Used Car Predictions
Lesson Guide
- Import Data
- Read Data
- Explore Data
- Data Cleaning
- Null values Market Category
- Null values Engine HP
- Null values Engine cylinders
- Null values Engine fuel type
- Null values Number of doors
- Segmentation
- Examine transmission_type Unknown
- Visualization of clean data
- Engine HP squared column
- Identifying outliers
- Dummies
- Train-Test
- Random forest
- Gradient Boosting
- K-neighbors
- Visualize pred
- Best Model less features
Import Data
# NumPy for numerical computing
import numpy as np
# Pandas for DataFrames
import pandas as pd
pd.set_option('display.max_columns', 100)
# Matplotlib for visualization
from matplotlib import pyplot as plt
# display plots in the notebook
%matplotlib inline
# Seaborn for easier visualization
import seaborn as sns
# For standardization
from sklearn.preprocessing import StandardScaler
# Helper for cross-validation
from sklearn.model_selection import GridSearchCV
# Function for splitting training and test set
from sklearn.model_selection import train_test_split # Scikit-Learn 0.18+
# Function for creating model pipelines
from sklearn.pipeline import make_pipeline
# Import Logistic Regression
from sklearn.linear_model import LogisticRegression
from sklearn import linear_model
# Import RandomForestClassifier and GradientBoostingClassifer
from sklearn.ensemble import RandomForestClassifier, RandomForestRegressor
from sklearn.neighbors import KNeighborsClassifier, KNeighborsRegressor
from sklearn.ensemble import GradientBoostingRegressor
from sklearn.preprocessing import PolynomialFeatures
from sklearn import model_selection
#import PipeLine, SelectKBest transformer, and RandomForest estimator classes
from sklearn.pipeline import Pipeline
from sklearn.feature_selection import SelectKBest, f_regression
from sklearn.metrics import r2_score, mean_absolute_error
import scipy.stats
Read Data
df = pd.read_csv('../Capstone/usedcarnew.csv')
Explore data
df.head()
| Make | Model | Year | Engine Fuel Type | Engine HP | Engine Cylinders | Transmission Type | Driven_Wheels | Number of Doors | Market Category | Vehicle Size | Vehicle Style | highway MPG | city mpg | Popularity | MSRP | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | BMW | 1 Series M | 2011 | premium unleaded (required) | 335.0 | 6.0 | MANUAL | rear wheel drive | 2.0 | Factory Tuner,Luxury,High-Performance | Compact | Coupe | 26 | 19 | 3916 | 46135 |
| 1 | BMW | 1 Series | 2011 | premium unleaded (required) | 300.0 | 6.0 | MANUAL | rear wheel drive | 2.0 | Luxury,Performance | Compact | Convertible | 28 | 19 | 3916 | 40650 |
| 2 | BMW | 1 Series | 2011 | premium unleaded (required) | 300.0 | 6.0 | MANUAL | rear wheel drive | 2.0 | Luxury,High-Performance | Compact | Coupe | 28 | 20 | 3916 | 36350 |
| 3 | BMW | 1 Series | 2011 | premium unleaded (required) | 230.0 | 6.0 | MANUAL | rear wheel drive | 2.0 | Luxury,Performance | Compact | Coupe | 28 | 18 | 3916 | 29450 |
| 4 | BMW | 1 Series | 2011 | premium unleaded (required) | 230.0 | 6.0 | MANUAL | rear wheel drive | 2.0 | Luxury | Compact | Convertible | 28 | 18 | 3916 | 34500 |
df.shape
(11914, 16)
# Drop duplicates
df = df.drop_duplicates()
print( df.shape )
(11199, 16)
# Make the figsize 7 x 6
plt.figure(figsize=(7,6))
# Plot heatmap of correlations
#sns.heatmap(correlations)
sns.heatmap(df.corr(), annot = True)
<matplotlib.axes._subplots.AxesSubplot at 0x10a239668>
correlations = df.corr()
# Generate a mask for the upper triangle
mask = np.zeros_like(correlations, dtype=np.bool)
mask[np.triu_indices_from(mask)] = True
#Make the figsize 10 x 8
plt.figure(figsize=(10,8))
# Plot heatmap of correlations
sns.heatmap(correlations * 100, annot=True, fmt='.0f', mask=mask)
<matplotlib.axes._subplots.AxesSubplot at 0x10a265208>
df.info()
<class 'pandas.core.frame.DataFrame'>
Int64Index: 11199 entries, 0 to 11913
Data columns (total 16 columns):
Make 11199 non-null object
Model 11199 non-null object
Year 11199 non-null int64
Engine Fuel Type 11196 non-null object
Engine HP 11130 non-null float64
Engine Cylinders 11169 non-null float64
Transmission Type 11199 non-null object
Driven_Wheels 11199 non-null object
Number of Doors 11193 non-null float64
Market Category 7823 non-null object
Vehicle Size 11199 non-null object
Vehicle Style 11199 non-null object
highway MPG 11199 non-null int64
city mpg 11199 non-null int64
Popularity 11199 non-null int64
MSRP 11199 non-null int64
dtypes: float64(3), int64(5), object(8)
memory usage: 1.5+ MB
# Display summary statistics for the numerical features.
#Summarize numerical features
df.describe()
| Year | Engine HP | Engine Cylinders | Number of Doors | highway MPG | city mpg | Popularity | MSRP | |
|---|---|---|---|---|---|---|---|---|
| count | 11199.000000 | 11130.000000 | 11169.000000 | 11193.000000 | 11199.000000 | 11199.000000 | 11199.000000 | 1.119900e+04 |
| mean | 2010.714528 | 253.388859 | 5.665950 | 3.454123 | 26.610590 | 19.731851 | 1558.483347 | 4.192593e+04 |
| std | 7.228211 | 110.150938 | 1.797021 | 0.872946 | 8.977641 | 9.177555 | 1445.668872 | 6.153505e+04 |
| min | 1990.000000 | 55.000000 | 0.000000 | 2.000000 | 12.000000 | 7.000000 | 2.000000 | 2.000000e+03 |
| 25% | 2007.000000 | 172.000000 | 4.000000 | 2.000000 | 22.000000 | 16.000000 | 549.000000 | 2.159950e+04 |
| 50% | 2015.000000 | 239.000000 | 6.000000 | 4.000000 | 25.000000 | 18.000000 | 1385.000000 | 3.067500e+04 |
| 75% | 2016.000000 | 303.000000 | 6.000000 | 4.000000 | 30.000000 | 22.000000 | 2009.000000 | 4.303250e+04 |
| max | 2017.000000 | 1001.000000 | 16.000000 | 4.000000 | 354.000000 | 137.000000 | 5657.000000 | 2.065902e+06 |
# xrot= argument that rotates x-axis labels counter-clockwise.
# Plot histogram grid
df.hist(figsize=(14,14), xrot=-45)
# Clear the text "residue"
plt.show()
# Summarize categorical features
df.describe(include=['object'])
| Make | Model | Engine Fuel Type | Transmission Type | Driven_Wheels | Market Category | Vehicle Size | Vehicle Style | |
|---|---|---|---|---|---|---|---|---|
| count | 11199 | 11199 | 11196 | 11199 | 11199 | 7823 | 11199 | 11199 |
| unique | 48 | 915 | 10 | 5 | 4 | 71 | 3 | 16 |
| top | Chevrolet | Silverado 1500 | regular unleaded | AUTOMATIC | front wheel drive | Crossover | Compact | Sedan |
| freq | 1083 | 156 | 6658 | 7932 | 4354 | 1075 | 4395 | 2843 |
# barchart for missing values in each column
nullvalues = df.isnull().sum()
nullvalues.plot.barh()
<matplotlib.axes._subplots.AxesSubplot at 0x1a116aeeb8>
df.head()
#df.Year.unique()
| Make | Model | Year | Engine Fuel Type | Engine HP | Engine Cylinders | Transmission Type | Driven_Wheels | Number of Doors | Market Category | Vehicle Size | Vehicle Style | highway MPG | city mpg | Popularity | MSRP | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | BMW | 1 Series M | 2011 | premium unleaded (required) | 335.0 | 6.0 | MANUAL | rear wheel drive | 2.0 | Factory Tuner,Luxury,High-Performance | Compact | Coupe | 26 | 19 | 3916 | 46135 |
| 1 | BMW | 1 Series | 2011 | premium unleaded (required) | 300.0 | 6.0 | MANUAL | rear wheel drive | 2.0 | Luxury,Performance | Compact | Convertible | 28 | 19 | 3916 | 40650 |
| 2 | BMW | 1 Series | 2011 | premium unleaded (required) | 300.0 | 6.0 | MANUAL | rear wheel drive | 2.0 | Luxury,High-Performance | Compact | Coupe | 28 | 20 | 3916 | 36350 |
| 3 | BMW | 1 Series | 2011 | premium unleaded (required) | 230.0 | 6.0 | MANUAL | rear wheel drive | 2.0 | Luxury,Performance | Compact | Coupe | 28 | 18 | 3916 | 29450 |
| 4 | BMW | 1 Series | 2011 | premium unleaded (required) | 230.0 | 6.0 | MANUAL | rear wheel drive | 2.0 | Luxury | Compact | Convertible | 28 | 18 | 3916 | 34500 |
SEGMENTATION
Cutting the data to observe the relationship between categorical features and numeric features
# Bar plot for Vehicle Style
plt.subplots(figsize=(10,10))
sns.countplot(y='Vehicle Style', data=df)
<matplotlib.axes._subplots.AxesSubplot at 0x1a116ceb00>
# Plot bar plot for each categorical feature
# Show count of observations
# for loop to plot bar plots of each of the categorical features.
# Some plots there is too many columns to visualize with this
plt.subplots(figsize=(20,15))
for feature in df.dtypes[df.dtypes == 'object'].index:
sns.countplot(y=feature, data=df)
plt.show()
# Segment tx_price by property_type and plot distributions
plt.subplots(figsize=(20,15))
sns.boxplot(y='Engine Fuel Type', x='MSRP', data=df)
<matplotlib.axes._subplots.AxesSubplot at 0x1a140cf780>
# Segment by Engine fuel Type and display the means within each class
# Maybe sue city/ Highway mpg to compare fuel type
df.groupby('Engine Fuel Type').mean()
| Year | Engine HP | Engine Cylinders | Number of Doors | highway MPG | city mpg | Popularity | MSRP | |
|---|---|---|---|---|---|---|---|---|
| Engine Fuel Type | ||||||||
| diesel | 2013.360000 | 184.100671 | 4.806667 | 3.720000 | 36.473333 | 26.346667 | 1649.240000 | 40449.680000 |
| electric | 2015.318182 | 145.318182 | 0.000000 | 3.901639 | 99.590909 | 112.696970 | 1773.454545 | 47943.030303 |
| flex-fuel (premium unleaded recommended/E85) | 2012.961538 | 283.346154 | 5.384615 | 3.307692 | 25.346154 | 16.923077 | 1332.807692 | 48641.923077 |
| flex-fuel (premium unleaded required/E85) | 2013.849057 | 514.716981 | 9.396226 | 3.358491 | 19.943396 | 13.283019 | 376.641509 | 160692.264151 |
| flex-fuel (unleaded/E85) | 2013.714769 | 286.213078 | 6.626832 | 3.523112 | 22.624577 | 16.160090 | 2278.855693 | 36279.217587 |
| flex-fuel (unleaded/natural gas) | 2016.000000 | NaN | 6.000000 | 4.000000 | 25.000000 | 17.000000 | 1385.000000 | 39194.166667 |
| natural gas | 2015.000000 | 110.000000 | 4.000000 | 4.000000 | 38.000000 | 27.000000 | 2202.000000 | 28065.000000 |
| premium unleaded (recommended) | 2014.686782 | 276.525937 | 5.173851 | 3.377155 | 28.407328 | 20.190374 | 1227.055316 | 41812.512213 |
| premium unleaded (required) | 2012.688650 | 375.906953 | 7.005157 | 3.062916 | 23.856851 | 16.649796 | 1449.656442 | 102814.088957 |
| regular unleaded | 2008.762391 | 207.901114 | 5.289106 | 3.566236 | 26.686092 | 20.010514 | 1570.338690 | 23833.156053 |
Data cleaning
df.isnull().sum()
Make 0
Model 0
Year 0
Engine Fuel Type 3
Engine HP 69
Engine Cylinders 30
Transmission Type 0
Driven_Wheels 0
Number of Doors 6
Market Category 3376
Vehicle Size 0
Vehicle Style 0
highway MPG 0
city mpg 0
Popularity 0
MSRP 0
dtype: int64
Examine how to deal with null values
# shows all null rows information
df.loc[(df['Market Category'].isnull()) |
(df['Engine HP'].isnull()) |
(df['Engine Cylinders'].isnull()) |
(df['Number of Doors'].isnull()) |
(df['Engine Fuel Type'].isnull())]
| Make | Model | Year | Engine Fuel Type | Engine HP | Engine Cylinders | Transmission Type | Driven_Wheels | Number of Doors | Market Category | Vehicle Size | Vehicle Style | highway MPG | city mpg | Popularity | MSRP | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 87 | Nissan | 200SX | 1996 | regular unleaded | 115.0 | 4.0 | MANUAL | front wheel drive | 2.0 | NaN | Compact | Coupe | 36 | 26 | 2009 | 2000 |
| 91 | Nissan | 200SX | 1997 | regular unleaded | 115.0 | 4.0 | MANUAL | front wheel drive | 2.0 | NaN | Compact | Coupe | 35 | 25 | 2009 | 2000 |
| 93 | Nissan | 200SX | 1998 | regular unleaded | 115.0 | 4.0 | MANUAL | front wheel drive | 2.0 | NaN | Compact | Coupe | 35 | 25 | 2009 | 2000 |
| 203 | Chrysler | 300 | 2015 | regular unleaded | 300.0 | 6.0 | AUTOMATIC | all wheel drive | 4.0 | NaN | Large | Sedan | 27 | 18 | 1013 | 37570 |
| 204 | Chrysler | 300 | 2015 | regular unleaded | 292.0 | 6.0 | AUTOMATIC | rear wheel drive | 4.0 | NaN | Large | Sedan | 31 | 19 | 1013 | 31695 |
| 205 | Chrysler | 300 | 2015 | regular unleaded | 292.0 | 6.0 | AUTOMATIC | rear wheel drive | 4.0 | NaN | Large | Sedan | 31 | 19 | 1013 | 38070 |
| 206 | Chrysler | 300 | 2015 | regular unleaded | 292.0 | 6.0 | AUTOMATIC | all wheel drive | 4.0 | NaN | Large | Sedan | 27 | 18 | 1013 | 44895 |
| 209 | Chrysler | 300 | 2015 | regular unleaded | 292.0 | 6.0 | AUTOMATIC | all wheel drive | 4.0 | NaN | Large | Sedan | 27 | 18 | 1013 | 34195 |
| 210 | Chrysler | 300 | 2015 | regular unleaded | 292.0 | 6.0 | AUTOMATIC | all wheel drive | 4.0 | NaN | Large | Sedan | 27 | 18 | 1013 | 40570 |
| 211 | Chrysler | 300 | 2016 | regular unleaded | 300.0 | 6.0 | AUTOMATIC | all wheel drive | 4.0 | NaN | Large | Sedan | 27 | 18 | 1013 | 38095 |
| 213 | Chrysler | 300 | 2016 | regular unleaded | 292.0 | 6.0 | AUTOMATIC | all wheel drive | 4.0 | NaN | Large | Sedan | 27 | 18 | 1013 | 45190 |
| 214 | Chrysler | 300 | 2016 | regular unleaded | 292.0 | 6.0 | AUTOMATIC | rear wheel drive | 4.0 | NaN | Large | Sedan | 31 | 19 | 1013 | 32260 |
| 215 | Chrysler | 300 | 2016 | regular unleaded | 292.0 | 6.0 | AUTOMATIC | all wheel drive | 4.0 | NaN | Large | Sedan | 27 | 18 | 1013 | 37755 |
| 216 | Chrysler | 300 | 2016 | regular unleaded | 292.0 | 6.0 | AUTOMATIC | all wheel drive | 4.0 | NaN | Large | Sedan | 27 | 18 | 1013 | 41055 |
| 219 | Chrysler | 300 | 2016 | regular unleaded | 292.0 | 6.0 | AUTOMATIC | rear wheel drive | 4.0 | NaN | Large | Sedan | 31 | 19 | 1013 | 38555 |
| 220 | Chrysler | 300 | 2016 | regular unleaded | 292.0 | 6.0 | AUTOMATIC | rear wheel drive | 4.0 | NaN | Large | Sedan | 31 | 19 | 1013 | 35255 |
| 221 | Chrysler | 300 | 2016 | regular unleaded | 300.0 | 6.0 | AUTOMATIC | all wheel drive | 4.0 | NaN | Large | Sedan | 27 | 18 | 1013 | 38590 |
| 222 | Chrysler | 300 | 2016 | regular unleaded | 292.0 | 6.0 | AUTOMATIC | all wheel drive | 4.0 | NaN | Large | Sedan | 27 | 18 | 1013 | 34760 |
| 223 | Chrysler | 300 | 2017 | regular unleaded | 292.0 | 6.0 | AUTOMATIC | all wheel drive | 4.0 | NaN | Large | Sedan | 27 | 18 | 1013 | 41135 |
| 224 | Chrysler | 300 | 2017 | regular unleaded | 292.0 | 6.0 | AUTOMATIC | all wheel drive | 4.0 | NaN | Large | Sedan | 27 | 18 | 1013 | 45270 |
| 225 | Chrysler | 300 | 2017 | regular unleaded | 300.0 | 6.0 | AUTOMATIC | all wheel drive | 4.0 | NaN | Large | Sedan | 27 | 18 | 1013 | 38670 |
| 228 | Chrysler | 300 | 2017 | regular unleaded | 300.0 | 6.0 | AUTOMATIC | all wheel drive | 4.0 | NaN | Large | Sedan | 27 | 18 | 1013 | 38175 |
| 229 | Chrysler | 300 | 2017 | regular unleaded | 292.0 | 6.0 | AUTOMATIC | rear wheel drive | 4.0 | NaN | Large | Sedan | 30 | 19 | 1013 | 32340 |
| 231 | Chrysler | 300 | 2017 | regular unleaded | 292.0 | 6.0 | AUTOMATIC | all wheel drive | 4.0 | NaN | Large | Sedan | 27 | 18 | 1013 | 34840 |
| 360 | Mazda | 3 | 2015 | regular unleaded | 155.0 | 4.0 | AUTOMATIC | front wheel drive | 4.0 | NaN | Compact | Sedan | 41 | 30 | 586 | 23795 |
| 361 | Mazda | 3 | 2015 | regular unleaded | 155.0 | 4.0 | MANUAL | front wheel drive | 4.0 | NaN | Compact | Sedan | 41 | 29 | 586 | 19595 |
| 362 | Mazda | 3 | 2015 | regular unleaded | 155.0 | 4.0 | MANUAL | front wheel drive | 4.0 | NaN | Compact | Sedan | 41 | 29 | 586 | 18445 |
| 368 | Mazda | 3 | 2015 | regular unleaded | 155.0 | 4.0 | AUTOMATIC | front wheel drive | 4.0 | NaN | Compact | Sedan | 41 | 30 | 586 | 19495 |
| 373 | Mazda | 3 | 2015 | regular unleaded | 155.0 | 4.0 | MANUAL | front wheel drive | 4.0 | NaN | Compact | Sedan | 41 | 29 | 586 | 16945 |
| 375 | Mazda | 3 | 2015 | regular unleaded | 155.0 | 4.0 | AUTOMATIC | front wheel drive | 4.0 | NaN | Compact | Sedan | 41 | 30 | 586 | 20645 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 11686 | Suzuki | XL-7 | 2006 | regular unleaded | 185.0 | 6.0 | AUTOMATIC | rear wheel drive | 4.0 | NaN | Midsize | 4dr SUV | 21 | 16 | 481 | 25499 |
| 11687 | Suzuki | XL-7 | 2006 | regular unleaded | 185.0 | 6.0 | AUTOMATIC | rear wheel drive | 4.0 | NaN | Midsize | 4dr SUV | 21 | 16 | 481 | 21999 |
| 11744 | Nissan | Xterra | 2013 | regular unleaded | 261.0 | 6.0 | AUTOMATIC | four wheel drive | 4.0 | NaN | Midsize | 4dr SUV | 20 | 15 | 2009 | 26900 |
| 11745 | Nissan | Xterra | 2013 | regular unleaded | 261.0 | 6.0 | MANUAL | four wheel drive | 4.0 | NaN | Midsize | 4dr SUV | 20 | 16 | 2009 | 29440 |
| 11746 | Nissan | Xterra | 2013 | regular unleaded | 261.0 | 6.0 | MANUAL | four wheel drive | 4.0 | NaN | Midsize | 4dr SUV | 20 | 16 | 2009 | 25850 |
| 11747 | Nissan | Xterra | 2013 | regular unleaded | 261.0 | 6.0 | AUTOMATIC | four wheel drive | 4.0 | NaN | Midsize | 4dr SUV | 20 | 15 | 2009 | 30490 |
| 11748 | Nissan | Xterra | 2013 | regular unleaded | 261.0 | 6.0 | AUTOMATIC | rear wheel drive | 4.0 | NaN | Midsize | 4dr SUV | 22 | 16 | 2009 | 24850 |
| 11749 | Nissan | Xterra | 2013 | regular unleaded | 261.0 | 6.0 | AUTOMATIC | four wheel drive | 4.0 | NaN | Midsize | 4dr SUV | 20 | 15 | 2009 | 24990 |
| 11750 | Nissan | Xterra | 2013 | regular unleaded | 261.0 | 6.0 | AUTOMATIC | rear wheel drive | 4.0 | NaN | Midsize | 4dr SUV | 22 | 16 | 2009 | 22940 |
| 11751 | Nissan | Xterra | 2014 | regular unleaded | 261.0 | 6.0 | AUTOMATIC | four wheel drive | 4.0 | NaN | Midsize | 4dr SUV | 20 | 15 | 2009 | 31370 |
| 11752 | Nissan | Xterra | 2014 | regular unleaded | 261.0 | 6.0 | AUTOMATIC | rear wheel drive | 4.0 | NaN | Midsize | 4dr SUV | 22 | 16 | 2009 | 25300 |
| 11753 | Nissan | Xterra | 2014 | regular unleaded | 261.0 | 6.0 | AUTOMATIC | four wheel drive | 4.0 | NaN | Midsize | 4dr SUV | 20 | 15 | 2009 | 27350 |
| 11754 | Nissan | Xterra | 2014 | regular unleaded | 261.0 | 6.0 | AUTOMATIC | four wheel drive | 4.0 | NaN | Midsize | 4dr SUV | 20 | 15 | 2009 | 25440 |
| 11755 | Nissan | Xterra | 2014 | regular unleaded | 261.0 | 6.0 | MANUAL | four wheel drive | 4.0 | NaN | Midsize | 4dr SUV | 20 | 16 | 2009 | 26300 |
| 11756 | Nissan | Xterra | 2014 | regular unleaded | 261.0 | 6.0 | AUTOMATIC | rear wheel drive | 4.0 | NaN | Midsize | 4dr SUV | 22 | 16 | 2009 | 23390 |
| 11757 | Nissan | Xterra | 2014 | regular unleaded | 261.0 | 6.0 | MANUAL | four wheel drive | 4.0 | NaN | Midsize | 4dr SUV | 20 | 16 | 2009 | 30320 |
| 11758 | Nissan | Xterra | 2015 | regular unleaded | 261.0 | 6.0 | MANUAL | four wheel drive | 4.0 | NaN | Midsize | 4dr SUV | 20 | 15 | 2009 | 26670 |
| 11759 | Nissan | Xterra | 2015 | regular unleaded | 261.0 | 6.0 | AUTOMATIC | four wheel drive | 4.0 | NaN | Midsize | 4dr SUV | 20 | 15 | 2009 | 27720 |
| 11760 | Nissan | Xterra | 2015 | regular unleaded | 261.0 | 6.0 | AUTOMATIC | four wheel drive | 4.0 | NaN | Midsize | 4dr SUV | 20 | 15 | 2009 | 25710 |
| 11761 | Nissan | Xterra | 2015 | regular unleaded | 261.0 | 6.0 | MANUAL | four wheel drive | 4.0 | NaN | Midsize | 4dr SUV | 20 | 15 | 2009 | 30590 |
| 11762 | Nissan | Xterra | 2015 | regular unleaded | 261.0 | 6.0 | AUTOMATIC | rear wheel drive | 4.0 | NaN | Midsize | 4dr SUV | 22 | 16 | 2009 | 23660 |
| 11763 | Nissan | Xterra | 2015 | regular unleaded | 261.0 | 6.0 | AUTOMATIC | four wheel drive | 4.0 | NaN | Midsize | 4dr SUV | 20 | 15 | 2009 | 31640 |
| 11764 | Nissan | Xterra | 2015 | regular unleaded | 261.0 | 6.0 | AUTOMATIC | rear wheel drive | 4.0 | NaN | Midsize | 4dr SUV | 22 | 16 | 2009 | 25670 |
| 11792 | Subaru | XT | 1991 | regular unleaded | 97.0 | 4.0 | MANUAL | front wheel drive | 2.0 | NaN | Compact | Coupe | 29 | 22 | 640 | 2000 |
| 11793 | Subaru | XT | 1991 | regular unleaded | 145.0 | 6.0 | AUTOMATIC | front wheel drive | 2.0 | NaN | Compact | Coupe | 26 | 18 | 640 | 2000 |
| 11794 | Subaru | XT | 1991 | regular unleaded | 145.0 | 6.0 | MANUAL | all wheel drive | 2.0 | NaN | Compact | Coupe | 23 | 16 | 640 | 2000 |
| 11809 | Toyota | Yaris iA | 2017 | regular unleaded | 106.0 | 4.0 | MANUAL | front wheel drive | 4.0 | NaN | Compact | Sedan | 39 | 30 | 2031 | 15950 |
| 11810 | Toyota | Yaris iA | 2017 | regular unleaded | 106.0 | 4.0 | AUTOMATIC | front wheel drive | 4.0 | NaN | Compact | Sedan | 40 | 32 | 2031 | 17050 |
| 11867 | GMC | Yukon | 2015 | premium unleaded (recommended) | 420.0 | 8.0 | AUTOMATIC | rear wheel drive | 4.0 | NaN | Large | 4dr SUV | 21 | 15 | 549 | 64520 |
| 11868 | GMC | Yukon | 2015 | premium unleaded (recommended) | 420.0 | 8.0 | AUTOMATIC | four wheel drive | 4.0 | NaN | Large | 4dr SUV | 21 | 14 | 549 | 67520 |
3464 rows × 16 columns
Null values in Market Category
# Examine the null values of Market Category
df[df['Market Category'].isnull()]
| Make | Model | Year | Engine Fuel Type | Engine HP | Engine Cylinders | Transmission Type | Driven_Wheels | Number of Doors | Market Category | Vehicle Size | Vehicle Style | highway MPG | city mpg | Popularity | MSRP | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 87 | Nissan | 200SX | 1996 | regular unleaded | 115.0 | 4.0 | MANUAL | front wheel drive | 2.0 | NaN | Compact | Coupe | 36 | 26 | 2009 | 2000 |
| 91 | Nissan | 200SX | 1997 | regular unleaded | 115.0 | 4.0 | MANUAL | front wheel drive | 2.0 | NaN | Compact | Coupe | 35 | 25 | 2009 | 2000 |
| 93 | Nissan | 200SX | 1998 | regular unleaded | 115.0 | 4.0 | MANUAL | front wheel drive | 2.0 | NaN | Compact | Coupe | 35 | 25 | 2009 | 2000 |
| 203 | Chrysler | 300 | 2015 | regular unleaded | 300.0 | 6.0 | AUTOMATIC | all wheel drive | 4.0 | NaN | Large | Sedan | 27 | 18 | 1013 | 37570 |
| 204 | Chrysler | 300 | 2015 | regular unleaded | 292.0 | 6.0 | AUTOMATIC | rear wheel drive | 4.0 | NaN | Large | Sedan | 31 | 19 | 1013 | 31695 |
| 205 | Chrysler | 300 | 2015 | regular unleaded | 292.0 | 6.0 | AUTOMATIC | rear wheel drive | 4.0 | NaN | Large | Sedan | 31 | 19 | 1013 | 38070 |
| 206 | Chrysler | 300 | 2015 | regular unleaded | 292.0 | 6.0 | AUTOMATIC | all wheel drive | 4.0 | NaN | Large | Sedan | 27 | 18 | 1013 | 44895 |
| 209 | Chrysler | 300 | 2015 | regular unleaded | 292.0 | 6.0 | AUTOMATIC | all wheel drive | 4.0 | NaN | Large | Sedan | 27 | 18 | 1013 | 34195 |
| 210 | Chrysler | 300 | 2015 | regular unleaded | 292.0 | 6.0 | AUTOMATIC | all wheel drive | 4.0 | NaN | Large | Sedan | 27 | 18 | 1013 | 40570 |
| 211 | Chrysler | 300 | 2016 | regular unleaded | 300.0 | 6.0 | AUTOMATIC | all wheel drive | 4.0 | NaN | Large | Sedan | 27 | 18 | 1013 | 38095 |
| 213 | Chrysler | 300 | 2016 | regular unleaded | 292.0 | 6.0 | AUTOMATIC | all wheel drive | 4.0 | NaN | Large | Sedan | 27 | 18 | 1013 | 45190 |
| 214 | Chrysler | 300 | 2016 | regular unleaded | 292.0 | 6.0 | AUTOMATIC | rear wheel drive | 4.0 | NaN | Large | Sedan | 31 | 19 | 1013 | 32260 |
| 215 | Chrysler | 300 | 2016 | regular unleaded | 292.0 | 6.0 | AUTOMATIC | all wheel drive | 4.0 | NaN | Large | Sedan | 27 | 18 | 1013 | 37755 |
| 216 | Chrysler | 300 | 2016 | regular unleaded | 292.0 | 6.0 | AUTOMATIC | all wheel drive | 4.0 | NaN | Large | Sedan | 27 | 18 | 1013 | 41055 |
| 219 | Chrysler | 300 | 2016 | regular unleaded | 292.0 | 6.0 | AUTOMATIC | rear wheel drive | 4.0 | NaN | Large | Sedan | 31 | 19 | 1013 | 38555 |
| 220 | Chrysler | 300 | 2016 | regular unleaded | 292.0 | 6.0 | AUTOMATIC | rear wheel drive | 4.0 | NaN | Large | Sedan | 31 | 19 | 1013 | 35255 |
| 221 | Chrysler | 300 | 2016 | regular unleaded | 300.0 | 6.0 | AUTOMATIC | all wheel drive | 4.0 | NaN | Large | Sedan | 27 | 18 | 1013 | 38590 |
| 222 | Chrysler | 300 | 2016 | regular unleaded | 292.0 | 6.0 | AUTOMATIC | all wheel drive | 4.0 | NaN | Large | Sedan | 27 | 18 | 1013 | 34760 |
| 223 | Chrysler | 300 | 2017 | regular unleaded | 292.0 | 6.0 | AUTOMATIC | all wheel drive | 4.0 | NaN | Large | Sedan | 27 | 18 | 1013 | 41135 |
| 224 | Chrysler | 300 | 2017 | regular unleaded | 292.0 | 6.0 | AUTOMATIC | all wheel drive | 4.0 | NaN | Large | Sedan | 27 | 18 | 1013 | 45270 |
| 225 | Chrysler | 300 | 2017 | regular unleaded | 300.0 | 6.0 | AUTOMATIC | all wheel drive | 4.0 | NaN | Large | Sedan | 27 | 18 | 1013 | 38670 |
| 228 | Chrysler | 300 | 2017 | regular unleaded | 300.0 | 6.0 | AUTOMATIC | all wheel drive | 4.0 | NaN | Large | Sedan | 27 | 18 | 1013 | 38175 |
| 229 | Chrysler | 300 | 2017 | regular unleaded | 292.0 | 6.0 | AUTOMATIC | rear wheel drive | 4.0 | NaN | Large | Sedan | 30 | 19 | 1013 | 32340 |
| 231 | Chrysler | 300 | 2017 | regular unleaded | 292.0 | 6.0 | AUTOMATIC | all wheel drive | 4.0 | NaN | Large | Sedan | 27 | 18 | 1013 | 34840 |
| 360 | Mazda | 3 | 2015 | regular unleaded | 155.0 | 4.0 | AUTOMATIC | front wheel drive | 4.0 | NaN | Compact | Sedan | 41 | 30 | 586 | 23795 |
| 361 | Mazda | 3 | 2015 | regular unleaded | 155.0 | 4.0 | MANUAL | front wheel drive | 4.0 | NaN | Compact | Sedan | 41 | 29 | 586 | 19595 |
| 362 | Mazda | 3 | 2015 | regular unleaded | 155.0 | 4.0 | MANUAL | front wheel drive | 4.0 | NaN | Compact | Sedan | 41 | 29 | 586 | 18445 |
| 368 | Mazda | 3 | 2015 | regular unleaded | 155.0 | 4.0 | AUTOMATIC | front wheel drive | 4.0 | NaN | Compact | Sedan | 41 | 30 | 586 | 19495 |
| 373 | Mazda | 3 | 2015 | regular unleaded | 155.0 | 4.0 | MANUAL | front wheel drive | 4.0 | NaN | Compact | Sedan | 41 | 29 | 586 | 16945 |
| 375 | Mazda | 3 | 2015 | regular unleaded | 155.0 | 4.0 | AUTOMATIC | front wheel drive | 4.0 | NaN | Compact | Sedan | 41 | 30 | 586 | 20645 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 11686 | Suzuki | XL-7 | 2006 | regular unleaded | 185.0 | 6.0 | AUTOMATIC | rear wheel drive | 4.0 | NaN | Midsize | 4dr SUV | 21 | 16 | 481 | 25499 |
| 11687 | Suzuki | XL-7 | 2006 | regular unleaded | 185.0 | 6.0 | AUTOMATIC | rear wheel drive | 4.0 | NaN | Midsize | 4dr SUV | 21 | 16 | 481 | 21999 |
| 11744 | Nissan | Xterra | 2013 | regular unleaded | 261.0 | 6.0 | AUTOMATIC | four wheel drive | 4.0 | NaN | Midsize | 4dr SUV | 20 | 15 | 2009 | 26900 |
| 11745 | Nissan | Xterra | 2013 | regular unleaded | 261.0 | 6.0 | MANUAL | four wheel drive | 4.0 | NaN | Midsize | 4dr SUV | 20 | 16 | 2009 | 29440 |
| 11746 | Nissan | Xterra | 2013 | regular unleaded | 261.0 | 6.0 | MANUAL | four wheel drive | 4.0 | NaN | Midsize | 4dr SUV | 20 | 16 | 2009 | 25850 |
| 11747 | Nissan | Xterra | 2013 | regular unleaded | 261.0 | 6.0 | AUTOMATIC | four wheel drive | 4.0 | NaN | Midsize | 4dr SUV | 20 | 15 | 2009 | 30490 |
| 11748 | Nissan | Xterra | 2013 | regular unleaded | 261.0 | 6.0 | AUTOMATIC | rear wheel drive | 4.0 | NaN | Midsize | 4dr SUV | 22 | 16 | 2009 | 24850 |
| 11749 | Nissan | Xterra | 2013 | regular unleaded | 261.0 | 6.0 | AUTOMATIC | four wheel drive | 4.0 | NaN | Midsize | 4dr SUV | 20 | 15 | 2009 | 24990 |
| 11750 | Nissan | Xterra | 2013 | regular unleaded | 261.0 | 6.0 | AUTOMATIC | rear wheel drive | 4.0 | NaN | Midsize | 4dr SUV | 22 | 16 | 2009 | 22940 |
| 11751 | Nissan | Xterra | 2014 | regular unleaded | 261.0 | 6.0 | AUTOMATIC | four wheel drive | 4.0 | NaN | Midsize | 4dr SUV | 20 | 15 | 2009 | 31370 |
| 11752 | Nissan | Xterra | 2014 | regular unleaded | 261.0 | 6.0 | AUTOMATIC | rear wheel drive | 4.0 | NaN | Midsize | 4dr SUV | 22 | 16 | 2009 | 25300 |
| 11753 | Nissan | Xterra | 2014 | regular unleaded | 261.0 | 6.0 | AUTOMATIC | four wheel drive | 4.0 | NaN | Midsize | 4dr SUV | 20 | 15 | 2009 | 27350 |
| 11754 | Nissan | Xterra | 2014 | regular unleaded | 261.0 | 6.0 | AUTOMATIC | four wheel drive | 4.0 | NaN | Midsize | 4dr SUV | 20 | 15 | 2009 | 25440 |
| 11755 | Nissan | Xterra | 2014 | regular unleaded | 261.0 | 6.0 | MANUAL | four wheel drive | 4.0 | NaN | Midsize | 4dr SUV | 20 | 16 | 2009 | 26300 |
| 11756 | Nissan | Xterra | 2014 | regular unleaded | 261.0 | 6.0 | AUTOMATIC | rear wheel drive | 4.0 | NaN | Midsize | 4dr SUV | 22 | 16 | 2009 | 23390 |
| 11757 | Nissan | Xterra | 2014 | regular unleaded | 261.0 | 6.0 | MANUAL | four wheel drive | 4.0 | NaN | Midsize | 4dr SUV | 20 | 16 | 2009 | 30320 |
| 11758 | Nissan | Xterra | 2015 | regular unleaded | 261.0 | 6.0 | MANUAL | four wheel drive | 4.0 | NaN | Midsize | 4dr SUV | 20 | 15 | 2009 | 26670 |
| 11759 | Nissan | Xterra | 2015 | regular unleaded | 261.0 | 6.0 | AUTOMATIC | four wheel drive | 4.0 | NaN | Midsize | 4dr SUV | 20 | 15 | 2009 | 27720 |
| 11760 | Nissan | Xterra | 2015 | regular unleaded | 261.0 | 6.0 | AUTOMATIC | four wheel drive | 4.0 | NaN | Midsize | 4dr SUV | 20 | 15 | 2009 | 25710 |
| 11761 | Nissan | Xterra | 2015 | regular unleaded | 261.0 | 6.0 | MANUAL | four wheel drive | 4.0 | NaN | Midsize | 4dr SUV | 20 | 15 | 2009 | 30590 |
| 11762 | Nissan | Xterra | 2015 | regular unleaded | 261.0 | 6.0 | AUTOMATIC | rear wheel drive | 4.0 | NaN | Midsize | 4dr SUV | 22 | 16 | 2009 | 23660 |
| 11763 | Nissan | Xterra | 2015 | regular unleaded | 261.0 | 6.0 | AUTOMATIC | four wheel drive | 4.0 | NaN | Midsize | 4dr SUV | 20 | 15 | 2009 | 31640 |
| 11764 | Nissan | Xterra | 2015 | regular unleaded | 261.0 | 6.0 | AUTOMATIC | rear wheel drive | 4.0 | NaN | Midsize | 4dr SUV | 22 | 16 | 2009 | 25670 |
| 11792 | Subaru | XT | 1991 | regular unleaded | 97.0 | 4.0 | MANUAL | front wheel drive | 2.0 | NaN | Compact | Coupe | 29 | 22 | 640 | 2000 |
| 11793 | Subaru | XT | 1991 | regular unleaded | 145.0 | 6.0 | AUTOMATIC | front wheel drive | 2.0 | NaN | Compact | Coupe | 26 | 18 | 640 | 2000 |
| 11794 | Subaru | XT | 1991 | regular unleaded | 145.0 | 6.0 | MANUAL | all wheel drive | 2.0 | NaN | Compact | Coupe | 23 | 16 | 640 | 2000 |
| 11809 | Toyota | Yaris iA | 2017 | regular unleaded | 106.0 | 4.0 | MANUAL | front wheel drive | 4.0 | NaN | Compact | Sedan | 39 | 30 | 2031 | 15950 |
| 11810 | Toyota | Yaris iA | 2017 | regular unleaded | 106.0 | 4.0 | AUTOMATIC | front wheel drive | 4.0 | NaN | Compact | Sedan | 40 | 32 | 2031 | 17050 |
| 11867 | GMC | Yukon | 2015 | premium unleaded (recommended) | 420.0 | 8.0 | AUTOMATIC | rear wheel drive | 4.0 | NaN | Large | 4dr SUV | 21 | 15 | 549 | 64520 |
| 11868 | GMC | Yukon | 2015 | premium unleaded (recommended) | 420.0 | 8.0 | AUTOMATIC | four wheel drive | 4.0 | NaN | Large | 4dr SUV | 21 | 14 | 549 | 67520 |
3376 rows × 16 columns
list(df['Market Category'].unique())
['Factory Tuner,Luxury,High-Performance',
'Luxury,Performance',
'Luxury,High-Performance',
'Luxury',
'Performance',
'Flex Fuel',
'Flex Fuel,Performance',
nan,
'Hatchback',
'Hatchback,Luxury,Performance',
'Hatchback,Luxury',
'Luxury,High-Performance,Hybrid',
'Diesel,Luxury',
'Hatchback,Performance',
'Hatchback,Factory Tuner,Performance',
'High-Performance',
'Factory Tuner,High-Performance',
'Exotic,High-Performance',
'Exotic,Factory Tuner,High-Performance',
'Factory Tuner,Performance',
'Crossover',
'Exotic,Luxury',
'Exotic,Luxury,High-Performance',
'Exotic,Luxury,Performance',
'Factory Tuner,Luxury,Performance',
'Flex Fuel,Luxury',
'Crossover,Luxury',
'Hatchback,Factory Tuner,Luxury,Performance',
'Crossover,Hatchback',
'Hybrid',
'Luxury,Performance,Hybrid',
'Crossover,Luxury,Performance,Hybrid',
'Crossover,Luxury,Performance',
'Exotic,Factory Tuner,Luxury,High-Performance',
'Flex Fuel,Luxury,High-Performance',
'Crossover,Flex Fuel',
'Diesel',
'Hatchback,Diesel',
'Crossover,Luxury,Diesel',
'Crossover,Luxury,High-Performance',
'Exotic,Flex Fuel,Factory Tuner,Luxury,High-Performance',
'Exotic,Flex Fuel,Luxury,High-Performance',
'Exotic,Factory Tuner,Luxury,Performance',
'Hatchback,Hybrid',
'Crossover,Hybrid',
'Hatchback,Luxury,Hybrid',
'Flex Fuel,Luxury,Performance',
'Crossover,Performance',
'Luxury,Hybrid',
'Crossover,Flex Fuel,Luxury,Performance',
'Crossover,Flex Fuel,Luxury',
'Crossover,Flex Fuel,Performance',
'Hatchback,Factory Tuner,High-Performance',
'Hatchback,Flex Fuel',
'Factory Tuner,Luxury',
'Crossover,Factory Tuner,Luxury,High-Performance',
'Crossover,Factory Tuner,Luxury,Performance',
'Crossover,Hatchback,Factory Tuner,Performance',
'Crossover,Hatchback,Performance',
'Flex Fuel,Hybrid',
'Flex Fuel,Performance,Hybrid',
'Crossover,Exotic,Luxury,High-Performance',
'Crossover,Exotic,Luxury,Performance',
'Exotic,Performance',
'Exotic,Luxury,High-Performance,Hybrid',
'Crossover,Luxury,Hybrid',
'Flex Fuel,Factory Tuner,Luxury,High-Performance',
'Performance,Hybrid',
'Crossover,Factory Tuner,Performance',
'Crossover,Diesel',
'Flex Fuel,Diesel',
'Crossover,Hatchback,Luxury']
# Decided to drop column becuse there are too many rows missing
# Plus other features can still describe the market Category
df.drop('Market Category', axis = 1, inplace=True)
df.head()
| Make | Model | Year | Engine Fuel Type | Engine HP | Engine Cylinders | Transmission Type | Driven_Wheels | Number of Doors | Vehicle Size | Vehicle Style | highway MPG | city mpg | Popularity | MSRP | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | BMW | 1 Series M | 2011 | premium unleaded (required) | 335.0 | 6.0 | MANUAL | rear wheel drive | 2.0 | Compact | Coupe | 26 | 19 | 3916 | 46135 |
| 1 | BMW | 1 Series | 2011 | premium unleaded (required) | 300.0 | 6.0 | MANUAL | rear wheel drive | 2.0 | Compact | Convertible | 28 | 19 | 3916 | 40650 |
| 2 | BMW | 1 Series | 2011 | premium unleaded (required) | 300.0 | 6.0 | MANUAL | rear wheel drive | 2.0 | Compact | Coupe | 28 | 20 | 3916 | 36350 |
| 3 | BMW | 1 Series | 2011 | premium unleaded (required) | 230.0 | 6.0 | MANUAL | rear wheel drive | 2.0 | Compact | Coupe | 28 | 18 | 3916 | 29450 |
| 4 | BMW | 1 Series | 2011 | premium unleaded (required) | 230.0 | 6.0 | MANUAL | rear wheel drive | 2.0 | Compact | Convertible | 28 | 18 | 3916 | 34500 |
All Null values in Engine HP
# Examine the null values of Engine HP
df[df['Engine HP'].isnull()]
| Make | Model | Year | Engine Fuel Type | Engine HP | Engine Cylinders | Transmission Type | Driven_Wheels | Number of Doors | Vehicle Size | Vehicle Style | highway MPG | city mpg | Popularity | MSRP | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 539 | FIAT | 500e | 2015 | electric | NaN | 0.0 | DIRECT_DRIVE | front wheel drive | 2.0 | Compact | 2dr Hatchback | 108 | 122 | 819 | 31800 |
| 540 | FIAT | 500e | 2016 | electric | NaN | 0.0 | DIRECT_DRIVE | front wheel drive | 2.0 | Compact | 2dr Hatchback | 103 | 121 | 819 | 31800 |
| 541 | FIAT | 500e | 2017 | electric | NaN | 0.0 | DIRECT_DRIVE | front wheel drive | 2.0 | Compact | 2dr Hatchback | 103 | 121 | 819 | 31800 |
| 2905 | Lincoln | Continental | 2017 | premium unleaded (recommended) | NaN | 6.0 | AUTOMATIC | all wheel drive | 4.0 | Large | Sedan | 25 | 17 | 61 | 55915 |
| 2906 | Lincoln | Continental | 2017 | premium unleaded (recommended) | NaN | 6.0 | AUTOMATIC | front wheel drive | 4.0 | Large | Sedan | 27 | 18 | 61 | 62915 |
| 2907 | Lincoln | Continental | 2017 | premium unleaded (recommended) | NaN | 6.0 | AUTOMATIC | front wheel drive | 4.0 | Large | Sedan | 27 | 18 | 61 | 53915 |
| 2908 | Lincoln | Continental | 2017 | premium unleaded (recommended) | NaN | 6.0 | AUTOMATIC | all wheel drive | 4.0 | Large | Sedan | 25 | 17 | 61 | 64915 |
| 4203 | Ford | Escape | 2017 | regular unleaded | NaN | 4.0 | AUTOMATIC | front wheel drive | 4.0 | Compact | 4dr SUV | 30 | 23 | 5657 | 29100 |
| 4204 | Ford | Escape | 2017 | regular unleaded | NaN | 4.0 | AUTOMATIC | all wheel drive | 4.0 | Compact | 4dr SUV | 28 | 22 | 5657 | 30850 |
| 4205 | Ford | Escape | 2017 | regular unleaded | NaN | 4.0 | AUTOMATIC | all wheel drive | 4.0 | Compact | 4dr SUV | 28 | 22 | 5657 | 26850 |
| 4206 | Ford | Escape | 2017 | regular unleaded | NaN | 4.0 | AUTOMATIC | front wheel drive | 4.0 | Compact | 4dr SUV | 30 | 23 | 5657 | 25100 |
| 4705 | Honda | Fit EV | 2013 | electric | NaN | 0.0 | DIRECT_DRIVE | front wheel drive | 4.0 | Compact | 4dr Hatchback | 105 | 132 | 2202 | 36625 |
| 4706 | Honda | Fit EV | 2014 | electric | NaN | 0.0 | DIRECT_DRIVE | front wheel drive | 4.0 | Compact | 4dr Hatchback | 105 | 132 | 2202 | 36625 |
| 4785 | Ford | Focus | 2015 | electric | NaN | 0.0 | DIRECT_DRIVE | front wheel drive | 4.0 | Compact | 4dr Hatchback | 99 | 110 | 5657 | 29170 |
| 4789 | Ford | Focus | 2016 | electric | NaN | 0.0 | DIRECT_DRIVE | front wheel drive | 4.0 | Compact | 4dr Hatchback | 99 | 110 | 5657 | 29170 |
| 4798 | Ford | Focus | 2017 | electric | NaN | 0.0 | DIRECT_DRIVE | front wheel drive | 4.0 | Compact | 4dr Hatchback | 99 | 110 | 5657 | 29120 |
| 4914 | Ford | Freestar | 2005 | regular unleaded | NaN | 6.0 | AUTOMATIC | front wheel drive | 4.0 | Midsize | Passenger Minivan | 22 | 16 | 5657 | 28030 |
| 4915 | Ford | Freestar | 2005 | regular unleaded | NaN | 6.0 | AUTOMATIC | front wheel drive | 4.0 | Midsize | Passenger Minivan | 22 | 16 | 5657 | 23930 |
| 4916 | Ford | Freestar | 2005 | regular unleaded | NaN | 6.0 | AUTOMATIC | front wheel drive | 4.0 | Midsize | Cargo Minivan | 22 | 16 | 5657 | 21630 |
| 4917 | Ford | Freestar | 2005 | regular unleaded | NaN | 6.0 | AUTOMATIC | front wheel drive | 4.0 | Midsize | Passenger Minivan | 22 | 16 | 5657 | 26530 |
| 4918 | Ford | Freestar | 2005 | regular unleaded | NaN | 6.0 | AUTOMATIC | front wheel drive | 4.0 | Midsize | Passenger Minivan | 21 | 16 | 5657 | 29030 |
| 4919 | Ford | Freestar | 2005 | regular unleaded | NaN | 6.0 | AUTOMATIC | front wheel drive | 4.0 | Midsize | Passenger Minivan | 21 | 16 | 5657 | 32755 |
| 5778 | Mitsubishi | i-MiEV | 2014 | electric | NaN | NaN | DIRECT_DRIVE | rear wheel drive | 4.0 | Compact | 4dr Hatchback | 99 | 126 | 436 | 22995 |
| 5825 | Chevrolet | Impala | 2015 | flex-fuel (unleaded/natural gas) | NaN | 6.0 | AUTOMATIC | front wheel drive | 4.0 | Large | Sedan | 25 | 17 | 1385 | 40660 |
| 5830 | Chevrolet | Impala | 2015 | flex-fuel (unleaded/natural gas) | NaN | 6.0 | AUTOMATIC | front wheel drive | 4.0 | Large | Sedan | 25 | 17 | 1385 | 37535 |
| 5831 | Chevrolet | Impala | 2016 | flex-fuel (unleaded/natural gas) | NaN | 6.0 | AUTOMATIC | front wheel drive | 4.0 | Large | Sedan | 25 | 17 | 1385 | 40810 |
| 5833 | Chevrolet | Impala | 2016 | flex-fuel (unleaded/natural gas) | NaN | 6.0 | AUTOMATIC | front wheel drive | 4.0 | Large | Sedan | 25 | 17 | 1385 | 37570 |
| 5839 | Chevrolet | Impala | 2017 | flex-fuel (unleaded/natural gas) | NaN | 6.0 | AUTOMATIC | front wheel drive | 4.0 | Large | Sedan | 25 | 17 | 1385 | 37675 |
| 5840 | Chevrolet | Impala | 2017 | flex-fuel (unleaded/natural gas) | NaN | 6.0 | AUTOMATIC | front wheel drive | 4.0 | Large | Sedan | 25 | 17 | 1385 | 40915 |
| 6385 | Nissan | Leaf | 2014 | electric | NaN | 0.0 | DIRECT_DRIVE | front wheel drive | 4.0 | Compact | 4dr Hatchback | 101 | 126 | 2009 | 35020 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 6578 | Mercedes-Benz | M-Class | 2015 | diesel | NaN | 4.0 | AUTOMATIC | all wheel drive | 4.0 | Midsize | 4dr SUV | 29 | 22 | 617 | 49800 |
| 6908 | Lincoln | MKZ | 2017 | regular unleaded | NaN | 4.0 | AUTOMATIC | front wheel drive | 4.0 | Midsize | Sedan | 38 | 41 | 61 | 35010 |
| 6910 | Lincoln | MKZ | 2017 | regular unleaded | NaN | 4.0 | AUTOMATIC | front wheel drive | 4.0 | Midsize | Sedan | 38 | 41 | 61 | 39510 |
| 6916 | Lincoln | MKZ | 2017 | regular unleaded | NaN | 4.0 | AUTOMATIC | front wheel drive | 4.0 | Midsize | Sedan | 38 | 41 | 61 | 36760 |
| 6918 | Lincoln | MKZ | 2017 | regular unleaded | NaN | 4.0 | AUTOMATIC | front wheel drive | 4.0 | Midsize | Sedan | 38 | 41 | 61 | 47670 |
| 6921 | Tesla | Model S | 2014 | electric | NaN | 0.0 | DIRECT_DRIVE | rear wheel drive | 4.0 | Large | Sedan | 90 | 88 | 1391 | 79900 |
| 6922 | Tesla | Model S | 2014 | electric | NaN | 0.0 | DIRECT_DRIVE | rear wheel drive | 4.0 | Large | Sedan | 97 | 94 | 1391 | 69900 |
| 6923 | Tesla | Model S | 2014 | electric | NaN | 0.0 | DIRECT_DRIVE | all wheel drive | 4.0 | Large | Sedan | 94 | 86 | 1391 | 104500 |
| 6924 | Tesla | Model S | 2014 | electric | NaN | 0.0 | DIRECT_DRIVE | rear wheel drive | 4.0 | Large | Sedan | 90 | 88 | 1391 | 93400 |
| 6925 | Tesla | Model S | 2015 | electric | NaN | 0.0 | DIRECT_DRIVE | rear wheel drive | 4.0 | Large | Sedan | 97 | 94 | 1391 | 69900 |
| 6926 | Tesla | Model S | 2015 | electric | NaN | 0.0 | DIRECT_DRIVE | all wheel drive | 4.0 | Large | Sedan | 102 | 101 | 1391 | 75000 |
| 6927 | Tesla | Model S | 2015 | electric | NaN | 0.0 | DIRECT_DRIVE | all wheel drive | 4.0 | Large | Sedan | 106 | 95 | 1391 | 85000 |
| 6928 | Tesla | Model S | 2015 | electric | NaN | 0.0 | DIRECT_DRIVE | all wheel drive | 4.0 | Large | Sedan | 98 | 89 | 1391 | 105000 |
| 6929 | Tesla | Model S | 2015 | electric | NaN | 0.0 | DIRECT_DRIVE | rear wheel drive | 4.0 | Large | Sedan | 90 | 88 | 1391 | 80000 |
| 6930 | Tesla | Model S | 2016 | electric | NaN | 0.0 | DIRECT_DRIVE | all wheel drive | NaN | Large | Sedan | 105 | 102 | 1391 | 79500 |
| 6931 | Tesla | Model S | 2016 | electric | NaN | 0.0 | DIRECT_DRIVE | all wheel drive | NaN | Large | Sedan | 101 | 98 | 1391 | 66000 |
| 6932 | Tesla | Model S | 2016 | electric | NaN | 0.0 | DIRECT_DRIVE | all wheel drive | NaN | Large | Sedan | 105 | 92 | 1391 | 134500 |
| 6933 | Tesla | Model S | 2016 | electric | NaN | 0.0 | DIRECT_DRIVE | rear wheel drive | NaN | Large | Sedan | 100 | 97 | 1391 | 74500 |
| 6934 | Tesla | Model S | 2016 | electric | NaN | 0.0 | DIRECT_DRIVE | all wheel drive | NaN | Large | Sedan | 107 | 101 | 1391 | 71000 |
| 6935 | Tesla | Model S | 2016 | electric | NaN | 0.0 | DIRECT_DRIVE | all wheel drive | 4.0 | Large | Sedan | 102 | 101 | 1391 | 75000 |
| 6936 | Tesla | Model S | 2016 | electric | NaN | 0.0 | DIRECT_DRIVE | all wheel drive | 4.0 | Large | Sedan | 107 | 101 | 1391 | 89500 |
| 6937 | Tesla | Model S | 2016 | electric | NaN | 0.0 | DIRECT_DRIVE | all wheel drive | 4.0 | Large | Sedan | 100 | 91 | 1391 | 112000 |
| 6938 | Tesla | Model S | 2016 | electric | NaN | 0.0 | DIRECT_DRIVE | rear wheel drive | 4.0 | Large | Sedan | 90 | 88 | 1391 | 70000 |
| 8374 | Toyota | RAV4 EV | 2013 | electric | NaN | 0.0 | DIRECT_DRIVE | front wheel drive | 4.0 | Midsize | 4dr SUV | 74 | 78 | 2031 | 49800 |
| 8375 | Toyota | RAV4 EV | 2014 | electric | NaN | 0.0 | DIRECT_DRIVE | front wheel drive | 4.0 | Midsize | 4dr SUV | 74 | 78 | 2031 | 49800 |
| 9850 | Kia | Soul EV | 2015 | electric | NaN | 0.0 | DIRECT_DRIVE | front wheel drive | 4.0 | Compact | Wagon | 92 | 120 | 1720 | 35700 |
| 9851 | Kia | Soul EV | 2015 | electric | NaN | 0.0 | DIRECT_DRIVE | front wheel drive | 4.0 | Compact | Wagon | 92 | 120 | 1720 | 33700 |
| 9852 | Kia | Soul EV | 2016 | electric | NaN | 0.0 | DIRECT_DRIVE | front wheel drive | 4.0 | Compact | Wagon | 92 | 120 | 1720 | 33950 |
| 9853 | Kia | Soul EV | 2016 | electric | NaN | 0.0 | DIRECT_DRIVE | front wheel drive | 4.0 | Compact | Wagon | 92 | 120 | 1720 | 31950 |
| 9854 | Kia | Soul EV | 2016 | electric | NaN | 0.0 | DIRECT_DRIVE | front wheel drive | 4.0 | Compact | Wagon | 92 | 120 | 1720 | 35950 |
69 rows × 15 columns
Null values for model Fiat model 500e
Fill in Engine HP
# Couldn't compare to anything else provided in data
df[df['Model'] == '500e']
| Make | Model | Year | Engine Fuel Type | Engine HP | Engine Cylinders | Transmission Type | Driven_Wheels | Number of Doors | Vehicle Size | Vehicle Style | highway MPG | city mpg | Popularity | MSRP | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 539 | FIAT | 500e | 2015 | electric | NaN | 0.0 | DIRECT_DRIVE | front wheel drive | 2.0 | Compact | 2dr Hatchback | 108 | 122 | 819 | 31800 |
| 540 | FIAT | 500e | 2016 | electric | NaN | 0.0 | DIRECT_DRIVE | front wheel drive | 2.0 | Compact | 2dr Hatchback | 103 | 121 | 819 | 31800 |
| 541 | FIAT | 500e | 2017 | electric | NaN | 0.0 | DIRECT_DRIVE | front wheel drive | 2.0 | Compact | 2dr Hatchback | 103 | 121 | 819 | 31800 |
# Searched Web for Engine HP and all years are the same
for i, j in df['Engine HP'].iteritems():
if np.isnan(df['Engine HP'][i]):
# print (i,j)
if df['Make'][i] == 'FIAT':
df['Engine HP'][i] = 111
/anaconda3/lib/python3.6/site-packages/ipykernel/__main__.py:7: SettingWithCopyWarning:
A value is trying to be set on a copy of a slice from a DataFrame
See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
df[df['Model'] == '500e']
| Make | Model | Year | Engine Fuel Type | Engine HP | Engine Cylinders | Transmission Type | Driven_Wheels | Number of Doors | Vehicle Size | Vehicle Style | highway MPG | city mpg | Popularity | MSRP | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 539 | FIAT | 500e | 2015 | electric | 111.0 | 0.0 | DIRECT_DRIVE | front wheel drive | 2.0 | Compact | 2dr Hatchback | 108 | 122 | 819 | 31800 |
| 540 | FIAT | 500e | 2016 | electric | 111.0 | 0.0 | DIRECT_DRIVE | front wheel drive | 2.0 | Compact | 2dr Hatchback | 103 | 121 | 819 | 31800 |
| 541 | FIAT | 500e | 2017 | electric | 111.0 | 0.0 | DIRECT_DRIVE | front wheel drive | 2.0 | Compact | 2dr Hatchback | 103 | 121 | 819 | 31800 |
df['Engine HP'].isnull().sum()
66
model Continental null values
Fill in Engine HP
df[df['Model'] == 'Continental']
| Make | Model | Year | Engine Fuel Type | Engine HP | Engine Cylinders | Transmission Type | Driven_Wheels | Number of Doors | Vehicle Size | Vehicle Style | highway MPG | city mpg | Popularity | MSRP | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2891 | Bentley | Continental | 2001 | premium unleaded (required) | 420.0 | 8.0 | AUTOMATIC | rear wheel drive | 2.0 | Large | Coupe | 15 | 10 | 520 | 299900 |
| 2892 | Bentley | Continental | 2001 | premium unleaded (required) | 400.0 | 8.0 | AUTOMATIC | rear wheel drive | 2.0 | Large | Coupe | 15 | 10 | 520 | 279900 |
| 2893 | Bentley | Continental | 2001 | premium unleaded (required) | 420.0 | 8.0 | AUTOMATIC | rear wheel drive | 2.0 | Large | Coupe | 15 | 10 | 520 | 309900 |
| 2894 | Bentley | Continental | 2001 | premium unleaded (required) | 420.0 | 8.0 | AUTOMATIC | rear wheel drive | 2.0 | Large | Coupe | 15 | 10 | 520 | 319900 |
| 2895 | Bentley | Continental | 2003 | premium unleaded (required) | 420.0 | 8.0 | AUTOMATIC | rear wheel drive | 2.0 | Large | Coupe | 15 | 10 | 520 | 328990 |
| 2896 | Bentley | Continental | 2003 | premium unleaded (required) | 420.0 | 8.0 | AUTOMATIC | rear wheel drive | 2.0 | Large | Coupe | 15 | 10 | 520 | 318990 |
| 2897 | Lincoln | Continental | 2001 | regular unleaded | 275.0 | 8.0 | AUTOMATIC | front wheel drive | 4.0 | Large | Sedan | 23 | 15 | 61 | 39660 |
| 2898 | Lincoln | Continental | 2002 | premium unleaded (required) | 275.0 | 8.0 | AUTOMATIC | front wheel drive | 4.0 | Large | Sedan | 23 | 15 | 61 | 39895 |
| 2899 | Lincoln | Continental | 2002 | premium unleaded (required) | 275.0 | 8.0 | AUTOMATIC | front wheel drive | 4.0 | Large | Sedan | 23 | 15 | 61 | 38185 |
| 2900 | Lincoln | Continental | 2002 | premium unleaded (required) | 275.0 | 8.0 | AUTOMATIC | front wheel drive | 4.0 | Large | Sedan | 23 | 15 | 61 | 38790 |
| 2901 | Lincoln | Continental | 2002 | premium unleaded (required) | 275.0 | 8.0 | AUTOMATIC | front wheel drive | 4.0 | Large | Sedan | 23 | 15 | 61 | 39775 |
| 2902 | Lincoln | Continental | 2017 | regular unleaded | 305.0 | 6.0 | AUTOMATIC | front wheel drive | 4.0 | Large | Sedan | 26 | 17 | 61 | 44560 |
| 2903 | Lincoln | Continental | 2017 | regular unleaded | 305.0 | 6.0 | AUTOMATIC | all wheel drive | 4.0 | Large | Sedan | 24 | 16 | 61 | 46560 |
| 2904 | Lincoln | Continental | 2017 | regular unleaded | 305.0 | 6.0 | AUTOMATIC | all wheel drive | 4.0 | Large | Sedan | 24 | 16 | 61 | 49515 |
| 2905 | Lincoln | Continental | 2017 | premium unleaded (recommended) | NaN | 6.0 | AUTOMATIC | all wheel drive | 4.0 | Large | Sedan | 25 | 17 | 61 | 55915 |
| 2906 | Lincoln | Continental | 2017 | premium unleaded (recommended) | NaN | 6.0 | AUTOMATIC | front wheel drive | 4.0 | Large | Sedan | 27 | 18 | 61 | 62915 |
| 2907 | Lincoln | Continental | 2017 | premium unleaded (recommended) | NaN | 6.0 | AUTOMATIC | front wheel drive | 4.0 | Large | Sedan | 27 | 18 | 61 | 53915 |
| 2908 | Lincoln | Continental | 2017 | premium unleaded (recommended) | NaN | 6.0 | AUTOMATIC | all wheel drive | 4.0 | Large | Sedan | 25 | 17 | 61 | 64915 |
| 2909 | Lincoln | Continental | 2017 | regular unleaded | 305.0 | 6.0 | AUTOMATIC | front wheel drive | 4.0 | Large | Sedan | 26 | 17 | 61 | 47515 |
df.loc[2905,'Model'] = 'Continental R'
df.loc[2906,'Model'] = 'Continental SR'
df.loc[2907,'Model'] = 'Continental SR'
df.loc[2908,'Model'] = 'Continental R'
df.loc[2905]
Make Lincoln
Model Continental R
Year 2017
Engine Fuel Type premium unleaded (recommended)
Engine HP NaN
Engine Cylinders 6
Transmission Type AUTOMATIC
Driven_Wheels all wheel drive
Number of Doors 4
Vehicle Size Large
Vehicle Style Sedan
highway MPG 25
city mpg 17
Popularity 61
MSRP 55915
Name: 2905, dtype: object
for i, j in df['Engine HP'].iteritems():
if np.isnan(df['Engine HP'][i]):
# print (i,j)
if df['Model'][i] == 'Continental R':
df['Engine HP'][i] = 400
elif df['Model'][i] == 'Continental SR':
df['Engine HP'][i] = 335
/anaconda3/lib/python3.6/site-packages/ipykernel/__main__.py:5: SettingWithCopyWarning:
A value is trying to be set on a copy of a slice from a DataFrame
See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
/anaconda3/lib/python3.6/site-packages/ipykernel/__main__.py:7: SettingWithCopyWarning:
A value is trying to be set on a copy of a slice from a DataFrame
See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
df[df['Model'] == 'Continental R']
| Make | Model | Year | Engine Fuel Type | Engine HP | Engine Cylinders | Transmission Type | Driven_Wheels | Number of Doors | Vehicle Size | Vehicle Style | highway MPG | city mpg | Popularity | MSRP | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2905 | Lincoln | Continental R | 2017 | premium unleaded (recommended) | 400.0 | 6.0 | AUTOMATIC | all wheel drive | 4.0 | Large | Sedan | 25 | 17 | 61 | 55915 |
| 2908 | Lincoln | Continental R | 2017 | premium unleaded (recommended) | 400.0 | 6.0 | AUTOMATIC | all wheel drive | 4.0 | Large | Sedan | 25 | 17 | 61 | 64915 |
# Number is still going down
df['Engine HP'].isnull().sum()
62
model Escape null values
Fill in Engine HP
df[df['Model'] == 'Escape']
| Make | Model | Year | Engine Fuel Type | Engine HP | Engine Cylinders | Transmission Type | Driven_Wheels | Number of Doors | Vehicle Size | Vehicle Style | highway MPG | city mpg | Popularity | MSRP | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 4192 | Ford | Escape | 2015 | premium unleaded (recommended) | 178.0 | 4.0 | AUTOMATIC | front wheel drive | 4.0 | Compact | 4dr SUV | 32 | 23 | 5657 | 29735 |
| 4193 | Ford | Escape | 2015 | premium unleaded (recommended) | 178.0 | 4.0 | AUTOMATIC | front wheel drive | 4.0 | Compact | 4dr SUV | 32 | 23 | 5657 | 25650 |
| 4194 | Ford | Escape | 2015 | premium unleaded (recommended) | 178.0 | 4.0 | AUTOMATIC | all wheel drive | 4.0 | Compact | 4dr SUV | 30 | 22 | 5657 | 27400 |
| 4195 | Ford | Escape | 2015 | regular unleaded | 168.0 | 4.0 | AUTOMATIC | front wheel drive | 4.0 | Compact | 4dr SUV | 31 | 22 | 5657 | 23450 |
| 4196 | Ford | Escape | 2015 | premium unleaded (recommended) | 178.0 | 4.0 | AUTOMATIC | all wheel drive | 4.0 | Compact | 4dr SUV | 30 | 22 | 5657 | 31485 |
| 4197 | Ford | Escape | 2016 | premium unleaded (recommended) | 178.0 | 4.0 | AUTOMATIC | all wheel drive | 4.0 | Compact | 4dr SUV | 29 | 22 | 5657 | 27540 |
| 4198 | Ford | Escape | 2016 | premium unleaded (recommended) | 178.0 | 4.0 | AUTOMATIC | front wheel drive | 4.0 | Compact | 4dr SUV | 32 | 23 | 5657 | 25790 |
| 4199 | Ford | Escape | 2016 | premium unleaded (recommended) | 178.0 | 4.0 | AUTOMATIC | front wheel drive | 4.0 | Compact | 4dr SUV | 32 | 23 | 5657 | 29995 |
| 4200 | Ford | Escape | 2016 | regular unleaded | 168.0 | 4.0 | AUTOMATIC | front wheel drive | 4.0 | Compact | 4dr SUV | 31 | 22 | 5657 | 23590 |
| 4201 | Ford | Escape | 2016 | premium unleaded (recommended) | 178.0 | 4.0 | AUTOMATIC | all wheel drive | 4.0 | Compact | 4dr SUV | 29 | 22 | 5657 | 31745 |
| 4202 | Ford | Escape | 2017 | regular unleaded | 168.0 | 4.0 | AUTOMATIC | front wheel drive | 4.0 | Compact | 4dr SUV | 29 | 21 | 5657 | 23600 |
| 4203 | Ford | Escape | 2017 | regular unleaded | NaN | 4.0 | AUTOMATIC | front wheel drive | 4.0 | Compact | 4dr SUV | 30 | 23 | 5657 | 29100 |
| 4204 | Ford | Escape | 2017 | regular unleaded | NaN | 4.0 | AUTOMATIC | all wheel drive | 4.0 | Compact | 4dr SUV | 28 | 22 | 5657 | 30850 |
| 4205 | Ford | Escape | 2017 | regular unleaded | NaN | 4.0 | AUTOMATIC | all wheel drive | 4.0 | Compact | 4dr SUV | 28 | 22 | 5657 | 26850 |
| 4206 | Ford | Escape | 2017 | regular unleaded | NaN | 4.0 | AUTOMATIC | front wheel drive | 4.0 | Compact | 4dr SUV | 30 | 23 | 5657 | 25100 |
df.loc[4203,'Model'] = 'Escape S'
df.loc[4204,'Model'] = 'Escape SE'
df.loc[4205,'Model'] = 'Escape SE'
df.loc[4206,'Model'] = 'Escape S'
df.loc[4205]
Make Ford
Model Escape SE
Year 2017
Engine Fuel Type regular unleaded
Engine HP NaN
Engine Cylinders 4
Transmission Type AUTOMATIC
Driven_Wheels all wheel drive
Number of Doors 4
Vehicle Size Compact
Vehicle Style 4dr SUV
highway MPG 28
city mpg 22
Popularity 5657
MSRP 26850
Name: 4205, dtype: object
for i, j in df['Engine HP'].iteritems():
if np.isnan(df['Engine HP'][i]):
# print (i,j)
if df['Model'][i] == 'Escape S':
df['Engine HP'][i] = 168
elif df['Model'][i] == 'Escape SE':
df['Engine HP'][i] = 179
/anaconda3/lib/python3.6/site-packages/ipykernel/__main__.py:5: SettingWithCopyWarning:
A value is trying to be set on a copy of a slice from a DataFrame
See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
/anaconda3/lib/python3.6/site-packages/ipykernel/__main__.py:7: SettingWithCopyWarning:
A value is trying to be set on a copy of a slice from a DataFrame
See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
# Number is still going down
df['Engine HP'].isnull().sum()
58
model Fit EV null values
Fill in Engine HP
df[df['Model'] == 'Fit EV']
| Make | Model | Year | Engine Fuel Type | Engine HP | Engine Cylinders | Transmission Type | Driven_Wheels | Number of Doors | Vehicle Size | Vehicle Style | highway MPG | city mpg | Popularity | MSRP | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 4705 | Honda | Fit EV | 2013 | electric | NaN | 0.0 | DIRECT_DRIVE | front wheel drive | 4.0 | Compact | 4dr Hatchback | 105 | 132 | 2202 | 36625 |
| 4706 | Honda | Fit EV | 2014 | electric | NaN | 0.0 | DIRECT_DRIVE | front wheel drive | 4.0 | Compact | 4dr Hatchback | 105 | 132 | 2202 | 36625 |
for i, j in df['Engine HP'].iteritems():
if np.isnan(df['Engine HP'][i]):
# print (i,j)
if df['Model'][i] == 'Fit EV':
df['Engine HP'][i] = 189
/anaconda3/lib/python3.6/site-packages/ipykernel/__main__.py:5: SettingWithCopyWarning:
A value is trying to be set on a copy of a slice from a DataFrame
See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
df[df['Model'] == 'Fit EV']
| Make | Model | Year | Engine Fuel Type | Engine HP | Engine Cylinders | Transmission Type | Driven_Wheels | Number of Doors | Vehicle Size | Vehicle Style | highway MPG | city mpg | Popularity | MSRP | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 4705 | Honda | Fit EV | 2013 | electric | 189.0 | 0.0 | DIRECT_DRIVE | front wheel drive | 4.0 | Compact | 4dr Hatchback | 105 | 132 | 2202 | 36625 |
| 4706 | Honda | Fit EV | 2014 | electric | 189.0 | 0.0 | DIRECT_DRIVE | front wheel drive | 4.0 | Compact | 4dr Hatchback | 105 | 132 | 2202 | 36625 |
# Number is still going down
df['Engine HP'].isnull().sum()
56
model Focus null values
Fill in Engine HP
df[df['Model'] == 'Focus']
| Make | Model | Year | Engine Fuel Type | Engine HP | Engine Cylinders | Transmission Type | Driven_Wheels | Number of Doors | Vehicle Size | Vehicle Style | highway MPG | city mpg | Popularity | MSRP | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 4780 | Ford | Focus | 2015 | flex-fuel (unleaded/E85) | 160.0 | 4.0 | MANUAL | front wheel drive | 4.0 | Compact | Sedan | 36 | 26 | 5657 | 18460 |
| 4781 | Ford | Focus | 2015 | flex-fuel (unleaded/E85) | 160.0 | 4.0 | MANUAL | front wheel drive | 4.0 | Compact | 4dr Hatchback | 36 | 26 | 5657 | 18960 |
| 4782 | Ford | Focus | 2015 | flex-fuel (unleaded/E85) | 160.0 | 4.0 | AUTOMATED_MANUAL | front wheel drive | 4.0 | Compact | Sedan | 40 | 27 | 5657 | 23170 |
| 4783 | Ford | Focus | 2015 | flex-fuel (unleaded/E85) | 160.0 | 4.0 | MANUAL | front wheel drive | 4.0 | Compact | Sedan | 36 | 26 | 5657 | 17170 |
| 4784 | Ford | Focus | 2015 | flex-fuel (unleaded/E85) | 160.0 | 4.0 | AUTOMATED_MANUAL | front wheel drive | 4.0 | Compact | 4dr Hatchback | 40 | 27 | 5657 | 23670 |
| 4785 | Ford | Focus | 2015 | electric | NaN | 0.0 | DIRECT_DRIVE | front wheel drive | 4.0 | Compact | 4dr Hatchback | 99 | 110 | 5657 | 29170 |
| 4786 | Ford | Focus | 2016 | flex-fuel (unleaded/E85) | 160.0 | 4.0 | MANUAL | front wheel drive | 4.0 | Compact | Sedan | 36 | 26 | 5657 | 18515 |
| 4787 | Ford | Focus | 2016 | flex-fuel (unleaded/E85) | 160.0 | 4.0 | AUTOMATED_MANUAL | front wheel drive | 4.0 | Compact | 4dr Hatchback | 40 | 27 | 5657 | 23725 |
| 4788 | Ford | Focus | 2016 | flex-fuel (unleaded/E85) | 160.0 | 4.0 | AUTOMATED_MANUAL | front wheel drive | 4.0 | Compact | Sedan | 40 | 27 | 5657 | 23225 |
| 4789 | Ford | Focus | 2016 | electric | NaN | 0.0 | DIRECT_DRIVE | front wheel drive | 4.0 | Compact | 4dr Hatchback | 99 | 110 | 5657 | 29170 |
| 4790 | Ford | Focus | 2016 | flex-fuel (unleaded/E85) | 160.0 | 4.0 | MANUAL | front wheel drive | 4.0 | Compact | 4dr Hatchback | 36 | 26 | 5657 | 19015 |
| 4791 | Ford | Focus | 2016 | flex-fuel (unleaded/E85) | 160.0 | 4.0 | MANUAL | front wheel drive | 4.0 | Compact | Sedan | 36 | 26 | 5657 | 17225 |
| 4792 | Ford | Focus | 2017 | flex-fuel (unleaded/E85) | 160.0 | 4.0 | AUTOMATED_MANUAL | front wheel drive | 4.0 | Compact | 4dr Hatchback | 40 | 27 | 5657 | 21675 |
| 4793 | Ford | Focus | 2017 | regular unleaded | 123.0 | 3.0 | MANUAL | front wheel drive | 4.0 | Compact | Sedan | 42 | 30 | 5657 | 18175 |
| 4794 | Ford | Focus | 2017 | flex-fuel (unleaded/E85) | 160.0 | 4.0 | MANUAL | front wheel drive | 4.0 | Compact | Sedan | 36 | 26 | 5657 | 16775 |
| 4795 | Ford | Focus | 2017 | flex-fuel (unleaded/E85) | 160.0 | 4.0 | AUTOMATED_MANUAL | front wheel drive | 4.0 | Compact | 4dr Hatchback | 40 | 27 | 5657 | 24075 |
| 4796 | Ford | Focus | 2017 | flex-fuel (unleaded/E85) | 160.0 | 4.0 | AUTOMATED_MANUAL | front wheel drive | 4.0 | Compact | Sedan | 40 | 27 | 5657 | 23575 |
| 4797 | Ford | Focus | 2017 | flex-fuel (unleaded/E85) | 160.0 | 4.0 | AUTOMATED_MANUAL | front wheel drive | 4.0 | Compact | Sedan | 40 | 27 | 5657 | 21175 |
| 4798 | Ford | Focus | 2017 | electric | NaN | 0.0 | DIRECT_DRIVE | front wheel drive | 4.0 | Compact | 4dr Hatchback | 99 | 110 | 5657 | 29120 |
| 4799 | Ford | Focus | 2017 | flex-fuel (unleaded/E85) | 160.0 | 4.0 | AUTOMATED_MANUAL | front wheel drive | 4.0 | Compact | 4dr Hatchback | 40 | 27 | 5657 | 19765 |
for i, j in df['Engine HP'].iteritems():
if np.isnan(df['Engine HP'][i]):
# print (i,j)
if df['Model'][i] == 'Focus':
df['Engine HP'][i] = 143
/anaconda3/lib/python3.6/site-packages/ipykernel/__main__.py:5: SettingWithCopyWarning:
A value is trying to be set on a copy of a slice from a DataFrame
See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
# Number is still going down
df['Engine HP'].isnull().sum()
53
model Freestar null values
Fill in Engine HP
df[df['Model'] == 'Freestar']
| Make | Model | Year | Engine Fuel Type | Engine HP | Engine Cylinders | Transmission Type | Driven_Wheels | Number of Doors | Vehicle Size | Vehicle Style | highway MPG | city mpg | Popularity | MSRP | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 4914 | Ford | Freestar | 2005 | regular unleaded | NaN | 6.0 | AUTOMATIC | front wheel drive | 4.0 | Midsize | Passenger Minivan | 22 | 16 | 5657 | 28030 |
| 4915 | Ford | Freestar | 2005 | regular unleaded | NaN | 6.0 | AUTOMATIC | front wheel drive | 4.0 | Midsize | Passenger Minivan | 22 | 16 | 5657 | 23930 |
| 4916 | Ford | Freestar | 2005 | regular unleaded | NaN | 6.0 | AUTOMATIC | front wheel drive | 4.0 | Midsize | Cargo Minivan | 22 | 16 | 5657 | 21630 |
| 4917 | Ford | Freestar | 2005 | regular unleaded | NaN | 6.0 | AUTOMATIC | front wheel drive | 4.0 | Midsize | Passenger Minivan | 22 | 16 | 5657 | 26530 |
| 4918 | Ford | Freestar | 2005 | regular unleaded | NaN | 6.0 | AUTOMATIC | front wheel drive | 4.0 | Midsize | Passenger Minivan | 21 | 16 | 5657 | 29030 |
| 4919 | Ford | Freestar | 2005 | regular unleaded | NaN | 6.0 | AUTOMATIC | front wheel drive | 4.0 | Midsize | Passenger Minivan | 21 | 16 | 5657 | 32755 |
| 4920 | Ford | Freestar | 2006 | regular unleaded | 193.0 | 6.0 | AUTOMATIC | front wheel drive | 4.0 | Midsize | Cargo Minivan | 22 | 16 | 5657 | 19650 |
| 4921 | Ford | Freestar | 2006 | regular unleaded | 201.0 | 6.0 | AUTOMATIC | front wheel drive | 4.0 | Midsize | Passenger Minivan | 21 | 15 | 5657 | 29575 |
| 4922 | Ford | Freestar | 2006 | regular unleaded | 193.0 | 6.0 | AUTOMATIC | front wheel drive | 4.0 | Midsize | Passenger Minivan | 22 | 16 | 5657 | 23655 |
| 4923 | Ford | Freestar | 2006 | regular unleaded | 201.0 | 6.0 | AUTOMATIC | front wheel drive | 4.0 | Midsize | Passenger Minivan | 21 | 15 | 5657 | 26615 |
| 4924 | Ford | Freestar | 2007 | regular unleaded | 201.0 | 6.0 | AUTOMATIC | front wheel drive | 4.0 | Midsize | Passenger Minivan | 21 | 15 | 5657 | 26665 |
| 4925 | Ford | Freestar | 2007 | regular unleaded | 201.0 | 6.0 | AUTOMATIC | front wheel drive | 4.0 | Midsize | Passenger Minivan | 21 | 15 | 5657 | 23705 |
| 4926 | Ford | Freestar | 2007 | regular unleaded | 201.0 | 6.0 | AUTOMATIC | front wheel drive | 4.0 | Midsize | Passenger Minivan | 21 | 15 | 5657 | 29575 |
| 4927 | Ford | Freestar | 2007 | regular unleaded | 201.0 | 6.0 | AUTOMATIC | front wheel drive | 4.0 | Midsize | Cargo Minivan | 21 | 15 | 5657 | 19700 |
for i, j in df['Engine HP'].iteritems():
if np.isnan(df['Engine HP'][i]):
# print (i,j)
if df['Model'][i] == 'Freestar':
df['Engine HP'][i] = 201
/anaconda3/lib/python3.6/site-packages/ipykernel/__main__.py:5: SettingWithCopyWarning:
A value is trying to be set on a copy of a slice from a DataFrame
See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
# Number is still going down
df['Engine HP'].isnull().sum()
47
model I-MIEV null values
Fill in Engine HP
df[df['Model'] == 'i-MiEV']
| Make | Model | Year | Engine Fuel Type | Engine HP | Engine Cylinders | Transmission Type | Driven_Wheels | Number of Doors | Vehicle Size | Vehicle Style | highway MPG | city mpg | Popularity | MSRP | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 5778 | Mitsubishi | i-MiEV | 2014 | electric | NaN | NaN | DIRECT_DRIVE | rear wheel drive | 4.0 | Compact | 4dr Hatchback | 99 | 126 | 436 | 22995 |
| 5779 | Mitsubishi | i-MiEV | 2016 | electric | 66.0 | NaN | DIRECT_DRIVE | rear wheel drive | 4.0 | Compact | 4dr Hatchback | 99 | 126 | 436 | 22995 |
| 5780 | Mitsubishi | i-MiEV | 2017 | electric | 66.0 | NaN | DIRECT_DRIVE | rear wheel drive | 4.0 | Compact | 4dr Hatchback | 102 | 121 | 436 | 22995 |
for i, j in df['Engine HP'].iteritems():
if np.isnan(df['Engine HP'][i]):
# print (i,j)
if df['Model'][i] == 'i-MiEV':
df['Engine HP'][i] = 201
/anaconda3/lib/python3.6/site-packages/ipykernel/__main__.py:5: SettingWithCopyWarning:
A value is trying to be set on a copy of a slice from a DataFrame
See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
# Number is still going down
df['Engine HP'].isnull().sum()
46
model Impala null values
Fill in Engine HP
# All years have the same HP for flex fuel v6
df[df['Model'] == 'Impala']
| Make | Model | Year | Engine Fuel Type | Engine HP | Engine Cylinders | Transmission Type | Driven_Wheels | Number of Doors | Vehicle Size | Vehicle Style | highway MPG | city mpg | Popularity | MSRP | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 5823 | Chevrolet | Impala | 2015 | regular unleaded | 195.0 | 4.0 | AUTOMATIC | front wheel drive | 4.0 | Large | Sedan | 31 | 21 | 1385 | 34465 |
| 5824 | Chevrolet | Impala | 2015 | regular unleaded | 195.0 | 4.0 | AUTOMATIC | front wheel drive | 4.0 | Large | Sedan | 31 | 21 | 1385 | 27060 |
| 5825 | Chevrolet | Impala | 2015 | flex-fuel (unleaded/natural gas) | NaN | 6.0 | AUTOMATIC | front wheel drive | 4.0 | Large | Sedan | 25 | 17 | 1385 | 40660 |
| 5826 | Chevrolet | Impala | 2015 | flex-fuel (unleaded/E85) | 305.0 | 6.0 | AUTOMATIC | front wheel drive | 4.0 | Large | Sedan | 29 | 19 | 1385 | 35440 |
| 5827 | Chevrolet | Impala | 2015 | flex-fuel (unleaded/E85) | 305.0 | 6.0 | AUTOMATIC | front wheel drive | 4.0 | Large | Sedan | 29 | 19 | 1385 | 30285 |
| 5828 | Chevrolet | Impala | 2015 | regular unleaded | 195.0 | 4.0 | AUTOMATIC | front wheel drive | 4.0 | Large | Sedan | 31 | 21 | 1385 | 29310 |
| 5830 | Chevrolet | Impala | 2015 | flex-fuel (unleaded/natural gas) | NaN | 6.0 | AUTOMATIC | front wheel drive | 4.0 | Large | Sedan | 25 | 17 | 1385 | 37535 |
| 5831 | Chevrolet | Impala | 2016 | flex-fuel (unleaded/natural gas) | NaN | 6.0 | AUTOMATIC | front wheel drive | 4.0 | Large | Sedan | 25 | 17 | 1385 | 40810 |
| 5832 | Chevrolet | Impala | 2016 | flex-fuel (unleaded/E85) | 305.0 | 6.0 | AUTOMATIC | front wheel drive | 4.0 | Large | Sedan | 29 | 19 | 1385 | 35540 |
| 5833 | Chevrolet | Impala | 2016 | flex-fuel (unleaded/natural gas) | NaN | 6.0 | AUTOMATIC | front wheel drive | 4.0 | Large | Sedan | 25 | 17 | 1385 | 37570 |
| 5834 | Chevrolet | Impala | 2016 | flex-fuel (unleaded/E85) | 305.0 | 6.0 | AUTOMATIC | front wheel drive | 4.0 | Large | Sedan | 29 | 19 | 1385 | 30435 |
| 5835 | Chevrolet | Impala | 2016 | regular unleaded | 195.0 | 4.0 | AUTOMATIC | front wheel drive | 4.0 | Large | Sedan | 31 | 22 | 1385 | 27095 |
| 5836 | Chevrolet | Impala | 2016 | regular unleaded | 195.0 | 4.0 | AUTOMATIC | front wheel drive | 4.0 | Large | Sedan | 31 | 22 | 1385 | 29460 |
| 5838 | Chevrolet | Impala | 2017 | flex-fuel (unleaded/E85) | 305.0 | 6.0 | AUTOMATIC | front wheel drive | 4.0 | Large | Sedan | 28 | 19 | 1385 | 35645 |
| 5839 | Chevrolet | Impala | 2017 | flex-fuel (unleaded/natural gas) | NaN | 6.0 | AUTOMATIC | front wheel drive | 4.0 | Large | Sedan | 25 | 17 | 1385 | 37675 |
| 5840 | Chevrolet | Impala | 2017 | flex-fuel (unleaded/natural gas) | NaN | 6.0 | AUTOMATIC | front wheel drive | 4.0 | Large | Sedan | 25 | 17 | 1385 | 40915 |
for i, j in df['Engine HP'].iteritems():
if np.isnan(df['Engine HP'][i]):
if df['Model'][i] == 'Impala':
df['Engine HP'][i] = 305
/anaconda3/lib/python3.6/site-packages/ipykernel/__main__.py:4: SettingWithCopyWarning:
A value is trying to be set on a copy of a slice from a DataFrame
See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
# Number is still going down
df['Engine HP'].isnull().sum()
40
model Leaf null values
Fill in Engine HP
df[df['Model'] == 'Leaf']
| Make | Model | Year | Engine Fuel Type | Engine HP | Engine Cylinders | Transmission Type | Driven_Wheels | Number of Doors | Vehicle Size | Vehicle Style | highway MPG | city mpg | Popularity | MSRP | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 6385 | Nissan | Leaf | 2014 | electric | NaN | 0.0 | DIRECT_DRIVE | front wheel drive | 4.0 | Compact | 4dr Hatchback | 101 | 126 | 2009 | 35020 |
| 6386 | Nissan | Leaf | 2014 | electric | NaN | 0.0 | DIRECT_DRIVE | front wheel drive | 4.0 | Compact | 4dr Hatchback | 101 | 126 | 2009 | 32000 |
| 6387 | Nissan | Leaf | 2014 | electric | NaN | 0.0 | DIRECT_DRIVE | front wheel drive | 4.0 | Compact | 4dr Hatchback | 101 | 126 | 2009 | 28980 |
| 6388 | Nissan | Leaf | 2015 | electric | NaN | 0.0 | DIRECT_DRIVE | front wheel drive | 4.0 | Compact | 4dr Hatchback | 101 | 126 | 2009 | 32100 |
| 6389 | Nissan | Leaf | 2015 | electric | NaN | 0.0 | DIRECT_DRIVE | front wheel drive | 4.0 | Compact | 4dr Hatchback | 101 | 126 | 2009 | 35120 |
| 6390 | Nissan | Leaf | 2015 | electric | NaN | 0.0 | DIRECT_DRIVE | front wheel drive | 4.0 | Compact | 4dr Hatchback | 101 | 126 | 2009 | 29010 |
| 6391 | Nissan | Leaf | 2015 | electric | NaN | 0.0 | DIRECT_DRIVE | front wheel drive | 4.0 | Compact | 4dr Hatchback | 101 | 126 | 2009 | 32000 |
| 6392 | Nissan | Leaf | 2016 | electric | NaN | 0.0 | DIRECT_DRIVE | front wheel drive | 4.0 | Compact | 4dr Hatchback | 101 | 126 | 2009 | 29010 |
| 6393 | Nissan | Leaf | 2016 | electric | NaN | 0.0 | DIRECT_DRIVE | front wheel drive | 4.0 | Compact | 4dr Hatchback | 101 | 124 | 2009 | 34200 |
| 6394 | Nissan | Leaf | 2016 | electric | NaN | 0.0 | DIRECT_DRIVE | front wheel drive | 4.0 | Compact | 4dr Hatchback | 101 | 124 | 2009 | 36790 |
# All leaf models are 110 HP
for i, j in df['Engine HP'].iteritems():
if np.isnan(df['Engine HP'][i]):
if df['Model'][i] == 'Leaf':
df['Engine HP'][i] = 110
/anaconda3/lib/python3.6/site-packages/ipykernel/__main__.py:5: SettingWithCopyWarning:
A value is trying to be set on a copy of a slice from a DataFrame
See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
# Number is still going down
df['Engine HP'].isnull().sum()
30
Null value for M-Class
df[df['Model'] == 'M-Class']
| Make | Model | Year | Engine Fuel Type | Engine HP | Engine Cylinders | Transmission Type | Driven_Wheels | Number of Doors | Vehicle Size | Vehicle Style | highway MPG | city mpg | Popularity | MSRP | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 6566 | Mercedes-Benz | M-Class | 2013 | diesel | 240.0 | 6.0 | AUTOMATIC | all wheel drive | 4.0 | Midsize | 4dr SUV | 28 | 20 | 617 | 51270 |
| 6567 | Mercedes-Benz | M-Class | 2013 | premium unleaded (required) | 518.0 | 8.0 | AUTOMATIC | all wheel drive | 4.0 | Midsize | 4dr SUV | 17 | 13 | 617 | 96100 |
| 6568 | Mercedes-Benz | M-Class | 2013 | premium unleaded (required) | 402.0 | 8.0 | AUTOMATIC | all wheel drive | 4.0 | Midsize | 4dr SUV | 20 | 14 | 617 | 58800 |
| 6569 | Mercedes-Benz | M-Class | 2013 | premium unleaded (required) | 302.0 | 6.0 | AUTOMATIC | all wheel drive | 4.0 | Midsize | 4dr SUV | 23 | 18 | 617 | 49770 |
| 6570 | Mercedes-Benz | M-Class | 2013 | premium unleaded (required) | 302.0 | 6.0 | AUTOMATIC | rear wheel drive | 4.0 | Midsize | 4dr SUV | 23 | 18 | 617 | 47270 |
| 6571 | Mercedes-Benz | M-Class | 2014 | premium unleaded (required) | 518.0 | 8.0 | AUTOMATIC | all wheel drive | 4.0 | Midsize | 4dr SUV | 17 | 13 | 617 | 97250 |
| 6572 | Mercedes-Benz | M-Class | 2014 | diesel | 240.0 | 6.0 | AUTOMATIC | all wheel drive | 4.0 | Midsize | 4dr SUV | 28 | 20 | 617 | 51790 |
| 6573 | Mercedes-Benz | M-Class | 2014 | premium unleaded (required) | 302.0 | 6.0 | AUTOMATIC | all wheel drive | 4.0 | Midsize | 4dr SUV | 22 | 17 | 617 | 50290 |
| 6574 | Mercedes-Benz | M-Class | 2014 | premium unleaded (required) | 402.0 | 8.0 | AUTOMATIC | all wheel drive | 4.0 | Midsize | 4dr SUV | 19 | 14 | 617 | 59450 |
| 6575 | Mercedes-Benz | M-Class | 2014 | premium unleaded (required) | 302.0 | 6.0 | AUTOMATIC | rear wheel drive | 4.0 | Midsize | 4dr SUV | 24 | 18 | 617 | 47790 |
| 6576 | Mercedes-Benz | M-Class | 2015 | premium unleaded (required) | 329.0 | 6.0 | AUTOMATIC | all wheel drive | 4.0 | Midsize | 4dr SUV | 22 | 18 | 617 | 62900 |
| 6577 | Mercedes-Benz | M-Class | 2015 | premium unleaded (required) | 302.0 | 6.0 | AUTOMATIC | rear wheel drive | 4.0 | Midsize | 4dr SUV | 24 | 18 | 617 | 48300 |
| 6578 | Mercedes-Benz | M-Class | 2015 | diesel | NaN | 4.0 | AUTOMATIC | all wheel drive | 4.0 | Midsize | 4dr SUV | 29 | 22 | 617 | 49800 |
| 6579 | Mercedes-Benz | M-Class | 2015 | premium unleaded (required) | 518.0 | 8.0 | AUTOMATIC | all wheel drive | 4.0 | Midsize | 4dr SUV | 17 | 13 | 617 | 98400 |
| 6580 | Mercedes-Benz | M-Class | 2015 | premium unleaded (required) | 302.0 | 6.0 | AUTOMATIC | all wheel drive | 4.0 | Midsize | 4dr SUV | 22 | 17 | 617 | 50800 |
for i, j in df['Engine HP'].iteritems():
if np.isnan(df['Engine HP'][i]):
if df['Model'][i] == 'M-Class':
df['Engine HP'][i] = 240
/anaconda3/lib/python3.6/site-packages/ipykernel/__main__.py:4: SettingWithCopyWarning:
A value is trying to be set on a copy of a slice from a DataFrame
See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
# Number is still going down
df['Engine HP'].isnull().sum()
29
Model MKZ
Fill in null Engine HP
# Only difference are the fuel type and Drivn_wheels
# 2.0L version can be front or all wheel drive
# Would still use 245 HP for year 2017
df[df['Model'] == 'MKZ']
| Make | Model | Year | Engine Fuel Type | Engine HP | Engine Cylinders | Transmission Type | Driven_Wheels | Number of Doors | Vehicle Size | Vehicle Style | highway MPG | city mpg | Popularity | MSRP | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 6896 | Lincoln | MKZ | 2015 | regular unleaded | 231.0 | 4.0 | AUTOMATIC | front wheel drive | 4.0 | Midsize | Sedan | 33 | 22 | 61 | 45555 |
| 6897 | Lincoln | MKZ | 2015 | regular unleaded | 231.0 | 4.0 | AUTOMATIC | front wheel drive | 4.0 | Midsize | Sedan | 33 | 22 | 61 | 35190 |
| 6898 | Lincoln | MKZ | 2015 | regular unleaded | 188.0 | 4.0 | AUTOMATIC | front wheel drive | 4.0 | Midsize | Sedan | 39 | 41 | 61 | 35190 |
| 6899 | Lincoln | MKZ | 2015 | regular unleaded | 231.0 | 4.0 | AUTOMATIC | all wheel drive | 4.0 | Midsize | Sedan | 31 | 22 | 61 | 37080 |
| 6900 | Lincoln | MKZ | 2015 | regular unleaded | 188.0 | 4.0 | AUTOMATIC | front wheel drive | 4.0 | Midsize | Sedan | 39 | 41 | 61 | 45555 |
| 6901 | Lincoln | MKZ | 2015 | regular unleaded | 231.0 | 4.0 | AUTOMATIC | all wheel drive | 4.0 | Midsize | Sedan | 31 | 22 | 61 | 47445 |
| 6902 | Lincoln | MKZ | 2016 | regular unleaded | 231.0 | 4.0 | AUTOMATIC | all wheel drive | 4.0 | Midsize | Sedan | 31 | 22 | 61 | 37080 |
| 6903 | Lincoln | MKZ | 2016 | regular unleaded | 188.0 | 4.0 | AUTOMATIC | front wheel drive | 4.0 | Midsize | Sedan | 39 | 41 | 61 | 45605 |
| 6904 | Lincoln | MKZ | 2016 | regular unleaded | 231.0 | 4.0 | AUTOMATIC | front wheel drive | 4.0 | Midsize | Sedan | 33 | 22 | 61 | 45605 |
| 6905 | Lincoln | MKZ | 2016 | regular unleaded | 231.0 | 4.0 | AUTOMATIC | all wheel drive | 4.0 | Midsize | Sedan | 31 | 22 | 61 | 47495 |
| 6906 | Lincoln | MKZ | 2016 | regular unleaded | 188.0 | 4.0 | AUTOMATIC | front wheel drive | 4.0 | Midsize | Sedan | 39 | 41 | 61 | 35190 |
| 6907 | Lincoln | MKZ | 2016 | regular unleaded | 231.0 | 4.0 | AUTOMATIC | front wheel drive | 4.0 | Midsize | Sedan | 33 | 22 | 61 | 35190 |
| 6908 | Lincoln | MKZ | 2017 | regular unleaded | NaN | 4.0 | AUTOMATIC | front wheel drive | 4.0 | Midsize | Sedan | 38 | 41 | 61 | 35010 |
| 6909 | Lincoln | MKZ | 2017 | premium unleaded (recommended) | 245.0 | 4.0 | AUTOMATIC | all wheel drive | 4.0 | Midsize | Sedan | 28 | 20 | 61 | 36900 |
| 6910 | Lincoln | MKZ | 2017 | regular unleaded | NaN | 4.0 | AUTOMATIC | front wheel drive | 4.0 | Midsize | Sedan | 38 | 41 | 61 | 39510 |
| 6911 | Lincoln | MKZ | 2017 | premium unleaded (recommended) | 245.0 | 4.0 | AUTOMATIC | front wheel drive | 4.0 | Midsize | Sedan | 31 | 21 | 61 | 39510 |
| 6912 | Lincoln | MKZ | 2017 | premium unleaded (recommended) | 245.0 | 4.0 | AUTOMATIC | all wheel drive | 4.0 | Midsize | Sedan | 28 | 20 | 61 | 49560 |
| 6913 | Lincoln | MKZ | 2017 | premium unleaded (recommended) | 245.0 | 4.0 | AUTOMATIC | front wheel drive | 4.0 | Midsize | Sedan | 31 | 21 | 61 | 35010 |
| 6914 | Lincoln | MKZ | 2017 | premium unleaded (recommended) | 245.0 | 4.0 | AUTOMATIC | all wheel drive | 4.0 | Midsize | Sedan | 28 | 20 | 61 | 41400 |
| 6915 | Lincoln | MKZ | 2017 | premium unleaded (recommended) | 245.0 | 4.0 | AUTOMATIC | front wheel drive | 4.0 | Midsize | Sedan | 31 | 21 | 61 | 47670 |
| 6916 | Lincoln | MKZ | 2017 | regular unleaded | NaN | 4.0 | AUTOMATIC | front wheel drive | 4.0 | Midsize | Sedan | 38 | 41 | 61 | 36760 |
| 6917 | Lincoln | MKZ | 2017 | premium unleaded (recommended) | 245.0 | 4.0 | AUTOMATIC | front wheel drive | 4.0 | Midsize | Sedan | 31 | 21 | 61 | 36760 |
| 6918 | Lincoln | MKZ | 2017 | regular unleaded | NaN | 4.0 | AUTOMATIC | front wheel drive | 4.0 | Midsize | Sedan | 38 | 41 | 61 | 47670 |
| 6919 | Lincoln | MKZ | 2017 | premium unleaded (recommended) | 245.0 | 4.0 | AUTOMATIC | all wheel drive | 4.0 | Midsize | Sedan | 28 | 20 | 61 | 38650 |
for i, j in df['Engine HP'].iteritems():
if np.isnan(df['Engine HP'][i]):
# print (i,j)
if df['Model'][i] == 'MKZ':
df['Engine HP'][i] = 245
/anaconda3/lib/python3.6/site-packages/ipykernel/__main__.py:5: SettingWithCopyWarning:
A value is trying to be set on a copy of a slice from a DataFrame
See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
# Number is still going down
df['Engine HP'].isnull().sum()
25
Model RAV4 EV
Fill in null Engine HP
# All model years are the same HP
df[df['Model'] == 'RAV4 EV']
| Make | Model | Year | Engine Fuel Type | Engine HP | Engine Cylinders | Transmission Type | Driven_Wheels | Number of Doors | Vehicle Size | Vehicle Style | highway MPG | city mpg | Popularity | MSRP | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 8373 | Toyota | RAV4 EV | 2012 | electric | 154.0 | NaN | DIRECT_DRIVE | front wheel drive | 4.0 | Midsize | 4dr SUV | 74 | 78 | 2031 | 49800 |
| 8374 | Toyota | RAV4 EV | 2013 | electric | NaN | 0.0 | DIRECT_DRIVE | front wheel drive | 4.0 | Midsize | 4dr SUV | 74 | 78 | 2031 | 49800 |
| 8375 | Toyota | RAV4 EV | 2014 | electric | NaN | 0.0 | DIRECT_DRIVE | front wheel drive | 4.0 | Midsize | 4dr SUV | 74 | 78 | 2031 | 49800 |
for i, j in df['Engine HP'].iteritems():
if np.isnan(df['Engine HP'][i]):
if df['Model'][i] == 'RAV4 EV':
df['Engine HP'][i] = 154
/anaconda3/lib/python3.6/site-packages/ipykernel/__main__.py:4: SettingWithCopyWarning:
A value is trying to be set on a copy of a slice from a DataFrame
See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
# Number is still going down
df['Engine HP'].isnull().sum()
23
Model Soul S
Fill in null Engine HP
df[df['Model'] == 'Soul EV']
| Make | Model | Year | Engine Fuel Type | Engine HP | Engine Cylinders | Transmission Type | Driven_Wheels | Number of Doors | Vehicle Size | Vehicle Style | highway MPG | city mpg | Popularity | MSRP | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 9850 | Kia | Soul EV | 2015 | electric | NaN | 0.0 | DIRECT_DRIVE | front wheel drive | 4.0 | Compact | Wagon | 92 | 120 | 1720 | 35700 |
| 9851 | Kia | Soul EV | 2015 | electric | NaN | 0.0 | DIRECT_DRIVE | front wheel drive | 4.0 | Compact | Wagon | 92 | 120 | 1720 | 33700 |
| 9852 | Kia | Soul EV | 2016 | electric | NaN | 0.0 | DIRECT_DRIVE | front wheel drive | 4.0 | Compact | Wagon | 92 | 120 | 1720 | 33950 |
| 9853 | Kia | Soul EV | 2016 | electric | NaN | 0.0 | DIRECT_DRIVE | front wheel drive | 4.0 | Compact | Wagon | 92 | 120 | 1720 | 31950 |
| 9854 | Kia | Soul EV | 2016 | electric | NaN | 0.0 | DIRECT_DRIVE | front wheel drive | 4.0 | Compact | Wagon | 92 | 120 | 1720 | 35950 |
for i, j in df['Engine HP'].iteritems():
if np.isnan(df['Engine HP'][i]):
if df['Model'][i] == 'Soul EV':
df['Engine HP'][i] = 109
/anaconda3/lib/python3.6/site-packages/ipykernel/__main__.py:4: SettingWithCopyWarning:
A value is trying to be set on a copy of a slice from a DataFrame
See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
# Number is still going down
df['Engine HP'].isnull().sum()
18
Model S
Fill in null Engine HP
df[df['Model'] == 'Model S']
| Make | Model | Year | Engine Fuel Type | Engine HP | Engine Cylinders | Transmission Type | Driven_Wheels | Number of Doors | Vehicle Size | Vehicle Style | highway MPG | city mpg | Popularity | MSRP | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 6921 | Tesla | Model S | 2014 | electric | NaN | 0.0 | DIRECT_DRIVE | rear wheel drive | 4.0 | Large | Sedan | 90 | 88 | 1391 | 79900 |
| 6922 | Tesla | Model S | 2014 | electric | NaN | 0.0 | DIRECT_DRIVE | rear wheel drive | 4.0 | Large | Sedan | 97 | 94 | 1391 | 69900 |
| 6923 | Tesla | Model S | 2014 | electric | NaN | 0.0 | DIRECT_DRIVE | all wheel drive | 4.0 | Large | Sedan | 94 | 86 | 1391 | 104500 |
| 6924 | Tesla | Model S | 2014 | electric | NaN | 0.0 | DIRECT_DRIVE | rear wheel drive | 4.0 | Large | Sedan | 90 | 88 | 1391 | 93400 |
| 6925 | Tesla | Model S | 2015 | electric | NaN | 0.0 | DIRECT_DRIVE | rear wheel drive | 4.0 | Large | Sedan | 97 | 94 | 1391 | 69900 |
| 6926 | Tesla | Model S | 2015 | electric | NaN | 0.0 | DIRECT_DRIVE | all wheel drive | 4.0 | Large | Sedan | 102 | 101 | 1391 | 75000 |
| 6927 | Tesla | Model S | 2015 | electric | NaN | 0.0 | DIRECT_DRIVE | all wheel drive | 4.0 | Large | Sedan | 106 | 95 | 1391 | 85000 |
| 6928 | Tesla | Model S | 2015 | electric | NaN | 0.0 | DIRECT_DRIVE | all wheel drive | 4.0 | Large | Sedan | 98 | 89 | 1391 | 105000 |
| 6929 | Tesla | Model S | 2015 | electric | NaN | 0.0 | DIRECT_DRIVE | rear wheel drive | 4.0 | Large | Sedan | 90 | 88 | 1391 | 80000 |
| 6930 | Tesla | Model S | 2016 | electric | NaN | 0.0 | DIRECT_DRIVE | all wheel drive | NaN | Large | Sedan | 105 | 102 | 1391 | 79500 |
| 6931 | Tesla | Model S | 2016 | electric | NaN | 0.0 | DIRECT_DRIVE | all wheel drive | NaN | Large | Sedan | 101 | 98 | 1391 | 66000 |
| 6932 | Tesla | Model S | 2016 | electric | NaN | 0.0 | DIRECT_DRIVE | all wheel drive | NaN | Large | Sedan | 105 | 92 | 1391 | 134500 |
| 6933 | Tesla | Model S | 2016 | electric | NaN | 0.0 | DIRECT_DRIVE | rear wheel drive | NaN | Large | Sedan | 100 | 97 | 1391 | 74500 |
| 6934 | Tesla | Model S | 2016 | electric | NaN | 0.0 | DIRECT_DRIVE | all wheel drive | NaN | Large | Sedan | 107 | 101 | 1391 | 71000 |
| 6935 | Tesla | Model S | 2016 | electric | NaN | 0.0 | DIRECT_DRIVE | all wheel drive | 4.0 | Large | Sedan | 102 | 101 | 1391 | 75000 |
| 6936 | Tesla | Model S | 2016 | electric | NaN | 0.0 | DIRECT_DRIVE | all wheel drive | 4.0 | Large | Sedan | 107 | 101 | 1391 | 89500 |
| 6937 | Tesla | Model S | 2016 | electric | NaN | 0.0 | DIRECT_DRIVE | all wheel drive | 4.0 | Large | Sedan | 100 | 91 | 1391 | 112000 |
| 6938 | Tesla | Model S | 2016 | electric | NaN | 0.0 | DIRECT_DRIVE | rear wheel drive | 4.0 | Large | Sedan | 90 | 88 | 1391 | 70000 |
# Change model name by the driven wheels
for i, j in df['Model'].iteritems():
if np.isnan(df['Engine HP'][i]):
# print (i,j)
if df['Driven_Wheels'][i] == 'all wheel drive':
df['Model'][i] = 'Model D'
elif df['Driven_Wheels'][i] == 'rear wheel drive':
df['Model'][i] = 'Model S'
/anaconda3/lib/python3.6/site-packages/ipykernel/__main__.py:9: SettingWithCopyWarning:
A value is trying to be set on a copy of a slice from a DataFrame
See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
/anaconda3/lib/python3.6/site-packages/ipykernel/__main__.py:7: SettingWithCopyWarning:
A value is trying to be set on a copy of a slice from a DataFrame
See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
df[df['Model'] == 'Model D']
| Make | Model | Year | Engine Fuel Type | Engine HP | Engine Cylinders | Transmission Type | Driven_Wheels | Number of Doors | Vehicle Size | Vehicle Style | highway MPG | city mpg | Popularity | MSRP | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 6923 | Tesla | Model D | 2014 | electric | NaN | 0.0 | DIRECT_DRIVE | all wheel drive | 4.0 | Large | Sedan | 94 | 86 | 1391 | 104500 |
| 6926 | Tesla | Model D | 2015 | electric | NaN | 0.0 | DIRECT_DRIVE | all wheel drive | 4.0 | Large | Sedan | 102 | 101 | 1391 | 75000 |
| 6927 | Tesla | Model D | 2015 | electric | NaN | 0.0 | DIRECT_DRIVE | all wheel drive | 4.0 | Large | Sedan | 106 | 95 | 1391 | 85000 |
| 6928 | Tesla | Model D | 2015 | electric | NaN | 0.0 | DIRECT_DRIVE | all wheel drive | 4.0 | Large | Sedan | 98 | 89 | 1391 | 105000 |
| 6930 | Tesla | Model D | 2016 | electric | NaN | 0.0 | DIRECT_DRIVE | all wheel drive | NaN | Large | Sedan | 105 | 102 | 1391 | 79500 |
| 6931 | Tesla | Model D | 2016 | electric | NaN | 0.0 | DIRECT_DRIVE | all wheel drive | NaN | Large | Sedan | 101 | 98 | 1391 | 66000 |
| 6932 | Tesla | Model D | 2016 | electric | NaN | 0.0 | DIRECT_DRIVE | all wheel drive | NaN | Large | Sedan | 105 | 92 | 1391 | 134500 |
| 6934 | Tesla | Model D | 2016 | electric | NaN | 0.0 | DIRECT_DRIVE | all wheel drive | NaN | Large | Sedan | 107 | 101 | 1391 | 71000 |
| 6935 | Tesla | Model D | 2016 | electric | NaN | 0.0 | DIRECT_DRIVE | all wheel drive | 4.0 | Large | Sedan | 102 | 101 | 1391 | 75000 |
| 6936 | Tesla | Model D | 2016 | electric | NaN | 0.0 | DIRECT_DRIVE | all wheel drive | 4.0 | Large | Sedan | 107 | 101 | 1391 | 89500 |
| 6937 | Tesla | Model D | 2016 | electric | NaN | 0.0 | DIRECT_DRIVE | all wheel drive | 4.0 | Large | Sedan | 100 | 91 | 1391 | 112000 |
for i, j in df['Engine HP'].iteritems():
if np.isnan(df['Engine HP'][i]):
if df['Model'][i] == 'Model S':
df['Engine HP'][i] = 362
elif df['Model'][i] == 'Model D':
df['Engine HP'][i] = 259
/anaconda3/lib/python3.6/site-packages/ipykernel/__main__.py:4: SettingWithCopyWarning:
A value is trying to be set on a copy of a slice from a DataFrame
See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
/anaconda3/lib/python3.6/site-packages/ipykernel/__main__.py:6: SettingWithCopyWarning:
A value is trying to be set on a copy of a slice from a DataFrame
See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
# Down to Zero
df['Engine HP'].isnull().sum()
0
NUll values in Engine Cylinders
# Examine the null values of Engine Cylinder
df[df['Engine Cylinders'].isnull()]
| Make | Model | Year | Engine Fuel Type | Engine HP | Engine Cylinders | Transmission Type | Driven_Wheels | Number of Doors | Vehicle Size | Vehicle Style | highway MPG | city mpg | Popularity | MSRP | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1983 | Chevrolet | Bolt EV | 2017 | electric | 200.0 | NaN | DIRECT_DRIVE | front wheel drive | 4.0 | Compact | 4dr Hatchback | 110 | 128 | 1385 | 40905 |
| 1984 | Chevrolet | Bolt EV | 2017 | electric | 200.0 | NaN | DIRECT_DRIVE | front wheel drive | 4.0 | Compact | 4dr Hatchback | 110 | 128 | 1385 | 36620 |
| 3716 | Volkswagen | e-Golf | 2015 | electric | 115.0 | NaN | DIRECT_DRIVE | front wheel drive | 4.0 | Compact | 4dr Hatchback | 105 | 126 | 873 | 33450 |
| 3717 | Volkswagen | e-Golf | 2015 | electric | 115.0 | NaN | DIRECT_DRIVE | front wheel drive | 4.0 | Compact | 4dr Hatchback | 105 | 126 | 873 | 35445 |
| 3718 | Volkswagen | e-Golf | 2016 | electric | 115.0 | NaN | DIRECT_DRIVE | front wheel drive | 4.0 | Compact | 4dr Hatchback | 105 | 126 | 873 | 28995 |
| 3719 | Volkswagen | e-Golf | 2016 | electric | 115.0 | NaN | DIRECT_DRIVE | front wheel drive | 4.0 | Compact | 4dr Hatchback | 105 | 126 | 873 | 35595 |
| 5778 | Mitsubishi | i-MiEV | 2014 | electric | 201.0 | NaN | DIRECT_DRIVE | rear wheel drive | 4.0 | Compact | 4dr Hatchback | 99 | 126 | 436 | 22995 |
| 5779 | Mitsubishi | i-MiEV | 2016 | electric | 66.0 | NaN | DIRECT_DRIVE | rear wheel drive | 4.0 | Compact | 4dr Hatchback | 99 | 126 | 436 | 22995 |
| 5780 | Mitsubishi | i-MiEV | 2017 | electric | 66.0 | NaN | DIRECT_DRIVE | rear wheel drive | 4.0 | Compact | 4dr Hatchback | 102 | 121 | 436 | 22995 |
| 8373 | Toyota | RAV4 EV | 2012 | electric | 154.0 | NaN | DIRECT_DRIVE | front wheel drive | 4.0 | Midsize | 4dr SUV | 74 | 78 | 2031 | 49800 |
| 8695 | Mazda | RX-7 | 1993 | regular unleaded | 255.0 | NaN | MANUAL | rear wheel drive | 2.0 | Compact | Coupe | 23 | 15 | 586 | 7523 |
| 8696 | Mazda | RX-7 | 1994 | regular unleaded | 255.0 | NaN | MANUAL | rear wheel drive | 2.0 | Compact | Coupe | 23 | 15 | 586 | 8147 |
| 8697 | Mazda | RX-7 | 1995 | regular unleaded | 255.0 | NaN | MANUAL | rear wheel drive | 2.0 | Compact | Coupe | 23 | 15 | 586 | 8839 |
| 8698 | Mazda | RX-8 | 2009 | premium unleaded (required) | 232.0 | NaN | MANUAL | rear wheel drive | 4.0 | Compact | Coupe | 22 | 16 | 586 | 31930 |
| 8699 | Mazda | RX-8 | 2009 | premium unleaded (required) | 212.0 | NaN | AUTOMATIC | rear wheel drive | 4.0 | Compact | Coupe | 23 | 16 | 586 | 26435 |
| 8700 | Mazda | RX-8 | 2009 | premium unleaded (required) | 232.0 | NaN | MANUAL | rear wheel drive | 4.0 | Compact | Coupe | 22 | 16 | 586 | 27860 |
| 8701 | Mazda | RX-8 | 2009 | premium unleaded (required) | 232.0 | NaN | MANUAL | rear wheel drive | 4.0 | Compact | Coupe | 22 | 16 | 586 | 31000 |
| 8702 | Mazda | RX-8 | 2009 | premium unleaded (required) | 232.0 | NaN | MANUAL | rear wheel drive | 4.0 | Compact | Coupe | 22 | 16 | 586 | 26435 |
| 8703 | Mazda | RX-8 | 2009 | premium unleaded (required) | 212.0 | NaN | AUTOMATIC | rear wheel drive | 4.0 | Compact | Coupe | 23 | 16 | 586 | 31700 |
| 8704 | Mazda | RX-8 | 2009 | premium unleaded (required) | 212.0 | NaN | AUTOMATIC | rear wheel drive | 4.0 | Compact | Coupe | 23 | 16 | 586 | 28560 |
| 8705 | Mazda | RX-8 | 2010 | premium unleaded (required) | 232.0 | NaN | MANUAL | rear wheel drive | 4.0 | Compact | Coupe | 22 | 16 | 586 | 32140 |
| 8706 | Mazda | RX-8 | 2010 | premium unleaded (required) | 212.0 | NaN | AUTOMATIC | rear wheel drive | 4.0 | Compact | Coupe | 23 | 16 | 586 | 26645 |
| 8707 | Mazda | RX-8 | 2010 | premium unleaded (required) | 212.0 | NaN | AUTOMATIC | rear wheel drive | 4.0 | Compact | Coupe | 23 | 16 | 586 | 32810 |
| 8708 | Mazda | RX-8 | 2010 | premium unleaded (required) | 232.0 | NaN | MANUAL | rear wheel drive | 4.0 | Compact | Coupe | 22 | 16 | 586 | 26645 |
| 8709 | Mazda | RX-8 | 2010 | premium unleaded (required) | 232.0 | NaN | MANUAL | rear wheel drive | 4.0 | Compact | Coupe | 22 | 16 | 586 | 32110 |
| 8710 | Mazda | RX-8 | 2011 | premium unleaded (required) | 212.0 | NaN | AUTOMATIC | rear wheel drive | 4.0 | Compact | Coupe | 23 | 16 | 586 | 32960 |
| 8711 | Mazda | RX-8 | 2011 | premium unleaded (required) | 232.0 | NaN | MANUAL | rear wheel drive | 4.0 | Compact | Coupe | 22 | 16 | 586 | 32260 |
| 8712 | Mazda | RX-8 | 2011 | premium unleaded (required) | 232.0 | NaN | MANUAL | rear wheel drive | 4.0 | Compact | Coupe | 22 | 16 | 586 | 32290 |
| 8713 | Mazda | RX-8 | 2011 | premium unleaded (required) | 232.0 | NaN | MANUAL | rear wheel drive | 4.0 | Compact | Coupe | 22 | 16 | 586 | 26795 |
| 8714 | Mazda | RX-8 | 2011 | premium unleaded (required) | 212.0 | NaN | AUTOMATIC | rear wheel drive | 4.0 | Compact | Coupe | 23 | 16 | 586 | 26795 |
# from what I can see this model is a rotary engine which means it have no cylinders
df[df['Model'] == 'RX-7']
| Make | Model | Year | Engine Fuel Type | Engine HP | Engine Cylinders | Transmission Type | Driven_Wheels | Number of Doors | Vehicle Size | Vehicle Style | highway MPG | city mpg | Popularity | MSRP | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 8695 | Mazda | RX-7 | 1993 | regular unleaded | 255.0 | NaN | MANUAL | rear wheel drive | 2.0 | Compact | Coupe | 23 | 15 | 586 | 7523 |
| 8696 | Mazda | RX-7 | 1994 | regular unleaded | 255.0 | NaN | MANUAL | rear wheel drive | 2.0 | Compact | Coupe | 23 | 15 | 586 | 8147 |
| 8697 | Mazda | RX-7 | 1995 | regular unleaded | 255.0 | NaN | MANUAL | rear wheel drive | 2.0 | Compact | Coupe | 23 | 15 | 586 | 8839 |
# from what I can see this model is a rotary engine which means it have no cylinders
df[df['Model'] == 'RX-8']
| Make | Model | Year | Engine Fuel Type | Engine HP | Engine Cylinders | Transmission Type | Driven_Wheels | Number of Doors | Vehicle Size | Vehicle Style | highway MPG | city mpg | Popularity | MSRP | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 8698 | Mazda | RX-8 | 2009 | premium unleaded (required) | 232.0 | NaN | MANUAL | rear wheel drive | 4.0 | Compact | Coupe | 22 | 16 | 586 | 31930 |
| 8699 | Mazda | RX-8 | 2009 | premium unleaded (required) | 212.0 | NaN | AUTOMATIC | rear wheel drive | 4.0 | Compact | Coupe | 23 | 16 | 586 | 26435 |
| 8700 | Mazda | RX-8 | 2009 | premium unleaded (required) | 232.0 | NaN | MANUAL | rear wheel drive | 4.0 | Compact | Coupe | 22 | 16 | 586 | 27860 |
| 8701 | Mazda | RX-8 | 2009 | premium unleaded (required) | 232.0 | NaN | MANUAL | rear wheel drive | 4.0 | Compact | Coupe | 22 | 16 | 586 | 31000 |
| 8702 | Mazda | RX-8 | 2009 | premium unleaded (required) | 232.0 | NaN | MANUAL | rear wheel drive | 4.0 | Compact | Coupe | 22 | 16 | 586 | 26435 |
| 8703 | Mazda | RX-8 | 2009 | premium unleaded (required) | 212.0 | NaN | AUTOMATIC | rear wheel drive | 4.0 | Compact | Coupe | 23 | 16 | 586 | 31700 |
| 8704 | Mazda | RX-8 | 2009 | premium unleaded (required) | 212.0 | NaN | AUTOMATIC | rear wheel drive | 4.0 | Compact | Coupe | 23 | 16 | 586 | 28560 |
| 8705 | Mazda | RX-8 | 2010 | premium unleaded (required) | 232.0 | NaN | MANUAL | rear wheel drive | 4.0 | Compact | Coupe | 22 | 16 | 586 | 32140 |
| 8706 | Mazda | RX-8 | 2010 | premium unleaded (required) | 212.0 | NaN | AUTOMATIC | rear wheel drive | 4.0 | Compact | Coupe | 23 | 16 | 586 | 26645 |
| 8707 | Mazda | RX-8 | 2010 | premium unleaded (required) | 212.0 | NaN | AUTOMATIC | rear wheel drive | 4.0 | Compact | Coupe | 23 | 16 | 586 | 32810 |
| 8708 | Mazda | RX-8 | 2010 | premium unleaded (required) | 232.0 | NaN | MANUAL | rear wheel drive | 4.0 | Compact | Coupe | 22 | 16 | 586 | 26645 |
| 8709 | Mazda | RX-8 | 2010 | premium unleaded (required) | 232.0 | NaN | MANUAL | rear wheel drive | 4.0 | Compact | Coupe | 22 | 16 | 586 | 32110 |
| 8710 | Mazda | RX-8 | 2011 | premium unleaded (required) | 212.0 | NaN | AUTOMATIC | rear wheel drive | 4.0 | Compact | Coupe | 23 | 16 | 586 | 32960 |
| 8711 | Mazda | RX-8 | 2011 | premium unleaded (required) | 232.0 | NaN | MANUAL | rear wheel drive | 4.0 | Compact | Coupe | 22 | 16 | 586 | 32260 |
| 8712 | Mazda | RX-8 | 2011 | premium unleaded (required) | 232.0 | NaN | MANUAL | rear wheel drive | 4.0 | Compact | Coupe | 22 | 16 | 586 | 32290 |
| 8713 | Mazda | RX-8 | 2011 | premium unleaded (required) | 232.0 | NaN | MANUAL | rear wheel drive | 4.0 | Compact | Coupe | 22 | 16 | 586 | 26795 |
| 8714 | Mazda | RX-8 | 2011 | premium unleaded (required) | 212.0 | NaN | AUTOMATIC | rear wheel drive | 4.0 | Compact | Coupe | 23 | 16 | 586 | 26795 |
# Also no electric cars have cylinders
# was able to repalce all nan values with 0
for i, j in df['Engine Cylinders'].iteritems():
if np.isnan(df['Engine Cylinders'][i]):
df['Engine Cylinders'][i] = 0
/anaconda3/lib/python3.6/site-packages/ipykernel/__main__.py:5: SettingWithCopyWarning:
A value is trying to be set on a copy of a slice from a DataFrame
See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
df[df['Engine Cylinders'] <=0]
| Make | Model | Year | Engine Fuel Type | Engine HP | Engine Cylinders | Transmission Type | Driven_Wheels | Number of Doors | Vehicle Size | Vehicle Style | highway MPG | city mpg | Popularity | MSRP | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 539 | FIAT | 500e | 2015 | electric | 111.0 | 0.0 | DIRECT_DRIVE | front wheel drive | 2.0 | Compact | 2dr Hatchback | 108 | 122 | 819 | 31800 |
| 540 | FIAT | 500e | 2016 | electric | 111.0 | 0.0 | DIRECT_DRIVE | front wheel drive | 2.0 | Compact | 2dr Hatchback | 103 | 121 | 819 | 31800 |
| 541 | FIAT | 500e | 2017 | electric | 111.0 | 0.0 | DIRECT_DRIVE | front wheel drive | 2.0 | Compact | 2dr Hatchback | 103 | 121 | 819 | 31800 |
| 1680 | Mercedes-Benz | B-Class Electric Drive | 2015 | electric | 177.0 | 0.0 | DIRECT_DRIVE | front wheel drive | 4.0 | Compact | 4dr Hatchback | 82 | 85 | 617 | 41450 |
| 1681 | Mercedes-Benz | B-Class Electric Drive | 2016 | electric | 177.0 | 0.0 | DIRECT_DRIVE | front wheel drive | 4.0 | Compact | 4dr Hatchback | 82 | 85 | 617 | 41450 |
| 1682 | Mercedes-Benz | B-Class Electric Drive | 2017 | electric | 177.0 | 0.0 | DIRECT_DRIVE | front wheel drive | 4.0 | Compact | 4dr Hatchback | 82 | 85 | 617 | 39900 |
| 1983 | Chevrolet | Bolt EV | 2017 | electric | 200.0 | 0.0 | DIRECT_DRIVE | front wheel drive | 4.0 | Compact | 4dr Hatchback | 110 | 128 | 1385 | 40905 |
| 1984 | Chevrolet | Bolt EV | 2017 | electric | 200.0 | 0.0 | DIRECT_DRIVE | front wheel drive | 4.0 | Compact | 4dr Hatchback | 110 | 128 | 1385 | 36620 |
| 3716 | Volkswagen | e-Golf | 2015 | electric | 115.0 | 0.0 | DIRECT_DRIVE | front wheel drive | 4.0 | Compact | 4dr Hatchback | 105 | 126 | 873 | 33450 |
| 3717 | Volkswagen | e-Golf | 2015 | electric | 115.0 | 0.0 | DIRECT_DRIVE | front wheel drive | 4.0 | Compact | 4dr Hatchback | 105 | 126 | 873 | 35445 |
| 3718 | Volkswagen | e-Golf | 2016 | electric | 115.0 | 0.0 | DIRECT_DRIVE | front wheel drive | 4.0 | Compact | 4dr Hatchback | 105 | 126 | 873 | 28995 |
| 3719 | Volkswagen | e-Golf | 2016 | electric | 115.0 | 0.0 | DIRECT_DRIVE | front wheel drive | 4.0 | Compact | 4dr Hatchback | 105 | 126 | 873 | 35595 |
| 4705 | Honda | Fit EV | 2013 | electric | 189.0 | 0.0 | DIRECT_DRIVE | front wheel drive | 4.0 | Compact | 4dr Hatchback | 105 | 132 | 2202 | 36625 |
| 4706 | Honda | Fit EV | 2014 | electric | 189.0 | 0.0 | DIRECT_DRIVE | front wheel drive | 4.0 | Compact | 4dr Hatchback | 105 | 132 | 2202 | 36625 |
| 4785 | Ford | Focus | 2015 | electric | 143.0 | 0.0 | DIRECT_DRIVE | front wheel drive | 4.0 | Compact | 4dr Hatchback | 99 | 110 | 5657 | 29170 |
| 4789 | Ford | Focus | 2016 | electric | 143.0 | 0.0 | DIRECT_DRIVE | front wheel drive | 4.0 | Compact | 4dr Hatchback | 99 | 110 | 5657 | 29170 |
| 4798 | Ford | Focus | 2017 | electric | 143.0 | 0.0 | DIRECT_DRIVE | front wheel drive | 4.0 | Compact | 4dr Hatchback | 99 | 110 | 5657 | 29120 |
| 5778 | Mitsubishi | i-MiEV | 2014 | electric | 201.0 | 0.0 | DIRECT_DRIVE | rear wheel drive | 4.0 | Compact | 4dr Hatchback | 99 | 126 | 436 | 22995 |
| 5779 | Mitsubishi | i-MiEV | 2016 | electric | 66.0 | 0.0 | DIRECT_DRIVE | rear wheel drive | 4.0 | Compact | 4dr Hatchback | 99 | 126 | 436 | 22995 |
| 5780 | Mitsubishi | i-MiEV | 2017 | electric | 66.0 | 0.0 | DIRECT_DRIVE | rear wheel drive | 4.0 | Compact | 4dr Hatchback | 102 | 121 | 436 | 22995 |
| 5790 | BMW | i3 | 2015 | electric | 170.0 | 0.0 | DIRECT_DRIVE | rear wheel drive | 4.0 | Compact | 4dr Hatchback | 111 | 137 | 3916 | 42400 |
| 5791 | BMW | i3 | 2016 | electric | 170.0 | 0.0 | DIRECT_DRIVE | rear wheel drive | 4.0 | Compact | 4dr Hatchback | 111 | 137 | 3916 | 42400 |
| 5792 | BMW | i3 | 2017 | electric | 170.0 | 0.0 | DIRECT_DRIVE | rear wheel drive | 4.0 | Compact | 4dr Hatchback | 111 | 137 | 3916 | 42400 |
| 5793 | BMW | i3 | 2017 | electric | 170.0 | 0.0 | DIRECT_DRIVE | rear wheel drive | 4.0 | Compact | 4dr Hatchback | 106 | 129 | 3916 | 43600 |
| 6385 | Nissan | Leaf | 2014 | electric | 110.0 | 0.0 | DIRECT_DRIVE | front wheel drive | 4.0 | Compact | 4dr Hatchback | 101 | 126 | 2009 | 35020 |
| 6386 | Nissan | Leaf | 2014 | electric | 110.0 | 0.0 | DIRECT_DRIVE | front wheel drive | 4.0 | Compact | 4dr Hatchback | 101 | 126 | 2009 | 32000 |
| 6387 | Nissan | Leaf | 2014 | electric | 110.0 | 0.0 | DIRECT_DRIVE | front wheel drive | 4.0 | Compact | 4dr Hatchback | 101 | 126 | 2009 | 28980 |
| 6388 | Nissan | Leaf | 2015 | electric | 110.0 | 0.0 | DIRECT_DRIVE | front wheel drive | 4.0 | Compact | 4dr Hatchback | 101 | 126 | 2009 | 32100 |
| 6389 | Nissan | Leaf | 2015 | electric | 110.0 | 0.0 | DIRECT_DRIVE | front wheel drive | 4.0 | Compact | 4dr Hatchback | 101 | 126 | 2009 | 35120 |
| 6390 | Nissan | Leaf | 2015 | electric | 110.0 | 0.0 | DIRECT_DRIVE | front wheel drive | 4.0 | Compact | 4dr Hatchback | 101 | 126 | 2009 | 29010 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 8696 | Mazda | RX-7 | 1994 | regular unleaded | 255.0 | 0.0 | MANUAL | rear wheel drive | 2.0 | Compact | Coupe | 23 | 15 | 586 | 8147 |
| 8697 | Mazda | RX-7 | 1995 | regular unleaded | 255.0 | 0.0 | MANUAL | rear wheel drive | 2.0 | Compact | Coupe | 23 | 15 | 586 | 8839 |
| 8698 | Mazda | RX-8 | 2009 | premium unleaded (required) | 232.0 | 0.0 | MANUAL | rear wheel drive | 4.0 | Compact | Coupe | 22 | 16 | 586 | 31930 |
| 8699 | Mazda | RX-8 | 2009 | premium unleaded (required) | 212.0 | 0.0 | AUTOMATIC | rear wheel drive | 4.0 | Compact | Coupe | 23 | 16 | 586 | 26435 |
| 8700 | Mazda | RX-8 | 2009 | premium unleaded (required) | 232.0 | 0.0 | MANUAL | rear wheel drive | 4.0 | Compact | Coupe | 22 | 16 | 586 | 27860 |
| 8701 | Mazda | RX-8 | 2009 | premium unleaded (required) | 232.0 | 0.0 | MANUAL | rear wheel drive | 4.0 | Compact | Coupe | 22 | 16 | 586 | 31000 |
| 8702 | Mazda | RX-8 | 2009 | premium unleaded (required) | 232.0 | 0.0 | MANUAL | rear wheel drive | 4.0 | Compact | Coupe | 22 | 16 | 586 | 26435 |
| 8703 | Mazda | RX-8 | 2009 | premium unleaded (required) | 212.0 | 0.0 | AUTOMATIC | rear wheel drive | 4.0 | Compact | Coupe | 23 | 16 | 586 | 31700 |
| 8704 | Mazda | RX-8 | 2009 | premium unleaded (required) | 212.0 | 0.0 | AUTOMATIC | rear wheel drive | 4.0 | Compact | Coupe | 23 | 16 | 586 | 28560 |
| 8705 | Mazda | RX-8 | 2010 | premium unleaded (required) | 232.0 | 0.0 | MANUAL | rear wheel drive | 4.0 | Compact | Coupe | 22 | 16 | 586 | 32140 |
| 8706 | Mazda | RX-8 | 2010 | premium unleaded (required) | 212.0 | 0.0 | AUTOMATIC | rear wheel drive | 4.0 | Compact | Coupe | 23 | 16 | 586 | 26645 |
| 8707 | Mazda | RX-8 | 2010 | premium unleaded (required) | 212.0 | 0.0 | AUTOMATIC | rear wheel drive | 4.0 | Compact | Coupe | 23 | 16 | 586 | 32810 |
| 8708 | Mazda | RX-8 | 2010 | premium unleaded (required) | 232.0 | 0.0 | MANUAL | rear wheel drive | 4.0 | Compact | Coupe | 22 | 16 | 586 | 26645 |
| 8709 | Mazda | RX-8 | 2010 | premium unleaded (required) | 232.0 | 0.0 | MANUAL | rear wheel drive | 4.0 | Compact | Coupe | 22 | 16 | 586 | 32110 |
| 8710 | Mazda | RX-8 | 2011 | premium unleaded (required) | 212.0 | 0.0 | AUTOMATIC | rear wheel drive | 4.0 | Compact | Coupe | 23 | 16 | 586 | 32960 |
| 8711 | Mazda | RX-8 | 2011 | premium unleaded (required) | 232.0 | 0.0 | MANUAL | rear wheel drive | 4.0 | Compact | Coupe | 22 | 16 | 586 | 32260 |
| 8712 | Mazda | RX-8 | 2011 | premium unleaded (required) | 232.0 | 0.0 | MANUAL | rear wheel drive | 4.0 | Compact | Coupe | 22 | 16 | 586 | 32290 |
| 8713 | Mazda | RX-8 | 2011 | premium unleaded (required) | 232.0 | 0.0 | MANUAL | rear wheel drive | 4.0 | Compact | Coupe | 22 | 16 | 586 | 26795 |
| 8714 | Mazda | RX-8 | 2011 | premium unleaded (required) | 212.0 | 0.0 | AUTOMATIC | rear wheel drive | 4.0 | Compact | Coupe | 23 | 16 | 586 | 26795 |
| 9850 | Kia | Soul EV | 2015 | electric | 109.0 | 0.0 | DIRECT_DRIVE | front wheel drive | 4.0 | Compact | Wagon | 92 | 120 | 1720 | 35700 |
| 9851 | Kia | Soul EV | 2015 | electric | 109.0 | 0.0 | DIRECT_DRIVE | front wheel drive | 4.0 | Compact | Wagon | 92 | 120 | 1720 | 33700 |
| 9852 | Kia | Soul EV | 2016 | electric | 109.0 | 0.0 | DIRECT_DRIVE | front wheel drive | 4.0 | Compact | Wagon | 92 | 120 | 1720 | 33950 |
| 9853 | Kia | Soul EV | 2016 | electric | 109.0 | 0.0 | DIRECT_DRIVE | front wheel drive | 4.0 | Compact | Wagon | 92 | 120 | 1720 | 31950 |
| 9854 | Kia | Soul EV | 2016 | electric | 109.0 | 0.0 | DIRECT_DRIVE | front wheel drive | 4.0 | Compact | Wagon | 92 | 120 | 1720 | 35950 |
| 9867 | Chevrolet | Spark EV | 2014 | electric | 140.0 | 0.0 | DIRECT_DRIVE | front wheel drive | 4.0 | Compact | 4dr Hatchback | 109 | 128 | 1385 | 26685 |
| 9868 | Chevrolet | Spark EV | 2014 | electric | 140.0 | 0.0 | DIRECT_DRIVE | front wheel drive | 4.0 | Compact | 4dr Hatchback | 109 | 128 | 1385 | 27010 |
| 9869 | Chevrolet | Spark EV | 2015 | electric | 140.0 | 0.0 | DIRECT_DRIVE | front wheel drive | 4.0 | Compact | 4dr Hatchback | 109 | 128 | 1385 | 25170 |
| 9870 | Chevrolet | Spark EV | 2015 | electric | 140.0 | 0.0 | DIRECT_DRIVE | front wheel drive | 4.0 | Compact | 4dr Hatchback | 109 | 128 | 1385 | 25560 |
| 9871 | Chevrolet | Spark EV | 2016 | electric | 140.0 | 0.0 | DIRECT_DRIVE | front wheel drive | 4.0 | Compact | 4dr Hatchback | 109 | 128 | 1385 | 25510 |
| 9872 | Chevrolet | Spark EV | 2016 | electric | 140.0 | 0.0 | DIRECT_DRIVE | front wheel drive | 4.0 | Compact | 4dr Hatchback | 109 | 128 | 1385 | 25120 |
86 rows × 15 columns
df.groupby(['Make','Model', 'Engine Cylinders']).count()
| Year | Engine Fuel Type | Engine HP | Transmission Type | Driven_Wheels | Number of Doors | Vehicle Size | Vehicle Style | highway MPG | city mpg | Popularity | MSRP | |||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Make | Model | Engine Cylinders | ||||||||||||
| Acura | CL | 6.0 | 9 | 9 | 9 | 9 | 9 | 9 | 9 | 9 | 9 | 9 | 9 | 9 |
| ILX | 4.0 | 16 | 16 | 16 | 16 | 16 | 16 | 16 | 16 | 16 | 16 | 16 | 16 | |
| ILX Hybrid | 4.0 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | |
| Integra | 4.0 | 24 | 24 | 24 | 24 | 24 | 24 | 24 | 24 | 24 | 24 | 24 | 24 | |
| Legend | 6.0 | 16 | 16 | 16 | 16 | 16 | 16 | 16 | 16 | 16 | 16 | 16 | 16 | |
| MDX | 6.0 | 34 | 34 | 34 | 34 | 34 | 34 | 34 | 34 | 34 | 34 | 34 | 34 | |
| NSX | 6.0 | 5 | 5 | 5 | 5 | 5 | 5 | 5 | 5 | 5 | 5 | 5 | 5 | |
| RDX | 6.0 | 24 | 24 | 24 | 24 | 24 | 24 | 24 | 24 | 24 | 24 | 24 | 24 | |
| RL | 6.0 | 9 | 9 | 9 | 9 | 9 | 9 | 9 | 9 | 9 | 9 | 9 | 9 | |
| RLX | 6.0 | 12 | 12 | 12 | 12 | 12 | 12 | 12 | 12 | 12 | 12 | 12 | 12 | |
| RSX | 4.0 | 15 | 15 | 15 | 15 | 15 | 15 | 15 | 15 | 15 | 15 | 15 | 15 | |
| SLX | 6.0 | 4 | 4 | 4 | 4 | 4 | 4 | 4 | 4 | 4 | 4 | 4 | 4 | |
| TL | 6.0 | 23 | 23 | 23 | 23 | 23 | 23 | 23 | 23 | 23 | 23 | 23 | 23 | |
| TLX | 4.0 | 6 | 6 | 6 | 6 | 6 | 6 | 6 | 6 | 6 | 6 | 6 | 6 | |
| 6.0 | 15 | 15 | 15 | 15 | 15 | 15 | 15 | 15 | 15 | 15 | 15 | 15 | ||
| TSX | 4.0 | 12 | 12 | 12 | 12 | 12 | 12 | 12 | 12 | 12 | 12 | 12 | 12 | |
| 6.0 | 4 | 4 | 4 | 4 | 4 | 4 | 4 | 4 | 4 | 4 | 4 | 4 | ||
| TSX Sport Wagon | 4.0 | 6 | 6 | 6 | 6 | 6 | 6 | 6 | 6 | 6 | 6 | 6 | 6 | |
| Vigor | 5.0 | 3 | 3 | 3 | 3 | 3 | 3 | 3 | 3 | 3 | 3 | 3 | 3 | |
| ZDX | 6.0 | 7 | 7 | 7 | 7 | 7 | 7 | 7 | 7 | 7 | 7 | 7 | 7 | |
| Alfa Romeo | 4C | 4.0 | 5 | 5 | 5 | 5 | 5 | 5 | 5 | 5 | 5 | 5 | 5 | 5 |
| Aston Martin | DB7 | 12.0 | 4 | 4 | 4 | 4 | 4 | 4 | 4 | 4 | 4 | 4 | 4 | 4 |
| DB9 | 12.0 | 8 | 8 | 8 | 8 | 8 | 8 | 8 | 8 | 8 | 8 | 8 | 8 | |
| DB9 GT | 12.0 | 3 | 3 | 3 | 3 | 3 | 3 | 3 | 3 | 3 | 3 | 3 | 3 | |
| DBS | 12.0 | 16 | 16 | 16 | 16 | 16 | 16 | 16 | 16 | 16 | 16 | 16 | 16 | |
| Rapide | 12.0 | 4 | 4 | 4 | 4 | 4 | 4 | 4 | 4 | 4 | 4 | 4 | 4 | |
| Rapide S | 12.0 | 3 | 3 | 3 | 3 | 3 | 3 | 3 | 3 | 3 | 3 | 3 | 3 | |
| V12 Vanquish | 12.0 | 4 | 4 | 4 | 4 | 4 | 4 | 4 | 4 | 4 | 4 | 4 | 4 | |
| V12 Vantage | 12.0 | 4 | 4 | 4 | 4 | 4 | 4 | 4 | 4 | 4 | 4 | 4 | 4 | |
| V12 Vantage S | 12.0 | 3 | 3 | 3 | 3 | 3 | 3 | 3 | 3 | 3 | 3 | 3 | 3 | |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| Volvo | C70 | 5.0 | 3 | 3 | 3 | 3 | 3 | 3 | 3 | 3 | 3 | 3 | 3 | 3 |
| Coupe | 4.0 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | |
| S40 | 5.0 | 8 | 8 | 8 | 8 | 8 | 8 | 8 | 8 | 8 | 8 | 8 | 8 | |
| S60 | 4.0 | 16 | 16 | 16 | 16 | 16 | 16 | 16 | 16 | 16 | 16 | 16 | 16 | |
| 5.0 | 7 | 7 | 7 | 7 | 7 | 7 | 7 | 7 | 7 | 7 | 7 | 7 | ||
| 6.0 | 5 | 5 | 5 | 5 | 5 | 5 | 5 | 5 | 5 | 5 | 5 | 5 | ||
| S60 Cross Country | 4.0 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | |
| S70 | 5.0 | 13 | 13 | 13 | 13 | 13 | 13 | 13 | 13 | 13 | 13 | 13 | 13 | |
| S80 | 4.0 | 4 | 4 | 4 | 4 | 4 | 4 | 4 | 4 | 4 | 4 | 4 | 4 | |
| 6.0 | 4 | 4 | 4 | 4 | 4 | 4 | 4 | 4 | 4 | 4 | 4 | 4 | ||
| S90 | 4.0 | 4 | 4 | 4 | 4 | 4 | 4 | 4 | 4 | 4 | 4 | 4 | 4 | |
| 6.0 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | ||
| V40 | 4.0 | 4 | 4 | 4 | 4 | 4 | 4 | 4 | 4 | 4 | 4 | 4 | 4 | |
| V50 | 5.0 | 6 | 6 | 6 | 6 | 6 | 6 | 6 | 6 | 6 | 6 | 6 | 6 | |
| V60 | 4.0 | 11 | 11 | 11 | 11 | 11 | 11 | 11 | 11 | 11 | 11 | 11 | 11 | |
| 5.0 | 5 | 5 | 5 | 5 | 5 | 5 | 5 | 5 | 5 | 5 | 5 | 5 | ||
| 6.0 | 5 | 5 | 5 | 5 | 5 | 5 | 5 | 5 | 5 | 5 | 5 | 5 | ||
| V60 Cross Country | 4.0 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | |
| 5.0 | 4 | 4 | 4 | 4 | 4 | 4 | 4 | 4 | 4 | 4 | 4 | 4 | ||
| V70 | 6.0 | 4 | 4 | 4 | 4 | 4 | 4 | 4 | 4 | 4 | 4 | 4 | 4 | |
| V90 | 6.0 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | |
| XC | 5.0 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | |
| XC60 | 4.0 | 26 | 26 | 26 | 26 | 26 | 26 | 26 | 26 | 26 | 26 | 26 | 26 | |
| 5.0 | 6 | 6 | 6 | 6 | 6 | 6 | 6 | 6 | 6 | 6 | 6 | 6 | ||
| 6.0 | 20 | 20 | 20 | 20 | 20 | 20 | 20 | 20 | 20 | 20 | 20 | 20 | ||
| XC70 | 4.0 | 7 | 7 | 7 | 7 | 7 | 7 | 7 | 7 | 7 | 7 | 7 | 7 | |
| 5.0 | 5 | 5 | 5 | 5 | 5 | 5 | 5 | 5 | 5 | 5 | 5 | 5 | ||
| 6.0 | 6 | 6 | 6 | 6 | 6 | 6 | 6 | 6 | 6 | 6 | 6 | 6 | ||
| XC90 | 4.0 | 15 | 15 | 15 | 15 | 15 | 15 | 15 | 15 | 15 | 15 | 15 | 15 | |
| 6.0 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 |
1160 rows × 12 columns
grouped = df.groupby('Year')
print(grouped.get_group(2014))
Make Model Year Engine Fuel Type \
120 Mazda 2 2014 regular unleaded
121 Mazda 2 2014 regular unleaded
122 Mazda 2 2014 regular unleaded
123 Mazda 2 2014 regular unleaded
468 Ferrari 458 Italia 2014 premium unleaded (required)
469 Ferrari 458 Italia 2014 premium unleaded (required)
470 Ferrari 458 Italia 2014 premium unleaded (required)
479 Toyota 4Runner 2014 regular unleaded
480 Toyota 4Runner 2014 regular unleaded
481 Toyota 4Runner 2014 regular unleaded
482 Toyota 4Runner 2014 regular unleaded
483 Toyota 4Runner 2014 regular unleaded
484 Toyota 4Runner 2014 regular unleaded
485 Toyota 4Runner 2014 regular unleaded
486 Toyota 4Runner 2014 regular unleaded
627 Mazda 5 2014 regular unleaded
628 Mazda 5 2014 regular unleaded
629 Mazda 5 2014 regular unleaded
630 Mazda 5 2014 regular unleaded
1171 Hyundai Accent 2014 regular unleaded
1172 Hyundai Accent 2014 regular unleaded
1173 Hyundai Accent 2014 regular unleaded
1174 Hyundai Accent 2014 regular unleaded
1175 Hyundai Accent 2014 regular unleaded
1176 Hyundai Accent 2014 regular unleaded
1202 Honda Accord Hybrid 2014 regular unleaded
1203 Honda Accord Hybrid 2014 regular unleaded
1204 Honda Accord Hybrid 2014 regular unleaded
1211 Honda Accord Plug-In Hybrid 2014 regular unleaded
1292 BMW ActiveHybrid 5 2014 premium unleaded (required)
... ... ... ... ...
11283 Toyota Venza 2014 regular unleaded
11284 Toyota Venza 2014 regular unleaded
11285 Toyota Venza 2014 regular unleaded
11286 Toyota Venza 2014 regular unleaded
11287 Toyota Venza 2014 regular unleaded
11448 Rolls-Royce Wraith 2014 premium unleaded (required)
11545 Scion xB 2014 regular unleaded
11546 Scion xB 2014 regular unleaded
11547 Scion xB 2014 regular unleaded
11605 Volvo XC70 2014 regular unleaded
11606 Volvo XC70 2014 regular unleaded
11623 Volvo XC90 2014 regular unleaded
11624 Volvo XC90 2014 regular unleaded
11649 Scion xD 2014 regular unleaded
11650 Scion xD 2014 regular unleaded
11751 Nissan Xterra 2014 regular unleaded
11752 Nissan Xterra 2014 regular unleaded
11753 Nissan Xterra 2014 regular unleaded
11754 Nissan Xterra 2014 regular unleaded
11755 Nissan Xterra 2014 regular unleaded
11756 Nissan Xterra 2014 regular unleaded
11757 Nissan Xterra 2014 regular unleaded
11798 Subaru XV Crosstrek 2014 regular unleaded
11799 Subaru XV Crosstrek 2014 regular unleaded
11800 Subaru XV Crosstrek 2014 regular unleaded
11801 Subaru XV Crosstrek 2014 regular unleaded
11802 Subaru XV Crosstrek 2014 regular unleaded
11894 BMW Z4 2014 premium unleaded (required)
11895 BMW Z4 2014 premium unleaded (required)
11896 BMW Z4 2014 premium unleaded (required)
Engine HP Engine Cylinders Transmission Type Driven_Wheels \
120 100.0 4.0 AUTOMATIC front wheel drive
121 100.0 4.0 AUTOMATIC front wheel drive
122 100.0 4.0 MANUAL front wheel drive
123 100.0 4.0 MANUAL front wheel drive
468 562.0 8.0 AUTOMATED_MANUAL rear wheel drive
469 597.0 8.0 AUTOMATED_MANUAL rear wheel drive
470 562.0 8.0 AUTOMATED_MANUAL rear wheel drive
479 270.0 6.0 AUTOMATIC rear wheel drive
480 270.0 6.0 AUTOMATIC rear wheel drive
481 270.0 6.0 AUTOMATIC four wheel drive
482 270.0 6.0 AUTOMATIC four wheel drive
483 270.0 6.0 AUTOMATIC four wheel drive
484 270.0 6.0 AUTOMATIC four wheel drive
485 270.0 6.0 AUTOMATIC rear wheel drive
486 270.0 6.0 AUTOMATIC four wheel drive
627 157.0 4.0 MANUAL front wheel drive
628 157.0 4.0 AUTOMATIC front wheel drive
629 157.0 4.0 AUTOMATIC front wheel drive
630 157.0 4.0 AUTOMATIC front wheel drive
1171 138.0 4.0 AUTOMATIC front wheel drive
1172 138.0 4.0 MANUAL front wheel drive
1173 138.0 4.0 AUTOMATIC front wheel drive
1174 138.0 4.0 AUTOMATIC front wheel drive
1175 138.0 4.0 MANUAL front wheel drive
1176 138.0 4.0 MANUAL front wheel drive
1202 195.0 4.0 AUTOMATIC front wheel drive
1203 195.0 4.0 AUTOMATIC front wheel drive
1204 195.0 4.0 AUTOMATIC front wheel drive
1211 196.0 4.0 AUTOMATIC front wheel drive
1292 335.0 6.0 AUTOMATIC rear wheel drive
... ... ... ... ...
11283 268.0 6.0 AUTOMATIC all wheel drive
11284 181.0 4.0 AUTOMATIC front wheel drive
11285 268.0 6.0 AUTOMATIC front wheel drive
11286 181.0 4.0 AUTOMATIC front wheel drive
11287 181.0 4.0 AUTOMATIC all wheel drive
11448 624.0 12.0 AUTOMATIC rear wheel drive
11545 158.0 4.0 MANUAL front wheel drive
11546 158.0 4.0 AUTOMATIC front wheel drive
11547 158.0 4.0 AUTOMATIC front wheel drive
11605 240.0 6.0 AUTOMATIC front wheel drive
11606 300.0 6.0 AUTOMATIC all wheel drive
11623 240.0 6.0 AUTOMATIC front wheel drive
11624 240.0 6.0 AUTOMATIC front wheel drive
11649 128.0 4.0 MANUAL front wheel drive
11650 128.0 4.0 AUTOMATIC front wheel drive
11751 261.0 6.0 AUTOMATIC four wheel drive
11752 261.0 6.0 AUTOMATIC rear wheel drive
11753 261.0 6.0 AUTOMATIC four wheel drive
11754 261.0 6.0 AUTOMATIC four wheel drive
11755 261.0 6.0 MANUAL four wheel drive
11756 261.0 6.0 AUTOMATIC rear wheel drive
11757 261.0 6.0 MANUAL four wheel drive
11798 160.0 4.0 AUTOMATIC all wheel drive
11799 160.0 4.0 AUTOMATIC all wheel drive
11800 148.0 4.0 AUTOMATIC all wheel drive
11801 148.0 4.0 AUTOMATIC all wheel drive
11802 148.0 4.0 MANUAL all wheel drive
11894 240.0 4.0 MANUAL rear wheel drive
11895 300.0 6.0 MANUAL rear wheel drive
11896 335.0 6.0 AUTOMATED_MANUAL rear wheel drive
Number of Doors Vehicle Size Vehicle Style highway MPG city mpg \
120 4.0 Compact 4dr Hatchback 34 28
121 4.0 Compact 4dr Hatchback 34 28
122 4.0 Compact 4dr Hatchback 35 29
123 4.0 Compact 4dr Hatchback 35 29
468 2.0 Compact Coupe 17 13
469 2.0 Compact Coupe 17 13
470 2.0 Compact Convertible 17 13
479 4.0 Midsize 4dr SUV 23 17
480 4.0 Midsize 4dr SUV 23 17
481 4.0 Midsize 4dr SUV 22 17
482 4.0 Midsize 4dr SUV 22 17
483 4.0 Midsize 4dr SUV 22 17
484 4.0 Midsize 4dr SUV 22 17
485 4.0 Midsize 4dr SUV 23 17
486 4.0 Midsize 4dr SUV 22 17
627 4.0 Compact Passenger Minivan 28 21
628 4.0 Compact Passenger Minivan 28 22
629 4.0 Compact Passenger Minivan 28 22
630 4.0 Compact Passenger Minivan 28 22
1171 4.0 Compact 4dr Hatchback 37 27
1172 4.0 Compact 4dr Hatchback 38 27
1173 4.0 Compact Sedan 37 27
1174 4.0 Compact 4dr Hatchback 37 27
1175 4.0 Compact Sedan 38 27
1176 4.0 Compact 4dr Hatchback 38 27
1202 4.0 Midsize Sedan 45 50
1203 4.0 Midsize Sedan 45 50
1204 4.0 Midsize Sedan 45 50
1211 4.0 Midsize Sedan 46 47
1292 4.0 Large Sedan 30 23
... ... ... ... ... ...
11283 4.0 Midsize Wagon 25 18
11284 4.0 Midsize Wagon 26 20
11285 4.0 Midsize Wagon 26 19
11286 4.0 Midsize Wagon 26 20
11287 4.0 Midsize Wagon 26 20
11448 2.0 Large Coupe 21 13
11545 4.0 Compact Wagon 28 22
11546 4.0 Compact Wagon 28 22
11547 4.0 Compact Wagon 28 22
11605 4.0 Midsize Wagon 26 18
11606 4.0 Midsize Wagon 24 17
11623 4.0 Midsize 4dr SUV 25 16
11624 4.0 Midsize 4dr SUV 25 16
11649 4.0 Compact 4dr Hatchback 33 27
11650 4.0 Compact 4dr Hatchback 33 27
11751 4.0 Midsize 4dr SUV 20 15
11752 4.0 Midsize 4dr SUV 22 16
11753 4.0 Midsize 4dr SUV 20 15
11754 4.0 Midsize 4dr SUV 20 15
11755 4.0 Midsize 4dr SUV 20 16
11756 4.0 Midsize 4dr SUV 22 16
11757 4.0 Midsize 4dr SUV 20 16
11798 4.0 Compact 4dr SUV 33 29
11799 4.0 Compact 4dr SUV 33 29
11800 4.0 Compact 4dr SUV 33 25
11801 4.0 Compact 4dr SUV 33 25
11802 4.0 Compact 4dr SUV 30 23
11894 2.0 Compact Convertible 34 22
11895 2.0 Compact Convertible 26 19
11896 2.0 Compact Convertible 24 17
Popularity MSRP
120 586 17050
121 586 15560
122 586 16210
123 586 14720
468 2774 233509
469 2774 288000
470 2774 257412
479 2031 41365
480 2031 35740
481 2031 37615
482 2031 34695
483 2031 35725
484 2031 43400
485 2031 32820
486 2031 38645
627 586 20140
628 586 24670
629 586 21140
630 586 22270
1171 1439 17395
1172 1439 14895
1173 1439 15645
1174 1439 16095
1175 1439 14645
1176 1439 16395
1202 2202 31905
1203 2202 29155
1204 2202 34905
1211 2202 39780
1292 3916 61400
... ... ...
11283 2031 31220
11284 2031 27950
11285 2031 38120
11286 2031 31810
11287 2031 33260
11448 86 284900
11545 105 16970
11546 105 20420
11547 105 17920
11605 870 34500
11606 870 40950
11623 870 39700
11624 870 42700
11649 105 15920
11650 105 16720
11751 2009 31370
11752 2009 25300
11753 2009 27350
11754 2009 25440
11755 2009 26300
11756 2009 23390
11757 2009 30320
11798 640 25995
11799 640 29295
11800 640 22995
11801 640 24495
11802 640 21995
11894 3916 48950
11895 3916 56950
11896 3916 65800
[554 rows x 15 columns]
Null values in Engine Fuel Type
# Examine the null values of Engine Fuel Type
df[df['Engine Fuel Type'].isnull()]
| Make | Model | Year | Engine Fuel Type | Engine HP | Engine Cylinders | Transmission Type | Driven_Wheels | Number of Doors | Vehicle Size | Vehicle Style | highway MPG | city mpg | Popularity | MSRP | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 11321 | Suzuki | Verona | 2004 | NaN | 155.0 | 6.0 | AUTOMATIC | front wheel drive | 4.0 | Midsize | Sedan | 25 | 17 | 481 | 17199 |
| 11322 | Suzuki | Verona | 2004 | NaN | 155.0 | 6.0 | AUTOMATIC | front wheel drive | 4.0 | Midsize | Sedan | 25 | 17 | 481 | 20199 |
| 11323 | Suzuki | Verona | 2004 | NaN | 155.0 | 6.0 | AUTOMATIC | front wheel drive | 4.0 | Midsize | Sedan | 25 | 17 | 481 | 18499 |
# look at other models with the same model
# Ended up searching the web to get answer. Since the years didn't match
df[df['Model'] == 'Verona']
| Make | Model | Year | Engine Fuel Type | Engine HP | Engine Cylinders | Transmission Type | Driven_Wheels | Number of Doors | Vehicle Size | Vehicle Style | highway MPG | city mpg | Popularity | MSRP | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 11321 | Suzuki | Verona | 2004 | NaN | 155.0 | 6.0 | AUTOMATIC | front wheel drive | 4.0 | Midsize | Sedan | 25 | 17 | 481 | 17199 |
| 11322 | Suzuki | Verona | 2004 | NaN | 155.0 | 6.0 | AUTOMATIC | front wheel drive | 4.0 | Midsize | Sedan | 25 | 17 | 481 | 20199 |
| 11323 | Suzuki | Verona | 2004 | NaN | 155.0 | 6.0 | AUTOMATIC | front wheel drive | 4.0 | Midsize | Sedan | 25 | 17 | 481 | 18499 |
| 11324 | Suzuki | Verona | 2005 | regular unleaded | 155.0 | 6.0 | AUTOMATIC | front wheel drive | 4.0 | Midsize | Sedan | 25 | 18 | 481 | 19349 |
| 11325 | Suzuki | Verona | 2005 | regular unleaded | 155.0 | 6.0 | AUTOMATIC | front wheel drive | 4.0 | Midsize | Sedan | 25 | 18 | 481 | 21049 |
| 11326 | Suzuki | Verona | 2005 | regular unleaded | 155.0 | 6.0 | AUTOMATIC | front wheel drive | 4.0 | Midsize | Sedan | 25 | 18 | 481 | 17549 |
| 11327 | Suzuki | Verona | 2005 | regular unleaded | 155.0 | 6.0 | AUTOMATIC | front wheel drive | 4.0 | Midsize | Sedan | 25 | 18 | 481 | 20549 |
| 11328 | Suzuki | Verona | 2006 | regular unleaded | 155.0 | 6.0 | AUTOMATIC | front wheel drive | 4.0 | Midsize | Sedan | 25 | 17 | 481 | 20299 |
| 11329 | Suzuki | Verona | 2006 | regular unleaded | 155.0 | 6.0 | AUTOMATIC | front wheel drive | 4.0 | Midsize | Sedan | 25 | 17 | 481 | 18299 |
# Couldn't get the for loop too work.
# Took the long way to solve instead of using so much time trying to figure it out
#
df.loc[11321,'Engine Fuel Type'] = 'regular unleaded'
df.loc[11322,'Engine Fuel Type'] = 'regular unleaded'
df.loc[11323,'Engine Fuel Type'] = 'regular unleaded'
df['Engine Fuel Type'].isnull().sum()
0
Null values in Number of Doors
# Examine the null values of number of Doors
df[df['Number of Doors'].isnull()]
| Make | Model | Year | Engine Fuel Type | Engine HP | Engine Cylinders | Transmission Type | Driven_Wheels | Number of Doors | Vehicle Size | Vehicle Style | highway MPG | city mpg | Popularity | MSRP | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 4666 | Ferrari | FF | 2013 | premium unleaded (required) | 651.0 | 12.0 | AUTOMATED_MANUAL | all wheel drive | NaN | Large | Coupe | 16 | 11 | 2774 | 295000 |
| 6930 | Tesla | Model D | 2016 | electric | 259.0 | 0.0 | DIRECT_DRIVE | all wheel drive | NaN | Large | Sedan | 105 | 102 | 1391 | 79500 |
| 6931 | Tesla | Model D | 2016 | electric | 259.0 | 0.0 | DIRECT_DRIVE | all wheel drive | NaN | Large | Sedan | 101 | 98 | 1391 | 66000 |
| 6932 | Tesla | Model D | 2016 | electric | 259.0 | 0.0 | DIRECT_DRIVE | all wheel drive | NaN | Large | Sedan | 105 | 92 | 1391 | 134500 |
| 6933 | Tesla | Model S | 2016 | electric | 362.0 | 0.0 | DIRECT_DRIVE | rear wheel drive | NaN | Large | Sedan | 100 | 97 | 1391 | 74500 |
| 6934 | Tesla | Model D | 2016 | electric | 259.0 | 0.0 | DIRECT_DRIVE | all wheel drive | NaN | Large | Sedan | 107 | 101 | 1391 | 71000 |
# np.isnan(df['Number of Doors'][6930])
# i will iterate through index
# j will show value of column 'Number of Doors'
# if statement will replace number of doors
for i, j in df['Number of Doors'].iteritems():
if np.isnan(df['Number of Doors'][i]):
# print (i,j)
if df['Vehicle Style'][i] == 'Coupe':
df['Number of Doors'][i] = 2
elif df['Vehicle Style'][i] == 'Sedan':
df['Number of Doors'][i] = 4
# print (i, j)
# if df['Number of Doors'].isnull():
# df['Vehicle Style'] == 'Coupe'
# else:
# df['Vehicle Style'] == 'Sedan'
/anaconda3/lib/python3.6/site-packages/ipykernel/__main__.py:9: SettingWithCopyWarning:
A value is trying to be set on a copy of a slice from a DataFrame
See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
/anaconda3/lib/python3.6/site-packages/ipykernel/__main__.py:11: SettingWithCopyWarning:
A value is trying to be set on a copy of a slice from a DataFrame
See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
df['Number of Doors'].isnull().sum()
0
df.isnull().sum()
Make 0
Model 0
Year 0
Engine Fuel Type 0
Engine HP 0
Engine Cylinders 0
Transmission Type 0
Driven_Wheels 0
Number of Doors 0
Vehicle Size 0
Vehicle Style 0
highway MPG 0
city mpg 0
Popularity 0
MSRP 0
dtype: int64
df.head()
| Make | Model | Year | Engine Fuel Type | Engine HP | Engine Cylinders | Transmission Type | Driven_Wheels | Number of Doors | Vehicle Size | Vehicle Style | highway MPG | city mpg | Popularity | MSRP | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | BMW | 1 Series M | 2011 | premium unleaded (required) | 335.0 | 6.0 | MANUAL | rear wheel drive | 2.0 | Compact | Coupe | 26 | 19 | 3916 | 46135 |
| 1 | BMW | 1 Series | 2011 | premium unleaded (required) | 300.0 | 6.0 | MANUAL | rear wheel drive | 2.0 | Compact | Convertible | 28 | 19 | 3916 | 40650 |
| 2 | BMW | 1 Series | 2011 | premium unleaded (required) | 300.0 | 6.0 | MANUAL | rear wheel drive | 2.0 | Compact | Coupe | 28 | 20 | 3916 | 36350 |
| 3 | BMW | 1 Series | 2011 | premium unleaded (required) | 230.0 | 6.0 | MANUAL | rear wheel drive | 2.0 | Compact | Coupe | 28 | 18 | 3916 | 29450 |
| 4 | BMW | 1 Series | 2011 | premium unleaded (required) | 230.0 | 6.0 | MANUAL | rear wheel drive | 2.0 | Compact | Convertible | 28 | 18 | 3916 | 34500 |
Examine transmission type Unknown
df[df['Transmission Type'] == 'UNKNOWN']
| Make | Model | Year | Engine Fuel Type | Engine HP | Engine Cylinders | Transmission Type | Driven_Wheels | Number of Doors | Vehicle Size | Vehicle Style | highway MPG | city mpg | Popularity | MSRP | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1289 | Oldsmobile | Achieva | 1997 | regular unleaded | 150.0 | 4.0 | UNKNOWN | front wheel drive | 2.0 | Midsize | Coupe | 29 | 19 | 26 | 2000 |
| 1290 | Oldsmobile | Achieva | 1997 | regular unleaded | 150.0 | 4.0 | UNKNOWN | front wheel drive | 4.0 | Midsize | Sedan | 29 | 19 | 26 | 2000 |
| 4691 | Pontiac | Firebird | 2000 | regular unleaded | 305.0 | 8.0 | UNKNOWN | rear wheel drive | 2.0 | Midsize | 2dr Hatchback | 23 | 15 | 210 | 6175 |
| 4692 | Pontiac | Firebird | 2000 | regular unleaded | 305.0 | 8.0 | UNKNOWN | rear wheel drive | 2.0 | Midsize | 2dr Hatchback | 23 | 15 | 210 | 8548 |
| 4693 | Pontiac | Firebird | 2000 | regular unleaded | 305.0 | 8.0 | UNKNOWN | rear wheel drive | 2.0 | Midsize | Convertible | 23 | 15 | 210 | 9567 |
| 6158 | GMC | Jimmy | 1999 | regular unleaded | 190.0 | 6.0 | UNKNOWN | rear wheel drive | 2.0 | Compact | 2dr SUV | 19 | 14 | 549 | 2182 |
| 6160 | GMC | Jimmy | 1999 | regular unleaded | 190.0 | 6.0 | UNKNOWN | four wheel drive | 2.0 | Compact | 2dr SUV | 19 | 14 | 549 | 2317 |
| 6165 | GMC | Jimmy | 2000 | regular unleaded | 190.0 | 6.0 | UNKNOWN | rear wheel drive | 2.0 | Compact | 2dr SUV | 20 | 15 | 549 | 2407 |
| 6174 | GMC | Jimmy | 2000 | regular unleaded | 190.0 | 6.0 | UNKNOWN | four wheel drive | 2.0 | Compact | 2dr SUV | 18 | 14 | 549 | 2578 |
| 6366 | Chrysler | Le Baron | 1993 | regular unleaded | 100.0 | 4.0 | UNKNOWN | front wheel drive | 2.0 | Compact | Coupe | 26 | 21 | 1013 | 2000 |
| 6368 | Chrysler | Le Baron | 1993 | regular unleaded | 100.0 | 4.0 | UNKNOWN | front wheel drive | 2.0 | Compact | Convertible | 24 | 18 | 1013 | 2000 |
| 8042 | Dodge | RAM 150 | 1991 | regular unleaded | 125.0 | 6.0 | UNKNOWN | rear wheel drive | 2.0 | Large | Regular Cab Pickup | 17 | 12 | 1851 | 2000 |
# Look through model firebird to see if I can use data to fill transmission type
df[df['Model'] == 'Firebird']
| Make | Model | Year | Engine Fuel Type | Engine HP | Engine Cylinders | Transmission Type | Driven_Wheels | Number of Doors | Vehicle Size | Vehicle Style | highway MPG | city mpg | Popularity | MSRP | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 4690 | Pontiac | Firebird | 2000 | regular unleaded | 200.0 | 6.0 | MANUAL | rear wheel drive | 2.0 | Midsize | 2dr Hatchback | 28 | 17 | 210 | 4677 |
| 4691 | Pontiac | Firebird | 2000 | regular unleaded | 305.0 | 8.0 | UNKNOWN | rear wheel drive | 2.0 | Midsize | 2dr Hatchback | 23 | 15 | 210 | 6175 |
| 4692 | Pontiac | Firebird | 2000 | regular unleaded | 305.0 | 8.0 | UNKNOWN | rear wheel drive | 2.0 | Midsize | 2dr Hatchback | 23 | 15 | 210 | 8548 |
| 4693 | Pontiac | Firebird | 2000 | regular unleaded | 305.0 | 8.0 | UNKNOWN | rear wheel drive | 2.0 | Midsize | Convertible | 23 | 15 | 210 | 9567 |
| 4694 | Pontiac | Firebird | 2000 | regular unleaded | 200.0 | 6.0 | MANUAL | rear wheel drive | 2.0 | Midsize | Convertible | 28 | 17 | 210 | 5844 |
| 4695 | Pontiac | Firebird | 2001 | premium unleaded (required) | 310.0 | 8.0 | AUTOMATIC | rear wheel drive | 2.0 | Midsize | 2dr Hatchback | 23 | 16 | 210 | 24035 |
| 4696 | Pontiac | Firebird | 2001 | regular unleaded | 200.0 | 6.0 | MANUAL | rear wheel drive | 2.0 | Midsize | Convertible | 28 | 17 | 210 | 25475 |
| 4697 | Pontiac | Firebird | 2001 | premium unleaded (required) | 310.0 | 8.0 | AUTOMATIC | rear wheel drive | 2.0 | Midsize | Convertible | 23 | 16 | 210 | 31215 |
| 4698 | Pontiac | Firebird | 2001 | regular unleaded | 200.0 | 6.0 | MANUAL | rear wheel drive | 2.0 | Midsize | 2dr Hatchback | 28 | 17 | 210 | 18855 |
| 4699 | Pontiac | Firebird | 2001 | premium unleaded (required) | 310.0 | 8.0 | AUTOMATIC | rear wheel drive | 2.0 | Midsize | 2dr Hatchback | 23 | 16 | 210 | 27145 |
| 4700 | Pontiac | Firebird | 2002 | premium unleaded (required) | 310.0 | 8.0 | AUTOMATIC | rear wheel drive | 2.0 | Midsize | Convertible | 23 | 16 | 210 | 32095 |
| 4701 | Pontiac | Firebird | 2002 | premium unleaded (required) | 310.0 | 8.0 | AUTOMATIC | rear wheel drive | 2.0 | Midsize | 2dr Hatchback | 23 | 16 | 210 | 25995 |
| 4702 | Pontiac | Firebird | 2002 | premium unleaded (required) | 310.0 | 8.0 | AUTOMATIC | rear wheel drive | 2.0 | Midsize | 2dr Hatchback | 23 | 16 | 210 | 28025 |
| 4703 | Pontiac | Firebird | 2002 | regular unleaded | 200.0 | 6.0 | MANUAL | rear wheel drive | 2.0 | Midsize | 2dr Hatchback | 29 | 17 | 210 | 20050 |
| 4704 | Pontiac | Firebird | 2002 | regular unleaded | 200.0 | 6.0 | AUTOMATIC | rear wheel drive | 2.0 | Midsize | Convertible | 28 | 17 | 210 | 26965 |
df[df['Model'] == 'Achieva']
| Make | Model | Year | Engine Fuel Type | Engine HP | Engine Cylinders | Transmission Type | Driven_Wheels | Number of Doors | Vehicle Size | Vehicle Style | highway MPG | city mpg | Popularity | MSRP | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1287 | Oldsmobile | Achieva | 1996 | regular unleaded | 150.0 | 4.0 | MANUAL | front wheel drive | 4.0 | Midsize | Sedan | 30 | 20 | 26 | 2000 |
| 1288 | Oldsmobile | Achieva | 1996 | regular unleaded | 150.0 | 4.0 | MANUAL | front wheel drive | 2.0 | Midsize | Coupe | 30 | 20 | 26 | 2000 |
| 1289 | Oldsmobile | Achieva | 1997 | regular unleaded | 150.0 | 4.0 | UNKNOWN | front wheel drive | 2.0 | Midsize | Coupe | 29 | 19 | 26 | 2000 |
| 1290 | Oldsmobile | Achieva | 1997 | regular unleaded | 150.0 | 4.0 | UNKNOWN | front wheel drive | 4.0 | Midsize | Sedan | 29 | 19 | 26 | 2000 |
| 1291 | Oldsmobile | Achieva | 1998 | regular unleaded | 150.0 | 6.0 | AUTOMATIC | front wheel drive | 4.0 | Midsize | Sedan | 27 | 18 | 26 | 2000 |
df[df['Model'] == 'Jimmy']
| Make | Model | Year | Engine Fuel Type | Engine HP | Engine Cylinders | Transmission Type | Driven_Wheels | Number of Doors | Vehicle Size | Vehicle Style | highway MPG | city mpg | Popularity | MSRP | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 6155 | GMC | Jimmy | 1999 | regular unleaded | 190.0 | 6.0 | MANUAL | four wheel drive | 2.0 | Compact | 2dr SUV | 16 | 13 | 549 | 2347 |
| 6156 | GMC | Jimmy | 1999 | regular unleaded | 190.0 | 6.0 | AUTOMATIC | rear wheel drive | 4.0 | Midsize | 4dr SUV | 19 | 14 | 549 | 2554 |
| 6157 | GMC | Jimmy | 1999 | regular unleaded | 190.0 | 6.0 | AUTOMATIC | four wheel drive | 4.0 | Midsize | 4dr SUV | 19 | 14 | 549 | 2590 |
| 6158 | GMC | Jimmy | 1999 | regular unleaded | 190.0 | 6.0 | UNKNOWN | rear wheel drive | 2.0 | Compact | 2dr SUV | 19 | 14 | 549 | 2182 |
| 6159 | GMC | Jimmy | 1999 | regular unleaded | 190.0 | 6.0 | AUTOMATIC | four wheel drive | 4.0 | Midsize | 4dr SUV | 19 | 14 | 549 | 2691 |
| 6160 | GMC | Jimmy | 1999 | regular unleaded | 190.0 | 6.0 | UNKNOWN | four wheel drive | 2.0 | Compact | 2dr SUV | 19 | 14 | 549 | 2317 |
| 6161 | GMC | Jimmy | 1999 | regular unleaded | 190.0 | 6.0 | AUTOMATIC | four wheel drive | 4.0 | Midsize | 4dr SUV | 19 | 14 | 549 | 2368 |
| 6162 | GMC | Jimmy | 1999 | regular unleaded | 190.0 | 6.0 | AUTOMATIC | rear wheel drive | 4.0 | Midsize | 4dr SUV | 19 | 14 | 549 | 2377 |
| 6163 | GMC | Jimmy | 1999 | regular unleaded | 190.0 | 6.0 | AUTOMATIC | rear wheel drive | 4.0 | Midsize | 4dr SUV | 19 | 14 | 549 | 2251 |
| 6164 | GMC | Jimmy | 1999 | regular unleaded | 190.0 | 6.0 | MANUAL | rear wheel drive | 2.0 | Compact | 2dr SUV | 21 | 15 | 549 | 2038 |
| 6165 | GMC | Jimmy | 2000 | regular unleaded | 190.0 | 6.0 | UNKNOWN | rear wheel drive | 2.0 | Compact | 2dr SUV | 20 | 15 | 549 | 2407 |
| 6166 | GMC | Jimmy | 2000 | regular unleaded | 190.0 | 6.0 | MANUAL | four wheel drive | 2.0 | Compact | 2dr SUV | 16 | 13 | 549 | 2463 |
| 6167 | GMC | Jimmy | 2000 | regular unleaded | 190.0 | 6.0 | AUTOMATIC | four wheel drive | 4.0 | Midsize | 4dr SUV | 18 | 14 | 549 | 2773 |
| 6168 | GMC | Jimmy | 2000 | regular unleaded | 190.0 | 6.0 | AUTOMATIC | rear wheel drive | 4.0 | Midsize | 4dr SUV | 20 | 15 | 549 | 2756 |
| 6169 | GMC | Jimmy | 2000 | regular unleaded | 190.0 | 6.0 | AUTOMATIC | rear wheel drive | 4.0 | Midsize | 4dr SUV | 20 | 15 | 549 | 2590 |
| 6170 | GMC | Jimmy | 2000 | regular unleaded | 190.0 | 6.0 | AUTOMATIC | four wheel drive | 4.0 | Midsize | 4dr SUV | 18 | 14 | 549 | 2916 |
| 6171 | GMC | Jimmy | 2000 | regular unleaded | 190.0 | 6.0 | MANUAL | rear wheel drive | 2.0 | Compact | 2dr SUV | 21 | 15 | 549 | 2322 |
| 6172 | GMC | Jimmy | 2000 | regular unleaded | 190.0 | 6.0 | AUTOMATIC | rear wheel drive | 4.0 | Midsize | 4dr SUV | 20 | 15 | 549 | 2623 |
| 6173 | GMC | Jimmy | 2000 | regular unleaded | 190.0 | 6.0 | AUTOMATIC | four wheel drive | 4.0 | Midsize | 4dr SUV | 18 | 14 | 549 | 2655 |
| 6174 | GMC | Jimmy | 2000 | regular unleaded | 190.0 | 6.0 | UNKNOWN | four wheel drive | 2.0 | Compact | 2dr SUV | 18 | 14 | 549 | 2578 |
| 6175 | GMC | Jimmy | 2001 | regular unleaded | 190.0 | 6.0 | AUTOMATIC | rear wheel drive | 4.0 | Midsize | 4dr SUV | 20 | 15 | 549 | 26770 |
| 6176 | GMC | Jimmy | 2001 | regular unleaded | 190.0 | 6.0 | MANUAL | four wheel drive | 2.0 | Compact | 2dr SUV | 16 | 13 | 549 | 22270 |
| 6177 | GMC | Jimmy | 2001 | regular unleaded | 190.0 | 6.0 | AUTOMATIC | four wheel drive | 2.0 | Compact | 2dr SUV | 18 | 14 | 549 | 25170 |
| 6178 | GMC | Jimmy | 2001 | regular unleaded | 190.0 | 6.0 | AUTOMATIC | four wheel drive | 4.0 | Midsize | 4dr SUV | 18 | 14 | 549 | 31925 |
| 6179 | GMC | Jimmy | 2001 | regular unleaded | 190.0 | 6.0 | MANUAL | rear wheel drive | 2.0 | Compact | 2dr SUV | 20 | 14 | 549 | 19270 |
| 6180 | GMC | Jimmy | 2001 | regular unleaded | 190.0 | 6.0 | AUTOMATIC | rear wheel drive | 2.0 | Compact | 2dr SUV | 20 | 15 | 549 | 22170 |
| 6181 | GMC | Jimmy | 2001 | regular unleaded | 190.0 | 6.0 | AUTOMATIC | four wheel drive | 4.0 | Midsize | 4dr SUV | 18 | 14 | 549 | 30225 |
| 6182 | GMC | Jimmy | 2001 | regular unleaded | 190.0 | 6.0 | AUTOMATIC | rear wheel drive | 4.0 | Midsize | 4dr SUV | 20 | 15 | 549 | 28225 |
| 6183 | GMC | Jimmy | 2001 | regular unleaded | 190.0 | 6.0 | AUTOMATIC | four wheel drive | 4.0 | Midsize | 4dr SUV | 18 | 14 | 549 | 33920 |
| 6184 | GMC | Jimmy | 2001 | regular unleaded | 190.0 | 6.0 | AUTOMATIC | four wheel drive | 4.0 | Midsize | 4dr SUV | 18 | 14 | 549 | 28770 |
| 6185 | GMC | Jimmy | 2001 | regular unleaded | 190.0 | 6.0 | AUTOMATIC | rear wheel drive | 4.0 | Midsize | 4dr SUV | 20 | 15 | 549 | 29925 |
df[df['Model'] == 'Le Baron']
| Make | Model | Year | Engine Fuel Type | Engine HP | Engine Cylinders | Transmission Type | Driven_Wheels | Number of Doors | Vehicle Size | Vehicle Style | highway MPG | city mpg | Popularity | MSRP | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 6364 | Chrysler | Le Baron | 1993 | regular unleaded | 141.0 | 6.0 | MANUAL | front wheel drive | 2.0 | Compact | Coupe | 26 | 17 | 1013 | 2000 |
| 6365 | Chrysler | Le Baron | 1993 | regular unleaded | 141.0 | 6.0 | AUTOMATIC | front wheel drive | 4.0 | Compact | Sedan | 26 | 18 | 1013 | 2000 |
| 6366 | Chrysler | Le Baron | 1993 | regular unleaded | 100.0 | 4.0 | UNKNOWN | front wheel drive | 2.0 | Compact | Coupe | 26 | 21 | 1013 | 2000 |
| 6367 | Chrysler | Le Baron | 1993 | regular unleaded | 141.0 | 6.0 | MANUAL | front wheel drive | 2.0 | Compact | Convertible | 24 | 17 | 1013 | 2000 |
| 6368 | Chrysler | Le Baron | 1993 | regular unleaded | 100.0 | 4.0 | UNKNOWN | front wheel drive | 2.0 | Compact | Convertible | 24 | 18 | 1013 | 2000 |
| 6369 | Chrysler | Le Baron | 1993 | regular unleaded | 141.0 | 6.0 | AUTOMATIC | front wheel drive | 2.0 | Compact | Coupe | 26 | 18 | 1013 | 2000 |
| 6370 | Chrysler | Le Baron | 1993 | regular unleaded | 100.0 | 4.0 | AUTOMATIC | front wheel drive | 4.0 | Compact | Sedan | 26 | 21 | 1013 | 2000 |
| 6371 | Chrysler | Le Baron | 1993 | regular unleaded | 141.0 | 6.0 | AUTOMATIC | front wheel drive | 2.0 | Compact | Convertible | 26 | 18 | 1013 | 2000 |
| 6372 | Chrysler | Le Baron | 1994 | regular unleaded | 141.0 | 6.0 | AUTOMATIC | front wheel drive | 2.0 | Compact | Convertible | 26 | 18 | 1013 | 2000 |
| 6373 | Chrysler | Le Baron | 1994 | regular unleaded | 142.0 | 6.0 | AUTOMATIC | front wheel drive | 4.0 | Compact | Sedan | 24 | 18 | 1013 | 2000 |
| 6374 | Chrysler | Le Baron | 1994 | regular unleaded | 142.0 | 6.0 | AUTOMATIC | front wheel drive | 4.0 | Compact | Sedan | 26 | 18 | 1013 | 2000 |
| 6375 | Chrysler | Le Baron | 1995 | regular unleaded | 141.0 | 6.0 | AUTOMATIC | front wheel drive | 2.0 | Compact | Convertible | 26 | 18 | 1013 | 2000 |
df[df['Model'] == 'RAM 150']
| Make | Model | Year | Engine Fuel Type | Engine HP | Engine Cylinders | Transmission Type | Driven_Wheels | Number of Doors | Vehicle Size | Vehicle Style | highway MPG | city mpg | Popularity | MSRP | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 8042 | Dodge | RAM 150 | 1991 | regular unleaded | 125.0 | 6.0 | UNKNOWN | rear wheel drive | 2.0 | Large | Regular Cab Pickup | 17 | 12 | 1851 | 2000 |
| 8044 | Dodge | RAM 150 | 1991 | regular unleaded | 170.0 | 8.0 | MANUAL | four wheel drive | 2.0 | Large | Extended Cab Pickup | 13 | 10 | 1851 | 2000 |
| 8045 | Dodge | RAM 150 | 1991 | regular unleaded | 170.0 | 8.0 | MANUAL | rear wheel drive | 2.0 | Large | Extended Cab Pickup | 14 | 11 | 1851 | 2000 |
| 8054 | Dodge | RAM 150 | 1992 | regular unleaded | 180.0 | 6.0 | MANUAL | four wheel drive | 2.0 | Large | Regular Cab Pickup | 16 | 11 | 1851 | 2000 |
| 8055 | Dodge | RAM 150 | 1992 | regular unleaded | 230.0 | 8.0 | MANUAL | four wheel drive | 2.0 | Large | Extended Cab Pickup | 15 | 12 | 1851 | 2000 |
| 8056 | Dodge | RAM 150 | 1992 | regular unleaded | 180.0 | 6.0 | MANUAL | rear wheel drive | 2.0 | Large | Regular Cab Pickup | 17 | 14 | 1851 | 2000 |
| 8058 | Dodge | RAM 150 | 1992 | regular unleaded | 230.0 | 8.0 | MANUAL | rear wheel drive | 2.0 | Large | Extended Cab Pickup | 16 | 11 | 1851 | 2000 |
| 8068 | Dodge | RAM 150 | 1993 | regular unleaded | 180.0 | 6.0 | MANUAL | four wheel drive | 2.0 | Large | Regular Cab Pickup | 16 | 11 | 1851 | 2000 |
| 8071 | Dodge | RAM 150 | 1993 | regular unleaded | 230.0 | 8.0 | MANUAL | rear wheel drive | 2.0 | Large | Extended Cab Pickup | 16 | 12 | 1851 | 2000 |
| 8072 | Dodge | RAM 150 | 1993 | regular unleaded | 180.0 | 6.0 | MANUAL | rear wheel drive | 2.0 | Large | Regular Cab Pickup | 17 | 14 | 1851 | 2000 |
| 8077 | Dodge | RAM 150 | 1993 | regular unleaded | 230.0 | 8.0 | MANUAL | four wheel drive | 2.0 | Large | Extended Cab Pickup | 15 | 12 | 1851 | 2008 |
| 8078 | Dodge | RAM 150 | 1993 | regular unleaded | 230.0 | 8.0 | MANUAL | four wheel drive | 2.0 | Large | Extended Cab Pickup | 15 | 12 | 1851 | 2083 |
df.loc[1289,'Transmission Type'] = 'MANUAL'
df.loc[1290,'Transmission Type'] = 'MANUAL'
df.loc[4691,'Transmission Type'] = 'MANUAL'
df.loc[4692,'Transmission Type'] = 'MANUAL'
df.loc[4693,'Transmission Type'] = 'MANUAL'
df.loc[6158,'Transmission Type'] = 'AUTOMATIC'
df.loc[6160,'Transmission Type'] = 'AUTOMATIC'
df.loc[6165,'Transmission Type'] = 'MANUAL'
df.loc[6174,'Transmission Type'] = 'AUTOMATIC'
df.loc[6366,'Transmission Type'] = 'AUTOMATIC'
df.loc[6368,'Transmission Type'] = 'AUTOMATIC'
df.loc[8042,'Transmission Type'] = 'MANUAL'
df[df['Transmission Type'] == 'UNKNOWN']
| Make | Model | Year | Engine Fuel Type | Engine HP | Engine Cylinders | Transmission Type | Driven_Wheels | Number of Doors | Vehicle Size | Vehicle Style | highway MPG | city mpg | Popularity | MSRP |
|---|
Visuals of clean data
df.head(10)
| Make | Model | Year | Engine Fuel Type | Engine HP | Engine Cylinders | Transmission Type | Driven_Wheels | Number of Doors | Vehicle Size | Vehicle Style | highway MPG | city mpg | Popularity | MSRP | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | BMW | 1 Series M | 2011 | premium unleaded (required) | 335.0 | 6.0 | MANUAL | rear wheel drive | 2.0 | Compact | Coupe | 26 | 19 | 3916 | 46135 |
| 1 | BMW | 1 Series | 2011 | premium unleaded (required) | 300.0 | 6.0 | MANUAL | rear wheel drive | 2.0 | Compact | Convertible | 28 | 19 | 3916 | 40650 |
| 2 | BMW | 1 Series | 2011 | premium unleaded (required) | 300.0 | 6.0 | MANUAL | rear wheel drive | 2.0 | Compact | Coupe | 28 | 20 | 3916 | 36350 |
| 3 | BMW | 1 Series | 2011 | premium unleaded (required) | 230.0 | 6.0 | MANUAL | rear wheel drive | 2.0 | Compact | Coupe | 28 | 18 | 3916 | 29450 |
| 4 | BMW | 1 Series | 2011 | premium unleaded (required) | 230.0 | 6.0 | MANUAL | rear wheel drive | 2.0 | Compact | Convertible | 28 | 18 | 3916 | 34500 |
| 5 | BMW | 1 Series | 2012 | premium unleaded (required) | 230.0 | 6.0 | MANUAL | rear wheel drive | 2.0 | Compact | Coupe | 28 | 18 | 3916 | 31200 |
| 6 | BMW | 1 Series | 2012 | premium unleaded (required) | 300.0 | 6.0 | MANUAL | rear wheel drive | 2.0 | Compact | Convertible | 26 | 17 | 3916 | 44100 |
| 7 | BMW | 1 Series | 2012 | premium unleaded (required) | 300.0 | 6.0 | MANUAL | rear wheel drive | 2.0 | Compact | Coupe | 28 | 20 | 3916 | 39300 |
| 8 | BMW | 1 Series | 2012 | premium unleaded (required) | 230.0 | 6.0 | MANUAL | rear wheel drive | 2.0 | Compact | Convertible | 28 | 18 | 3916 | 36900 |
| 9 | BMW | 1 Series | 2013 | premium unleaded (required) | 230.0 | 6.0 | MANUAL | rear wheel drive | 2.0 | Compact | Convertible | 27 | 18 | 3916 | 37200 |
df.corr()
| Year | Engine HP | Engine Cylinders | Number of Doors | highway MPG | city mpg | Popularity | MSRP | |
|---|---|---|---|---|---|---|---|---|
| Year | 1.000000 | 0.334092 | -0.033038 | 0.247648 | 0.244972 | 0.188417 | 0.085874 | 0.209635 |
| Engine HP | 0.334092 | 1.000000 | 0.771169 | -0.129639 | -0.374234 | -0.373267 | 0.039185 | 0.658245 |
| Engine Cylinders | -0.033038 | 0.771169 | 1.000000 | -0.152048 | -0.610338 | -0.585333 | 0.043010 | 0.533431 |
| Number of Doors | 0.247648 | -0.129639 | -0.152048 | 1.000000 | 0.115311 | 0.121194 | -0.057379 | -0.145179 |
| highway MPG | 0.244972 | -0.374234 | -0.610338 | 0.115311 | 1.000000 | 0.886299 | -0.017159 | -0.166631 |
| city mpg | 0.188417 | -0.373267 | -0.585333 | 0.121194 | 0.886299 | 1.000000 | -0.000549 | -0.162343 |
| Popularity | 0.085874 | 0.039185 | 0.043010 | -0.057379 | -0.017159 | -0.000549 | 1.000000 | -0.048371 |
| MSRP | 0.209635 | 0.658245 | 0.533431 | -0.145179 | -0.166631 | -0.162343 | -0.048371 | 1.000000 |
# Can see that Engine HP, Engine cylinders, and popularity have strongest correlation with price
# Make the figsize 7 x 6
plt.figure(figsize=(7,6))
# Plot heatmap of correlations
#sns.heatmap(correlations)
sns.heatmap(df.corr(), annot = True)
<matplotlib.axes._subplots.AxesSubplot at 0x1a13def320>
EC = df['Engine Cylinders'].value_counts()
EC.plot.barh()
plt.xlabel('Count')
plt.ylabel('Engine Cylinders')
Text(0,0.5,'Engine Cylinders')
sns.set_style('whitegrid')
df['Popularity'].hist(bins=30)
plt.xlabel('Popularity')
plt.ylabel('Count')
Text(0,0.5,'Count')
# Create a jointplot showing MSRP versus Popularity.
sns.jointplot(x='Popularity',y='MSRP',data=df)
<seaborn.axisgrid.JointGrid at 0x1a13f45da0>
# plot showing the relationship between the independent
# and dependent variables.
sns.lmplot(x='Engine HP', y='MSRP', data=df)
plt.show()
sns.lmplot(x='Engine Cylinders', y='MSRP', data=df)
plt.show()
sns.lmplot(x='Popularity', y='MSRP', data=df)
plt.show()
# Create a jointplot showing MSRP versus engine cylinders.
sns.jointplot(x='Engine Cylinders',y='MSRP',data=df)
<seaborn.axisgrid.JointGrid at 0x1a11bc6c88>
# Create a jointplot showing MSRP versus Engine HP.
sns.jointplot(x='Engine HP',y='MSRP',data=df)
<seaborn.axisgrid.JointGrid at 0x1a136ceb70>
sns.set_style('whitegrid')
df['Engine HP'].hist(bins=30)
plt.xlabel('Engine HP')
Text(0.5,0,'Engine HP')
sns.set_style('whitegrid')
df['Engine HP'].hist(bins=30)
plt.xlabel('Engine HP')
Text(0.5,0,'Engine HP')
Create Engine HP squared column
Seeing if Engine HP squared would perform better fit in model
df['Engine HP^2'] = (df['Engine HP'] * df['Engine HP']).astype(int)
df.head()
| Make | Model | Year | Engine Fuel Type | Engine HP | Engine Cylinders | Transmission Type | Driven_Wheels | Number of Doors | Vehicle Size | Vehicle Style | highway MPG | city mpg | Popularity | MSRP | Engine HP^2 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | BMW | 1 Series M | 2011 | premium unleaded (required) | 335.0 | 6.0 | MANUAL | rear wheel drive | 2.0 | Compact | Coupe | 26 | 19 | 3916 | 46135 | 112225 |
| 1 | BMW | 1 Series | 2011 | premium unleaded (required) | 300.0 | 6.0 | MANUAL | rear wheel drive | 2.0 | Compact | Convertible | 28 | 19 | 3916 | 40650 | 90000 |
| 2 | BMW | 1 Series | 2011 | premium unleaded (required) | 300.0 | 6.0 | MANUAL | rear wheel drive | 2.0 | Compact | Coupe | 28 | 20 | 3916 | 36350 | 90000 |
| 3 | BMW | 1 Series | 2011 | premium unleaded (required) | 230.0 | 6.0 | MANUAL | rear wheel drive | 2.0 | Compact | Coupe | 28 | 18 | 3916 | 29450 | 52900 |
| 4 | BMW | 1 Series | 2011 | premium unleaded (required) | 230.0 | 6.0 | MANUAL | rear wheel drive | 2.0 | Compact | Convertible | 28 | 18 | 3916 | 34500 | 52900 |
# Create a jointplot showing MSRP versus Engine HP.
sns.jointplot(x='Engine HP^2',y='MSRP',data=df)
<seaborn.axisgrid.JointGrid at 0x1a13e31128>
sns.set_style('whitegrid')
df['Engine HP^2'].hist(bins=30)
plt.xlabel('Engine HP^2')
Text(0.5,0,'Engine HP^2')
sns.lmplot(x='Engine HP^2', y='MSRP', data=df)
plt.show()
Identifying outliers
# outliers are cars such as the Bugatti which have 16 cylinders and 0ver a thousand HP
# which will have higher MSRP
df[df['Engine HP'] >= 1000]
| Make | Model | Year | Engine Fuel Type | Engine HP | Engine Cylinders | Transmission Type | Driven_Wheels | Number of Doors | Vehicle Size | Vehicle Style | highway MPG | city mpg | Popularity | MSRP | Engine HP^2 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 11362 | Bugatti | Veyron 16.4 | 2008 | premium unleaded (required) | 1001.0 | 16.0 | AUTOMATED_MANUAL | all wheel drive | 2.0 | Compact | Coupe | 14 | 8 | 820 | 2065902 | 1002001 |
| 11363 | Bugatti | Veyron 16.4 | 2008 | premium unleaded (required) | 1001.0 | 16.0 | AUTOMATED_MANUAL | all wheel drive | 2.0 | Compact | Coupe | 14 | 8 | 820 | 1500000 | 1002001 |
| 11364 | Bugatti | Veyron 16.4 | 2009 | premium unleaded (required) | 1001.0 | 16.0 | AUTOMATED_MANUAL | all wheel drive | 2.0 | Compact | Coupe | 14 | 8 | 820 | 1705769 | 1002001 |
df[df['MSRP'] >= 150000]
| Make | Model | Year | Engine Fuel Type | Engine HP | Engine Cylinders | Transmission Type | Driven_Wheels | Number of Doors | Vehicle Size | Vehicle Style | highway MPG | city mpg | Popularity | MSRP | Engine HP^2 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 294 | Ferrari | 360 | 2002 | premium unleaded (required) | 400.0 | 8.0 | MANUAL | rear wheel drive | 2.0 | Compact | Convertible | 15 | 10 | 2774 | 160829 | 160000 |
| 296 | Ferrari | 360 | 2002 | premium unleaded (required) | 400.0 | 8.0 | AUTOMATED_MANUAL | rear wheel drive | 2.0 | Compact | Coupe | 15 | 10 | 2774 | 150694 | 160000 |
| 297 | Ferrari | 360 | 2002 | premium unleaded (required) | 400.0 | 8.0 | AUTOMATED_MANUAL | rear wheel drive | 2.0 | Compact | Convertible | 15 | 10 | 2774 | 170829 | 160000 |
| 298 | Ferrari | 360 | 2003 | premium unleaded (required) | 400.0 | 8.0 | MANUAL | rear wheel drive | 2.0 | Compact | Convertible | 15 | 10 | 2774 | 165986 | 160000 |
| 299 | Ferrari | 360 | 2003 | premium unleaded (required) | 400.0 | 8.0 | AUTOMATED_MANUAL | rear wheel drive | 2.0 | Compact | Coupe | 15 | 10 | 2774 | 154090 | 160000 |
| 301 | Ferrari | 360 | 2003 | premium unleaded (required) | 400.0 | 8.0 | AUTOMATED_MANUAL | rear wheel drive | 2.0 | Compact | Convertible | 15 | 10 | 2774 | 176287 | 160000 |
| 302 | Ferrari | 360 | 2004 | premium unleaded (required) | 400.0 | 8.0 | AUTOMATED_MANUAL | rear wheel drive | 2.0 | Compact | Coupe | 15 | 10 | 2774 | 157767 | 160000 |
| 303 | Ferrari | 360 | 2004 | premium unleaded (required) | 425.0 | 8.0 | AUTOMATED_MANUAL | rear wheel drive | 2.0 | Compact | Coupe | 15 | 10 | 2774 | 187124 | 180625 |
| 305 | Ferrari | 360 | 2004 | premium unleaded (required) | 400.0 | 8.0 | MANUAL | rear wheel drive | 2.0 | Compact | Convertible | 15 | 10 | 2774 | 169900 | 160000 |
| 306 | Ferrari | 360 | 2004 | premium unleaded (required) | 400.0 | 8.0 | AUTOMATED_MANUAL | rear wheel drive | 2.0 | Compact | Convertible | 15 | 10 | 2774 | 180408 | 160000 |
| 460 | Ferrari | 456M | 2001 | premium unleaded (required) | 442.0 | 12.0 | AUTOMATIC | rear wheel drive | 2.0 | Compact | Coupe | 14 | 9 | 2774 | 223970 | 195364 |
| 461 | Ferrari | 456M | 2001 | premium unleaded (required) | 442.0 | 12.0 | MANUAL | rear wheel drive | 2.0 | Compact | Coupe | 15 | 9 | 2774 | 219775 | 195364 |
| 462 | Ferrari | 456M | 2002 | premium unleaded (required) | 442.0 | 12.0 | AUTOMATIC | rear wheel drive | 2.0 | Compact | Coupe | 14 | 9 | 2774 | 228625 | 195364 |
| 463 | Ferrari | 456M | 2002 | premium unleaded (required) | 442.0 | 12.0 | MANUAL | rear wheel drive | 2.0 | Compact | Coupe | 15 | 9 | 2774 | 224585 | 195364 |
| 464 | Ferrari | 456M | 2003 | premium unleaded (required) | 442.0 | 12.0 | AUTOMATIC | rear wheel drive | 2.0 | Compact | Coupe | 14 | 9 | 2774 | 228625 | 195364 |
| 465 | Ferrari | 456M | 2003 | premium unleaded (required) | 442.0 | 12.0 | MANUAL | rear wheel drive | 2.0 | Compact | Coupe | 15 | 9 | 2774 | 224585 | 195364 |
| 466 | Ferrari | 458 Italia | 2013 | premium unleaded (required) | 562.0 | 8.0 | AUTOMATED_MANUAL | rear wheel drive | 2.0 | Compact | Convertible | 17 | 13 | 2774 | 257412 | 315844 |
| 467 | Ferrari | 458 Italia | 2013 | premium unleaded (required) | 562.0 | 8.0 | AUTOMATED_MANUAL | rear wheel drive | 2.0 | Compact | Coupe | 17 | 13 | 2774 | 233509 | 315844 |
| 468 | Ferrari | 458 Italia | 2014 | premium unleaded (required) | 562.0 | 8.0 | AUTOMATED_MANUAL | rear wheel drive | 2.0 | Compact | Coupe | 17 | 13 | 2774 | 233509 | 315844 |
| 469 | Ferrari | 458 Italia | 2014 | premium unleaded (required) | 597.0 | 8.0 | AUTOMATED_MANUAL | rear wheel drive | 2.0 | Compact | Coupe | 17 | 13 | 2774 | 288000 | 356409 |
| 470 | Ferrari | 458 Italia | 2014 | premium unleaded (required) | 562.0 | 8.0 | AUTOMATED_MANUAL | rear wheel drive | 2.0 | Compact | Convertible | 17 | 13 | 2774 | 257412 | 315844 |
| 471 | Ferrari | 458 Italia | 2015 | premium unleaded (required) | 562.0 | 8.0 | AUTOMATED_MANUAL | rear wheel drive | 2.0 | Compact | Coupe | 17 | 13 | 2774 | 239340 | 315844 |
| 472 | Ferrari | 458 Italia | 2015 | premium unleaded (required) | 562.0 | 8.0 | AUTOMATED_MANUAL | rear wheel drive | 2.0 | Compact | Convertible | 17 | 13 | 2774 | 263553 | 315844 |
| 473 | Ferrari | 458 Italia | 2015 | premium unleaded (required) | 597.0 | 8.0 | AUTOMATED_MANUAL | rear wheel drive | 2.0 | Compact | Coupe | 17 | 13 | 2774 | 291744 | 356409 |
| 598 | Ferrari | 550 | 2001 | premium unleaded (required) | 485.0 | 12.0 | MANUAL | rear wheel drive | 2.0 | Compact | Convertible | 12 | 8 | 2774 | 248500 | 235225 |
| 599 | Ferrari | 550 | 2001 | premium unleaded (required) | 485.0 | 12.0 | MANUAL | rear wheel drive | 2.0 | Compact | Coupe | 12 | 8 | 2774 | 205840 | 235225 |
| 604 | McLaren | 570S | 2016 | premium unleaded (required) | 562.0 | 8.0 | AUTOMATED_MANUAL | rear wheel drive | 2.0 | Compact | Coupe | 23 | 16 | 416 | 184900 | 315844 |
| 605 | Ferrari | 575M | 2002 | premium unleaded (required) | 515.0 | 12.0 | MANUAL | rear wheel drive | 2.0 | Compact | Coupe | 15 | 9 | 2774 | 214670 | 265225 |
| 606 | Ferrari | 575M | 2002 | premium unleaded (required) | 515.0 | 12.0 | AUTOMATED_MANUAL | rear wheel drive | 2.0 | Compact | Coupe | 16 | 9 | 2774 | 224670 | 265225 |
| 607 | Ferrari | 575M | 2003 | premium unleaded (required) | 515.0 | 12.0 | MANUAL | rear wheel drive | 2.0 | Compact | Coupe | 15 | 9 | 2774 | 217890 | 265225 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 11092 | Aston Martin | V12 Vanquish | 2004 | premium unleaded (required) | 460.0 | 12.0 | AUTOMATIC | rear wheel drive | 2.0 | Compact | Coupe | 18 | 11 | 259 | 234260 | 211600 |
| 11093 | Aston Martin | V12 Vanquish | 2005 | premium unleaded (required) | 460.0 | 12.0 | AUTOMATED_MANUAL | rear wheel drive | 2.0 | Compact | Coupe | 16 | 10 | 259 | 234260 | 211600 |
| 11094 | Aston Martin | V12 Vanquish | 2005 | premium unleaded (required) | 520.0 | 12.0 | AUTOMATED_MANUAL | rear wheel drive | 2.0 | Compact | Coupe | 16 | 10 | 259 | 255000 | 270400 |
| 11095 | Aston Martin | V12 Vanquish | 2006 | premium unleaded (required) | 520.0 | 12.0 | AUTOMATED_MANUAL | rear wheel drive | 2.0 | Compact | Coupe | 16 | 10 | 259 | 260000 | 270400 |
| 11096 | Aston Martin | V12 Vantage S | 2015 | premium unleaded (required) | 565.0 | 12.0 | AUTOMATED_MANUAL | rear wheel drive | 2.0 | Compact | Coupe | 18 | 12 | 259 | 182395 | 319225 |
| 11097 | Aston Martin | V12 Vantage S | 2016 | premium unleaded (required) | 565.0 | 12.0 | AUTOMATED_MANUAL | rear wheel drive | 2.0 | Compact | Convertible | 18 | 12 | 259 | 198195 | 319225 |
| 11098 | Aston Martin | V12 Vantage S | 2016 | premium unleaded (required) | 565.0 | 12.0 | AUTOMATED_MANUAL | rear wheel drive | 2.0 | Compact | Coupe | 18 | 12 | 259 | 183695 | 319225 |
| 11099 | Aston Martin | V12 Vantage | 2011 | premium unleaded (required) | 510.0 | 12.0 | MANUAL | rear wheel drive | 2.0 | Compact | Coupe | 17 | 11 | 259 | 191995 | 260100 |
| 11100 | Aston Martin | V12 Vantage | 2011 | premium unleaded (required) | 510.0 | 12.0 | MANUAL | rear wheel drive | 2.0 | Compact | Coupe | 17 | 11 | 259 | 180535 | 260100 |
| 11101 | Aston Martin | V12 Vantage | 2012 | premium unleaded (required) | 510.0 | 12.0 | MANUAL | rear wheel drive | 2.0 | Compact | Coupe | 17 | 11 | 259 | 180535 | 260100 |
| 11102 | Aston Martin | V12 Vantage | 2012 | premium unleaded (required) | 510.0 | 12.0 | MANUAL | rear wheel drive | 2.0 | Compact | Coupe | 17 | 11 | 259 | 195895 | 260100 |
| 11153 | Aston Martin | V8 Vantage | 2014 | premium unleaded (required) | 430.0 | 8.0 | AUTOMATED_MANUAL | rear wheel drive | 2.0 | Compact | Convertible | 21 | 14 | 259 | 150900 | 184900 |
| 11159 | Aston Martin | V8 Vantage | 2015 | premium unleaded (required) | 430.0 | 8.0 | AUTOMATED_MANUAL | rear wheel drive | 2.0 | Compact | Convertible | 21 | 14 | 259 | 153195 | 184900 |
| 11173 | Aston Martin | V8 Vantage | 2016 | premium unleaded (required) | 430.0 | 8.0 | AUTOMATED_MANUAL | rear wheel drive | 2.0 | Compact | Convertible | 21 | 14 | 259 | 154495 | 184900 |
| 11206 | Aston Martin | Vanquish | 2014 | premium unleaded (required) | 565.0 | 12.0 | AUTOMATIC | rear wheel drive | 2.0 | Midsize | Convertible | 19 | 13 | 259 | 296295 | 319225 |
| 11207 | Aston Martin | Vanquish | 2014 | premium unleaded (required) | 565.0 | 12.0 | AUTOMATIC | rear wheel drive | 2.0 | Midsize | Coupe | 19 | 13 | 259 | 278295 | 319225 |
| 11208 | Aston Martin | Vanquish | 2015 | premium unleaded (required) | 568.0 | 12.0 | AUTOMATIC | rear wheel drive | 2.0 | Midsize | Convertible | 21 | 13 | 259 | 301695 | 322624 |
| 11209 | Aston Martin | Vanquish | 2015 | premium unleaded (required) | 568.0 | 12.0 | AUTOMATIC | rear wheel drive | 2.0 | Midsize | Coupe | 21 | 13 | 259 | 283695 | 322624 |
| 11210 | Aston Martin | Vanquish | 2016 | premium unleaded (required) | 568.0 | 12.0 | AUTOMATIC | rear wheel drive | 2.0 | Midsize | Coupe | 21 | 13 | 259 | 287650 | 322624 |
| 11211 | Aston Martin | Vanquish | 2016 | premium unleaded (required) | 568.0 | 12.0 | AUTOMATIC | rear wheel drive | 2.0 | Midsize | Convertible | 21 | 13 | 259 | 305650 | 322624 |
| 11212 | Aston Martin | Vanquish | 2016 | premium unleaded (required) | 568.0 | 12.0 | AUTOMATIC | rear wheel drive | 2.0 | Midsize | Coupe | 21 | 13 | 259 | 302695 | 322624 |
| 11213 | Aston Martin | Vanquish | 2016 | premium unleaded (required) | 568.0 | 12.0 | AUTOMATIC | rear wheel drive | 2.0 | Midsize | Convertible | 21 | 13 | 259 | 320695 | 322624 |
| 11362 | Bugatti | Veyron 16.4 | 2008 | premium unleaded (required) | 1001.0 | 16.0 | AUTOMATED_MANUAL | all wheel drive | 2.0 | Compact | Coupe | 14 | 8 | 820 | 2065902 | 1002001 |
| 11363 | Bugatti | Veyron 16.4 | 2008 | premium unleaded (required) | 1001.0 | 16.0 | AUTOMATED_MANUAL | all wheel drive | 2.0 | Compact | Coupe | 14 | 8 | 820 | 1500000 | 1002001 |
| 11364 | Bugatti | Veyron 16.4 | 2009 | premium unleaded (required) | 1001.0 | 16.0 | AUTOMATED_MANUAL | all wheel drive | 2.0 | Compact | Coupe | 14 | 8 | 820 | 1705769 | 1002001 |
| 11394 | Aston Martin | Virage | 2012 | premium unleaded (required) | 490.0 | 12.0 | AUTOMATIC | rear wheel drive | 2.0 | Midsize | Coupe | 18 | 13 | 259 | 208295 | 240100 |
| 11395 | Aston Martin | Virage | 2012 | premium unleaded (required) | 490.0 | 12.0 | AUTOMATIC | rear wheel drive | 2.0 | Midsize | Convertible | 18 | 13 | 259 | 223295 | 240100 |
| 11448 | Rolls-Royce | Wraith | 2014 | premium unleaded (required) | 624.0 | 12.0 | AUTOMATIC | rear wheel drive | 2.0 | Large | Coupe | 21 | 13 | 86 | 284900 | 389376 |
| 11449 | Rolls-Royce | Wraith | 2015 | premium unleaded (required) | 624.0 | 12.0 | AUTOMATIC | rear wheel drive | 2.0 | Large | Coupe | 21 | 13 | 86 | 294025 | 389376 |
| 11450 | Rolls-Royce | Wraith | 2016 | premium unleaded (required) | 624.0 | 12.0 | AUTOMATIC | rear wheel drive | 2.0 | Large | Coupe | 21 | 13 | 86 | 304350 | 389376 |
408 rows × 16 columns
Save data
# Save undummy data
df1 = df.to_csv('carnotdummied.csv')
Dummies for categorical columns
df = pd.get_dummies(df, columns=['Make','Model', 'Engine Fuel Type', 'Transmission Type', 'Driven_Wheels',
'Vehicle Size', 'Vehicle Style'])
df.head()
| Year | Engine HP | Engine Cylinders | Number of Doors | highway MPG | city mpg | Popularity | MSRP | Engine HP^2 | Make_Acura | Make_Alfa Romeo | Make_Aston Martin | Make_Audi | Make_BMW | Make_Bentley | Make_Bugatti | Make_Buick | Make_Cadillac | Make_Chevrolet | Make_Chrysler | Make_Dodge | Make_FIAT | Make_Ferrari | Make_Ford | Make_GMC | Make_Genesis | Make_HUMMER | Make_Honda | Make_Hyundai | Make_Infiniti | Make_Kia | Make_Lamborghini | Make_Land Rover | Make_Lexus | Make_Lincoln | Make_Lotus | Make_Maserati | Make_Maybach | Make_Mazda | Make_McLaren | Make_Mercedes-Benz | Make_Mitsubishi | Make_Nissan | Make_Oldsmobile | Make_Plymouth | Make_Pontiac | Make_Porsche | Make_Rolls-Royce | Make_Saab | Make_Scion | ... | Model_Zephyr | Model_allroad | Model_allroad quattro | Model_e-Golf | Model_i-MiEV | Model_i3 | Model_iA | Model_iM | Model_iQ | Model_tC | Model_xA | Model_xB | Model_xD | Engine Fuel Type_diesel | Engine Fuel Type_electric | Engine Fuel Type_flex-fuel (premium unleaded recommended/E85) | Engine Fuel Type_flex-fuel (premium unleaded required/E85) | Engine Fuel Type_flex-fuel (unleaded/E85) | Engine Fuel Type_flex-fuel (unleaded/natural gas) | Engine Fuel Type_natural gas | Engine Fuel Type_premium unleaded (recommended) | Engine Fuel Type_premium unleaded (required) | Engine Fuel Type_regular unleaded | Transmission Type_AUTOMATED_MANUAL | Transmission Type_AUTOMATIC | Transmission Type_DIRECT_DRIVE | Transmission Type_MANUAL | Driven_Wheels_all wheel drive | Driven_Wheels_four wheel drive | Driven_Wheels_front wheel drive | Driven_Wheels_rear wheel drive | Vehicle Size_Compact | Vehicle Size_Large | Vehicle Size_Midsize | Vehicle Style_2dr Hatchback | Vehicle Style_2dr SUV | Vehicle Style_4dr Hatchback | Vehicle Style_4dr SUV | Vehicle Style_Cargo Minivan | Vehicle Style_Cargo Van | Vehicle Style_Convertible | Vehicle Style_Convertible SUV | Vehicle Style_Coupe | Vehicle Style_Crew Cab Pickup | Vehicle Style_Extended Cab Pickup | Vehicle Style_Passenger Minivan | Vehicle Style_Passenger Van | Vehicle Style_Regular Cab Pickup | Vehicle Style_Sedan | Vehicle Style_Wagon | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 2011 | 335.0 | 6.0 | 2.0 | 26 | 19 | 3916 | 46135 | 112225 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 1 | 2011 | 300.0 | 6.0 | 2.0 | 28 | 19 | 3916 | 40650 | 90000 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 2 | 2011 | 300.0 | 6.0 | 2.0 | 28 | 20 | 3916 | 36350 | 90000 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 3 | 2011 | 230.0 | 6.0 | 2.0 | 28 | 18 | 3916 | 29450 | 52900 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 4 | 2011 | 230.0 | 6.0 | 2.0 | 28 | 18 | 3916 | 34500 | 52900 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
5 rows × 1014 columns
df.to_csv('cardummied.csv')
Train test spit
# Create separate object for target variable
y = df.MSRP
# Create separate object for input features
X = df.drop('MSRP', axis=1)
# Split X and y into train and test sets
X_train, X_test, y_train, y_test = train_test_split(X, y,
test_size=0.2, # set aside 20% of observations for the test set.
random_state=1234)
# verify test and train set shape
X_test.shape,y_train.shape
((2240, 1013), (8959,))
# Print number of observations in X_train, X_test, y_train, and y_test
print(len(X_train), len(X_test), len(y_train), len(y_test))
8959 2240 8959 2240
X_cols = df.drop('MSRP', axis=1).columns
# MSE - the average absolute difference between predicted and actual values for our target variable.
# R2 - The percentt of the variation in the target variable that can be explained by the model
Pipeline with Random Forest Model
#initialize randomforest and selectKbest
selector = SelectKBest(f_regression) # select k best
clf = RandomForestRegressor() # Model I want to use
#place SelectKbest transformer and RandomForest estimator into Pipeine
pipe = Pipeline(steps=[
# ('poly', PolynomialFeatures()), # Did not need because I created dummies for categorial columns which made to many columns
('selector', selector), # feature selection
('clf', clf) # Model
])
#Create the parameter grid, entering the values to use for each parameter selected in the RandomForest estimator
parameters = {
'selector__k':[50,100], # params to search through
# 'poly__degree': [2],
'clf__n_estimators':[20, 100,150], # Start, stop, number of trees
'clf__min_samples_split': [5], # max number of samples required to split an internal node:
'clf__max_features': ['auto'], # max number of features considered for splitting a node
'clf__max_depth': [ 3, 5, 7] # max number of splits per tree
}
#Perform grid search on the classifier using 'scorer' as the scoring method using GridSearchCV()
g_search = GridSearchCV(pipe, parameters, cv=3, n_jobs=1, verbose=2)
#Fit the grid search object to the training data and find the optimal parameters using fit()
g_fit = g_search.fit(X_train, y_train)
#Get the best estimator and print out the estimator model
best_clf = g_fit.best_estimator_
print (best_clf)
#Use best estimator to make predictions on the test set
best_predictions = best_clf.predict(X_test)
#metrics
#print(mean_absolute_error(y_true = y_test, y_pred = best_predictions))
#print(r2_score(y_true = y_test, y_pred = best_predictions))
print("MAE: " + str(mean_absolute_error(y_true = y_test, y_pred = best_predictions)))
print("R2 Score: " + str(r2_score(y_true = y_test, y_pred = best_predictions)))
Fitting 3 folds for each of 18 candidates, totalling 54 fits
[CV] clf__max_depth=3, clf__max_features=auto, clf__min_samples_split=5, clf__n_estimators=20, selector__k=50
/anaconda3/lib/python3.6/site-packages/sklearn/feature_selection/univariate_selection.py:298: RuntimeWarning: invalid value encountered in true_divide
corr /= X_norms
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in greater
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in less
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:1821: RuntimeWarning: invalid value encountered in less_equal
cond2 = cond0 & (x <= self.a)
[Parallel(n_jobs=1)]: Done 1 out of 1 | elapsed: 0.3s remaining: 0.0s
[CV] clf__max_depth=3, clf__max_features=auto, clf__min_samples_split=5, clf__n_estimators=20, selector__k=50, total= 0.3s
[CV] clf__max_depth=3, clf__max_features=auto, clf__min_samples_split=5, clf__n_estimators=20, selector__k=50
/anaconda3/lib/python3.6/site-packages/sklearn/feature_selection/univariate_selection.py:298: RuntimeWarning: invalid value encountered in true_divide
corr /= X_norms
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in greater
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in less
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:1821: RuntimeWarning: invalid value encountered in less_equal
cond2 = cond0 & (x <= self.a)
[CV] clf__max_depth=3, clf__max_features=auto, clf__min_samples_split=5, clf__n_estimators=20, selector__k=50, total= 0.3s
[CV] clf__max_depth=3, clf__max_features=auto, clf__min_samples_split=5, clf__n_estimators=20, selector__k=50
/anaconda3/lib/python3.6/site-packages/sklearn/feature_selection/univariate_selection.py:298: RuntimeWarning: invalid value encountered in true_divide
corr /= X_norms
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in greater
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in less
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:1821: RuntimeWarning: invalid value encountered in less_equal
cond2 = cond0 & (x <= self.a)
[CV] clf__max_depth=3, clf__max_features=auto, clf__min_samples_split=5, clf__n_estimators=20, selector__k=50, total= 0.2s
[CV] clf__max_depth=3, clf__max_features=auto, clf__min_samples_split=5, clf__n_estimators=20, selector__k=100
/anaconda3/lib/python3.6/site-packages/sklearn/feature_selection/univariate_selection.py:298: RuntimeWarning: invalid value encountered in true_divide
corr /= X_norms
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in greater
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in less
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:1821: RuntimeWarning: invalid value encountered in less_equal
cond2 = cond0 & (x <= self.a)
[CV] clf__max_depth=3, clf__max_features=auto, clf__min_samples_split=5, clf__n_estimators=20, selector__k=100, total= 0.3s
[CV] clf__max_depth=3, clf__max_features=auto, clf__min_samples_split=5, clf__n_estimators=20, selector__k=100
/anaconda3/lib/python3.6/site-packages/sklearn/feature_selection/univariate_selection.py:298: RuntimeWarning: invalid value encountered in true_divide
corr /= X_norms
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in greater
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in less
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:1821: RuntimeWarning: invalid value encountered in less_equal
cond2 = cond0 & (x <= self.a)
[CV] clf__max_depth=3, clf__max_features=auto, clf__min_samples_split=5, clf__n_estimators=20, selector__k=100, total= 0.2s
[CV] clf__max_depth=3, clf__max_features=auto, clf__min_samples_split=5, clf__n_estimators=20, selector__k=100
/anaconda3/lib/python3.6/site-packages/sklearn/feature_selection/univariate_selection.py:298: RuntimeWarning: invalid value encountered in true_divide
corr /= X_norms
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in greater
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in less
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:1821: RuntimeWarning: invalid value encountered in less_equal
cond2 = cond0 & (x <= self.a)
[CV] clf__max_depth=3, clf__max_features=auto, clf__min_samples_split=5, clf__n_estimators=20, selector__k=100, total= 0.3s
[CV] clf__max_depth=3, clf__max_features=auto, clf__min_samples_split=5, clf__n_estimators=100, selector__k=50
/anaconda3/lib/python3.6/site-packages/sklearn/feature_selection/univariate_selection.py:298: RuntimeWarning: invalid value encountered in true_divide
corr /= X_norms
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in greater
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in less
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:1821: RuntimeWarning: invalid value encountered in less_equal
cond2 = cond0 & (x <= self.a)
[CV] clf__max_depth=3, clf__max_features=auto, clf__min_samples_split=5, clf__n_estimators=100, selector__k=50, total= 0.7s
[CV] clf__max_depth=3, clf__max_features=auto, clf__min_samples_split=5, clf__n_estimators=100, selector__k=50
/anaconda3/lib/python3.6/site-packages/sklearn/feature_selection/univariate_selection.py:298: RuntimeWarning: invalid value encountered in true_divide
corr /= X_norms
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in greater
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in less
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:1821: RuntimeWarning: invalid value encountered in less_equal
cond2 = cond0 & (x <= self.a)
[CV] clf__max_depth=3, clf__max_features=auto, clf__min_samples_split=5, clf__n_estimators=100, selector__k=50, total= 0.6s
[CV] clf__max_depth=3, clf__max_features=auto, clf__min_samples_split=5, clf__n_estimators=100, selector__k=50
/anaconda3/lib/python3.6/site-packages/sklearn/feature_selection/univariate_selection.py:298: RuntimeWarning: invalid value encountered in true_divide
corr /= X_norms
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in greater
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in less
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:1821: RuntimeWarning: invalid value encountered in less_equal
cond2 = cond0 & (x <= self.a)
[CV] clf__max_depth=3, clf__max_features=auto, clf__min_samples_split=5, clf__n_estimators=100, selector__k=50, total= 0.6s
[CV] clf__max_depth=3, clf__max_features=auto, clf__min_samples_split=5, clf__n_estimators=100, selector__k=100
/anaconda3/lib/python3.6/site-packages/sklearn/feature_selection/univariate_selection.py:298: RuntimeWarning: invalid value encountered in true_divide
corr /= X_norms
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in greater
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in less
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:1821: RuntimeWarning: invalid value encountered in less_equal
cond2 = cond0 & (x <= self.a)
[CV] clf__max_depth=3, clf__max_features=auto, clf__min_samples_split=5, clf__n_estimators=100, selector__k=100, total= 0.8s
[CV] clf__max_depth=3, clf__max_features=auto, clf__min_samples_split=5, clf__n_estimators=100, selector__k=100
/anaconda3/lib/python3.6/site-packages/sklearn/feature_selection/univariate_selection.py:298: RuntimeWarning: invalid value encountered in true_divide
corr /= X_norms
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in greater
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in less
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:1821: RuntimeWarning: invalid value encountered in less_equal
cond2 = cond0 & (x <= self.a)
[CV] clf__max_depth=3, clf__max_features=auto, clf__min_samples_split=5, clf__n_estimators=100, selector__k=100, total= 0.7s
[CV] clf__max_depth=3, clf__max_features=auto, clf__min_samples_split=5, clf__n_estimators=100, selector__k=100
/anaconda3/lib/python3.6/site-packages/sklearn/feature_selection/univariate_selection.py:298: RuntimeWarning: invalid value encountered in true_divide
corr /= X_norms
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in greater
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in less
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:1821: RuntimeWarning: invalid value encountered in less_equal
cond2 = cond0 & (x <= self.a)
[CV] clf__max_depth=3, clf__max_features=auto, clf__min_samples_split=5, clf__n_estimators=100, selector__k=100, total= 0.8s
[CV] clf__max_depth=3, clf__max_features=auto, clf__min_samples_split=5, clf__n_estimators=150, selector__k=50
/anaconda3/lib/python3.6/site-packages/sklearn/feature_selection/univariate_selection.py:298: RuntimeWarning: invalid value encountered in true_divide
corr /= X_norms
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in greater
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in less
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:1821: RuntimeWarning: invalid value encountered in less_equal
cond2 = cond0 & (x <= self.a)
[CV] clf__max_depth=3, clf__max_features=auto, clf__min_samples_split=5, clf__n_estimators=150, selector__k=50, total= 0.8s
[CV] clf__max_depth=3, clf__max_features=auto, clf__min_samples_split=5, clf__n_estimators=150, selector__k=50
/anaconda3/lib/python3.6/site-packages/sklearn/feature_selection/univariate_selection.py:298: RuntimeWarning: invalid value encountered in true_divide
corr /= X_norms
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in greater
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in less
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:1821: RuntimeWarning: invalid value encountered in less_equal
cond2 = cond0 & (x <= self.a)
[CV] clf__max_depth=3, clf__max_features=auto, clf__min_samples_split=5, clf__n_estimators=150, selector__k=50, total= 0.9s
[CV] clf__max_depth=3, clf__max_features=auto, clf__min_samples_split=5, clf__n_estimators=150, selector__k=50
/anaconda3/lib/python3.6/site-packages/sklearn/feature_selection/univariate_selection.py:298: RuntimeWarning: invalid value encountered in true_divide
corr /= X_norms
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in greater
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in less
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:1821: RuntimeWarning: invalid value encountered in less_equal
cond2 = cond0 & (x <= self.a)
[CV] clf__max_depth=3, clf__max_features=auto, clf__min_samples_split=5, clf__n_estimators=150, selector__k=50, total= 0.8s
[CV] clf__max_depth=3, clf__max_features=auto, clf__min_samples_split=5, clf__n_estimators=150, selector__k=100
/anaconda3/lib/python3.6/site-packages/sklearn/feature_selection/univariate_selection.py:298: RuntimeWarning: invalid value encountered in true_divide
corr /= X_norms
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in greater
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in less
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:1821: RuntimeWarning: invalid value encountered in less_equal
cond2 = cond0 & (x <= self.a)
[CV] clf__max_depth=3, clf__max_features=auto, clf__min_samples_split=5, clf__n_estimators=150, selector__k=100, total= 1.1s
[CV] clf__max_depth=3, clf__max_features=auto, clf__min_samples_split=5, clf__n_estimators=150, selector__k=100
/anaconda3/lib/python3.6/site-packages/sklearn/feature_selection/univariate_selection.py:298: RuntimeWarning: invalid value encountered in true_divide
corr /= X_norms
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in greater
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in less
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:1821: RuntimeWarning: invalid value encountered in less_equal
cond2 = cond0 & (x <= self.a)
[CV] clf__max_depth=3, clf__max_features=auto, clf__min_samples_split=5, clf__n_estimators=150, selector__k=100, total= 1.1s
[CV] clf__max_depth=3, clf__max_features=auto, clf__min_samples_split=5, clf__n_estimators=150, selector__k=100
/anaconda3/lib/python3.6/site-packages/sklearn/feature_selection/univariate_selection.py:298: RuntimeWarning: invalid value encountered in true_divide
corr /= X_norms
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in greater
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in less
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:1821: RuntimeWarning: invalid value encountered in less_equal
cond2 = cond0 & (x <= self.a)
[CV] clf__max_depth=3, clf__max_features=auto, clf__min_samples_split=5, clf__n_estimators=150, selector__k=100, total= 1.1s
[CV] clf__max_depth=5, clf__max_features=auto, clf__min_samples_split=5, clf__n_estimators=20, selector__k=50
/anaconda3/lib/python3.6/site-packages/sklearn/feature_selection/univariate_selection.py:298: RuntimeWarning: invalid value encountered in true_divide
corr /= X_norms
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in greater
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in less
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:1821: RuntimeWarning: invalid value encountered in less_equal
cond2 = cond0 & (x <= self.a)
[CV] clf__max_depth=5, clf__max_features=auto, clf__min_samples_split=5, clf__n_estimators=20, selector__k=50, total= 0.2s
[CV] clf__max_depth=5, clf__max_features=auto, clf__min_samples_split=5, clf__n_estimators=20, selector__k=50
/anaconda3/lib/python3.6/site-packages/sklearn/feature_selection/univariate_selection.py:298: RuntimeWarning: invalid value encountered in true_divide
corr /= X_norms
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in greater
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in less
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:1821: RuntimeWarning: invalid value encountered in less_equal
cond2 = cond0 & (x <= self.a)
[CV] clf__max_depth=5, clf__max_features=auto, clf__min_samples_split=5, clf__n_estimators=20, selector__k=50, total= 0.2s
[CV] clf__max_depth=5, clf__max_features=auto, clf__min_samples_split=5, clf__n_estimators=20, selector__k=50
/anaconda3/lib/python3.6/site-packages/sklearn/feature_selection/univariate_selection.py:298: RuntimeWarning: invalid value encountered in true_divide
corr /= X_norms
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in greater
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in less
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:1821: RuntimeWarning: invalid value encountered in less_equal
cond2 = cond0 & (x <= self.a)
[CV] clf__max_depth=5, clf__max_features=auto, clf__min_samples_split=5, clf__n_estimators=20, selector__k=50, total= 0.2s
[CV] clf__max_depth=5, clf__max_features=auto, clf__min_samples_split=5, clf__n_estimators=20, selector__k=100
/anaconda3/lib/python3.6/site-packages/sklearn/feature_selection/univariate_selection.py:298: RuntimeWarning: invalid value encountered in true_divide
corr /= X_norms
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in greater
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in less
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:1821: RuntimeWarning: invalid value encountered in less_equal
cond2 = cond0 & (x <= self.a)
[CV] clf__max_depth=5, clf__max_features=auto, clf__min_samples_split=5, clf__n_estimators=20, selector__k=100, total= 0.3s
[CV] clf__max_depth=5, clf__max_features=auto, clf__min_samples_split=5, clf__n_estimators=20, selector__k=100
/anaconda3/lib/python3.6/site-packages/sklearn/feature_selection/univariate_selection.py:298: RuntimeWarning: invalid value encountered in true_divide
corr /= X_norms
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in greater
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in less
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:1821: RuntimeWarning: invalid value encountered in less_equal
cond2 = cond0 & (x <= self.a)
[CV] clf__max_depth=5, clf__max_features=auto, clf__min_samples_split=5, clf__n_estimators=20, selector__k=100, total= 0.3s
[CV] clf__max_depth=5, clf__max_features=auto, clf__min_samples_split=5, clf__n_estimators=20, selector__k=100
/anaconda3/lib/python3.6/site-packages/sklearn/feature_selection/univariate_selection.py:298: RuntimeWarning: invalid value encountered in true_divide
corr /= X_norms
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in greater
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in less
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:1821: RuntimeWarning: invalid value encountered in less_equal
cond2 = cond0 & (x <= self.a)
[CV] clf__max_depth=5, clf__max_features=auto, clf__min_samples_split=5, clf__n_estimators=20, selector__k=100, total= 0.3s
[CV] clf__max_depth=5, clf__max_features=auto, clf__min_samples_split=5, clf__n_estimators=100, selector__k=50
/anaconda3/lib/python3.6/site-packages/sklearn/feature_selection/univariate_selection.py:298: RuntimeWarning: invalid value encountered in true_divide
corr /= X_norms
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in greater
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in less
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:1821: RuntimeWarning: invalid value encountered in less_equal
cond2 = cond0 & (x <= self.a)
[CV] clf__max_depth=5, clf__max_features=auto, clf__min_samples_split=5, clf__n_estimators=100, selector__k=50, total= 0.8s
[CV] clf__max_depth=5, clf__max_features=auto, clf__min_samples_split=5, clf__n_estimators=100, selector__k=50
/anaconda3/lib/python3.6/site-packages/sklearn/feature_selection/univariate_selection.py:298: RuntimeWarning: invalid value encountered in true_divide
corr /= X_norms
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in greater
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in less
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:1821: RuntimeWarning: invalid value encountered in less_equal
cond2 = cond0 & (x <= self.a)
[CV] clf__max_depth=5, clf__max_features=auto, clf__min_samples_split=5, clf__n_estimators=100, selector__k=50, total= 0.9s
[CV] clf__max_depth=5, clf__max_features=auto, clf__min_samples_split=5, clf__n_estimators=100, selector__k=50
/anaconda3/lib/python3.6/site-packages/sklearn/feature_selection/univariate_selection.py:298: RuntimeWarning: invalid value encountered in true_divide
corr /= X_norms
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in greater
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in less
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:1821: RuntimeWarning: invalid value encountered in less_equal
cond2 = cond0 & (x <= self.a)
[CV] clf__max_depth=5, clf__max_features=auto, clf__min_samples_split=5, clf__n_estimators=100, selector__k=50, total= 0.8s
[CV] clf__max_depth=5, clf__max_features=auto, clf__min_samples_split=5, clf__n_estimators=100, selector__k=100
/anaconda3/lib/python3.6/site-packages/sklearn/feature_selection/univariate_selection.py:298: RuntimeWarning: invalid value encountered in true_divide
corr /= X_norms
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in greater
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in less
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:1821: RuntimeWarning: invalid value encountered in less_equal
cond2 = cond0 & (x <= self.a)
[CV] clf__max_depth=5, clf__max_features=auto, clf__min_samples_split=5, clf__n_estimators=100, selector__k=100, total= 1.1s
[CV] clf__max_depth=5, clf__max_features=auto, clf__min_samples_split=5, clf__n_estimators=100, selector__k=100
/anaconda3/lib/python3.6/site-packages/sklearn/feature_selection/univariate_selection.py:298: RuntimeWarning: invalid value encountered in true_divide
corr /= X_norms
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in greater
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in less
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:1821: RuntimeWarning: invalid value encountered in less_equal
cond2 = cond0 & (x <= self.a)
[CV] clf__max_depth=5, clf__max_features=auto, clf__min_samples_split=5, clf__n_estimators=100, selector__k=100, total= 1.0s
[CV] clf__max_depth=5, clf__max_features=auto, clf__min_samples_split=5, clf__n_estimators=100, selector__k=100
/anaconda3/lib/python3.6/site-packages/sklearn/feature_selection/univariate_selection.py:298: RuntimeWarning: invalid value encountered in true_divide
corr /= X_norms
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in greater
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in less
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:1821: RuntimeWarning: invalid value encountered in less_equal
cond2 = cond0 & (x <= self.a)
[CV] clf__max_depth=5, clf__max_features=auto, clf__min_samples_split=5, clf__n_estimators=100, selector__k=100, total= 1.1s
[CV] clf__max_depth=5, clf__max_features=auto, clf__min_samples_split=5, clf__n_estimators=150, selector__k=50
/anaconda3/lib/python3.6/site-packages/sklearn/feature_selection/univariate_selection.py:298: RuntimeWarning: invalid value encountered in true_divide
corr /= X_norms
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in greater
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in less
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:1821: RuntimeWarning: invalid value encountered in less_equal
cond2 = cond0 & (x <= self.a)
[CV] clf__max_depth=5, clf__max_features=auto, clf__min_samples_split=5, clf__n_estimators=150, selector__k=50, total= 1.2s
[CV] clf__max_depth=5, clf__max_features=auto, clf__min_samples_split=5, clf__n_estimators=150, selector__k=50
/anaconda3/lib/python3.6/site-packages/sklearn/feature_selection/univariate_selection.py:298: RuntimeWarning: invalid value encountered in true_divide
corr /= X_norms
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in greater
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in less
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:1821: RuntimeWarning: invalid value encountered in less_equal
cond2 = cond0 & (x <= self.a)
[CV] clf__max_depth=5, clf__max_features=auto, clf__min_samples_split=5, clf__n_estimators=150, selector__k=50, total= 1.1s
[CV] clf__max_depth=5, clf__max_features=auto, clf__min_samples_split=5, clf__n_estimators=150, selector__k=50
/anaconda3/lib/python3.6/site-packages/sklearn/feature_selection/univariate_selection.py:298: RuntimeWarning: invalid value encountered in true_divide
corr /= X_norms
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in greater
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in less
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:1821: RuntimeWarning: invalid value encountered in less_equal
cond2 = cond0 & (x <= self.a)
[CV] clf__max_depth=5, clf__max_features=auto, clf__min_samples_split=5, clf__n_estimators=150, selector__k=50, total= 1.2s
[CV] clf__max_depth=5, clf__max_features=auto, clf__min_samples_split=5, clf__n_estimators=150, selector__k=100
/anaconda3/lib/python3.6/site-packages/sklearn/feature_selection/univariate_selection.py:298: RuntimeWarning: invalid value encountered in true_divide
corr /= X_norms
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in greater
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in less
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:1821: RuntimeWarning: invalid value encountered in less_equal
cond2 = cond0 & (x <= self.a)
[CV] clf__max_depth=5, clf__max_features=auto, clf__min_samples_split=5, clf__n_estimators=150, selector__k=100, total= 1.5s
[CV] clf__max_depth=5, clf__max_features=auto, clf__min_samples_split=5, clf__n_estimators=150, selector__k=100
/anaconda3/lib/python3.6/site-packages/sklearn/feature_selection/univariate_selection.py:298: RuntimeWarning: invalid value encountered in true_divide
corr /= X_norms
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in greater
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in less
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:1821: RuntimeWarning: invalid value encountered in less_equal
cond2 = cond0 & (x <= self.a)
[CV] clf__max_depth=5, clf__max_features=auto, clf__min_samples_split=5, clf__n_estimators=150, selector__k=100, total= 1.4s
[CV] clf__max_depth=5, clf__max_features=auto, clf__min_samples_split=5, clf__n_estimators=150, selector__k=100
/anaconda3/lib/python3.6/site-packages/sklearn/feature_selection/univariate_selection.py:298: RuntimeWarning: invalid value encountered in true_divide
corr /= X_norms
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in greater
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in less
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:1821: RuntimeWarning: invalid value encountered in less_equal
cond2 = cond0 & (x <= self.a)
[CV] clf__max_depth=5, clf__max_features=auto, clf__min_samples_split=5, clf__n_estimators=150, selector__k=100, total= 1.5s
[CV] clf__max_depth=7, clf__max_features=auto, clf__min_samples_split=5, clf__n_estimators=20, selector__k=50
/anaconda3/lib/python3.6/site-packages/sklearn/feature_selection/univariate_selection.py:298: RuntimeWarning: invalid value encountered in true_divide
corr /= X_norms
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in greater
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in less
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:1821: RuntimeWarning: invalid value encountered in less_equal
cond2 = cond0 & (x <= self.a)
[CV] clf__max_depth=7, clf__max_features=auto, clf__min_samples_split=5, clf__n_estimators=20, selector__k=50, total= 0.3s
[CV] clf__max_depth=7, clf__max_features=auto, clf__min_samples_split=5, clf__n_estimators=20, selector__k=50
/anaconda3/lib/python3.6/site-packages/sklearn/feature_selection/univariate_selection.py:298: RuntimeWarning: invalid value encountered in true_divide
corr /= X_norms
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in greater
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in less
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:1821: RuntimeWarning: invalid value encountered in less_equal
cond2 = cond0 & (x <= self.a)
[CV] clf__max_depth=7, clf__max_features=auto, clf__min_samples_split=5, clf__n_estimators=20, selector__k=50, total= 0.4s
[CV] clf__max_depth=7, clf__max_features=auto, clf__min_samples_split=5, clf__n_estimators=20, selector__k=50
/anaconda3/lib/python3.6/site-packages/sklearn/feature_selection/univariate_selection.py:298: RuntimeWarning: invalid value encountered in true_divide
corr /= X_norms
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in greater
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in less
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:1821: RuntimeWarning: invalid value encountered in less_equal
cond2 = cond0 & (x <= self.a)
[CV] clf__max_depth=7, clf__max_features=auto, clf__min_samples_split=5, clf__n_estimators=20, selector__k=50, total= 0.3s
[CV] clf__max_depth=7, clf__max_features=auto, clf__min_samples_split=5, clf__n_estimators=20, selector__k=100
/anaconda3/lib/python3.6/site-packages/sklearn/feature_selection/univariate_selection.py:298: RuntimeWarning: invalid value encountered in true_divide
corr /= X_norms
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in greater
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in less
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:1821: RuntimeWarning: invalid value encountered in less_equal
cond2 = cond0 & (x <= self.a)
[CV] clf__max_depth=7, clf__max_features=auto, clf__min_samples_split=5, clf__n_estimators=20, selector__k=100, total= 0.4s
[CV] clf__max_depth=7, clf__max_features=auto, clf__min_samples_split=5, clf__n_estimators=20, selector__k=100
/anaconda3/lib/python3.6/site-packages/sklearn/feature_selection/univariate_selection.py:298: RuntimeWarning: invalid value encountered in true_divide
corr /= X_norms
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in greater
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in less
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:1821: RuntimeWarning: invalid value encountered in less_equal
cond2 = cond0 & (x <= self.a)
[CV] clf__max_depth=7, clf__max_features=auto, clf__min_samples_split=5, clf__n_estimators=20, selector__k=100, total= 0.3s
[CV] clf__max_depth=7, clf__max_features=auto, clf__min_samples_split=5, clf__n_estimators=20, selector__k=100
/anaconda3/lib/python3.6/site-packages/sklearn/feature_selection/univariate_selection.py:298: RuntimeWarning: invalid value encountered in true_divide
corr /= X_norms
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in greater
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in less
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:1821: RuntimeWarning: invalid value encountered in less_equal
cond2 = cond0 & (x <= self.a)
[CV] clf__max_depth=7, clf__max_features=auto, clf__min_samples_split=5, clf__n_estimators=20, selector__k=100, total= 0.4s
[CV] clf__max_depth=7, clf__max_features=auto, clf__min_samples_split=5, clf__n_estimators=100, selector__k=50
/anaconda3/lib/python3.6/site-packages/sklearn/feature_selection/univariate_selection.py:298: RuntimeWarning: invalid value encountered in true_divide
corr /= X_norms
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in greater
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in less
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:1821: RuntimeWarning: invalid value encountered in less_equal
cond2 = cond0 & (x <= self.a)
[CV] clf__max_depth=7, clf__max_features=auto, clf__min_samples_split=5, clf__n_estimators=100, selector__k=50, total= 1.1s
[CV] clf__max_depth=7, clf__max_features=auto, clf__min_samples_split=5, clf__n_estimators=100, selector__k=50
/anaconda3/lib/python3.6/site-packages/sklearn/feature_selection/univariate_selection.py:298: RuntimeWarning: invalid value encountered in true_divide
corr /= X_norms
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in greater
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in less
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:1821: RuntimeWarning: invalid value encountered in less_equal
cond2 = cond0 & (x <= self.a)
[CV] clf__max_depth=7, clf__max_features=auto, clf__min_samples_split=5, clf__n_estimators=100, selector__k=50, total= 1.2s
[CV] clf__max_depth=7, clf__max_features=auto, clf__min_samples_split=5, clf__n_estimators=100, selector__k=50
/anaconda3/lib/python3.6/site-packages/sklearn/feature_selection/univariate_selection.py:298: RuntimeWarning: invalid value encountered in true_divide
corr /= X_norms
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in greater
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in less
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:1821: RuntimeWarning: invalid value encountered in less_equal
cond2 = cond0 & (x <= self.a)
[CV] clf__max_depth=7, clf__max_features=auto, clf__min_samples_split=5, clf__n_estimators=100, selector__k=50, total= 1.1s
[CV] clf__max_depth=7, clf__max_features=auto, clf__min_samples_split=5, clf__n_estimators=100, selector__k=100
/anaconda3/lib/python3.6/site-packages/sklearn/feature_selection/univariate_selection.py:298: RuntimeWarning: invalid value encountered in true_divide
corr /= X_norms
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in greater
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in less
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:1821: RuntimeWarning: invalid value encountered in less_equal
cond2 = cond0 & (x <= self.a)
[CV] clf__max_depth=7, clf__max_features=auto, clf__min_samples_split=5, clf__n_estimators=100, selector__k=100, total= 1.6s
[CV] clf__max_depth=7, clf__max_features=auto, clf__min_samples_split=5, clf__n_estimators=100, selector__k=100
/anaconda3/lib/python3.6/site-packages/sklearn/feature_selection/univariate_selection.py:298: RuntimeWarning: invalid value encountered in true_divide
corr /= X_norms
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in greater
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in less
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:1821: RuntimeWarning: invalid value encountered in less_equal
cond2 = cond0 & (x <= self.a)
[CV] clf__max_depth=7, clf__max_features=auto, clf__min_samples_split=5, clf__n_estimators=100, selector__k=100, total= 1.4s
[CV] clf__max_depth=7, clf__max_features=auto, clf__min_samples_split=5, clf__n_estimators=100, selector__k=100
/anaconda3/lib/python3.6/site-packages/sklearn/feature_selection/univariate_selection.py:298: RuntimeWarning: invalid value encountered in true_divide
corr /= X_norms
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in greater
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in less
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:1821: RuntimeWarning: invalid value encountered in less_equal
cond2 = cond0 & (x <= self.a)
[CV] clf__max_depth=7, clf__max_features=auto, clf__min_samples_split=5, clf__n_estimators=100, selector__k=100, total= 1.3s
[CV] clf__max_depth=7, clf__max_features=auto, clf__min_samples_split=5, clf__n_estimators=150, selector__k=50
/anaconda3/lib/python3.6/site-packages/sklearn/feature_selection/univariate_selection.py:298: RuntimeWarning: invalid value encountered in true_divide
corr /= X_norms
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in greater
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in less
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:1821: RuntimeWarning: invalid value encountered in less_equal
cond2 = cond0 & (x <= self.a)
[CV] clf__max_depth=7, clf__max_features=auto, clf__min_samples_split=5, clf__n_estimators=150, selector__k=50, total= 1.4s
[CV] clf__max_depth=7, clf__max_features=auto, clf__min_samples_split=5, clf__n_estimators=150, selector__k=50
/anaconda3/lib/python3.6/site-packages/sklearn/feature_selection/univariate_selection.py:298: RuntimeWarning: invalid value encountered in true_divide
corr /= X_norms
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in greater
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in less
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:1821: RuntimeWarning: invalid value encountered in less_equal
cond2 = cond0 & (x <= self.a)
[CV] clf__max_depth=7, clf__max_features=auto, clf__min_samples_split=5, clf__n_estimators=150, selector__k=50, total= 1.4s
[CV] clf__max_depth=7, clf__max_features=auto, clf__min_samples_split=5, clf__n_estimators=150, selector__k=50
/anaconda3/lib/python3.6/site-packages/sklearn/feature_selection/univariate_selection.py:298: RuntimeWarning: invalid value encountered in true_divide
corr /= X_norms
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in greater
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in less
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:1821: RuntimeWarning: invalid value encountered in less_equal
cond2 = cond0 & (x <= self.a)
[CV] clf__max_depth=7, clf__max_features=auto, clf__min_samples_split=5, clf__n_estimators=150, selector__k=50, total= 1.4s
[CV] clf__max_depth=7, clf__max_features=auto, clf__min_samples_split=5, clf__n_estimators=150, selector__k=100
/anaconda3/lib/python3.6/site-packages/sklearn/feature_selection/univariate_selection.py:298: RuntimeWarning: invalid value encountered in true_divide
corr /= X_norms
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in greater
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in less
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:1821: RuntimeWarning: invalid value encountered in less_equal
cond2 = cond0 & (x <= self.a)
[CV] clf__max_depth=7, clf__max_features=auto, clf__min_samples_split=5, clf__n_estimators=150, selector__k=100, total= 2.2s
[CV] clf__max_depth=7, clf__max_features=auto, clf__min_samples_split=5, clf__n_estimators=150, selector__k=100
/anaconda3/lib/python3.6/site-packages/sklearn/feature_selection/univariate_selection.py:298: RuntimeWarning: invalid value encountered in true_divide
corr /= X_norms
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in greater
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in less
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:1821: RuntimeWarning: invalid value encountered in less_equal
cond2 = cond0 & (x <= self.a)
[CV] clf__max_depth=7, clf__max_features=auto, clf__min_samples_split=5, clf__n_estimators=150, selector__k=100, total= 2.1s
[CV] clf__max_depth=7, clf__max_features=auto, clf__min_samples_split=5, clf__n_estimators=150, selector__k=100
/anaconda3/lib/python3.6/site-packages/sklearn/feature_selection/univariate_selection.py:298: RuntimeWarning: invalid value encountered in true_divide
corr /= X_norms
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in greater
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in less
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:1821: RuntimeWarning: invalid value encountered in less_equal
cond2 = cond0 & (x <= self.a)
[CV] clf__max_depth=7, clf__max_features=auto, clf__min_samples_split=5, clf__n_estimators=150, selector__k=100, total= 2.1s
[Parallel(n_jobs=1)]: Done 54 out of 54 | elapsed: 49.7s finished
/anaconda3/lib/python3.6/site-packages/sklearn/feature_selection/univariate_selection.py:298: RuntimeWarning: invalid value encountered in true_divide
corr /= X_norms
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in greater
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in less
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:1821: RuntimeWarning: invalid value encountered in less_equal
cond2 = cond0 & (x <= self.a)
Pipeline(memory=None,
steps=[('selector', SelectKBest(k=100, score_func=<function f_regression at 0x109eeb950>)), ('clf', RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=7,
max_features='auto', max_leaf_nodes=None,
min_impurity_decrease=0.0, min_impurity_split=None,
min_samples_leaf=1, min_samples_split=5,
min_weight_fraction_leaf=0.0, n_estimators=150, n_jobs=1,
oob_score=False, random_state=None, verbose=0, warm_start=False))])
MAE: 6898.5361470355265
R2 Score: 0.9249219699040897
rf_pred = g_search.predict(X_test)
plt.scatter(rf_pred, y_test)
plt.plot(y_test, y_test)
plt.xlabel('predicted')
plt.ylabel('actual')
plt.show()
Pipeline with Gradient Boost
#initialize gradient boosting and selectKbest
selector = SelectKBest(f_regression) # select k best
clf = GradientBoostingRegressor() # Model I want to use
#place SelectKbest transformer and RandomForest estimator into Pipeine
pipe = Pipeline(steps=[
('Scale',StandardScaler()),
#('poly', PolynomialFeatures()),
('selector', selector),
('clf', clf)
])
#Create the parameter grid, entering the values to use for each parameter selected in the RandomForest estimator
parameters = {
'selector__k':[50,100], # params to search through
#'poly__degree': [2],
'clf__n_estimators': [20, 100], # num of boosting stages to perform. larger number usually better performance
'clf__learning_rate':[0.05, 0.1, 0.2], # shrinks the contibution of each tree. trade off between n_estimators and learning rate
'clf__max_depth': [1, 3, 5] # max depth of the individual regression estimators
}
#Perform grid search on the classifier using 'scorer' as the scoring method using GridSearchCV()
g_search = GridSearchCV(pipe, parameters, cv=3, n_jobs=1, verbose=2)
#Fit the grid search object to the training data and find the optimal parameters using fit()
g_fit = g_search.fit(X_train, y_train)
#Get the best estimator and print out the estimator model
best_clf = g_fit.best_estimator_
print (best_clf)
#Use best estimator to make predictions on the test set
best_predictions = best_clf.predict(X_test)
#metrics
#print(mean_absolute_error(y_true = y_test, y_pred = best_predictions))
#print(r2_score(y_true = y_test, y_pred = best_predictions))
print("MAE: " + str(mean_absolute_error(y_true = y_test, y_pred = best_predictions)))
print("R2 Score: " + str(r2_score(y_true = y_test, y_pred = best_predictions)))
Fitting 3 folds for each of 36 candidates, totalling 108 fits
[CV] clf__learning_rate=0.05, clf__max_depth=1, clf__n_estimators=20, selector__k=50
/anaconda3/lib/python3.6/site-packages/sklearn/feature_selection/univariate_selection.py:298: RuntimeWarning: invalid value encountered in true_divide
corr /= X_norms
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in greater
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in less
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:1821: RuntimeWarning: invalid value encountered in less_equal
cond2 = cond0 & (x <= self.a)
[CV] clf__learning_rate=0.05, clf__max_depth=1, clf__n_estimators=20, selector__k=50, total= 0.4s
[CV] clf__learning_rate=0.05, clf__max_depth=1, clf__n_estimators=20, selector__k=50
[Parallel(n_jobs=1)]: Done 1 out of 1 | elapsed: 0.5s remaining: 0.0s
/anaconda3/lib/python3.6/site-packages/sklearn/feature_selection/univariate_selection.py:298: RuntimeWarning: invalid value encountered in true_divide
corr /= X_norms
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in greater
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in less
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:1821: RuntimeWarning: invalid value encountered in less_equal
cond2 = cond0 & (x <= self.a)
[CV] clf__learning_rate=0.05, clf__max_depth=1, clf__n_estimators=20, selector__k=50, total= 0.4s
[CV] clf__learning_rate=0.05, clf__max_depth=1, clf__n_estimators=20, selector__k=50
/anaconda3/lib/python3.6/site-packages/sklearn/feature_selection/univariate_selection.py:298: RuntimeWarning: invalid value encountered in true_divide
corr /= X_norms
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in greater
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in less
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:1821: RuntimeWarning: invalid value encountered in less_equal
cond2 = cond0 & (x <= self.a)
[CV] clf__learning_rate=0.05, clf__max_depth=1, clf__n_estimators=20, selector__k=50, total= 0.3s
[CV] clf__learning_rate=0.05, clf__max_depth=1, clf__n_estimators=20, selector__k=100
/anaconda3/lib/python3.6/site-packages/sklearn/feature_selection/univariate_selection.py:298: RuntimeWarning: invalid value encountered in true_divide
corr /= X_norms
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in greater
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in less
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:1821: RuntimeWarning: invalid value encountered in less_equal
cond2 = cond0 & (x <= self.a)
[CV] clf__learning_rate=0.05, clf__max_depth=1, clf__n_estimators=20, selector__k=100, total= 0.4s
[CV] clf__learning_rate=0.05, clf__max_depth=1, clf__n_estimators=20, selector__k=100
/anaconda3/lib/python3.6/site-packages/sklearn/feature_selection/univariate_selection.py:298: RuntimeWarning: invalid value encountered in true_divide
corr /= X_norms
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in greater
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in less
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:1821: RuntimeWarning: invalid value encountered in less_equal
cond2 = cond0 & (x <= self.a)
[CV] clf__learning_rate=0.05, clf__max_depth=1, clf__n_estimators=20, selector__k=100, total= 0.4s
[CV] clf__learning_rate=0.05, clf__max_depth=1, clf__n_estimators=20, selector__k=100
/anaconda3/lib/python3.6/site-packages/sklearn/feature_selection/univariate_selection.py:298: RuntimeWarning: invalid value encountered in true_divide
corr /= X_norms
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in greater
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in less
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:1821: RuntimeWarning: invalid value encountered in less_equal
cond2 = cond0 & (x <= self.a)
[CV] clf__learning_rate=0.05, clf__max_depth=1, clf__n_estimators=20, selector__k=100, total= 0.3s
[CV] clf__learning_rate=0.05, clf__max_depth=1, clf__n_estimators=100, selector__k=50
/anaconda3/lib/python3.6/site-packages/sklearn/feature_selection/univariate_selection.py:298: RuntimeWarning: invalid value encountered in true_divide
corr /= X_norms
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in greater
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in less
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:1821: RuntimeWarning: invalid value encountered in less_equal
cond2 = cond0 & (x <= self.a)
[CV] clf__learning_rate=0.05, clf__max_depth=1, clf__n_estimators=100, selector__k=50, total= 0.4s
[CV] clf__learning_rate=0.05, clf__max_depth=1, clf__n_estimators=100, selector__k=50
/anaconda3/lib/python3.6/site-packages/sklearn/feature_selection/univariate_selection.py:298: RuntimeWarning: invalid value encountered in true_divide
corr /= X_norms
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in greater
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in less
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:1821: RuntimeWarning: invalid value encountered in less_equal
cond2 = cond0 & (x <= self.a)
[CV] clf__learning_rate=0.05, clf__max_depth=1, clf__n_estimators=100, selector__k=50, total= 0.4s
[CV] clf__learning_rate=0.05, clf__max_depth=1, clf__n_estimators=100, selector__k=50
/anaconda3/lib/python3.6/site-packages/sklearn/feature_selection/univariate_selection.py:298: RuntimeWarning: invalid value encountered in true_divide
corr /= X_norms
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in greater
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in less
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:1821: RuntimeWarning: invalid value encountered in less_equal
cond2 = cond0 & (x <= self.a)
[CV] clf__learning_rate=0.05, clf__max_depth=1, clf__n_estimators=100, selector__k=50, total= 0.4s
[CV] clf__learning_rate=0.05, clf__max_depth=1, clf__n_estimators=100, selector__k=100
/anaconda3/lib/python3.6/site-packages/sklearn/feature_selection/univariate_selection.py:298: RuntimeWarning: invalid value encountered in true_divide
corr /= X_norms
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in greater
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in less
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:1821: RuntimeWarning: invalid value encountered in less_equal
cond2 = cond0 & (x <= self.a)
[CV] clf__learning_rate=0.05, clf__max_depth=1, clf__n_estimators=100, selector__k=100, total= 0.6s
[CV] clf__learning_rate=0.05, clf__max_depth=1, clf__n_estimators=100, selector__k=100
/anaconda3/lib/python3.6/site-packages/sklearn/feature_selection/univariate_selection.py:298: RuntimeWarning: invalid value encountered in true_divide
corr /= X_norms
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in greater
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in less
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:1821: RuntimeWarning: invalid value encountered in less_equal
cond2 = cond0 & (x <= self.a)
[CV] clf__learning_rate=0.05, clf__max_depth=1, clf__n_estimators=100, selector__k=100, total= 0.7s
[CV] clf__learning_rate=0.05, clf__max_depth=1, clf__n_estimators=100, selector__k=100
/anaconda3/lib/python3.6/site-packages/sklearn/feature_selection/univariate_selection.py:298: RuntimeWarning: invalid value encountered in true_divide
corr /= X_norms
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in greater
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in less
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:1821: RuntimeWarning: invalid value encountered in less_equal
cond2 = cond0 & (x <= self.a)
[CV] clf__learning_rate=0.05, clf__max_depth=1, clf__n_estimators=100, selector__k=100, total= 0.6s
[CV] clf__learning_rate=0.05, clf__max_depth=3, clf__n_estimators=20, selector__k=50
/anaconda3/lib/python3.6/site-packages/sklearn/feature_selection/univariate_selection.py:298: RuntimeWarning: invalid value encountered in true_divide
corr /= X_norms
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in greater
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in less
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:1821: RuntimeWarning: invalid value encountered in less_equal
cond2 = cond0 & (x <= self.a)
[CV] clf__learning_rate=0.05, clf__max_depth=3, clf__n_estimators=20, selector__k=50, total= 0.5s
[CV] clf__learning_rate=0.05, clf__max_depth=3, clf__n_estimators=20, selector__k=50
/anaconda3/lib/python3.6/site-packages/sklearn/feature_selection/univariate_selection.py:298: RuntimeWarning: invalid value encountered in true_divide
corr /= X_norms
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in greater
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in less
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:1821: RuntimeWarning: invalid value encountered in less_equal
cond2 = cond0 & (x <= self.a)
[CV] clf__learning_rate=0.05, clf__max_depth=3, clf__n_estimators=20, selector__k=50, total= 0.4s
[CV] clf__learning_rate=0.05, clf__max_depth=3, clf__n_estimators=20, selector__k=50
/anaconda3/lib/python3.6/site-packages/sklearn/feature_selection/univariate_selection.py:298: RuntimeWarning: invalid value encountered in true_divide
corr /= X_norms
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in greater
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in less
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:1821: RuntimeWarning: invalid value encountered in less_equal
cond2 = cond0 & (x <= self.a)
[CV] clf__learning_rate=0.05, clf__max_depth=3, clf__n_estimators=20, selector__k=50, total= 0.4s
[CV] clf__learning_rate=0.05, clf__max_depth=3, clf__n_estimators=20, selector__k=100
/anaconda3/lib/python3.6/site-packages/sklearn/feature_selection/univariate_selection.py:298: RuntimeWarning: invalid value encountered in true_divide
corr /= X_norms
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in greater
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in less
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:1821: RuntimeWarning: invalid value encountered in less_equal
cond2 = cond0 & (x <= self.a)
[CV] clf__learning_rate=0.05, clf__max_depth=3, clf__n_estimators=20, selector__k=100, total= 0.5s
[CV] clf__learning_rate=0.05, clf__max_depth=3, clf__n_estimators=20, selector__k=100
/anaconda3/lib/python3.6/site-packages/sklearn/feature_selection/univariate_selection.py:298: RuntimeWarning: invalid value encountered in true_divide
corr /= X_norms
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in greater
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in less
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:1821: RuntimeWarning: invalid value encountered in less_equal
cond2 = cond0 & (x <= self.a)
[CV] clf__learning_rate=0.05, clf__max_depth=3, clf__n_estimators=20, selector__k=100, total= 0.5s
[CV] clf__learning_rate=0.05, clf__max_depth=3, clf__n_estimators=20, selector__k=100
/anaconda3/lib/python3.6/site-packages/sklearn/feature_selection/univariate_selection.py:298: RuntimeWarning: invalid value encountered in true_divide
corr /= X_norms
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in greater
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in less
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:1821: RuntimeWarning: invalid value encountered in less_equal
cond2 = cond0 & (x <= self.a)
[CV] clf__learning_rate=0.05, clf__max_depth=3, clf__n_estimators=20, selector__k=100, total= 0.5s
[CV] clf__learning_rate=0.05, clf__max_depth=3, clf__n_estimators=100, selector__k=50
/anaconda3/lib/python3.6/site-packages/sklearn/feature_selection/univariate_selection.py:298: RuntimeWarning: invalid value encountered in true_divide
corr /= X_norms
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in greater
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in less
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:1821: RuntimeWarning: invalid value encountered in less_equal
cond2 = cond0 & (x <= self.a)
[CV] clf__learning_rate=0.05, clf__max_depth=3, clf__n_estimators=100, selector__k=50, total= 0.9s
[CV] clf__learning_rate=0.05, clf__max_depth=3, clf__n_estimators=100, selector__k=50
/anaconda3/lib/python3.6/site-packages/sklearn/feature_selection/univariate_selection.py:298: RuntimeWarning: invalid value encountered in true_divide
corr /= X_norms
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in greater
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in less
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:1821: RuntimeWarning: invalid value encountered in less_equal
cond2 = cond0 & (x <= self.a)
[CV] clf__learning_rate=0.05, clf__max_depth=3, clf__n_estimators=100, selector__k=50, total= 0.9s
[CV] clf__learning_rate=0.05, clf__max_depth=3, clf__n_estimators=100, selector__k=50
/anaconda3/lib/python3.6/site-packages/sklearn/feature_selection/univariate_selection.py:298: RuntimeWarning: invalid value encountered in true_divide
corr /= X_norms
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in greater
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in less
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:1821: RuntimeWarning: invalid value encountered in less_equal
cond2 = cond0 & (x <= self.a)
[CV] clf__learning_rate=0.05, clf__max_depth=3, clf__n_estimators=100, selector__k=50, total= 0.8s
[CV] clf__learning_rate=0.05, clf__max_depth=3, clf__n_estimators=100, selector__k=100
/anaconda3/lib/python3.6/site-packages/sklearn/feature_selection/univariate_selection.py:298: RuntimeWarning: invalid value encountered in true_divide
corr /= X_norms
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in greater
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in less
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:1821: RuntimeWarning: invalid value encountered in less_equal
cond2 = cond0 & (x <= self.a)
[CV] clf__learning_rate=0.05, clf__max_depth=3, clf__n_estimators=100, selector__k=100, total= 1.6s
[CV] clf__learning_rate=0.05, clf__max_depth=3, clf__n_estimators=100, selector__k=100
/anaconda3/lib/python3.6/site-packages/sklearn/feature_selection/univariate_selection.py:298: RuntimeWarning: invalid value encountered in true_divide
corr /= X_norms
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in greater
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in less
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:1821: RuntimeWarning: invalid value encountered in less_equal
cond2 = cond0 & (x <= self.a)
[CV] clf__learning_rate=0.05, clf__max_depth=3, clf__n_estimators=100, selector__k=100, total= 1.5s
[CV] clf__learning_rate=0.05, clf__max_depth=3, clf__n_estimators=100, selector__k=100
/anaconda3/lib/python3.6/site-packages/sklearn/feature_selection/univariate_selection.py:298: RuntimeWarning: invalid value encountered in true_divide
corr /= X_norms
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in greater
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in less
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:1821: RuntimeWarning: invalid value encountered in less_equal
cond2 = cond0 & (x <= self.a)
[CV] clf__learning_rate=0.05, clf__max_depth=3, clf__n_estimators=100, selector__k=100, total= 1.2s
[CV] clf__learning_rate=0.05, clf__max_depth=5, clf__n_estimators=20, selector__k=50
/anaconda3/lib/python3.6/site-packages/sklearn/feature_selection/univariate_selection.py:298: RuntimeWarning: invalid value encountered in true_divide
corr /= X_norms
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in greater
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in less
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:1821: RuntimeWarning: invalid value encountered in less_equal
cond2 = cond0 & (x <= self.a)
[CV] clf__learning_rate=0.05, clf__max_depth=5, clf__n_estimators=20, selector__k=50, total= 0.5s
[CV] clf__learning_rate=0.05, clf__max_depth=5, clf__n_estimators=20, selector__k=50
/anaconda3/lib/python3.6/site-packages/sklearn/feature_selection/univariate_selection.py:298: RuntimeWarning: invalid value encountered in true_divide
corr /= X_norms
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in greater
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in less
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:1821: RuntimeWarning: invalid value encountered in less_equal
cond2 = cond0 & (x <= self.a)
[CV] clf__learning_rate=0.05, clf__max_depth=5, clf__n_estimators=20, selector__k=50, total= 0.7s
[CV] clf__learning_rate=0.05, clf__max_depth=5, clf__n_estimators=20, selector__k=50
/anaconda3/lib/python3.6/site-packages/sklearn/feature_selection/univariate_selection.py:298: RuntimeWarning: invalid value encountered in true_divide
corr /= X_norms
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in greater
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in less
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:1821: RuntimeWarning: invalid value encountered in less_equal
cond2 = cond0 & (x <= self.a)
[CV] clf__learning_rate=0.05, clf__max_depth=5, clf__n_estimators=20, selector__k=50, total= 0.6s
[CV] clf__learning_rate=0.05, clf__max_depth=5, clf__n_estimators=20, selector__k=100
/anaconda3/lib/python3.6/site-packages/sklearn/feature_selection/univariate_selection.py:298: RuntimeWarning: invalid value encountered in true_divide
corr /= X_norms
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in greater
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in less
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:1821: RuntimeWarning: invalid value encountered in less_equal
cond2 = cond0 & (x <= self.a)
[CV] clf__learning_rate=0.05, clf__max_depth=5, clf__n_estimators=20, selector__k=100, total= 0.7s
[CV] clf__learning_rate=0.05, clf__max_depth=5, clf__n_estimators=20, selector__k=100
/anaconda3/lib/python3.6/site-packages/sklearn/feature_selection/univariate_selection.py:298: RuntimeWarning: invalid value encountered in true_divide
corr /= X_norms
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in greater
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in less
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:1821: RuntimeWarning: invalid value encountered in less_equal
cond2 = cond0 & (x <= self.a)
[CV] clf__learning_rate=0.05, clf__max_depth=5, clf__n_estimators=20, selector__k=100, total= 0.7s
[CV] clf__learning_rate=0.05, clf__max_depth=5, clf__n_estimators=20, selector__k=100
/anaconda3/lib/python3.6/site-packages/sklearn/feature_selection/univariate_selection.py:298: RuntimeWarning: invalid value encountered in true_divide
corr /= X_norms
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in greater
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in less
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:1821: RuntimeWarning: invalid value encountered in less_equal
cond2 = cond0 & (x <= self.a)
[CV] clf__learning_rate=0.05, clf__max_depth=5, clf__n_estimators=20, selector__k=100, total= 0.7s
[CV] clf__learning_rate=0.05, clf__max_depth=5, clf__n_estimators=100, selector__k=50
/anaconda3/lib/python3.6/site-packages/sklearn/feature_selection/univariate_selection.py:298: RuntimeWarning: invalid value encountered in true_divide
corr /= X_norms
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in greater
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in less
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:1821: RuntimeWarning: invalid value encountered in less_equal
cond2 = cond0 & (x <= self.a)
[CV] clf__learning_rate=0.05, clf__max_depth=5, clf__n_estimators=100, selector__k=50, total= 1.9s
[CV] clf__learning_rate=0.05, clf__max_depth=5, clf__n_estimators=100, selector__k=50
/anaconda3/lib/python3.6/site-packages/sklearn/feature_selection/univariate_selection.py:298: RuntimeWarning: invalid value encountered in true_divide
corr /= X_norms
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in greater
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in less
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:1821: RuntimeWarning: invalid value encountered in less_equal
cond2 = cond0 & (x <= self.a)
[CV] clf__learning_rate=0.05, clf__max_depth=5, clf__n_estimators=100, selector__k=50, total= 1.4s
[CV] clf__learning_rate=0.05, clf__max_depth=5, clf__n_estimators=100, selector__k=50
/anaconda3/lib/python3.6/site-packages/sklearn/feature_selection/univariate_selection.py:298: RuntimeWarning: invalid value encountered in true_divide
corr /= X_norms
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in greater
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in less
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:1821: RuntimeWarning: invalid value encountered in less_equal
cond2 = cond0 & (x <= self.a)
[CV] clf__learning_rate=0.05, clf__max_depth=5, clf__n_estimators=100, selector__k=50, total= 1.4s
[CV] clf__learning_rate=0.05, clf__max_depth=5, clf__n_estimators=100, selector__k=100
/anaconda3/lib/python3.6/site-packages/sklearn/feature_selection/univariate_selection.py:298: RuntimeWarning: invalid value encountered in true_divide
corr /= X_norms
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in greater
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in less
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:1821: RuntimeWarning: invalid value encountered in less_equal
cond2 = cond0 & (x <= self.a)
[CV] clf__learning_rate=0.05, clf__max_depth=5, clf__n_estimators=100, selector__k=100, total= 2.5s
[CV] clf__learning_rate=0.05, clf__max_depth=5, clf__n_estimators=100, selector__k=100
/anaconda3/lib/python3.6/site-packages/sklearn/feature_selection/univariate_selection.py:298: RuntimeWarning: invalid value encountered in true_divide
corr /= X_norms
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in greater
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in less
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:1821: RuntimeWarning: invalid value encountered in less_equal
cond2 = cond0 & (x <= self.a)
[CV] clf__learning_rate=0.05, clf__max_depth=5, clf__n_estimators=100, selector__k=100, total= 2.5s
[CV] clf__learning_rate=0.05, clf__max_depth=5, clf__n_estimators=100, selector__k=100
/anaconda3/lib/python3.6/site-packages/sklearn/feature_selection/univariate_selection.py:298: RuntimeWarning: invalid value encountered in true_divide
corr /= X_norms
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in greater
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in less
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:1821: RuntimeWarning: invalid value encountered in less_equal
cond2 = cond0 & (x <= self.a)
[CV] clf__learning_rate=0.05, clf__max_depth=5, clf__n_estimators=100, selector__k=100, total= 2.7s
[CV] clf__learning_rate=0.1, clf__max_depth=1, clf__n_estimators=20, selector__k=50
/anaconda3/lib/python3.6/site-packages/sklearn/feature_selection/univariate_selection.py:298: RuntimeWarning: invalid value encountered in true_divide
corr /= X_norms
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in greater
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in less
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:1821: RuntimeWarning: invalid value encountered in less_equal
cond2 = cond0 & (x <= self.a)
[CV] clf__learning_rate=0.1, clf__max_depth=1, clf__n_estimators=20, selector__k=50, total= 0.3s
[CV] clf__learning_rate=0.1, clf__max_depth=1, clf__n_estimators=20, selector__k=50
/anaconda3/lib/python3.6/site-packages/sklearn/feature_selection/univariate_selection.py:298: RuntimeWarning: invalid value encountered in true_divide
corr /= X_norms
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in greater
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in less
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:1821: RuntimeWarning: invalid value encountered in less_equal
cond2 = cond0 & (x <= self.a)
[CV] clf__learning_rate=0.1, clf__max_depth=1, clf__n_estimators=20, selector__k=50, total= 0.3s
[CV] clf__learning_rate=0.1, clf__max_depth=1, clf__n_estimators=20, selector__k=50
/anaconda3/lib/python3.6/site-packages/sklearn/feature_selection/univariate_selection.py:298: RuntimeWarning: invalid value encountered in true_divide
corr /= X_norms
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in greater
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in less
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:1821: RuntimeWarning: invalid value encountered in less_equal
cond2 = cond0 & (x <= self.a)
[CV] clf__learning_rate=0.1, clf__max_depth=1, clf__n_estimators=20, selector__k=50, total= 0.3s
[CV] clf__learning_rate=0.1, clf__max_depth=1, clf__n_estimators=20, selector__k=100
/anaconda3/lib/python3.6/site-packages/sklearn/feature_selection/univariate_selection.py:298: RuntimeWarning: invalid value encountered in true_divide
corr /= X_norms
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in greater
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in less
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:1821: RuntimeWarning: invalid value encountered in less_equal
cond2 = cond0 & (x <= self.a)
[CV] clf__learning_rate=0.1, clf__max_depth=1, clf__n_estimators=20, selector__k=100, total= 0.3s
[CV] clf__learning_rate=0.1, clf__max_depth=1, clf__n_estimators=20, selector__k=100
/anaconda3/lib/python3.6/site-packages/sklearn/feature_selection/univariate_selection.py:298: RuntimeWarning: invalid value encountered in true_divide
corr /= X_norms
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in greater
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in less
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:1821: RuntimeWarning: invalid value encountered in less_equal
cond2 = cond0 & (x <= self.a)
[CV] clf__learning_rate=0.1, clf__max_depth=1, clf__n_estimators=20, selector__k=100, total= 0.3s
[CV] clf__learning_rate=0.1, clf__max_depth=1, clf__n_estimators=20, selector__k=100
/anaconda3/lib/python3.6/site-packages/sklearn/feature_selection/univariate_selection.py:298: RuntimeWarning: invalid value encountered in true_divide
corr /= X_norms
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in greater
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in less
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:1821: RuntimeWarning: invalid value encountered in less_equal
cond2 = cond0 & (x <= self.a)
[CV] clf__learning_rate=0.1, clf__max_depth=1, clf__n_estimators=20, selector__k=100, total= 0.3s
[CV] clf__learning_rate=0.1, clf__max_depth=1, clf__n_estimators=100, selector__k=50
/anaconda3/lib/python3.6/site-packages/sklearn/feature_selection/univariate_selection.py:298: RuntimeWarning: invalid value encountered in true_divide
corr /= X_norms
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in greater
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in less
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:1821: RuntimeWarning: invalid value encountered in less_equal
cond2 = cond0 & (x <= self.a)
[CV] clf__learning_rate=0.1, clf__max_depth=1, clf__n_estimators=100, selector__k=50, total= 0.4s
[CV] clf__learning_rate=0.1, clf__max_depth=1, clf__n_estimators=100, selector__k=50
/anaconda3/lib/python3.6/site-packages/sklearn/feature_selection/univariate_selection.py:298: RuntimeWarning: invalid value encountered in true_divide
corr /= X_norms
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in greater
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in less
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:1821: RuntimeWarning: invalid value encountered in less_equal
cond2 = cond0 & (x <= self.a)
[CV] clf__learning_rate=0.1, clf__max_depth=1, clf__n_estimators=100, selector__k=50, total= 0.4s
[CV] clf__learning_rate=0.1, clf__max_depth=1, clf__n_estimators=100, selector__k=50
/anaconda3/lib/python3.6/site-packages/sklearn/feature_selection/univariate_selection.py:298: RuntimeWarning: invalid value encountered in true_divide
corr /= X_norms
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in greater
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in less
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:1821: RuntimeWarning: invalid value encountered in less_equal
cond2 = cond0 & (x <= self.a)
[CV] clf__learning_rate=0.1, clf__max_depth=1, clf__n_estimators=100, selector__k=50, total= 0.4s
[CV] clf__learning_rate=0.1, clf__max_depth=1, clf__n_estimators=100, selector__k=100
/anaconda3/lib/python3.6/site-packages/sklearn/feature_selection/univariate_selection.py:298: RuntimeWarning: invalid value encountered in true_divide
corr /= X_norms
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in greater
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in less
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:1821: RuntimeWarning: invalid value encountered in less_equal
cond2 = cond0 & (x <= self.a)
[CV] clf__learning_rate=0.1, clf__max_depth=1, clf__n_estimators=100, selector__k=100, total= 0.5s
[CV] clf__learning_rate=0.1, clf__max_depth=1, clf__n_estimators=100, selector__k=100
/anaconda3/lib/python3.6/site-packages/sklearn/feature_selection/univariate_selection.py:298: RuntimeWarning: invalid value encountered in true_divide
corr /= X_norms
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in greater
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in less
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:1821: RuntimeWarning: invalid value encountered in less_equal
cond2 = cond0 & (x <= self.a)
[CV] clf__learning_rate=0.1, clf__max_depth=1, clf__n_estimators=100, selector__k=100, total= 0.5s
[CV] clf__learning_rate=0.1, clf__max_depth=1, clf__n_estimators=100, selector__k=100
/anaconda3/lib/python3.6/site-packages/sklearn/feature_selection/univariate_selection.py:298: RuntimeWarning: invalid value encountered in true_divide
corr /= X_norms
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in greater
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in less
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:1821: RuntimeWarning: invalid value encountered in less_equal
cond2 = cond0 & (x <= self.a)
[CV] clf__learning_rate=0.1, clf__max_depth=1, clf__n_estimators=100, selector__k=100, total= 0.5s
[CV] clf__learning_rate=0.1, clf__max_depth=3, clf__n_estimators=20, selector__k=50
/anaconda3/lib/python3.6/site-packages/sklearn/feature_selection/univariate_selection.py:298: RuntimeWarning: invalid value encountered in true_divide
corr /= X_norms
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in greater
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in less
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:1821: RuntimeWarning: invalid value encountered in less_equal
cond2 = cond0 & (x <= self.a)
[CV] clf__learning_rate=0.1, clf__max_depth=3, clf__n_estimators=20, selector__k=50, total= 0.4s
[CV] clf__learning_rate=0.1, clf__max_depth=3, clf__n_estimators=20, selector__k=50
/anaconda3/lib/python3.6/site-packages/sklearn/feature_selection/univariate_selection.py:298: RuntimeWarning: invalid value encountered in true_divide
corr /= X_norms
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in greater
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in less
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:1821: RuntimeWarning: invalid value encountered in less_equal
cond2 = cond0 & (x <= self.a)
[CV] clf__learning_rate=0.1, clf__max_depth=3, clf__n_estimators=20, selector__k=50, total= 0.4s
[CV] clf__learning_rate=0.1, clf__max_depth=3, clf__n_estimators=20, selector__k=50
/anaconda3/lib/python3.6/site-packages/sklearn/feature_selection/univariate_selection.py:298: RuntimeWarning: invalid value encountered in true_divide
corr /= X_norms
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in greater
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in less
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:1821: RuntimeWarning: invalid value encountered in less_equal
cond2 = cond0 & (x <= self.a)
[CV] clf__learning_rate=0.1, clf__max_depth=3, clf__n_estimators=20, selector__k=50, total= 0.4s
[CV] clf__learning_rate=0.1, clf__max_depth=3, clf__n_estimators=20, selector__k=100
/anaconda3/lib/python3.6/site-packages/sklearn/feature_selection/univariate_selection.py:298: RuntimeWarning: invalid value encountered in true_divide
corr /= X_norms
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in greater
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in less
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:1821: RuntimeWarning: invalid value encountered in less_equal
cond2 = cond0 & (x <= self.a)
[CV] clf__learning_rate=0.1, clf__max_depth=3, clf__n_estimators=20, selector__k=100, total= 0.5s
[CV] clf__learning_rate=0.1, clf__max_depth=3, clf__n_estimators=20, selector__k=100
/anaconda3/lib/python3.6/site-packages/sklearn/feature_selection/univariate_selection.py:298: RuntimeWarning: invalid value encountered in true_divide
corr /= X_norms
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in greater
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in less
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:1821: RuntimeWarning: invalid value encountered in less_equal
cond2 = cond0 & (x <= self.a)
[CV] clf__learning_rate=0.1, clf__max_depth=3, clf__n_estimators=20, selector__k=100, total= 0.5s
[CV] clf__learning_rate=0.1, clf__max_depth=3, clf__n_estimators=20, selector__k=100
/anaconda3/lib/python3.6/site-packages/sklearn/feature_selection/univariate_selection.py:298: RuntimeWarning: invalid value encountered in true_divide
corr /= X_norms
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in greater
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in less
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:1821: RuntimeWarning: invalid value encountered in less_equal
cond2 = cond0 & (x <= self.a)
[CV] clf__learning_rate=0.1, clf__max_depth=3, clf__n_estimators=20, selector__k=100, total= 0.5s
[CV] clf__learning_rate=0.1, clf__max_depth=3, clf__n_estimators=100, selector__k=50
/anaconda3/lib/python3.6/site-packages/sklearn/feature_selection/univariate_selection.py:298: RuntimeWarning: invalid value encountered in true_divide
corr /= X_norms
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in greater
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in less
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:1821: RuntimeWarning: invalid value encountered in less_equal
cond2 = cond0 & (x <= self.a)
[CV] clf__learning_rate=0.1, clf__max_depth=3, clf__n_estimators=100, selector__k=50, total= 0.8s
[CV] clf__learning_rate=0.1, clf__max_depth=3, clf__n_estimators=100, selector__k=50
/anaconda3/lib/python3.6/site-packages/sklearn/feature_selection/univariate_selection.py:298: RuntimeWarning: invalid value encountered in true_divide
corr /= X_norms
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in greater
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in less
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:1821: RuntimeWarning: invalid value encountered in less_equal
cond2 = cond0 & (x <= self.a)
[CV] clf__learning_rate=0.1, clf__max_depth=3, clf__n_estimators=100, selector__k=50, total= 0.8s
[CV] clf__learning_rate=0.1, clf__max_depth=3, clf__n_estimators=100, selector__k=50
/anaconda3/lib/python3.6/site-packages/sklearn/feature_selection/univariate_selection.py:298: RuntimeWarning: invalid value encountered in true_divide
corr /= X_norms
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in greater
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in less
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:1821: RuntimeWarning: invalid value encountered in less_equal
cond2 = cond0 & (x <= self.a)
[CV] clf__learning_rate=0.1, clf__max_depth=3, clf__n_estimators=100, selector__k=50, total= 0.8s
[CV] clf__learning_rate=0.1, clf__max_depth=3, clf__n_estimators=100, selector__k=100
/anaconda3/lib/python3.6/site-packages/sklearn/feature_selection/univariate_selection.py:298: RuntimeWarning: invalid value encountered in true_divide
corr /= X_norms
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in greater
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in less
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:1821: RuntimeWarning: invalid value encountered in less_equal
cond2 = cond0 & (x <= self.a)
[CV] clf__learning_rate=0.1, clf__max_depth=3, clf__n_estimators=100, selector__k=100, total= 1.5s
[CV] clf__learning_rate=0.1, clf__max_depth=3, clf__n_estimators=100, selector__k=100
/anaconda3/lib/python3.6/site-packages/sklearn/feature_selection/univariate_selection.py:298: RuntimeWarning: invalid value encountered in true_divide
corr /= X_norms
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in greater
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in less
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:1821: RuntimeWarning: invalid value encountered in less_equal
cond2 = cond0 & (x <= self.a)
[CV] clf__learning_rate=0.1, clf__max_depth=3, clf__n_estimators=100, selector__k=100, total= 1.3s
[CV] clf__learning_rate=0.1, clf__max_depth=3, clf__n_estimators=100, selector__k=100
/anaconda3/lib/python3.6/site-packages/sklearn/feature_selection/univariate_selection.py:298: RuntimeWarning: invalid value encountered in true_divide
corr /= X_norms
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in greater
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in less
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:1821: RuntimeWarning: invalid value encountered in less_equal
cond2 = cond0 & (x <= self.a)
[CV] clf__learning_rate=0.1, clf__max_depth=3, clf__n_estimators=100, selector__k=100, total= 1.3s
[CV] clf__learning_rate=0.1, clf__max_depth=5, clf__n_estimators=20, selector__k=50
/anaconda3/lib/python3.6/site-packages/sklearn/feature_selection/univariate_selection.py:298: RuntimeWarning: invalid value encountered in true_divide
corr /= X_norms
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in greater
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in less
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:1821: RuntimeWarning: invalid value encountered in less_equal
cond2 = cond0 & (x <= self.a)
[CV] clf__learning_rate=0.1, clf__max_depth=5, clf__n_estimators=20, selector__k=50, total= 0.5s
[CV] clf__learning_rate=0.1, clf__max_depth=5, clf__n_estimators=20, selector__k=50
/anaconda3/lib/python3.6/site-packages/sklearn/feature_selection/univariate_selection.py:298: RuntimeWarning: invalid value encountered in true_divide
corr /= X_norms
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in greater
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in less
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:1821: RuntimeWarning: invalid value encountered in less_equal
cond2 = cond0 & (x <= self.a)
[CV] clf__learning_rate=0.1, clf__max_depth=5, clf__n_estimators=20, selector__k=50, total= 0.5s
[CV] clf__learning_rate=0.1, clf__max_depth=5, clf__n_estimators=20, selector__k=50
/anaconda3/lib/python3.6/site-packages/sklearn/feature_selection/univariate_selection.py:298: RuntimeWarning: invalid value encountered in true_divide
corr /= X_norms
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in greater
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in less
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:1821: RuntimeWarning: invalid value encountered in less_equal
cond2 = cond0 & (x <= self.a)
[CV] clf__learning_rate=0.1, clf__max_depth=5, clf__n_estimators=20, selector__k=50, total= 0.6s
[CV] clf__learning_rate=0.1, clf__max_depth=5, clf__n_estimators=20, selector__k=100
/anaconda3/lib/python3.6/site-packages/sklearn/feature_selection/univariate_selection.py:298: RuntimeWarning: invalid value encountered in true_divide
corr /= X_norms
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in greater
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in less
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:1821: RuntimeWarning: invalid value encountered in less_equal
cond2 = cond0 & (x <= self.a)
[CV] clf__learning_rate=0.1, clf__max_depth=5, clf__n_estimators=20, selector__k=100, total= 0.8s
[CV] clf__learning_rate=0.1, clf__max_depth=5, clf__n_estimators=20, selector__k=100
/anaconda3/lib/python3.6/site-packages/sklearn/feature_selection/univariate_selection.py:298: RuntimeWarning: invalid value encountered in true_divide
corr /= X_norms
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in greater
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in less
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:1821: RuntimeWarning: invalid value encountered in less_equal
cond2 = cond0 & (x <= self.a)
[CV] clf__learning_rate=0.1, clf__max_depth=5, clf__n_estimators=20, selector__k=100, total= 0.7s
[CV] clf__learning_rate=0.1, clf__max_depth=5, clf__n_estimators=20, selector__k=100
/anaconda3/lib/python3.6/site-packages/sklearn/feature_selection/univariate_selection.py:298: RuntimeWarning: invalid value encountered in true_divide
corr /= X_norms
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in greater
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in less
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:1821: RuntimeWarning: invalid value encountered in less_equal
cond2 = cond0 & (x <= self.a)
[CV] clf__learning_rate=0.1, clf__max_depth=5, clf__n_estimators=20, selector__k=100, total= 0.7s
[CV] clf__learning_rate=0.1, clf__max_depth=5, clf__n_estimators=100, selector__k=50
/anaconda3/lib/python3.6/site-packages/sklearn/feature_selection/univariate_selection.py:298: RuntimeWarning: invalid value encountered in true_divide
corr /= X_norms
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in greater
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in less
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:1821: RuntimeWarning: invalid value encountered in less_equal
cond2 = cond0 & (x <= self.a)
[CV] clf__learning_rate=0.1, clf__max_depth=5, clf__n_estimators=100, selector__k=50, total= 1.4s
[CV] clf__learning_rate=0.1, clf__max_depth=5, clf__n_estimators=100, selector__k=50
/anaconda3/lib/python3.6/site-packages/sklearn/feature_selection/univariate_selection.py:298: RuntimeWarning: invalid value encountered in true_divide
corr /= X_norms
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in greater
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in less
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:1821: RuntimeWarning: invalid value encountered in less_equal
cond2 = cond0 & (x <= self.a)
[CV] clf__learning_rate=0.1, clf__max_depth=5, clf__n_estimators=100, selector__k=50, total= 1.3s
[CV] clf__learning_rate=0.1, clf__max_depth=5, clf__n_estimators=100, selector__k=50
/anaconda3/lib/python3.6/site-packages/sklearn/feature_selection/univariate_selection.py:298: RuntimeWarning: invalid value encountered in true_divide
corr /= X_norms
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in greater
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in less
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:1821: RuntimeWarning: invalid value encountered in less_equal
cond2 = cond0 & (x <= self.a)
[CV] clf__learning_rate=0.1, clf__max_depth=5, clf__n_estimators=100, selector__k=50, total= 1.5s
[CV] clf__learning_rate=0.1, clf__max_depth=5, clf__n_estimators=100, selector__k=100
/anaconda3/lib/python3.6/site-packages/sklearn/feature_selection/univariate_selection.py:298: RuntimeWarning: invalid value encountered in true_divide
corr /= X_norms
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in greater
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in less
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:1821: RuntimeWarning: invalid value encountered in less_equal
cond2 = cond0 & (x <= self.a)
[CV] clf__learning_rate=0.1, clf__max_depth=5, clf__n_estimators=100, selector__k=100, total= 2.5s
[CV] clf__learning_rate=0.1, clf__max_depth=5, clf__n_estimators=100, selector__k=100
/anaconda3/lib/python3.6/site-packages/sklearn/feature_selection/univariate_selection.py:298: RuntimeWarning: invalid value encountered in true_divide
corr /= X_norms
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in greater
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in less
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:1821: RuntimeWarning: invalid value encountered in less_equal
cond2 = cond0 & (x <= self.a)
[CV] clf__learning_rate=0.1, clf__max_depth=5, clf__n_estimators=100, selector__k=100, total= 2.1s
[CV] clf__learning_rate=0.1, clf__max_depth=5, clf__n_estimators=100, selector__k=100
/anaconda3/lib/python3.6/site-packages/sklearn/feature_selection/univariate_selection.py:298: RuntimeWarning: invalid value encountered in true_divide
corr /= X_norms
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in greater
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in less
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:1821: RuntimeWarning: invalid value encountered in less_equal
cond2 = cond0 & (x <= self.a)
[CV] clf__learning_rate=0.1, clf__max_depth=5, clf__n_estimators=100, selector__k=100, total= 2.2s
[CV] clf__learning_rate=0.2, clf__max_depth=1, clf__n_estimators=20, selector__k=50
/anaconda3/lib/python3.6/site-packages/sklearn/feature_selection/univariate_selection.py:298: RuntimeWarning: invalid value encountered in true_divide
corr /= X_norms
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in greater
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in less
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:1821: RuntimeWarning: invalid value encountered in less_equal
cond2 = cond0 & (x <= self.a)
[CV] clf__learning_rate=0.2, clf__max_depth=1, clf__n_estimators=20, selector__k=50, total= 0.3s
[CV] clf__learning_rate=0.2, clf__max_depth=1, clf__n_estimators=20, selector__k=50
/anaconda3/lib/python3.6/site-packages/sklearn/feature_selection/univariate_selection.py:298: RuntimeWarning: invalid value encountered in true_divide
corr /= X_norms
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in greater
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in less
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:1821: RuntimeWarning: invalid value encountered in less_equal
cond2 = cond0 & (x <= self.a)
[CV] clf__learning_rate=0.2, clf__max_depth=1, clf__n_estimators=20, selector__k=50, total= 0.3s
[CV] clf__learning_rate=0.2, clf__max_depth=1, clf__n_estimators=20, selector__k=50
/anaconda3/lib/python3.6/site-packages/sklearn/feature_selection/univariate_selection.py:298: RuntimeWarning: invalid value encountered in true_divide
corr /= X_norms
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in greater
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in less
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:1821: RuntimeWarning: invalid value encountered in less_equal
cond2 = cond0 & (x <= self.a)
[CV] clf__learning_rate=0.2, clf__max_depth=1, clf__n_estimators=20, selector__k=50, total= 0.3s
[CV] clf__learning_rate=0.2, clf__max_depth=1, clf__n_estimators=20, selector__k=100
/anaconda3/lib/python3.6/site-packages/sklearn/feature_selection/univariate_selection.py:298: RuntimeWarning: invalid value encountered in true_divide
corr /= X_norms
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in greater
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in less
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:1821: RuntimeWarning: invalid value encountered in less_equal
cond2 = cond0 & (x <= self.a)
[CV] clf__learning_rate=0.2, clf__max_depth=1, clf__n_estimators=20, selector__k=100, total= 0.3s
[CV] clf__learning_rate=0.2, clf__max_depth=1, clf__n_estimators=20, selector__k=100
/anaconda3/lib/python3.6/site-packages/sklearn/feature_selection/univariate_selection.py:298: RuntimeWarning: invalid value encountered in true_divide
corr /= X_norms
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in greater
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in less
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:1821: RuntimeWarning: invalid value encountered in less_equal
cond2 = cond0 & (x <= self.a)
[CV] clf__learning_rate=0.2, clf__max_depth=1, clf__n_estimators=20, selector__k=100, total= 0.4s
[CV] clf__learning_rate=0.2, clf__max_depth=1, clf__n_estimators=20, selector__k=100
/anaconda3/lib/python3.6/site-packages/sklearn/feature_selection/univariate_selection.py:298: RuntimeWarning: invalid value encountered in true_divide
corr /= X_norms
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in greater
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in less
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:1821: RuntimeWarning: invalid value encountered in less_equal
cond2 = cond0 & (x <= self.a)
[CV] clf__learning_rate=0.2, clf__max_depth=1, clf__n_estimators=20, selector__k=100, total= 0.4s
[CV] clf__learning_rate=0.2, clf__max_depth=1, clf__n_estimators=100, selector__k=50
/anaconda3/lib/python3.6/site-packages/sklearn/feature_selection/univariate_selection.py:298: RuntimeWarning: invalid value encountered in true_divide
corr /= X_norms
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in greater
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in less
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:1821: RuntimeWarning: invalid value encountered in less_equal
cond2 = cond0 & (x <= self.a)
[CV] clf__learning_rate=0.2, clf__max_depth=1, clf__n_estimators=100, selector__k=50, total= 0.5s
[CV] clf__learning_rate=0.2, clf__max_depth=1, clf__n_estimators=100, selector__k=50
/anaconda3/lib/python3.6/site-packages/sklearn/feature_selection/univariate_selection.py:298: RuntimeWarning: invalid value encountered in true_divide
corr /= X_norms
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in greater
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in less
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:1821: RuntimeWarning: invalid value encountered in less_equal
cond2 = cond0 & (x <= self.a)
[CV] clf__learning_rate=0.2, clf__max_depth=1, clf__n_estimators=100, selector__k=50, total= 0.5s
[CV] clf__learning_rate=0.2, clf__max_depth=1, clf__n_estimators=100, selector__k=50
/anaconda3/lib/python3.6/site-packages/sklearn/feature_selection/univariate_selection.py:298: RuntimeWarning: invalid value encountered in true_divide
corr /= X_norms
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in greater
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in less
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:1821: RuntimeWarning: invalid value encountered in less_equal
cond2 = cond0 & (x <= self.a)
[CV] clf__learning_rate=0.2, clf__max_depth=1, clf__n_estimators=100, selector__k=50, total= 0.5s
[CV] clf__learning_rate=0.2, clf__max_depth=1, clf__n_estimators=100, selector__k=100
/anaconda3/lib/python3.6/site-packages/sklearn/feature_selection/univariate_selection.py:298: RuntimeWarning: invalid value encountered in true_divide
corr /= X_norms
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in greater
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in less
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:1821: RuntimeWarning: invalid value encountered in less_equal
cond2 = cond0 & (x <= self.a)
[CV] clf__learning_rate=0.2, clf__max_depth=1, clf__n_estimators=100, selector__k=100, total= 0.7s
[CV] clf__learning_rate=0.2, clf__max_depth=1, clf__n_estimators=100, selector__k=100
/anaconda3/lib/python3.6/site-packages/sklearn/feature_selection/univariate_selection.py:298: RuntimeWarning: invalid value encountered in true_divide
corr /= X_norms
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in greater
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in less
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:1821: RuntimeWarning: invalid value encountered in less_equal
cond2 = cond0 & (x <= self.a)
[CV] clf__learning_rate=0.2, clf__max_depth=1, clf__n_estimators=100, selector__k=100, total= 0.6s
[CV] clf__learning_rate=0.2, clf__max_depth=1, clf__n_estimators=100, selector__k=100
/anaconda3/lib/python3.6/site-packages/sklearn/feature_selection/univariate_selection.py:298: RuntimeWarning: invalid value encountered in true_divide
corr /= X_norms
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in greater
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in less
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:1821: RuntimeWarning: invalid value encountered in less_equal
cond2 = cond0 & (x <= self.a)
[CV] clf__learning_rate=0.2, clf__max_depth=1, clf__n_estimators=100, selector__k=100, total= 0.6s
[CV] clf__learning_rate=0.2, clf__max_depth=3, clf__n_estimators=20, selector__k=50
/anaconda3/lib/python3.6/site-packages/sklearn/feature_selection/univariate_selection.py:298: RuntimeWarning: invalid value encountered in true_divide
corr /= X_norms
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in greater
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in less
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:1821: RuntimeWarning: invalid value encountered in less_equal
cond2 = cond0 & (x <= self.a)
[CV] clf__learning_rate=0.2, clf__max_depth=3, clf__n_estimators=20, selector__k=50, total= 0.4s
[CV] clf__learning_rate=0.2, clf__max_depth=3, clf__n_estimators=20, selector__k=50
/anaconda3/lib/python3.6/site-packages/sklearn/feature_selection/univariate_selection.py:298: RuntimeWarning: invalid value encountered in true_divide
corr /= X_norms
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in greater
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in less
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:1821: RuntimeWarning: invalid value encountered in less_equal
cond2 = cond0 & (x <= self.a)
[CV] clf__learning_rate=0.2, clf__max_depth=3, clf__n_estimators=20, selector__k=50, total= 0.4s
[CV] clf__learning_rate=0.2, clf__max_depth=3, clf__n_estimators=20, selector__k=50
/anaconda3/lib/python3.6/site-packages/sklearn/feature_selection/univariate_selection.py:298: RuntimeWarning: invalid value encountered in true_divide
corr /= X_norms
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in greater
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in less
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:1821: RuntimeWarning: invalid value encountered in less_equal
cond2 = cond0 & (x <= self.a)
[CV] clf__learning_rate=0.2, clf__max_depth=3, clf__n_estimators=20, selector__k=50, total= 0.4s
[CV] clf__learning_rate=0.2, clf__max_depth=3, clf__n_estimators=20, selector__k=100
/anaconda3/lib/python3.6/site-packages/sklearn/feature_selection/univariate_selection.py:298: RuntimeWarning: invalid value encountered in true_divide
corr /= X_norms
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in greater
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in less
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:1821: RuntimeWarning: invalid value encountered in less_equal
cond2 = cond0 & (x <= self.a)
[CV] clf__learning_rate=0.2, clf__max_depth=3, clf__n_estimators=20, selector__k=100, total= 0.5s
[CV] clf__learning_rate=0.2, clf__max_depth=3, clf__n_estimators=20, selector__k=100
/anaconda3/lib/python3.6/site-packages/sklearn/feature_selection/univariate_selection.py:298: RuntimeWarning: invalid value encountered in true_divide
corr /= X_norms
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in greater
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in less
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:1821: RuntimeWarning: invalid value encountered in less_equal
cond2 = cond0 & (x <= self.a)
[CV] clf__learning_rate=0.2, clf__max_depth=3, clf__n_estimators=20, selector__k=100, total= 0.5s
[CV] clf__learning_rate=0.2, clf__max_depth=3, clf__n_estimators=20, selector__k=100
/anaconda3/lib/python3.6/site-packages/sklearn/feature_selection/univariate_selection.py:298: RuntimeWarning: invalid value encountered in true_divide
corr /= X_norms
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in greater
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in less
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:1821: RuntimeWarning: invalid value encountered in less_equal
cond2 = cond0 & (x <= self.a)
[CV] clf__learning_rate=0.2, clf__max_depth=3, clf__n_estimators=20, selector__k=100, total= 0.5s
[CV] clf__learning_rate=0.2, clf__max_depth=3, clf__n_estimators=100, selector__k=50
/anaconda3/lib/python3.6/site-packages/sklearn/feature_selection/univariate_selection.py:298: RuntimeWarning: invalid value encountered in true_divide
corr /= X_norms
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in greater
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in less
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:1821: RuntimeWarning: invalid value encountered in less_equal
cond2 = cond0 & (x <= self.a)
[CV] clf__learning_rate=0.2, clf__max_depth=3, clf__n_estimators=100, selector__k=50, total= 0.8s
[CV] clf__learning_rate=0.2, clf__max_depth=3, clf__n_estimators=100, selector__k=50
/anaconda3/lib/python3.6/site-packages/sklearn/feature_selection/univariate_selection.py:298: RuntimeWarning: invalid value encountered in true_divide
corr /= X_norms
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in greater
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in less
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:1821: RuntimeWarning: invalid value encountered in less_equal
cond2 = cond0 & (x <= self.a)
[CV] clf__learning_rate=0.2, clf__max_depth=3, clf__n_estimators=100, selector__k=50, total= 0.8s
[CV] clf__learning_rate=0.2, clf__max_depth=3, clf__n_estimators=100, selector__k=50
/anaconda3/lib/python3.6/site-packages/sklearn/feature_selection/univariate_selection.py:298: RuntimeWarning: invalid value encountered in true_divide
corr /= X_norms
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in greater
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in less
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:1821: RuntimeWarning: invalid value encountered in less_equal
cond2 = cond0 & (x <= self.a)
[CV] clf__learning_rate=0.2, clf__max_depth=3, clf__n_estimators=100, selector__k=50, total= 0.8s
[CV] clf__learning_rate=0.2, clf__max_depth=3, clf__n_estimators=100, selector__k=100
/anaconda3/lib/python3.6/site-packages/sklearn/feature_selection/univariate_selection.py:298: RuntimeWarning: invalid value encountered in true_divide
corr /= X_norms
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in greater
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in less
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:1821: RuntimeWarning: invalid value encountered in less_equal
cond2 = cond0 & (x <= self.a)
[CV] clf__learning_rate=0.2, clf__max_depth=3, clf__n_estimators=100, selector__k=100, total= 1.2s
[CV] clf__learning_rate=0.2, clf__max_depth=3, clf__n_estimators=100, selector__k=100
/anaconda3/lib/python3.6/site-packages/sklearn/feature_selection/univariate_selection.py:298: RuntimeWarning: invalid value encountered in true_divide
corr /= X_norms
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in greater
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in less
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:1821: RuntimeWarning: invalid value encountered in less_equal
cond2 = cond0 & (x <= self.a)
[CV] clf__learning_rate=0.2, clf__max_depth=3, clf__n_estimators=100, selector__k=100, total= 1.4s
[CV] clf__learning_rate=0.2, clf__max_depth=3, clf__n_estimators=100, selector__k=100
/anaconda3/lib/python3.6/site-packages/sklearn/feature_selection/univariate_selection.py:298: RuntimeWarning: invalid value encountered in true_divide
corr /= X_norms
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in greater
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in less
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:1821: RuntimeWarning: invalid value encountered in less_equal
cond2 = cond0 & (x <= self.a)
[CV] clf__learning_rate=0.2, clf__max_depth=3, clf__n_estimators=100, selector__k=100, total= 1.2s
[CV] clf__learning_rate=0.2, clf__max_depth=5, clf__n_estimators=20, selector__k=50
/anaconda3/lib/python3.6/site-packages/sklearn/feature_selection/univariate_selection.py:298: RuntimeWarning: invalid value encountered in true_divide
corr /= X_norms
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in greater
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in less
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:1821: RuntimeWarning: invalid value encountered in less_equal
cond2 = cond0 & (x <= self.a)
[CV] clf__learning_rate=0.2, clf__max_depth=5, clf__n_estimators=20, selector__k=50, total= 0.6s
[CV] clf__learning_rate=0.2, clf__max_depth=5, clf__n_estimators=20, selector__k=50
/anaconda3/lib/python3.6/site-packages/sklearn/feature_selection/univariate_selection.py:298: RuntimeWarning: invalid value encountered in true_divide
corr /= X_norms
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in greater
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in less
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:1821: RuntimeWarning: invalid value encountered in less_equal
cond2 = cond0 & (x <= self.a)
[CV] clf__learning_rate=0.2, clf__max_depth=5, clf__n_estimators=20, selector__k=50, total= 0.6s
[CV] clf__learning_rate=0.2, clf__max_depth=5, clf__n_estimators=20, selector__k=50
/anaconda3/lib/python3.6/site-packages/sklearn/feature_selection/univariate_selection.py:298: RuntimeWarning: invalid value encountered in true_divide
corr /= X_norms
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in greater
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in less
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:1821: RuntimeWarning: invalid value encountered in less_equal
cond2 = cond0 & (x <= self.a)
[CV] clf__learning_rate=0.2, clf__max_depth=5, clf__n_estimators=20, selector__k=50, total= 0.8s
[CV] clf__learning_rate=0.2, clf__max_depth=5, clf__n_estimators=20, selector__k=100
/anaconda3/lib/python3.6/site-packages/sklearn/feature_selection/univariate_selection.py:298: RuntimeWarning: invalid value encountered in true_divide
corr /= X_norms
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in greater
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in less
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:1821: RuntimeWarning: invalid value encountered in less_equal
cond2 = cond0 & (x <= self.a)
[CV] clf__learning_rate=0.2, clf__max_depth=5, clf__n_estimators=20, selector__k=100, total= 1.0s
[CV] clf__learning_rate=0.2, clf__max_depth=5, clf__n_estimators=20, selector__k=100
/anaconda3/lib/python3.6/site-packages/sklearn/feature_selection/univariate_selection.py:298: RuntimeWarning: invalid value encountered in true_divide
corr /= X_norms
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in greater
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in less
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:1821: RuntimeWarning: invalid value encountered in less_equal
cond2 = cond0 & (x <= self.a)
[CV] clf__learning_rate=0.2, clf__max_depth=5, clf__n_estimators=20, selector__k=100, total= 0.7s
[CV] clf__learning_rate=0.2, clf__max_depth=5, clf__n_estimators=20, selector__k=100
/anaconda3/lib/python3.6/site-packages/sklearn/feature_selection/univariate_selection.py:298: RuntimeWarning: invalid value encountered in true_divide
corr /= X_norms
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in greater
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in less
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:1821: RuntimeWarning: invalid value encountered in less_equal
cond2 = cond0 & (x <= self.a)
[CV] clf__learning_rate=0.2, clf__max_depth=5, clf__n_estimators=20, selector__k=100, total= 0.9s
[CV] clf__learning_rate=0.2, clf__max_depth=5, clf__n_estimators=100, selector__k=50
/anaconda3/lib/python3.6/site-packages/sklearn/feature_selection/univariate_selection.py:298: RuntimeWarning: invalid value encountered in true_divide
corr /= X_norms
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in greater
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in less
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:1821: RuntimeWarning: invalid value encountered in less_equal
cond2 = cond0 & (x <= self.a)
[CV] clf__learning_rate=0.2, clf__max_depth=5, clf__n_estimators=100, selector__k=50, total= 1.7s
[CV] clf__learning_rate=0.2, clf__max_depth=5, clf__n_estimators=100, selector__k=50
/anaconda3/lib/python3.6/site-packages/sklearn/feature_selection/univariate_selection.py:298: RuntimeWarning: invalid value encountered in true_divide
corr /= X_norms
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in greater
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in less
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:1821: RuntimeWarning: invalid value encountered in less_equal
cond2 = cond0 & (x <= self.a)
[CV] clf__learning_rate=0.2, clf__max_depth=5, clf__n_estimators=100, selector__k=50, total= 1.4s
[CV] clf__learning_rate=0.2, clf__max_depth=5, clf__n_estimators=100, selector__k=50
/anaconda3/lib/python3.6/site-packages/sklearn/feature_selection/univariate_selection.py:298: RuntimeWarning: invalid value encountered in true_divide
corr /= X_norms
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in greater
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in less
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:1821: RuntimeWarning: invalid value encountered in less_equal
cond2 = cond0 & (x <= self.a)
[CV] clf__learning_rate=0.2, clf__max_depth=5, clf__n_estimators=100, selector__k=50, total= 1.4s
[CV] clf__learning_rate=0.2, clf__max_depth=5, clf__n_estimators=100, selector__k=100
/anaconda3/lib/python3.6/site-packages/sklearn/feature_selection/univariate_selection.py:298: RuntimeWarning: invalid value encountered in true_divide
corr /= X_norms
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in greater
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in less
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:1821: RuntimeWarning: invalid value encountered in less_equal
cond2 = cond0 & (x <= self.a)
[CV] clf__learning_rate=0.2, clf__max_depth=5, clf__n_estimators=100, selector__k=100, total= 2.2s
[CV] clf__learning_rate=0.2, clf__max_depth=5, clf__n_estimators=100, selector__k=100
/anaconda3/lib/python3.6/site-packages/sklearn/feature_selection/univariate_selection.py:298: RuntimeWarning: invalid value encountered in true_divide
corr /= X_norms
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in greater
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in less
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:1821: RuntimeWarning: invalid value encountered in less_equal
cond2 = cond0 & (x <= self.a)
[CV] clf__learning_rate=0.2, clf__max_depth=5, clf__n_estimators=100, selector__k=100, total= 2.1s
[CV] clf__learning_rate=0.2, clf__max_depth=5, clf__n_estimators=100, selector__k=100
/anaconda3/lib/python3.6/site-packages/sklearn/feature_selection/univariate_selection.py:298: RuntimeWarning: invalid value encountered in true_divide
corr /= X_norms
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in greater
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in less
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:1821: RuntimeWarning: invalid value encountered in less_equal
cond2 = cond0 & (x <= self.a)
[CV] clf__learning_rate=0.2, clf__max_depth=5, clf__n_estimators=100, selector__k=100, total= 2.4s
[Parallel(n_jobs=1)]: Done 108 out of 108 | elapsed: 1.7min finished
/anaconda3/lib/python3.6/site-packages/sklearn/feature_selection/univariate_selection.py:298: RuntimeWarning: invalid value encountered in true_divide
corr /= X_norms
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in greater
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in less
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:1821: RuntimeWarning: invalid value encountered in less_equal
cond2 = cond0 & (x <= self.a)
Pipeline(memory=None,
steps=[('Scale', StandardScaler(copy=True, with_mean=True, with_std=True)), ('selector', SelectKBest(k=50, score_func=<function f_regression at 0x109eeb950>)), ('clf', GradientBoostingRegressor(alpha=0.9, criterion='friedman_mse', init=None,
learning_rate=0.2, loss='ls', max_depth=5, ma...s=100, presort='auto', random_state=None,
subsample=1.0, verbose=0, warm_start=False))])
MAE: 4569.530409739521
R2 Score: 0.9531386762746651
len(best_clf.steps[1][1].get_support())
1013
feature = list(X_cols[best_clf.steps[1][1].get_support()])
X_cols = df.drop('MSRP', axis=1).columns
feature_import = best_clf.steps[2][1].feature_importances_
dict(zip(feature,feature_import))
{'Driven_Wheels_all wheel drive': 0.010180044520391722,
'Driven_Wheels_front wheel drive': 0.0030040474201914486,
'Driven_Wheels_rear wheel drive': 0.006939920130830467,
'Engine Cylinders': 0.056492723143649226,
'Engine Fuel Type_flex-fuel (premium unleaded required/E85)': 0.004622118920666116,
'Engine Fuel Type_premium unleaded (required)': 0.02054458099422069,
'Engine Fuel Type_regular unleaded': 0.009821706110858336,
'Engine HP': 0.16784824407025872,
'Engine HP^2': 0.19046619308817184,
'Make_Aston Martin': 0.005356143350255724,
'Make_Bentley': 0.012146657996343466,
'Make_Bugatti': 0.0007619216530423492,
'Make_Ferrari': 0.008570128324786444,
'Make_Lamborghini': 0.004904571887663365,
'Make_Maybach': 0.004338514012278021,
'Make_Porsche': 0.008641787530394593,
'Make_Rolls-Royce': 0.014741114807662128,
'Model_458 Italia': 0.005405439308733585,
'Model_57': 0.0018458020543991557,
'Model_62': 0.005726411807380986,
'Model_911': 0.0028952290716332068,
'Model_Arnage': 0.0013392577032392242,
'Model_Aventador': 0.0018176714628395,
'Model_Carrera GT': 5.697533669976291e-06,
'Model_Continental GT': 0.0004635586330599047,
'Model_DBS': 0.010426194135688379,
'Model_Enzo': 0.0009188950381704875,
'Model_F430': 0.0006565191018580336,
'Model_Gallardo': 0.0006422988939341604,
'Model_Ghost': 0.0007490001611653036,
'Model_Ghost Series II': 1.4323460133123379e-05,
'Model_Landaulet': 0.006437036957277748,
'Model_Murcielago': 0.0024864400711675848,
'Model_Phantom': 0.0005154999674636062,
'Model_Phantom Coupe': 1.0202661652350821e-06,
'Model_Phantom Drophead Coupe': 6.784444922574109e-05,
'Model_R8': 0.0014865141821076694,
'Model_Reventon': 0.007777224543114406,
'Model_SLR McLaren': 0.00503380396179973,
'Model_Vanquish': 0.008172928102421984,
'Model_Veyron 16.4': 0.0014414448847538625,
'Number of Doors': 0.013405820394224634,
'Transmission Type_AUTOMATED_MANUAL': 0.02306096094915099,
'Transmission Type_MANUAL': 0.026773955122458785,
'Vehicle Size_Large': 0.040271703766337305,
'Vehicle Style_Convertible': 0.012202993884355669,
'Vehicle Style_Coupe': 0.014985916031109263,
'Year': 0.0644912715023895,
'city mpg': 0.10877401504437749,
'highway MPG': 0.1003268895925291}
pd.DataFrame(feature_import,
index=feature).sort_values(0,
ascending = False).plot.barh()
<matplotlib.axes._subplots.AxesSubplot at 0x1a160acda0>
pd.DataFrame(feature_import,
index=feature).sort_values(0,
ascending = False)
| 0 | |
|---|---|
| Engine HP^2 | 0.190466 |
| Engine HP | 0.167848 |
| city mpg | 0.108774 |
| highway MPG | 0.100327 |
| Year | 0.064491 |
| Engine Cylinders | 0.056493 |
| Vehicle Size_Large | 0.040272 |
| Transmission Type_MANUAL | 0.026774 |
| Transmission Type_AUTOMATED_MANUAL | 0.023061 |
| Engine Fuel Type_premium unleaded (required) | 0.020545 |
| Vehicle Style_Coupe | 0.014986 |
| Make_Rolls-Royce | 0.014741 |
| Number of Doors | 0.013406 |
| Vehicle Style_Convertible | 0.012203 |
| Make_Bentley | 0.012147 |
| Model_DBS | 0.010426 |
| Driven_Wheels_all wheel drive | 0.010180 |
| Engine Fuel Type_regular unleaded | 0.009822 |
| Make_Porsche | 0.008642 |
| Make_Ferrari | 0.008570 |
| Model_Vanquish | 0.008173 |
| Model_Reventon | 0.007777 |
| Driven_Wheels_rear wheel drive | 0.006940 |
| Model_Landaulet | 0.006437 |
| Model_62 | 0.005726 |
| Model_458 Italia | 0.005405 |
| Make_Aston Martin | 0.005356 |
| Model_SLR McLaren | 0.005034 |
| Make_Lamborghini | 0.004905 |
| Engine Fuel Type_flex-fuel (premium unleaded required/E85) | 0.004622 |
| Make_Maybach | 0.004339 |
| Driven_Wheels_front wheel drive | 0.003004 |
| Model_911 | 0.002895 |
| Model_Murcielago | 0.002486 |
| Model_57 | 0.001846 |
| Model_Aventador | 0.001818 |
| Model_R8 | 0.001487 |
| Model_Veyron 16.4 | 0.001441 |
| Model_Arnage | 0.001339 |
| Model_Enzo | 0.000919 |
| Make_Bugatti | 0.000762 |
| Model_Ghost | 0.000749 |
| Model_F430 | 0.000657 |
| Model_Gallardo | 0.000642 |
| Model_Phantom | 0.000515 |
| Model_Continental GT | 0.000464 |
| Model_Phantom Drophead Coupe | 0.000068 |
| Model_Ghost Series II | 0.000014 |
| Model_Carrera GT | 0.000006 |
| Model_Phantom Coupe | 0.000001 |
gb_pred = g_search.predict(X_test)
plt.scatter(gb_pred, y_test)
plt.plot(y_test, y_test)
plt.xlabel('predicted')
plt.ylabel('actual')
plt.show()
Pipeline with Kneighbors
#initialize k-neighbors and selectKbest
selector = SelectKBest(f_regression) # select k best
clf = KNeighborsRegressor()
#place SelectKbest transformer and RandomForest estimator into Pipeine
pipe = Pipeline(steps=[
('Scale',StandardScaler()),
# ('poly', PolynomialFeatures()),
('selector', selector),
('clf', clf)
])
#Create the parameter grid, entering the values to use for each parameter selected in the RandomForest estimator
parameters = {
'selector__k':[50], # params to search through
#'poly__degree': [2],
'clf__n_neighbors':[3,5,7], # number of neighbors to use
'clf__weights': ['uniform'], # weight function used in prediction
'clf__algorithm':['auto']} # Algorithm used to compute the nearest neighbors
#Perform grid search on the classifier using 'scorer' as the scoring method using GridSearchCV()
g_search = GridSearchCV(pipe, parameters, cv=3, n_jobs=1, verbose=2)
#Fit the grid search object to the training data and find the optimal parameters using fit()
g_fit = g_search.fit(X_train, y_train)
#Get the best estimator and print out the estimator model
best_clf = g_fit.best_estimator_
print (best_clf)
#Use best estimator to make predictions on the test set
best_predictions = best_clf.predict(X_test)
#metrics
#print(mean_absolute_error(y_true = y_test, y_pred = best_predictions))
#print(r2_score(y_true = y_test, y_pred = best_predictions))
print("MAE: " + str(mean_absolute_error(y_true = y_test, y_pred = best_predictions)))
print("R2 Score: " + str(r2_score(y_true = y_test, y_pred = best_predictions)))
Fitting 3 folds for each of 3 candidates, totalling 9 fits
[CV] clf__algorithm=auto, clf__n_neighbors=3, clf__weights=uniform, selector__k=50
/anaconda3/lib/python3.6/site-packages/sklearn/feature_selection/univariate_selection.py:298: RuntimeWarning: invalid value encountered in true_divide
corr /= X_norms
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in greater
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in less
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:1821: RuntimeWarning: invalid value encountered in less_equal
cond2 = cond0 & (x <= self.a)
[CV] clf__algorithm=auto, clf__n_neighbors=3, clf__weights=uniform, selector__k=50, total= 1.5s
[CV] clf__algorithm=auto, clf__n_neighbors=3, clf__weights=uniform, selector__k=50
[Parallel(n_jobs=1)]: Done 1 out of 1 | elapsed: 3.8s remaining: 0.0s
/anaconda3/lib/python3.6/site-packages/sklearn/feature_selection/univariate_selection.py:298: RuntimeWarning: invalid value encountered in true_divide
corr /= X_norms
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in greater
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in less
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:1821: RuntimeWarning: invalid value encountered in less_equal
cond2 = cond0 & (x <= self.a)
[CV] clf__algorithm=auto, clf__n_neighbors=3, clf__weights=uniform, selector__k=50, total= 1.5s
[CV] clf__algorithm=auto, clf__n_neighbors=3, clf__weights=uniform, selector__k=50
/anaconda3/lib/python3.6/site-packages/sklearn/feature_selection/univariate_selection.py:298: RuntimeWarning: invalid value encountered in true_divide
corr /= X_norms
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in greater
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in less
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:1821: RuntimeWarning: invalid value encountered in less_equal
cond2 = cond0 & (x <= self.a)
[CV] clf__algorithm=auto, clf__n_neighbors=3, clf__weights=uniform, selector__k=50, total= 1.6s
[CV] clf__algorithm=auto, clf__n_neighbors=5, clf__weights=uniform, selector__k=50
/anaconda3/lib/python3.6/site-packages/sklearn/feature_selection/univariate_selection.py:298: RuntimeWarning: invalid value encountered in true_divide
corr /= X_norms
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in greater
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in less
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:1821: RuntimeWarning: invalid value encountered in less_equal
cond2 = cond0 & (x <= self.a)
[CV] clf__algorithm=auto, clf__n_neighbors=5, clf__weights=uniform, selector__k=50, total= 1.8s
[CV] clf__algorithm=auto, clf__n_neighbors=5, clf__weights=uniform, selector__k=50
/anaconda3/lib/python3.6/site-packages/sklearn/feature_selection/univariate_selection.py:298: RuntimeWarning: invalid value encountered in true_divide
corr /= X_norms
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in greater
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in less
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:1821: RuntimeWarning: invalid value encountered in less_equal
cond2 = cond0 & (x <= self.a)
[CV] clf__algorithm=auto, clf__n_neighbors=5, clf__weights=uniform, selector__k=50, total= 1.9s
[CV] clf__algorithm=auto, clf__n_neighbors=5, clf__weights=uniform, selector__k=50
/anaconda3/lib/python3.6/site-packages/sklearn/feature_selection/univariate_selection.py:298: RuntimeWarning: invalid value encountered in true_divide
corr /= X_norms
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in greater
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in less
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:1821: RuntimeWarning: invalid value encountered in less_equal
cond2 = cond0 & (x <= self.a)
[CV] clf__algorithm=auto, clf__n_neighbors=5, clf__weights=uniform, selector__k=50, total= 1.7s
[CV] clf__algorithm=auto, clf__n_neighbors=7, clf__weights=uniform, selector__k=50
/anaconda3/lib/python3.6/site-packages/sklearn/feature_selection/univariate_selection.py:298: RuntimeWarning: invalid value encountered in true_divide
corr /= X_norms
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in greater
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in less
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:1821: RuntimeWarning: invalid value encountered in less_equal
cond2 = cond0 & (x <= self.a)
[CV] clf__algorithm=auto, clf__n_neighbors=7, clf__weights=uniform, selector__k=50, total= 1.7s
[CV] clf__algorithm=auto, clf__n_neighbors=7, clf__weights=uniform, selector__k=50
/anaconda3/lib/python3.6/site-packages/sklearn/feature_selection/univariate_selection.py:298: RuntimeWarning: invalid value encountered in true_divide
corr /= X_norms
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in greater
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in less
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:1821: RuntimeWarning: invalid value encountered in less_equal
cond2 = cond0 & (x <= self.a)
[CV] clf__algorithm=auto, clf__n_neighbors=7, clf__weights=uniform, selector__k=50, total= 1.8s
[CV] clf__algorithm=auto, clf__n_neighbors=7, clf__weights=uniform, selector__k=50
/anaconda3/lib/python3.6/site-packages/sklearn/feature_selection/univariate_selection.py:298: RuntimeWarning: invalid value encountered in true_divide
corr /= X_norms
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in greater
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in less
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:1821: RuntimeWarning: invalid value encountered in less_equal
cond2 = cond0 & (x <= self.a)
[CV] clf__algorithm=auto, clf__n_neighbors=7, clf__weights=uniform, selector__k=50, total= 1.6s
[Parallel(n_jobs=1)]: Done 9 out of 9 | elapsed: 38.2s finished
/anaconda3/lib/python3.6/site-packages/sklearn/feature_selection/univariate_selection.py:298: RuntimeWarning: invalid value encountered in true_divide
corr /= X_norms
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in greater
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in less
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:1821: RuntimeWarning: invalid value encountered in less_equal
cond2 = cond0 & (x <= self.a)
Pipeline(memory=None,
steps=[('Scale', StandardScaler(copy=True, with_mean=True, with_std=True)), ('selector', SelectKBest(k=50, score_func=<function f_regression at 0x109eeb950>)), ('clf', KNeighborsRegressor(algorithm='auto', leaf_size=30, metric='minkowski',
metric_params=None, n_jobs=1, n_neighbors=3, p=2,
weights='uniform'))])
MAE: 4840.068601190476
R2 Score: 0.8896617420551712
kn_pred = g_search.predict(X_test)
plt.scatter(kn_pred, y_test)
plt.plot(y_test, y_test)
plt.xlabel('predicted')
plt.ylabel('actual')
plt.show()
Visualize predictions
# plot y-pred vs y
# plot residulas vs y
# Residual histogram (see if it looks normal)
# Y-hat vs y + y vs y
# Hyperparameter tuning curves
# Metrics vs model
def lin_reg(x,y):
# SLR, the correlation coefficient multiplied by the standard
# deviation of y divided by standard deviation of x is the optimal slope.
beta_1 = (scipy.stats.pearsonr(x,y)[0])*(np.std(y)/np.std(x))
# The optimal beta is found by: mean(y) - b1 * mean(x).
beta_0 = np.mean(y)-(beta_1*np.mean(x))
return beta_0, beta_1
x = df['Engine Cylinders'].values
y = df['MSRP'].values
beta0, beta1 = lin_reg(x,y)
#Print the optimal values.
print('The Optimal Y Intercept is ', beta0)
print('The Optimal slope is ', beta1)
The Optimal Y Intercept is -60081.168704419215
The Optimal slope is 18051.885440336122
y_pred = beta0 + beta1*x
# Appending the predicted values:
df['Pred'] = y_pred
# Residuals equals the difference between Y-True and Y-Pred:
df['Residuals'] = abs(df['MSRP']-df['Pred'])
# how our predictions compare to the true values.
sns.lmplot(x='MSRP', y='Pred', data=df)
<seaborn.axisgrid.FacetGrid at 0x1a15302860>
x = df['Engine HP'].values
y = df['MSRP'].values
beta0, beta1 = lin_reg(x,y)
#Print the optimal values.
print('The Optimal Y Intercept is ', beta0)
print('The Optimal slope is ', beta1)
The Optimal Y Intercept is -51242.12399479592
The Optimal slope is 367.99756098271956
# Appending the predicted values
df['Pred'] = y_pred
# Residuals equals the difference between Y-True and Y-Pred:
df['Residuals'] = abs(df['MSRP']-df['Pred'])
sns.lmplot(x='MSRP', y='Pred', data=df)
<seaborn.axisgrid.FacetGrid at 0x1a15320c88>
# Assumptions for my model
plt.figure(figsize=(8,5))
df['Residuals'] = df['MSRP'] - df['Pred']
sns.distplot(df['Residuals'])
<matplotlib.axes._subplots.AxesSubplot at 0x1a15630828>
Best Model for less features
#initialize gradient boosting and selectKbest
selector = SelectKBest(f_regression) # select k best
clf = GradientBoostingRegressor() # Model I want to use
#place SelectKbest transformer and RandomForest estimator into Pipeine
pipe = Pipeline(steps=[
('Scale',StandardScaler()),
('selector', selector),
('clf', clf)
])
#Create the parameter grid, entering the values to use for each parameter selected in the RandomForest estimator
parameters = {
'selector__k':[12], # params to search through
'clf__n_estimators': [20, 100], # num of boosting stages to perform. larger number usually better performance
'clf__learning_rate':[0.05, 0.1, 0.2], # shrinks the contibution of each tree. trade off between n_estimators and learning rate
'clf__max_depth': [1, 3, 5] # max depth of the individual regression estimators
}
#Perform grid search on the classifier using 'scorer' as the scoring method using GridSearchCV()
g_search = GridSearchCV(pipe, parameters, cv=3, n_jobs=1, verbose=2)
#Fit the grid search object to the training data and find the optimal parameters using fit()
g_fit = g_search.fit(X_train, y_train)
#Get the best estimator and print out the estimator model
best_clf = g_fit.best_estimator_
print (best_clf)
#Use best estimator to make predictions on the test set
best_predictions = best_clf.predict(X_test)
#metrics
#print(mean_absolute_error(y_true = y_test, y_pred = best_predictions))
#print(r2_score(y_true = y_test, y_pred = best_predictions))
print("MAE: " + str(mean_absolute_error(y_true = y_test, y_pred = best_predictions)))
print("R2 Score: " + str(r2_score(y_true = y_test, y_pred = best_predictions)))
Fitting 3 folds for each of 18 candidates, totalling 54 fits
[CV] clf__learning_rate=0.05, clf__max_depth=1, clf__n_estimators=20, selector__k=12
/anaconda3/lib/python3.6/site-packages/sklearn/feature_selection/univariate_selection.py:298: RuntimeWarning: invalid value encountered in true_divide
corr /= X_norms
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in greater
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in less
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:1821: RuntimeWarning: invalid value encountered in less_equal
cond2 = cond0 & (x <= self.a)
[Parallel(n_jobs=1)]: Done 1 out of 1 | elapsed: 0.5s remaining: 0.0s
[CV] clf__learning_rate=0.05, clf__max_depth=1, clf__n_estimators=20, selector__k=12, total= 0.4s
[CV] clf__learning_rate=0.05, clf__max_depth=1, clf__n_estimators=20, selector__k=12
/anaconda3/lib/python3.6/site-packages/sklearn/feature_selection/univariate_selection.py:298: RuntimeWarning: invalid value encountered in true_divide
corr /= X_norms
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in greater
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in less
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:1821: RuntimeWarning: invalid value encountered in less_equal
cond2 = cond0 & (x <= self.a)
[CV] clf__learning_rate=0.05, clf__max_depth=1, clf__n_estimators=20, selector__k=12, total= 0.4s
[CV] clf__learning_rate=0.05, clf__max_depth=1, clf__n_estimators=20, selector__k=12
/anaconda3/lib/python3.6/site-packages/sklearn/feature_selection/univariate_selection.py:298: RuntimeWarning: invalid value encountered in true_divide
corr /= X_norms
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in greater
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in less
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:1821: RuntimeWarning: invalid value encountered in less_equal
cond2 = cond0 & (x <= self.a)
[CV] clf__learning_rate=0.05, clf__max_depth=1, clf__n_estimators=20, selector__k=12, total= 0.3s
[CV] clf__learning_rate=0.05, clf__max_depth=1, clf__n_estimators=100, selector__k=12
/anaconda3/lib/python3.6/site-packages/sklearn/feature_selection/univariate_selection.py:298: RuntimeWarning: invalid value encountered in true_divide
corr /= X_norms
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in greater
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in less
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:1821: RuntimeWarning: invalid value encountered in less_equal
cond2 = cond0 & (x <= self.a)
[CV] clf__learning_rate=0.05, clf__max_depth=1, clf__n_estimators=100, selector__k=12, total= 0.4s
[CV] clf__learning_rate=0.05, clf__max_depth=1, clf__n_estimators=100, selector__k=12
/anaconda3/lib/python3.6/site-packages/sklearn/feature_selection/univariate_selection.py:298: RuntimeWarning: invalid value encountered in true_divide
corr /= X_norms
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in greater
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in less
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:1821: RuntimeWarning: invalid value encountered in less_equal
cond2 = cond0 & (x <= self.a)
[CV] clf__learning_rate=0.05, clf__max_depth=1, clf__n_estimators=100, selector__k=12, total= 0.4s
[CV] clf__learning_rate=0.05, clf__max_depth=1, clf__n_estimators=100, selector__k=12
/anaconda3/lib/python3.6/site-packages/sklearn/feature_selection/univariate_selection.py:298: RuntimeWarning: invalid value encountered in true_divide
corr /= X_norms
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in greater
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in less
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:1821: RuntimeWarning: invalid value encountered in less_equal
cond2 = cond0 & (x <= self.a)
[CV] clf__learning_rate=0.05, clf__max_depth=1, clf__n_estimators=100, selector__k=12, total= 0.3s
[CV] clf__learning_rate=0.05, clf__max_depth=3, clf__n_estimators=20, selector__k=12
/anaconda3/lib/python3.6/site-packages/sklearn/feature_selection/univariate_selection.py:298: RuntimeWarning: invalid value encountered in true_divide
corr /= X_norms
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in greater
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in less
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:1821: RuntimeWarning: invalid value encountered in less_equal
cond2 = cond0 & (x <= self.a)
[CV] clf__learning_rate=0.05, clf__max_depth=3, clf__n_estimators=20, selector__k=12, total= 0.3s
[CV] clf__learning_rate=0.05, clf__max_depth=3, clf__n_estimators=20, selector__k=12
/anaconda3/lib/python3.6/site-packages/sklearn/feature_selection/univariate_selection.py:298: RuntimeWarning: invalid value encountered in true_divide
corr /= X_norms
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in greater
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in less
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:1821: RuntimeWarning: invalid value encountered in less_equal
cond2 = cond0 & (x <= self.a)
[CV] clf__learning_rate=0.05, clf__max_depth=3, clf__n_estimators=20, selector__k=12, total= 0.3s
[CV] clf__learning_rate=0.05, clf__max_depth=3, clf__n_estimators=20, selector__k=12
/anaconda3/lib/python3.6/site-packages/sklearn/feature_selection/univariate_selection.py:298: RuntimeWarning: invalid value encountered in true_divide
corr /= X_norms
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in greater
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in less
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:1821: RuntimeWarning: invalid value encountered in less_equal
cond2 = cond0 & (x <= self.a)
[CV] clf__learning_rate=0.05, clf__max_depth=3, clf__n_estimators=20, selector__k=12, total= 0.3s
[CV] clf__learning_rate=0.05, clf__max_depth=3, clf__n_estimators=100, selector__k=12
/anaconda3/lib/python3.6/site-packages/sklearn/feature_selection/univariate_selection.py:298: RuntimeWarning: invalid value encountered in true_divide
corr /= X_norms
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in greater
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in less
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:1821: RuntimeWarning: invalid value encountered in less_equal
cond2 = cond0 & (x <= self.a)
[CV] clf__learning_rate=0.05, clf__max_depth=3, clf__n_estimators=100, selector__k=12, total= 0.5s
[CV] clf__learning_rate=0.05, clf__max_depth=3, clf__n_estimators=100, selector__k=12
/anaconda3/lib/python3.6/site-packages/sklearn/feature_selection/univariate_selection.py:298: RuntimeWarning: invalid value encountered in true_divide
corr /= X_norms
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in greater
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in less
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:1821: RuntimeWarning: invalid value encountered in less_equal
cond2 = cond0 & (x <= self.a)
[CV] clf__learning_rate=0.05, clf__max_depth=3, clf__n_estimators=100, selector__k=12, total= 0.5s
[CV] clf__learning_rate=0.05, clf__max_depth=3, clf__n_estimators=100, selector__k=12
/anaconda3/lib/python3.6/site-packages/sklearn/feature_selection/univariate_selection.py:298: RuntimeWarning: invalid value encountered in true_divide
corr /= X_norms
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in greater
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in less
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:1821: RuntimeWarning: invalid value encountered in less_equal
cond2 = cond0 & (x <= self.a)
[CV] clf__learning_rate=0.05, clf__max_depth=3, clf__n_estimators=100, selector__k=12, total= 0.5s
[CV] clf__learning_rate=0.05, clf__max_depth=5, clf__n_estimators=20, selector__k=12
/anaconda3/lib/python3.6/site-packages/sklearn/feature_selection/univariate_selection.py:298: RuntimeWarning: invalid value encountered in true_divide
corr /= X_norms
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in greater
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in less
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:1821: RuntimeWarning: invalid value encountered in less_equal
cond2 = cond0 & (x <= self.a)
[CV] clf__learning_rate=0.05, clf__max_depth=5, clf__n_estimators=20, selector__k=12, total= 0.4s
[CV] clf__learning_rate=0.05, clf__max_depth=5, clf__n_estimators=20, selector__k=12
/anaconda3/lib/python3.6/site-packages/sklearn/feature_selection/univariate_selection.py:298: RuntimeWarning: invalid value encountered in true_divide
corr /= X_norms
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in greater
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in less
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:1821: RuntimeWarning: invalid value encountered in less_equal
cond2 = cond0 & (x <= self.a)
[CV] clf__learning_rate=0.05, clf__max_depth=5, clf__n_estimators=20, selector__k=12, total= 0.4s
[CV] clf__learning_rate=0.05, clf__max_depth=5, clf__n_estimators=20, selector__k=12
/anaconda3/lib/python3.6/site-packages/sklearn/feature_selection/univariate_selection.py:298: RuntimeWarning: invalid value encountered in true_divide
corr /= X_norms
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in greater
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in less
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:1821: RuntimeWarning: invalid value encountered in less_equal
cond2 = cond0 & (x <= self.a)
[CV] clf__learning_rate=0.05, clf__max_depth=5, clf__n_estimators=20, selector__k=12, total= 0.3s
[CV] clf__learning_rate=0.05, clf__max_depth=5, clf__n_estimators=100, selector__k=12
/anaconda3/lib/python3.6/site-packages/sklearn/feature_selection/univariate_selection.py:298: RuntimeWarning: invalid value encountered in true_divide
corr /= X_norms
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in greater
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in less
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:1821: RuntimeWarning: invalid value encountered in less_equal
cond2 = cond0 & (x <= self.a)
[CV] clf__learning_rate=0.05, clf__max_depth=5, clf__n_estimators=100, selector__k=12, total= 0.6s
[CV] clf__learning_rate=0.05, clf__max_depth=5, clf__n_estimators=100, selector__k=12
/anaconda3/lib/python3.6/site-packages/sklearn/feature_selection/univariate_selection.py:298: RuntimeWarning: invalid value encountered in true_divide
corr /= X_norms
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in greater
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in less
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:1821: RuntimeWarning: invalid value encountered in less_equal
cond2 = cond0 & (x <= self.a)
[CV] clf__learning_rate=0.05, clf__max_depth=5, clf__n_estimators=100, selector__k=12, total= 0.7s
[CV] clf__learning_rate=0.05, clf__max_depth=5, clf__n_estimators=100, selector__k=12
/anaconda3/lib/python3.6/site-packages/sklearn/feature_selection/univariate_selection.py:298: RuntimeWarning: invalid value encountered in true_divide
corr /= X_norms
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in greater
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in less
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:1821: RuntimeWarning: invalid value encountered in less_equal
cond2 = cond0 & (x <= self.a)
[CV] clf__learning_rate=0.05, clf__max_depth=5, clf__n_estimators=100, selector__k=12, total= 0.7s
[CV] clf__learning_rate=0.1, clf__max_depth=1, clf__n_estimators=20, selector__k=12
/anaconda3/lib/python3.6/site-packages/sklearn/feature_selection/univariate_selection.py:298: RuntimeWarning: invalid value encountered in true_divide
corr /= X_norms
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in greater
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in less
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:1821: RuntimeWarning: invalid value encountered in less_equal
cond2 = cond0 & (x <= self.a)
[CV] clf__learning_rate=0.1, clf__max_depth=1, clf__n_estimators=20, selector__k=12, total= 0.3s
[CV] clf__learning_rate=0.1, clf__max_depth=1, clf__n_estimators=20, selector__k=12
/anaconda3/lib/python3.6/site-packages/sklearn/feature_selection/univariate_selection.py:298: RuntimeWarning: invalid value encountered in true_divide
corr /= X_norms
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in greater
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in less
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:1821: RuntimeWarning: invalid value encountered in less_equal
cond2 = cond0 & (x <= self.a)
[CV] clf__learning_rate=0.1, clf__max_depth=1, clf__n_estimators=20, selector__k=12, total= 0.3s
[CV] clf__learning_rate=0.1, clf__max_depth=1, clf__n_estimators=20, selector__k=12
/anaconda3/lib/python3.6/site-packages/sklearn/feature_selection/univariate_selection.py:298: RuntimeWarning: invalid value encountered in true_divide
corr /= X_norms
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in greater
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in less
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:1821: RuntimeWarning: invalid value encountered in less_equal
cond2 = cond0 & (x <= self.a)
[CV] clf__learning_rate=0.1, clf__max_depth=1, clf__n_estimators=20, selector__k=12, total= 0.3s
[CV] clf__learning_rate=0.1, clf__max_depth=1, clf__n_estimators=100, selector__k=12
/anaconda3/lib/python3.6/site-packages/sklearn/feature_selection/univariate_selection.py:298: RuntimeWarning: invalid value encountered in true_divide
corr /= X_norms
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in greater
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in less
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:1821: RuntimeWarning: invalid value encountered in less_equal
cond2 = cond0 & (x <= self.a)
[CV] clf__learning_rate=0.1, clf__max_depth=1, clf__n_estimators=100, selector__k=12, total= 0.4s
[CV] clf__learning_rate=0.1, clf__max_depth=1, clf__n_estimators=100, selector__k=12
/anaconda3/lib/python3.6/site-packages/sklearn/feature_selection/univariate_selection.py:298: RuntimeWarning: invalid value encountered in true_divide
corr /= X_norms
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in greater
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in less
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:1821: RuntimeWarning: invalid value encountered in less_equal
cond2 = cond0 & (x <= self.a)
[CV] clf__learning_rate=0.1, clf__max_depth=1, clf__n_estimators=100, selector__k=12, total= 0.3s
[CV] clf__learning_rate=0.1, clf__max_depth=1, clf__n_estimators=100, selector__k=12
/anaconda3/lib/python3.6/site-packages/sklearn/feature_selection/univariate_selection.py:298: RuntimeWarning: invalid value encountered in true_divide
corr /= X_norms
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in greater
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in less
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:1821: RuntimeWarning: invalid value encountered in less_equal
cond2 = cond0 & (x <= self.a)
[CV] clf__learning_rate=0.1, clf__max_depth=1, clf__n_estimators=100, selector__k=12, total= 0.3s
[CV] clf__learning_rate=0.1, clf__max_depth=3, clf__n_estimators=20, selector__k=12
/anaconda3/lib/python3.6/site-packages/sklearn/feature_selection/univariate_selection.py:298: RuntimeWarning: invalid value encountered in true_divide
corr /= X_norms
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in greater
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in less
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:1821: RuntimeWarning: invalid value encountered in less_equal
cond2 = cond0 & (x <= self.a)
[CV] clf__learning_rate=0.1, clf__max_depth=3, clf__n_estimators=20, selector__k=12, total= 0.3s
[CV] clf__learning_rate=0.1, clf__max_depth=3, clf__n_estimators=20, selector__k=12
/anaconda3/lib/python3.6/site-packages/sklearn/feature_selection/univariate_selection.py:298: RuntimeWarning: invalid value encountered in true_divide
corr /= X_norms
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in greater
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in less
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:1821: RuntimeWarning: invalid value encountered in less_equal
cond2 = cond0 & (x <= self.a)
[CV] clf__learning_rate=0.1, clf__max_depth=3, clf__n_estimators=20, selector__k=12, total= 0.3s
[CV] clf__learning_rate=0.1, clf__max_depth=3, clf__n_estimators=20, selector__k=12
/anaconda3/lib/python3.6/site-packages/sklearn/feature_selection/univariate_selection.py:298: RuntimeWarning: invalid value encountered in true_divide
corr /= X_norms
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in greater
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in less
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:1821: RuntimeWarning: invalid value encountered in less_equal
cond2 = cond0 & (x <= self.a)
[CV] clf__learning_rate=0.1, clf__max_depth=3, clf__n_estimators=20, selector__k=12, total= 0.4s
[CV] clf__learning_rate=0.1, clf__max_depth=3, clf__n_estimators=100, selector__k=12
/anaconda3/lib/python3.6/site-packages/sklearn/feature_selection/univariate_selection.py:298: RuntimeWarning: invalid value encountered in true_divide
corr /= X_norms
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in greater
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in less
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:1821: RuntimeWarning: invalid value encountered in less_equal
cond2 = cond0 & (x <= self.a)
[CV] clf__learning_rate=0.1, clf__max_depth=3, clf__n_estimators=100, selector__k=12, total= 0.5s
[CV] clf__learning_rate=0.1, clf__max_depth=3, clf__n_estimators=100, selector__k=12
/anaconda3/lib/python3.6/site-packages/sklearn/feature_selection/univariate_selection.py:298: RuntimeWarning: invalid value encountered in true_divide
corr /= X_norms
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in greater
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in less
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:1821: RuntimeWarning: invalid value encountered in less_equal
cond2 = cond0 & (x <= self.a)
[CV] clf__learning_rate=0.1, clf__max_depth=3, clf__n_estimators=100, selector__k=12, total= 0.5s
[CV] clf__learning_rate=0.1, clf__max_depth=3, clf__n_estimators=100, selector__k=12
/anaconda3/lib/python3.6/site-packages/sklearn/feature_selection/univariate_selection.py:298: RuntimeWarning: invalid value encountered in true_divide
corr /= X_norms
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in greater
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in less
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:1821: RuntimeWarning: invalid value encountered in less_equal
cond2 = cond0 & (x <= self.a)
[CV] clf__learning_rate=0.1, clf__max_depth=3, clf__n_estimators=100, selector__k=12, total= 0.5s
[CV] clf__learning_rate=0.1, clf__max_depth=5, clf__n_estimators=20, selector__k=12
/anaconda3/lib/python3.6/site-packages/sklearn/feature_selection/univariate_selection.py:298: RuntimeWarning: invalid value encountered in true_divide
corr /= X_norms
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in greater
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in less
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:1821: RuntimeWarning: invalid value encountered in less_equal
cond2 = cond0 & (x <= self.a)
[CV] clf__learning_rate=0.1, clf__max_depth=5, clf__n_estimators=20, selector__k=12, total= 0.4s
[CV] clf__learning_rate=0.1, clf__max_depth=5, clf__n_estimators=20, selector__k=12
/anaconda3/lib/python3.6/site-packages/sklearn/feature_selection/univariate_selection.py:298: RuntimeWarning: invalid value encountered in true_divide
corr /= X_norms
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in greater
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in less
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:1821: RuntimeWarning: invalid value encountered in less_equal
cond2 = cond0 & (x <= self.a)
[CV] clf__learning_rate=0.1, clf__max_depth=5, clf__n_estimators=20, selector__k=12, total= 0.4s
[CV] clf__learning_rate=0.1, clf__max_depth=5, clf__n_estimators=20, selector__k=12
/anaconda3/lib/python3.6/site-packages/sklearn/feature_selection/univariate_selection.py:298: RuntimeWarning: invalid value encountered in true_divide
corr /= X_norms
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in greater
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in less
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:1821: RuntimeWarning: invalid value encountered in less_equal
cond2 = cond0 & (x <= self.a)
[CV] clf__learning_rate=0.1, clf__max_depth=5, clf__n_estimators=20, selector__k=12, total= 0.4s
[CV] clf__learning_rate=0.1, clf__max_depth=5, clf__n_estimators=100, selector__k=12
/anaconda3/lib/python3.6/site-packages/sklearn/feature_selection/univariate_selection.py:298: RuntimeWarning: invalid value encountered in true_divide
corr /= X_norms
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in greater
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in less
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:1821: RuntimeWarning: invalid value encountered in less_equal
cond2 = cond0 & (x <= self.a)
[CV] clf__learning_rate=0.1, clf__max_depth=5, clf__n_estimators=100, selector__k=12, total= 0.6s
[CV] clf__learning_rate=0.1, clf__max_depth=5, clf__n_estimators=100, selector__k=12
/anaconda3/lib/python3.6/site-packages/sklearn/feature_selection/univariate_selection.py:298: RuntimeWarning: invalid value encountered in true_divide
corr /= X_norms
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in greater
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in less
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:1821: RuntimeWarning: invalid value encountered in less_equal
cond2 = cond0 & (x <= self.a)
[CV] clf__learning_rate=0.1, clf__max_depth=5, clf__n_estimators=100, selector__k=12, total= 0.6s
[CV] clf__learning_rate=0.1, clf__max_depth=5, clf__n_estimators=100, selector__k=12
/anaconda3/lib/python3.6/site-packages/sklearn/feature_selection/univariate_selection.py:298: RuntimeWarning: invalid value encountered in true_divide
corr /= X_norms
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in greater
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in less
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:1821: RuntimeWarning: invalid value encountered in less_equal
cond2 = cond0 & (x <= self.a)
[CV] clf__learning_rate=0.1, clf__max_depth=5, clf__n_estimators=100, selector__k=12, total= 0.6s
[CV] clf__learning_rate=0.2, clf__max_depth=1, clf__n_estimators=20, selector__k=12
/anaconda3/lib/python3.6/site-packages/sklearn/feature_selection/univariate_selection.py:298: RuntimeWarning: invalid value encountered in true_divide
corr /= X_norms
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in greater
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in less
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:1821: RuntimeWarning: invalid value encountered in less_equal
cond2 = cond0 & (x <= self.a)
[CV] clf__learning_rate=0.2, clf__max_depth=1, clf__n_estimators=20, selector__k=12, total= 0.3s
[CV] clf__learning_rate=0.2, clf__max_depth=1, clf__n_estimators=20, selector__k=12
/anaconda3/lib/python3.6/site-packages/sklearn/feature_selection/univariate_selection.py:298: RuntimeWarning: invalid value encountered in true_divide
corr /= X_norms
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in greater
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in less
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:1821: RuntimeWarning: invalid value encountered in less_equal
cond2 = cond0 & (x <= self.a)
[CV] clf__learning_rate=0.2, clf__max_depth=1, clf__n_estimators=20, selector__k=12, total= 0.3s
[CV] clf__learning_rate=0.2, clf__max_depth=1, clf__n_estimators=20, selector__k=12
/anaconda3/lib/python3.6/site-packages/sklearn/feature_selection/univariate_selection.py:298: RuntimeWarning: invalid value encountered in true_divide
corr /= X_norms
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in greater
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in less
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:1821: RuntimeWarning: invalid value encountered in less_equal
cond2 = cond0 & (x <= self.a)
[CV] clf__learning_rate=0.2, clf__max_depth=1, clf__n_estimators=20, selector__k=12, total= 0.3s
[CV] clf__learning_rate=0.2, clf__max_depth=1, clf__n_estimators=100, selector__k=12
/anaconda3/lib/python3.6/site-packages/sklearn/feature_selection/univariate_selection.py:298: RuntimeWarning: invalid value encountered in true_divide
corr /= X_norms
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in greater
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in less
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:1821: RuntimeWarning: invalid value encountered in less_equal
cond2 = cond0 & (x <= self.a)
[CV] clf__learning_rate=0.2, clf__max_depth=1, clf__n_estimators=100, selector__k=12, total= 0.3s
[CV] clf__learning_rate=0.2, clf__max_depth=1, clf__n_estimators=100, selector__k=12
/anaconda3/lib/python3.6/site-packages/sklearn/feature_selection/univariate_selection.py:298: RuntimeWarning: invalid value encountered in true_divide
corr /= X_norms
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in greater
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in less
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:1821: RuntimeWarning: invalid value encountered in less_equal
cond2 = cond0 & (x <= self.a)
[CV] clf__learning_rate=0.2, clf__max_depth=1, clf__n_estimators=100, selector__k=12, total= 0.3s
[CV] clf__learning_rate=0.2, clf__max_depth=1, clf__n_estimators=100, selector__k=12
/anaconda3/lib/python3.6/site-packages/sklearn/feature_selection/univariate_selection.py:298: RuntimeWarning: invalid value encountered in true_divide
corr /= X_norms
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in greater
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in less
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:1821: RuntimeWarning: invalid value encountered in less_equal
cond2 = cond0 & (x <= self.a)
[CV] clf__learning_rate=0.2, clf__max_depth=1, clf__n_estimators=100, selector__k=12, total= 0.3s
[CV] clf__learning_rate=0.2, clf__max_depth=3, clf__n_estimators=20, selector__k=12
/anaconda3/lib/python3.6/site-packages/sklearn/feature_selection/univariate_selection.py:298: RuntimeWarning: invalid value encountered in true_divide
corr /= X_norms
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in greater
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in less
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:1821: RuntimeWarning: invalid value encountered in less_equal
cond2 = cond0 & (x <= self.a)
[CV] clf__learning_rate=0.2, clf__max_depth=3, clf__n_estimators=20, selector__k=12, total= 0.3s
[CV] clf__learning_rate=0.2, clf__max_depth=3, clf__n_estimators=20, selector__k=12
/anaconda3/lib/python3.6/site-packages/sklearn/feature_selection/univariate_selection.py:298: RuntimeWarning: invalid value encountered in true_divide
corr /= X_norms
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in greater
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in less
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:1821: RuntimeWarning: invalid value encountered in less_equal
cond2 = cond0 & (x <= self.a)
[CV] clf__learning_rate=0.2, clf__max_depth=3, clf__n_estimators=20, selector__k=12, total= 0.3s
[CV] clf__learning_rate=0.2, clf__max_depth=3, clf__n_estimators=20, selector__k=12
/anaconda3/lib/python3.6/site-packages/sklearn/feature_selection/univariate_selection.py:298: RuntimeWarning: invalid value encountered in true_divide
corr /= X_norms
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in greater
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in less
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:1821: RuntimeWarning: invalid value encountered in less_equal
cond2 = cond0 & (x <= self.a)
[CV] clf__learning_rate=0.2, clf__max_depth=3, clf__n_estimators=20, selector__k=12, total= 0.3s
[CV] clf__learning_rate=0.2, clf__max_depth=3, clf__n_estimators=100, selector__k=12
/anaconda3/lib/python3.6/site-packages/sklearn/feature_selection/univariate_selection.py:298: RuntimeWarning: invalid value encountered in true_divide
corr /= X_norms
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in greater
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in less
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:1821: RuntimeWarning: invalid value encountered in less_equal
cond2 = cond0 & (x <= self.a)
[CV] clf__learning_rate=0.2, clf__max_depth=3, clf__n_estimators=100, selector__k=12, total= 0.5s
[CV] clf__learning_rate=0.2, clf__max_depth=3, clf__n_estimators=100, selector__k=12
/anaconda3/lib/python3.6/site-packages/sklearn/feature_selection/univariate_selection.py:298: RuntimeWarning: invalid value encountered in true_divide
corr /= X_norms
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in greater
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in less
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:1821: RuntimeWarning: invalid value encountered in less_equal
cond2 = cond0 & (x <= self.a)
[CV] clf__learning_rate=0.2, clf__max_depth=3, clf__n_estimators=100, selector__k=12, total= 0.4s
[CV] clf__learning_rate=0.2, clf__max_depth=3, clf__n_estimators=100, selector__k=12
/anaconda3/lib/python3.6/site-packages/sklearn/feature_selection/univariate_selection.py:298: RuntimeWarning: invalid value encountered in true_divide
corr /= X_norms
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in greater
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in less
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:1821: RuntimeWarning: invalid value encountered in less_equal
cond2 = cond0 & (x <= self.a)
[CV] clf__learning_rate=0.2, clf__max_depth=3, clf__n_estimators=100, selector__k=12, total= 0.4s
[CV] clf__learning_rate=0.2, clf__max_depth=5, clf__n_estimators=20, selector__k=12
/anaconda3/lib/python3.6/site-packages/sklearn/feature_selection/univariate_selection.py:298: RuntimeWarning: invalid value encountered in true_divide
corr /= X_norms
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in greater
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in less
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:1821: RuntimeWarning: invalid value encountered in less_equal
cond2 = cond0 & (x <= self.a)
[CV] clf__learning_rate=0.2, clf__max_depth=5, clf__n_estimators=20, selector__k=12, total= 0.3s
[CV] clf__learning_rate=0.2, clf__max_depth=5, clf__n_estimators=20, selector__k=12
/anaconda3/lib/python3.6/site-packages/sklearn/feature_selection/univariate_selection.py:298: RuntimeWarning: invalid value encountered in true_divide
corr /= X_norms
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in greater
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in less
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:1821: RuntimeWarning: invalid value encountered in less_equal
cond2 = cond0 & (x <= self.a)
[CV] clf__learning_rate=0.2, clf__max_depth=5, clf__n_estimators=20, selector__k=12, total= 0.4s
[CV] clf__learning_rate=0.2, clf__max_depth=5, clf__n_estimators=20, selector__k=12
/anaconda3/lib/python3.6/site-packages/sklearn/feature_selection/univariate_selection.py:298: RuntimeWarning: invalid value encountered in true_divide
corr /= X_norms
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in greater
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in less
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:1821: RuntimeWarning: invalid value encountered in less_equal
cond2 = cond0 & (x <= self.a)
[CV] clf__learning_rate=0.2, clf__max_depth=5, clf__n_estimators=20, selector__k=12, total= 0.3s
[CV] clf__learning_rate=0.2, clf__max_depth=5, clf__n_estimators=100, selector__k=12
/anaconda3/lib/python3.6/site-packages/sklearn/feature_selection/univariate_selection.py:298: RuntimeWarning: invalid value encountered in true_divide
corr /= X_norms
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in greater
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in less
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:1821: RuntimeWarning: invalid value encountered in less_equal
cond2 = cond0 & (x <= self.a)
[CV] clf__learning_rate=0.2, clf__max_depth=5, clf__n_estimators=100, selector__k=12, total= 0.9s
[CV] clf__learning_rate=0.2, clf__max_depth=5, clf__n_estimators=100, selector__k=12
/anaconda3/lib/python3.6/site-packages/sklearn/feature_selection/univariate_selection.py:298: RuntimeWarning: invalid value encountered in true_divide
corr /= X_norms
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in greater
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in less
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:1821: RuntimeWarning: invalid value encountered in less_equal
cond2 = cond0 & (x <= self.a)
[CV] clf__learning_rate=0.2, clf__max_depth=5, clf__n_estimators=100, selector__k=12, total= 0.8s
[CV] clf__learning_rate=0.2, clf__max_depth=5, clf__n_estimators=100, selector__k=12
/anaconda3/lib/python3.6/site-packages/sklearn/feature_selection/univariate_selection.py:298: RuntimeWarning: invalid value encountered in true_divide
corr /= X_norms
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in greater
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in less
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:1821: RuntimeWarning: invalid value encountered in less_equal
cond2 = cond0 & (x <= self.a)
[CV] clf__learning_rate=0.2, clf__max_depth=5, clf__n_estimators=100, selector__k=12, total= 0.7s
[Parallel(n_jobs=1)]: Done 54 out of 54 | elapsed: 28.0s finished
/anaconda3/lib/python3.6/site-packages/sklearn/feature_selection/univariate_selection.py:298: RuntimeWarning: invalid value encountered in true_divide
corr /= X_norms
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in greater
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in less
return (self.a < x) & (x < self.b)
/anaconda3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:1821: RuntimeWarning: invalid value encountered in less_equal
cond2 = cond0 & (x <= self.a)
Pipeline(memory=None,
steps=[('Scale', StandardScaler(copy=True, with_mean=True, with_std=True)), ('selector', SelectKBest(k=12, score_func=<function f_regression at 0x109eeb950>)), ('clf', GradientBoostingRegressor(alpha=0.9, criterion='friedman_mse', init=None,
learning_rate=0.2, loss='ls', max_depth=5, ma...s=100, presort='auto', random_state=None,
subsample=1.0, verbose=0, warm_start=False))])
MAE: 7224.197106401388
R2 Score: 0.944446769451272
best_clf.steps[1][1].get_support()
array([False, True, True, ..., False, False, False])
feature = list(X_cols[best_clf.steps[1][1].get_support()])
feature_import = best_clf.steps[2][1].feature_importances_
dict(zip(feature,feature_import))
{'Engine Cylinders': 0.161652154815887,
'Engine Fuel Type_premium unleaded (required)': 0.03686347996660804,
'Engine Fuel Type_regular unleaded': 0.022067087420454934,
'Engine HP': 0.3288154443057195,
'Engine HP^2': 0.39578180065792545,
'Make_Bentley': 0.011090018954312117,
'Make_Bugatti': 0.0011565166088123209,
'Make_Lamborghini': 0.016016273040587527,
'Make_Maybach': 0.007658665277021095,
'Make_Rolls-Royce': 0.009172297191849426,
'Model_Landaulet': 0.008039050208676577,
'Model_Veyron 16.4': 0.0016872115521459321}
pd.DataFrame(feature_import,
index=feature).sort_values(0,
ascending = False).plot.barh()
<matplotlib.axes._subplots.AxesSubplot at 0x1a118de978>
pd.DataFrame(feature_import,
index=feature).sort_values(0,
ascending = False)
| 0 | |
|---|---|
| Engine HP^2 | 0.395782 |
| Engine HP | 0.328815 |
| Engine Cylinders | 0.161652 |
| Engine Fuel Type_premium unleaded (required) | 0.036863 |
| Engine Fuel Type_regular unleaded | 0.022067 |
| Make_Lamborghini | 0.016016 |
| Make_Bentley | 0.011090 |
| Make_Rolls-Royce | 0.009172 |
| Model_Landaulet | 0.008039 |
| Make_Maybach | 0.007659 |
| Model_Veyron 16.4 | 0.001687 |
| Make_Bugatti | 0.001157 |
import statsmodels.api as sm
model = sm.OLS(y,X).fit()
model.summary()
| Dep. Variable: | y | R-squared: | 0.988 |
|---|---|---|---|
| Model: | OLS | Adj. R-squared: | 0.986 |
| Method: | Least Squares | F-statistic: | 847.9 |
| Date: | Tue, 17 Jul 2018 | Prob (F-statistic): | 0.00 |
| Time: | 00:34:14 | Log-Likelihood: | -1.1478e+05 |
| No. Observations: | 11199 | AIC: | 2.315e+05 |
| Df Residuals: | 10233 | BIC: | 2.386e+05 |
| Df Model: | 965 | ||
| Covariance Type: | nonrobust |
| coef | std err | t | P>|t| | [0.025 | 0.975] | |
|---|---|---|---|---|---|---|
| Year | 744.0213 | 81.465 | 9.133 | 0.000 | 584.334 | 903.709 |
| Engine HP | -127.7246 | 8.198 | -15.579 | 0.000 | -143.795 | -111.654 |
| Engine Cylinders | 1195.3531 | 183.652 | 6.509 | 0.000 | 835.359 | 1555.347 |
| Number of Doors | 943.7598 | 508.433 | 1.856 | 0.063 | -52.869 | 1940.389 |
| highway MPG | -5.4721 | 22.103 | -0.248 | 0.804 | -48.798 | 37.854 |
| city mpg | -61.3339 | 63.666 | -0.963 | 0.335 | -186.131 | 63.463 |
| Popularity | -39.2920 | 2.871 | -13.686 | 0.000 | -44.920 | -33.664 |
| Engine HP^2 | 0.3414 | 0.010 | 34.979 | 0.000 | 0.322 | 0.361 |
| Make_Acura | -1.135e+05 | 5682.850 | -19.980 | 0.000 | -1.25e+05 | -1.02e+05 |
| Make_Alfa Romeo | -5.201e+04 | 3788.710 | -13.727 | 0.000 | -5.94e+04 | -4.46e+04 |
| Make_Aston Martin | 1.192e+04 | 5828.697 | 2.045 | 0.041 | 492.905 | 2.33e+04 |
| Make_Audi | -1.102e+04 | 4096.029 | -2.691 | 0.007 | -1.91e+04 | -2994.038 |
| Make_BMW | 3.836e+04 | 5130.895 | 7.476 | 0.000 | 2.83e+04 | 4.84e+04 |
| Make_Bentley | 1.088e+05 | 5137.073 | 21.184 | 0.000 | 9.88e+04 | 1.19e+05 |
| Make_Bugatti | 6.922e+05 | 3637.595 | 190.291 | 0.000 | 6.85e+05 | 6.99e+05 |
| Make_Buick | -1.244e+05 | 5587.367 | -22.272 | 0.000 | -1.35e+05 | -1.13e+05 |
| Make_Cadillac | -5.911e+04 | 2011.354 | -29.391 | 0.000 | -6.31e+04 | -5.52e+04 |
| Make_Chevrolet | -7.326e+04 | 3239.041 | -22.618 | 0.000 | -7.96e+04 | -6.69e+04 |
| Make_Chrysler | -8.716e+04 | 5579.228 | -15.622 | 0.000 | -9.81e+04 | -7.62e+04 |
| Make_Dodge | -6.135e+04 | 4591.941 | -13.360 | 0.000 | -7.03e+04 | -5.23e+04 |
| Make_FIAT | -9.24e+04 | 4262.418 | -21.678 | 0.000 | -1.01e+05 | -8.4e+04 |
| Make_Ferrari | 1.577e+05 | 1845.857 | 85.440 | 0.000 | 1.54e+05 | 1.61e+05 |
| Make_Ford | 8.029e+04 | 9493.408 | 8.457 | 0.000 | 6.17e+04 | 9.89e+04 |
| Make_GMC | -1.397e+05 | 4730.569 | -29.537 | 0.000 | -1.49e+05 | -1.3e+05 |
| Make_Genesis | -6.559e+04 | 4125.760 | -15.898 | 0.000 | -7.37e+04 | -5.75e+04 |
| Make_HUMMER | -8.554e+04 | 4542.425 | -18.832 | 0.000 | -9.44e+04 | -7.66e+04 |
| Make_Honda | -4.774e+04 | 847.841 | -56.307 | 0.000 | -4.94e+04 | -4.61e+04 |
| Make_Hyundai | -7.791e+04 | 2552.363 | -30.526 | 0.000 | -8.29e+04 | -7.29e+04 |
| Make_Infiniti | -1.151e+05 | 6802.790 | -16.924 | 0.000 | -1.28e+05 | -1.02e+05 |
| Make_Kia | -6.594e+04 | 1928.668 | -34.190 | 0.000 | -6.97e+04 | -6.22e+04 |
| Make_Lamborghini | 2.758e+05 | 3477.301 | 79.324 | 0.000 | 2.69e+05 | 2.83e+05 |
| Make_Land Rover | -1.01e+05 | 5289.941 | -19.098 | 0.000 | -1.11e+05 | -9.07e+04 |
| Make_Lexus | -9.733e+04 | 5290.597 | -18.396 | 0.000 | -1.08e+05 | -8.7e+04 |
| Make_Lincoln | -1.662e+05 | 6841.451 | -24.292 | 0.000 | -1.8e+05 | -1.53e+05 |
| Make_Lotus | -5.633e+04 | 4512.174 | -12.484 | 0.000 | -6.52e+04 | -4.75e+04 |
| Make_Maserati | -6.931e+04 | 8053.531 | -8.606 | 0.000 | -8.51e+04 | -5.35e+04 |
| Make_Maybach | 3.867e+05 | 5252.038 | 73.632 | 0.000 | 3.76e+05 | 3.97e+05 |
| Make_Mazda | -1.081e+05 | 5126.187 | -21.080 | 0.000 | -1.18e+05 | -9.8e+04 |
| Make_McLaren | 1.821e+04 | 5448.645 | 3.343 | 0.001 | 7534.517 | 2.89e+04 |
| Make_Mercedes-Benz | -7.765e+04 | 4660.746 | -16.660 | 0.000 | -8.68e+04 | -6.85e+04 |
| Make_Mitsubishi | -1.187e+05 | 5027.187 | -23.609 | 0.000 | -1.29e+05 | -1.09e+05 |
| Make_Nissan | -6.219e+04 | 1327.931 | -46.830 | 0.000 | -6.48e+04 | -5.96e+04 |
| Make_Oldsmobile | -1.345e+05 | 5311.273 | -25.325 | 0.000 | -1.45e+05 | -1.24e+05 |
| Make_Plymouth | -1.176e+05 | 6913.364 | -17.006 | 0.000 | -1.31e+05 | -1.04e+05 |
| Make_Pontiac | -1.242e+05 | 5275.567 | -23.541 | 0.000 | -1.35e+05 | -1.14e+05 |
| Make_Porsche | -1.65e+04 | 1861.879 | -8.859 | 0.000 | -2.01e+04 | -1.28e+04 |
| Make_Rolls-Royce | 1.306e+05 | 6175.348 | 21.146 | 0.000 | 1.18e+05 | 1.43e+05 |
| Make_Saab | -1.044e+05 | 4746.725 | -22.004 | 0.000 | -1.14e+05 | -9.51e+04 |
| Make_Scion | -1.238e+05 | 6088.127 | -20.334 | 0.000 | -1.36e+05 | -1.12e+05 |
| Make_Spyker | 1.723e+04 | 4178.446 | 4.123 | 0.000 | 9038.610 | 2.54e+04 |
| Make_Subaru | -1.084e+05 | 4497.695 | -24.107 | 0.000 | -1.17e+05 | -9.96e+04 |
| Make_Suzuki | -1.162e+05 | 4660.367 | -24.925 | 0.000 | -1.25e+05 | -1.07e+05 |
| Make_Tesla | -2.708e+04 | 3844.997 | -7.043 | 0.000 | -3.46e+04 | -1.95e+04 |
| Make_Toyota | -5.62e+04 | 1048.016 | -53.625 | 0.000 | -5.83e+04 | -5.41e+04 |
| Make_Volkswagen | -9.44e+04 | 5930.929 | -15.916 | 0.000 | -1.06e+05 | -8.28e+04 |
| Make_Volvo | -9.449e+04 | 8082.457 | -11.691 | 0.000 | -1.1e+05 | -7.86e+04 |
| Model_1 Series | -1.404e+04 | 2760.376 | -5.087 | 0.000 | -1.95e+04 | -8630.641 |
| Model_1 Series M | -5595.7536 | 7351.070 | -0.761 | 0.447 | -2e+04 | 8813.783 |
| Model_100 | -1.348e+04 | 3384.461 | -3.982 | 0.000 | -2.01e+04 | -6841.128 |
| Model_124 Spider | -1.86e+04 | 3795.760 | -4.900 | 0.000 | -2.6e+04 | -1.12e+04 |
| Model_190-Class | -4.065e+04 | 3207.910 | -12.671 | 0.000 | -4.69e+04 | -3.44e+04 |
| Model_2 | -1.63e+04 | 2599.390 | -6.270 | 0.000 | -2.14e+04 | -1.12e+04 |
| Model_2 Series | -1.557e+04 | 2776.120 | -5.608 | 0.000 | -2.1e+04 | -1.01e+04 |
| Model_200 | -1.051e+04 | 3035.543 | -3.461 | 0.001 | -1.65e+04 | -4556.092 |
| Model_200SX | -4814.4195 | 3235.365 | -1.488 | 0.137 | -1.12e+04 | 1527.529 |
| Model_240 | -1.38e+04 | 7953.630 | -1.735 | 0.083 | -2.94e+04 | 1787.295 |
| Model_240SX | -1407.3579 | 2911.295 | -0.483 | 0.629 | -7114.066 | 4299.350 |
| Model_3 | -8919.4721 | 1833.143 | -4.866 | 0.000 | -1.25e+04 | -5326.154 |
| Model_3 Series | -1.019e+04 | 2573.842 | -3.960 | 0.000 | -1.52e+04 | -5148.171 |
| Model_3 Series Gran Turismo | -9297.1967 | 3712.991 | -2.504 | 0.012 | -1.66e+04 | -2019.007 |
| Model_300 | -6248.6887 | 3460.540 | -1.806 | 0.071 | -1.3e+04 | 534.647 |
| Model_300-Class | -4.478e+04 | 1898.369 | -23.591 | 0.000 | -4.85e+04 | -4.11e+04 |
| Model_3000GT | -1.399e+04 | 2581.584 | -5.420 | 0.000 | -1.91e+04 | -8931.928 |
| Model_300M | -1652.7505 | 4550.296 | -0.363 | 0.716 | -1.06e+04 | 7266.721 |
| Model_300ZX | -6729.5740 | 2575.053 | -2.613 | 0.009 | -1.18e+04 | -1681.967 |
| Model_323 | -1.602e+04 | 4084.128 | -3.923 | 0.000 | -2.4e+04 | -8014.785 |
| Model_350-Class | -4.81e+04 | 3900.158 | -12.334 | 0.000 | -5.57e+04 | -4.05e+04 |
| Model_350Z | 1.104e+04 | 2000.519 | 5.517 | 0.000 | 7115.830 | 1.5e+04 |
| Model_360 | -6.618e+04 | 2283.939 | -28.974 | 0.000 | -7.07e+04 | -6.17e+04 |
| Model_370Z | 7372.2050 | 2173.103 | 3.392 | 0.001 | 3112.498 | 1.16e+04 |
| Model_4 Series | -6421.2427 | 2619.523 | -2.451 | 0.014 | -1.16e+04 | -1286.464 |
| Model_4 Series Gran Coupe | -7047.1577 | 3021.806 | -2.332 | 0.020 | -1.3e+04 | -1123.827 |
| Model_400-Class | -4.859e+04 | 3757.763 | -12.931 | 0.000 | -5.6e+04 | -4.12e+04 |
| Model_420-Class | -4.581e+04 | 5206.282 | -8.799 | 0.000 | -5.6e+04 | -3.56e+04 |
| Model_456M | -7616.5847 | 3057.726 | -2.491 | 0.013 | -1.36e+04 | -1622.842 |
| Model_458 Italia | -1.459e+04 | 2677.492 | -5.450 | 0.000 | -1.98e+04 | -9344.111 |
| Model_4C | -5.201e+04 | 3788.710 | -13.727 | 0.000 | -5.94e+04 | -4.46e+04 |
| Model_4Runner | 4077.9483 | 1541.771 | 2.645 | 0.008 | 1055.774 | 7100.122 |
| Model_5 | -7275.5055 | 4330.227 | -1.680 | 0.093 | -1.58e+04 | 1212.587 |
| Model_5 Series | -1965.2143 | 3009.560 | -0.653 | 0.514 | -7864.541 | 3934.112 |
| Model_5 Series Gran Turismo | -1275.4174 | 3366.882 | -0.379 | 0.705 | -7875.166 | 5324.331 |
| Model_500 | -2.28e+04 | 2000.608 | -11.397 | 0.000 | -2.67e+04 | -1.89e+04 |
| Model_500-Class | -5.35e+04 | 2812.166 | -19.025 | 0.000 | -5.9e+04 | -4.8e+04 |
| Model_500L | -1.676e+04 | 2385.210 | -7.028 | 0.000 | -2.14e+04 | -1.21e+04 |
| Model_500X | -1.734e+04 | 2171.919 | -7.985 | 0.000 | -2.16e+04 | -1.31e+04 |
| Model_500e | -1.689e+04 | 5274.069 | -3.203 | 0.001 | -2.72e+04 | -6553.978 |
| Model_550 | -1.615e+04 | 4906.165 | -3.291 | 0.001 | -2.58e+04 | -6530.178 |
| Model_560-Class | -4.649e+04 | 3859.938 | -12.045 | 0.000 | -5.41e+04 | -3.89e+04 |
| Model_57 | -2.083e+05 | 3008.203 | -69.252 | 0.000 | -2.14e+05 | -2.02e+05 |
| Model_570S | -3.67e+04 | 6292.221 | -5.833 | 0.000 | -4.9e+04 | -2.44e+04 |
| Model_575M | -2.795e+04 | 2998.765 | -9.322 | 0.000 | -3.38e+04 | -2.21e+04 |
| Model_599 | 5.16e+04 | 3248.072 | 15.887 | 0.000 | 4.52e+04 | 5.8e+04 |
| Model_6 | -5439.0689 | 2423.321 | -2.244 | 0.025 | -1.02e+04 | -688.885 |
| Model_6 Series | 1.794e+04 | 2638.790 | 6.797 | 0.000 | 1.28e+04 | 2.31e+04 |
| Model_6 Series Gran Coupe | 1.838e+04 | 3184.342 | 5.773 | 0.000 | 1.21e+04 | 2.46e+04 |
| Model_600-Class | -6.699e+04 | 3796.974 | -17.643 | 0.000 | -7.44e+04 | -5.95e+04 |
| Model_6000 | -1.23e+04 | 2853.952 | -4.311 | 0.000 | -1.79e+04 | -6708.436 |
| Model_612 Scaglietti | 4.971e+04 | 4112.969 | 12.086 | 0.000 | 4.16e+04 | 5.78e+04 |
| Model_62 | -1.572e+05 | 3008.203 | -52.260 | 0.000 | -1.63e+05 | -1.51e+05 |
| Model_626 | -7121.8836 | 2688.252 | -2.649 | 0.008 | -1.24e+04 | -1852.383 |
| Model_650S Coupe | 2.228e+04 | 6277.261 | 3.549 | 0.000 | 9975.894 | 3.46e+04 |
| Model_650S Spider | 2.891e+04 | 6284.110 | 4.600 | 0.000 | 1.66e+04 | 4.12e+04 |
| Model_7 Series | 2.032e+04 | 3011.819 | 6.748 | 0.000 | 1.44e+04 | 2.62e+04 |
| Model_718 Cayman | -2.447e+04 | 4865.942 | -5.029 | 0.000 | -3.4e+04 | -1.49e+04 |
| Model_740 | -1.295e+04 | 7733.650 | -1.675 | 0.094 | -2.81e+04 | 2208.655 |
| Model_760 | -1.321e+04 | 8530.321 | -1.549 | 0.121 | -2.99e+04 | 3506.309 |
| Model_780 | -1.327e+04 | 9002.131 | -1.474 | 0.141 | -3.09e+04 | 4378.901 |
| Model_8 Series | -3.779e+04 | 3546.699 | -10.656 | 0.000 | -4.47e+04 | -3.08e+04 |
| Model_80 | -1.095e+04 | 3927.950 | -2.787 | 0.005 | -1.86e+04 | -3246.699 |
| Model_850 | -1.852e+04 | 7639.575 | -2.424 | 0.015 | -3.35e+04 | -3543.743 |
| Model_86 | -258.4745 | 5058.406 | -0.051 | 0.959 | -1.02e+04 | 9656.992 |
| Model_9-2X | -1.26e+04 | 3400.664 | -3.705 | 0.000 | -1.93e+04 | -5933.393 |
| Model_9-3 | -5468.5127 | 1697.810 | -3.221 | 0.001 | -8796.552 | -2140.473 |
| Model_9-3 Griffin | -7178.5647 | 2518.016 | -2.851 | 0.004 | -1.21e+04 | -2242.760 |
| Model_9-4X | -1.025e+04 | 3077.219 | -3.330 | 0.001 | -1.63e+04 | -4216.049 |
| Model_9-5 | -1258.6110 | 2309.845 | -0.545 | 0.586 | -5786.359 | 3269.137 |
| Model_9-7X | -9297.0307 | 2471.521 | -3.762 | 0.000 | -1.41e+04 | -4452.367 |
| Model_90 | -1.338e+04 | 3909.331 | -3.423 | 0.001 | -2.1e+04 | -5719.795 |
| Model_900 | -3.069e+04 | 1709.977 | -17.946 | 0.000 | -3.4e+04 | -2.73e+04 |
| Model_9000 | -2.771e+04 | 2288.093 | -12.109 | 0.000 | -3.22e+04 | -2.32e+04 |
| Model_911 | 1.634e+04 | 1581.258 | 10.333 | 0.000 | 1.32e+04 | 1.94e+04 |
| Model_928 | -6.793e+04 | 4156.843 | -16.342 | 0.000 | -7.61e+04 | -5.98e+04 |
| Model_929 | -1.471e+04 | 4512.682 | -3.260 | 0.001 | -2.36e+04 | -5864.662 |
| Model_940 | -1.594e+04 | 7744.494 | -2.058 | 0.040 | -3.11e+04 | -756.001 |
| Model_944 | -5.969e+04 | 3722.740 | -16.033 | 0.000 | -6.7e+04 | -5.24e+04 |
| Model_960 | -1.902e+04 | 8013.282 | -2.374 | 0.018 | -3.47e+04 | -3314.807 |
| Model_968 | -6.054e+04 | 3092.200 | -19.578 | 0.000 | -6.66e+04 | -5.45e+04 |
| Model_A3 | -811.0673 | 3299.922 | -0.246 | 0.806 | -7279.560 | 5657.425 |
| Model_A4 | 6722.7995 | 3418.730 | 1.966 | 0.049 | 21.419 | 1.34e+04 |
| Model_A4 allroad | 8357.5461 | 5199.247 | 1.607 | 0.108 | -1833.997 | 1.85e+04 |
| Model_A5 | 9898.2838 | 4043.286 | 2.448 | 0.014 | 1972.651 | 1.78e+04 |
| Model_A6 | 1.799e+04 | 3459.859 | 5.200 | 0.000 | 1.12e+04 | 2.48e+04 |
| Model_A7 | 2.808e+04 | 3868.644 | 7.259 | 0.000 | 2.05e+04 | 3.57e+04 |
| Model_A8 | 3.955e+04 | 4298.463 | 9.201 | 0.000 | 3.11e+04 | 4.8e+04 |
| Model_ALPINA B6 Gran Coupe | 1.109e+04 | 4801.175 | 2.311 | 0.021 | 1682.027 | 2.05e+04 |
| Model_ALPINA B7 | 3.364e+04 | 3411.585 | 9.862 | 0.000 | 2.7e+04 | 4.03e+04 |
| Model_AMG GT | 2.548e+04 | 4221.282 | 6.036 | 0.000 | 1.72e+04 | 3.38e+04 |
| Model_ATS | -2993.8122 | 1535.403 | -1.950 | 0.051 | -6003.504 | 15.879 |
| Model_ATS Coupe | -261.0055 | 1702.778 | -0.153 | 0.878 | -3598.783 | 3076.772 |
| Model_ATS-V | -6017.3505 | 3704.529 | -1.624 | 0.104 | -1.33e+04 | 1244.252 |
| Model_Acadia | 3.041e+04 | 3015.387 | 10.083 | 0.000 | 2.45e+04 | 3.63e+04 |
| Model_Acadia Limited | 3.364e+04 | 5707.609 | 5.893 | 0.000 | 2.24e+04 | 4.48e+04 |
| Model_Accent | -1.219e+04 | 1883.235 | -6.475 | 0.000 | -1.59e+04 | -8502.325 |
| Model_Acclaim | -7741.2907 | 5874.094 | -1.318 | 0.188 | -1.93e+04 | 3773.083 |
| Model_Accord | -936.1508 | 1301.867 | -0.719 | 0.472 | -3488.064 | 1615.763 |
| Model_Accord Crosstour | 2447.1240 | 2439.955 | 1.003 | 0.316 | -2335.666 | 7229.914 |
| Model_Accord Hybrid | 6569.1866 | 2773.200 | 2.369 | 0.018 | 1133.172 | 1.2e+04 |
| Model_Accord Plug-In Hybrid | 1.492e+04 | 6966.629 | 2.142 | 0.032 | 1267.134 | 2.86e+04 |
| Model_Achieva | -1.016e+04 | 3184.931 | -3.190 | 0.001 | -1.64e+04 | -3915.369 |
| Model_ActiveHybrid 5 | 4109.9401 | 4751.596 | 0.865 | 0.387 | -5204.119 | 1.34e+04 |
| Model_ActiveHybrid 7 | 2.623e+04 | 4738.263 | 5.536 | 0.000 | 1.69e+04 | 3.55e+04 |
| Model_ActiveHybrid X6 | 6405.2906 | 5442.959 | 1.177 | 0.239 | -4263.975 | 1.71e+04 |
| Model_Aerio | -4746.3315 | 1435.568 | -3.306 | 0.001 | -7560.327 | -1932.336 |
| Model_Aerostar | -5570.1393 | 4624.856 | -1.204 | 0.228 | -1.46e+04 | 3495.485 |
| Model_Alero | 1890.5518 | 1856.897 | 1.018 | 0.309 | -1749.329 | 5530.433 |
| Model_Allante | -3.437e+04 | 4328.608 | -7.939 | 0.000 | -4.29e+04 | -2.59e+04 |
| Model_Alpina | 7.4e+04 | 7347.060 | 10.072 | 0.000 | 5.96e+04 | 8.84e+04 |
| Model_Altima | 4138.0542 | 2323.920 | 1.781 | 0.075 | -417.284 | 8693.392 |
| Model_Altima Hybrid | 1.078e+04 | 4411.113 | 2.444 | 0.015 | 2135.565 | 1.94e+04 |
| Model_Amanti | -1794.8874 | 3991.615 | -0.450 | 0.653 | -9619.235 | 6029.460 |
| Model_Armada | 8858.5532 | 2405.492 | 3.683 | 0.000 | 4143.319 | 1.36e+04 |
| Model_Arnage | -3.292e+04 | 3112.412 | -10.576 | 0.000 | -3.9e+04 | -2.68e+04 |
| Model_Aspen | -8972.7260 | 4341.690 | -2.067 | 0.039 | -1.75e+04 | -462.164 |
| Model_Aspire | -8417.4378 | 3326.220 | -2.531 | 0.011 | -1.49e+04 | -1897.395 |
| Model_Astro | -4501.6512 | 5081.463 | -0.886 | 0.376 | -1.45e+04 | 5459.012 |
| Model_Astro Cargo | -2940.4214 | 5301.089 | -0.555 | 0.579 | -1.33e+04 | 7450.751 |
| Model_Aurora | 9687.7605 | 3257.563 | 2.974 | 0.003 | 3302.299 | 1.61e+04 |
| Model_Avalanche | -1.085e+04 | 2398.742 | -4.525 | 0.000 | -1.56e+04 | -6152.063 |
| Model_Avalon | 5349.3984 | 2047.145 | 2.613 | 0.009 | 1336.593 | 9362.204 |
| Model_Avalon Hybrid | 1.392e+04 | 2612.936 | 5.327 | 0.000 | 8796.440 | 1.9e+04 |
| Model_Avenger | -5058.4362 | 4746.910 | -1.066 | 0.287 | -1.44e+04 | 4246.437 |
| Model_Aventador | -3.804e+04 | 2688.112 | -14.151 | 0.000 | -4.33e+04 | -3.28e+04 |
| Model_Aveo | -1.755e+04 | 2211.886 | -7.936 | 0.000 | -2.19e+04 | -1.32e+04 |
| Model_Aviator | 4.394e+04 | 3143.731 | 13.976 | 0.000 | 3.78e+04 | 5.01e+04 |
| Model_Axxess | 179.8388 | 5433.977 | 0.033 | 0.974 | -1.05e+04 | 1.08e+04 |
| Model_Azera | 2371.4562 | 2954.695 | 0.803 | 0.422 | -3420.326 | 8163.238 |
| Model_Aztek | -4599.4181 | 2893.852 | -1.589 | 0.112 | -1.03e+04 | 1073.098 |
| Model_Azure | 4.783e+04 | 4565.942 | 10.475 | 0.000 | 3.89e+04 | 5.68e+04 |
| Model_Azure T | 6.783e+04 | 7295.792 | 9.298 | 0.000 | 5.35e+04 | 8.21e+04 |
| Model_B-Class Electric Drive | -3.068e+04 | 6228.531 | -4.925 | 0.000 | -4.29e+04 | -1.85e+04 |
| Model_B-Series | -9485.9804 | 2447.952 | -3.875 | 0.000 | -1.43e+04 | -4687.516 |
| Model_B-Series Pickup | -2.235e+04 | 1872.186 | -11.936 | 0.000 | -2.6e+04 | -1.87e+04 |
| Model_B-Series Truck | -1.199e+04 | 2640.882 | -4.540 | 0.000 | -1.72e+04 | -6812.004 |
| Model_B9 Tribeca | 2858.9747 | 1875.199 | 1.525 | 0.127 | -816.782 | 6534.732 |
| Model_BRZ | -84.9446 | 2338.316 | -0.036 | 0.971 | -4668.501 | 4498.612 |
| Model_Baja | -8044.0389 | 2148.427 | -3.744 | 0.000 | -1.23e+04 | -3832.702 |
| Model_Beetle | -1.086e+04 | 5732.510 | -1.894 | 0.058 | -2.21e+04 | 380.365 |
| Model_Beetle Convertible | -1.314e+04 | 5695.114 | -2.308 | 0.021 | -2.43e+04 | -1981.114 |
| Model_Beretta | -1.767e+04 | 3527.827 | -5.007 | 0.000 | -2.46e+04 | -1.08e+04 |
| Model_Black Diamond Avalanche | -1.321e+04 | 3283.858 | -4.024 | 0.000 | -1.96e+04 | -6775.921 |
| Model_Blackwood | 4.745e+04 | 7167.034 | 6.620 | 0.000 | 3.34e+04 | 6.15e+04 |
| Model_Blazer | -5671.0180 | 2617.027 | -2.167 | 0.030 | -1.08e+04 | -541.133 |
| Model_Bolt EV | -5034.7444 | 6877.099 | -0.732 | 0.464 | -1.85e+04 | 8445.717 |
| Model_Bonneville | 3693.4302 | 2589.120 | 1.427 | 0.154 | -1381.753 | 8768.613 |
| Model_Borrego | -4688.9815 | 2404.845 | -1.950 | 0.051 | -9402.948 | 24.985 |
| Model_Boxster | -3.02e+04 | 2687.040 | -11.239 | 0.000 | -3.55e+04 | -2.49e+04 |
| Model_Bravada | 8562.3898 | 2767.956 | 3.093 | 0.002 | 3136.654 | 1.4e+04 |
| Model_Breeze | -8275.6075 | 5767.933 | -1.435 | 0.151 | -1.96e+04 | 3030.670 |
| Model_Bronco | -9022.1320 | 3243.733 | -2.781 | 0.005 | -1.54e+04 | -2663.780 |
| Model_Bronco II | -2713.5384 | 5491.270 | -0.494 | 0.621 | -1.35e+04 | 8050.426 |
| Model_Brooklands | 4.76e+04 | 5374.330 | 8.858 | 0.000 | 3.71e+04 | 5.81e+04 |
| Model_Brougham | -2.54e+04 | 4333.065 | -5.862 | 0.000 | -3.39e+04 | -1.69e+04 |
| Model_C-Class | -2.297e+04 | 1617.539 | -14.201 | 0.000 | -2.61e+04 | -1.98e+04 |
| Model_C-Max Hybrid | 5595.1606 | 3310.469 | 1.690 | 0.091 | -894.007 | 1.21e+04 |
| Model_C/K 1500 Series | -2.686e+04 | 1883.631 | -14.260 | 0.000 | -3.06e+04 | -2.32e+04 |
| Model_C/K 2500 Series | -2.869e+04 | 2974.113 | -9.647 | 0.000 | -3.45e+04 | -2.29e+04 |
| Model_C30 | -7302.2787 | 8305.760 | -0.879 | 0.379 | -2.36e+04 | 8978.638 |
| Model_C36 AMG | -4.647e+04 | 4213.632 | -11.027 | 0.000 | -5.47e+04 | -3.82e+04 |
| Model_C43 AMG | -5.205e+04 | 4173.485 | -12.472 | 0.000 | -6.02e+04 | -4.39e+04 |
| Model_C70 | -1174.6724 | 8693.229 | -0.135 | 0.893 | -1.82e+04 | 1.59e+04 |
| Model_C8 | 1.723e+04 | 4178.446 | 4.123 | 0.000 | 9038.610 | 2.54e+04 |
| Model_CC | -1909.6530 | 5909.877 | -0.323 | 0.747 | -1.35e+04 | 9674.862 |
| Model_CL | -4630.3642 | 2437.461 | -1.900 | 0.058 | -9408.266 | 147.537 |
| Model_CL-Class | 5.177e+04 | 2344.802 | 22.078 | 0.000 | 4.72e+04 | 5.64e+04 |
| Model_CLA-Class | -3.034e+04 | 2639.690 | -11.495 | 0.000 | -3.55e+04 | -2.52e+04 |
| Model_CLK-Class | -1.008e+04 | 1983.465 | -5.084 | 0.000 | -1.4e+04 | -6195.382 |
| Model_CLS-Class | -7135.9362 | 2241.011 | -3.184 | 0.001 | -1.15e+04 | -2743.116 |
| Model_CR-V | -3213.6652 | 1473.669 | -2.181 | 0.029 | -6102.345 | -324.985 |
| Model_CR-Z | -6523.0908 | 1979.873 | -3.295 | 0.001 | -1.04e+04 | -2642.153 |
| Model_CT 200h | -1.698e+04 | 4380.215 | -3.877 | 0.000 | -2.56e+04 | -8397.809 |
| Model_CT6 | 1.138e+04 | 1914.544 | 5.946 | 0.000 | 7630.399 | 1.51e+04 |
| Model_CTS | 7077.5706 | 1447.444 | 4.890 | 0.000 | 4240.298 | 9914.843 |
| Model_CTS Coupe | -1386.5680 | 1927.828 | -0.719 | 0.472 | -5165.488 | 2392.352 |
| Model_CTS Wagon | 36.2807 | 1865.185 | 0.019 | 0.984 | -3619.846 | 3692.408 |
| Model_CTS-V | -2.941e+04 | 4172.870 | -7.048 | 0.000 | -3.76e+04 | -2.12e+04 |
| Model_CTS-V Coupe | -2.227e+04 | 4179.548 | -5.329 | 0.000 | -3.05e+04 | -1.41e+04 |
| Model_CTS-V Wagon | -2.453e+04 | 4206.155 | -5.832 | 0.000 | -3.28e+04 | -1.63e+04 |
| Model_CX-3 | -1.287e+04 | 2522.807 | -5.102 | 0.000 | -1.78e+04 | -7927.327 |
| Model_CX-5 | -8339.9586 | 1949.773 | -4.277 | 0.000 | -1.22e+04 | -4518.022 |
| Model_CX-7 | -5210.5659 | 2109.468 | -2.470 | 0.014 | -9345.536 | -1075.596 |
| Model_CX-9 | -4694.9725 | 2277.174 | -2.062 | 0.039 | -9158.679 | -231.266 |
| Model_Cabrio | -1.333e+04 | 5650.137 | -2.360 | 0.018 | -2.44e+04 | -2256.611 |
| Model_Cabriolet | -2.342e+04 | 4145.391 | -5.650 | 0.000 | -3.15e+04 | -1.53e+04 |
| Model_Cadenza | 3311.6281 | 2580.484 | 1.283 | 0.199 | -1746.626 | 8369.882 |
| Model_Caliber | -5587.4684 | 4647.545 | -1.202 | 0.229 | -1.47e+04 | 3522.630 |
| Model_California | -5.71e+04 | 4054.406 | -14.084 | 0.000 | -6.51e+04 | -4.92e+04 |
| Model_California T | -7.455e+04 | 6789.074 | -10.981 | 0.000 | -8.79e+04 | -6.12e+04 |
| Model_Camaro | -1.982e+04 | 1935.692 | -10.240 | 0.000 | -2.36e+04 | -1.6e+04 |
| Model_Camry | -768.9230 | 1973.190 | -0.390 | 0.697 | -4636.763 | 3098.917 |
| Model_Camry Hybrid | 3239.0193 | 2631.128 | 1.231 | 0.218 | -1918.507 | 8396.546 |
| Model_Camry Solara | 1462.3325 | 1561.648 | 0.936 | 0.349 | -1598.803 | 4523.467 |
| Model_Canyon | 1.762e+04 | 3396.372 | 5.188 | 0.000 | 1.1e+04 | 2.43e+04 |
| Model_Caprice | -2.454e+04 | 3552.930 | -6.907 | 0.000 | -3.15e+04 | -1.76e+04 |
| Model_Captiva Sport | -8903.6780 | 2440.098 | -3.649 | 0.000 | -1.37e+04 | -4120.608 |
| Model_Caravan | -2256.5822 | 6507.581 | -0.347 | 0.729 | -1.5e+04 | 1.05e+04 |
| Model_Carrera GT | 2.958e+05 | 4838.435 | 61.129 | 0.000 | 2.86e+05 | 3.05e+05 |
| Model_Cascada | -6397.6226 | 3467.733 | -1.845 | 0.065 | -1.32e+04 | 399.813 |
| Model_Catera | -2.02e+04 | 3626.343 | -5.571 | 0.000 | -2.73e+04 | -1.31e+04 |
| Model_Cavalier | -9759.2314 | 2220.297 | -4.395 | 0.000 | -1.41e+04 | -5407.015 |
| Model_Cayenne | -1.671e+04 | 2186.647 | -7.644 | 0.000 | -2.1e+04 | -1.24e+04 |
| Model_Cayman | -2.183e+04 | 2682.490 | -8.139 | 0.000 | -2.71e+04 | -1.66e+04 |
| Model_Cayman S | -1.731e+04 | 6669.614 | -2.595 | 0.009 | -3.04e+04 | -4233.505 |
| Model_Celebrity | -1.675e+04 | 5549.932 | -3.018 | 0.003 | -2.76e+04 | -5871.722 |
| Model_Celica | 1453.2343 | 2092.012 | 0.695 | 0.487 | -2647.519 | 5553.988 |
| Model_Century | -3744.3741 | 4041.879 | -0.926 | 0.354 | -1.17e+04 | 4178.500 |
| Model_Challenger | -1.753e+04 | 4466.184 | -3.924 | 0.000 | -2.63e+04 | -8771.066 |
| Model_Charger | -1.71e+04 | 4440.526 | -3.850 | 0.000 | -2.58e+04 | -8393.928 |
| Model_Chevy Van | -3.153e+04 | 3826.739 | -8.240 | 0.000 | -3.9e+04 | -2.4e+04 |
| Model_Ciera | -1.249e+04 | 4031.859 | -3.097 | 0.002 | -2.04e+04 | -4585.243 |
| Model_Cirrus | -2.324e+04 | 5463.644 | -4.253 | 0.000 | -3.39e+04 | -1.25e+04 |
| Model_City Express | -9749.5732 | 4894.168 | -1.992 | 0.046 | -1.93e+04 | -156.046 |
| Model_Civic | -5528.5440 | 1225.838 | -4.510 | 0.000 | -7931.427 | -3125.661 |
| Model_Civic CRX | -1.046e+04 | 3353.808 | -3.120 | 0.002 | -1.7e+04 | -3888.493 |
| Model_Civic del Sol | -1.065e+04 | 2667.870 | -3.994 | 0.000 | -1.59e+04 | -5425.350 |
| Model_Classic | -7115.7111 | 5226.247 | -1.362 | 0.173 | -1.74e+04 | 3128.757 |
| Model_Cobalt | -1.143e+04 | 2064.231 | -5.539 | 0.000 | -1.55e+04 | -7386.484 |
| Model_Colorado | -1.735e+04 | 2040.887 | -8.499 | 0.000 | -2.13e+04 | -1.33e+04 |
| Model_Colt | -7737.6769 | 4513.986 | -1.714 | 0.087 | -1.66e+04 | 1110.621 |
| Model_Concorde | -5574.0592 | 4230.577 | -1.318 | 0.188 | -1.39e+04 | 2718.700 |
| Model_Continental | 4.42e+04 | 2083.440 | 21.216 | 0.000 | 4.01e+04 | 4.83e+04 |
| Model_Continental Flying Spur | -1.219e+05 | 4780.760 | -25.501 | 0.000 | -1.31e+05 | -1.13e+05 |
| Model_Continental Flying Spur Speed | -1.103e+05 | 4689.084 | -23.525 | 0.000 | -1.2e+05 | -1.01e+05 |
| Model_Continental GT | -9.3e+04 | 2825.916 | -32.911 | 0.000 | -9.85e+04 | -8.75e+04 |
| Model_Continental GT Speed | -1.072e+05 | 5449.821 | -19.673 | 0.000 | -1.18e+05 | -9.65e+04 |
| Model_Continental GT Speed Convertible | -9.42e+04 | 7450.412 | -12.643 | 0.000 | -1.09e+05 | -7.96e+04 |
| Model_Continental GT3-R | 2.856e+04 | 7340.429 | 3.890 | 0.000 | 1.42e+04 | 4.29e+04 |
| Model_Continental GTC | -9.997e+04 | 3675.191 | -27.202 | 0.000 | -1.07e+05 | -9.28e+04 |
| Model_Continental GTC Speed | -9.059e+04 | 5441.016 | -16.650 | 0.000 | -1.01e+05 | -7.99e+04 |
| Model_Continental R | 4.282e+04 | 5397.907 | 7.932 | 0.000 | 3.22e+04 | 5.34e+04 |
| Model_Continental SR | 5.084e+04 | 5392.769 | 9.428 | 0.000 | 4.03e+04 | 6.14e+04 |
| Model_Continental Supersports | -5.408e+04 | 4769.635 | -11.339 | 0.000 | -6.34e+04 | -4.47e+04 |
| Model_Continental Supersports Convertible | -4.915e+04 | 5565.476 | -8.831 | 0.000 | -6.01e+04 | -3.82e+04 |
| Model_Contour | -5801.8538 | 3741.030 | -1.551 | 0.121 | -1.31e+04 | 1531.297 |
| Model_Contour SVT | -5898.0901 | 4257.310 | -1.385 | 0.166 | -1.42e+04 | 2447.071 |
| Model_Corniche | 5.97e+04 | 6658.734 | 8.966 | 0.000 | 4.67e+04 | 7.28e+04 |
| Model_Corolla | -6613.6446 | 1515.544 | -4.364 | 0.000 | -9584.408 | -3642.881 |
| Model_Corolla iM | -9384.7681 | 5042.362 | -1.861 | 0.063 | -1.93e+04 | 499.250 |
| Model_Corrado | -1.69e+04 | 6353.662 | -2.659 | 0.008 | -2.94e+04 | -4442.753 |
| Model_Corsica | -1.89e+04 | 5387.116 | -3.509 | 0.000 | -2.95e+04 | -8340.982 |
| Model_Corvette | -1.072e+04 | 1802.848 | -5.948 | 0.000 | -1.43e+04 | -7188.582 |
| Model_Corvette Stingray | -8236.3542 | 3843.596 | -2.143 | 0.032 | -1.58e+04 | -702.153 |
| Model_Coupe | -1.24e+04 | 4632.458 | -2.678 | 0.007 | -2.15e+04 | -3323.919 |
| Model_Cressida | -7550.9591 | 4389.473 | -1.720 | 0.085 | -1.62e+04 | 1053.267 |
| Model_Crossfire | 858.5848 | 3925.820 | 0.219 | 0.827 | -6836.790 | 8553.960 |
| Model_Crosstour | 1307.5126 | 1795.701 | 0.728 | 0.467 | -2212.413 | 4827.438 |
| Model_Crosstrek | -9423.0195 | 2373.482 | -3.970 | 0.000 | -1.41e+04 | -4770.529 |
| Model_Crown Victoria | 7760.4925 | 4207.571 | 1.844 | 0.065 | -487.171 | 1.6e+04 |
| Model_Cruze | -1.385e+04 | 1949.323 | -7.104 | 0.000 | -1.77e+04 | -1e+04 |
| Model_Cruze Limited | -1.416e+04 | 2719.543 | -5.207 | 0.000 | -1.95e+04 | -8830.071 |
| Model_Cube | -2906.9453 | 2839.926 | -1.024 | 0.306 | -8473.756 | 2659.866 |
| Model_Custom Cruiser | -1.348e+04 | 4104.465 | -3.283 | 0.001 | -2.15e+04 | -5429.837 |
| Model_Cutlass | -1.448e+04 | 3195.851 | -4.530 | 0.000 | -2.07e+04 | -8211.544 |
| Model_Cutlass Calais | -5888.1219 | 2262.419 | -2.603 | 0.009 | -1.03e+04 | -1453.338 |
| Model_Cutlass Ciera | -1.015e+04 | 2237.890 | -4.537 | 0.000 | -1.45e+04 | -5766.258 |
| Model_Cutlass Supreme | -1.372e+04 | 2733.388 | -5.020 | 0.000 | -1.91e+04 | -8363.661 |
| Model_DB7 | -3.336e+04 | 3473.144 | -9.604 | 0.000 | -4.02e+04 | -2.65e+04 |
| Model_DB9 | -1.584e+04 | 2621.844 | -6.043 | 0.000 | -2.1e+04 | -1.07e+04 |
| Model_DB9 GT | -3638.7353 | 4011.196 | -0.907 | 0.364 | -1.15e+04 | 4223.994 |
| Model_DBS | 7.216e+04 | 1979.936 | 36.445 | 0.000 | 6.83e+04 | 7.6e+04 |
| Model_DTS | 6246.2727 | 2049.911 | 3.047 | 0.002 | 2228.046 | 1.03e+04 |
| Model_Dakota | -7758.2896 | 3802.217 | -2.040 | 0.041 | -1.52e+04 | -305.201 |
| Model_Dart | -5989.8452 | 4447.239 | -1.347 | 0.178 | -1.47e+04 | 2727.615 |
| Model_Dawn | -2.283e+04 | 6709.398 | -3.403 | 0.001 | -3.6e+04 | -9682.055 |
| Model_Daytona | -1.076e+04 | 5258.659 | -2.046 | 0.041 | -2.11e+04 | -450.317 |
| Model_DeVille | 7899.2486 | 2370.696 | 3.332 | 0.001 | 3252.221 | 1.25e+04 |
| Model_Defender | -1.051e+04 | 3614.954 | -2.908 | 0.004 | -1.76e+04 | -3425.886 |
| Model_Diablo | -1.433e+05 | 6281.066 | -22.819 | 0.000 | -1.56e+05 | -1.31e+05 |
| Model_Diamante | 6455.1585 | 2559.640 | 2.522 | 0.012 | 1437.762 | 1.15e+04 |
| Model_Discovery | -1.502e+04 | 2844.998 | -5.280 | 0.000 | -2.06e+04 | -9443.976 |
| Model_Discovery Series II | -2.109e+04 | 2851.318 | -7.395 | 0.000 | -2.67e+04 | -1.55e+04 |
| Model_Discovery Sport | -1.617e+04 | 2546.141 | -6.352 | 0.000 | -2.12e+04 | -1.12e+04 |
| Model_Durango | -2589.8977 | 4379.560 | -0.591 | 0.554 | -1.12e+04 | 5994.897 |
| Model_Dynasty | -1.138e+04 | 5583.332 | -2.039 | 0.042 | -2.23e+04 | -438.241 |
| Model_E-150 | -1.652e+04 | 2565.060 | -6.440 | 0.000 | -2.15e+04 | -1.15e+04 |
| Model_E-250 | -1.77e+04 | 3764.811 | -4.703 | 0.000 | -2.51e+04 | -1.03e+04 |
| Model_E-Class | -1.445e+04 | 1600.698 | -9.030 | 0.000 | -1.76e+04 | -1.13e+04 |
| Model_E-Series Van | -3762.9223 | 3459.070 | -1.088 | 0.277 | -1.05e+04 | 3017.533 |
| Model_E-Series Wagon | 331.7101 | 3539.919 | 0.094 | 0.925 | -6607.224 | 7270.644 |
| Model_E55 AMG | -5.546e+04 | 5043.680 | -10.995 | 0.000 | -6.53e+04 | -4.56e+04 |
| Model_ECHO | -7482.5604 | 2198.576 | -3.403 | 0.001 | -1.18e+04 | -3172.921 |
| Model_ES 250 | -2.791e+04 | 5243.837 | -5.323 | 0.000 | -3.82e+04 | -1.76e+04 |
| Model_ES 300 | -8160.9948 | 4143.079 | -1.970 | 0.049 | -1.63e+04 | -39.749 |
| Model_ES 300h | -4798.6505 | 4255.719 | -1.128 | 0.260 | -1.31e+04 | 3543.393 |
| Model_ES 330 | -9872.6342 | 4130.734 | -2.390 | 0.017 | -1.8e+04 | -1775.586 |
| Model_ES 350 | -1.275e+04 | 3635.742 | -3.506 | 0.000 | -1.99e+04 | -5620.774 |
| Model_EX | -1.092e+04 | 2983.006 | -3.660 | 0.000 | -1.68e+04 | -5069.887 |
| Model_EX35 | -1.055e+04 | 3030.366 | -3.480 | 0.001 | -1.65e+04 | -4606.260 |
| Model_Eclipse | -2345.9532 | 2280.931 | -1.029 | 0.304 | -6817.025 | 2125.119 |
| Model_Eclipse Spyder | -4005.2953 | 2852.147 | -1.404 | 0.160 | -9596.061 | 1585.471 |
| Model_Edge | 6783.4784 | 1678.392 | 4.042 | 0.000 | 3493.501 | 1.01e+04 |
| Model_Eighty-Eight | -1.617e+04 | 2936.468 | -5.508 | 0.000 | -2.19e+04 | -1.04e+04 |
| Model_Eighty-Eight Royale | -1.335e+04 | 3501.397 | -3.814 | 0.000 | -2.02e+04 | -6490.251 |
| Model_Elantra | -7906.4937 | 1823.564 | -4.336 | 0.000 | -1.15e+04 | -4331.950 |
| Model_Elantra Coupe | -6295.3732 | 2591.543 | -2.429 | 0.015 | -1.14e+04 | -1215.441 |
| Model_Elantra GT | -9449.8329 | 2987.021 | -3.164 | 0.002 | -1.53e+04 | -3594.687 |
| Model_Elantra Touring | -7696.6369 | 2178.723 | -3.533 | 0.000 | -1.2e+04 | -3425.912 |
| Model_Eldorado | -6665.2227 | 2837.142 | -2.349 | 0.019 | -1.22e+04 | -1103.870 |
| Model_Electra | -1.327e+04 | 6918.733 | -1.918 | 0.055 | -2.68e+04 | 288.977 |
| Model_Element | -4287.0100 | 1760.899 | -2.435 | 0.015 | -7738.718 | -835.302 |
| Model_Elise | -3.272e+04 | 2758.701 | -11.861 | 0.000 | -3.81e+04 | -2.73e+04 |
| Model_Enclave | 3583.1762 | 2172.192 | 1.650 | 0.099 | -674.746 | 7841.099 |
| Model_Encore | -8592.4600 | 2055.049 | -4.181 | 0.000 | -1.26e+04 | -4564.162 |
| Model_Endeavor | 325.8131 | 2605.384 | 0.125 | 0.900 | -4781.249 | 5432.875 |
| Model_Entourage | 1891.0362 | 4972.327 | 0.380 | 0.704 | -7855.698 | 1.16e+04 |
| Model_Envision | 3204.7407 | 2751.525 | 1.165 | 0.244 | -2188.788 | 8598.269 |
| Model_Envoy | 2.746e+04 | 3010.208 | 9.123 | 0.000 | 2.16e+04 | 3.34e+04 |
| Model_Envoy XL | 2.905e+04 | 3052.913 | 9.517 | 0.000 | 2.31e+04 | 3.5e+04 |
| Model_Envoy XUV | 2.942e+04 | 4316.887 | 6.815 | 0.000 | 2.1e+04 | 3.79e+04 |
| Model_Enzo | 3.508e+05 | 6796.271 | 51.616 | 0.000 | 3.37e+05 | 3.64e+05 |
| Model_Eos | -8147.9780 | 6229.597 | -1.308 | 0.191 | -2.04e+04 | 4063.251 |
| Model_Equator | -1.036e+04 | 2282.731 | -4.540 | 0.000 | -1.48e+04 | -5888.426 |
| Model_Equinox | -1.094e+04 | 2054.672 | -5.322 | 0.000 | -1.5e+04 | -6907.646 |
| Model_Equus | 1.05e+04 | 3082.777 | 3.407 | 0.001 | 4459.502 | 1.65e+04 |
| Model_Escalade | 1.275e+04 | 1573.645 | 8.102 | 0.000 | 9665.202 | 1.58e+04 |
| Model_Escalade ESV | 1.574e+04 | 1573.292 | 10.005 | 0.000 | 1.27e+04 | 1.88e+04 |
| Model_Escalade EXT | -4156.0943 | 3649.218 | -1.139 | 0.255 | -1.13e+04 | 2997.088 |
| Model_Escalade Hybrid | 2.698e+04 | 2201.400 | 12.256 | 0.000 | 2.27e+04 | 3.13e+04 |
| Model_Escape | 2596.3741 | 2322.289 | 1.118 | 0.264 | -1955.768 | 7148.516 |
| Model_Escape Hybrid | 1.281e+04 | 2343.399 | 5.468 | 0.000 | 8220.074 | 1.74e+04 |
| Model_Escape S | 2928.3231 | 5033.343 | 0.582 | 0.561 | -6938.016 | 1.28e+04 |
| Model_Escape SE | 2760.5117 | 5031.701 | 0.549 | 0.583 | -7102.607 | 1.26e+04 |
| Model_Escort | 3561.9762 | 2613.767 | 1.363 | 0.173 | -1561.518 | 8685.470 |
| Model_Esprit | 6102.7552 | 3746.255 | 1.629 | 0.103 | -1240.637 | 1.34e+04 |
| Model_Estate Wagon | -1.817e+04 | 6989.214 | -2.599 | 0.009 | -3.19e+04 | -4467.168 |
| Model_Esteem | -3854.1047 | 1401.264 | -2.750 | 0.006 | -6600.856 | -1107.354 |
| Model_EuroVan | -541.6895 | 7177.792 | -0.075 | 0.940 | -1.46e+04 | 1.35e+04 |
| Model_Evora | -1.418e+04 | 2651.137 | -5.348 | 0.000 | -1.94e+04 | -8980.507 |
| Model_Evora 400 | -7202.0651 | 6203.034 | -1.161 | 0.246 | -1.94e+04 | 4957.096 |
| Model_Excel | -1.176e+04 | 2648.187 | -4.439 | 0.000 | -1.69e+04 | -6564.632 |
| Model_Exige | -8331.0739 | 2994.804 | -2.782 | 0.005 | -1.42e+04 | -2460.671 |
| Model_Expedition | 1.753e+04 | 1560.147 | 11.236 | 0.000 | 1.45e+04 | 2.06e+04 |
| Model_Explorer | 7186.2483 | 1642.262 | 4.376 | 0.000 | 3967.093 | 1.04e+04 |
| Model_Explorer Sport | 1.046e+04 | 2505.840 | 4.173 | 0.000 | 5545.558 | 1.54e+04 |
| Model_Explorer Sport Trac | -698.5569 | 1809.072 | -0.386 | 0.699 | -4244.693 | 2847.579 |
| Model_Expo | -1.045e+04 | 2328.279 | -4.488 | 0.000 | -1.5e+04 | -5884.693 |
| Model_Express | -1.721e+04 | 3740.525 | -4.602 | 0.000 | -2.45e+04 | -9882.371 |
| Model_Express Cargo | -1.975e+04 | 5737.856 | -3.441 | 0.001 | -3.1e+04 | -8499.255 |
| Model_F-150 | -352.4856 | 1560.728 | -0.226 | 0.821 | -3411.819 | 2706.847 |
| Model_F-150 Heritage | 2366.0724 | 1780.245 | 1.329 | 0.184 | -1123.556 | 5855.700 |
| Model_F-150 SVT Lightning | -1.978e+04 | 4107.582 | -4.816 | 0.000 | -2.78e+04 | -1.17e+04 |
| Model_F-250 | -1.559e+04 | 1380.408 | -11.290 | 0.000 | -1.83e+04 | -1.29e+04 |
| Model_F12 Berlinetta | -7410.5426 | 4183.174 | -1.772 | 0.077 | -1.56e+04 | 789.297 |
| Model_F430 | -4.023e+04 | 2213.489 | -18.177 | 0.000 | -4.46e+04 | -3.59e+04 |
| Model_FF | -6825.8426 | 4161.237 | -1.640 | 0.101 | -1.5e+04 | 1330.997 |
| Model_FJ Cruiser | -3781.4535 | 2422.343 | -1.561 | 0.119 | -8529.720 | 966.813 |
| Model_FR-S | -5485.9244 | 2440.716 | -2.248 | 0.025 | -1.03e+04 | -701.642 |
| Model_FX | -2478.2846 | 3032.563 | -0.817 | 0.414 | -8422.702 | 3466.133 |
| Model_FX35 | -3498.8299 | 3637.542 | -0.962 | 0.336 | -1.06e+04 | 3631.465 |
| Model_FX45 | 97.2683 | 4670.082 | 0.021 | 0.983 | -9057.006 | 9251.543 |
| Model_FX50 | -910.7358 | 5447.786 | -0.167 | 0.867 | -1.16e+04 | 9767.992 |
| Model_Festiva | -5513.6229 | 4514.340 | -1.221 | 0.222 | -1.44e+04 | 3335.367 |
| Model_Fiesta | -4229.1341 | 1893.630 | -2.233 | 0.026 | -7941.019 | -517.249 |
| Model_Firebird | -9031.3201 | 2152.470 | -4.196 | 0.000 | -1.33e+04 | -4812.058 |
| Model_Fit | -1.146e+04 | 1919.155 | -5.970 | 0.000 | -1.52e+04 | -7694.529 |
| Model_Fit EV | 2285.4278 | 6610.322 | 0.346 | 0.730 | -1.07e+04 | 1.52e+04 |
| Model_Five Hundred | 6948.4731 | 2058.712 | 3.375 | 0.001 | 2912.994 | 1.1e+04 |
| Model_Fleetwood | -2.982e+04 | 4212.156 | -7.079 | 0.000 | -3.81e+04 | -2.16e+04 |
| Model_Flex | 4699.1277 | 2069.082 | 2.271 | 0.023 | 643.321 | 8754.935 |
| Model_Flying Spur | -1.059e+05 | 3919.575 | -27.012 | 0.000 | -1.14e+05 | -9.82e+04 |
| Model_Focus | -1363.4022 | 2047.707 | -0.666 | 0.506 | -5377.310 | 2650.505 |
| Model_Focus RS | 2537.1329 | 7143.701 | 0.355 | 0.722 | -1.15e+04 | 1.65e+04 |
| Model_Focus ST | 1493.9100 | 4238.342 | 0.352 | 0.724 | -6814.071 | 9801.891 |
| Model_Forenza | -6333.1441 | 1457.589 | -4.345 | 0.000 | -9190.305 | -3475.984 |
| Model_Forester | -6057.2423 | 1670.430 | -3.626 | 0.000 | -9331.613 | -2782.872 |
| Model_Forte | -9381.3041 | 1600.719 | -5.861 | 0.000 | -1.25e+04 | -6243.581 |
| Model_Fox | -1.623e+04 | 5984.031 | -2.712 | 0.007 | -2.8e+04 | -4499.575 |
| Model_Freelander | -2.222e+04 | 2409.406 | -9.222 | 0.000 | -2.69e+04 | -1.75e+04 |
| Model_Freestar | 1.141e+04 | 4141.683 | 2.755 | 0.006 | 3289.774 | 1.95e+04 |
| Model_Freestyle | 8772.4949 | 2138.669 | 4.102 | 0.000 | 4580.285 | 1.3e+04 |
| Model_Frontier | -6303.3976 | 2513.788 | -2.508 | 0.012 | -1.12e+04 | -1375.881 |
| Model_Fusion | 7236.2215 | 1983.741 | 3.648 | 0.000 | 3347.700 | 1.11e+04 |
| Model_Fusion Hybrid | 1.01e+04 | 2732.584 | 3.698 | 0.000 | 4747.657 | 1.55e+04 |
| Model_G Convertible | -1916.5040 | 3399.917 | -0.564 | 0.573 | -8581.007 | 4747.999 |
| Model_G Coupe | -2632.5733 | 2875.305 | -0.916 | 0.360 | -8268.734 | 3003.587 |
| Model_G Sedan | -6895.6434 | 2662.588 | -2.590 | 0.010 | -1.21e+04 | -1676.449 |
| Model_G-Class | 3.737e+04 | 3257.596 | 11.472 | 0.000 | 3.1e+04 | 4.38e+04 |
| Model_G20 | -1.094e+04 | 3640.658 | -3.005 | 0.003 | -1.81e+04 | -3804.527 |
| Model_G3 | -1.238e+04 | 6971.974 | -1.776 | 0.076 | -2.6e+04 | 1286.792 |
| Model_G35 | -6645.3788 | 2891.747 | -2.298 | 0.022 | -1.23e+04 | -976.988 |
| Model_G37 | -7952.2174 | 3011.628 | -2.641 | 0.008 | -1.39e+04 | -2048.837 |
| Model_G37 Convertible | -3103.3646 | 4694.922 | -0.661 | 0.509 | -1.23e+04 | 6099.602 |
| Model_G37 Coupe | -4505.0531 | 3921.478 | -1.149 | 0.251 | -1.22e+04 | 3181.812 |
| Model_G37 Sedan | -7618.0758 | 3657.130 | -2.083 | 0.037 | -1.48e+04 | -449.384 |
| Model_G5 | -5050.4474 | 3086.627 | -1.636 | 0.102 | -1.11e+04 | 999.946 |
| Model_G6 | -2760.3215 | 1944.223 | -1.420 | 0.156 | -6571.380 | 1050.736 |
| Model_G8 | -7319.2788 | 3381.497 | -2.165 | 0.030 | -1.39e+04 | -690.882 |
| Model_G80 | -6.559e+04 | 4125.760 | -15.898 | 0.000 | -7.37e+04 | -5.75e+04 |
| Model_GL-Class | -6939.1568 | 2281.748 | -3.041 | 0.002 | -1.14e+04 | -2466.484 |
| Model_GLA-Class | -3.341e+04 | 2640.891 | -12.652 | 0.000 | -3.86e+04 | -2.82e+04 |
| Model_GLC-Class | -2.594e+04 | 3616.879 | -7.171 | 0.000 | -3.3e+04 | -1.88e+04 |
| Model_GLE-Class | -1.608e+04 | 2299.582 | -6.994 | 0.000 | -2.06e+04 | -1.16e+04 |
| Model_GLE-Class Coupe | -1.267e+04 | 3611.975 | -3.509 | 0.000 | -1.98e+04 | -5594.667 |
| Model_GLI | -6348.7730 | 5915.289 | -1.073 | 0.283 | -1.79e+04 | 5246.352 |
| Model_GLK-Class | -3.057e+04 | 2478.874 | -12.334 | 0.000 | -3.54e+04 | -2.57e+04 |
| Model_GLS-Class | -5990.8696 | 4178.866 | -1.434 | 0.152 | -1.42e+04 | 2200.526 |
| Model_GS 200t | 1374.5798 | 3646.163 | 0.377 | 0.706 | -5772.614 | 8521.774 |
| Model_GS 300 | -1674.9146 | 3579.352 | -0.468 | 0.640 | -8691.145 | 5341.316 |
| Model_GS 350 | -3325.2877 | 2311.111 | -1.439 | 0.150 | -7855.518 | 1204.942 |
| Model_GS 400 | -3.942e+04 | 4183.473 | -9.422 | 0.000 | -4.76e+04 | -3.12e+04 |
| Model_GS 430 | 1683.5586 | 4112.592 | 0.409 | 0.682 | -6377.926 | 9745.044 |
| Model_GS 450h | 5607.3364 | 4175.286 | 1.343 | 0.179 | -2577.042 | 1.38e+04 |
| Model_GS 460 | -1581.6376 | 4109.589 | -0.385 | 0.700 | -9637.238 | 6473.963 |
| Model_GS F | 5473.4215 | 7070.125 | 0.774 | 0.439 | -8385.407 | 1.93e+04 |
| Model_GT | 8.848e+04 | 5140.179 | 17.214 | 0.000 | 7.84e+04 | 9.86e+04 |
| Model_GT-R | 4.061e+04 | 3240.461 | 12.531 | 0.000 | 3.43e+04 | 4.7e+04 |
| Model_GTI | -7339.7977 | 5655.325 | -1.298 | 0.194 | -1.84e+04 | 3745.746 |
| Model_GTO | -8330.6239 | 4167.056 | -1.999 | 0.046 | -1.65e+04 | -162.378 |
| Model_GX 460 | -5663.9611 | 2989.359 | -1.895 | 0.058 | -1.15e+04 | 195.769 |
| Model_GX 470 | -6165.4598 | 4108.080 | -1.501 | 0.133 | -1.42e+04 | 1887.182 |
| Model_Galant | -71.3072 | 3026.543 | -0.024 | 0.981 | -6003.925 | 5861.310 |
| Model_Gallardo | -2.327e+05 | 2173.279 | -107.060 | 0.000 | -2.37e+05 | -2.28e+05 |
| Model_Genesis | -375.5624 | 2706.801 | -0.139 | 0.890 | -5681.423 | 4930.298 |
| Model_Genesis Coupe | -5899.4833 | 2037.314 | -2.896 | 0.004 | -9893.018 | -1905.949 |
| Model_Ghibli | -2.551e+04 | 6005.249 | -4.248 | 0.000 | -3.73e+04 | -1.37e+04 |
| Model_Ghost | -7.1e+04 | 3107.976 | -22.846 | 0.000 | -7.71e+04 | -6.49e+04 |
| Model_Ghost Series II | -4.156e+04 | 3672.688 | -11.315 | 0.000 | -4.88e+04 | -3.44e+04 |
| Model_Golf | -1.312e+04 | 5787.441 | -2.266 | 0.023 | -2.45e+04 | -1772.193 |
| Model_Golf Alltrack | -1.003e+04 | 6492.352 | -1.545 | 0.122 | -2.28e+04 | 2696.514 |
| Model_Golf GTI | -8953.4626 | 5717.602 | -1.566 | 0.117 | -2.02e+04 | 2254.156 |
| Model_Golf R | -6399.0442 | 6090.654 | -1.051 | 0.293 | -1.83e+04 | 5539.830 |
| Model_Golf SportWagen | -9574.5266 | 5862.176 | -1.633 | 0.102 | -2.11e+04 | 1916.486 |
| Model_GranSport | 52.4676 | 6464.070 | 0.008 | 0.994 | -1.26e+04 | 1.27e+04 |
| Model_GranTurismo | 3.155e+04 | 6003.930 | 5.254 | 0.000 | 1.98e+04 | 4.33e+04 |
| Model_GranTurismo Convertible | 3.573e+04 | 5858.816 | 6.099 | 0.000 | 2.42e+04 | 4.72e+04 |
| Model_Grand Am | -1011.3114 | 1977.461 | -0.511 | 0.609 | -4887.522 | 2864.899 |
| Model_Grand Caravan | -5622.4215 | 5970.301 | -0.942 | 0.346 | -1.73e+04 | 6080.537 |
| Model_Grand Prix | -4274.0167 | 2689.190 | -1.589 | 0.112 | -9545.355 | 997.322 |
| Model_Grand Vitara | -4928.6849 | 1878.525 | -2.624 | 0.009 | -8610.962 | -1246.407 |
| Model_Grand Voyager | -1.53e+04 | 6204.352 | -2.467 | 0.014 | -2.75e+04 | -3142.967 |
| Model_H3 | -3.706e+04 | 2446.062 | -15.150 | 0.000 | -4.19e+04 | -3.23e+04 |
| Model_H3T | -4.848e+04 | 3383.000 | -14.332 | 0.000 | -5.51e+04 | -4.19e+04 |
| Model_HHR | -9131.8275 | 2404.014 | -3.799 | 0.000 | -1.38e+04 | -4419.490 |
| Model_HR-V | -9876.8469 | 1932.098 | -5.112 | 0.000 | -1.37e+04 | -6089.555 |
| Model_HS 250h | -5107.1942 | 3039.285 | -1.680 | 0.093 | -1.11e+04 | 850.400 |
| Model_Highlander | 3244.9610 | 1622.560 | 2.000 | 0.046 | 64.425 | 6425.497 |
| Model_Highlander Hybrid | 1.378e+04 | 2925.260 | 4.709 | 0.000 | 8040.955 | 1.95e+04 |
| Model_Horizon | -5536.0600 | 8359.220 | -0.662 | 0.508 | -2.19e+04 | 1.08e+04 |
| Model_Huracan | -2.33e+05 | 3562.137 | -65.409 | 0.000 | -2.4e+05 | -2.26e+05 |
| Model_I30 | -1.885e+04 | 3824.607 | -4.928 | 0.000 | -2.63e+04 | -1.13e+04 |
| Model_I35 | -5280.7397 | 4731.279 | -1.116 | 0.264 | -1.46e+04 | 3993.495 |
| Model_ILX | -1.447e+04 | 2161.744 | -6.695 | 0.000 | -1.87e+04 | -1.02e+04 |
| Model_ILX Hybrid | -9875.2888 | 5031.747 | -1.963 | 0.050 | -1.97e+04 | -12.079 |
| Model_IS 200t | -1.081e+04 | 5052.529 | -2.140 | 0.032 | -2.07e+04 | -906.467 |
| Model_IS 250 | -1.117e+04 | 2634.811 | -4.240 | 0.000 | -1.63e+04 | -6005.766 |
| Model_IS 250 C | -1.359e+04 | 4201.047 | -3.234 | 0.001 | -2.18e+04 | -5352.556 |
| Model_IS 300 | -1.257e+04 | 3230.617 | -3.891 | 0.000 | -1.89e+04 | -6238.825 |
| Model_IS 350 | -1.379e+04 | 2992.562 | -4.608 | 0.000 | -1.97e+04 | -7922.987 |
| Model_IS 350 C | -1.418e+04 | 4190.285 | -3.383 | 0.001 | -2.24e+04 | -5963.244 |
| Model_IS F | -5664.0569 | 4129.355 | -1.372 | 0.170 | -1.38e+04 | 2430.287 |
| Model_Impala | -7987.9424 | 2608.122 | -3.063 | 0.002 | -1.31e+04 | -2875.513 |
| Model_Impala Limited | -1.418e+04 | 2731.223 | -5.193 | 0.000 | -1.95e+04 | -8828.712 |
| Model_Imperial | -2.059e+04 | 5713.429 | -3.604 | 0.000 | -3.18e+04 | -9394.259 |
| Model_Impreza | -1.029e+04 | 1637.066 | -6.285 | 0.000 | -1.35e+04 | -7080.009 |
| Model_Impreza WRX | -1504.8215 | 1946.392 | -0.773 | 0.439 | -5320.131 | 2310.488 |
| Model_Insight | -7854.1296 | 2309.717 | -3.400 | 0.001 | -1.24e+04 | -3326.633 |
| Model_Integra | -1.773e+04 | 1685.356 | -10.518 | 0.000 | -2.1e+04 | -1.44e+04 |
| Model_Intrepid | -132.2169 | 5109.305 | -0.026 | 0.979 | -1.01e+04 | 9883.021 |
| Model_Intrigue | -1114.0959 | 2522.393 | -0.442 | 0.659 | -6058.481 | 3830.289 |
| Model_J30 | -2.519e+04 | 4136.317 | -6.091 | 0.000 | -3.33e+04 | -1.71e+04 |
| Model_JX | -6960.7729 | 5410.745 | -1.286 | 0.198 | -1.76e+04 | 3645.347 |
| Model_Jetta | -1.073e+04 | 5701.019 | -1.882 | 0.060 | -2.19e+04 | 444.510 |
| Model_Jetta GLI | -6656.5437 | 5754.146 | -1.157 | 0.247 | -1.79e+04 | 4622.709 |
| Model_Jetta Hybrid | -6747.9146 | 6219.758 | -1.085 | 0.278 | -1.89e+04 | 5444.029 |
| Model_Jetta SportWagen | -9991.3322 | 5717.422 | -1.748 | 0.081 | -2.12e+04 | 1215.934 |
| Model_Jimmy | 1.694e+04 | 2730.691 | 6.202 | 0.000 | 1.16e+04 | 2.23e+04 |
| Model_Journey | -7462.3776 | 4296.148 | -1.737 | 0.082 | -1.59e+04 | 958.914 |
| Model_Juke | 670.7645 | 2288.463 | 0.293 | 0.769 | -3815.072 | 5156.601 |
| Model_Justy | -1.377e+04 | 2509.200 | -5.487 | 0.000 | -1.87e+04 | -8849.831 |
| Model_K900 | 8367.4174 | 3277.709 | 2.553 | 0.011 | 1942.466 | 1.48e+04 |
| Model_Kizashi | -644.2447 | 1728.018 | -0.373 | 0.709 | -4031.498 | 2743.009 |
| Model_LFA | 2.732e+05 | 7117.954 | 38.381 | 0.000 | 2.59e+05 | 2.87e+05 |
| Model_LHS | -1.89e+04 | 5469.287 | -3.455 | 0.001 | -2.96e+04 | -8176.530 |
| Model_LR2 | -1.803e+04 | 2560.829 | -7.040 | 0.000 | -2.3e+04 | -1.3e+04 |
| Model_LR3 | -1.087e+04 | 3896.577 | -2.790 | 0.005 | -1.85e+04 | -3232.038 |
| Model_LR4 | -9743.8066 | 2425.525 | -4.017 | 0.000 | -1.45e+04 | -4989.302 |
| Model_LS | 4.732e+04 | 3080.881 | 15.358 | 0.000 | 4.13e+04 | 5.34e+04 |
| Model_LS 400 | -3.778e+04 | 4181.339 | -9.036 | 0.000 | -4.6e+04 | -2.96e+04 |
| Model_LS 430 | 6705.9389 | 4192.580 | 1.599 | 0.110 | -1512.338 | 1.49e+04 |
| Model_LS 460 | 1.125e+04 | 2233.772 | 5.038 | 0.000 | 6875.516 | 1.56e+04 |
| Model_LS 600h L | 4.284e+04 | 4217.611 | 10.158 | 0.000 | 3.46e+04 | 5.11e+04 |
| Model_LSS | -1.616e+04 | 2935.196 | -5.507 | 0.000 | -2.19e+04 | -1.04e+04 |
| Model_LTD Crown Victoria | -6459.1412 | 4030.310 | -1.603 | 0.109 | -1.44e+04 | 1441.055 |
| Model_LX 450 | -3.63e+04 | 5139.659 | -7.062 | 0.000 | -4.64e+04 | -2.62e+04 |
| Model_LX 470 | 1.528e+04 | 4134.820 | 3.695 | 0.000 | 7171.120 | 2.34e+04 |
| Model_LX 570 | 1.47e+04 | 4173.179 | 3.522 | 0.000 | 6516.499 | 2.29e+04 |
| Model_LaCrosse | -3084.4680 | 2172.127 | -1.420 | 0.156 | -7342.262 | 1173.326 |
| Model_Lancer | -6891.0780 | 2106.077 | -3.272 | 0.001 | -1.1e+04 | -2762.755 |
| Model_Lancer Evolution | 3637.8815 | 2966.317 | 1.226 | 0.220 | -2176.680 | 9452.443 |
| Model_Lancer Sportback | -6257.2809 | 3060.269 | -2.045 | 0.041 | -1.23e+04 | -258.554 |
| Model_Land Cruiser | 3.389e+04 | 4101.230 | 8.264 | 0.000 | 2.59e+04 | 4.19e+04 |
| Model_Landaulet | 7.522e+05 | 4345.128 | 173.124 | 0.000 | 7.44e+05 | 7.61e+05 |
| Model_Laser | -6625.2790 | 4733.420 | -1.400 | 0.162 | -1.59e+04 | 2653.151 |
| Model_Le Baron | -2.313e+04 | 4334.749 | -5.337 | 0.000 | -3.16e+04 | -1.46e+04 |
| Model_Le Mans | -1.273e+04 | 2598.505 | -4.900 | 0.000 | -1.78e+04 | -7638.416 |
| Model_LeSabre | 1584.2894 | 2949.848 | 0.537 | 0.591 | -4197.989 | 7366.568 |
| Model_Leaf | 1390.2705 | 5347.259 | 0.260 | 0.795 | -9091.404 | 1.19e+04 |
| Model_Legacy | -5599.5718 | 1956.264 | -2.862 | 0.004 | -9434.233 | -1764.910 |
| Model_Legend | -2.394e+04 | 2031.840 | -11.784 | 0.000 | -2.79e+04 | -2e+04 |
| Model_Levante | -3.097e+04 | 7427.546 | -4.169 | 0.000 | -4.55e+04 | -1.64e+04 |
| Model_Loyale | -1.265e+04 | 2567.861 | -4.927 | 0.000 | -1.77e+04 | -7617.910 |
| Model_Lucerne | 1979.4939 | 1930.216 | 1.026 | 0.305 | -1804.108 | 5763.096 |
| Model_Lumina | -1.896e+04 | 3945.446 | -4.805 | 0.000 | -2.67e+04 | -1.12e+04 |
| Model_Lumina Minivan | -1.818e+04 | 5205.160 | -3.493 | 0.000 | -2.84e+04 | -7979.107 |
| Model_M | -784.9873 | 2032.232 | -0.386 | 0.699 | -4768.560 | 3198.586 |
| Model_M-Class | -1.85e+04 | 1982.054 | -9.335 | 0.000 | -2.24e+04 | -1.46e+04 |
| Model_M2 | -7521.3778 | 5450.104 | -1.380 | 0.168 | -1.82e+04 | 3161.893 |
| Model_M3 | -3923.3553 | 4652.308 | -0.843 | 0.399 | -1.3e+04 | 5196.079 |
| Model_M30 | -2.446e+04 | 4338.824 | -5.637 | 0.000 | -3.3e+04 | -1.6e+04 |
| Model_M35 | 1832.5974 | 3591.863 | 0.510 | 0.610 | -5208.157 | 8873.352 |
| Model_M37 | -1105.1860 | 5425.624 | -0.204 | 0.839 | -1.17e+04 | 9530.099 |
| Model_M4 | -1461.1448 | 3634.788 | -0.402 | 0.688 | -8586.040 | 5663.751 |
| Model_M4 GTS | 4.784e+04 | 7420.256 | 6.447 | 0.000 | 3.33e+04 | 6.24e+04 |
| Model_M45 | 3298.6865 | 3593.629 | 0.918 | 0.359 | -3745.530 | 1.03e+04 |
| Model_M5 | -1.051e+04 | 4790.744 | -2.194 | 0.028 | -1.99e+04 | -1121.862 |
| Model_M56 | -3202.6369 | 5415.114 | -0.591 | 0.554 | -1.38e+04 | 7412.046 |
| Model_M6 | 9363.6697 | 3691.472 | 2.537 | 0.011 | 2127.662 | 1.66e+04 |
| Model_M6 Gran Coupe | 1.201e+04 | 4802.251 | 2.501 | 0.012 | 2596.634 | 2.14e+04 |
| Model_MDX | -1416.6606 | 1529.375 | -0.926 | 0.354 | -4414.536 | 1581.215 |
| Model_MKC | 4.121e+04 | 2759.702 | 14.934 | 0.000 | 3.58e+04 | 4.66e+04 |
| Model_MKS | 3.501e+04 | 3172.592 | 11.036 | 0.000 | 2.88e+04 | 4.12e+04 |
| Model_MKT | 3.666e+04 | 3655.399 | 10.030 | 0.000 | 2.95e+04 | 4.38e+04 |
| Model_MKX | 4.037e+04 | 2659.838 | 15.176 | 0.000 | 3.52e+04 | 4.56e+04 |
| Model_MKZ | 4.545e+04 | 2644.995 | 17.183 | 0.000 | 4.03e+04 | 5.06e+04 |
| Model_MKZ Hybrid | 4.595e+04 | 7367.178 | 6.237 | 0.000 | 3.15e+04 | 6.04e+04 |
| Model_ML55 AMG | -6.109e+04 | 7034.609 | -8.684 | 0.000 | -7.49e+04 | -4.73e+04 |
| Model_MP4-12C | 3729.8231 | 4860.062 | 0.767 | 0.443 | -5796.851 | 1.33e+04 |
| Model_MPV | -840.0421 | 4638.817 | -0.181 | 0.856 | -9933.031 | 8252.947 |
| Model_MR2 | -6326.5774 | 3236.439 | -1.955 | 0.051 | -1.27e+04 | 17.477 |
| Model_MR2 Spyder | -1745.7910 | 3030.804 | -0.576 | 0.565 | -7686.760 | 4195.178 |
| Model_MX-3 | -1.639e+04 | 3737.544 | -4.385 | 0.000 | -2.37e+04 | -9064.461 |
| Model_MX-5 Miata | -1.016e+04 | 2119.902 | -4.793 | 0.000 | -1.43e+04 | -6005.283 |
| Model_MX-6 | -1.517e+04 | 3381.938 | -4.484 | 0.000 | -2.18e+04 | -8536.525 |
| Model_Macan | -3.893e+04 | 2689.996 | -14.471 | 0.000 | -4.42e+04 | -3.37e+04 |
| Model_Magnum | -4824.3304 | 4796.725 | -1.006 | 0.315 | -1.42e+04 | 4578.191 |
| Model_Malibu | -9407.7336 | 2268.534 | -4.147 | 0.000 | -1.39e+04 | -4960.963 |
| Model_Malibu Classic | -1.144e+04 | 4316.345 | -2.651 | 0.008 | -1.99e+04 | -2983.193 |
| Model_Malibu Hybrid | -4229.3867 | 4309.215 | -0.981 | 0.326 | -1.27e+04 | 4217.519 |
| Model_Malibu Limited | -9467.5757 | 4293.193 | -2.205 | 0.027 | -1.79e+04 | -1052.076 |
| Model_Malibu Maxx | -9560.3987 | 2673.641 | -3.576 | 0.000 | -1.48e+04 | -4319.539 |
| Model_Mark LT | 3.156e+04 | 3096.891 | 10.191 | 0.000 | 2.55e+04 | 3.76e+04 |
| Model_Mark VII | 2.019e+04 | 4863.469 | 4.150 | 0.000 | 1.07e+04 | 2.97e+04 |
| Model_Mark VIII | 1.33e+04 | 3987.982 | 3.334 | 0.001 | 5480.674 | 2.11e+04 |
| Model_Matrix | -5093.3753 | 2027.899 | -2.512 | 0.012 | -9068.454 | -1118.297 |
| Model_Maxima | 6828.9394 | 2773.623 | 2.462 | 0.014 | 1392.095 | 1.23e+04 |
| Model_Maybach | 7.901e+04 | 5129.745 | 15.402 | 0.000 | 6.9e+04 | 8.91e+04 |
| Model_Mazdaspeed 3 | -7452.7927 | 4392.794 | -1.697 | 0.090 | -1.61e+04 | 1157.944 |
| Model_Mazdaspeed 6 | 898.2330 | 3890.465 | 0.231 | 0.817 | -6727.840 | 8524.306 |
| Model_Mazdaspeed MX-5 Miata | -3747.2574 | 4374.400 | -0.857 | 0.392 | -1.23e+04 | 4827.423 |
| Model_Mazdaspeed Protege | -965.0405 | 5219.163 | -0.185 | 0.853 | -1.12e+04 | 9265.540 |
| Model_Metris | -2.897e+04 | 6385.695 | -4.537 | 0.000 | -4.15e+04 | -1.65e+04 |
| Model_Metro | -2.245e+04 | 3275.606 | -6.853 | 0.000 | -2.89e+04 | -1.6e+04 |
| Model_Mighty Max Pickup | -1.378e+04 | 3192.122 | -4.316 | 0.000 | -2e+04 | -7521.028 |
| Model_Millenia | -2545.4407 | 2872.889 | -0.886 | 0.376 | -8176.865 | 3085.984 |
| Model_Mirage | -1.484e+04 | 2393.763 | -6.197 | 0.000 | -1.95e+04 | -1.01e+04 |
| Model_Mirage G4 | -1.366e+04 | 4235.426 | -3.225 | 0.001 | -2.2e+04 | -5357.613 |
| Model_Model D | -3726.3687 | 2508.038 | -1.486 | 0.137 | -8642.615 | 1189.877 |
| Model_Model S | -2.336e+04 | 2699.272 | -8.652 | 0.000 | -2.86e+04 | -1.81e+04 |
| Model_Monaco | -1.08e+04 | 6319.182 | -1.710 | 0.087 | -2.32e+04 | 1583.093 |
| Model_Montana | 4302.0981 | 3844.360 | 1.119 | 0.263 | -3233.600 | 1.18e+04 |
| Model_Montana SV6 | 411.2000 | 5933.381 | 0.069 | 0.945 | -1.12e+04 | 1.2e+04 |
| Model_Monte Carlo | -7170.7976 | 2630.461 | -2.726 | 0.006 | -1.23e+04 | -2014.580 |
| Model_Montero | 9739.9702 | 4045.074 | 2.408 | 0.016 | 1810.832 | 1.77e+04 |
| Model_Montero Sport | 4788.0098 | 1806.451 | 2.651 | 0.008 | 1247.012 | 8329.008 |
| Model_Mulsanne | 1.706e+04 | 4201.410 | 4.060 | 0.000 | 8822.090 | 2.53e+04 |
| Model_Murano | 7795.7258 | 2075.863 | 3.755 | 0.000 | 3726.628 | 1.19e+04 |
| Model_Murano CrossCabriolet | 1.396e+04 | 4693.098 | 2.975 | 0.003 | 4763.293 | 2.32e+04 |
| Model_Murcielago | -9.985e+04 | 2629.525 | -37.971 | 0.000 | -1.05e+05 | -9.47e+04 |
| Model_Mustang | -3058.8967 | 1868.180 | -1.637 | 0.102 | -6720.896 | 603.102 |
| Model_Mustang SVT Cobra | -1.511e+04 | 3210.533 | -4.708 | 0.000 | -2.14e+04 | -8821.441 |
| Model_NSX | 5.312e+04 | 3202.387 | 16.588 | 0.000 | 4.68e+04 | 5.94e+04 |
| Model_NV200 | 2320.8189 | 4990.618 | 0.465 | 0.642 | -7461.769 | 1.21e+04 |
| Model_NX | -3028.7121 | 3372.106 | -0.898 | 0.369 | -9638.700 | 3581.276 |
| Model_NX 200t | -1.573e+04 | 2206.290 | -7.130 | 0.000 | -2.01e+04 | -1.14e+04 |
| Model_NX 300h | -9709.9497 | 3315.209 | -2.929 | 0.003 | -1.62e+04 | -3211.490 |
| Model_Navajo | -1.54e+04 | 3778.941 | -4.076 | 0.000 | -2.28e+04 | -7997.336 |
| Model_Navigator | 5.484e+04 | 2695.557 | 20.344 | 0.000 | 4.96e+04 | 6.01e+04 |
| Model_Neon | -4362.1242 | 4369.753 | -0.998 | 0.318 | -1.29e+04 | 4203.448 |
| Model_New Beetle | -1.231e+04 | 5733.366 | -2.147 | 0.032 | -2.35e+04 | -1071.747 |
| Model_New Yorker | -2.26e+04 | 5613.552 | -4.026 | 0.000 | -3.36e+04 | -1.16e+04 |
| Model_Ninety-Eight | -1.385e+04 | 3160.792 | -4.382 | 0.000 | -2e+04 | -7654.103 |
| Model_Nitro | -5735.3082 | 4469.337 | -1.283 | 0.199 | -1.45e+04 | 3025.467 |
| Model_Odyssey | 4066.6707 | 4478.069 | 0.908 | 0.364 | -4711.221 | 1.28e+04 |
| Model_Omni | -1.005e+04 | 8604.851 | -1.168 | 0.243 | -2.69e+04 | 6818.987 |
| Model_Optima | -1371.1320 | 1879.143 | -0.730 | 0.466 | -5054.621 | 2312.357 |
| Model_Optima Hybrid | 1139.1152 | 2998.690 | 0.380 | 0.704 | -4738.905 | 7017.135 |
| Model_Outback | -4343.8414 | 1782.717 | -2.437 | 0.015 | -7838.317 | -849.366 |
| Model_Outlander | -5050.7123 | 1971.623 | -2.562 | 0.010 | -8915.480 | -1185.945 |
| Model_Outlander Sport | -7290.9516 | 1751.847 | -4.162 | 0.000 | -1.07e+04 | -3856.989 |
| Model_PT Cruiser | -1.179e+04 | 4160.059 | -2.835 | 0.005 | -1.99e+04 | -3638.648 |
| Model_Pacifica | -6666.4631 | 3947.429 | -1.689 | 0.091 | -1.44e+04 | 1071.270 |
| Model_Panamera | 9011.5754 | 2152.165 | 4.187 | 0.000 | 4792.910 | 1.32e+04 |
| Model_Park Avenue | 9386.2134 | 2957.505 | 3.174 | 0.002 | 3588.924 | 1.52e+04 |
| Model_Park Ward | -3.565e+04 | 4821.699 | -7.394 | 0.000 | -4.51e+04 | -2.62e+04 |
| Model_Paseo | -1.286e+04 | 3735.632 | -3.443 | 0.001 | -2.02e+04 | -5538.139 |
| Model_Passat | -7372.0319 | 5765.820 | -1.279 | 0.201 | -1.87e+04 | 3930.105 |
| Model_Passport | -426.0252 | 2001.632 | -0.213 | 0.831 | -4349.616 | 3497.566 |
| Model_Pathfinder | 4248.8570 | 2211.292 | 1.921 | 0.055 | -85.709 | 8583.423 |
| Model_Phaeton | 3.075e+04 | 5854.820 | 5.252 | 0.000 | 1.93e+04 | 4.22e+04 |
| Model_Phantom | 1.209e+05 | 3150.830 | 38.375 | 0.000 | 1.15e+05 | 1.27e+05 |
| Model_Phantom Coupe | 1.155e+05 | 4116.543 | 28.061 | 0.000 | 1.07e+05 | 1.24e+05 |
| Model_Phantom Drophead Coupe | 1.491e+05 | 4117.446 | 36.206 | 0.000 | 1.41e+05 | 1.57e+05 |
| Model_Pickup | -1.448e+04 | 1853.402 | -7.812 | 0.000 | -1.81e+04 | -1.08e+04 |
| Model_Pilot | 51.6121 | 1317.505 | 0.039 | 0.969 | -2530.956 | 2634.180 |
| Model_Precis | -1.062e+04 | 6983.092 | -1.520 | 0.129 | -2.43e+04 | 3073.057 |
| Model_Prelude | -3053.2410 | 2858.075 | -1.068 | 0.285 | -8655.628 | 2549.146 |
| Model_Previa | -9489.0721 | 4639.740 | -2.045 | 0.041 | -1.86e+04 | -394.273 |
| Model_Prius | -462.8847 | 2483.306 | -0.186 | 0.852 | -5330.650 | 4404.881 |
| Model_Prius Prime | 1488.5464 | 4473.467 | 0.333 | 0.739 | -7280.325 | 1.03e+04 |
| Model_Prius c | -6688.2681 | 2664.700 | -2.510 | 0.012 | -1.19e+04 | -1464.935 |
| Model_Prius v | 2385.6769 | 2498.000 | 0.955 | 0.340 | -2510.892 | 7282.246 |
| Model_Prizm | -1.225e+04 | 3355.982 | -3.651 | 0.000 | -1.88e+04 | -5672.737 |
| Model_Probe | -5976.1594 | 3298.962 | -1.812 | 0.070 | -1.24e+04 | 490.453 |
| Model_Protege | -5851.7125 | 2628.688 | -2.226 | 0.026 | -1.1e+04 | -698.969 |
| Model_Protege5 | -4983.8724 | 5236.561 | -0.952 | 0.341 | -1.52e+04 | 5280.813 |
| Model_Prowler | 5712.3133 | 4778.000 | 1.196 | 0.232 | -3653.503 | 1.51e+04 |
| Model_Pulsar | -1575.8871 | 7249.402 | -0.217 | 0.828 | -1.58e+04 | 1.26e+04 |
| Model_Q3 | 1544.2599 | 3545.293 | 0.436 | 0.663 | -5405.209 | 8493.729 |
| Model_Q40 | -1.489e+04 | 5453.553 | -2.730 | 0.006 | -2.56e+04 | -4196.094 |
| Model_Q45 | 1.134e+04 | 4189.839 | 2.708 | 0.007 | 3131.096 | 1.96e+04 |
| Model_Q5 | 1.004e+04 | 3357.402 | 2.990 | 0.003 | 3457.062 | 1.66e+04 |
| Model_Q50 | -5942.4857 | 2488.843 | -2.388 | 0.017 | -1.08e+04 | -1063.866 |
| Model_Q60 Convertible | -1301.1088 | 3687.387 | -0.353 | 0.724 | -8529.108 | 5926.891 |
| Model_Q60 Coupe | -2820.6768 | 2827.677 | -0.998 | 0.319 | -8363.478 | 2722.125 |
| Model_Q7 | 1.39e+04 | 3492.407 | 3.980 | 0.000 | 7054.181 | 2.07e+04 |
| Model_Q70 | -447.3480 | 2541.894 | -0.176 | 0.860 | -5429.959 | 4535.263 |
| Model_QX | -1864.0524 | 4108.921 | -0.454 | 0.650 | -9918.342 | 6190.238 |
| Model_QX4 | -2027.6247 | 3794.643 | -0.534 | 0.593 | -9465.868 | 5410.618 |
| Model_QX50 | -1.707e+04 | 3282.311 | -5.200 | 0.000 | -2.35e+04 | -1.06e+04 |
| Model_QX56 | 2126.9897 | 3609.277 | 0.589 | 0.556 | -4947.900 | 9201.880 |
| Model_QX60 | -3824.2082 | 3057.790 | -1.251 | 0.211 | -9818.075 | 2169.658 |
| Model_QX70 | -6321.2750 | 3575.790 | -1.768 | 0.077 | -1.33e+04 | 687.974 |
| Model_QX80 | 1824.9559 | 3385.079 | 0.539 | 0.590 | -4810.462 | 8460.374 |
| Model_Quattroporte | 4941.4690 | 5921.859 | 0.834 | 0.404 | -6666.534 | 1.65e+04 |
| Model_Quest | 8889.1278 | 4265.761 | 2.084 | 0.037 | 527.401 | 1.73e+04 |
| Model_R-Class | -1.659e+04 | 3134.073 | -5.294 | 0.000 | -2.27e+04 | -1.04e+04 |
| Model_R32 | -5058.8065 | 7395.470 | -0.684 | 0.494 | -1.96e+04 | 9437.764 |
| Model_R8 | 7.984e+04 | 3443.482 | 23.185 | 0.000 | 7.31e+04 | 8.66e+04 |
| Model_RAM 150 | -1.86e+04 | 4531.071 | -4.106 | 0.000 | -2.75e+04 | -9720.899 |
| Model_RAM 250 | -1.959e+04 | 4440.781 | -4.411 | 0.000 | -2.83e+04 | -1.09e+04 |
| Model_RAV4 | -1222.3593 | 1557.203 | -0.785 | 0.432 | -4274.783 | 1830.064 |
| Model_RAV4 EV | 1.29e+04 | 6313.465 | 2.043 | 0.041 | 520.769 | 2.53e+04 |
| Model_RAV4 Hybrid | 855.1799 | 3229.060 | 0.265 | 0.791 | -5474.409 | 7184.769 |
| Model_RC 200t | -8219.5141 | 5069.598 | -1.621 | 0.105 | -1.82e+04 | 1717.890 |
| Model_RC 300 | -1.153e+04 | 5065.455 | -2.276 | 0.023 | -2.15e+04 | -1601.995 |
| Model_RC 350 | -1.176e+04 | 3036.022 | -3.874 | 0.000 | -1.77e+04 | -5811.516 |
| Model_RC F | -1.574e+04 | 4184.607 | -3.762 | 0.000 | -2.39e+04 | -7541.489 |
| Model_RDX | -1.182e+04 | 1710.819 | -6.909 | 0.000 | -1.52e+04 | -8466.346 |
| Model_RL | 4561.7908 | 2457.248 | 1.856 | 0.063 | -254.897 | 9378.479 |
| Model_RLX | 5113.0781 | 2263.681 | 2.259 | 0.024 | 675.821 | 9550.336 |
| Model_RS 4 | 2.05e+04 | 4994.657 | 4.105 | 0.000 | 1.07e+04 | 3.03e+04 |
| Model_RS 5 | 6877.1486 | 4260.223 | 1.614 | 0.106 | -1473.723 | 1.52e+04 |
| Model_RS 6 | 3.154e+04 | 7624.190 | 4.136 | 0.000 | 1.66e+04 | 4.65e+04 |
| Model_RS 7 | 2.215e+04 | 5205.010 | 4.256 | 0.000 | 1.19e+04 | 3.24e+04 |
| Model_RSX | -1.223e+04 | 2075.918 | -5.894 | 0.000 | -1.63e+04 | -8165.318 |
| Model_RX 300 | -8457.1160 | 3031.784 | -2.789 | 0.005 | -1.44e+04 | -2514.225 |
| Model_RX 330 | -9289.7511 | 3001.332 | -3.095 | 0.002 | -1.52e+04 | -3406.553 |
| Model_RX 350 | -1.123e+04 | 2274.406 | -4.937 | 0.000 | -1.57e+04 | -6770.389 |
| Model_RX 400h | -5295.0538 | 3003.875 | -1.763 | 0.078 | -1.12e+04 | 593.129 |
| Model_RX 450h | -6036.3818 | 2854.888 | -2.114 | 0.035 | -1.16e+04 | -440.242 |
| Model_RX-7 | -1741.4610 | 4692.174 | -0.371 | 0.711 | -1.09e+04 | 7456.119 |
| Model_RX-8 | 5787.5040 | 2696.665 | 2.146 | 0.032 | 501.513 | 1.11e+04 |
| Model_Rabbit | -1.319e+04 | 5838.965 | -2.259 | 0.024 | -2.46e+04 | -1745.935 |
| Model_Raider | -7074.9759 | 2311.784 | -3.060 | 0.002 | -1.16e+04 | -2543.426 |
| Model_Rainier | -854.0842 | 2904.857 | -0.294 | 0.769 | -6548.173 | 4840.005 |
| Model_Rally Wagon | 2929.2805 | 4942.627 | 0.593 | 0.553 | -6759.236 | 1.26e+04 |
| Model_Ram 50 Pickup | -1.422e+04 | 4585.638 | -3.102 | 0.002 | -2.32e+04 | -5233.783 |
| Model_Ram Cargo | -1.12e+04 | 4456.287 | -2.514 | 0.012 | -1.99e+04 | -2469.363 |
| Model_Ram Pickup 1500 | -1.239e+04 | 3792.530 | -3.266 | 0.001 | -1.98e+04 | -4952.867 |
| Model_Ram Van | -2.76e+04 | 4541.865 | -6.078 | 0.000 | -3.65e+04 | -1.87e+04 |
| Model_Ram Wagon | -1.645e+04 | 5171.735 | -3.181 | 0.001 | -2.66e+04 | -6313.249 |
| Model_Ramcharger | -1.518e+04 | 5847.886 | -2.596 | 0.009 | -2.66e+04 | -3718.005 |
| Model_Range Rover | 3.16e+04 | 1974.910 | 16.003 | 0.000 | 2.77e+04 | 3.55e+04 |
| Model_Range Rover Evoque | -7069.1429 | 1919.384 | -3.683 | 0.000 | -1.08e+04 | -3306.774 |
| Model_Range Rover Sport | -1904.5533 | 1939.169 | -0.982 | 0.326 | -5705.705 | 1896.599 |
| Model_Ranger | -311.4846 | 1685.089 | -0.185 | 0.853 | -3614.589 | 2991.620 |
| Model_Rapide | 1.027e+04 | 3554.144 | 2.889 | 0.004 | 3302.327 | 1.72e+04 |
| Model_Rapide S | -1.781e+04 | 4066.298 | -4.381 | 0.000 | -2.58e+04 | -9842.198 |
| Model_Reatta | -1.8e+04 | 3663.000 | -4.914 | 0.000 | -2.52e+04 | -1.08e+04 |
| Model_Regal | -3517.9792 | 2279.214 | -1.544 | 0.123 | -7985.686 | 949.727 |
| Model_Regency | -1.564e+04 | 4881.554 | -3.203 | 0.001 | -2.52e+04 | -6067.976 |
| Model_Rendezvous | -3242.3175 | 2899.424 | -1.118 | 0.263 | -8925.757 | 2441.122 |
| Model_Reno | -8240.2347 | 1996.303 | -4.128 | 0.000 | -1.22e+04 | -4327.089 |
| Model_Reventon | 1.023e+06 | 6277.591 | 162.915 | 0.000 | 1.01e+06 | 1.04e+06 |
| Model_Ridgeline | -1.102e+04 | 2181.819 | -5.052 | 0.000 | -1.53e+04 | -6744.891 |
| Model_Rio | -1.309e+04 | 1787.698 | -7.322 | 0.000 | -1.66e+04 | -9584.748 |
| Model_Riviera | -2.132e+04 | 3578.051 | -5.959 | 0.000 | -2.83e+04 | -1.43e+04 |
| Model_Roadmaster | -2.26e+04 | 2837.458 | -7.965 | 0.000 | -2.82e+04 | -1.7e+04 |
| Model_Rogue | 1554.7892 | 2229.232 | 0.697 | 0.486 | -2814.941 | 5924.520 |
| Model_Rogue Select | -2910.9433 | 3816.582 | -0.763 | 0.446 | -1.04e+04 | 4570.306 |
| Model_Rondo | -6158.9060 | 1997.234 | -3.084 | 0.002 | -1.01e+04 | -2243.937 |
| Model_Routan | 1105.6252 | 6623.958 | 0.167 | 0.867 | -1.19e+04 | 1.41e+04 |
| Model_S-10 | -1.197e+04 | 2001.302 | -5.979 | 0.000 | -1.59e+04 | -8043.581 |
| Model_S-10 Blazer | -1.935e+04 | 3129.798 | -6.181 | 0.000 | -2.55e+04 | -1.32e+04 |
| Model_S-15 | 1.387e+04 | 4229.162 | 3.279 | 0.001 | 5575.353 | 2.22e+04 |
| Model_S-15 Jimmy | 1.598e+04 | 3889.237 | 4.108 | 0.000 | 8354.973 | 2.36e+04 |
| Model_S-Class | 4.323e+04 | 1894.892 | 22.816 | 0.000 | 3.95e+04 | 4.69e+04 |
| Model_S2000 | 5904.5165 | 2845.525 | 2.075 | 0.038 | 326.729 | 1.15e+04 |
| Model_S3 | 4987.6116 | 4306.613 | 1.158 | 0.247 | -3454.193 | 1.34e+04 |
| Model_S4 | 9840.5744 | 3707.649 | 2.654 | 0.008 | 2572.856 | 1.71e+04 |
| Model_S40 | -1809.8580 | 8054.125 | -0.225 | 0.822 | -1.76e+04 | 1.4e+04 |
| Model_S5 | 1.134e+04 | 3970.103 | 2.857 | 0.004 | 3558.621 | 1.91e+04 |
| Model_S6 | 9617.8437 | 4491.461 | 2.141 | 0.032 | 813.700 | 1.84e+04 |
| Model_S60 | 2548.4420 | 7767.467 | 0.328 | 0.743 | -1.27e+04 | 1.78e+04 |
| Model_S60 Cross Country | 7025.9078 | 1.05e+04 | 0.672 | 0.502 | -1.35e+04 | 2.75e+04 |
| Model_S7 | 1.655e+04 | 4778.958 | 3.463 | 0.001 | 7181.699 | 2.59e+04 |
| Model_S70 | -1.986e+04 | 7743.799 | -2.564 | 0.010 | -3.5e+04 | -4676.059 |
| Model_S8 | 2.728e+04 | 4777.932 | 5.710 | 0.000 | 1.79e+04 | 3.66e+04 |
| Model_S80 | 6456.4802 | 8040.393 | 0.803 | 0.422 | -9304.264 | 2.22e+04 |
| Model_S90 | 3894.0124 | 8249.331 | 0.472 | 0.637 | -1.23e+04 | 2.01e+04 |
| Model_SC 300 | -3.31e+04 | 4200.621 | -7.880 | 0.000 | -4.13e+04 | -2.49e+04 |
| Model_SC 400 | -3.833e+04 | 4199.452 | -9.127 | 0.000 | -4.66e+04 | -3.01e+04 |
| Model_SC 430 | 8262.4433 | 4167.462 | 1.983 | 0.047 | 93.403 | 1.64e+04 |
| Model_SL-Class | 2.948e+04 | 2375.544 | 12.409 | 0.000 | 2.48e+04 | 3.41e+04 |
| Model_SLC-Class | -2.224e+04 | 5109.162 | -4.354 | 0.000 | -3.23e+04 | -1.22e+04 |
| Model_SLK-Class | -1.988e+04 | 2621.874 | -7.581 | 0.000 | -2.5e+04 | -1.47e+04 |
| Model_SLR McLaren | 3.589e+05 | 4195.964 | 85.535 | 0.000 | 3.51e+05 | 3.67e+05 |
| Model_SLS AMG | 7.185e+04 | 4191.832 | 17.141 | 0.000 | 6.36e+04 | 8.01e+04 |
| Model_SLS AMG GT | 9.076e+04 | 3372.872 | 26.907 | 0.000 | 8.41e+04 | 9.74e+04 |
| Model_SLS AMG GT Final Edition | 9.795e+04 | 5106.940 | 19.179 | 0.000 | 8.79e+04 | 1.08e+05 |
| Model_SLX | -3.199e+04 | 3558.009 | -8.990 | 0.000 | -3.9e+04 | -2.5e+04 |
| Model_SQ5 | 9976.0264 | 4133.944 | 2.413 | 0.016 | 1872.687 | 1.81e+04 |
| Model_SRT Viper | -319.8561 | 5603.341 | -0.057 | 0.954 | -1.13e+04 | 1.07e+04 |
| Model_SRX | -3932.5893 | 1751.713 | -2.245 | 0.025 | -7366.290 | -498.889 |
| Model_SS | -1.388e+04 | 4328.610 | -3.206 | 0.001 | -2.24e+04 | -5391.483 |
| Model_SSR | -6005.8802 | 4362.049 | -1.377 | 0.169 | -1.46e+04 | 2544.590 |
| Model_STS | 8844.8959 | 2322.667 | 3.808 | 0.000 | 4292.013 | 1.34e+04 |
| Model_STS-V | 1.071e+04 | 4086.957 | 2.621 | 0.009 | 2700.470 | 1.87e+04 |
| Model_SVX | -1.681e+04 | 2755.824 | -6.101 | 0.000 | -2.22e+04 | -1.14e+04 |
| Model_SX4 | -8137.7835 | 1621.759 | -5.018 | 0.000 | -1.13e+04 | -4958.818 |
| Model_Safari | 2.912e+04 | 5306.020 | 5.487 | 0.000 | 1.87e+04 | 3.95e+04 |
| Model_Safari Cargo | 3.068e+04 | 5517.567 | 5.560 | 0.000 | 1.99e+04 | 4.15e+04 |
| Model_Samurai | -1.634e+04 | 3895.736 | -4.194 | 0.000 | -2.4e+04 | -8703.192 |
| Model_Santa Fe | -2916.6799 | 1854.053 | -1.573 | 0.116 | -6550.987 | 717.628 |
| Model_Santa Fe Sport | -2611.7869 | 1963.983 | -1.330 | 0.184 | -6461.577 | 1238.004 |
| Model_Savana | 1.646e+04 | 4815.000 | 3.419 | 0.001 | 7022.706 | 2.59e+04 |
| Model_Savana Cargo | 1.379e+04 | 4788.430 | 2.880 | 0.004 | 4406.014 | 2.32e+04 |
| Model_Scoupe | -1.043e+04 | 3143.813 | -3.317 | 0.001 | -1.66e+04 | -4264.800 |
| Model_Sebring | -8766.3300 | 3647.681 | -2.403 | 0.016 | -1.59e+04 | -1616.160 |
| Model_Sedona | -502.0923 | 4143.195 | -0.121 | 0.904 | -8623.567 | 7619.382 |
| Model_Sentra | -2336.3333 | 2446.492 | -0.955 | 0.340 | -7131.937 | 2459.270 |
| Model_Sephia | -9144.6454 | 2980.684 | -3.068 | 0.002 | -1.5e+04 | -3301.921 |
| Model_Sequoia | 5962.6236 | 1791.976 | 3.327 | 0.001 | 2449.999 | 9475.248 |
| Model_Seville | 7551.0906 | 3237.314 | 2.333 | 0.020 | 1205.322 | 1.39e+04 |
| Model_Shadow | -1.274e+04 | 5014.385 | -2.540 | 0.011 | -2.26e+04 | -2908.801 |
| Model_Shelby GT350 | -7641.9247 | 3156.927 | -2.421 | 0.016 | -1.38e+04 | -1453.730 |
| Model_Shelby GT500 | -3.998e+04 | 3483.549 | -11.477 | 0.000 | -4.68e+04 | -3.32e+04 |
| Model_Sidekick | -1.606e+04 | 1732.997 | -9.266 | 0.000 | -1.95e+04 | -1.27e+04 |
| Model_Sienna | 4094.2276 | 4159.211 | 0.984 | 0.325 | -4058.641 | 1.22e+04 |
| Model_Sierra 1500 | 2.027e+04 | 3325.383 | 6.097 | 0.000 | 1.38e+04 | 2.68e+04 |
| Model_Sierra 1500 Classic | 1.677e+04 | 2936.011 | 5.711 | 0.000 | 1.1e+04 | 2.25e+04 |
| Model_Sierra 1500 Hybrid | 2.343e+04 | 3644.550 | 6.430 | 0.000 | 1.63e+04 | 3.06e+04 |
| Model_Sierra 1500HD | 1.819e+04 | 3931.248 | 4.626 | 0.000 | 1.05e+04 | 2.59e+04 |
| Model_Sierra C3 | 2.615e+04 | 7429.015 | 3.520 | 0.000 | 1.16e+04 | 4.07e+04 |
| Model_Sierra Classic 1500 | 2873.2521 | 4270.203 | 0.673 | 0.501 | -5497.182 | 1.12e+04 |
| Model_Sigma | -8541.2755 | 7001.327 | -1.220 | 0.223 | -2.23e+04 | 5182.697 |
| Model_Silhouette | 1.284e+04 | 4143.129 | 3.100 | 0.002 | 4722.274 | 2.1e+04 |
| Model_Silver Seraph | -7.215e+04 | 4821.699 | -14.964 | 0.000 | -8.16e+04 | -6.27e+04 |
| Model_Silverado 1500 | -1.411e+04 | 1782.018 | -7.920 | 0.000 | -1.76e+04 | -1.06e+04 |
| Model_Silverado 1500 Classic | -1.688e+04 | 1784.263 | -9.463 | 0.000 | -2.04e+04 | -1.34e+04 |
| Model_Silverado 1500 Hybrid | -1.058e+04 | 2584.297 | -4.092 | 0.000 | -1.56e+04 | -5509.714 |
| Model_Sixty Special | -2.741e+04 | 7085.070 | -3.869 | 0.000 | -4.13e+04 | -1.35e+04 |
| Model_Skylark | -1.67e+04 | 2752.404 | -6.066 | 0.000 | -2.21e+04 | -1.13e+04 |
| Model_Solstice | -3207.2485 | 2502.009 | -1.282 | 0.200 | -8111.677 | 1697.180 |
| Model_Sonata | -2631.7164 | 1824.247 | -1.443 | 0.149 | -6207.598 | 944.166 |
| Model_Sonata Hybrid | 1230.6205 | 3062.960 | 0.402 | 0.688 | -4773.380 | 7234.621 |
| Model_Sonic | -1.714e+04 | 1791.524 | -9.566 | 0.000 | -2.06e+04 | -1.36e+04 |
| Model_Sonoma | 2.029e+04 | 3154.740 | 6.431 | 0.000 | 1.41e+04 | 2.65e+04 |
| Model_Sorento | -2785.7455 | 1326.785 | -2.100 | 0.036 | -5386.503 | -184.988 |
| Model_Soul | -1.113e+04 | 2204.278 | -5.048 | 0.000 | -1.54e+04 | -6805.778 |
| Model_Soul EV | -4489.2907 | 5363.485 | -0.837 | 0.403 | -1.5e+04 | 6024.190 |
| Model_Spark | -2.312e+04 | 2295.416 | -10.071 | 0.000 | -2.76e+04 | -1.86e+04 |
| Model_Spark EV | -1.717e+04 | 5523.384 | -3.109 | 0.002 | -2.8e+04 | -6344.650 |
| Model_Spectra | -7514.7144 | 1633.146 | -4.601 | 0.000 | -1.07e+04 | -4313.429 |
| Model_Spirit | -1.228e+04 | 5871.612 | -2.091 | 0.037 | -2.38e+04 | -765.650 |
| Model_Sportage | -6712.4776 | 1820.623 | -3.687 | 0.000 | -1.03e+04 | -3143.699 |
| Model_Sportvan | -3.05e+04 | 4502.495 | -6.774 | 0.000 | -3.93e+04 | -2.17e+04 |
| Model_Spyder | -1.549e+04 | 6153.534 | -2.517 | 0.012 | -2.75e+04 | -3425.288 |
| Model_Stanza | -904.8963 | 3886.231 | -0.233 | 0.816 | -8522.670 | 6712.877 |
| Model_Stealth | -1.387e+04 | 5162.647 | -2.687 | 0.007 | -2.4e+04 | -3754.707 |
| Model_Stratus | 485.3919 | 4685.037 | 0.104 | 0.917 | -8698.198 | 9668.981 |
| Model_Suburban | 3966.9170 | 1704.826 | 2.327 | 0.020 | 625.124 | 7308.710 |
| Model_Sunbird | -1.375e+04 | 2272.058 | -6.053 | 0.000 | -1.82e+04 | -9298.968 |
| Model_Sundance | -7654.4349 | 4724.893 | -1.620 | 0.105 | -1.69e+04 | 1607.281 |
| Model_Sunfire | -6995.1226 | 3269.093 | -2.140 | 0.032 | -1.34e+04 | -587.060 |
| Model_Superamerica | 2.422e+04 | 4866.398 | 4.976 | 0.000 | 1.47e+04 | 3.38e+04 |
| Model_Supersports Convertible ISR | -4.176e+04 | 7442.525 | -5.612 | 0.000 | -5.64e+04 | -2.72e+04 |
| Model_Supra | 3463.6133 | 3167.607 | 1.093 | 0.274 | -2745.516 | 9672.743 |
| Model_Swift | -1.266e+04 | 2940.974 | -4.304 | 0.000 | -1.84e+04 | -6892.867 |
| Model_Syclone | 1.048e+04 | 7389.569 | 1.418 | 0.156 | -4003.726 | 2.5e+04 |
| Model_T100 | -1.677e+04 | 2170.160 | -7.726 | 0.000 | -2.1e+04 | -1.25e+04 |
| Model_TC | -2.456e+04 | 5721.628 | -4.292 | 0.000 | -3.58e+04 | -1.33e+04 |
| Model_TL | -5893.7952 | 1714.588 | -3.437 | 0.001 | -9254.723 | -2532.867 |
| Model_TLX | -9083.9467 | 1871.007 | -4.855 | 0.000 | -1.28e+04 | -5416.407 |
| Model_TSX | -7318.5437 | 1948.468 | -3.756 | 0.000 | -1.11e+04 | -3499.165 |
| Model_TSX Sport Wagon | -6745.2858 | 3014.661 | -2.237 | 0.025 | -1.27e+04 | -835.959 |
| Model_TT | 2963.5479 | 4287.950 | 0.691 | 0.489 | -5441.674 | 1.14e+04 |
| Model_TT RS | 1.735e+04 | 5880.296 | 2.950 | 0.003 | 5819.930 | 2.89e+04 |
| Model_TTS | 1.02e+04 | 4774.494 | 2.136 | 0.033 | 840.047 | 1.96e+04 |
| Model_Tacoma | -8871.6472 | 1786.167 | -4.967 | 0.000 | -1.24e+04 | -5370.410 |
| Model_Tahoe | 1297.5840 | 2176.609 | 0.596 | 0.551 | -2968.995 | 5564.163 |
| Model_Tahoe Hybrid | 4860.8629 | 3236.552 | 1.502 | 0.133 | -1483.412 | 1.12e+04 |
| Model_Tahoe Limited/Z71 | -3.139e+04 | 5355.067 | -5.861 | 0.000 | -4.19e+04 | -2.09e+04 |
| Model_Taurus | 3552.5668 | 2009.682 | 1.768 | 0.077 | -386.804 | 7491.938 |
| Model_Taurus X | 7902.9242 | 2353.857 | 3.357 | 0.001 | 3288.903 | 1.25e+04 |
| Model_Tempo | -2663.5800 | 3006.554 | -0.886 | 0.376 | -8557.014 | 3229.854 |
| Model_Tercel | -1.118e+04 | 2939.075 | -3.805 | 0.000 | -1.69e+04 | -5422.791 |
| Model_Terrain | 2.549e+04 | 3114.212 | 8.187 | 0.000 | 1.94e+04 | 3.16e+04 |
| Model_Terraza | 2596.5818 | 4288.599 | 0.605 | 0.545 | -5809.913 | 1.1e+04 |
| Model_Thunderbird | 1.125e+04 | 2813.556 | 3.997 | 0.000 | 5730.050 | 1.68e+04 |
| Model_Tiburon | -4526.8179 | 1853.820 | -2.442 | 0.015 | -8160.668 | -892.968 |
| Model_Tiguan | -7255.2987 | 5737.694 | -1.264 | 0.206 | -1.85e+04 | 3991.706 |
| Model_Titan | -6838.4376 | 2645.306 | -2.585 | 0.010 | -1.2e+04 | -1653.120 |
| Model_Toronado | -1.084e+04 | 4037.385 | -2.684 | 0.007 | -1.88e+04 | -2922.016 |
| Model_Torrent | -5093.0362 | 2437.502 | -2.089 | 0.037 | -9871.017 | -315.055 |
| Model_Touareg | 8560.5889 | 5787.862 | 1.479 | 0.139 | -2784.754 | 1.99e+04 |
| Model_Touareg 2 | 3747.5116 | 6124.145 | 0.612 | 0.541 | -8257.013 | 1.58e+04 |
| Model_Town Car | 5.392e+04 | 3317.034 | 16.257 | 0.000 | 4.74e+04 | 6.04e+04 |
| Model_Town and Country | -3991.5621 | 5271.696 | -0.757 | 0.449 | -1.43e+04 | 6341.994 |
| Model_Tracker | -1.043e+04 | 2413.813 | -4.323 | 0.000 | -1.52e+04 | -5702.525 |
| Model_TrailBlazer | -1.103e+04 | 1968.636 | -5.602 | 0.000 | -1.49e+04 | -7168.998 |
| Model_TrailBlazer EXT | -7273.8578 | 2659.639 | -2.735 | 0.006 | -1.25e+04 | -2060.445 |
| Model_Trans Sport | -1.585e+04 | 4397.099 | -3.605 | 0.000 | -2.45e+04 | -7230.582 |
| Model_Transit Connect | 6729.0682 | 3859.820 | 1.743 | 0.081 | -836.935 | 1.43e+04 |
| Model_Transit Wagon | 1956.6327 | 3402.060 | 0.575 | 0.565 | -4712.071 | 8625.336 |
| Model_Traverse | -7836.3791 | 1907.681 | -4.108 | 0.000 | -1.16e+04 | -4096.951 |
| Model_Trax | -1.588e+04 | 2158.291 | -7.355 | 0.000 | -2.01e+04 | -1.16e+04 |
| Model_Tribeca | -2037.5493 | 3113.260 | -0.654 | 0.513 | -8140.149 | 4065.050 |
| Model_Tribute | -6904.8873 | 1882.421 | -3.668 | 0.000 | -1.06e+04 | -3214.973 |
| Model_Tribute Hybrid | -136.6361 | 2928.529 | -0.047 | 0.963 | -5877.126 | 5603.854 |
| Model_Truck | -9119.4585 | 1516.283 | -6.014 | 0.000 | -1.21e+04 | -6147.247 |
| Model_Tucson | -8014.5433 | 1695.435 | -4.727 | 0.000 | -1.13e+04 | -4691.158 |
| Model_Tundra | -1.602e+04 | 1741.079 | -9.203 | 0.000 | -1.94e+04 | -1.26e+04 |
| Model_Typhoon | 1.335e+04 | 5729.522 | 2.330 | 0.020 | 2117.196 | 2.46e+04 |
| Model_Uplander | -4627.6946 | 4183.113 | -1.106 | 0.269 | -1.28e+04 | 3572.026 |
| Model_V12 Vanquish | 4.478e+04 | 3471.997 | 12.897 | 0.000 | 3.8e+04 | 5.16e+04 |
| Model_V12 Vantage | -1.796e+04 | 3492.722 | -5.141 | 0.000 | -2.48e+04 | -1.11e+04 |
| Model_V12 Vantage S | -4.166e+04 | 4045.337 | -10.298 | 0.000 | -4.96e+04 | -3.37e+04 |
| Model_V40 | 58.6781 | 8387.084 | 0.007 | 0.994 | -1.64e+04 | 1.65e+04 |
| Model_V50 | -1551.8789 | 8181.495 | -0.190 | 0.850 | -1.76e+04 | 1.45e+04 |
| Model_V60 | 3231.6013 | 7809.118 | 0.414 | 0.679 | -1.21e+04 | 1.85e+04 |
| Model_V60 Cross Country | 4866.2712 | 8187.169 | 0.594 | 0.552 | -1.12e+04 | 2.09e+04 |
| Model_V70 | 388.5196 | 8368.821 | 0.046 | 0.963 | -1.6e+04 | 1.68e+04 |
| Model_V8 | -1.998e+04 | 4887.987 | -4.087 | 0.000 | -2.96e+04 | -1.04e+04 |
| Model_V8 Vantage | -6.463e+04 | 1810.445 | -35.699 | 0.000 | -6.82e+04 | -6.11e+04 |
| Model_V90 | -2.063e+04 | 1.03e+04 | -1.999 | 0.046 | -4.09e+04 | -401.551 |
| Model_Van | 270.3994 | 6496.831 | 0.042 | 0.967 | -1.25e+04 | 1.3e+04 |
| Model_Vanagon | -1.508e+04 | 7516.603 | -2.006 | 0.045 | -2.98e+04 | -341.199 |
| Model_Vandura | 4129.5787 | 4342.870 | 0.951 | 0.342 | -4383.298 | 1.26e+04 |
| Model_Vanquish | 7.024e+04 | 2650.993 | 26.495 | 0.000 | 6.5e+04 | 7.54e+04 |
| Model_Vanwagon | -4315.3589 | 6515.683 | -0.662 | 0.508 | -1.71e+04 | 8456.655 |
| Model_Veloster | -9483.5280 | 2037.625 | -4.654 | 0.000 | -1.35e+04 | -5489.383 |
| Model_Venture | -702.6295 | 4190.675 | -0.168 | 0.867 | -8917.174 | 7511.914 |
| Model_Venza | 3942.6831 | 1735.417 | 2.272 | 0.023 | 540.926 | 7344.440 |
| Model_Veracruz | -87.8289 | 2068.162 | -0.042 | 0.966 | -4141.831 | 3966.173 |
| Model_Verano | -7289.8133 | 2358.712 | -3.091 | 0.002 | -1.19e+04 | -2666.276 |
| Model_Verona | -2698.1187 | 2434.873 | -1.108 | 0.268 | -7470.946 | 2074.709 |
| Model_Versa | -8079.5541 | 2546.041 | -3.173 | 0.002 | -1.31e+04 | -3088.814 |
| Model_Versa Note | -7301.2561 | 2598.885 | -2.809 | 0.005 | -1.24e+04 | -2206.933 |
| Model_Veyron 16.4 | 6.922e+05 | 3637.595 | 190.291 | 0.000 | 6.85e+05 | 6.99e+05 |
| Model_Vibe | -7911.7489 | 2688.313 | -2.943 | 0.003 | -1.32e+04 | -2642.129 |
| Model_Vigor | -2.14e+04 | 4082.171 | -5.242 | 0.000 | -2.94e+04 | -1.34e+04 |
| Model_Viper | -5478.9419 | 4818.341 | -1.137 | 0.256 | -1.49e+04 | 3965.950 |
| Model_Virage | 9369.8515 | 4787.657 | 1.957 | 0.050 | -14.894 | 1.88e+04 |
| Model_Vitara | -4263.7013 | 1875.648 | -2.273 | 0.023 | -7940.339 | -587.064 |
| Model_Voyager | -7322.7481 | 5714.577 | -1.281 | 0.200 | -1.85e+04 | 3878.942 |
| Model_WRX | -1881.8088 | 1870.918 | -1.006 | 0.315 | -5549.174 | 1785.556 |
| Model_Windstar | 1.642e+04 | 4133.749 | 3.972 | 0.000 | 8314.357 | 2.45e+04 |
| Model_Windstar Cargo | 1.228e+04 | 5765.948 | 2.129 | 0.033 | 974.800 | 2.36e+04 |
| Model_Wraith | -7.142e+04 | 4148.676 | -17.216 | 0.000 | -7.96e+04 | -6.33e+04 |
| Model_X-90 | -1.416e+04 | 3179.579 | -4.454 | 0.000 | -2.04e+04 | -7930.280 |
| Model_X1 | -1.882e+04 | 3641.154 | -5.168 | 0.000 | -2.6e+04 | -1.17e+04 |
| Model_X3 | -1.226e+04 | 3023.142 | -4.054 | 0.000 | -1.82e+04 | -6329.807 |
| Model_X4 | -8311.1132 | 3477.253 | -2.390 | 0.017 | -1.51e+04 | -1495.017 |
| Model_X5 | -3476.6013 | 3014.214 | -1.153 | 0.249 | -9385.051 | 2431.849 |
| Model_X5 M | -7987.7850 | 4673.345 | -1.709 | 0.087 | -1.71e+04 | 1172.887 |
| Model_X6 | 817.6930 | 3231.291 | 0.253 | 0.800 | -5516.270 | 7151.656 |
| Model_X6 M | -4587.7850 | 4673.345 | -0.982 | 0.326 | -1.37e+04 | 4572.887 |
| Model_XC | 8400.7091 | 1.03e+04 | 0.812 | 0.417 | -1.19e+04 | 2.87e+04 |
| Model_XC60 | 2038.5189 | 7723.952 | 0.264 | 0.792 | -1.31e+04 | 1.72e+04 |
| Model_XC70 | 3116.4594 | 7825.969 | 0.398 | 0.690 | -1.22e+04 | 1.85e+04 |
| Model_XC90 | 7137.6710 | 7883.778 | 0.905 | 0.365 | -8316.078 | 2.26e+04 |
| Model_XG300 | 4931.3553 | 4934.727 | 0.999 | 0.318 | -4741.676 | 1.46e+04 |
| Model_XG350 | 3433.5025 | 2933.017 | 1.171 | 0.242 | -2315.785 | 9182.790 |
| Model_XL-7 | -780.9299 | 1469.636 | -0.531 | 0.595 | -3661.704 | 2099.844 |
| Model_XL7 | -1952.5671 | 1394.892 | -1.400 | 0.162 | -4686.828 | 781.694 |
| Model_XLR | 3.031e+04 | 2820.946 | 10.744 | 0.000 | 2.48e+04 | 3.58e+04 |
| Model_XLR-V | 3.092e+04 | 3631.263 | 8.516 | 0.000 | 2.38e+04 | 3.8e+04 |
| Model_XT | -1.106e+04 | 4093.781 | -2.702 | 0.007 | -1.91e+04 | -3037.430 |
| Model_XT5 | -2273.7220 | 3004.936 | -0.757 | 0.449 | -8163.985 | 3616.541 |
| Model_XTS | 5534.1500 | 1657.966 | 3.338 | 0.001 | 2284.212 | 8784.088 |
| Model_XV Crosstrek | -7724.6162 | 2031.091 | -3.803 | 0.000 | -1.17e+04 | -3743.279 |
| Model_Xterra | -567.9202 | 2178.321 | -0.261 | 0.794 | -4837.856 | 3702.016 |
| Model_Yaris | -1.296e+04 | 1850.016 | -7.005 | 0.000 | -1.66e+04 | -9332.543 |
| Model_Yaris iA | -1.169e+04 | 5034.211 | -2.322 | 0.020 | -2.16e+04 | -1823.275 |
| Model_Yukon | 3.457e+04 | 3295.285 | 10.492 | 0.000 | 2.81e+04 | 4.1e+04 |
| Model_Yukon Denali | 1454.0864 | 7457.491 | 0.195 | 0.845 | -1.32e+04 | 1.61e+04 |
| Model_Yukon Hybrid | 4.281e+04 | 3375.931 | 12.681 | 0.000 | 3.62e+04 | 4.94e+04 |
| Model_Yukon XL | 3.725e+04 | 3294.053 | 11.307 | 0.000 | 3.08e+04 | 4.37e+04 |
| Model_Z3 | -1.747e+04 | 3133.168 | -5.575 | 0.000 | -2.36e+04 | -1.13e+04 |
| Model_Z4 | -4503.2799 | 3193.990 | -1.410 | 0.159 | -1.08e+04 | 1757.567 |
| Model_Z4 M | -1692.8037 | 4056.365 | -0.417 | 0.676 | -9644.074 | 6258.467 |
| Model_Z8 | 6.67e+04 | 4558.143 | 14.633 | 0.000 | 5.78e+04 | 7.56e+04 |
| Model_ZDX | 2208.2458 | 2807.982 | 0.786 | 0.432 | -3295.950 | 7712.441 |
| Model_Zephyr | 3.972e+04 | 7287.768 | 5.450 | 0.000 | 2.54e+04 | 5.4e+04 |
| Model_allroad | 1.209e+04 | 3974.600 | 3.042 | 0.002 | 4300.671 | 1.99e+04 |
| Model_allroad quattro | 1.398e+04 | 3716.910 | 3.762 | 0.000 | 6696.257 | 2.13e+04 |
| Model_e-Golf | -1.016e+04 | 8074.348 | -1.258 | 0.208 | -2.6e+04 | 5668.882 |
| Model_i-MiEV | -1.446e+04 | 6069.106 | -2.383 | 0.017 | -2.64e+04 | -2565.293 |
| Model_i3 | -1.153e+04 | 6359.136 | -1.814 | 0.070 | -2.4e+04 | 930.301 |
| Model_iA | -1.921e+04 | 4713.862 | -4.074 | 0.000 | -2.84e+04 | -9964.967 |
| Model_iM | -1.701e+04 | 4699.879 | -3.618 | 0.000 | -2.62e+04 | -7792.638 |
| Model_iQ | -2.076e+04 | 3519.450 | -5.898 | 0.000 | -2.77e+04 | -1.39e+04 |
| Model_tC | -1.309e+04 | 2280.205 | -5.742 | 0.000 | -1.76e+04 | -8623.135 |
| Model_xA | -1.607e+04 | 2826.893 | -5.684 | 0.000 | -2.16e+04 | -1.05e+04 |
| Model_xB | -1.502e+04 | 2533.439 | -5.930 | 0.000 | -2e+04 | -1.01e+04 |
| Model_xD | -1.716e+04 | 2403.513 | -7.138 | 0.000 | -2.19e+04 | -1.24e+04 |
| Engine Fuel Type_diesel | -1.311e+05 | 1.58e+04 | -8.307 | 0.000 | -1.62e+05 | -1e+05 |
| Engine Fuel Type_electric | -1.211e+05 | 1.69e+04 | -7.172 | 0.000 | -1.54e+05 | -8.8e+04 |
| Engine Fuel Type_flex-fuel (premium unleaded recommended/E85) | -1.354e+05 | 1.6e+04 | -8.448 | 0.000 | -1.67e+05 | -1.04e+05 |
| Engine Fuel Type_flex-fuel (premium unleaded required/E85) | -1.375e+05 | 1.58e+04 | -8.686 | 0.000 | -1.69e+05 | -1.06e+05 |
| Engine Fuel Type_flex-fuel (unleaded/E85) | -1.361e+05 | 1.58e+04 | -8.608 | 0.000 | -1.67e+05 | -1.05e+05 |
| Engine Fuel Type_flex-fuel (unleaded/natural gas) | -1.321e+05 | 1.62e+04 | -8.155 | 0.000 | -1.64e+05 | -1e+05 |
| Engine Fuel Type_natural gas | -1.311e+05 | 1.65e+04 | -7.954 | 0.000 | -1.63e+05 | -9.88e+04 |
| Engine Fuel Type_premium unleaded (recommended) | -1.342e+05 | 1.58e+04 | -8.481 | 0.000 | -1.65e+05 | -1.03e+05 |
| Engine Fuel Type_premium unleaded (required) | -1.346e+05 | 1.58e+04 | -8.523 | 0.000 | -1.66e+05 | -1.04e+05 |
| Engine Fuel Type_regular unleaded | -1.353e+05 | 1.58e+04 | -8.561 | 0.000 | -1.66e+05 | -1.04e+05 |
| Transmission Type_AUTOMATED_MANUAL | -3.297e+05 | 3.94e+04 | -8.378 | 0.000 | -4.07e+05 | -2.53e+05 |
| Transmission Type_AUTOMATIC | -3.337e+05 | 3.94e+04 | -8.480 | 0.000 | -4.11e+05 | -2.57e+05 |
| Transmission Type_DIRECT_DRIVE | -3.297e+05 | 3.95e+04 | -8.343 | 0.000 | -4.07e+05 | -2.52e+05 |
| Transmission Type_MANUAL | -3.354e+05 | 3.93e+04 | -8.524 | 0.000 | -4.12e+05 | -2.58e+05 |
| Driven_Wheels_all wheel drive | -3.309e+05 | 3.93e+04 | -8.414 | 0.000 | -4.08e+05 | -2.54e+05 |
| Driven_Wheels_four wheel drive | -3.311e+05 | 3.93e+04 | -8.423 | 0.000 | -4.08e+05 | -2.54e+05 |
| Driven_Wheels_front wheel drive | -3.329e+05 | 3.93e+04 | -8.465 | 0.000 | -4.1e+05 | -2.56e+05 |
| Driven_Wheels_rear wheel drive | -3.336e+05 | 3.93e+04 | -8.487 | 0.000 | -4.11e+05 | -2.57e+05 |
| Vehicle Size_Compact | -4.432e+05 | 5.24e+04 | -8.461 | 0.000 | -5.46e+05 | -3.41e+05 |
| Vehicle Size_Large | -4.417e+05 | 5.25e+04 | -8.419 | 0.000 | -5.45e+05 | -3.39e+05 |
| Vehicle Size_Midsize | -4.436e+05 | 5.24e+04 | -8.461 | 0.000 | -5.46e+05 | -3.41e+05 |
| Vehicle Style_2dr Hatchback | -8.375e+04 | 1.01e+04 | -8.310 | 0.000 | -1.04e+05 | -6.4e+04 |
| Vehicle Style_2dr SUV | -8.441e+04 | 1.04e+04 | -8.128 | 0.000 | -1.05e+05 | -6.41e+04 |
| Vehicle Style_4dr Hatchback | -8.582e+04 | 1.01e+04 | -8.527 | 0.000 | -1.06e+05 | -6.61e+04 |
| Vehicle Style_4dr SUV | -8.456e+04 | 1.03e+04 | -8.249 | 0.000 | -1.05e+05 | -6.45e+04 |
| Vehicle Style_Cargo Minivan | -9.065e+04 | 1.14e+04 | -7.965 | 0.000 | -1.13e+05 | -6.83e+04 |
| Vehicle Style_Cargo Van | -7.634e+04 | 8939.359 | -8.539 | 0.000 | -9.39e+04 | -5.88e+04 |
| Vehicle Style_Convertible | -7.739e+04 | 1.01e+04 | -7.683 | 0.000 | -9.71e+04 | -5.76e+04 |
| Vehicle Style_Convertible SUV | -8.242e+04 | 1.04e+04 | -7.931 | 0.000 | -1.03e+05 | -6.21e+04 |
| Vehicle Style_Coupe | -8.549e+04 | 1.01e+04 | -8.496 | 0.000 | -1.05e+05 | -6.58e+04 |
| Vehicle Style_Crew Cab Pickup | -7.797e+04 | 9324.862 | -8.362 | 0.000 | -9.63e+04 | -5.97e+04 |
| Vehicle Style_Extended Cab Pickup | -8.08e+04 | 9318.610 | -8.671 | 0.000 | -9.91e+04 | -6.25e+04 |
| Vehicle Style_Passenger Minivan | -8.786e+04 | 1.13e+04 | -7.789 | 0.000 | -1.1e+05 | -6.58e+04 |
| Vehicle Style_Passenger Van | -7.63e+04 | 8989.535 | -8.488 | 0.000 | -9.39e+04 | -5.87e+04 |
| Vehicle Style_Regular Cab Pickup | -8.086e+04 | 9333.654 | -8.663 | 0.000 | -9.92e+04 | -6.26e+04 |
| Vehicle Style_Sedan | -8.728e+04 | 1e+04 | -8.697 | 0.000 | -1.07e+05 | -6.76e+04 |
| Vehicle Style_Wagon | -8.661e+04 | 1e+04 | -8.619 | 0.000 | -1.06e+05 | -6.69e+04 |
| Omnibus: | 12341.470 | Durbin-Watson: | 2.356 |
|---|---|---|---|
| Prob(Omnibus): | 0.000 | Jarque-Bera (JB): | 148226190.597 |
| Skew: | 4.098 | Prob(JB): | 0.00 |
| Kurtosis: | 566.550 | Cond. No. | 1.26e+22 |
Warnings:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
[2] The smallest eigenvalue is 7.9e-31. This might indicate that there are
strong multicollinearity problems or that the design matrix is singular.
Plotting true msrp vs the predicted msrp to evalate
X = df[['Driven_Wheels_all wheel drive','Driven_Wheels_front wheel drive',
'Driven_Wheels_rear wheel drive','Engine Cylinders']].values
y = df['MSRP'].values
model =RandomForestRegressor()
model.fit(X, y)
y_pred = model.predict(X)
df['y_pred'] = y_pred
sns.lmplot(x='MSRP', y='y_pred', data=df)
<seaborn.axisgrid.FacetGrid at 0x1a19232b70>
Future interests
Other info
#Get examples from ebay and see if I can predict price or correct price.
# Against my price?
# Create another model for outliers
# Classification for normal cars vs outliers(determine higher prices)
Written on September 30, 2018