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In [1]:

    
# import common APIs
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline
from mpl_toolkits.mplot3d import Axes3D
import seaborn as sns
import os
from sklearn.preprocessing import MinMaxScaler, StandardScaler, LabelEncoder, OneHotEncoder
from sklearn.decomposition import PCA
from sklearn import cross_validation, naive_bayes, tree, svm, ensemble
from sklearn.metrics import classification_report,confusion_matrix,precision_recall_curve,auc,roc_auc_score,roc_curve
from xgboost import XGBRegressor









    



//anaconda/lib/python3.5/site-packages/sklearn/cross_validation.py:41: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.
  "This module will be removed in 0.20.", DeprecationWarning)



In [2]:

    
# Data observation
filepath = '/Users/mac/Desktop/Kaggle_datasets/Safe_Driver(Reg)/'
filename01 = 'train.csv'

df = pd.read_csv(os.path.join(filepath, filename01))
df.head()









    Out[2]:






  
    
      
      id
      target
      ps_ind_01
      ps_ind_02_cat
      ps_ind_03
      ps_ind_04_cat
      ps_ind_05_cat
      ps_ind_06_bin
      ps_ind_07_bin
      ps_ind_08_bin
      ...
      ps_calc_11
      ps_calc_12
      ps_calc_13
      ps_calc_14
      ps_calc_15_bin
      ps_calc_16_bin
      ps_calc_17_bin
      ps_calc_18_bin
      ps_calc_19_bin
      ps_calc_20_bin
    
  
  
    
      0
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      0
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      1
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      2
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      16
      0
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      ...
      3
      1
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      0
    
  

5 rows × 59 columns



In [3]:

    
df.info()









    



<class 'pandas.core.frame.DataFrame'>
RangeIndex: 595212 entries, 0 to 595211
Data columns (total 59 columns):
id                595212 non-null int64
target            595212 non-null int64
ps_ind_01         595212 non-null int64
ps_ind_02_cat     595212 non-null int64
ps_ind_03         595212 non-null int64
ps_ind_04_cat     595212 non-null int64
ps_ind_05_cat     595212 non-null int64
ps_ind_06_bin     595212 non-null int64
ps_ind_07_bin     595212 non-null int64
ps_ind_08_bin     595212 non-null int64
ps_ind_09_bin     595212 non-null int64
ps_ind_10_bin     595212 non-null int64
ps_ind_11_bin     595212 non-null int64
ps_ind_12_bin     595212 non-null int64
ps_ind_13_bin     595212 non-null int64
ps_ind_14         595212 non-null int64
ps_ind_15         595212 non-null int64
ps_ind_16_bin     595212 non-null int64
ps_ind_17_bin     595212 non-null int64
ps_ind_18_bin     595212 non-null int64
ps_reg_01         595212 non-null float64
ps_reg_02         595212 non-null float64
ps_reg_03         595212 non-null float64
ps_car_01_cat     595212 non-null int64
ps_car_02_cat     595212 non-null int64
ps_car_03_cat     595212 non-null int64
ps_car_04_cat     595212 non-null int64
ps_car_05_cat     595212 non-null int64
ps_car_06_cat     595212 non-null int64
ps_car_07_cat     595212 non-null int64
ps_car_08_cat     595212 non-null int64
ps_car_09_cat     595212 non-null int64
ps_car_10_cat     595212 non-null int64
ps_car_11_cat     595212 non-null int64
ps_car_11         595212 non-null int64
ps_car_12         595212 non-null float64
ps_car_13         595212 non-null float64
ps_car_14         595212 non-null float64
ps_car_15         595212 non-null float64
ps_calc_01        595212 non-null float64
ps_calc_02        595212 non-null float64
ps_calc_03        595212 non-null float64
ps_calc_04        595212 non-null int64
ps_calc_05        595212 non-null int64
ps_calc_06        595212 non-null int64
ps_calc_07        595212 non-null int64
ps_calc_08        595212 non-null int64
ps_calc_09        595212 non-null int64
ps_calc_10        595212 non-null int64
ps_calc_11        595212 non-null int64
ps_calc_12        595212 non-null int64
ps_calc_13        595212 non-null int64
ps_calc_14        595212 non-null int64
ps_calc_15_bin    595212 non-null int64
ps_calc_16_bin    595212 non-null int64
ps_calc_17_bin    595212 non-null int64
ps_calc_18_bin    595212 non-null int64
ps_calc_19_bin    595212 non-null int64
ps_calc_20_bin    595212 non-null int64
dtypes: float64(10), int64(49)
memory usage: 267.9 MB



In [4]:

    
df.target.value_counts()









    Out[4]:





0    573518
1     21694
Name: target, dtype: int64



In [5]:

    
filepath = '/Users/mac/Desktop/Kaggle_datasets/Safe_Driver(Reg)/'
filename02 = 'test.csv'

df2 = pd.read_csv(os.path.join(filepath, filename02))
df2.head()









    Out[5]:






  
    
      
      id
      ps_ind_01
      ps_ind_02_cat
      ps_ind_03
      ps_ind_04_cat
      ps_ind_05_cat
      ps_ind_06_bin
      ps_ind_07_bin
      ps_ind_08_bin
      ps_ind_09_bin
      ...
      ps_calc_11
      ps_calc_12
      ps_calc_13
      ps_calc_14
      ps_calc_15_bin
      ps_calc_16_bin
      ps_calc_17_bin
      ps_calc_18_bin
      ps_calc_19_bin
      ps_calc_20_bin
    
  
  
    
      0
      0
      0
      1
      8
      1
      0
      0
      1
      0
      0
      ...
      1
      1
      1
      12
      0
      1
      1
      0
      0
      1
    
    
      1
      1
      4
      2
      5
      1
      0
      0
      0
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      ...
      2
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      10
      0
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      1
    
    
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      ...
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      0
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      4
      0
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      1
    
  

5 rows × 58 columns



In [6]:

    
filepath = '/Users/mac/Desktop/Kaggle_datasets/Safe_Driver(Reg)/'
filename03 = 'sample_submission.csv'

df3 = pd.read_csv(os.path.join(filepath, filename03))
df3.head()



In [7]:

    
# Data preprocessing
from sklearn.utils import shuffle

df.drop('id', axis=1, inplace=True)
shuffle_df = shuffle(df, random_state=42)

df_label = shuffle_df['target']
df_feature = shuffle_df.drop('target', axis=1)

cut_point = round(len(df)*0.6)
train_feature = np.array(df_feature.values[:cut_point,:])
train_label = np.array(df_label.values[:cut_point])
test_feature = np.array(df_feature.values[cut_point:,:])
test_label = np.array(df_label.values[cut_point:])



In [13]:

    
### tree.DecisionTreeRegressor()
from sklearn import cross_validation, tree
X_train,X_test,y_train,y_test = cross_validation.train_test_split(train_feature,train_label, 
                                              test_size=0.25, random_state=0)
regr=tree.DecisionTreeRegressor()
regr.fit(X_train,y_train)
print("Traing Score:%f"%regr.score(X_train,y_train))
print("Testing Score:%f"%regr.score(X_test,y_test))
#np.mean(np.abs((regr.predict(test_feature)-test_label)/test_label)) #平均誤差百分比









    



Traing Score:1.000000
Testing Score:-1.303998



In [37]:

    
### ensemble.RandomForestRegressor()
from sklearn import cross_validation,ensemble
X_train,X_test,y_train,y_test = cross_validation.train_test_split(train_feature,train_label, 
                                                test_size=0.25, random_state=0)
regr=ensemble.RandomForestRegressor()
regr.fit(X_train,y_train)
print("Traing Score:%f"%regr.score(X_train,y_train))
print("Testing Score:%f"%regr.score(X_test,y_test))









    



Traing Score:0.790828
Testing Score:-0.152782



In [38]:

    
### ensemble.AdaBoostRegressor()
from sklearn import cross_validation,ensemble
X_train,X_test,y_train,y_test = cross_validation.train_test_split(train_feature,train_label,
                                                                  test_size=0.25, random_state=0)
regr=ensemble.AdaBoostRegressor()
regr.fit(X_train,y_train)
print("Traing Score:%f"%regr.score(X_train,y_train))
print("Testing Score:%f"%regr.score(X_test,y_test))









    



Traing Score:0.004517
Testing Score:0.004009

GradientBoostingRegressor() 跟 XGB伯仲之間，可是訓練時間超久！！



In [39]:

    
### ensemble.GradientBoostingRegressor()
from sklearn import cross_validation,ensemble
X_train,X_test,y_train,y_test = cross_validation.train_test_split(train_feature,train_label,
                                                                  test_size=0.25, random_state=0)
regr=ensemble.GradientBoostingRegressor()
regr.fit(X_train,y_train)
print("Traing Score:%f"%regr.score(X_train,y_train))
print("Testing Score:%f"%regr.score(X_test,y_test))









    



Traing Score:0.016172
Testing Score:0.009060

XGB 94強...



In [8]:

    
# XGBRegressor
from xgboost import XGBRegressor
X_train,X_test,y_train,y_test = cross_validation.train_test_split(train_feature,train_label,
                                          test_size=0.25,random_state=0)
regr=XGBRegressor()
regr.fit(X_train,y_train)
print("Traing Score:%f"%regr.score(X_train,y_train))
print("Testing Score:%f"%regr.score(X_test,y_test))









    



Traing Score:0.015627
Testing Score:0.009405

Tuning the parameters



In [18]:

    
# XGBRegressor
# Default: XGBRegressor(max_depth=3, learning_rate=0.1, n_estimators=100, silent=True, objective='reg:linear', booster='gbtree', n_jobs=1, nthread=None, gamma=0, min_child_weight=1, max_delta_step=0, subsample=1, colsample_bytree=1, colsample_bylevel=1, reg_alpha=0, reg_lambda=1, scale_pos_weight=1, base_score=0.5, random_state=0, seed=None, missing=None, **kwargs)

from xgboost import XGBRegressor
X_train,X_test,y_train,y_test = cross_validation.train_test_split(train_feature,train_label,
                                          test_size=0.25,random_state=0)
regr = XGBRegressor(
    max_depth=10, 
    learning_rate=0.2, 
    n_estimators=200, 
    silent=True, 
    objective='reg:linear', 
    #nthread=None, 
    #gamma=0, 
    #min_child_weight=1, 
    #max_delta_step=0, 
    #subsample=1, 
    #colsample_bytree=1, 
    #colsample_bylevel=1, 
    #reg_alpha=0, 
    #reg_lambda=1, 
    #scale_pos_weight=1, 
    #base_score=0.5, 
    #seed=None, 
    #missing=None,
    )

regr.fit(X_train,y_train)
print("Traing Score:%f"%regr.score(X_train,y_train))
print("Testing Score:%f"%regr.score(X_test,y_test))









    



Traing Score:0.656000
Testing Score:-0.069946



In [24]:

    
from sklearn.grid_search import GridSearchCV

#用dict傳參數
parameters={
    'max_depth':[2,3,4], 
    'learning_rate':[0.1,0.2], 
    'n_estimators':[50,100],
}

model = XGBRegressor()
grid = GridSearchCV(estimator=model, 
                    param_grid=parameters,
                    cv=3,
                    verbose=5,)

grid.fit(X_train,y_train)

print(grid.best_score_)
print(grid.best_estimator_)









    



Fitting 3 folds for each of 12 candidates, totalling 36 fits
[CV] n_estimators=50, learning_rate=0.1, max_depth=2 .................
[CV]  n_estimators=50, learning_rate=0.1, max_depth=2, score=0.008441 -   7.2s
[CV] n_estimators=50, learning_rate=0.1, max_depth=2 .................






    



[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    7.2s remaining:    0.0s






    



[CV]  n_estimators=50, learning_rate=0.1, max_depth=2, score=0.007748 -   7.4s
[CV] n_estimators=50, learning_rate=0.1, max_depth=2 .................






    



[Parallel(n_jobs=1)]: Done   2 out of   2 | elapsed:   14.7s remaining:    0.0s






    



[CV]  n_estimators=50, learning_rate=0.1, max_depth=2, score=0.008667 -   9.7s
[CV] n_estimators=100, learning_rate=0.1, max_depth=2 ................






    



[Parallel(n_jobs=1)]: Done   3 out of   3 | elapsed:   24.4s remaining:    0.0s






    



[CV]  n_estimators=100, learning_rate=0.1, max_depth=2, score=0.009753 -  16.7s
[CV] n_estimators=100, learning_rate=0.1, max_depth=2 ................






    



[Parallel(n_jobs=1)]: Done   4 out of   4 | elapsed:   41.1s remaining:    0.0s






    



[CV]  n_estimators=100, learning_rate=0.1, max_depth=2, score=0.008819 -  18.7s
[CV] n_estimators=100, learning_rate=0.1, max_depth=2 ................
[CV]  n_estimators=100, learning_rate=0.1, max_depth=2, score=0.009918 -  21.5s
[CV] n_estimators=50, learning_rate=0.1, max_depth=3 .................
[CV]  n_estimators=50, learning_rate=0.1, max_depth=3, score=0.009031 -  15.2s
[CV] n_estimators=50, learning_rate=0.1, max_depth=3 .................
[CV]  n_estimators=50, learning_rate=0.1, max_depth=3, score=0.008158 -  14.6s
[CV] n_estimators=50, learning_rate=0.1, max_depth=3 .................
[CV]  n_estimators=50, learning_rate=0.1, max_depth=3, score=0.009717 -  13.7s
[CV] n_estimators=100, learning_rate=0.1, max_depth=3 ................
[CV]  n_estimators=100, learning_rate=0.1, max_depth=3, score=0.009725 -  29.4s
[CV] n_estimators=100, learning_rate=0.1, max_depth=3 ................
[CV]  n_estimators=100, learning_rate=0.1, max_depth=3, score=0.008598 -  35.2s
[CV] n_estimators=100, learning_rate=0.1, max_depth=3 ................
[CV]  n_estimators=100, learning_rate=0.1, max_depth=3, score=0.010174 -  29.1s
[CV] n_estimators=50, learning_rate=0.1, max_depth=4 .................
[CV]  n_estimators=50, learning_rate=0.1, max_depth=4, score=0.009351 -  24.0s
[CV] n_estimators=50, learning_rate=0.1, max_depth=4 .................
[CV]  n_estimators=50, learning_rate=0.1, max_depth=4, score=0.008460 -  22.7s
[CV] n_estimators=50, learning_rate=0.1, max_depth=4 .................
[CV]  n_estimators=50, learning_rate=0.1, max_depth=4, score=0.009792 -  20.5s
[CV] n_estimators=100, learning_rate=0.1, max_depth=4 ................
[CV]  n_estimators=100, learning_rate=0.1, max_depth=4, score=0.009582 -  49.0s
[CV] n_estimators=100, learning_rate=0.1, max_depth=4 ................
[CV]  n_estimators=100, learning_rate=0.1, max_depth=4, score=0.008706 -  36.8s
[CV] n_estimators=100, learning_rate=0.1, max_depth=4 ................
[CV]  n_estimators=100, learning_rate=0.1, max_depth=4, score=0.009671 -  45.3s
[CV] n_estimators=50, learning_rate=0.2, max_depth=2 .................
[CV]  n_estimators=50, learning_rate=0.2, max_depth=2, score=0.009964 -   9.6s
[CV] n_estimators=50, learning_rate=0.2, max_depth=2 .................
[CV]  n_estimators=50, learning_rate=0.2, max_depth=2, score=0.008996 -   9.5s
[CV] n_estimators=50, learning_rate=0.2, max_depth=2 .................
[CV]  n_estimators=50, learning_rate=0.2, max_depth=2, score=0.009602 -   9.0s
[CV] n_estimators=100, learning_rate=0.2, max_depth=2 ................
[CV]  n_estimators=100, learning_rate=0.2, max_depth=2, score=0.010249 -  17.7s
[CV] n_estimators=100, learning_rate=0.2, max_depth=2 ................
[CV]  n_estimators=100, learning_rate=0.2, max_depth=2, score=0.008991 -  17.3s
[CV] n_estimators=100, learning_rate=0.2, max_depth=2 ................
[CV]  n_estimators=100, learning_rate=0.2, max_depth=2, score=0.009665 -  25.7s
[CV] n_estimators=50, learning_rate=0.2, max_depth=3 .................
[CV]  n_estimators=50, learning_rate=0.2, max_depth=3, score=0.009394 -  17.0s
[CV] n_estimators=50, learning_rate=0.2, max_depth=3 .................
[CV]  n_estimators=50, learning_rate=0.2, max_depth=3, score=0.008368 -  14.4s
[CV] n_estimators=50, learning_rate=0.2, max_depth=3 .................
[CV]  n_estimators=50, learning_rate=0.2, max_depth=3, score=0.009895 -  13.8s
[CV] n_estimators=100, learning_rate=0.2, max_depth=3 ................
[CV]  n_estimators=100, learning_rate=0.2, max_depth=3, score=0.009535 -  28.2s
[CV] n_estimators=100, learning_rate=0.2, max_depth=3 ................
[CV]  n_estimators=100, learning_rate=0.2, max_depth=3, score=0.008117 -  35.1s
[CV] n_estimators=100, learning_rate=0.2, max_depth=3 ................
[CV]  n_estimators=100, learning_rate=0.2, max_depth=3, score=0.009039 -  26.7s
[CV] n_estimators=50, learning_rate=0.2, max_depth=4 .................
[CV]  n_estimators=50, learning_rate=0.2, max_depth=4, score=0.009296 -  25.6s
[CV] n_estimators=50, learning_rate=0.2, max_depth=4 .................
[CV]  n_estimators=50, learning_rate=0.2, max_depth=4, score=0.008131 -  27.7s
[CV] n_estimators=50, learning_rate=0.2, max_depth=4 .................
[CV]  n_estimators=50, learning_rate=0.2, max_depth=4, score=0.008773 -  27.7s
[CV] n_estimators=100, learning_rate=0.2, max_depth=4 ................
[CV]  n_estimators=100, learning_rate=0.2, max_depth=4, score=0.007926 -  44.9s
[CV] n_estimators=100, learning_rate=0.2, max_depth=4 ................
[CV]  n_estimators=100, learning_rate=0.2, max_depth=4, score=0.006208 -  36.1s
[CV] n_estimators=100, learning_rate=0.2, max_depth=4 ................
[CV]  n_estimators=100, learning_rate=0.2, max_depth=4, score=0.007413 -  39.3s






    



[Parallel(n_jobs=1)]: Done  36 out of  36 | elapsed: 14.1min finished






    



0.00963492633263067
XGBRegressor(base_score=0.5, colsample_bylevel=1, colsample_bytree=1, gamma=0,
       learning_rate=0.2, max_delta_step=0, max_depth=2,
       min_child_weight=1, missing=None, n_estimators=100, nthread=-1,
       objective='reg:linear', reg_alpha=0, reg_lambda=1,
       scale_pos_weight=1, seed=0, silent=True, subsample=1)



In [30]:

    
grid.best_params_









    Out[30]:





{'learning_rate': 0.2, 'max_depth': 2, 'n_estimators': 100}



In [9]:

    
# 調整成最佳參數的XGBRegressor
from xgboost import XGBRegressor
X_train,X_test,y_train,y_test = cross_validation.train_test_split(train_feature,train_label,
                                          test_size=0.25,random_state=0)
regr= XGBRegressor(base_score=0.5, colsample_bylevel=1, colsample_bytree=1, gamma=0,
       learning_rate=0.2, max_delta_step=0, max_depth=2,
       min_child_weight=1, missing=None, n_estimators=100, nthread=-1,
       objective='reg:linear', reg_alpha=0, reg_lambda=1,
       scale_pos_weight=1, seed=0, silent=True, subsample=1)

regr.fit(X_train,y_train)

print("Traing Score:%f"%regr.score(X_train,y_train))
print("Testing Score:%f"%regr.score(X_test,y_test))









    



Traing Score:0.013634
Testing Score:0.008926



In [32]:

    
ans_train = regr.predict(test_feature)
ans_train









    Out[32]:





array([ 0.02281702,  0.04230547,  0.03215387, ...,  0.050439  ,
        0.02625221,  0.02031264], dtype=float32)



In [33]:

    
sns.kdeplot(ans_train)









    Out[33]:





<matplotlib.axes._subplots.AxesSubplot at 0x11098d160>



In [10]:

    
df2.drop('id', axis=1, inplace=True)
df2_feature = np.array(df2.values)



In [11]:

    
ans_test = regr.predict(df2_feature)
ans_test









    Out[11]:





array([ 0.02853408,  0.03256738,  0.03038755, ...,  0.03900239,
        0.02681893,  0.03101519], dtype=float32)



In [36]:

    
sns.kdeplot(ans_test)









    Out[36]:





<matplotlib.axes._subplots.AxesSubplot at 0x111a1fc50>



In [13]:

    
positive = np.where(ans_test<0, 0, ans_test)



In [14]:

    
sns.kdeplot(positive)









    Out[14]:





<matplotlib.axes._subplots.AxesSubplot at 0x1109bc470>



In [15]:

    
df3['target'] = positive
df3.head()



In [16]:

    
df3.describe()









    Out[16]:






  
    
      
      id
      target
    
  
  
    
      count
      8.928160e+05
      892816.000000
    
    
      mean
      7.441535e+05
      0.036624
    
    
      std
      4.296830e+05
      0.018261
    
    
      min
      0.000000e+00
      0.000000
    
    
      25%
      3.720218e+05
      0.024369
    
    
      50%
      7.443070e+05
      0.033682
    
    
      75%
      1.116308e+06
      0.045204
    
    
      max
      1.488026e+06
      0.689391



In [17]:

    
df3.to_csv('GBreg_param_Ans.csv', index=0)



In [ ]:

	id	target
0	0	0.0364
1	1	0.0364
2	2	0.0364
3	3	0.0364
4	4	0.0364

	id	ps_ind_01	ps_ind_02_cat	ps_ind_03	ps_ind_04_cat	ps_ind_06_bin	ps_ind_07_bin	ps_ind_08_bin	...	ps_calc_11	ps_calc_12	ps_calc_13	ps_calc_14	ps_calc_16_bin	ps_calc_17_bin	ps_calc_18_bin	ps_calc_19_bin	ps_calc_20_bin
0	7	2	2	5	1	0	1	0	...	9	1	5	8	1	1	0	0	1
1	9	1	1	7	0	0	0	1	...	3	1	1	9	1	1	0	1	0
2	13	5	4	9	1	0	0	1	...	4	2	7	7	1	1	0	1	0
3	16	0	1	2	0	1	0	0	...	2	2	4	9	0	0	0	0	0
4	17	0	2	0	1	1	0	0	...	3	1	1	3	0	0	1	1	0

	id	ps_ind_01	ps_ind_02_cat	ps_ind_03	ps_ind_04_cat	ps_ind_06_bin	ps_ind_07_bin	ps_ind_09_bin	...	ps_calc_11	ps_calc_12	ps_calc_13	ps_calc_14	ps_calc_15_bin	ps_calc_16_bin	ps_calc_17_bin	ps_calc_18_bin	ps_calc_20_bin
0	0	0	1	8	1	0	1	0	...	1	1	1	12	0	1	1	0	1
1	1	4	2	5	1	0	0	1	...	2	0	3	10	0	0	1	1	1
2	2	5	1	3	0	0	0	1	...	4	0	2	4	0	0	0	0	0
3	3	0	1	6	0	1	0	0	...	5	1	0	5	1	0	1	0	0
4	4	5	1	7	0	0	0	1	...	4	0	0	4	0	1	1	0	1

	id	target
count	8.928160e+05	892816.000000
mean	7.441535e+05	0.036624
std	4.296830e+05	0.018261
min	0.000000e+00	0.000000
25%	3.720218e+05	0.024369
50%	7.443070e+05	0.033682
75%	1.116308e+06	0.045204
max	1.488026e+06	0.689391

	id	ps_ind_01	ps_ind_02_cat	ps_ind_03	ps_ind_04_cat	ps_ind_06_bin	ps_ind_07_bin	ps_ind_08_bin	...	ps_calc_11	ps_calc_12	ps_calc_13	ps_calc_14	ps_calc_16_bin	ps_calc_17_bin	ps_calc_18_bin	ps_calc_19_bin	ps_calc_20_bin
0	7	2	2	5	1	0	1	0	...	9	1	5	8	1	1	0	0	1
1	9	1	1	7	0	0	0	1	...	3	1	1	9	1	1	0	1	0
2	13	5	4	9	1	0	0	1	...	4	2	7	7	1	1	0	1	0
3	16	0	1	2	0	1	0	0	...	2	2	4	9	0	0	0	0	0
4	17	0	2	0	1	1	0	0	...	3	1	1	3	0	0	1	1	0

	id	ps_ind_01	ps_ind_02_cat	ps_ind_03	ps_ind_04_cat	ps_ind_06_bin	ps_ind_07_bin	ps_ind_09_bin	...	ps_calc_11	ps_calc_12	ps_calc_13	ps_calc_14	ps_calc_15_bin	ps_calc_16_bin	ps_calc_17_bin	ps_calc_18_bin	ps_calc_20_bin
0	0	0	1	8	1	0	1	0	...	1	1	1	12	0	1	1	0	1
1	1	4	2	5	1	0	0	1	...	2	0	3	10	0	0	1	1	1
2	2	5	1	3	0	0	0	1	...	4	0	2	4	0	0	0	0	0
3	3	0	1	6	0	1	0	0	...	5	1	0	5	1	0	1	0	0
4	4	5	1	7	0	0	0	1	...	4	0	0	4	0	1	1	0	1

	id	ps_ind_01	ps_ind_02_cat	ps_ind_03	ps_ind_04_cat	ps_ind_06_bin	ps_ind_07_bin	ps_ind_08_bin	...	ps_calc_11	ps_calc_12	ps_calc_13	ps_calc_14	ps_calc_16_bin	ps_calc_17_bin	ps_calc_18_bin	ps_calc_19_bin	ps_calc_20_bin
0	7	2	2	5	1	0	1	0	...	9	1	5	8	1	1	0	0	1
1	9	1	1	7	0	0	0	1	...	3	1	1	9	1	1	0	1	0
2	13	5	4	9	1	0	0	1	...	4	2	7	7	1	1	0	1	0
3	16	0	1	2	0	1	0	0	...	2	2	4	9	0	0	0	0	0
4	17	0	2	0	1	1	0	0	...	3	1	1	3	0	0	1	1	0

	id	ps_ind_01	ps_ind_02_cat	ps_ind_03	ps_ind_04_cat	ps_ind_06_bin	ps_ind_07_bin	ps_ind_09_bin	...	ps_calc_11	ps_calc_12	ps_calc_13	ps_calc_14	ps_calc_15_bin	ps_calc_16_bin	ps_calc_17_bin	ps_calc_18_bin	ps_calc_20_bin
0	0	0	1	8	1	0	1	0	...	1	1	1	12	0	1	1	0	1
1	1	4	2	5	1	0	0	1	...	2	0	3	10	0	0	1	1	1
2	2	5	1	3	0	0	0	1	...	4	0	2	4	0	0	0	0	0
3	3	0	1	6	0	1	0	0	...	5	1	0	5	1	0	1	0	0
4	4	5	1	7	0	0	0	1	...	4	0	0	4	0	1	1	0	1