Table of Contents

  • 1  Interconnect Churn
    • 1.1  Purpose
    • 1.2  Feature Engineering
    • 1.3  Clustering
    • 1.4  Modeling
    • 1.5  Final Work Plan
    • 1.6  Feature Engineering
    • 1.7  EDA
    • 1.8  Preprocessing
      • 1.8.1  K Means Clustering
    • 1.9  Modeling
      • 1.9.1  Classifier Pipeline
        • 1.9.1.1  ADA Pipeline Feature Importance
        • 1.9.1.2  XGBoost Pipeline Feature Importance
        • 1.9.1.3  LGBM Pipeline Feature Importance
        • 1.9.1.4  Gradient Boost Pipeline Feature Importance
        • 1.9.1.5  CatBoost Pipeline Feature Importance
      • 1.9.2  Comparing Models
    • 1.10  Final Model
      • 1.10.1  Final Model Evaluation Metric
      • 1.10.2  Final Model Feature Importances
    • 1.11  Overall Conclusions
    • 1.12  Solution Report

Interconnect Churn¶

Purpose¶

The telecom operator Interconnect would like to forecast churn of their clients. To ensure loyalty, those who are predicted to leave will be offered promotional codes and special plans.

In [ ]:
# import libraries
import pandas as pd  
import numpy as np  
import plotly_express as px  
import seaborn as sns
from ydata_profiling.profile_report import ProfileReport
from sklearn.preprocessing import OrdinalEncoder
from sklearn.preprocessing import StandardScaler 
from sklearn.model_selection import cross_val_score, GridSearchCV, KFold, train_test_split
from sklearn.ensemble import AdaBoostClassifier, GradientBoostingClassifier, VotingClassifier
from catboost import CatBoostClassifier, Pool, cv 
from xgboost import XGBClassifier
import lightgbm as lgb
from imblearn.over_sampling import SMOTE
from imblearn.pipeline import Pipeline
from sklearn.utils import shuffle
from sklearn.metrics import roc_auc_score, roc_curve, auc
# show graphs in html
import plotly.io as pio
pio.renderers.default = "plotly_mimetype+notebook"
In [ ]:
# load data
contract = pd.read_csv('datasets/contract.csv', parse_dates=[1, 2])
internet = pd.read_csv('datasets/internet.csv')
personal = pd.read_csv('datasets/personal.csv')
phone = pd.read_csv('datasets/phone.csv')
In [ ]:
# EDA profile reports
con_prof = ProfileReport(contract, title= 'Contract Profile')
int_prof = ProfileReport(internet, title= 'Internet Profile')
per_prof = ProfileReport(personal, title= 'Personal Profile')
pho_prof = ProfileReport(phone, title= 'Contract Profile')
In [ ]:
# contract profile report
con_prof

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The contract dataset contains a few features, and the target variable in the form of end date. If the customer has churned, there will be an end date, otherwise the customer is still with the company. Therefore, we can see class imbalance, which will need to be rectified to improve the score of the model. The column names will need to be changed to lowercase, and underscores will be added to separate words. Customer Id is a common column among all the datasets, therefore we will use that to combine them. Customer id contains 7043 values, and all are unique. Of note, begin date has moderate correlation with type. This dataset does not contain missing values, ut has categorical columns that may need encoding.



















In [ ]:



     


# internet profile report
int_prof



     






















    
    








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The internet dataset contains a customer id column with a few other categorical features. The dataset contains 5517 observations with no missing values. This profile report also shows us the distributions of the different features. Most of the features have two distinct values, the absence or presence of a service.



















In [ ]:



     


# personal profile report
per_prof



     






















    
    








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EDA shows us that gender among the personal dataset is even. A vast majority of customers are not senior citizens, and the distribution of having a partner is fairly even. A majority of customers have dependents. The dataset is free of missing values.



















In [ ]:



     


# phone profile report
pho_prof



     






















    
    








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This dataset has one column that has a yes or no value for multiple lines, paired with customer id. The multiple lines feature is fairly balanced with no values missing.























Feature Engineering¶






















The different datasets will be combined into one full dataset by joining on the column id. The dates column will be parsed to create new features. Furthermore, the end date column will be label encoded.























Clustering¶






















Analysis will be done to se if the the full dataset contains clusters of customers.























Modeling¶






















Some boosting models work with categorical features natively, but we may have to label encode those features. We will also need to scale the data, and balance the classes for churn by upsampling. We will then test across a couple of models, and optimize the AUCROC score. Finally, we will see if a voting classifier improves the AUCROC further.























Final Work Plan¶






















    

  1. Read data
    2. 

  2. Combine data
    3. 

  3. Feature engineering/ change column names and types
    4. 

  4. EDA
    5. 

  5. KNN clustering
    6. 

  6. Preprocessing, upscaling, standard scaling
    7. 

  7. Modeling with boost methods and cross validation
    8. 

  8. Hyperparameter tuning
    9. 

  9. Feature importance
    10. 

  10. Visualizing modeling results 
    11. 

  11. Voting Classifier 
    12. 

  12. Visualize final model, feature importance
    13. 

  13. Conclude
    14. 
























Feature Engineering¶


















In [ ]:



     


# contract columns
contract.columns



     






















    
    
Out[ ]:








Index(['customerID', 'BeginDate', 'EndDate', 'Type', 'PaperlessBilling',
       'PaymentMethod', 'MonthlyCharges', 'TotalCharges'],
      dtype='object')
























In [ ]:



     


# change column names
contract.columns = ['customer_id', 'begin_date', 'end_date', 'type', 'paperless_billing', 'payment_method', 'monthly_charges', 'total_charges']
internet.columns = [ 'customer_id', 'internet_service', 'online_security', 'online_backup', 'device_protection', 'tech_support', 'streaming_tv', 'streaming_movies']
personal.columns = ['customer_id', 'gender', 'senior_citizen', 'partner', 'dependents']
phone.columns = ['customer_id', 'multiple_lines']



     



















In [ ]:



     


# new contract columns
contract.columns



     






















    
    
Out[ ]:








Index(['customer_id', 'begin_date', 'end_date', 'type', 'paperless_billing',
       'payment_method', 'monthly_charges', 'total_charges'],
      dtype='object')
























In [ ]:



     


# new internet columns
internet.columns



     






















    
    
Out[ ]:








Index(['customer_id', 'internet_service', 'online_security', 'online_backup',
       'device_protection', 'tech_support', 'streaming_tv',
       'streaming_movies'],
      dtype='object')
























In [ ]:



     


# new personal columns
personal.columns



     






















    
    
Out[ ]:








Index(['customer_id', 'gender', 'senior_citizen', 'partner', 'dependents'], dtype='object')
























In [ ]:



     


# new phone columns
phone.columns



     






















    
    
Out[ ]:








Index(['customer_id', 'multiple_lines'], dtype='object')
























In [ ]:



     


# first merge
df2 = contract.merge(internet, how='left')



     



















In [ ]:



     


# results of first merge 
df2.columns



     






















    
    
Out[ ]:








Index(['customer_id', 'begin_date', 'end_date', 'type', 'paperless_billing',
       'payment_method', 'monthly_charges', 'total_charges',
       'internet_service', 'online_security', 'online_backup',
       'device_protection', 'tech_support', 'streaming_tv',
       'streaming_movies'],
      dtype='object')
























In [ ]:



     


# second merge
df3 = df2.merge(personal, how='left')



     



















In [ ]:



     


# second merge results
df3.columns



     






















    
    
Out[ ]:








Index(['customer_id', 'begin_date', 'end_date', 'type', 'paperless_billing',
       'payment_method', 'monthly_charges', 'total_charges',
       'internet_service', 'online_security', 'online_backup',
       'device_protection', 'tech_support', 'streaming_tv', 'streaming_movies',
       'gender', 'senior_citizen', 'partner', 'dependents'],
      dtype='object')
























In [ ]:



     


# final merge
df = df3.merge(phone, how='left')



     



















In [ ]:



     


# final dataset columns
df.columns



     






















    
    
Out[ ]:








Index(['customer_id', 'begin_date', 'end_date', 'type', 'paperless_billing',
       'payment_method', 'monthly_charges', 'total_charges',
       'internet_service', 'online_security', 'online_backup',
       'device_protection', 'tech_support', 'streaming_tv', 'streaming_movies',
       'gender', 'senior_citizen', 'partner', 'dependents', 'multiple_lines'],
      dtype='object')
























In [ ]:



     


# info on the columns
df.info()



     






















    
    






<class 'pandas.core.frame.DataFrame'>
Int64Index: 7043 entries, 0 to 7042
Data columns (total 20 columns):
 #   Column             Non-Null Count  Dtype         
---  ------             --------------  -----         
 0   customer_id        7043 non-null   object        
 1   begin_date         7043 non-null   datetime64[ns]
 2   end_date           7043 non-null   object        
 3   type               7043 non-null   object        
 4   paperless_billing  7043 non-null   object        
 5   payment_method     7043 non-null   object        
 6   monthly_charges    7043 non-null   float64       
 7   total_charges      7043 non-null   object        
 8   internet_service   5517 non-null   object        
 9   online_security    5517 non-null   object        
 10  online_backup      5517 non-null   object        
 11  device_protection  5517 non-null   object        
 12  tech_support       5517 non-null   object        
 13  streaming_tv       5517 non-null   object        
 14  streaming_movies   5517 non-null   object        
 15  gender             7043 non-null   object        
 16  senior_citizen     7043 non-null   int64         
 17  partner            7043 non-null   object        
 18  dependents         7043 non-null   object        
 19  multiple_lines     6361 non-null   object        
dtypes: datetime64[ns](1), float64(1), int64(1), object(17)
memory usage: 1.1+ MB
























In [ ]:



     


# look at df
df.head()



     






















    
    
Out[ ]:











  
    

      
      customer_id
      begin_date
      end_date
      type
      paperless_billing
      payment_method
      monthly_charges
      total_charges
      internet_service
      online_security
      online_backup
      device_protection
      tech_support
      streaming_tv
      streaming_movies
      gender
      senior_citizen
      partner
      dependents
      multiple_lines
    

  
  
    

      0
      7590-VHVEG
      2020-01-01
      No
      Month-to-month
      Yes
      Electronic check
      29.85
      29.85
      DSL
      No
      Yes
      No
      No
      No
      No
      Female
      0
      Yes
      No
      NaN
    

    

      1
      5575-GNVDE
      2017-04-01
      No
      One year
      No
      Mailed check
      56.95
      1889.5
      DSL
      Yes
      No
      Yes
      No
      No
      No
      Male
      0
      No
      No
      No
    

    

      2
      3668-QPYBK
      2019-10-01
      2019-12-01 00:00:00
      Month-to-month
      Yes
      Mailed check
      53.85
      108.15
      DSL
      Yes
      Yes
      No
      No
      No
      No
      Male
      0
      No
      No
      No
    

    

      3
      7795-CFOCW
      2016-05-01
      No
      One year
      No
      Bank transfer (automatic)
      42.30
      1840.75
      DSL
      Yes
      No
      Yes
      Yes
      No
      No
      Male
      0
      No
      No
      NaN
    

    

      4
      9237-HQITU
      2019-09-01
      2019-11-01 00:00:00
      Month-to-month
      Yes
      Electronic check
      70.70
      151.65
      Fiber optic
      No
      No
      No
      No
      No
      No
      Female
      0
      No
      No
      No
    

  



























In [ ]:



     


# extracting new features from begin date
df['year'] = df.begin_date.dt.year
df['month'] = df.begin_date.dt.month
df['day'] = df.begin_date.dt.day
df['quarter'] = df.begin_date.dt.quarter



     



















In [ ]:



     


# create churn column
df['churn'] = np.where(df['end_date'] == 'No', 0, 1)



     



















In [ ]:



     


# drop ed_date for churn 
df.drop(columns=['end_date', 'customer_id', 'begin_date'], inplace=True)



     



















In [ ]:



     


# look at the data
df.head()



     






















    
    
Out[ ]:











  
    

      
      type
      paperless_billing
      payment_method
      monthly_charges
      total_charges
      internet_service
      online_security
      online_backup
      device_protection
      tech_support
      ...
      gender
      senior_citizen
      partner
      dependents
      multiple_lines
      year
      month
      day
      quarter
      churn
    

  
  
    

      0
      Month-to-month
      Yes
      Electronic check
      29.85
      29.85
      DSL
      No
      Yes
      No
      No
      ...
      Female
      0
      Yes
      No
      NaN
      2020
      1
      1
      1
      0
    

    

      1
      One year
      No
      Mailed check
      56.95
      1889.5
      DSL
      Yes
      No
      Yes
      No
      ...
      Male
      0
      No
      No
      No
      2017
      4
      1
      2
      0
    

    

      2
      Month-to-month
      Yes
      Mailed check
      53.85
      108.15
      DSL
      Yes
      Yes
      No
      No
      ...
      Male
      0
      No
      No
      No
      2019
      10
      1
      4
      1
    

    

      3
      One year
      No
      Bank transfer (automatic)
      42.30
      1840.75
      DSL
      Yes
      No
      Yes
      Yes
      ...
      Male
      0
      No
      No
      NaN
      2016
      5
      1
      2
      0
    

    

      4
      Month-to-month
      Yes
      Electronic check
      70.70
      151.65
      Fiber optic
      No
      No
      No
      No
      ...
      Female
      0
      No
      No
      No
      2019
      9
      1
      3
      1
    

  



5 rows × 22 columns


























In [ ]:



     


# check for missing values
df.isna().sum()



     






















    
    
Out[ ]:








type                    0
paperless_billing       0
payment_method          0
monthly_charges         0
total_charges           0
internet_service     1526
online_security      1526
online_backup        1526
device_protection    1526
tech_support         1526
streaming_tv         1526
streaming_movies     1526
gender                  0
senior_citizen          0
partner                 0
dependents              0
multiple_lines        682
year                    0
month                   0
day                     0
quarter                 0
churn                   0
dtype: int64
























In [ ]:



     


# fill missing values with No, representing no service
df.fillna('No', inplace=True)



     



















In [ ]:



     


# recheck for missing values
df.isna().sum().sum()



     






















    
    
Out[ ]:








0
























In [ ]:



     


# we found empty strings
df.query("total_charges==' '")



     






















    
    
Out[ ]:











  
    

      
      type
      paperless_billing
      payment_method
      monthly_charges
      total_charges
      internet_service
      online_security
      online_backup
      device_protection
      tech_support
      ...
      gender
      senior_citizen
      partner
      dependents
      multiple_lines
      year
      month
      day
      quarter
      churn
    

  
  
    

      488
      Two year
      Yes
      Bank transfer (automatic)
      52.55
      
      DSL
      Yes
      No
      Yes
      Yes
      ...
      Female
      0
      Yes
      Yes
      No
      2020
      2
      1
      1
      0
    

    

      753
      Two year
      No
      Mailed check
      20.25
      
      No
      No
      No
      No
      No
      ...
      Male
      0
      No
      Yes
      No
      2020
      2
      1
      1
      0
    

    

      936
      Two year
      No
      Mailed check
      80.85
      
      DSL
      Yes
      Yes
      Yes
      No
      ...
      Female
      0
      Yes
      Yes
      No
      2020
      2
      1
      1
      0
    

    

      1082
      Two year
      No
      Mailed check
      25.75
      
      No
      No
      No
      No
      No
      ...
      Male
      0
      Yes
      Yes
      Yes
      2020
      2
      1
      1
      0
    

    

      1340
      Two year
      No
      Credit card (automatic)
      56.05
      
      DSL
      Yes
      Yes
      Yes
      Yes
      ...
      Female
      0
      Yes
      Yes
      No
      2020
      2
      1
      1
      0
    

    

      3331
      Two year
      No
      Mailed check
      19.85
      
      No
      No
      No
      No
      No
      ...
      Male
      0
      Yes
      Yes
      No
      2020
      2
      1
      1
      0
    

    

      3826
      Two year
      No
      Mailed check
      25.35
      
      No
      No
      No
      No
      No
      ...
      Male
      0
      Yes
      Yes
      Yes
      2020
      2
      1
      1
      0
    

    

      4380
      Two year
      No
      Mailed check
      20.00
      
      No
      No
      No
      No
      No
      ...
      Female
      0
      Yes
      Yes
      No
      2020
      2
      1
      1
      0
    

    

      5218
      One year
      Yes
      Mailed check
      19.70
      
      No
      No
      No
      No
      No
      ...
      Male
      0
      Yes
      Yes
      No
      2020
      2
      1
      1
      0
    

    

      6670
      Two year
      No
      Mailed check
      73.35
      
      DSL
      No
      Yes
      Yes
      Yes
      ...
      Female
      0
      Yes
      Yes
      Yes
      2020
      2
      1
      1
      0
    

    

      6754
      Two year
      Yes
      Bank transfer (automatic)
      61.90
      
      DSL
      Yes
      Yes
      No
      Yes
      ...
      Male
      0
      No
      Yes
      Yes
      2020
      2
      1
      1
      0
    

  



11 rows × 22 columns


























In [ ]:



     


# percentage of missing values
df.query("total_charges==' '")['total_charges'].count()/len(df) * 100



     






















    
    
Out[ ]:








0.1561834445548772




























Less than 0.2 % of the data has missing total charges.



















In [ ]:



     


# drop those rows where total charges is an empty string
df = df.drop(df[df['total_charges'] == ' '].index)



     























Initially, we thought to fill in these missing total charges with zero, but that would mean we made an inference that these customers did not use any services during their contract. We could have tried imputing the values, but since it represents only 0.2% of the data, we will drop it.



















In [ ]:



     


# checking proper implementation
df.query("total_charges==' '")



     






















    
    
Out[ ]:











  
    

      
      type
      paperless_billing
      payment_method
      monthly_charges
      total_charges
      internet_service
      online_security
      online_backup
      device_protection
      tech_support
      ...
      gender
      senior_citizen
      partner
      dependents
      multiple_lines
      year
      month
      day
      quarter
      churn
    

  
  
  



0 rows × 22 columns


























In [ ]:



     


# convert total charges to float
df.total_charges = pd.to_numeric(df.total_charges)



     



















In [ ]:



     


# looking at dtypes of columns
df.info()



     






















    
    






<class 'pandas.core.frame.DataFrame'>
Int64Index: 7032 entries, 0 to 7042
Data columns (total 22 columns):
 #   Column             Non-Null Count  Dtype  
---  ------             --------------  -----  
 0   type               7032 non-null   object 
 1   paperless_billing  7032 non-null   object 
 2   payment_method     7032 non-null   object 
 3   monthly_charges    7032 non-null   float64
 4   total_charges      7032 non-null   float64
 5   internet_service   7032 non-null   object 
 6   online_security    7032 non-null   object 
 7   online_backup      7032 non-null   object 
 8   device_protection  7032 non-null   object 
 9   tech_support       7032 non-null   object 
 10  streaming_tv       7032 non-null   object 
 11  streaming_movies   7032 non-null   object 
 12  gender             7032 non-null   object 
 13  senior_citizen     7032 non-null   int64  
 14  partner            7032 non-null   object 
 15  dependents         7032 non-null   object 
 16  multiple_lines     7032 non-null   object 
 17  year               7032 non-null   int64  
 18  month              7032 non-null   int64  
 19  day                7032 non-null   int64  
 20  quarter            7032 non-null   int64  
 21  churn              7032 non-null   int32  
dtypes: float64(2), int32(1), int64(5), object(14)
memory usage: 1.2+ MB




























We added a few features to the dataset by parsing the dates, and separating them into their individual components. We then converted the end date column into a binary column named churn. After combining the datasets, we introduced missing values. We dropped those missing values so we could convert total charges into a float.























EDA¶


















In [ ]:



     


# summart stats 
df.describe()



     






















    
    
Out[ ]:











  
    

      
      monthly_charges
      total_charges
      senior_citizen
      year
      month
      day
      quarter
      churn
    

  
  
    

      count
      7032.000000
      7032.000000
      7032.000000
      7032.00000
      7032.000000
      7032.0
      7032.000000
      7032.000000
    

    

      mean
      64.798208
      2283.300441
      0.162400
      2016.87372
      6.428185
      1.0
      2.471985
      0.265785
    

    

      std
      30.085974
      2266.771362
      0.368844
      1.96824
      3.576972
      0.0
      1.157273
      0.441782
    

    

      min
      18.250000
      18.800000
      0.000000
      2013.00000
      1.000000
      1.0
      1.000000
      0.000000
    

    

      25%
      35.587500
      401.450000
      0.000000
      2015.00000
      3.000000
      1.0
      1.000000
      0.000000
    

    

      50%
      70.350000
      1397.475000
      0.000000
      2017.00000
      6.000000
      1.0
      2.000000
      0.000000
    

    

      75%
      89.862500
      3794.737500
      0.000000
      2019.00000
      10.000000
      1.0
      4.000000
      1.000000
    

    

      max
      118.750000
      8684.800000
      1.000000
      2020.00000
      12.000000
      1.0
      4.000000
      1.000000
    

  



























In [ ]:



     


# correlations
df.corr()



     






















    
    
Out[ ]:











  
    

      
      monthly_charges
      total_charges
      senior_citizen
      year
      month
      day
      quarter
      churn
    

  
  
    

      monthly_charges
      1.000000
      0.651065
      0.219874
      -0.263086
      -0.019613
      NaN
      -0.029945
      0.192858
    

    

      total_charges
      0.651065
      1.000000
      0.102411
      -0.819304
      -0.196624
      NaN
      -0.209060
      -0.199484
    

    

      senior_citizen
      0.219874
      0.102411
      1.000000
      -0.027386
      0.028569
      NaN
      0.020656
      0.150541
    

    

      year
      -0.263086
      -0.819304
      -0.027386
      1.000000
      0.084872
      NaN
      0.103909
      0.293770
    

    

      month
      -0.019613
      -0.196624
      0.028569
      0.084872
      1.000000
      NaN
      0.974156
      0.180522
    

    

      day
      NaN
      NaN
      NaN
      NaN
      NaN
      NaN
      NaN
      NaN
    

    

      quarter
      -0.029945
      -0.209060
      0.020656
      0.103909
      0.974156
      NaN
      1.000000
      0.173829
    

    

      churn
      0.192858
      -0.199484
      0.150541
      0.293770
      0.180522
      NaN
      0.173829
      1.000000
    

  



























In [ ]:



     


# target values
df.churn.value_counts()



     






















    
    
Out[ ]:








0    5163
1    1869
Name: churn, dtype: int64




























We see summary statistics on the full dataset. Looking at correlations, we see the begin date year has the strongest correlation with churn amongst the other features, yet is is a weak corelation. The monthly charges have a moderate negative correlation with the year.























Preprocessing¶


















In [ ]:



     


# reset index 
df = df.reset_index(drop=True)



     



















In [ ]:



     


# encoding categorical columns
columns = ['type', 'paperless_billing',
       'payment_method',
       'internet_service', 'online_security', 'online_backup',
       'device_protection', 'tech_support', 'streaming_tv', 'streaming_movies',
       'gender', 'senior_citizen', 'partner', 'dependents', 'multiple_lines']
encoder = OrdinalEncoder()
df_ordinal = pd.DataFrame(encoder.fit_transform(df[columns]), columns=df[columns].columns)



     



















In [ ]:



     


# ordinal dataset
df_ordinal.head()



     






















    
    
Out[ ]:











  
    

      
      type
      paperless_billing
      payment_method
      internet_service
      online_security
      online_backup
      device_protection
      tech_support
      streaming_tv
      streaming_movies
      gender
      senior_citizen
      partner
      dependents
      multiple_lines
    

  
  
    

      0
      0.0
      1.0
      2.0
      0.0
      0.0
      1.0
      0.0
      0.0
      0.0
      0.0
      0.0
      0.0
      1.0
      0.0
      0.0
    

    

      1
      1.0
      0.0
      3.0
      0.0
      1.0
      0.0
      1.0
      0.0
      0.0
      0.0
      1.0
      0.0
      0.0
      0.0
      0.0
    

    

      2
      0.0
      1.0
      3.0
      0.0
      1.0
      1.0
      0.0
      0.0
      0.0
      0.0
      1.0
      0.0
      0.0
      0.0
      0.0
    

    

      3
      1.0
      0.0
      0.0
      0.0
      1.0
      0.0
      1.0
      1.0
      0.0
      0.0
      1.0
      0.0
      0.0
      0.0
      0.0
    

    

      4
      0.0
      1.0
      2.0
      1.0
      0.0
      0.0
      0.0
      0.0
      0.0
      0.0
      0.0
      0.0
      0.0
      0.0
      0.0
    

  



























In [ ]:



     


# replace categorical columns with ordinal columns
df[columns] = df_ordinal



     



















In [ ]:



     


# look at resulting dataframe
df.head()



     






















    
    
Out[ ]:











  
    

      
      type
      paperless_billing
      payment_method
      monthly_charges
      total_charges
      internet_service
      online_security
      online_backup
      device_protection
      tech_support
      ...
      gender
      senior_citizen
      partner
      dependents
      multiple_lines
      year
      month
      day
      quarter
      churn
    

  
  
    

      0
      0.0
      1.0
      2.0
      29.85
      29.85
      0.0
      0.0
      1.0
      0.0
      0.0
      ...
      0.0
      0.0
      1.0
      0.0
      0.0
      2020
      1
      1
      1
      0
    

    

      1
      1.0
      0.0
      3.0
      56.95
      1889.50
      0.0
      1.0
      0.0
      1.0
      0.0
      ...
      1.0
      0.0
      0.0
      0.0
      0.0
      2017
      4
      1
      2
      0
    

    

      2
      0.0
      1.0
      3.0
      53.85
      108.15
      0.0
      1.0
      1.0
      0.0
      0.0
      ...
      1.0
      0.0
      0.0
      0.0
      0.0
      2019
      10
      1
      4
      1
    

    

      3
      1.0
      0.0
      0.0
      42.30
      1840.75
      0.0
      1.0
      0.0
      1.0
      1.0
      ...
      1.0
      0.0
      0.0
      0.0
      0.0
      2016
      5
      1
      2
      0
    

    

      4
      0.0
      1.0
      2.0
      70.70
      151.65
      1.0
      0.0
      0.0
      0.0
      0.0
      ...
      0.0
      0.0
      0.0
      0.0
      0.0
      2019
      9
      1
      3
      1
    

  



5 rows × 22 columns


























In [ ]:



     


# features and target
X = df.drop(columns='churn')
y = df.churn



     



















In [ ]:



     


# train test split
X_train, X_test, y_train, y_test = train_test_split(
    X, y, test_size=0.25, random_state=19)



     



















In [ ]:



     


# target value counts
y.value_counts()



     






















    
    
Out[ ]:








0    5163
1    1869
Name: churn, dtype: int64
























In [ ]:



     


# look at training features
X_train.head()



     






















    
    
Out[ ]:











  
    

      
      type
      paperless_billing
      payment_method
      monthly_charges
      total_charges
      internet_service
      online_security
      online_backup
      device_protection
      tech_support
      ...
      streaming_movies
      gender
      senior_citizen
      partner
      dependents
      multiple_lines
      year
      month
      day
      quarter
    

  
  
    

      2272
      0.0
      1.0
      2.0
      109.15
      6941.20
      1.0
      0.0
      1.0
      1.0
      1.0
      ...
      1.0
      1.0
      0.0
      0.0
      0.0
      1.0
      2014
      8
      1
      3
    

    

      4321
      0.0
      1.0
      2.0
      51.10
      711.15
      0.0
      0.0
      0.0
      0.0
      0.0
      ...
      0.0
      0.0
      0.0
      1.0
      0.0
      1.0
      2018
      11
      1
      4
    

    

      3545
      0.0
      1.0
      2.0
      79.60
      79.60
      1.0
      0.0
      0.0
      0.0
      0.0
      ...
      1.0
      0.0
      0.0
      0.0
      1.0
      0.0
      2019
      9
      1
      3
    

    

      3940
      0.0
      0.0
      1.0
      55.80
      300.40
      0.0
      1.0
      0.0
      1.0
      0.0
      ...
      0.0
      0.0
      0.0
      0.0
      0.0
      0.0
      2019
      9
      1
      3
    

    

      125
      2.0
      1.0
      2.0
      88.95
      3027.65
      1.0
      1.0
      0.0
      0.0
      1.0
      ...
      0.0
      1.0
      0.0
      0.0
      0.0
      0.0
      2017
      5
      1
      2
    

  



5 rows × 21 columns






























First, we encoding all the categorical columns with label encoding. we followed up by separating the data into features and targets. We then noticed the class imbalance in churn, sow e used upsampling to rectify this. Then, we concluded preprocessing with the standard scaling on the numerical columns. Looking at our final training dataset, we are prepared for modeling.























K Means Clustering¶


















In [ ]:



     


# make copy of df
df_copy = df.copy()



     



















In [ ]:



     


# K means
from sklearn.cluster import KMeans
km = KMeans(n_clusters=2, random_state=19)
km.fit(df_copy)

print('Cluster centroids:')
print(km.cluster_centers_)
print()
print(km.inertia_)



     






















    
    






Cluster centroids:
[[1.17328865e+00 7.01292484e-01 1.12733365e+00 9.12255625e+01
  5.36420701e+03 6.68262326e-01 5.42364768e-01 6.74964098e-01
  6.84059359e-01 5.57204404e-01 7.25227382e-01 7.28099569e-01
  4.98803255e-01 2.12063188e-01 6.83580661e-01 3.35088559e-01
  7.24269986e-01 2.01474964e+03 5.65581618e+00 1.00000000e+00
  2.20631881e+00 1.53662039e-01]
 [4.83714344e-01 5.46833907e-01 1.76168319e+00 5.36295367e+01
  9.81254350e+02 9.58931823e-01 1.78434149e-01 2.05340886e-01
  2.00080923e-01 1.77220312e-01 2.40339875e-01 2.44790613e-01
  5.07181873e-01 1.41412098e-01 3.97531863e-01 2.83026502e-01
  2.94153348e-01 2.01777139e+03 6.75460247e+00 1.00000000e+00
  2.58426057e+00 3.13170140e-01]]

7922736403.502683
























In [ ]:



     


# pairplot 

centroids = pd.DataFrame(km.cluster_centers_, columns=df_copy.columns)
df_copy['label'] = km.labels_.astype(str)
centroids['label'] = ['0 centroid', '1 centroid']
data_all = pd.concat([df_copy, centroids], ignore_index=True)

# Plot the graph
sns.pairplot(data_all, hue='label', diag_kind='hist')



     






















    
    
Out[ ]:








<seaborn.axisgrid.PairGrid at 0x1fa91477eb0>










    
    



































Looking at the pairplot, we can clearly see the two clusters in various category pairs.























Modeling¶






















Classifier Pipeline¶


















In [ ]:



     


# K fold cross val
kf = KFold(n_splits=5, shuffle=True, random_state=19)



     























We initially used K-fold cross validation for hyperparameter tuning using gridsearch cv.



















In [ ]:



     


# Classifier pipeline
pipe_ada = Pipeline([('scalar1', StandardScaler()), ('oversampler1', SMOTE(random_state=19)), ('ada_classifier', AdaBoostClassifier(n_estimators=500,  random_state=19, learning_rate=0.1))])

pipe_xg = Pipeline([('scalar1', StandardScaler()), ('oversampler1', SMOTE(random_state=19)), ('xg_classifier', XGBClassifier(learning_rate=0.5, max_depth=5, n_estimators=1500))])

pipe_lgbm = Pipeline([('scalar1', StandardScaler()), ('oversampler1', SMOTE(random_state=19)), ('lgbm_classifier', lgb.LGBMClassifier(num_boost_round=500, objective='binary', metric='auc', random_state=19, learning_rate=0.1, n_estimators=350, num_leaves=30, max_depth=20, boosting_type='gbdt'))])

pipe_gb = Pipeline([('scalar1', StandardScaler()), ('oversampler1', SMOTE(random_state=19)), ('gb_classifier', GradientBoostingClassifier(n_estimators=1500, learning_rate=0.1, random_state=19, max_depth=5))])

pipe_cat = Pipeline([('scalar1', StandardScaler()), ('oversampler1', SMOTE(random_state=19)), ('cat_classifier', CatBoostClassifier(task_type='GPU', loss_function='Logloss', eval_metric='AUC', iterations=1500, random_seed=19))])

pipe_vote = Pipeline([('scalar1', StandardScaler()), ('oversampler1', SMOTE(random_state=19)), ('vote_classifier', VotingClassifier(estimators=[('ada', pipe_ada), ('xgb', pipe_xg), ('lgb', pipe_lgbm), ('cat', pipe_cat)], verbose=1, voting='soft'))])

pipelines = [pipe_ada, pipe_xg, pipe_lgbm, pipe_gb, pipe_cat, pipe_vote]

best_auc = 0
best_classifier = 0
best_pipeline = ""

pipe_dict = {0: 'ADA Boost', 1: 'XG Boost', 2: 'Light GBM', 3: 'Gradient Boost', 4: 'Cat Boost', 5: 'Voting Classifier'}

# Use cross-validation to evaluate the models
for i, model in enumerate(pipelines):
    scores = cross_val_score(model, X_train, y_train, cv=5, scoring='roc_auc')
    print('{} Cross-Validation AUC-ROC: {:.2f}'.format(pipe_dict[i], scores.mean()))
    if scores.mean() > best_auc:
        best_auc = scores.mean()
        best_pipeline = model
        best_classifier = i

# Print the best classifier
print('\nClassifier with the best AUC-ROC: {}'.format(pipe_dict[best_classifier]))



     






















    
    






ADA Boost Cross-Validation AUC-ROC: 0.87
XG Boost Cross-Validation AUC-ROC: 0.93
[LightGBM] [Warning] num_iterations is set=500, num_boost_round=500 will be ignored. Current value: num_iterations=500










    
    






c:\Users\XIX\anaconda3\lib\site-packages\lightgbm\engine.py:177: UserWarning:

Found `num_boost_round` in params. Will use it instead of argument

c:\Users\XIX\anaconda3\lib\site-packages\lightgbm\engine.py:177: UserWarning:

Found `num_boost_round` in params. Will use it instead of argument











    
    






[LightGBM] [Warning] num_iterations is set=500, num_boost_round=500 will be ignored. Current value: num_iterations=500










    
    






c:\Users\XIX\anaconda3\lib\site-packages\lightgbm\engine.py:177: UserWarning:

Found `num_boost_round` in params. Will use it instead of argument











    
    






[LightGBM] [Warning] num_iterations is set=500, num_boost_round=500 will be ignored. Current value: num_iterations=500










    
    






c:\Users\XIX\anaconda3\lib\site-packages\lightgbm\engine.py:177: UserWarning:

Found `num_boost_round` in params. Will use it instead of argument











    
    






[LightGBM] [Warning] num_iterations is set=500, num_boost_round=500 will be ignored. Current value: num_iterations=500










    
    






c:\Users\XIX\anaconda3\lib\site-packages\lightgbm\engine.py:177: UserWarning:

Found `num_boost_round` in params. Will use it instead of argument











    
    






[LightGBM] [Warning] num_iterations is set=500, num_boost_round=500 will be ignored. Current value: num_iterations=500
Light GBM Cross-Validation AUC-ROC: 0.92
Gradient Boost Cross-Validation AUC-ROC: 0.91
Learning rate set to 0.021203










    
    






Default metric period is 5 because AUC is/are not implemented for GPU










    
    






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1410:	total: 1m 6s	remaining: 4.2s
1411:	total: 1m 6s	remaining: 4.15s
1412:	total: 1m 6s	remaining: 4.1s
1413:	total: 1m 6s	remaining: 4.05s
1414:	total: 1m 6s	remaining: 4.01s
1415:	total: 1m 6s	remaining: 3.96s
1416:	total: 1m 6s	remaining: 3.91s
1417:	total: 1m 6s	remaining: 3.87s
1418:	total: 1m 6s	remaining: 3.82s
1419:	total: 1m 6s	remaining: 3.77s
1420:	total: 1m 6s	remaining: 3.72s
1421:	total: 1m 7s	remaining: 3.68s
1422:	total: 1m 7s	remaining: 3.63s
1423:	total: 1m 7s	remaining: 3.58s
1424:	total: 1m 7s	remaining: 3.53s
1425:	total: 1m 7s	remaining: 3.49s
1426:	total: 1m 7s	remaining: 3.44s
1427:	total: 1m 7s	remaining: 3.39s
1428:	total: 1m 7s	remaining: 3.34s
1429:	total: 1m 7s	remaining: 3.3s
1430:	total: 1m 7s	remaining: 3.25s
1431:	total: 1m 7s	remaining: 3.2s
1432:	total: 1m 7s	remaining: 3.15s
1433:	total: 1m 7s	remaining: 3.11s
1434:	total: 1m 7s	remaining: 3.06s
1435:	total: 1m 7s	remaining: 3.01s
1436:	total: 1m 7s	remaining: 2.96s
1437:	total: 1m 7s	remaining: 2.92s
1438:	total: 1m 7s	remaining: 2.87s
1439:	total: 1m 7s	remaining: 2.82s
1440:	total: 1m 7s	remaining: 2.77s
1441:	total: 1m 7s	remaining: 2.73s
1442:	total: 1m 7s	remaining: 2.68s
1443:	total: 1m 7s	remaining: 2.63s
1444:	total: 1m 8s	remaining: 2.59s
1445:	total: 1m 8s	remaining: 2.54s
1446:	total: 1m 8s	remaining: 2.49s
1447:	total: 1m 8s	remaining: 2.45s
1448:	total: 1m 8s	remaining: 2.4s
1449:	total: 1m 8s	remaining: 2.35s
1450:	total: 1m 8s	remaining: 2.31s
1451:	total: 1m 8s	remaining: 2.26s
1452:	total: 1m 8s	remaining: 2.21s
1453:	total: 1m 8s	remaining: 2.16s
1454:	total: 1m 8s	remaining: 2.12s
1455:	total: 1m 8s	remaining: 2.07s
1456:	total: 1m 8s	remaining: 2.02s
1457:	total: 1m 8s	remaining: 1.97s
1458:	total: 1m 8s	remaining: 1.93s
1459:	total: 1m 8s	remaining: 1.88s
1460:	total: 1m 8s	remaining: 1.83s
1461:	total: 1m 8s	remaining: 1.78s
1462:	total: 1m 8s	remaining: 1.74s
1463:	total: 1m 8s	remaining: 1.69s
1464:	total: 1m 8s	remaining: 1.64s
1465:	total: 1m 8s	remaining: 1.6s
1466:	total: 1m 8s	remaining: 1.55s
1467:	total: 1m 8s	remaining: 1.5s
1468:	total: 1m 8s	remaining: 1.46s
1469:	total: 1m 9s	remaining: 1.41s
1470:	total: 1m 9s	remaining: 1.36s
1471:	total: 1m 9s	remaining: 1.31s
1472:	total: 1m 9s	remaining: 1.27s
1473:	total: 1m 9s	remaining: 1.22s
1474:	total: 1m 9s	remaining: 1.17s
1475:	total: 1m 9s	remaining: 1.13s
1476:	total: 1m 9s	remaining: 1.08s
1477:	total: 1m 9s	remaining: 1.03s
1478:	total: 1m 9s	remaining: 985ms
1479:	total: 1m 9s	remaining: 938ms
1480:	total: 1m 9s	remaining: 891ms
1481:	total: 1m 9s	remaining: 844ms
1482:	total: 1m 9s	remaining: 797ms
1483:	total: 1m 9s	remaining: 750ms
1484:	total: 1m 9s	remaining: 703ms
1485:	total: 1m 9s	remaining: 656ms
1486:	total: 1m 9s	remaining: 609ms
1487:	total: 1m 9s	remaining: 562ms
1488:	total: 1m 9s	remaining: 515ms
1489:	total: 1m 9s	remaining: 469ms
1490:	total: 1m 9s	remaining: 422ms
1491:	total: 1m 9s	remaining: 375ms
1492:	total: 1m 9s	remaining: 328ms
1493:	total: 1m 9s	remaining: 281ms
1494:	total: 1m 10s	remaining: 234ms
1495:	total: 1m 10s	remaining: 187ms
1496:	total: 1m 10s	remaining: 140ms
1497:	total: 1m 10s	remaining: 93.7ms
1498:	total: 1m 10s	remaining: 46.8ms
1499:	total: 1m 10s	remaining: 0us
Learning rate set to 0.021203
0:	total: 46.8ms	remaining: 1m 10s
1:	total: 95.7ms	remaining: 1m 11s
2:	total: 147ms	remaining: 1m 13s










    
    






Default metric period is 5 because AUC is/are not implemented for GPU










    
    






3:	total: 205ms	remaining: 1m 16s
4:	total: 254ms	remaining: 1m 15s
5:	total: 303ms	remaining: 1m 15s
6:	total: 356ms	remaining: 1m 15s
7:	total: 417ms	remaining: 1m 17s
8:	total: 467ms	remaining: 1m 17s
9:	total: 517ms	remaining: 1m 16s
10:	total: 561ms	remaining: 1m 15s
11:	total: 605ms	remaining: 1m 15s
12:	total: 654ms	remaining: 1m 14s
13:	total: 714ms	remaining: 1m 15s
14:	total: 761ms	remaining: 1m 15s
15:	total: 815ms	remaining: 1m 15s
16:	total: 864ms	remaining: 1m 15s
17:	total: 916ms	remaining: 1m 15s
18:	total: 962ms	remaining: 1m 15s
19:	total: 1.01s	remaining: 1m 15s
20:	total: 1.06s	remaining: 1m 14s
21:	total: 1.12s	remaining: 1m 15s
22:	total: 1.18s	remaining: 1m 15s
23:	total: 1.23s	remaining: 1m 15s
24:	total: 1.28s	remaining: 1m 15s
25:	total: 1.33s	remaining: 1m 15s
26:	total: 1.4s	remaining: 1m 16s
27:	total: 1.44s	remaining: 1m 15s
28:	total: 1.49s	remaining: 1m 15s
29:	total: 1.55s	remaining: 1m 15s
30:	total: 1.59s	remaining: 1m 15s
31:	total: 1.65s	remaining: 1m 15s
32:	total: 1.7s	remaining: 1m 15s
33:	total: 1.76s	remaining: 1m 15s
34:	total: 1.81s	remaining: 1m 15s
35:	total: 1.86s	remaining: 1m 15s
36:	total: 1.92s	remaining: 1m 15s
37:	total: 1.97s	remaining: 1m 15s
38:	total: 2.02s	remaining: 1m 15s
39:	total: 2.07s	remaining: 1m 15s
40:	total: 2.12s	remaining: 1m 15s
41:	total: 2.17s	remaining: 1m 15s
42:	total: 2.22s	remaining: 1m 15s
43:	total: 2.27s	remaining: 1m 15s
44:	total: 2.32s	remaining: 1m 15s
45:	total: 2.38s	remaining: 1m 15s
46:	total: 2.44s	remaining: 1m 15s
47:	total: 2.48s	remaining: 1m 15s
48:	total: 2.53s	remaining: 1m 14s
49:	total: 2.58s	remaining: 1m 14s
50:	total: 2.63s	remaining: 1m 14s
51:	total: 2.68s	remaining: 1m 14s
52:	total: 2.73s	remaining: 1m 14s
53:	total: 2.78s	remaining: 1m 14s
54:	total: 2.83s	remaining: 1m 14s
55:	total: 2.88s	remaining: 1m 14s
56:	total: 2.93s	remaining: 1m 14s
57:	total: 2.97s	remaining: 1m 13s
58:	total: 3.02s	remaining: 1m 13s
59:	total: 3.07s	remaining: 1m 13s
60:	total: 3.12s	remaining: 1m 13s
61:	total: 3.18s	remaining: 1m 13s
62:	total: 3.23s	remaining: 1m 13s
63:	total: 3.28s	remaining: 1m 13s
64:	total: 3.33s	remaining: 1m 13s
65:	total: 3.39s	remaining: 1m 13s
66:	total: 3.44s	remaining: 1m 13s
67:	total: 3.48s	remaining: 1m 13s
68:	total: 3.53s	remaining: 1m 13s
69:	total: 3.58s	remaining: 1m 13s
70:	total: 3.63s	remaining: 1m 13s
71:	total: 3.69s	remaining: 1m 13s
72:	total: 3.74s	remaining: 1m 13s
73:	total: 3.79s	remaining: 1m 12s
74:	total: 3.84s	remaining: 1m 12s
75:	total: 3.89s	remaining: 1m 12s
76:	total: 3.93s	remaining: 1m 12s
77:	total: 3.98s	remaining: 1m 12s
78:	total: 4.03s	remaining: 1m 12s
79:	total: 4.08s	remaining: 1m 12s
80:	total: 4.13s	remaining: 1m 12s
81:	total: 4.19s	remaining: 1m 12s
82:	total: 4.24s	remaining: 1m 12s
83:	total: 4.28s	remaining: 1m 12s
84:	total: 4.33s	remaining: 1m 12s
85:	total: 4.39s	remaining: 1m 12s
86:	total: 4.45s	remaining: 1m 12s
87:	total: 4.5s	remaining: 1m 12s
88:	total: 4.54s	remaining: 1m 12s
89:	total: 4.6s	remaining: 1m 12s
90:	total: 4.65s	remaining: 1m 12s
91:	total: 4.7s	remaining: 1m 11s
92:	total: 4.75s	remaining: 1m 11s
93:	total: 4.8s	remaining: 1m 11s
94:	total: 4.85s	remaining: 1m 11s
95:	total: 4.9s	remaining: 1m 11s
96:	total: 4.94s	remaining: 1m 11s
97:	total: 4.99s	remaining: 1m 11s
98:	total: 5.05s	remaining: 1m 11s
99:	total: 5.09s	remaining: 1m 11s
100:	total: 5.15s	remaining: 1m 11s
101:	total: 5.2s	remaining: 1m 11s
102:	total: 5.25s	remaining: 1m 11s
103:	total: 5.29s	remaining: 1m 11s
104:	total: 5.36s	remaining: 1m 11s
105:	total: 5.41s	remaining: 1m 11s
106:	total: 5.46s	remaining: 1m 11s
107:	total: 5.51s	remaining: 1m 11s
108:	total: 5.55s	remaining: 1m 10s
109:	total: 5.61s	remaining: 1m 10s
110:	total: 5.66s	remaining: 1m 10s
111:	total: 5.71s	remaining: 1m 10s
112:	total: 5.76s	remaining: 1m 10s
113:	total: 5.81s	remaining: 1m 10s
114:	total: 5.86s	remaining: 1m 10s
115:	total: 5.91s	remaining: 1m 10s
116:	total: 5.96s	remaining: 1m 10s
117:	total: 6s	remaining: 1m 10s
118:	total: 6.06s	remaining: 1m 10s
119:	total: 6.16s	remaining: 1m 10s
120:	total: 6.2s	remaining: 1m 10s
121:	total: 6.25s	remaining: 1m 10s
122:	total: 6.3s	remaining: 1m 10s
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130:	total: 6.7s	remaining: 1m 10s
131:	total: 6.75s	remaining: 1m 9s
132:	total: 6.8s	remaining: 1m 9s
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135:	total: 6.94s	remaining: 1m 9s
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139:	total: 7.13s	remaining: 1m 9s
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141:	total: 7.22s	remaining: 1m 9s
142:	total: 7.27s	remaining: 1m 8s
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151:	total: 7.69s	remaining: 1m 8s
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153:	total: 7.79s	remaining: 1m 8s
154:	total: 7.83s	remaining: 1m 7s
155:	total: 7.88s	remaining: 1m 7s
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161:	total: 8.17s	remaining: 1m 7s
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164:	total: 8.31s	remaining: 1m 7s
165:	total: 8.37s	remaining: 1m 7s
166:	total: 8.41s	remaining: 1m 7s
167:	total: 8.46s	remaining: 1m 7s
168:	total: 8.51s	remaining: 1m 6s
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171:	total: 8.64s	remaining: 1m 6s
172:	total: 8.69s	remaining: 1m 6s
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179:	total: 9s	remaining: 1m 6s
180:	total: 9.05s	remaining: 1m 5s
181:	total: 9.1s	remaining: 1m 5s
182:	total: 9.15s	remaining: 1m 5s
183:	total: 9.2s	remaining: 1m 5s
184:	total: 9.25s	remaining: 1m 5s
185:	total: 9.3s	remaining: 1m 5s
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197:	total: 9.88s	remaining: 1m 4s
198:	total: 9.92s	remaining: 1m 4s
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220:	total: 11s	remaining: 1m 3s
221:	total: 11s	remaining: 1m 3s
222:	total: 11s	remaining: 1m 3s
223:	total: 11.1s	remaining: 1m 3s
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225:	total: 11.2s	remaining: 1m 3s
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227:	total: 11.3s	remaining: 1m 2s
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231:	total: 11.5s	remaining: 1m 2s
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235:	total: 11.7s	remaining: 1m 2s
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237:	total: 11.8s	remaining: 1m 2s
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239:	total: 11.9s	remaining: 1m 2s
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242:	total: 12s	remaining: 1m 2s
243:	total: 12s	remaining: 1m 1s
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252:	total: 12.5s	remaining: 1m 1s
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254:	total: 12.6s	remaining: 1m 1s
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256:	total: 12.7s	remaining: 1m 1s
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261:	total: 12.9s	remaining: 1m
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276:	total: 13.6s	remaining: 1m
277:	total: 13.6s	remaining: 59.9s
278:	total: 13.7s	remaining: 59.9s
279:	total: 13.7s	remaining: 59.8s
280:	total: 13.8s	remaining: 59.7s
281:	total: 13.8s	remaining: 59.7s
282:	total: 13.9s	remaining: 59.6s
283:	total: 13.9s	remaining: 59.5s
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285:	total: 14s	remaining: 59.4s
286:	total: 14s	remaining: 59.3s
287:	total: 14.1s	remaining: 59.3s
288:	total: 14.1s	remaining: 59.2s
289:	total: 14.2s	remaining: 59.2s
290:	total: 14.2s	remaining: 59.1s
291:	total: 14.3s	remaining: 59.1s
292:	total: 14.3s	remaining: 59.1s
293:	total: 14.4s	remaining: 59s
294:	total: 14.4s	remaining: 58.9s
295:	total: 14.5s	remaining: 58.9s
296:	total: 14.5s	remaining: 58.8s
297:	total: 14.6s	remaining: 58.8s
298:	total: 14.6s	remaining: 58.7s
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304:	total: 14.9s	remaining: 58.3s
305:	total: 14.9s	remaining: 58.2s
306:	total: 15s	remaining: 58.2s
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310:	total: 15.2s	remaining: 58s
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321:	total: 15.7s	remaining: 57.3s
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325:	total: 15.9s	remaining: 57.1s
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327:	total: 16s	remaining: 57s
328:	total: 16s	remaining: 56.9s
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1197:	total: 56.7s	remaining: 14.3s
1198:	total: 56.8s	remaining: 14.3s
1199:	total: 56.8s	remaining: 14.2s
1200:	total: 56.9s	remaining: 14.2s
1201:	total: 56.9s	remaining: 14.1s
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1204:	total: 57.1s	remaining: 14s
1205:	total: 57.1s	remaining: 13.9s
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1211:	total: 57.4s	remaining: 13.6s
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1220:	total: 57.8s	remaining: 13.2s
1221:	total: 57.8s	remaining: 13.2s
1222:	total: 57.9s	remaining: 13.1s
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1226:	total: 58s	remaining: 12.9s
1227:	total: 58.1s	remaining: 12.9s
1228:	total: 58.2s	remaining: 12.8s
1229:	total: 58.2s	remaining: 12.8s
1230:	total: 58.2s	remaining: 12.7s
1231:	total: 58.3s	remaining: 12.7s
1232:	total: 58.3s	remaining: 12.6s
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1234:	total: 58.4s	remaining: 12.5s
1235:	total: 58.5s	remaining: 12.5s
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1238:	total: 58.6s	remaining: 12.3s
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1247:	total: 59s	remaining: 11.9s
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1255:	total: 59.3s	remaining: 11.5s
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1259:	total: 59.5s	remaining: 11.3s
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1265:	total: 59.8s	remaining: 11s
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1267:	total: 59.9s	remaining: 11s
1268:	total: 59.9s	remaining: 10.9s
1269:	total: 59.9s	remaining: 10.9s
1270:	total: 60s	remaining: 10.8s
1271:	total: 1m	remaining: 10.8s
1272:	total: 1m	remaining: 10.7s
1273:	total: 1m	remaining: 10.7s
1274:	total: 1m	remaining: 10.6s
1275:	total: 1m	remaining: 10.6s
1276:	total: 1m	remaining: 10.5s
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1279:	total: 1m	remaining: 10.4s
1280:	total: 1m	remaining: 10.3s
1281:	total: 1m	remaining: 10.3s
1282:	total: 1m	remaining: 10.2s
1283:	total: 1m	remaining: 10.2s
1284:	total: 1m	remaining: 10.1s
1285:	total: 1m	remaining: 10.1s
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1287:	total: 1m	remaining: 10s
1288:	total: 1m	remaining: 9.95s
1289:	total: 1m	remaining: 9.9s
1290:	total: 1m	remaining: 9.86s
1291:	total: 1m	remaining: 9.81s
1292:	total: 1m	remaining: 9.76s
1293:	total: 1m 1s	remaining: 9.71s
1294:	total: 1m 1s	remaining: 9.66s
1295:	total: 1m 1s	remaining: 9.62s
1296:	total: 1m 1s	remaining: 9.57s
1297:	total: 1m 1s	remaining: 9.53s
1298:	total: 1m 1s	remaining: 9.47s
1299:	total: 1m 1s	remaining: 9.43s
1300:	total: 1m 1s	remaining: 9.38s
1301:	total: 1m 1s	remaining: 9.33s
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1303:	total: 1m 1s	remaining: 9.24s
1304:	total: 1m 1s	remaining: 9.19s
1305:	total: 1m 1s	remaining: 9.14s
1306:	total: 1m 1s	remaining: 9.09s
1307:	total: 1m 1s	remaining: 9.04s
1308:	total: 1m 1s	remaining: 9s
1309:	total: 1m 1s	remaining: 8.95s
1310:	total: 1m 1s	remaining: 8.9s
1311:	total: 1m 1s	remaining: 8.85s
1312:	total: 1m 1s	remaining: 8.81s
1313:	total: 1m 1s	remaining: 8.76s
1314:	total: 1m 1s	remaining: 8.71s
1315:	total: 1m 1s	remaining: 8.66s
1316:	total: 1m 2s	remaining: 8.62s
1317:	total: 1m 2s	remaining: 8.57s
1318:	total: 1m 2s	remaining: 8.52s
1319:	total: 1m 2s	remaining: 8.48s
1320:	total: 1m 2s	remaining: 8.43s
1321:	total: 1m 2s	remaining: 8.38s
1322:	total: 1m 2s	remaining: 8.34s
1323:	total: 1m 2s	remaining: 8.29s
1324:	total: 1m 2s	remaining: 8.24s
1325:	total: 1m 2s	remaining: 8.19s
1326:	total: 1m 2s	remaining: 8.14s
1327:	total: 1m 2s	remaining: 8.1s
1328:	total: 1m 2s	remaining: 8.05s
1329:	total: 1m 2s	remaining: 8s
1330:	total: 1m 2s	remaining: 7.95s
1331:	total: 1m 2s	remaining: 7.91s
1332:	total: 1m 2s	remaining: 7.86s
1333:	total: 1m 2s	remaining: 7.81s
1334:	total: 1m 2s	remaining: 7.76s
1335:	total: 1m 2s	remaining: 7.72s
1336:	total: 1m 2s	remaining: 7.67s
1337:	total: 1m 2s	remaining: 7.62s
1338:	total: 1m 3s	remaining: 7.57s
1339:	total: 1m 3s	remaining: 7.53s
1340:	total: 1m 3s	remaining: 7.48s
1341:	total: 1m 3s	remaining: 7.43s
1342:	total: 1m 3s	remaining: 7.39s
1343:	total: 1m 3s	remaining: 7.34s
1344:	total: 1m 3s	remaining: 7.29s
1345:	total: 1m 3s	remaining: 7.25s
1346:	total: 1m 3s	remaining: 7.2s
1347:	total: 1m 3s	remaining: 7.15s
1348:	total: 1m 3s	remaining: 7.1s
1349:	total: 1m 3s	remaining: 7.05s
1350:	total: 1m 3s	remaining: 7s
1351:	total: 1m 3s	remaining: 6.96s
1352:	total: 1m 3s	remaining: 6.91s
1353:	total: 1m 3s	remaining: 6.86s
1354:	total: 1m 3s	remaining: 6.81s
1355:	total: 1m 3s	remaining: 6.76s
1356:	total: 1m 3s	remaining: 6.72s
1357:	total: 1m 3s	remaining: 6.67s
1358:	total: 1m 3s	remaining: 6.62s
1359:	total: 1m 3s	remaining: 6.58s
1360:	total: 1m 3s	remaining: 6.53s
1361:	total: 1m 4s	remaining: 6.48s
1362:	total: 1m 4s	remaining: 6.44s
1363:	total: 1m 4s	remaining: 6.39s
1364:	total: 1m 4s	remaining: 6.34s
1365:	total: 1m 4s	remaining: 6.3s
1366:	total: 1m 4s	remaining: 6.25s
1367:	total: 1m 4s	remaining: 6.2s
1368:	total: 1m 4s	remaining: 6.16s
1369:	total: 1m 4s	remaining: 6.11s
1370:	total: 1m 4s	remaining: 6.06s
1371:	total: 1m 4s	remaining: 6.01s
1372:	total: 1m 4s	remaining: 5.97s
1373:	total: 1m 4s	remaining: 5.92s
1374:	total: 1m 4s	remaining: 5.87s
1375:	total: 1m 4s	remaining: 5.82s
1376:	total: 1m 4s	remaining: 5.78s
1377:	total: 1m 4s	remaining: 5.73s
1378:	total: 1m 4s	remaining: 5.68s
1379:	total: 1m 4s	remaining: 5.63s
1380:	total: 1m 4s	remaining: 5.59s
1381:	total: 1m 4s	remaining: 5.54s
1382:	total: 1m 4s	remaining: 5.49s
1383:	total: 1m 4s	remaining: 5.45s
1384:	total: 1m 5s	remaining: 5.4s
1385:	total: 1m 5s	remaining: 5.35s
1386:	total: 1m 5s	remaining: 5.31s
1387:	total: 1m 5s	remaining: 5.26s
1388:	total: 1m 5s	remaining: 5.21s
1389:	total: 1m 5s	remaining: 5.16s
1390:	total: 1m 5s	remaining: 5.12s
1391:	total: 1m 5s	remaining: 5.07s
1392:	total: 1m 5s	remaining: 5.02s
1393:	total: 1m 5s	remaining: 4.98s
1394:	total: 1m 5s	remaining: 4.93s
1395:	total: 1m 5s	remaining: 4.88s
1396:	total: 1m 5s	remaining: 4.83s
1397:	total: 1m 5s	remaining: 4.79s
1398:	total: 1m 5s	remaining: 4.74s
1399:	total: 1m 5s	remaining: 4.69s
1400:	total: 1m 5s	remaining: 4.65s
1401:	total: 1m 5s	remaining: 4.6s
1402:	total: 1m 5s	remaining: 4.55s
1403:	total: 1m 5s	remaining: 4.51s
1404:	total: 1m 5s	remaining: 4.46s
1405:	total: 1m 5s	remaining: 4.41s
1406:	total: 1m 6s	remaining: 4.36s
1407:	total: 1m 6s	remaining: 4.32s
1408:	total: 1m 6s	remaining: 4.27s
1409:	total: 1m 6s	remaining: 4.22s
1410:	total: 1m 6s	remaining: 4.18s
1411:	total: 1m 6s	remaining: 4.13s
1412:	total: 1m 6s	remaining: 4.08s
1413:	total: 1m 6s	remaining: 4.04s
1414:	total: 1m 6s	remaining: 3.99s
1415:	total: 1m 6s	remaining: 3.94s
1416:	total: 1m 6s	remaining: 3.9s
1417:	total: 1m 6s	remaining: 3.85s
1418:	total: 1m 6s	remaining: 3.8s
1419:	total: 1m 6s	remaining: 3.75s
1420:	total: 1m 6s	remaining: 3.71s
1421:	total: 1m 6s	remaining: 3.66s
1422:	total: 1m 6s	remaining: 3.61s
1423:	total: 1m 6s	remaining: 3.56s
1424:	total: 1m 6s	remaining: 3.52s
1425:	total: 1m 6s	remaining: 3.47s
1426:	total: 1m 6s	remaining: 3.42s
1427:	total: 1m 6s	remaining: 3.38s
1428:	total: 1m 7s	remaining: 3.33s
1429:	total: 1m 7s	remaining: 3.28s
1430:	total: 1m 7s	remaining: 3.24s
1431:	total: 1m 7s	remaining: 3.19s
1432:	total: 1m 7s	remaining: 3.14s
1433:	total: 1m 7s	remaining: 3.1s
1434:	total: 1m 7s	remaining: 3.05s
1435:	total: 1m 7s	remaining: 3s
1436:	total: 1m 7s	remaining: 2.95s
1437:	total: 1m 7s	remaining: 2.91s
1438:	total: 1m 7s	remaining: 2.86s
1439:	total: 1m 7s	remaining: 2.81s
1440:	total: 1m 7s	remaining: 2.77s
1441:	total: 1m 7s	remaining: 2.72s
1442:	total: 1m 7s	remaining: 2.67s
1443:	total: 1m 7s	remaining: 2.63s
1444:	total: 1m 7s	remaining: 2.58s
1445:	total: 1m 7s	remaining: 2.53s
1446:	total: 1m 7s	remaining: 2.48s
1447:	total: 1m 7s	remaining: 2.44s
1448:	total: 1m 7s	remaining: 2.39s
1449:	total: 1m 7s	remaining: 2.34s
1450:	total: 1m 7s	remaining: 2.29s
1451:	total: 1m 8s	remaining: 2.25s
1452:	total: 1m 8s	remaining: 2.2s
1453:	total: 1m 8s	remaining: 2.15s
1454:	total: 1m 8s	remaining: 2.11s
1455:	total: 1m 8s	remaining: 2.06s
1456:	total: 1m 8s	remaining: 2.01s
1457:	total: 1m 8s	remaining: 1.97s
1458:	total: 1m 8s	remaining: 1.92s
1459:	total: 1m 8s	remaining: 1.87s
1460:	total: 1m 8s	remaining: 1.83s
1461:	total: 1m 8s	remaining: 1.78s
1462:	total: 1m 8s	remaining: 1.73s
1463:	total: 1m 8s	remaining: 1.69s
1464:	total: 1m 8s	remaining: 1.64s
1465:	total: 1m 8s	remaining: 1.59s
1466:	total: 1m 8s	remaining: 1.54s
1467:	total: 1m 8s	remaining: 1.5s
1468:	total: 1m 8s	remaining: 1.45s
1469:	total: 1m 8s	remaining: 1.41s
1470:	total: 1m 8s	remaining: 1.36s
1471:	total: 1m 8s	remaining: 1.31s
1472:	total: 1m 8s	remaining: 1.26s
1473:	total: 1m 9s	remaining: 1.22s
1474:	total: 1m 9s	remaining: 1.17s
1475:	total: 1m 9s	remaining: 1.12s
1476:	total: 1m 9s	remaining: 1.08s
1477:	total: 1m 9s	remaining: 1.03s
1478:	total: 1m 9s	remaining: 983ms
1479:	total: 1m 9s	remaining: 936ms
1480:	total: 1m 9s	remaining: 890ms
1481:	total: 1m 9s	remaining: 843ms
1482:	total: 1m 9s	remaining: 796ms
1483:	total: 1m 9s	remaining: 749ms
1484:	total: 1m 9s	remaining: 702ms
1485:	total: 1m 9s	remaining: 655ms
1486:	total: 1m 9s	remaining: 609ms
1487:	total: 1m 9s	remaining: 562ms
1488:	total: 1m 9s	remaining: 515ms
1489:	total: 1m 9s	remaining: 468ms
1490:	total: 1m 9s	remaining: 421ms
1491:	total: 1m 9s	remaining: 374ms
1492:	total: 1m 9s	remaining: 328ms
1493:	total: 1m 9s	remaining: 281ms
1494:	total: 1m 9s	remaining: 234ms
1495:	total: 1m 10s	remaining: 187ms
1496:	total: 1m 10s	remaining: 140ms
1497:	total: 1m 10s	remaining: 93.6ms
1498:	total: 1m 10s	remaining: 46.8ms
1499:	total: 1m 10s	remaining: 0us
Learning rate set to 0.021203
0:	total: 48ms	remaining: 1m 11s
1:	total: 101ms	remaining: 1m 15s
2:	total: 153ms	remaining: 1m 16s










    
    






Default metric period is 5 because AUC is/are not implemented for GPU










    
    






3:	total: 202ms	remaining: 1m 15s
4:	total: 249ms	remaining: 1m 14s
5:	total: 295ms	remaining: 1m 13s
6:	total: 346ms	remaining: 1m 13s
7:	total: 399ms	remaining: 1m 14s
8:	total: 445ms	remaining: 1m 13s
9:	total: 496ms	remaining: 1m 13s
10:	total: 545ms	remaining: 1m 13s
11:	total: 610ms	remaining: 1m 15s
12:	total: 660ms	remaining: 1m 15s
13:	total: 706ms	remaining: 1m 14s
14:	total: 753ms	remaining: 1m 14s
15:	total: 801ms	remaining: 1m 14s
16:	total: 847ms	remaining: 1m 13s
17:	total: 892ms	remaining: 1m 13s
18:	total: 938ms	remaining: 1m 13s
19:	total: 984ms	remaining: 1m 12s
20:	total: 1.03s	remaining: 1m 12s
21:	total: 1.07s	remaining: 1m 12s
22:	total: 1.12s	remaining: 1m 11s
23:	total: 1.17s	remaining: 1m 11s
24:	total: 1.22s	remaining: 1m 11s
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27:	total: 1.36s	remaining: 1m 11s
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36:	total: 1.79s	remaining: 1m 10s
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40:	total: 1.99s	remaining: 1m 10s
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47:	total: 2.31s	remaining: 1m 9s
48:	total: 2.37s	remaining: 1m 10s
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55:	total: 2.71s	remaining: 1m 9s
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61:	total: 2.99s	remaining: 1m 9s
62:	total: 3.04s	remaining: 1m 9s
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65:	total: 3.18s	remaining: 1m 9s
66:	total: 3.22s	remaining: 1m 8s
67:	total: 3.27s	remaining: 1m 8s
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78:	total: 3.81s	remaining: 1m 8s
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80:	total: 3.9s	remaining: 1m 8s
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83:	total: 4.03s	remaining: 1m 7s
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99:	total: 4.78s	remaining: 1m 6s
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106:	total: 5.1s	remaining: 1m 6s
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108:	total: 5.2s	remaining: 1m 6s
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1402:	total: 1m	remaining: 4.16s
1403:	total: 1m	remaining: 4.12s
1404:	total: 1m	remaining: 4.08s
1405:	total: 1m	remaining: 4.03s
1406:	total: 1m	remaining: 3.99s
1407:	total: 1m	remaining: 3.95s
1408:	total: 1m	remaining: 3.9s
1409:	total: 1m	remaining: 3.86s
1410:	total: 1m	remaining: 3.82s
1411:	total: 1m	remaining: 3.77s
1412:	total: 1m	remaining: 3.73s
1413:	total: 1m	remaining: 3.69s
1414:	total: 1m	remaining: 3.65s
1415:	total: 1m	remaining: 3.6s
1416:	total: 1m	remaining: 3.56s
1417:	total: 1m	remaining: 3.52s
1418:	total: 1m	remaining: 3.47s
1419:	total: 1m	remaining: 3.43s
1420:	total: 1m	remaining: 3.39s
1421:	total: 1m	remaining: 3.34s
1422:	total: 1m	remaining: 3.3s
1423:	total: 1m 1s	remaining: 3.26s
1424:	total: 1m 1s	remaining: 3.21s
1425:	total: 1m 1s	remaining: 3.17s
1426:	total: 1m 1s	remaining: 3.13s
1427:	total: 1m 1s	remaining: 3.09s
1428:	total: 1m 1s	remaining: 3.04s
1429:	total: 1m 1s	remaining: 3s
1430:	total: 1m 1s	remaining: 2.96s
1431:	total: 1m 1s	remaining: 2.91s
1432:	total: 1m 1s	remaining: 2.87s
1433:	total: 1m 1s	remaining: 2.83s
1434:	total: 1m 1s	remaining: 2.78s
1435:	total: 1m 1s	remaining: 2.74s
1436:	total: 1m 1s	remaining: 2.7s
1437:	total: 1m 1s	remaining: 2.65s
1438:	total: 1m 1s	remaining: 2.61s
1439:	total: 1m 1s	remaining: 2.57s
1440:	total: 1m 1s	remaining: 2.53s
1441:	total: 1m 1s	remaining: 2.48s
1442:	total: 1m 1s	remaining: 2.44s
1443:	total: 1m 1s	remaining: 2.4s
1444:	total: 1m 1s	remaining: 2.35s
1445:	total: 1m 1s	remaining: 2.31s
1446:	total: 1m 1s	remaining: 2.27s
1447:	total: 1m 1s	remaining: 2.23s
1448:	total: 1m 2s	remaining: 2.18s
1449:	total: 1m 2s	remaining: 2.14s
1450:	total: 1m 2s	remaining: 2.1s
1451:	total: 1m 2s	remaining: 2.05s
1452:	total: 1m 2s	remaining: 2.01s
1453:	total: 1m 2s	remaining: 1.97s
1454:	total: 1m 2s	remaining: 1.93s
1455:	total: 1m 2s	remaining: 1.88s
1456:	total: 1m 2s	remaining: 1.84s
1457:	total: 1m 2s	remaining: 1.8s
1458:	total: 1m 2s	remaining: 1.75s
1459:	total: 1m 2s	remaining: 1.71s
1460:	total: 1m 2s	remaining: 1.67s
1461:	total: 1m 2s	remaining: 1.62s
1462:	total: 1m 2s	remaining: 1.58s
1463:	total: 1m 2s	remaining: 1.54s
1464:	total: 1m 2s	remaining: 1.5s
1465:	total: 1m 2s	remaining: 1.45s
1466:	total: 1m 2s	remaining: 1.41s
1467:	total: 1m 2s	remaining: 1.37s
1468:	total: 1m 2s	remaining: 1.32s
1469:	total: 1m 2s	remaining: 1.28s
1470:	total: 1m 2s	remaining: 1.24s
1471:	total: 1m 2s	remaining: 1.2s
1472:	total: 1m 2s	remaining: 1.15s
1473:	total: 1m 2s	remaining: 1.11s
1474:	total: 1m 3s	remaining: 1.07s
1475:	total: 1m 3s	remaining: 1.02s
1476:	total: 1m 3s	remaining: 982ms
1477:	total: 1m 3s	remaining: 940ms
1478:	total: 1m 3s	remaining: 897ms
1479:	total: 1m 3s	remaining: 854ms
1480:	total: 1m 3s	remaining: 811ms
1481:	total: 1m 3s	remaining: 769ms
1482:	total: 1m 3s	remaining: 726ms
1483:	total: 1m 3s	remaining: 683ms
1484:	total: 1m 3s	remaining: 640ms
1485:	total: 1m 3s	remaining: 598ms
1486:	total: 1m 3s	remaining: 555ms
1487:	total: 1m 3s	remaining: 512ms
1488:	total: 1m 3s	remaining: 470ms
1489:	total: 1m 3s	remaining: 427ms
1490:	total: 1m 3s	remaining: 384ms
1491:	total: 1m 3s	remaining: 341ms
1492:	total: 1m 3s	remaining: 299ms
1493:	total: 1m 3s	remaining: 256ms
1494:	total: 1m 3s	remaining: 213ms
1495:	total: 1m 3s	remaining: 171ms
1496:	total: 1m 3s	remaining: 128ms
1497:	total: 1m 3s	remaining: 85.3ms
1498:	total: 1m 3s	remaining: 42.7ms
1499:	total: 1m 3s	remaining: 0us
Learning rate set to 0.021203
0:	total: 47.9ms	remaining: 1m 11s
1:	total: 96.9ms	remaining: 1m 12s
2:	total: 146ms	remaining: 1m 12s
3:	total: 191ms	remaining: 1m 11s










    
    






Default metric period is 5 because AUC is/are not implemented for GPU










    
    






4:	total: 245ms	remaining: 1m 13s
5:	total: 294ms	remaining: 1m 13s
6:	total: 342ms	remaining: 1m 13s
7:	total: 393ms	remaining: 1m 13s
8:	total: 440ms	remaining: 1m 12s
9:	total: 489ms	remaining: 1m 12s
10:	total: 535ms	remaining: 1m 12s
11:	total: 582ms	remaining: 1m 12s
12:	total: 628ms	remaining: 1m 11s
13:	total: 676ms	remaining: 1m 11s
14:	total: 730ms	remaining: 1m 12s
15:	total: 783ms	remaining: 1m 12s
16:	total: 830ms	remaining: 1m 12s
17:	total: 879ms	remaining: 1m 12s
18:	total: 942ms	remaining: 1m 13s
19:	total: 993ms	remaining: 1m 13s
20:	total: 1.04s	remaining: 1m 13s
21:	total: 1.09s	remaining: 1m 13s
22:	total: 1.14s	remaining: 1m 13s
23:	total: 1.19s	remaining: 1m 13s
24:	total: 1.24s	remaining: 1m 13s
25:	total: 1.29s	remaining: 1m 13s
26:	total: 1.33s	remaining: 1m 12s
27:	total: 1.39s	remaining: 1m 12s
28:	total: 1.43s	remaining: 1m 12s
29:	total: 1.48s	remaining: 1m 12s
30:	total: 1.52s	remaining: 1m 12s
31:	total: 1.57s	remaining: 1m 12s
32:	total: 1.61s	remaining: 1m 11s
33:	total: 1.66s	remaining: 1m 11s
34:	total: 1.71s	remaining: 1m 11s
35:	total: 1.77s	remaining: 1m 11s
36:	total: 1.81s	remaining: 1m 11s
37:	total: 1.86s	remaining: 1m 11s
38:	total: 1.91s	remaining: 1m 11s
39:	total: 1.96s	remaining: 1m 11s
40:	total: 2.01s	remaining: 1m 11s
41:	total: 2.05s	remaining: 1m 11s
42:	total: 2.1s	remaining: 1m 11s
43:	total: 2.15s	remaining: 1m 11s
44:	total: 2.19s	remaining: 1m 10s
45:	total: 2.24s	remaining: 1m 10s
46:	total: 2.29s	remaining: 1m 10s
47:	total: 2.33s	remaining: 1m 10s
48:	total: 2.38s	remaining: 1m 10s
49:	total: 2.44s	remaining: 1m 10s
50:	total: 2.48s	remaining: 1m 10s
51:	total: 2.53s	remaining: 1m 10s
52:	total: 2.58s	remaining: 1m 10s
53:	total: 2.63s	remaining: 1m 10s
54:	total: 2.68s	remaining: 1m 10s
55:	total: 2.73s	remaining: 1m 10s
56:	total: 2.78s	remaining: 1m 10s
57:	total: 2.83s	remaining: 1m 10s
58:	total: 2.88s	remaining: 1m 10s
59:	total: 2.94s	remaining: 1m 10s
60:	total: 2.99s	remaining: 1m 10s
61:	total: 3.04s	remaining: 1m 10s
62:	total: 3.09s	remaining: 1m 10s
63:	total: 3.13s	remaining: 1m 10s
64:	total: 3.19s	remaining: 1m 10s
65:	total: 3.23s	remaining: 1m 10s
66:	total: 3.27s	remaining: 1m 10s
67:	total: 3.32s	remaining: 1m 9s
68:	total: 3.37s	remaining: 1m 9s
69:	total: 3.42s	remaining: 1m 9s
70:	total: 3.46s	remaining: 1m 9s
71:	total: 3.51s	remaining: 1m 9s
72:	total: 3.56s	remaining: 1m 9s
73:	total: 3.61s	remaining: 1m 9s
74:	total: 3.65s	remaining: 1m 9s
75:	total: 3.71s	remaining: 1m 9s
76:	total: 3.75s	remaining: 1m 9s
77:	total: 3.8s	remaining: 1m 9s
78:	total: 3.84s	remaining: 1m 9s
79:	total: 3.9s	remaining: 1m 9s
80:	total: 3.95s	remaining: 1m 9s
81:	total: 4s	remaining: 1m 9s
82:	total: 4.05s	remaining: 1m 9s
83:	total: 4.1s	remaining: 1m 9s
84:	total: 4.15s	remaining: 1m 9s
85:	total: 4.2s	remaining: 1m 9s
86:	total: 4.25s	remaining: 1m 9s
87:	total: 4.3s	remaining: 1m 9s
88:	total: 4.35s	remaining: 1m 8s
89:	total: 4.4s	remaining: 1m 8s
90:	total: 4.45s	remaining: 1m 8s
91:	total: 4.5s	remaining: 1m 8s
92:	total: 4.55s	remaining: 1m 8s
93:	total: 4.6s	remaining: 1m 8s
94:	total: 4.65s	remaining: 1m 8s
95:	total: 4.71s	remaining: 1m 8s
96:	total: 4.76s	remaining: 1m 8s
97:	total: 4.81s	remaining: 1m 8s
98:	total: 4.86s	remaining: 1m 8s
99:	total: 4.93s	remaining: 1m 8s
100:	total: 4.98s	remaining: 1m 8s
101:	total: 5.03s	remaining: 1m 8s
102:	total: 5.08s	remaining: 1m 8s
103:	total: 5.13s	remaining: 1m 8s
104:	total: 5.18s	remaining: 1m 8s
105:	total: 5.23s	remaining: 1m 8s
106:	total: 5.29s	remaining: 1m 8s
107:	total: 5.35s	remaining: 1m 8s
108:	total: 5.4s	remaining: 1m 8s
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110:	total: 5.51s	remaining: 1m 9s
111:	total: 5.57s	remaining: 1m 9s
112:	total: 5.63s	remaining: 1m 9s
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114:	total: 5.76s	remaining: 1m 9s
115:	total: 5.82s	remaining: 1m 9s
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118:	total: 6s	remaining: 1m 9s
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120:	total: 6.13s	remaining: 1m 9s
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140:	total: 7.16s	remaining: 1m 9s
141:	total: 7.21s	remaining: 1m 8s
142:	total: 7.25s	remaining: 1m 8s
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166:	total: 8.39s	remaining: 1m 6s
167:	total: 8.44s	remaining: 1m 6s
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181:	total: 9.11s	remaining: 1m 5s
182:	total: 9.16s	remaining: 1m 5s
183:	total: 9.21s	remaining: 1m 5s
184:	total: 9.25s	remaining: 1m 5s
185:	total: 9.3s	remaining: 1m 5s
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194:	total: 9.73s	remaining: 1m 5s
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196:	total: 9.82s	remaining: 1m 4s
197:	total: 9.87s	remaining: 1m 4s
198:	total: 9.93s	remaining: 1m 4s
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212:	total: 10.6s	remaining: 1m 3s
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219:	total: 11s	remaining: 1m 3s
220:	total: 11s	remaining: 1m 3s
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222:	total: 11.1s	remaining: 1m 3s
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230:	total: 11.5s	remaining: 1m 3s
231:	total: 11.5s	remaining: 1m 2s
232:	total: 11.6s	remaining: 1m 2s
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234:	total: 11.7s	remaining: 1m 2s
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239:	total: 11.9s	remaining: 1m 2s
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241:	total: 12s	remaining: 1m 2s
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281:	total: 13.9s	remaining: 1m
282:	total: 14s	remaining: 1m
283:	total: 14s	remaining: 59.9s
284:	total: 14.1s	remaining: 59.9s
285:	total: 14.1s	remaining: 59.8s
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287:	total: 14.2s	remaining: 59.8s
288:	total: 14.2s	remaining: 59.7s
289:	total: 14.3s	remaining: 59.6s
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291:	total: 14.4s	remaining: 59.5s
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295:	total: 14.6s	remaining: 59.3s
296:	total: 14.6s	remaining: 59.2s
297:	total: 14.7s	remaining: 59.1s
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301:	total: 14.9s	remaining: 59s
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303:	total: 15s	remaining: 58.8s
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308:	total: 15.2s	remaining: 58.6s
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310:	total: 15.3s	remaining: 58.4s
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316:	total: 15.6s	remaining: 58.1s
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318:	total: 15.7s	remaining: 58s
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320:	total: 15.8s	remaining: 57.9s
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1195:	total: 54s	remaining: 13.7s
1196:	total: 54s	remaining: 13.7s
1197:	total: 54.1s	remaining: 13.6s
1198:	total: 54.1s	remaining: 13.6s
1199:	total: 54.1s	remaining: 13.5s
1200:	total: 54.1s	remaining: 13.5s
1201:	total: 54.2s	remaining: 13.4s
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1211:	total: 54.5s	remaining: 13s
1212:	total: 54.6s	remaining: 12.9s
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1214:	total: 54.7s	remaining: 12.8s
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1220:	total: 54.9s	remaining: 12.5s
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1222:	total: 54.9s	remaining: 12.4s
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1225:	total: 55.1s	remaining: 12.3s
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1227:	total: 55.1s	remaining: 12.2s
1228:	total: 55.2s	remaining: 12.2s
1229:	total: 55.2s	remaining: 12.1s
1230:	total: 55.3s	remaining: 12.1s
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1233:	total: 55.4s	remaining: 11.9s
1234:	total: 55.4s	remaining: 11.9s
1235:	total: 55.5s	remaining: 11.8s
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1237:	total: 55.6s	remaining: 11.8s
1238:	total: 55.6s	remaining: 11.7s
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1250:	total: 56.1s	remaining: 11.2s
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1254:	total: 56.3s	remaining: 11s
1255:	total: 56.3s	remaining: 10.9s
1256:	total: 56.4s	remaining: 10.9s
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1258:	total: 56.5s	remaining: 10.8s
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1260:	total: 56.5s	remaining: 10.7s
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1270:	total: 57s	remaining: 10.3s
1271:	total: 57s	remaining: 10.2s
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1276:	total: 57.2s	remaining: 9.98s
1277:	total: 57.2s	remaining: 9.94s
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1279:	total: 57.3s	remaining: 9.84s
1280:	total: 57.3s	remaining: 9.79s
1281:	total: 57.3s	remaining: 9.74s
1282:	total: 57.3s	remaining: 9.7s
1283:	total: 57.4s	remaining: 9.65s
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1289:	total: 57.6s	remaining: 9.37s
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1314:	total: 58.4s	remaining: 8.21s
1315:	total: 58.4s	remaining: 8.17s
1316:	total: 58.5s	remaining: 8.12s
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1318:	total: 58.5s	remaining: 8.03s
1319:	total: 58.6s	remaining: 7.98s
1320:	total: 58.6s	remaining: 7.94s
1321:	total: 58.6s	remaining: 7.89s
1322:	total: 58.6s	remaining: 7.85s
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1333:	total: 59s	remaining: 7.35s
1334:	total: 59.1s	remaining: 7.3s
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1337:	total: 59.2s	remaining: 7.17s
1338:	total: 59.3s	remaining: 7.13s
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1340:	total: 59.4s	remaining: 7.04s
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1342:	total: 59.5s	remaining: 6.95s
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1345:	total: 59.5s	remaining: 6.81s
1346:	total: 59.6s	remaining: 6.77s
1347:	total: 59.7s	remaining: 6.73s
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1350:	total: 59.8s	remaining: 6.59s
1351:	total: 59.8s	remaining: 6.55s
1352:	total: 59.8s	remaining: 6.5s
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1354:	total: 59.9s	remaining: 6.41s
1355:	total: 60s	remaining: 6.37s
1356:	total: 60s	remaining: 6.32s
1357:	total: 1m	remaining: 6.28s
1358:	total: 1m	remaining: 6.23s
1359:	total: 1m	remaining: 6.18s
1360:	total: 1m	remaining: 6.14s
1361:	total: 1m	remaining: 6.1s
1362:	total: 1m	remaining: 6.05s
1363:	total: 1m	remaining: 6.01s
1364:	total: 1m	remaining: 5.96s
1365:	total: 1m	remaining: 5.92s
1366:	total: 1m	remaining: 5.87s
1367:	total: 1m	remaining: 5.83s
1368:	total: 1m	remaining: 5.78s
1369:	total: 1m	remaining: 5.74s
1370:	total: 1m	remaining: 5.69s
1371:	total: 1m	remaining: 5.65s
1372:	total: 1m	remaining: 5.6s
1373:	total: 1m	remaining: 5.56s
1374:	total: 1m	remaining: 5.51s
1375:	total: 1m	remaining: 5.47s
1376:	total: 1m	remaining: 5.42s
1377:	total: 1m	remaining: 5.38s
1378:	total: 1m	remaining: 5.33s
1379:	total: 1m	remaining: 5.29s
1380:	total: 1m	remaining: 5.24s
1381:	total: 1m	remaining: 5.2s
1382:	total: 1m	remaining: 5.15s
1383:	total: 1m	remaining: 5.11s
1384:	total: 1m	remaining: 5.06s
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1386:	total: 1m 1s	remaining: 4.98s
1387:	total: 1m 1s	remaining: 4.93s
1388:	total: 1m 1s	remaining: 4.89s
1389:	total: 1m 1s	remaining: 4.84s
1390:	total: 1m 1s	remaining: 4.8s
1391:	total: 1m 1s	remaining: 4.75s
1392:	total: 1m 1s	remaining: 4.71s
1393:	total: 1m 1s	remaining: 4.67s
1394:	total: 1m 1s	remaining: 4.62s
1395:	total: 1m 1s	remaining: 4.58s
1396:	total: 1m 1s	remaining: 4.53s
1397:	total: 1m 1s	remaining: 4.49s
1398:	total: 1m 1s	remaining: 4.45s
1399:	total: 1m 1s	remaining: 4.4s
1400:	total: 1m 1s	remaining: 4.36s
1401:	total: 1m 1s	remaining: 4.31s
1402:	total: 1m 1s	remaining: 4.27s
1403:	total: 1m 1s	remaining: 4.22s
1404:	total: 1m 1s	remaining: 4.18s
1405:	total: 1m 1s	remaining: 4.13s
1406:	total: 1m 1s	remaining: 4.09s
1407:	total: 1m 1s	remaining: 4.05s
1408:	total: 1m 1s	remaining: 4s
1409:	total: 1m 2s	remaining: 3.96s
1410:	total: 1m 2s	remaining: 3.91s
1411:	total: 1m 2s	remaining: 3.87s
1412:	total: 1m 2s	remaining: 3.83s
1413:	total: 1m 2s	remaining: 3.78s
1414:	total: 1m 2s	remaining: 3.74s
1415:	total: 1m 2s	remaining: 3.69s
1416:	total: 1m 2s	remaining: 3.65s
1417:	total: 1m 2s	remaining: 3.61s
1418:	total: 1m 2s	remaining: 3.56s
1419:	total: 1m 2s	remaining: 3.52s
1420:	total: 1m 2s	remaining: 3.47s
1421:	total: 1m 2s	remaining: 3.43s
1422:	total: 1m 2s	remaining: 3.38s
1423:	total: 1m 2s	remaining: 3.34s
1424:	total: 1m 2s	remaining: 3.29s
1425:	total: 1m 2s	remaining: 3.25s
1426:	total: 1m 2s	remaining: 3.21s
1427:	total: 1m 2s	remaining: 3.16s
1428:	total: 1m 2s	remaining: 3.12s
1429:	total: 1m 2s	remaining: 3.07s
1430:	total: 1m 2s	remaining: 3.03s
1431:	total: 1m 2s	remaining: 2.98s
1432:	total: 1m 2s	remaining: 2.94s
1433:	total: 1m 2s	remaining: 2.9s
1434:	total: 1m 2s	remaining: 2.85s
1435:	total: 1m 2s	remaining: 2.81s
1436:	total: 1m 3s	remaining: 2.76s
1437:	total: 1m 3s	remaining: 2.72s
1438:	total: 1m 3s	remaining: 2.67s
1439:	total: 1m 3s	remaining: 2.63s
1440:	total: 1m 3s	remaining: 2.59s
1441:	total: 1m 3s	remaining: 2.54s
1442:	total: 1m 3s	remaining: 2.5s
1443:	total: 1m 3s	remaining: 2.46s
1444:	total: 1m 3s	remaining: 2.41s
1445:	total: 1m 3s	remaining: 2.37s
1446:	total: 1m 3s	remaining: 2.32s
1447:	total: 1m 3s	remaining: 2.28s
1448:	total: 1m 3s	remaining: 2.23s
1449:	total: 1m 3s	remaining: 2.19s
1450:	total: 1m 3s	remaining: 2.15s
1451:	total: 1m 3s	remaining: 2.1s
1452:	total: 1m 3s	remaining: 2.06s
1453:	total: 1m 3s	remaining: 2.02s
1454:	total: 1m 3s	remaining: 1.97s
1455:	total: 1m 3s	remaining: 1.93s
1456:	total: 1m 3s	remaining: 1.88s
1457:	total: 1m 3s	remaining: 1.84s
1458:	total: 1m 3s	remaining: 1.8s
1459:	total: 1m 3s	remaining: 1.75s
1460:	total: 1m 4s	remaining: 1.71s
1461:	total: 1m 4s	remaining: 1.66s
1462:	total: 1m 4s	remaining: 1.62s
1463:	total: 1m 4s	remaining: 1.58s
1464:	total: 1m 4s	remaining: 1.53s
1465:	total: 1m 4s	remaining: 1.49s
1466:	total: 1m 4s	remaining: 1.45s
1467:	total: 1m 4s	remaining: 1.4s
1468:	total: 1m 4s	remaining: 1.36s
1469:	total: 1m 4s	remaining: 1.31s
1470:	total: 1m 4s	remaining: 1.27s
1471:	total: 1m 4s	remaining: 1.23s
1472:	total: 1m 4s	remaining: 1.18s
1473:	total: 1m 4s	remaining: 1.14s
1474:	total: 1m 4s	remaining: 1.09s
1475:	total: 1m 4s	remaining: 1.05s
1476:	total: 1m 4s	remaining: 1.01s
1477:	total: 1m 4s	remaining: 964ms
1478:	total: 1m 4s	remaining: 920ms
1479:	total: 1m 4s	remaining: 876ms
1480:	total: 1m 4s	remaining: 832ms
1481:	total: 1m 4s	remaining: 788ms
1482:	total: 1m 4s	remaining: 744ms
1483:	total: 1m 4s	remaining: 700ms
1484:	total: 1m 4s	remaining: 656ms
1485:	total: 1m 5s	remaining: 613ms
1486:	total: 1m 5s	remaining: 569ms
1487:	total: 1m 5s	remaining: 525ms
1488:	total: 1m 5s	remaining: 481ms
1489:	total: 1m 5s	remaining: 437ms
1490:	total: 1m 5s	remaining: 394ms
1491:	total: 1m 5s	remaining: 350ms
1492:	total: 1m 5s	remaining: 306ms
1493:	total: 1m 5s	remaining: 262ms
1494:	total: 1m 5s	remaining: 219ms
1495:	total: 1m 5s	remaining: 175ms
1496:	total: 1m 5s	remaining: 131ms
1497:	total: 1m 5s	remaining: 87.4ms
1498:	total: 1m 5s	remaining: 43.7ms
1499:	total: 1m 5s	remaining: 0us
Learning rate set to 0.021203
0:	total: 58.6ms	remaining: 1m 27s
1:	total: 117ms	remaining: 1m 27s
2:	total: 177ms	remaining: 1m 28s










    
    






Default metric period is 5 because AUC is/are not implemented for GPU










    
    






3:	total: 238ms	remaining: 1m 29s
4:	total: 295ms	remaining: 1m 28s
5:	total: 352ms	remaining: 1m 27s
6:	total: 408ms	remaining: 1m 27s
7:	total: 480ms	remaining: 1m 29s
8:	total: 540ms	remaining: 1m 29s
9:	total: 603ms	remaining: 1m 29s
10:	total: 670ms	remaining: 1m 30s
11:	total: 737ms	remaining: 1m 31s
12:	total: 798ms	remaining: 1m 31s
13:	total: 860ms	remaining: 1m 31s
14:	total: 924ms	remaining: 1m 31s
15:	total: 985ms	remaining: 1m 31s
16:	total: 1.04s	remaining: 1m 31s
17:	total: 1.1s	remaining: 1m 30s
18:	total: 1.15s	remaining: 1m 29s
19:	total: 1.2s	remaining: 1m 28s
20:	total: 1.25s	remaining: 1m 28s
21:	total: 1.3s	remaining: 1m 27s
22:	total: 1.35s	remaining: 1m 26s
23:	total: 1.4s	remaining: 1m 25s
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27:	total: 1.6s	remaining: 1m 24s
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29:	total: 1.71s	remaining: 1m 23s
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49:	total: 2.68s	remaining: 1m 17s
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52:	total: 2.82s	remaining: 1m 16s
53:	total: 2.87s	remaining: 1m 16s
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55:	total: 2.97s	remaining: 1m 16s
56:	total: 3.01s	remaining: 1m 16s
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58:	total: 3.11s	remaining: 1m 15s
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60:	total: 3.2s	remaining: 1m 15s
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63:	total: 3.34s	remaining: 1m 14s
64:	total: 3.39s	remaining: 1m 14s
65:	total: 3.45s	remaining: 1m 14s
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67:	total: 3.54s	remaining: 1m 14s
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70:	total: 3.7s	remaining: 1m 14s
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72:	total: 3.79s	remaining: 1m 14s
73:	total: 3.84s	remaining: 1m 13s
74:	total: 3.88s	remaining: 1m 13s
75:	total: 3.93s	remaining: 1m 13s
76:	total: 3.98s	remaining: 1m 13s
77:	total: 4.03s	remaining: 1m 13s
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80:	total: 4.17s	remaining: 1m 13s
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92:	total: 4.75s	remaining: 1m 11s
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98:	total: 5.02s	remaining: 1m 11s
99:	total: 5.07s	remaining: 1m 10s
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1477:	total: 1m 9s	remaining: 1.03s
1478:	total: 1m 9s	remaining: 986ms
1479:	total: 1m 9s	remaining: 939ms
1480:	total: 1m 9s	remaining: 892ms
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1482:	total: 1m 9s	remaining: 798ms
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1485:	total: 1m 9s	remaining: 657ms
1486:	total: 1m 9s	remaining: 610ms
1487:	total: 1m 9s	remaining: 563ms
1488:	total: 1m 9s	remaining: 516ms
1489:	total: 1m 9s	remaining: 469ms
1490:	total: 1m 9s	remaining: 422ms
1491:	total: 1m 9s	remaining: 375ms
1492:	total: 1m 10s	remaining: 328ms
1493:	total: 1m 10s	remaining: 281ms
1494:	total: 1m 10s	remaining: 234ms
1495:	total: 1m 10s	remaining: 188ms
1496:	total: 1m 10s	remaining: 141ms
1497:	total: 1m 10s	remaining: 93.8ms
1498:	total: 1m 10s	remaining: 46.9ms
1499:	total: 1m 10s	remaining: 0us
Cat Boost Cross-Validation AUC-ROC: 0.92
[Voting] ...................... (1 of 4) Processing ada, total=   3.4s
[Voting] ...................... (2 of 4) Processing xgb, total=   5.7s
[LightGBM] [Warning] num_iterations is set=500, num_boost_round=500 will be ignored. Current value: num_iterations=500










    
    






c:\Users\XIX\anaconda3\lib\site-packages\lightgbm\engine.py:177: UserWarning:

Found `num_boost_round` in params. Will use it instead of argument











    
    






[Voting] ...................... (3 of 4) Processing lgb, total=   1.0s
Learning rate set to 0.021203
0:	total: 49.6ms	remaining: 1m 14s
1:	total: 102ms	remaining: 1m 16s
2:	total: 150ms	remaining: 1m 14s










    
    






Default metric period is 5 because AUC is/are not implemented for GPU










    
    






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[Voting] ...................... (4 of 4) Processing cat, total= 1.2min
[Voting] ...................... (1 of 4) Processing ada, total=   3.4s
[Voting] ...................... (2 of 4) Processing xgb, total=   5.7s
[LightGBM] [Warning] num_iterations is set=500, num_boost_round=500 will be ignored. Current value: num_iterations=500










    
    






c:\Users\XIX\anaconda3\lib\site-packages\lightgbm\engine.py:177: UserWarning:

Found `num_boost_round` in params. Will use it instead of argument











    
    






[Voting] ...................... (3 of 4) Processing lgb, total=   1.1s
Learning rate set to 0.021203
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1:	total: 127ms	remaining: 1m 35s










    
    






Default metric period is 5 because AUC is/are not implemented for GPU










    
    






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[Voting] ...................... (4 of 4) Processing cat, total= 1.3min
[Voting] ...................... (1 of 4) Processing ada, total=   4.2s
[Voting] ...................... (2 of 4) Processing xgb, total=   6.1s
[LightGBM] [Warning] num_iterations is set=500, num_boost_round=500 will be ignored. Current value: num_iterations=500










    
    






c:\Users\XIX\anaconda3\lib\site-packages\lightgbm\engine.py:177: UserWarning:

Found `num_boost_round` in params. Will use it instead of argument











    
    






[Voting] ...................... (3 of 4) Processing lgb, total=   1.2s
Learning rate set to 0.021203
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Default metric period is 5 because AUC is/are not implemented for GPU










    
    






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[Voting] ...................... (4 of 4) Processing cat, total= 1.2min
[Voting] ...................... (1 of 4) Processing ada, total=   3.5s
[Voting] ...................... (2 of 4) Processing xgb, total=   6.1s
[LightGBM] [Warning] num_iterations is set=500, num_boost_round=500 will be ignored. Current value: num_iterations=500










    
    






c:\Users\XIX\anaconda3\lib\site-packages\lightgbm\engine.py:177: UserWarning:

Found `num_boost_round` in params. Will use it instead of argument











    
    






[Voting] ...................... (3 of 4) Processing lgb, total=   1.2s
Learning rate set to 0.021203
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1:	total: 131ms	remaining: 1m 37s










    
    






Default metric period is 5 because AUC is/are not implemented for GPU










    
    






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[Voting] ...................... (4 of 4) Processing cat, total= 1.2min
[Voting] ...................... (1 of 4) Processing ada, total=   4.3s
[Voting] ...................... (2 of 4) Processing xgb, total=   6.5s
[LightGBM] [Warning] num_iterations is set=500, num_boost_round=500 will be ignored. Current value: num_iterations=500










    
    






c:\Users\XIX\anaconda3\lib\site-packages\lightgbm\engine.py:177: UserWarning:

Found `num_boost_round` in params. Will use it instead of argument











    
    






[Voting] ...................... (3 of 4) Processing lgb, total=   1.2s
Learning rate set to 0.021203
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Default metric period is 5 because AUC is/are not implemented for GPU










    
    






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[Voting] ...................... (4 of 4) Processing cat, total= 1.2min
Voting Classifier Cross-Validation AUC-ROC: 0.93

Classifier with the best AUC-ROC: XG Boost




























We used a pipeline to determine the best classifier to use. We used a standard scaler and SMOTE for preprocessing. The models with the best cross validation scores on the training set was the voting classifier and XG Boost. Therefore, we will implement the voting classifier and measure the model against the test data set.























ADA Pipeline Feature Importance¶


















In [ ]:



     


# Ada pipeline feature importance
pipe_ada.fit(X_train, y_train)

ada_classifier = pipe_ada.named_steps['ada_classifier']
ada_importances = ada_classifier.feature_importances_
ada_indices = np.argsort(ada_importances)[::-1]

top_10_features = []
for f in range(10):
    feature_index = ada_indices[f]
    feature_name = X_train.columns[feature_index]
    top_10_features.append((feature_name, ada_importances[feature_index]))

ada_top_10_df = pd.DataFrame(top_10_features, columns=['Feature', 'Importance'])



     



















In [ ]:



     


# ADA feature imprtance
fig1 = px.pie(ada_top_10_df, names='Feature', values='Importance', title='ADA Boost Feature Importance', template='ggplot2')
fig1



     






















    
    






        
        









    
    







                            
                    
























In [ ]:



     


# fig1.write_html('figures/fig1.html')



     























XGBoost Pipeline Feature Importance¶


















In [ ]:



     


# XG Boost pipeline feature importance
pipe_xg.fit(X_train, y_train)

xg_classifier = pipe_xg.named_steps['xg_classifier']
xg_importances = xg_classifier.feature_importances_
xg_indices = np.argsort(xg_importances)[::-1]

top_10_features = []
for f in range(10):
    feature_index = xg_indices[f]
    feature_name = X_train.columns[feature_index]
    top_10_features.append((feature_name, xg_importances[feature_index]))

xg_top_10_df = pd.DataFrame(top_10_features, columns=['Feature', 'Importance'])



     



















In [ ]:



     


# XGB feature importance
fig2 = px.pie(xg_top_10_df, names='Feature', values='Importance', title='XG Boost Feature Importance', template='ggplot2')
fig2



     






















    
    







                            
                    
























In [ ]:



     


# fig2.write_html('figures/fig2.html')



     























LGBM Pipeline Feature Importance¶


















In [ ]:



     


# Light GBM pipeline feature importance
pipe_lgbm.fit(X_train, y_train)

lg_classifier = pipe_lgbm.named_steps['lgbm_classifier']
lg_importances = lg_classifier.feature_importances_
lg_indices = np.argsort(lg_importances)[::-1]

top_10_features = []
for f in range(10):
    feature_index = lg_indices[f]
    feature_name = X_train.columns[feature_index]
    top_10_features.append((feature_name, lg_importances[feature_index]))

lg_top_10_df = pd.DataFrame(top_10_features, columns=['Feature', 'Importance'])



     






















    
    






c:\Users\XIX\anaconda3\lib\site-packages\lightgbm\engine.py:177: UserWarning:

Found `num_boost_round` in params. Will use it instead of argument











    
    






[LightGBM] [Warning] num_iterations is set=500, num_boost_round=500 will be ignored. Current value: num_iterations=500
























In [ ]:



     


# LGBM feature importance
fig3 = px.pie(lg_top_10_df, names='Feature', values='Importance', title='Light GBM Feature Importance', template='ggplot2')
fig3



     






















    
    







                            
                    
























In [ ]:



     


# fig3.write_html('figures/fig3.html')



     























Gradient Boost Pipeline Feature Importance¶


















In [ ]:



     


# Gradient Boost pipeline feature importance
pipe_gb.fit(X_train, y_train)

gb_classifier = pipe_gb.named_steps['gb_classifier']
gb_importances = gb_classifier.feature_importances_
gb_indices = np.argsort(gb_importances)[::-1]

top_10_features = []
for f in range(10):
    feature_index = gb_indices[f]
    feature_name = X_train.columns[feature_index]
    top_10_features.append((feature_name, gb_importances[feature_index]))

gb_top_10_df = pd.DataFrame(top_10_features, columns=['Feature', 'Importance'])



     



















In [ ]:



     


# gb feature importance
fig4 = px.pie(gb_top_10_df, names='Feature', values='Importance', title='Gradient Boost Feature Importance', template='ggplot2')
fig4



     






















    
    







                            
                    
























In [ ]:



     


# fig4.write_html('figures/fig4.html')



     























CatBoost Pipeline Feature Importance¶


















In [ ]:



     


# Cat Boost pipeline feature importance
pipe_cat.fit(X_train, y_train)

cat_classifier = pipe_cat.named_steps['cat_classifier']
cat_importances = cat_classifier.feature_importances_
cat_indices = np.argsort(cat_importances)[::-1]

top_10_features = []
for f in range(10):
    feature_index = cat_indices[f]
    feature_name = X_train.columns[feature_index]
    top_10_features.append((feature_name, cat_importances[feature_index]))

cat_top_10_df = pd.DataFrame(top_10_features, columns=['Feature', 'Importance'])



     






















    
    






Learning rate set to 0.020944
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Default metric period is 5 because AUC is/are not implemented for GPU










    
    






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In [ ]:



     


# cat boost feature importance
fig5 = px.pie(cat_top_10_df, names='Feature', values='Importance', title='Cat Boost Feature Importance', template='ggplot2')
fig5



     






















    
    







                            
                    
























In [ ]:



     


# fig5.write_html('figures/fig5.html')



     























Comparing Models¶


















In [ ]:



     


# series of model scores
data = {'Ada Boost': 0.87, 'XG Boost': 0.93 , 'LGBM': 0.92, 'Gradient Boost': 0.91, 'Cat Boost': 0.92, 'Voting Classifier': 0.93 }
comp = pd.Series(data, name='Auc-Roc Score')
comp



     






















    
    
Out[ ]:








Ada Boost            0.87
XG Boost             0.93
LGBM                 0.92
Gradient Boost       0.91
Cat Boost            0.92
Voting Classifier    0.93
Name: Auc-Roc Score, dtype: float64
























In [ ]:



     


# model scores
fig6 = px.scatter(comp, color=comp.index, size=comp, title='Model Comparison', symbol=comp, labels={'index': 'Model', 'value': 'AUC-ROC Score'})
fig6



     






















    
    







                            
                    
























In [ ]:



     


# fig6.write_html('figures/fig6.html')



     























Final Model¶


















In [ ]:



     


# Final model

class1 = AdaBoostClassifier(n_estimators=400, random_state=19, learning_rate=0.1)
class2 = GradientBoostingClassifier(n_estimators=1500, learning_rate=0.1, random_state=19, max_depth=5)
class3 = XGBClassifier(learning_rate=0.5, max_depth=5, n_estimators=1500) 
class4 = lgb.LGBMClassifier(objective='binary', metric='auc', boosting_type='gbdt', learning_rate=0.1, max_depth=20, n_estimators=350, num_leaves=30, random_state=19) 
class5 = CatBoostClassifier(task_type='GPU', loss_function='Logloss', eval_metric='AUC', iterations=1500, random_seed=19)
 

final = VotingClassifier(estimators=[('ada', class1), 
                                #('gbr', class2), 
                                ('xgb', class3), 
                                ('lgb', class4), 
                                ('cat', class5)], 
                                verbose=1, voting='soft')
final = final.fit(X_train, y_train)

# Make predictions on the test set
final_predictions = final.predict(X_test)

result = roc_auc_score(y_test, final_predictions)
print()
print("Voting Classifier model on the test set: ", result)



     






















    
    






[Voting] ...................... (1 of 4) Processing ada, total=   2.7s
[Voting] ...................... (2 of 4) Processing xgb, total=   6.8s
[Voting] ...................... (3 of 4) Processing lgb, total=   0.8s
Learning rate set to 0.021393










    
    






Default metric period is 5 because AUC is/are not implemented for GPU










    
    






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[Voting] ...................... (4 of 4) Processing cat, total= 1.2min

Voting Classifier model on the test set:  0.8538050855126234




























Final Model Evaluation Metric¶


















In [ ]:



     


# Display AUC ROC
probabilities_valid = final.predict_proba(X_test)
probabilities_one_valid = probabilities_valid[:, 1]

auc_roc = roc_auc_score(y_test, probabilities_one_valid)

print(auc_roc)

# ROC AUC curve of results
fpr, tpr, thresholds = roc_curve(y_test, probabilities_one_valid)

fig7 = px.area(
    x=fpr, y=tpr,
    title=f'ROC Curve (AUC={auc(fpr, tpr):.4f})',
    labels=dict(x='False Positive Rate', y='True Positive Rate'),
    width=800, height=600
)
fig7.add_shape(
    type='line', line=dict(dash='dash'),
    x0=0, x1=1, y0=0, y1=1
)

fig7.update_yaxes(scaleanchor="x", scaleratio=1)
fig7.update_xaxes(constrain='domain')
fig7.show()



     






















    
    






0.94068163308956










    
    







                            
                    
























In [ ]:



     


# fig7.write_html('figures/fig7.html')



     























Final Model Feature Importances¶


















In [ ]:



     


# function for extracting feature importance of each model in voting classifier
def compute_feature_importance(voting_clf, weights):
    """ Function to compute feature importance of Voting Classifier """
    
    feature_importance = dict()
    for est in voting_clf.estimators_:
        feature_importance[str(est)] = est.feature_importances_
    
    fe_scores = [0]*len(list(feature_importance.values())[0])
    for idx, imp_score in enumerate(feature_importance.values()):
        imp_score_with_weight = imp_score*weights[idx]
        fe_scores = list(np.add(fe_scores, list(imp_score_with_weight)))
    return fe_scores



     



















In [ ]:



     


# creating feature importance dataframe
fedf = pd.DataFrame()
fedf['Feature'] = X_train.columns
fedf['Feature Importance'] = compute_feature_importance(final, [1, 1, 1, 1])
fedf = fedf.sort_values('Feature Importance', ascending=False).head(10)



     



















In [ ]:



     


# voting classifier feature importance
fig8 = px.pie(fedf, names='Feature', values='Feature Importance', title='Top 10 Features', template='ggplot2')
fig8



     






















    
    







                            
                    
























In [ ]:



     


# fig8.write_html('figures/fig8.html')



     























Overall Conclusions¶






















We succeeded in achieving our target AUC ROC score of 0.75. Our final score with the test set is 0.9409, which is roughly 25% more than our target. The boosting models generally had good scores with the training set, but to ensure a better score with the test set, we used a voting classifier. We also looked into the feature importance of the various models in our pipeline, and finally, the important features in our final model. We see that total charges and monthly charges are the most important features, followed by the time frame of the account opening with the year and month. It is reasonable to conclude features such as charges and the length of time a customer has had service, outweigh other factors such as demographics.


Therefore, Interconnect can utilize this model to predict churn. Alongside marketing strategies, the company can target customers likely to leave, and therefore implement strategies to prevent churn. They may also be interested in looking at trends in the charges of customers alongside the length of service, to periodically implement marketing strategies to prevent churn.























Solution Report¶






















All steps of the final work plan were performed, yet we could have done without the K Means analysis, as the results of that model were difficult to interpret.


Since we used a voting classifier as our final model, we found it difficult to illustrate the inner workings of this black box model. Therefore, we did extensive research to write a code that could extract the feature importance of each model in the ensemble, and give the average feature importance as the overall summary of the model.


Key steps to a successful model and prediction were parsing the start dates into months and years. Furthermore, scaling the dataset and using SMOTE in the pipeline likely helped to achieve a good AUC-ROC score. The final model is a voting classifier with an AUC-ROC score of 0.9409. Choosing the voting classifier was a good decision, yet and XG Boost classifier may have been equally as good.