Telecom Churn Case Study

With 21 predictor variables we need to predict whether a particular customer will switch to another telecom provider or not. In telecom terminology, this is referred to as churning and not churning, respectively.

Importing and Merging Data

# Importing Pandas and NumPy
import pandas as pd
import numpy as np

# Importing all datasets
churn_data = pd.read_csv("C:/Users/crv00/OneDrive/Documents/churn_data.csv")
customer_data = pd.read_csv("C:/Users/crv00/OneDrive/Documents/customer_data.csv")
internet_data = pd.read_csv("C:/Users/crv00/OneDrive/Documents/internet_data.csv")

#Merging on 'customerID'
df_1 = pd.merge(churn_data, customer_data, how='inner', on='customerID')

#Final dataframe with all predictor variables
telecom = pd.merge(df_1, internet_data, how='inner', on='customerID')

Let's understand the structure of our dataframe

# Let's see the head of our master dataset
telecom.head()

	customerID	tenure	PhoneService	Contract	PaperlessBilling	PaymentMethod	MonthlyCharges	TotalCharges	Churn	gender	...	Partner	Dependents	MultipleLines	InternetService	OnlineSecurity	OnlineBackup	DeviceProtection	TechSupport	StreamingTV	StreamingMovies
0	7590-VHVEG	1	No	Month-to-month	Yes	Electronic check	29.85	29.85	No	Female	...	Yes	No	No phone service	DSL	No	Yes	No	No	No	No
1	5575-GNVDE	34	Yes	One year	No	Mailed check	56.95	1889.5	No	Male	...	No	No	No	DSL	Yes	No	Yes	No	No	No
2	3668-QPYBK	2	Yes	Month-to-month	Yes	Mailed check	53.85	108.15	Yes	Male	...	No	No	No	DSL	Yes	Yes	No	No	No	No
3	7795-CFOCW	45	No	One year	No	Bank transfer (automatic)	42.30	1840.75	No	Male	...	No	No	No phone service	DSL	Yes	No	Yes	Yes	No	No
4	9237-HQITU	2	Yes	Month-to-month	Yes	Electronic check	70.70	151.65	Yes	Female	...	No	No	No	Fiber optic	No	No	No	No	No	No

5 rows × 21 columns

np.shape(telecom)

(7043, 21)

telecom.describe()

	tenure	MonthlyCharges	SeniorCitizen
count	7043.000000	7043.000000	7043.000000
mean	32.371149	64.761692	0.162147
std	24.559481	30.090047	0.368612
min	0.000000	18.250000	0.000000
25%	9.000000	35.500000	0.000000
50%	29.000000	70.350000	0.000000
75%	55.000000	89.850000	0.000000
max	72.000000	118.750000	1.000000

# Let's see the type of each column
telecom.info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 7043 entries, 0 to 7042
Data columns (total 21 columns):
 #   Column            Non-Null Count  Dtype  
---  ------            --------------  -----  
 0   customerID        7043 non-null   object 
 1   tenure            7043 non-null   int64  
 2   PhoneService      7043 non-null   object 
 3   Contract          7043 non-null   object 
 4   PaperlessBilling  7043 non-null   object 
 5   PaymentMethod     7043 non-null   object 
 6   MonthlyCharges    7043 non-null   float64
 7   TotalCharges      7043 non-null   object 
 8   Churn             7043 non-null   object 
 9   gender            7043 non-null   object 
 10  SeniorCitizen     7043 non-null   int64  
 11  Partner           7043 non-null   object 
 12  Dependents        7043 non-null   object 
 13  MultipleLines     7043 non-null   object 
 14  InternetService   7043 non-null   object 
 15  OnlineSecurity    7043 non-null   object 
 16  OnlineBackup      7043 non-null   object 
 17  DeviceProtection  7043 non-null   object 
 18  TechSupport       7043 non-null   object 
 19  StreamingTV       7043 non-null   object 
 20  StreamingMovies   7043 non-null   object 
dtypes: float64(1), int64(2), object(18)
memory usage: 1.2+ MB

Data Preparation

telecom.head()

	customerID	tenure	PhoneService	Contract	PaperlessBilling	PaymentMethod	MonthlyCharges	TotalCharges	Churn	gender	...	Partner	Dependents	MultipleLines	InternetService	OnlineSecurity	OnlineBackup	DeviceProtection	TechSupport	StreamingTV	StreamingMovies
0	7590-VHVEG	1	No	Month-to-month	Yes	Electronic check	29.85	29.85	No	Female	...	Yes	No	No phone service	DSL	No	Yes	No	No	No	No
1	5575-GNVDE	34	Yes	One year	No	Mailed check	56.95	1889.5	No	Male	...	No	No	No	DSL	Yes	No	Yes	No	No	No
2	3668-QPYBK	2	Yes	Month-to-month	Yes	Mailed check	53.85	108.15	Yes	Male	...	No	No	No	DSL	Yes	Yes	No	No	No	No
3	7795-CFOCW	45	No	One year	No	Bank transfer (automatic)	42.30	1840.75	No	Male	...	No	No	No phone service	DSL	Yes	No	Yes	Yes	No	No
4	9237-HQITU	2	Yes	Month-to-month	Yes	Electronic check	70.70	151.65	Yes	Female	...	No	No	No	Fiber optic	No	No	No	No	No	No

5 rows × 21 columns

# Converting Yes to 1 and No to 0
telecom['PhoneService'] = telecom['PhoneService'].map({'Yes': 1, 'No': 0})
telecom['PaperlessBilling'] = telecom['PaperlessBilling'].map({'Yes': 1, 'No': 0})
telecom['Churn'] = telecom['Churn'].map({'Yes': 1, 'No': 0})
telecom['Partner'] = telecom['Partner'].map({'Yes': 1, 'No': 0})
telecom['Dependents'] = telecom['Dependents'].map({'Yes': 1, 'No': 0})

telecom

	customerID	tenure	PhoneService	Contract	PaperlessBilling	PaymentMethod	MonthlyCharges	TotalCharges	Churn	gender	...	Partner	Dependents	MultipleLines	InternetService	OnlineSecurity	OnlineBackup	DeviceProtection	TechSupport	StreamingTV	StreamingMovies
0	7590-VHVEG	1	0	Month-to-month	1	Electronic check	29.85	29.85	0	Female	...	1	0	No phone service	DSL	No	Yes	No	No	No	No
1	5575-GNVDE	34	1	One year	0	Mailed check	56.95	1889.5	0	Male	...	0	0	No	DSL	Yes	No	Yes	No	No	No
2	3668-QPYBK	2	1	Month-to-month	1	Mailed check	53.85	108.15	1	Male	...	0	0	No	DSL	Yes	Yes	No	No	No	No
3	7795-CFOCW	45	0	One year	0	Bank transfer (automatic)	42.30	1840.75	0	Male	...	0	0	No phone service	DSL	Yes	No	Yes	Yes	No	No
4	9237-HQITU	2	1	Month-to-month	1	Electronic check	70.70	151.65	1	Female	...	0	0	No	Fiber optic	No	No	No	No	No	No
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
7038	6840-RESVB	24	1	One year	1	Mailed check	84.80	1990.5	0	Male	...	1	1	Yes	DSL	Yes	No	Yes	Yes	Yes	Yes
7039	2234-XADUH	72	1	One year	1	Credit card (automatic)	103.20	7362.9	0	Female	...	1	1	Yes	Fiber optic	No	Yes	Yes	No	Yes	Yes
7040	4801-JZAZL	11	0	Month-to-month	1	Electronic check	29.60	346.45	0	Female	...	1	1	No phone service	DSL	Yes	No	No	No	No	No
7041	8361-LTMKD	4	1	Month-to-month	1	Mailed check	74.40	306.6	1	Male	...	1	0	Yes	Fiber optic	No	No	No	No	No	No
7042	3186-AJIEK	66	1	Two year	1	Bank transfer (automatic)	105.65	6844.5	0	Male	...	0	0	No	Fiber optic	Yes	No	Yes	Yes	Yes	Yes

7043 rows × 21 columns

Dummy Variable Creation

# Creating a dummy variable for the variable 'Contract' and dropping the first one.
cont = pd.get_dummies(telecom['Contract'],prefix='Contract',drop_first=True)
#Adding the results to the master dataframe
telecom = pd.concat([telecom,cont],axis=1)

# Creating a dummy variable for the variable 'PaymentMethod' and dropping the first one.
pm = pd.get_dummies(telecom['PaymentMethod'],prefix='PaymentMethod',drop_first=True)
#Adding the results to the master dataframe
telecom = pd.concat([telecom,pm],axis=1)

# Creating a dummy variable for the variable 'gender' and dropping the first one.
gen = pd.get_dummies(telecom['gender'],prefix='gender',drop_first=True)
#Adding the results to the master dataframe
telecom = pd.concat([telecom,gen],axis=1)

# Creating a dummy variable for the variable 'MultipleLines' and dropping the first one.
ml = pd.get_dummies(telecom['MultipleLines'],prefix='MultipleLines')
#  dropping MultipleLines_No phone service column
ml1 = ml.drop(['MultipleLines_No phone service'],1)
#Adding the results to the master dataframe
telecom = pd.concat([telecom,ml1],axis=1)

# Creating a dummy variable for the variable 'InternetService' and dropping the first one.
iser = pd.get_dummies(telecom['InternetService'],prefix='InternetService',drop_first=True)
#Adding the results to the master dataframe
telecom = pd.concat([telecom,iser],axis=1)

# Creating a dummy variable for the variable 'OnlineSecurity'.
os = pd.get_dummies(telecom['OnlineSecurity'],prefix='OnlineSecurity')
os1= os.drop(['OnlineSecurity_No internet service'],1)
#Adding the results to the master dataframe
telecom = pd.concat([telecom,os1],axis=1)

# Creating a dummy variable for the variable 'OnlineBackup'.
ob =pd.get_dummies(telecom['OnlineBackup'],prefix='OnlineBackup')
ob1 =ob.drop(['OnlineBackup_No internet service'],1)
#Adding the results to the master dataframe
telecom = pd.concat([telecom,ob1],axis=1)

# Creating a dummy variable for the variable 'DeviceProtection'. 
dp =pd.get_dummies(telecom['DeviceProtection'],prefix='DeviceProtection')
dp1 = dp.drop(['DeviceProtection_No internet service'],1)
#Adding the results to the master dataframe
telecom = pd.concat([telecom,dp1],axis=1)

# Creating a dummy variable for the variable 'TechSupport'. 
ts =pd.get_dummies(telecom['TechSupport'],prefix='TechSupport')
ts1 = ts.drop(['TechSupport_No internet service'],1)
#Adding the results to the master dataframe
telecom = pd.concat([telecom,ts1],axis=1)

# Creating a dummy variable for the variable 'StreamingTV'.
st =pd.get_dummies(telecom['StreamingTV'],prefix='StreamingTV')
st1 = st.drop(['StreamingTV_No internet service'],1)
#Adding the results to the master dataframe
telecom = pd.concat([telecom,st1],axis=1)

# Creating a dummy variable for the variable 'StreamingMovies'. 
sm =pd.get_dummies(telecom['StreamingMovies'],prefix='StreamingMovies')
sm1 = sm.drop(['StreamingMovies_No internet service'],1)
#Adding the results to the master dataframe
telecom = pd.concat([telecom,sm1],axis=1)

#telecom['MultipleLines'].value_counts()
telecom.head()

	customerID	tenure	PhoneService	Contract	PaperlessBilling	PaymentMethod	MonthlyCharges	TotalCharges	Churn	gender	...	OnlineBackup_No	OnlineBackup_Yes	DeviceProtection_No	DeviceProtection_Yes	TechSupport_No	TechSupport_Yes	StreamingTV_No	StreamingTV_Yes	StreamingMovies_No	StreamingMovies_Yes
0	7590-VHVEG	1	0	Month-to-month	1	Electronic check	29.85	29.85	0	Female	...	0	1	1	0	1	0	1	0	1	0
1	5575-GNVDE	34	1	One year	0	Mailed check	56.95	1889.5	0	Male	...	1	0	0	1	1	0	1	0	1	0
2	3668-QPYBK	2	1	Month-to-month	1	Mailed check	53.85	108.15	1	Male	...	0	1	1	0	1	0	1	0	1	0
3	7795-CFOCW	45	0	One year	0	Bank transfer (automatic)	42.30	1840.75	0	Male	...	1	0	0	1	0	1	1	0	1	0
4	9237-HQITU	2	1	Month-to-month	1	Electronic check	70.70	151.65	1	Female	...	1	0	1	0	1	0	1	0	1	0

5 rows × 43 columns

Dropping the repeated variables

# We have created dummies for the below variables, so we can drop them
telecom = telecom.drop(['Contract','PaymentMethod','gender','MultipleLines','InternetService', 'OnlineSecurity', 'OnlineBackup', 'DeviceProtection',
       'TechSupport', 'StreamingTV', 'StreamingMovies'], 1)

#The varaible was imported as a string we need to convert it to float
#telecom['TotalCharges'] =telecom['TotalCharges'].convert_objects(convert_numeric=True)
#telecom['tenure'] = telecom['tenure'].astype(int).astype(float)

telecom['TotalCharges']=pd.to_numeric(telecom['TotalCharges'],errors='coerce')

#telecom['TotalCharges']=pd.to_numeric(telecom['TotalCharges'],downcast="integer")

telecom.info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 7043 entries, 0 to 7042
Data columns (total 32 columns):
 #   Column                                 Non-Null Count  Dtype  
---  ------                                 --------------  -----  
 0   customerID                             7043 non-null   object 
 1   tenure                                 7043 non-null   int64  
 2   PhoneService                           7043 non-null   int64  
 3   PaperlessBilling                       7043 non-null   int64  
 4   MonthlyCharges                         7043 non-null   float64
 5   TotalCharges                           7032 non-null   float64
 6   Churn                                  7043 non-null   int64  
 7   SeniorCitizen                          7043 non-null   int64  
 8   Partner                                7043 non-null   int64  
 9   Dependents                             7043 non-null   int64  
 10  Contract_One year                      7043 non-null   uint8  
 11  Contract_Two year                      7043 non-null   uint8  
 12  PaymentMethod_Credit card (automatic)  7043 non-null   uint8  
 13  PaymentMethod_Electronic check         7043 non-null   uint8  
 14  PaymentMethod_Mailed check             7043 non-null   uint8  
 15  gender_Male                            7043 non-null   uint8  
 16  MultipleLines_No                       7043 non-null   uint8  
 17  MultipleLines_Yes                      7043 non-null   uint8  
 18  InternetService_Fiber optic            7043 non-null   uint8  
 19  InternetService_No                     7043 non-null   uint8  
 20  OnlineSecurity_No                      7043 non-null   uint8  
 21  OnlineSecurity_Yes                     7043 non-null   uint8  
 22  OnlineBackup_No                        7043 non-null   uint8  
 23  OnlineBackup_Yes                       7043 non-null   uint8  
 24  DeviceProtection_No                    7043 non-null   uint8  
 25  DeviceProtection_Yes                   7043 non-null   uint8  
 26  TechSupport_No                         7043 non-null   uint8  
 27  TechSupport_Yes                        7043 non-null   uint8  
 28  StreamingTV_No                         7043 non-null   uint8  
 29  StreamingTV_Yes                        7043 non-null   uint8  
 30  StreamingMovies_No                     7043 non-null   uint8  
 31  StreamingMovies_Yes                    7043 non-null   uint8  
dtypes: float64(2), int64(7), object(1), uint8(22)
memory usage: 756.6+ KB

Now we can see we have all variables as integer.

Checking for Outliers

# Checking for outliers in the continuous variables
num_telecom = telecom[['tenure','MonthlyCharges','SeniorCitizen','TotalCharges']]

# Checking outliers at 25%,50%,75%,90%,95% and 99%
num_telecom.describe(percentiles=[.25,.5,.75,.90,.95,.99])

	tenure	MonthlyCharges	SeniorCitizen	TotalCharges
count	7043.000000	7043.000000	7043.000000	7032.000000
mean	32.371149	64.761692	0.162147	2283.300441
std	24.559481	30.090047	0.368612	2266.771362
min	0.000000	18.250000	0.000000	18.800000
25%	9.000000	35.500000	0.000000	401.450000
50%	29.000000	70.350000	0.000000	1397.475000
75%	55.000000	89.850000	0.000000	3794.737500
90%	69.000000	102.600000	1.000000	5976.640000
95%	72.000000	107.400000	1.000000	6923.590000
99%	72.000000	114.729000	1.000000	8039.883000
max	72.000000	118.750000	1.000000	8684.800000

From the distribution shown above, you can see that there no outliner in your data. The numbers are gradually increasing.

Checking for Missing Values and Inputing Them

# Adding up the missing values (column-wise)
telecom.isnull().sum()

customerID                                0
tenure                                    0
PhoneService                              0
PaperlessBilling                          0
MonthlyCharges                            0
TotalCharges                             11
Churn                                     0
SeniorCitizen                             0
Partner                                   0
Dependents                                0
Contract_One year                         0
Contract_Two year                         0
PaymentMethod_Credit card (automatic)     0
PaymentMethod_Electronic check            0
PaymentMethod_Mailed check                0
gender_Male                               0
MultipleLines_No                          0
MultipleLines_Yes                         0
InternetService_Fiber optic               0
InternetService_No                        0
OnlineSecurity_No                         0
OnlineSecurity_Yes                        0
OnlineBackup_No                           0
OnlineBackup_Yes                          0
DeviceProtection_No                       0
DeviceProtection_Yes                      0
TechSupport_No                            0
TechSupport_Yes                           0
StreamingTV_No                            0
StreamingTV_Yes                           0
StreamingMovies_No                        0
StreamingMovies_Yes                       0
dtype: int64

It means that 11/7043 = 0.001561834 i.e 0.1%, best is to remove these observations from the analysis

# Checking the percentage of missing values
round(100*(telecom.isnull().sum()/len(telecom.index)), 2)

customerID                               0.00
tenure                                   0.00
PhoneService                             0.00
PaperlessBilling                         0.00
MonthlyCharges                           0.00
TotalCharges                             0.16
Churn                                    0.00
SeniorCitizen                            0.00
Partner                                  0.00
Dependents                               0.00
Contract_One year                        0.00
Contract_Two year                        0.00
PaymentMethod_Credit card (automatic)    0.00
PaymentMethod_Electronic check           0.00
PaymentMethod_Mailed check               0.00
gender_Male                              0.00
MultipleLines_No                         0.00
MultipleLines_Yes                        0.00
InternetService_Fiber optic              0.00
InternetService_No                       0.00
OnlineSecurity_No                        0.00
OnlineSecurity_Yes                       0.00
OnlineBackup_No                          0.00
OnlineBackup_Yes                         0.00
DeviceProtection_No                      0.00
DeviceProtection_Yes                     0.00
TechSupport_No                           0.00
TechSupport_Yes                          0.00
StreamingTV_No                           0.00
StreamingTV_Yes                          0.00
StreamingMovies_No                       0.00
StreamingMovies_Yes                      0.00
dtype: float64

# Removing NaN TotalCharges rows
telecom = telecom[~np.isnan(telecom['TotalCharges'])]

# Checking percentage of missing values after removing the missing values
round(100*(telecom.isnull().sum()/len(telecom.index)), 2)

customerID                               0.0
tenure                                   0.0
PhoneService                             0.0
PaperlessBilling                         0.0
MonthlyCharges                           0.0
TotalCharges                             0.0
Churn                                    0.0
SeniorCitizen                            0.0
Partner                                  0.0
Dependents                               0.0
Contract_One year                        0.0
Contract_Two year                        0.0
PaymentMethod_Credit card (automatic)    0.0
PaymentMethod_Electronic check           0.0
PaymentMethod_Mailed check               0.0
gender_Male                              0.0
MultipleLines_No                         0.0
MultipleLines_Yes                        0.0
InternetService_Fiber optic              0.0
InternetService_No                       0.0
OnlineSecurity_No                        0.0
OnlineSecurity_Yes                       0.0
OnlineBackup_No                          0.0
OnlineBackup_Yes                         0.0
DeviceProtection_No                      0.0
DeviceProtection_Yes                     0.0
TechSupport_No                           0.0
TechSupport_Yes                          0.0
StreamingTV_No                           0.0
StreamingTV_Yes                          0.0
StreamingMovies_No                       0.0
StreamingMovies_Yes                      0.0
dtype: float64

Now we don't have any missing values

Feature Standardisation

# Normalising continuous features
df = telecom[['tenure','MonthlyCharges','TotalCharges']]

normalized_df=(df-df.mean())/df.std()

telecom = telecom.drop(['tenure','MonthlyCharges','TotalCharges'], 1)

telecom = pd.concat([telecom,normalized_df],axis=1)

telecom

	customerID	PhoneService	PaperlessBilling	Churn	SeniorCitizen	Partner	Dependents	Contract_One year	Contract_Two year	PaymentMethod_Credit card (automatic)	...	DeviceProtection_Yes	TechSupport_No	TechSupport_Yes	StreamingTV_No	StreamingTV_Yes	StreamingMovies_No	StreamingMovies_Yes	tenure	MonthlyCharges	TotalCharges
0	7590-VHVEG	0	1	0	0	1	0	0	0	0	...	0	1	0	1	0	1	0	-1.280157	-1.161611	-0.994123
1	5575-GNVDE	1	0	0	0	0	0	1	0	0	...	1	1	0	1	0	1	0	0.064298	-0.260859	-0.173727
2	3668-QPYBK	1	1	1	0	0	0	0	0	0	...	0	1	0	1	0	1	0	-1.239416	-0.363897	-0.959581
3	7795-CFOCW	0	0	0	0	0	0	1	0	0	...	1	0	1	1	0	1	0	0.512450	-0.747797	-0.195234
4	9237-HQITU	1	1	1	0	0	0	0	0	0	...	0	1	0	1	0	1	0	-1.239416	0.196164	-0.940391
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
7038	6840-RESVB	1	1	0	0	1	1	1	0	0	...	1	0	1	0	1	0	1	-0.343113	0.664821	-0.129171
7039	2234-XADUH	1	1	0	0	1	1	1	0	1	...	1	1	0	0	1	0	1	1.612459	1.276402	2.240896
7040	4801-JZAZL	0	1	0	0	1	1	0	0	0	...	0	1	0	1	0	1	0	-0.872746	-1.169921	-0.854453
7041	8361-LTMKD	1	1	1	1	1	0	0	0	0	...	0	1	0	1	0	1	0	-1.157934	0.319145	-0.872033
7042	3186-AJIEK	1	1	0	0	0	0	0	1	0	...	1	0	1	0	1	0	1	1.368012	1.357835	2.012201

7032 rows × 32 columns

Checking the Churn Rate

churn = (sum(telecom['Churn'])/len(telecom['Churn'].index))*100

churn

26.578498293515356

We have almost 27% churn rate

Model Building

Let's start by splitting our data into a training set and a test set.

Splitting Data into Training and Test Sets

from sklearn.model_selection import train_test_split

# Putting feature variable to X
X = telecom.drop(['Churn','customerID'],axis=1)

# Putting response variable to y
y = telecom['Churn']

y.head()

0    0
1    0
2    1
3    0
4    1
Name: Churn, dtype: int64

# Splitting the data into train and test
X_train, X_test, y_train, y_test = train_test_split(X,y, train_size=0.7,test_size=0.3,random_state=100)

Running Your First Training Model

import statsmodels.api as sm

# Logistic regression model
logm1 = sm.GLM(y_train,(sm.add_constant(X_train)), family = sm.families.Binomial())
logm1.fit().summary()

Generalized Linear Model Regression Results

Dep. Variable:	Churn	No. Observations:	4922
Model:	GLM	Df Residuals:	4898
Model Family:	Binomial	Df Model:	23
Link Function:	logit	Scale:	1.0
Method:	IRLS	Log-Likelihood:	-2004.7
Date:	Thu, 01 Mar 2018	Deviance:	4009.4
Time:	14:21:09	Pearson chi2:	6.07e+03
No. Iterations:	7

	coef	std err	z	P>|z|	[0.025	0.975]
const	-3.2783	1.187	-2.762	0.006	-5.605	-0.952
PhoneService	0.8213	0.588	1.396	0.163	-0.332	1.974
PaperlessBilling	0.3254	0.090	3.614	0.000	0.149	0.502
SeniorCitizen	0.3984	0.102	3.924	0.000	0.199	0.597
Partner	0.0374	0.094	0.399	0.690	-0.146	0.221
Dependents	-0.1430	0.107	-1.332	0.183	-0.353	0.067
Contract_One year	-0.6578	0.129	-5.106	0.000	-0.910	-0.405
Contract_Two year	-1.2455	0.212	-5.874	0.000	-1.661	-0.830
PaymentMethod_Credit card (automatic)	-0.2577	0.137	-1.883	0.060	-0.526	0.011
PaymentMethod_Electronic check	0.1615	0.113	1.434	0.152	-0.059	0.382
PaymentMethod_Mailed check	-0.2536	0.137	-1.845	0.065	-0.523	0.016
gender_Male	-0.0346	0.078	-0.442	0.658	-0.188	0.119
MultipleLines_No	0.1295	0.205	0.632	0.527	-0.272	0.531
MultipleLines_Yes	0.6918	0.392	1.763	0.078	-0.077	1.461
InternetService_Fiber optic	2.5124	0.967	2.599	0.009	0.618	4.407
InternetService_No	-3.4348	1.324	-2.594	0.009	-6.030	-0.839
OnlineSecurity_No	0.0905	0.058	1.558	0.119	-0.023	0.204
OnlineSecurity_Yes	0.0660	0.174	0.380	0.704	-0.275	0.407
OnlineBackup_No	-0.0088	0.055	-0.161	0.872	-0.116	0.098
OnlineBackup_Yes	0.1653	0.172	0.960	0.337	-0.172	0.503
DeviceProtection_No	-0.0832	0.056	-1.487	0.137	-0.193	0.026
DeviceProtection_Yes	0.2397	0.174	1.379	0.168	-0.101	0.580
TechSupport_No	0.0935	0.058	1.604	0.109	-0.021	0.208
TechSupport_Yes	0.0630	0.174	0.362	0.717	-0.278	0.404
StreamingTV_No	-0.4016	0.133	-3.027	0.002	-0.662	-0.142
StreamingTV_Yes	0.5581	0.267	2.094	0.036	0.036	1.081
StreamingMovies_No	-0.3459	0.133	-2.609	0.009	-0.606	-0.086
StreamingMovies_Yes	0.5024	0.266	1.886	0.059	-0.020	1.025
tenure	-1.5198	0.190	-8.015	0.000	-1.891	-1.148
MonthlyCharges	-2.1817	1.160	-1.880	0.060	-4.456	0.092
TotalCharges	0.7329	0.198	3.705	0.000	0.345	1.121

Correlation Matrix

# Importing matplotlib and seaborn
import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline

# Let's see the correlation matrix 
plt.figure(figsize = (20,10))        # Size of the figure
sns.heatmap(telecom.corr(),annot = True)

<matplotlib.axes._subplots.AxesSubplot at 0x1959803b7b8>

Dropping highly correlated variables.

X_test2 = X_test.drop(['MultipleLines_No','OnlineSecurity_No','OnlineBackup_No','DeviceProtection_No','TechSupport_No','StreamingTV_No','StreamingMovies_No'],1)
X_train2 = X_train.drop(['MultipleLines_No','OnlineSecurity_No','OnlineBackup_No','DeviceProtection_No','TechSupport_No','StreamingTV_No','StreamingMovies_No'],1)

Checking the Correlation Matrix

After dropping highly correlated variables now let's check the correlation matrix again.

plt.figure(figsize = (20,10))
sns.heatmap(X_train2.corr(),annot = True)

<matplotlib.axes._subplots.AxesSubplot at 0x19595aa3dd8>

Re-Running the Model

Now let's run our model again after dropping highly correlated variables

logm2 = sm.GLM(y_train,(sm.add_constant(X_train2)), family = sm.families.Binomial())
logm2.fit().summary()

Generalized Linear Model Regression Results

Dep. Variable:	Churn	No. Observations:	4922
Model:	GLM	Df Residuals:	4898
Model Family:	Binomial	Df Model:	23
Link Function:	logit	Scale:	1.0
Method:	IRLS	Log-Likelihood:	-2004.7
Date:	Thu, 01 Mar 2018	Deviance:	4009.4
Time:	14:21:18	Pearson chi2:	6.07e+03
No. Iterations:	7

	coef	std err	z	P>|z|	[0.025	0.975]
const	-3.9338	1.545	-2.545	0.011	-6.963	-0.905
PhoneService	0.9507	0.789	1.205	0.228	-0.595	2.497
PaperlessBilling	0.3254	0.090	3.614	0.000	0.149	0.502
SeniorCitizen	0.3984	0.102	3.924	0.000	0.199	0.597
Partner	0.0374	0.094	0.399	0.690	-0.146	0.221
Dependents	-0.1430	0.107	-1.332	0.183	-0.353	0.067
Contract_One year	-0.6578	0.129	-5.106	0.000	-0.910	-0.405
Contract_Two year	-1.2455	0.212	-5.874	0.000	-1.661	-0.830
PaymentMethod_Credit card (automatic)	-0.2577	0.137	-1.883	0.060	-0.526	0.011
PaymentMethod_Electronic check	0.1615	0.113	1.434	0.152	-0.059	0.382
PaymentMethod_Mailed check	-0.2536	0.137	-1.845	0.065	-0.523	0.016
gender_Male	-0.0346	0.078	-0.442	0.658	-0.188	0.119
MultipleLines_Yes	0.5623	0.214	2.628	0.009	0.143	0.982
InternetService_Fiber optic	2.5124	0.967	2.599	0.009	0.618	4.407
InternetService_No	-2.7792	0.982	-2.831	0.005	-4.703	-0.855
OnlineSecurity_Yes	-0.0245	0.216	-0.113	0.910	-0.448	0.399
OnlineBackup_Yes	0.1740	0.212	0.822	0.411	-0.241	0.589
DeviceProtection_Yes	0.3229	0.215	1.501	0.133	-0.099	0.744
TechSupport_Yes	-0.0305	0.216	-0.141	0.888	-0.455	0.394
StreamingTV_Yes	0.9598	0.396	2.423	0.015	0.183	1.736
StreamingMovies_Yes	0.8484	0.396	2.143	0.032	0.072	1.624
tenure	-1.5198	0.190	-8.015	0.000	-1.891	-1.148
MonthlyCharges	-2.1817	1.160	-1.880	0.060	-4.456	0.092
TotalCharges	0.7329	0.198	3.705	0.000	0.345	1.121

Feature Selection Using RFE

from sklearn.linear_model import LogisticRegression
logreg = LogisticRegression()
from sklearn.feature_selection import RFE
rfe = RFE(logreg, 13)             # running RFE with 13 variables as output
rfe = rfe.fit(X,y)
print(rfe.support_)           # Printing the boolean results
print(rfe.ranking_)           # Printing the ranking

[ True  True False False False  True  True False  True False False  True
 False  True  True False  True False False False False False  True False
 False  True False  True False  True]
[ 1  1  2 18  6  1  1 11  1 12 14  1  8  1  1  4  1 15  5 13 10  7  1  3 16
  1 17  1  9  1]

# Variables selected by RFE 
col = ['PhoneService', 'PaperlessBilling', 'Contract_One year', 'Contract_Two year',
       'PaymentMethod_Electronic check','MultipleLines_No','InternetService_Fiber optic', 'InternetService_No',
       'OnlineSecurity_Yes','TechSupport_Yes','StreamingMovies_No','tenure','TotalCharges']

# Let's run the model using the selected variables
from sklearn.linear_model import LogisticRegression
from sklearn import metrics
logsk = LogisticRegression()
logsk.fit(X_train[col], y_train)

LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,
          intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,
          penalty='l2', random_state=None, solver='liblinear', tol=0.0001,
          verbose=0, warm_start=False)

#Comparing the model with StatsModels
logm4 = sm.GLM(y_train,(sm.add_constant(X_train[col])), family = sm.families.Binomial())
logm4.fit().summary()

Generalized Linear Model Regression Results

Dep. Variable:	Churn	No. Observations:	4922
Model:	GLM	Df Residuals:	4908
Model Family:	Binomial	Df Model:	13
Link Function:	logit	Scale:	1.0
Method:	IRLS	Log-Likelihood:	-2024.2
Date:	Thu, 01 Mar 2018	Deviance:	4048.4
Time:	14:21:19	Pearson chi2:	6.19e+03
No. Iterations:	7

	coef	std err	z	P>|z|	[0.025	0.975]
const	-1.0162	0.169	-6.017	0.000	-1.347	-0.685
PhoneService	-0.3090	0.173	-1.784	0.074	-0.648	0.030
PaperlessBilling	0.3595	0.089	4.029	0.000	0.185	0.534
Contract_One year	-0.7012	0.127	-5.516	0.000	-0.950	-0.452
Contract_Two year	-1.3187	0.210	-6.271	0.000	-1.731	-0.907
PaymentMethod_Electronic check	0.3668	0.083	4.446	0.000	0.205	0.529
MultipleLines_No	-0.2311	0.095	-2.435	0.015	-0.417	-0.045
InternetService_Fiber optic	0.7937	0.116	6.836	0.000	0.566	1.021
InternetService_No	-1.1832	0.182	-6.484	0.000	-1.541	-0.826
OnlineSecurity_Yes	-0.4107	0.102	-4.031	0.000	-0.610	-0.211
TechSupport_Yes	-0.4181	0.101	-4.135	0.000	-0.616	-0.220
StreamingMovies_No	-0.2024	0.094	-2.160	0.031	-0.386	-0.019
tenure	-1.4974	0.181	-8.251	0.000	-1.853	-1.142
TotalCharges	0.7373	0.186	3.965	0.000	0.373	1.102

# UDF for calculating vif value
def vif_cal(input_data, dependent_col):
    vif_df = pd.DataFrame( columns = ['Var', 'Vif'])
    x_vars=input_data.drop([dependent_col], axis=1)
    xvar_names=x_vars.columns
    for i in range(0,xvar_names.shape[0]):
        y=x_vars[xvar_names[i]] 
        x=x_vars[xvar_names.drop(xvar_names[i])]
        rsq=sm.OLS(y,x).fit().rsquared  
        vif=round(1/(1-rsq),2)
        vif_df.loc[i] = [xvar_names[i], vif]
    return vif_df.sort_values(by = 'Vif', axis=0, ascending=False, inplace=False)

telecom.columns
['PhoneService', 'PaperlessBilling', 'Contract_One year', 'Contract_Two year',
       'PaymentMethod_Electronic check','MultipleLines_No','InternetService_Fiber optic', 'InternetService_No',
       'OnlineSecurity_Yes','TechSupport_Yes','StreamingMovies_No','tenure','TotalCharges']

['PhoneService',
 'PaperlessBilling',
 'Contract_One year',
 'Contract_Two year',
 'PaymentMethod_Electronic check',
 'MultipleLines_No',
 'InternetService_Fiber optic',
 'InternetService_No',
 'OnlineSecurity_Yes',
 'TechSupport_Yes',
 'StreamingMovies_No',
 'tenure',
 'TotalCharges']

# Calculating Vif value
vif_cal(input_data=telecom.drop(['customerID','SeniorCitizen', 'Partner', 'Dependents',
                                 'PaymentMethod_Credit card (automatic)','PaymentMethod_Mailed check',
                                 'gender_Male','MultipleLines_Yes','OnlineSecurity_No','OnlineBackup_No',
                                 'OnlineBackup_Yes', 'DeviceProtection_No', 'DeviceProtection_Yes',
                                 'TechSupport_No','StreamingTV_No','StreamingTV_Yes','StreamingMovies_Yes',
                                 'MonthlyCharges'], axis=1), dependent_col='Churn')

	Var	Vif
0	PhoneService	10.87
12	TotalCharges	8.58
11	tenure	6.80
1	PaperlessBilling	2.61
7	InternetService_No	0.65
3	Contract_Two year	0.28
2	Contract_One year	0.24
9	TechSupport_Yes	0.24
8	OnlineSecurity_Yes	0.21
10	StreamingMovies_No	0.19
4	PaymentMethod_Electronic check	0.05
5	MultipleLines_No	0.05
6	InternetService_Fiber optic	0.03

Dropping Variable with high VIF

col = ['PaperlessBilling', 'Contract_One year', 'Contract_Two year',
       'PaymentMethod_Electronic check','MultipleLines_No','InternetService_Fiber optic', 'InternetService_No',
       'OnlineSecurity_Yes','TechSupport_Yes','StreamingMovies_No','tenure','TotalCharges']

logm5 = sm.GLM(y_train,(sm.add_constant(X_train[col])), family = sm.families.Binomial())
logm5.fit().summary()

Generalized Linear Model Regression Results

Dep. Variable:	Churn	No. Observations:	4922
Model:	GLM	Df Residuals:	4909
Model Family:	Binomial	Df Model:	12
Link Function:	logit	Scale:	1.0
Method:	IRLS	Log-Likelihood:	-2025.8
Date:	Thu, 01 Mar 2018	Deviance:	4051.5
Time:	14:21:20	Pearson chi2:	6.00e+03
No. Iterations:	7

	coef	std err	z	P>|z|	[0.025	0.975]
const	-1.1915	0.138	-8.607	0.000	-1.463	-0.920
PaperlessBilling	0.3563	0.089	3.998	0.000	0.182	0.531
Contract_One year	-0.6965	0.127	-5.483	0.000	-0.945	-0.448
Contract_Two year	-1.3078	0.210	-6.230	0.000	-1.719	-0.896
PaymentMethod_Electronic check	0.3700	0.082	4.487	0.000	0.208	0.532
MultipleLines_No	-0.2990	0.087	-3.442	0.001	-0.469	-0.129
InternetService_Fiber optic	0.7227	0.108	6.666	0.000	0.510	0.935
InternetService_No	-1.2732	0.175	-7.276	0.000	-1.616	-0.930
OnlineSecurity_Yes	-0.4100	0.102	-4.025	0.000	-0.610	-0.210
TechSupport_Yes	-0.4202	0.101	-4.157	0.000	-0.618	-0.222
StreamingMovies_No	-0.2205	0.093	-2.366	0.018	-0.403	-0.038
tenure	-1.4276	0.177	-8.066	0.000	-1.774	-1.081
TotalCharges	0.6495	0.179	3.622	0.000	0.298	1.001

# Calculating Vif value
vif_cal(input_data=telecom.drop(['customerID','PhoneService','SeniorCitizen', 'Partner', 'Dependents',
                                 'PaymentMethod_Credit card (automatic)','PaymentMethod_Mailed check',
                                 'gender_Male','MultipleLines_Yes','OnlineSecurity_No','OnlineBackup_No',
                                 'OnlineBackup_Yes', 'DeviceProtection_No', 'DeviceProtection_Yes',
                                 'TechSupport_No','StreamingTV_No','StreamingTV_Yes','StreamingMovies_Yes',
                                 'MonthlyCharges'], axis=1), dependent_col='Churn')

	Var	Vif
11	TotalCharges	8.24
10	tenure	6.56
0	PaperlessBilling	2.44
6	InternetService_No	0.45
2	Contract_Two year	0.26
8	TechSupport_Yes	0.24
1	Contract_One year	0.23
7	OnlineSecurity_Yes	0.21
9	StreamingMovies_No	0.17
3	PaymentMethod_Electronic check	0.05
4	MultipleLines_No	0.04
5	InternetService_Fiber optic	0.02

# Let's run the model using the selected variables
from sklearn.linear_model import LogisticRegression
from sklearn import metrics
logsk = LogisticRegression()
logsk.fit(X_train[col], y_train)

LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,
          intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,
          penalty='l2', random_state=None, solver='liblinear', tol=0.0001,
          verbose=0, warm_start=False)

Making Predictions

# Predicted probabilities
y_pred = logsk.predict_proba(X_test[col])

# Converting y_pred to a dataframe which is an array
y_pred_df = pd.DataFrame(y_pred)

# Converting to column dataframe
y_pred_1 = y_pred_df.iloc[:,[1]]

# Let's see the head
y_pred_1.head()

	1
0	0.499083
1	0.372696
2	0.006738
3	0.635453
4	0.007533

# Converting y_test to dataframe
y_test_df = pd.DataFrame(y_test)

# Putting CustID to index
y_test_df['CustID'] = y_test_df.index

# Removing index for both dataframes to append them side by side 
y_pred_1.reset_index(drop=True, inplace=True)
y_test_df.reset_index(drop=True, inplace=True)

# Appending y_test_df and y_pred_1
y_pred_final = pd.concat([y_test_df,y_pred_1],axis=1)

# Renaming the column 
y_pred_final= y_pred_final.rename(columns={ 1 : 'Churn_Prob'})

# Rearranging the columns
y_pred_final = y_pred_final.reindex_axis(['CustID','Churn','Churn_Prob'], axis=1)

# Let's see the head of y_pred_final
y_pred_final.head()

	CustID	Churn	Churn_Prob
0	942	0	0.499083
1	3730	1	0.372696
2	1761	0	0.006738
3	2283	1	0.635453
4	1872	0	0.007533

# Creating new column 'predicted' with 1 if Churn_Prob>0.5 else 0
y_pred_final['predicted'] = y_pred_final.Churn_Prob.map( lambda x: 1 if x > 0.5 else 0)

# Let's see the head
y_pred_final.head()

	CustID	Churn	Churn_Prob	predicted
0	942	0	0.499083	0
1	3730	1	0.372696	0
2	1761	0	0.006738	0
3	2283	1	0.635453	1
4	1872	0	0.007533	0

Model Evaluation

from sklearn import metrics

help(metrics.confusion_matrix)

Help on function confusion_matrix in module sklearn.metrics.classification:

confusion_matrix(y_true, y_pred, labels=None, sample_weight=None)
    Compute confusion matrix to evaluate the accuracy of a classification
    
    By definition a confusion matrix :math:`C` is such that :math:`C_{i, j}`
    is equal to the number of observations known to be in group :math:`i` but
    predicted to be in group :math:`j`.
    
    Thus in binary classification, the count of true negatives is
    :math:`C_{0,0}`, false negatives is :math:`C_{1,0}`, true positives is
    :math:`C_{1,1}` and false positives is :math:`C_{0,1}`.
    
    Read more in the :ref:`User Guide <confusion_matrix>`.
    
    Parameters
    ----------
    y_true : array, shape = [n_samples]
        Ground truth (correct) target values.
    
    y_pred : array, shape = [n_samples]
        Estimated targets as returned by a classifier.
    
    labels : array, shape = [n_classes], optional
        List of labels to index the matrix. This may be used to reorder
        or select a subset of labels.
        If none is given, those that appear at least once
        in ``y_true`` or ``y_pred`` are used in sorted order.
    
    sample_weight : array-like of shape = [n_samples], optional
        Sample weights.
    
    Returns
    -------
    C : array, shape = [n_classes, n_classes]
        Confusion matrix
    
    References
    ----------
    .. [1] `Wikipedia entry for the Confusion matrix
           <https://en.wikipedia.org/wiki/Confusion_matrix>`_
    
    Examples
    --------
    >>> from sklearn.metrics import confusion_matrix
    >>> y_true = [2, 0, 2, 2, 0, 1]
    >>> y_pred = [0, 0, 2, 2, 0, 2]
    >>> confusion_matrix(y_true, y_pred)
    array([[2, 0, 0],
           [0, 0, 1],
           [1, 0, 2]])
    
    >>> y_true = ["cat", "ant", "cat", "cat", "ant", "bird"]
    >>> y_pred = ["ant", "ant", "cat", "cat", "ant", "cat"]
    >>> confusion_matrix(y_true, y_pred, labels=["ant", "bird", "cat"])
    array([[2, 0, 0],
           [0, 0, 1],
           [1, 0, 2]])
    
    In the binary case, we can extract true positives, etc as follows:
    
    >>> tn, fp, fn, tp = confusion_matrix([0, 1, 0, 1], [1, 1, 1, 0]).ravel()
    >>> (tn, fp, fn, tp)
    (0, 2, 1, 1)

# Confusion matrix 
confusion = metrics.confusion_matrix( y_pred_final.Churn, y_pred_final.predicted )
confusion

array([[1362,  166],
       [ 249,  333]], dtype=int64)

# Predicted     not_churn    churn
# Actual
# not_churn        1326      166
# churn            249       333  

#Let's check the overall accuracy.
metrics.accuracy_score( y_pred_final.Churn, y_pred_final.predicted)

0.80331753554502372

TP = confusion[0,0] # true positive 
TN = confusion[1,1] # true negatives
FP = confusion[0,1] # false positives
FN = confusion[1,0] # false negatives

# Let's see the sensitivity of our logistic regression model
TP / float(TP+FN)

0.84543761638733705

# Let us calculate specificity
TN / float(TN+FP)

0.66733466933867736

# Calculate false postive rate - predicting churn when customer does not have churned
print(FP/ float(TN+FP))

0.332665330661

# positive predictive value 
print (TP / float(TP+FP))

0.891361256545

# Negative predictive value
print (TN / float(TN+ FN))

0.572164948454

ROC Curve

An ROC curve demonstrates several things:

• It shows the tradeoff between sensitivity and specificity (any increase in sensitivity will be accompanied by a decrease in specificity).
• The closer the curve follows the left-hand border and then the top border of the ROC space, the more accurate the test.
• The closer the curve comes to the 45-degree diagonal of the ROC space, the less accurate the test.

def draw_roc( actual, probs ):
    fpr, tpr, thresholds = metrics.roc_curve( actual, probs,
                                              drop_intermediate = False )
    auc_score = metrics.roc_auc_score( actual, probs )
    plt.figure(figsize=(6, 4))
    plt.plot( fpr, tpr, label='ROC curve (area = %0.2f)' % auc_score )
    plt.plot([0, 1], [0, 1], 'k--')
    plt.xlim([0.0, 1.0])
    plt.ylim([0.0, 1.05])
    plt.xlabel('False Positive Rate or [1 - True Negative Rate]')
    plt.ylabel('True Positive Rate')
    plt.title('Receiver operating characteristic example')
    plt.legend(loc="lower right")
    plt.show()

    return fpr, tpr, thresholds

draw_roc(y_pred_final.Churn, y_pred_final.predicted)

(array([ 0.        ,  0.10863874,  1.        ]),
 array([ 0.        ,  0.57216495,  1.        ]),
 array([2, 1, 0], dtype=int64))

Finding Optimal Cutoff Point

Optimal cutoff probability is that prob where we get balanced sensitivity and specificity

# Let's create columns with different probability cutoffs 
numbers = [float(x)/10 for x in range(10)]
for i in numbers:
    y_pred_final[i]= y_pred_final.Churn_Prob.map( lambda x: 1 if x > i else 0)
y_pred_final.head()

	CustID	Churn	Churn_Prob	predicted	0.0	0.1	0.2	0.3	0.4	0.5	0.6	0.7	0.8	0.9
0	942	0	0.499083	0	1	1	1	1	1	0	0	0	0	0
1	3730	1	0.372696	0	1	1	1	1	0	0	0	0	0	0
2	1761	0	0.006738	0	1	0	0	0	0	0	0	0	0	0
3	2283	1	0.635453	1	1	1	1	1	1	1	1	0	0	0
4	1872	0	0.007533	0	1	0	0	0	0	0	0	0	0	0

# Now let's calculate accuracy sensitivity and specificity for various probability cutoffs.
cutoff_df = pd.DataFrame( columns = ['prob','accuracy','sensi','speci'])
from sklearn.metrics import confusion_matrix
num = [0.0,0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9]
for i in num:
    cm1 = metrics.confusion_matrix( y_pred_final.Churn, y_pred_final[i] )
    total1=sum(sum(cm1))
    accuracy = (cm1[0,0]+cm1[1,1])/total1
    sensi = cm1[0,0]/(cm1[0,0]+cm1[0,1])
    speci = cm1[1,1]/(cm1[1,0]+cm1[1,1])
    cutoff_df.loc[i] =[ i ,accuracy,sensi,speci]
print(cutoff_df)

     prob  accuracy     sensi     speci
0.0   0.0  0.275829  0.000000  1.000000
0.1   0.1  0.605687  0.477094  0.943299
0.2   0.2  0.695261  0.643325  0.831615
0.3   0.3  0.750237  0.752618  0.743986
0.4   0.4  0.783886  0.828534  0.666667
0.5   0.5  0.803318  0.891361  0.572165
0.6   0.6  0.795735  0.941754  0.412371
0.7   0.7  0.757820  0.978403  0.178694
0.8   0.8  0.727962  1.000000  0.013746
0.9   0.9  0.724171  1.000000  0.000000

# Let's plot accuracy sensitivity and specificity for various probabilities.
cutoff_df.plot.line(x='prob', y=['accuracy','sensi','speci'])

<matplotlib.axes._subplots.AxesSubplot at 0x1959961e550>

From the curve above, 0.3 is the optimum point to take it as a cutoff probability.

y_pred_final['final_predicted'] = y_pred_final.Churn_Prob.map( lambda x: 1 if x > 0.3 else 0)

y_pred_final.head()

	CustID	Churn	Churn_Prob	predicted	0.0	0.1	0.2	0.3	0.4	0.5	0.6	0.7	0.8	0.9	final_predicted
0	942	0	0.499083	0	1	1	1	1	1	0	0	0	0	0	1
1	3730	1	0.372696	0	1	1	1	1	0	0	0	0	0	0	1
2	1761	0	0.006738	0	1	0	0	0	0	0	0	0	0	0	0
3	2283	1	0.635453	1	1	1	1	1	1	1	1	0	0	0	1
4	1872	0	0.007533	0	1	0	0	0	0	0	0	0	0	0	0

#Let's check the overall accuracy.
metrics.accuracy_score( y_pred_final.Churn, y_pred_final.final_predicted)

0.75023696682464458

metrics.confusion_matrix( y_pred_final.Churn, y_pred_final.final_predicted )

array([[1150,  378],
       [ 149,  433]], dtype=int64)