By Aman Kharwal

Predict Customer Churn with Python and Machine Learning

Prediction of Customer Churn with Machine Learning

In this project we will be building a model that Predicts customer churn with Machine Learning. We do this by implementing a predictive model with the help of python.

Prediction of Customer Churn means our beloved customers with the intention of leaving us in the future.

Let’s Start by Importing the required Libraries

import numpy as np
import pandas as pd
import sklearn
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.preprocessing import LabelEncoder
from sklearn.preprocessing import StandardScaler
from sklearn.metrics import classification_report
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split

Download the data set

Let’s read and look at the data

df = pd.read_csv("churn.csv")

To show the number of rows and columns


(7043, 21)

To see all column names

array(['customerID', 'gender', 'SeniorCitizen', 'Partner', 'Dependents',
       'tenure', 'PhoneService', 'MultipleLines', 'InternetService',
       'OnlineSecurity', 'OnlineBackup', 'DeviceProtection',
       'TechSupport', 'StreamingTV', 'StreamingMovies', 'Contract',
       'PaperlessBilling', 'PaymentMethod', 'MonthlyCharges',
       'TotalCharges', 'Churn'], dtype=object)

To check for NA or missing values

customerID          0
gender              0
SeniorCitizen       0
Partner             0
Dependents          0
tenure              0
PhoneService        0
MultipleLines       0
InternetService     0
OnlineSecurity      0
OnlineBackup        0
DeviceProtection    0
TechSupport         0
StreamingTV         0
StreamingMovies     0
Contract            0
PaperlessBilling    0
PaymentMethod       0
MonthlyCharges      0
TotalCharges        0
Churn               0
dtype: int64

To show some statistics


To get Customer Churn count

No     5174
Yes    1869
Name: Churn, dtype: int64

Visualize the count of customer churn


To see the percentage of customers that are leaving

numRetained = df[df.Churn == 'No'].shape[0]
numChurned = df[df.Churn == 'Yes'].shape[0]

# print the percentage of customers that stayed
print(numRetained/(numRetained + numChurned) * 100,'% of customers stayed in the company')
# peint the percentage of customers that left
print(numChurned/(numRetained + numChurned) * 100, '% of customers left with the company')
73.4630129206304 % of customers stayed in the company
26.536987079369588 % of customers left with the company

Visualize the churn count for both males and females

sns.countplot(x ='gender', hue='Churn', data=df)

Visualize the churn count for the internet service

sns.countplot(x='InternetService', hue='Churn', data=df)

To Visualize Numeric data

numericFeatures = ['tenure', 'MonthlyCharges']
fig, ax = plt.subplots(1,2, figsize=(28, 8))
df[df.Churn == "No"][numericFeatures].hist(bins=20, color='blue', alpha=0.5, ax=ax)
df[df.Churn == "Yes"][numericFeatures].hist(bins=20, color='orange', alpha=0.5, ax=ax)

To remove unnecessary columns

cleanDF = df.drop('customerID', axis=1)

Convert all the non-numeric columns to numeric

Convert all the non-numeric columns to numeric
for column in cleanDF.columns:
  if cleanDF[column].dtype == np.number:
  cleanDF[column] = LabelEncoder().fit_transform(cleanDF[column])

To show the data types

gender                int64
SeniorCitizen         int64
Partner               int64
Dependents            int64
tenure                int64
PhoneService          int64
MultipleLines         int64
InternetService       int64
OnlineSecurity        int64
OnlineBackup          int64
DeviceProtection      int64
TechSupport           int64
StreamingTV           int64
StreamingMovies       int64
Contract              int64
PaperlessBilling      int64
PaymentMethod         int64
MonthlyCharges      float64
TotalCharges          int64
Churn                 int64
dtype: object

To show first 5 rows of the new data

Scale the data

Scaled the data
x = cleanDF.drop('Churn', axis=1)
y = cleanDF['Churn']
x = StandardScaler().fit_transform(x)

Split the data into 80% training and 20% testing

xtrain, xtest, ytrain, ytest = train_test_split(x,y, test_size=0.2, random_state=42)

Create and Train the model

model = LogisticRegression()
# Train the model, ytrain)
LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,
                   intercept_scaling=1, l1_ratio=None, max_iter=100,
                   multi_class='auto', n_jobs=None, penalty='l2',
                   random_state=None, solver='lbfgs', tol=0.0001, verbose=0,

Create the predictions on the test data

predictions = model.predict(xtest)

# print the predictions
[1 0 0 ... 0 0 0]

And Finally check the precision, recall and f1-score

print(classification_report(ytest, predictions))
              precision    recall  f1-score   support

           0       0.85      0.91      0.88      1036
           1       0.69      0.56      0.62       373

    accuracy                           0.82      1409
   macro avg       0.77      0.74      0.75      1409
weighted avg       0.81      0.82      0.81      1409

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