Telco Customer Churn

# import useful libraries import numpy as np import pandas as pd import matplotlib.pyplot as plt import seaborn as sns

df = pd.read_csv('Telco-Customer-Churn.csv')

# 20 features and 1 label # 7032 rows (customers) df.shape

# there seems no missing value # many of the columns are categorical columns df.info()

# let's first check churn column # there are less people who discontinued the service plt.figure(figsize=(8,6)) sns.countplot(x='Churn',data=df);

# our data is little bit imbalanced, but not too extreme df['Churn'].value_counts()

df.info()

# distribution of tenure bettween Churn categories with a violin plot # people with 'No' churn tend to have longer tenure plt.figure(figsize=(10,8)) sns.violinplot(x='Churn',y='tenure',data=df);

# kde plot of tenure betwen Churn categories # it seems that the longer the tenure the less likely people turn away plt.figure(figsize=(10,8)) sns.kdeplot(data=df,x='tenure',hue='Churn');

# distribution of MonthlyCharges bettween Churn categories with a violin plot # people with 'No' churn tend to spend less for monthly charges plt.figure(figsize=(10,8)) sns.violinplot(x='Churn',y='MonthlyCharges',data=df);

# kde plot of MonthlyCharges betwen Churn categories # it seems that when monthly charge is lager than 60, density increases rapidly plt.figure(figsize=(10,8)) sns.kdeplot(data=df,x='MonthlyCharges',hue='Churn');

# distribution of TotalCharges bettween Churn categories with a violin plot # people with 'No' churn tend to spend more on total charges # this makes sense, they usually used the service for longer period of time plt.figure(figsize=(10,8)) sns.violinplot(x='Churn',y='TotalCharges',data=df);

# countplot of InternetService with hue equals to Churn plt.figure(figsize=(10,4)) sns.countplot(data=df,x='InternetService',hue='Churn');

mask = (df['InternetService']=='Fiber optic') &(df['Churn']=='Yes') # percentage of people churn after using fiber optic Internet service (len(df[mask])/len(df[df['InternetService']=='Fiber optic']))*100

plt.figure(figsize=(10,4)) sns.countplot(data=df,x='Contract',hue='Churn');

plt.figure(figsize=(10,4)) sns.countplot(data=df,x='PaymentMethod',hue='Churn');

# create a pd.series with groupby method # groubpy [churn,tenure] -> count number of elements -> transpose -> # only choose columns with 'yes' -> look at first row yes_churn_tenure = df.groupby(['Churn','tenure']).count().T['Yes'].loc['customerID'] # same for no churn no_churn_tenure = df.groupby(['Churn','tenure']).count().T['No'].loc['customerID'] # churn rate churn_rate = yes_churn_tenure*100/(yes_churn_tenure+no_churn_tenure)

churn_rate

# we can visualize this churn rate plt.figure(figsize=(10,8)) churn_rate.plot() plt.scatter(x=churn_rate.index,y=churn_rate) plt.ylabel('Churn Rate') plt.xlabel('Tenure');

# define a function for apply method def cohort(tenure): if tenure<13: return '0-12 Months' elif tenure<25: return '12-24 Monhts' elif tenure<49: return '24-48 Months' else: return 'Over 48 Months' # apply the function to create a new column 'Cohort' df['Cohort'] = df['tenure'].apply(cohort) df['Cohort']

df['Cohort'].value_counts(sort=False)

df[df['Churn']=='Yes']['Cohort'].value_counts(sort=False)

# churn rate drops significantly after a customer uses the service of 1 year churn_rate_cohort = (df[df['Churn']=='Yes']['Cohort'].value_counts(sort=False))/(df['Cohort'].value_counts(sort=False)) churn_rate_cohort

plt.figure(figsize=(10,8)) sns.countplot(x='Cohort',data=df,hue='Churn');

# catplot with cohort and contract method # people with monthly contract and less than 12 month tenure are very likely to churn plt.figure(figsize=(12,10)) sns.catplot(data=df,x='Cohort',hue='Churn',col='Contract',kind='count');

# MonthlyCharges vs. TotalCharges with hue = Cohort plt.figure(figsize=(12,8)) sns.scatterplot(data=df,x='MonthlyCharges',y='TotalCharges',hue='Cohort',palette='mako',alpha=.77);

df