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This notebook analyzes customer churn for a telecom company. It explores factors like demographics, services, contract type, and payment methods to understand why customers leave. Key visualizations highlight churn rates by tenure, contract, and payment method, revealing insights into customer behavior and potential areas for improvement.

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import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
import seaborn as sns
df=pd.read_csv(r"D:\Project work\11. Customer Churn Analysis\Customer Churn.csv")
df
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customerID gender SeniorCitizen Partner Dependents tenure PhoneService MultipleLines InternetService OnlineSecurity ... DeviceProtection TechSupport StreamingTV StreamingMovies Contract PaperlessBilling PaymentMethod MonthlyCharges TotalCharges Churn
0 7590-VHVEG Female 0 Yes No 1 No No phone service DSL No ... No No No No Month-to-month Yes Electronic check 29.85 29.85 No
1 5575-GNVDE Male 0 No No 34 Yes No DSL Yes ... Yes No No No One year No Mailed check 56.95 1889.5 No
2 3668-QPYBK Male 0 No No 2 Yes No DSL Yes ... No No No No Month-to-month Yes Mailed check 53.85 108.15 Yes
3 7795-CFOCW Male 0 No No 45 No No phone service DSL Yes ... Yes Yes No No One year No Bank transfer (automatic) 42.30 1840.75 No
4 9237-HQITU Female 0 No No 2 Yes No Fiber optic No ... No No No No Month-to-month Yes Electronic check 70.70 151.65 Yes
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
7038 6840-RESVB Male 0 Yes Yes 24 Yes Yes DSL Yes ... Yes Yes Yes Yes One year Yes Mailed check 84.80 1990.5 No
7039 2234-XADUH Female 0 Yes Yes 72 Yes Yes Fiber optic No ... Yes No Yes Yes One year Yes Credit card (automatic) 103.20 7362.9 No
7040 4801-JZAZL Female 0 Yes Yes 11 No No phone service DSL Yes ... No No No No Month-to-month Yes Electronic check 29.60 346.45 No
7041 8361-LTMKD Male 1 Yes No 4 Yes Yes Fiber optic No ... No No No No Month-to-month Yes Mailed check 74.40 306.6 Yes
7042 3186-AJIEK Male 0 No No 66 Yes No Fiber optic Yes ... Yes Yes Yes Yes Two year Yes Bank transfer (automatic) 105.65 6844.5 No

7043 rows × 21 columns

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

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

5 rows × 21 columns

df.tail()
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customerID gender SeniorCitizen Partner Dependents tenure PhoneService MultipleLines InternetService OnlineSecurity ... DeviceProtection TechSupport StreamingTV StreamingMovies Contract PaperlessBilling PaymentMethod MonthlyCharges TotalCharges Churn
7038 6840-RESVB Male 0 Yes Yes 24 Yes Yes DSL Yes ... Yes Yes Yes Yes One year Yes Mailed check 84.80 1990.5 No
7039 2234-XADUH Female 0 Yes Yes 72 Yes Yes Fiber optic No ... Yes No Yes Yes One year Yes Credit card (automatic) 103.20 7362.9 No
7040 4801-JZAZL Female 0 Yes Yes 11 No No phone service DSL Yes ... No No No No Month-to-month Yes Electronic check 29.60 346.45 No
7041 8361-LTMKD Male 1 Yes No 4 Yes Yes Fiber optic No ... No No No No Month-to-month Yes Mailed check 74.40 306.6 Yes
7042 3186-AJIEK Male 0 No No 66 Yes No Fiber optic Yes ... Yes Yes Yes Yes Two year Yes Bank transfer (automatic) 105.65 6844.5 No

5 rows × 21 columns

df.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 7043 entries, 0 to 7042
Data columns (total 21 columns):
 #   Column            Non-Null Count  Dtype  
---  ------            --------------  -----  
 0   customerID        7043 non-null   object 
 1   gender            7043 non-null   object 
 2   SeniorCitizen     7043 non-null   int64  
 3   Partner           7043 non-null   object 
 4   Dependents        7043 non-null   object 
 5   tenure            7043 non-null   int64  
 6   PhoneService      7043 non-null   object 
 7   MultipleLines     7043 non-null   object 
 8   InternetService   7043 non-null   object 
 9   OnlineSecurity    7043 non-null   object 
 10  OnlineBackup      7043 non-null   object 
 11  DeviceProtection  7043 non-null   object 
 12  TechSupport       7043 non-null   object 
 13  StreamingTV       7043 non-null   object 
 14  StreamingMovies   7043 non-null   object 
 15  Contract          7043 non-null   object 
 16  PaperlessBilling  7043 non-null   object 
 17  PaymentMethod     7043 non-null   object 
 18  MonthlyCharges    7043 non-null   float64
 19  TotalCharges      7043 non-null   object 
 20  Churn             7043 non-null   object 
dtypes: float64(1), int64(2), object(18)
memory usage: 1.1+ MB

# change data type
df['TotalCharges']=df['TotalCharges'].replace(" ","0")
df['TotalCharges']=df['TotalCharges'].astype(float)
df.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 7043 entries, 0 to 7042
Data columns (total 21 columns):
 #   Column            Non-Null Count  Dtype  
---  ------            --------------  -----  
 0   customerID        7043 non-null   object 
 1   gender            7043 non-null   object 
 2   SeniorCitizen     7043 non-null   int64  
 3   Partner           7043 non-null   object 
 4   Dependents        7043 non-null   object 
 5   tenure            7043 non-null   int64  
 6   PhoneService      7043 non-null   object 
 7   MultipleLines     7043 non-null   object 
 8   InternetService   7043 non-null   object 
 9   OnlineSecurity    7043 non-null   object 
 10  OnlineBackup      7043 non-null   object 
 11  DeviceProtection  7043 non-null   object 
 12  TechSupport       7043 non-null   object 
 13  StreamingTV       7043 non-null   object 
 14  StreamingMovies   7043 non-null   object 
 15  Contract          7043 non-null   object 
 16  PaperlessBilling  7043 non-null   object 
 17  PaymentMethod     7043 non-null   object 
 18  MonthlyCharges    7043 non-null   float64
 19  TotalCharges      7043 non-null   float64
 20  Churn             7043 non-null   object 
dtypes: float64(2), int64(2), object(17)
memory usage: 1.1+ MB

df.isnull().sum().sum()
np.int64(0)

df.describe()
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SeniorCitizen tenure MonthlyCharges TotalCharges
count 7043.000000 7043.000000 7043.000000 7043.000000
mean 0.162147 32.371149 64.761692 2279.734304
std 0.368612 24.559481 30.090047 2266.794470
min 0.000000 0.000000 18.250000 0.000000
25% 0.000000 9.000000 35.500000 398.550000
50% 0.000000 29.000000 70.350000 1394.550000
75% 0.000000 55.000000 89.850000 3786.600000
max 1.000000 72.000000 118.750000 8684.800000 
df.duplicated(["customerID"]).sum()
np.int64(0)

# Converted 0 and 1 values to yes or no to make it easier to understand.
def conv(value):
    if value==1:
        return "Yes"
    else:
        return "No"
        
df['SeniorCitizen']=df['SeniorCitizen'].apply(conv)
df.head()
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customerID gender SeniorCitizen Partner Dependents tenure PhoneService MultipleLines InternetService OnlineSecurity ... DeviceProtection TechSupport StreamingTV StreamingMovies Contract PaperlessBilling PaymentMethod MonthlyCharges TotalCharges Churn
0 7590-VHVEG Female No Yes No 1 No No phone service DSL No ... No No No No Month-to-month Yes Electronic check 29.85 29.85 No
1 5575-GNVDE Male No No No 34 Yes No DSL Yes ... Yes No No No One year No Mailed check 56.95 1889.50 No
2 3668-QPYBK Male No No No 2 Yes No DSL Yes ... No No No No Month-to-month Yes Mailed check 53.85 108.15 Yes
3 7795-CFOCW Male No No No 45 No No phone service DSL Yes ... Yes Yes No No One year No Bank transfer (automatic) 42.30 1840.75 No
4 9237-HQITU Female No No No 2 Yes No Fiber optic No ... No No No No Month-to-month Yes Electronic check 70.70 151.65 Yes

5 rows × 21 columns

EDA

plt.figure(figsize=(4,4))
ax=sns.countplot(x="Churn",data=df)

for bars in ax.containers:
    ax.bar_label(bars)
plt.title("Count of Customers by Churn")
plt.show()

png

plt.figure(figsize=(4,4))
gb=df.groupby("Churn").agg({"Churn":"count"})
plt.pie(gb['Churn'], labels=gb.index,autopct="%1.2f%%")
plt.title("Percentage of Churned Customers")
plt.show()
gb

png

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Churn
Churn
No 5174
Yes 1869

From the given pie chart we can conclude that 26.54% of cutomers have churned out.

Now lets explore the reason behind it.

plt.figure(figsize=(4,4))
ax=sns.countplot(x='gender',data=df,hue='Churn')

for bars in ax.containers:
    plt.bar_label(bars)
plt.title("Churn by Gender")
plt.show()

png

plt.figure(figsize=(4,4))
ax=sns.countplot(x='SeniorCitizen',data=df,hue="Churn")

for bars in ax.containers:
    plt.bar_label(bars)
plt.title("Churn by Senior Citizen")
plt.show()

png

import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt

# Example DataFrame
data = {
    'SeniorCitizen': [0, 1, 0, 1, 0, 0, 1, 0, 1, 0],
    'Churn': ['Yes', 'No', 'Yes', 'Yes', 'No', 'No', 'No', 'Yes', 'Yes', 'No']
}
df = pd.DataFrame(data)

# Calculate percentages
stacked_data = (
    df.groupby(['SeniorCitizen', 'Churn'])
    .size()
    .unstack(fill_value=0)
)

stacked_data_percentage = (stacked_data.T / stacked_data.T.sum()).T * 100

# Plot stacked bar chart
stacked_data_percentage.plot(
    kind='bar',
    stacked=True,
    figsize=(6, 4),
    color=['#76c7c0', '#ff6f61']
)

# Add percentage labels
for i, row in stacked_data_percentage.iterrows():
    cumulative = 0
    for col in stacked_data_percentage.columns:
        plt.text(
            i, 
            cumulative + row[col] / 2,  # Position the label in the middle of the stack
            f"{row[col]:.1f}%", 
            ha='center', 
            va='center', 
            color='white', 
            fontsize=10
        )
        cumulative += row[col]

plt.title("Churn by Senior Citizen", fontsize=14)
plt.ylabel("Percentage")
plt.xlabel("Senior Citizen")
plt.xticks(ticks=[0, 1], labels=["No", "Yes"], rotation=0)
plt.legend(title="Churn", loc="upper right")
plt.show()

png

Comparative a greater percentage of people in senior citizen category have churned.

plt.figure(figsize=(9,5))
sns.histplot(x='tenure',data=df,bins=50,hue="Churn")
plt.show()

png

People who have used our services for a long time have stayed and people who have used our services for 1 and 2 months have churned.

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

plt.figure(figsize=(5,4))
ax=sns.countplot(x="Contract",data=df,hue="Churn")
plt.title("Count of Customers by Contract")
for bars in ax.containers:
    ax.bar_label(bars)

png

People who have month to month contract are likely to churn thean from those who have 1 or 2 years of contract.

# Assuming your data is in a pandas DataFrame named 'df'

# List of columns for subplots
columns = ['PhoneService', 'MultipleLines', 'InternetService',
           'OnlineSecurity', 'OnlineBackup', 'DeviceProtection', 'TechSupport',
           'StreamingTV', 'StreamingMovies']

# Create subplots
fig, axes = plt.subplots(nrows=3, ncols=3, figsize=(15, 10)) 

# Iterate through columns and create countplots
for i, column in enumerate(columns):
    row = i // 3  # Calculate row index
    col = i % 3  # Calculate column index
    sns.countplot(data=df, x=column, ax=axes[row, col],hue=df['Churn'])
    axes[row, col].set_title(column)
    axes[row, col].tick_params(axis='x', rotation=45)  # Rotate x-axis labels for better readability

# Adjust layout for better spacing
plt.tight_layout()

# Show the plot
plt.show()

png

The countplots reveal that the majority of customers have phone service and internet service, with DSL being the most common type. A significant portion of customers do not subscribe to online security, online backup, device protection, or tech support. Streaming TV and movies are popular services among customers with internet service.

plt.figure(figsize=(10,4))
ax=sns.countplot(x='PaymentMethod',data=df,hue="Churn")
plt.title("Churned Customers by PaymentMethod")
for bars in ax.containers:
    ax.bar_label(bars)

png

Customers is likely to churn when he is using electronic check as a payment method.



   




      


  
      



  

    

      

  
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        This notebook analyzes customer churn for a telecom company. It explores factors like demographics, services, contract type, and payment methods to understand why customers leave. Key visualizations highlight churn rates by tenure, contract, and payment method, revealing insights into customer behavior and potential areas for improvement.
      



    
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