This project analyzes customer churn in a telecommunications company. The dataset contains customer demographics, service usage, and contract details to identify patterns associated with churn.

The goal is to:

• Perform Exploratory Data Analysis (EDA) to identify trends.
• Handle data preprocessing and feature engineering.
• Use visualizations for insights.
• Optionally, apply machine learning models to predict churn.

The dataset includes:

• Customer ID: Unique identifier.
• Demographics: Gender, senior citizen status, partner, and dependents.
• Service Information: Internet service, online security, streaming TV, etc.
• Contract Details: Contract type, paperless billing, payment method.
• Churn Label: Whether the customer left the service (Yes or No).

📌 Data Cleaning Steps:

• Handled missing values.
• Converted categorical variables.
• Engineered new features for analysis.

git clone https://github.com/rizz1406/Customer-Churn-Analysis.git
cd Customer-Churn-Analysis

Ensure you have Python 3.x installed, then install required libraries:

pip install pandas numpy matplotlib seaborn scikit-learn

jupyter notebook

Open Telco Customer Churn.ipynb and execute all cells.

import pandas as pd

df = pd.read_csv("Customer Churn.csv")
print(df.info())  # Dataset structure
print(df.describe())  # Statistical summary
print(df.isnull().sum())  # Check missing values

import seaborn as sns
import matplotlib.pyplot as plt

plt.figure(figsize=(6,4))
sns.countplot(x='Churn', data=df, palette='coolwarm')
plt.title("Customer Churn Distribution")
plt.show()

📊 Insight: Helps understand the proportion of customers who churned vs. stayed.

plt.figure(figsize=(10,6))
sns.heatmap(df.corr(), annot=True, cmap='Blues')
plt.title("Feature Correlation Heatmap")
plt.show()

📊 Insight: Identifies relationships between different variables.

Some feature transformations:

• Encoding categorical variables (Yes/No, Male/Female → 0/1).
• Creating new aggregated features.
• Removing redundant columns.

Example transformation:

df['SeniorCitizen'] = df['SeniorCitizen'].map({0: 'No', 1: 'Yes'})
df = pd.get_dummies(df, drop_first=True)  # Convert categorical to numerical

from sklearn.model_selection import train_test_split

X = df.drop(columns=['Churn'])
y = df['Churn']

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score

model = RandomForestClassifier(n_estimators=100, random_state=42)
model.fit(X_train, y_train)

y_pred = model.predict(X_test)
accuracy = accuracy_score(y_test, y_pred)
print(f"Model Accuracy: {accuracy:.2f}")

📊 Insight: This gives a baseline model to predict churn.

plt.figure(figsize=(8,5))
sns.countplot(x='Contract', hue='Churn', data=df)
plt.title("Churn Rate by Contract Type")
plt.show()

📊 Insight: Customers with month-to-month contracts have a higher churn rate.

plt.figure(figsize=(8,5))
sns.boxplot(x="Churn", y="MonthlyCharges", data=df)
plt.title("Monthly Charges vs Churn")
plt.show()

📊 Insight: Higher monthly charges correlate with increased churn.

• Customers with month-to-month contracts are more likely to churn.
• Senior citizens have a slightly higher churn rate.
• Paperless billing customers churn more frequently.
• Long-term contract customers are more loyal.

📢 Business Recommendation: Offer incentives for long-term contracts to reduce churn.

• ✅ Improve feature selection for better model accuracy.
• ✅ Implement hyperparameter tuning for the ML model.
• ✅ Deploy the model via Flask or Streamlit.

• Feel free to contribute by submitting pull requests.
• Licensed under MIT License.