📊 Customer Churn Analysis and Predictive Modeling

📌 Overview

This project analyzes customer churn patterns and builds a predictive model to identify customers likely to leave the company.

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🔍 Key Insights

• Overall Churn Rate: 26.5%
• New customers (low tenure) churn more.
• Month-to-month contract customers have the highest churn.
• Customers with higher monthly charges are more likely to churn.
• Customers without dependents and partner churn more.
• Lack of tech support and online security increases churn.
• Customers using electronic check payment method churn more.

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🤖 Predictive Modeling

Model Used: Logistic Regression

Initial Model Performance:

• Recall (Churn): 0.47
• F1 Score: 0.52

After Handling Class Imbalance (class_weight='balanced'):

• Accuracy score: 0.74
• Recall (Churn): 0.79
• F1 Score: 0.62

The improvement significantly increased the model’s ability to detect at-risk customers.

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VISUALIZATION

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🛠 Tools & Technologies

• Python
• Pandas
• NumPy
• Matplotlib
• Seaborn
• Scikit-learn
• Jupyter Notebook

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💡 Conclusion

This project demonstrates how data analysis and predictive modeling can help businesses identify high-risk customers and design targeted retention strategies.