📊 Telecom Customer Churn Analysis

📌 Project Overview

This project analyzes telecom customer data to identify factors that lead to customer churn.

The goal is to understand customer behavior and discover patterns that help businesses reduce churn.

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🛠 Tools & Technologies

• Python
• Pandas
• NumPy
• Matplotlib
• Seaborn

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📂 Dataset

The dataset contains customer information such as:

• CustomerID
• Contract Type
• Satisfaction Score
• Customer Support Calls
• App Usage Time
• Monthly Spend
• Discount Usage
• Last Purchase Days
• Churn Status

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📊 Key Analysis Performed

✔ Churn Rate Calculation
✔ Churn by Contract Type
✔ Churn vs Satisfaction Score
✔ Customer Support Calls Impact
✔ App Usage vs Churn
✔ Revenue Risk Analysis
✔ High Risk Customer Identification

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📈 Visualizations

• Churn vs Non-Churn Distribution
• Contract Type vs Churn
• Satisfaction Score vs Churn
• Support Calls vs Churn
• App Usage Distribution
• Monthly Spend vs Churn
• Discount Usage vs Churn
• Last Purchase vs App Usage
• Churn Rate vs Days Since Last Purchase

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🚀 Business Insights

• Customers with low satisfaction score churn more.
• High support calls indicate potential churn risk.
• Longer time since last purchase increases churn probability.
• Certain contract types show higher churn rates.

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🎯 Conclusion

Customer churn can be reduced by:

• Improving customer satisfaction
• Reducing support issues
• Increasing engagement through app usage
• Providing personalized offers

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👨‍💻 Author

Abhinav
Aspiring Data Analyst