📊 Telco Customer Churn Analysis

📌 Project Overview

This project analyzes customer churn for a telecom company to identify the key factors influencing customer retention and churn.

The dataset contains 7,043 customers and 21 variables, including customer demographics, tenure, services, contract type, payment method, and charges.

🎯 Objectives

• Identify factors associated with customer churn
• Analyze high-risk customer segments
• Understand the impact of contracts, services, tenure, and payment methods
• Provide actionable retention recommendations

🧹 Data Preprocessing

• Converted TotalCharges to numeric
• Replaced blank TotalCharges values with 0
• Converted SeniorCitizen from 0/1 to Yes/No
• Verified 0 missing values and 0 duplicate customer IDs

📈 Key Findings

• 🔴 26.54% of customers have churned
• ⏳ Short-tenure customers show higher churn
• 📄 Month-to-month customers have the highest churn
• 🌐 Fiber Optic customers show higher churn than DSL customers
• 💳 Electronic Check users show higher churn
• 🔐 Customers without security/support services churn more
• 👵 Senior citizens have a higher churn rate
• ⚧️ Gender shows no strong churn differentiation
• 📺 Streaming services show a relatively balanced churn pattern

💡 Business Recommendations

• Improve onboarding and engagement for new customers
• Encourage month-to-month customers to adopt longer contracts
• Promote security and technical-support services
• Investigate Fiber Optic pricing and service quality
• Encourage customers to use automatic payment methods
• Create targeted retention programs for senior citizens

🛠️ Tools & Technologies

Python | Pandas | NumPy | Matplotlib | Seaborn | Google Colab

📌 Conclusion

The analysis shows that tenure, contract type, internet service, payment method, and additional services are important factors associated with customer churn. These insights can help the company develop targeted strategies to reduce churn and improve customer retention.