Customer Churn Prediction

About the Project

This project focuses on predicting customer churn using machine learning techniques. By analyzing historical customer data, the model aims to identify patterns and behaviors that lead to customer attrition.

Objectives

• Analyze customer data to identify key churn indicators.
• Build a predictive model to classify customers likely to churn.
• Provide insights to reduce customer attrition through data-driven strategies.

Technologies Used

• Python (Pandas, NumPy, Scikit-learn, Matplotlib, Seaborn)
• Machine Learning (Logistic Regression, Decision Trees, Random Forest, XGBoost)
• Data Visualization (Power BI, Matplotlib, Seaborn)
• SQL for Data Handling

Key Findings

• Customers with lower engagement are more likely to churn.
• High subscription costs and lack of personalized offers contribute significantly to attrition.
• Machine learning models achieved an accuracy of over 85% in predicting churn.

Source Code & Dataset

GitHub Repository: Customer churn Prediction

Dataset: Kaggle Customer Dataset

Conclusion

The Customer Churn Prediction project helps businesses retain customers by identifying potential churners early. By implementing targeted retention strategies, companies can minimize churn rates and improve customer satisfaction.