Customer Churn Prediction using ML

This project focuses on building a machine learning model that predicts whether a customer is likely to leave a service (churn). The model analyzes customer behavior, usage patterns, service history, demographics, and complaint data.

Steps include data cleaning, feature engineering, handling imbalanced data, model training using Logistic Regression, Random Forest, or XGBoost, and evaluating results using accuracy, F1-score, and ROC-AUC.

This helps companies improve customer retention and take preventive actions before customers churn.