Customer Churn Prediction Using Machine Learning

Overview:

This project focuses on predicting customer churn using machine learning techniques. By analyzing customer behavior, transaction history, and engagement patterns, the model helps businesses identify customers at risk of leaving and take proactive retention measures.

Approach:

Collected and preprocessed customer data (e.g., demographics, usage patterns, transactions).

Performed exploratory data analysis (EDA) to identify key churn indicators.

Implemented various machine learning models (Logistic Regression, Random Forest, XGBoost, etc.).

Optimized model performance using feature engineering and hyperparameter tuning.

Evaluated the model using accuracy, precision, recall, and F1-score.

Deployed the model using Flask/Streamlit for easy access and integration.