Machine Learning project to predict customer churn using Decision Tree and XGBoost
Machine Learning project to predict customer churn using Decision Tree and XGBoost
This dashboard visualizes telecom customer churn using interactive charts and machine learning insights. It includes customer analysis, RFM segmentation, model evaluation, and a live churn prediction tool. The screenshots below show the main pages of the application.
A Streamlit web app that predicts bank customer churn using a Decision Tree Classifier tuned with GridSearchCV. Features full EDA, class imbalance handling, and real-time single-customer predictions via an interactive sidebar.
Part 3 of the D2C churn project: predictive churn modeling using machine learning. Trains and evaluates classification models to estimate churn probability, with feature engineering, metrics tracking, and explainability for business decisions.
Customer churn prediction and retention analysis using Python, Machine Learning, and business analytics.
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