uilt an end-to-end customer churn

uilt an end-to-end customer churn prediction system using a 20,000-row telecom-style dataset (tenure, monthly charges, contract type, payment method, internet service, support calls, etc.).

The workflow covered data cleaning, exploratory data analysis, stratified train-test splitting, feature encoding (one-hot & binary), and model comparison between Logistic Regression and Random Forest. Model performance was evaluated using precision, recall, F1-score, and ROC-AUC, with SHAP used to interpret which features drive churn risk.

The final model was deployed as an interactive Streamlit dashboard, allowing users to input customer data and get a live churn risk prediction along with the key factors behind it.

This project reflects my focus on building practical, interpretable ML solutions — from raw data to a usable, deployed tool.