A hands-on walkthrough for building a telecom customer churn detection model using Python and scikit-learn. Covers the full pipeline: generating a synthetic dataset with 3,333 customers and 14.3% churn rate, exploratory data analysis revealing that support call volume and international plan holders are the strongest churn signals, preprocessing with stratified splits and standard scaling, training a Random Forest classifier with class_weight='balanced' to handle imbalance, and evaluating with ROC-AUC (0.798). Explains threshold tuning via Youden's J statistic, showing that lowering the decision boundary from 0.5 to 0.376 raises recall from 48% to 67.4%. Also covers feature importance analysis and scoring new customers with risk tiers. Closes with expert notes on SHAP values, LightGBM/XGBoost as stronger alternatives, and uplift modeling as the next frontier.
