Two Full ML Projects: House Prices and Customer Churn, Start to Finish

Two end-to-end tabular machine learning projects, built with one repeatable template. 📖 Read this lesson as an article, chapter by chapter: https://easy-explain-app.vercel.app/lesson... 📱 Free Android app: https://easy-explain-app.vercel.app/downlo... In this capstone lesson we predict house prices with a log target, RMSE, MAE and residual diagnostics. Then we tackle customer churn with imbalanced data, precision-recall curves, a business-cost threshold, calibration and SHAP. We finish by contrasting how metric choice changes between regression and classification. All numbers shown in worked examples are small illustrative ones, and the lesson stresses that you should report only results from your own runs. It builds on lessons 33 to 35, 40, 42 and 45. 0:00 Intro 0:43 The Project Template 1:43 House Price: Setup and EDA 3:05 House Price: Models 4:16 House Price: Evaluation and Diagnostics 5:51 Churn: Setup and Metrics 7:34 Churn: Threshold, Calibration, SHAP 9:27 Contrasting the Two Projects 10:34 Recap and Next Steps #MachineLearning #DataScience #Python #Capstone #Regression