XGBoost in Python: Complete Beginner-to-Advanced Guide for Housing Price Prediction
In this video, we build and explain an XGBoost regression model in Python from first principles. Rather than treating XGBoost as a black box, we break down the mathematics and logic behind boosting, residual learning, gain calculations, tree splitting, pruning, regularization, and hyperparameter tuning.
Topics covered include:
✅ Where XGBoost fits in the Machine Learning landscape
✅ Tree-based models and Gradient Boosting fundamentals
✅ Root nodes, branches, leaves, partitions, and tree depth
✅ Initial prediction using the mean target value (F₀)
✅ Residual calculation and why boosting trees learn errors rather than the original target
✅ How Tree 1, Tree 2, and subsequent boosting trees are constructed sequentially
✅ Gain formula, similarity scores, gradients, and Hessians
✅ How XGBoost chooses the optimal split at each node
✅ Leaf correction values and prediction updates
✅ Pruning with Gamma (γ) and L2 regularization with Lambda (λ)
✅ Learning Rate (η), Max Depth, and Number of Estimators
✅ Hyperparameter Tuning using Grid Search Cross-Validation
✅ Regularization techniques to reduce overfitting
✅ Early Stopping for automatic complexity control
✅ Model evaluation using R², MAE, RMSE, and MAPE
✅ Feature Importance and SHAP-based model interpretation
Using a real housing price prediction example, we demonstrate how XGBoost builds an ensemble of correction trees that progressively reduce prediction errors and achieve powerful predictive performance on structured data. The video also compares XGBoost with OLS regression and explains when each approach is most appropriate.
https://drive.google.com/file/d/1FAnH...
https://drive.google.com/file/d/19p8H...