Housing Price Lab

Project overview

Examining housing-price models against a simple baseline.

An ensemble-regression study using a 545-row classroom housing dataset. The project engineers property features, compares model weights with cross-validation and reserves a separate test set for evaluation.

• Python
• Regression
• Ensemble Learning
• Evaluation

Inside this build

• Prepared a dataset spanning five cities and three renovation categories, with no missing values or duplicate identifiers.
• Engineered total-room, area-per-bedroom, toilet-to-bedroom and storey-area predictors while excluding identifiers and price from inputs.
• Used fold-fitted imputation, scaling and one-hot encoding to prevent preprocessing leakage.
• Compared four ensemble-weight candidates with five-fold validation on 436 training rows, selecting a 2:1:1 Ridge, random-forest and gradient-boosting ensemble.
• Evaluated 109 held-out rows: R² 0.5353, MAE 113,249.95 and RMSE 153,267.51, with lower error than the median baseline.
• Added 19 tests and documented the lack of external validation, prediction intervals and subgroup analysis; this remains a classroom study.

Source code & links