ai

Housing Prices ML

End-to-end regression pipeline for California housing prices with feature engineering and model comparison.

2021coursework4 min read
Source Code
scikit-learn / Jupyter

Project Snapshot

  • ImpactBuilt end-to-end regression pipelines for California housing-price prediction with feature engineering and cross-validation.
  • Tagsregression · feature-engineering · scikit-learn · housing-data
  • DatasetCalifornia Census Housing
  • ModelsLinear, DT, Random Forest
  • Libraryscikit-learn

01. Overview

An end-to-end ML pipeline based on the California census housing dataset. Covers data loading and exploration, feature engineering (imputation, scaling, encoding), training multiple regression models (Linear, Decision Tree, Random Forest), and comparing performance via cross-validation — following the Hands-On Machine Learning curriculum.