Boston Housing Regression

This project performs regression analysis on the Boston Housing dataset to predict housing prices. The goal is to identify the best-fitting model by evaluating key metrics such as R², MSE, RMSE, and MAE. The workflow includes essential steps like data pre-processing and transformation, examining correlations and collinearity between variables, applying regularization, performing hyperparameter tuning, and assessing statistical significance, all aimed at ensuring robust and interpretable results.