Jupyter Notebook applying statistical theory to housing price prediction, using techniques like the Kolmogorov–Smirnov test, Box-Cox transformation, Pearson/Spearman correlations, chi-square tests, and feature importance, with analysis of prediction accuracy across price ranges.

This project uses various regression models to predict the housing value in California. Experimenting with different models, this notebook explores the differences in performances. Furthermore, we select the best model for this endeavor to wrap it up.