This project implements and compares three regression models—Linear Regression, Ridge Regression, and Decision Tree Regressor—on the California Housing Dataset to predict median house prices. The goal is to evaluate how different machine learning models perform on the same dataset using standard preprocessing, training, and evaluation techniques.