This notebook explores linear regression, detailing its mathematical foundation and practical application. It guides users through data loading, exploration, model training, and evaluation using metrics like R-squared, MSE, and RMSE. The notebook emphasizes the importance of testing on unseen data to ensure model generalization and avoid overfitting, providing a strong foundation in this fundamental machine learning algorithm.