Feature Selection and Regression for House Value Prediction

This report investigates the application of feature selection and regression techniques, including LASSO, Ridge Regression, and Elastic Net, in predicting house values. By analyzing these methods, we aim to identify the most influential factors driving house prices and assess their relative importance. Our study involves a comprehensive evaluation of various linear regression models to determine their effectiveness and consistency in predicting house prices. The results reveal that while all examined techniques demonstrate robust performance, certain methods, particularly Elastic Net, offer superior predictive accuracy and better handle multicollinearity among features. This report provides valuable insights for real estate professionals and data scientists seeking to refine their predictive models and make informed decisions based on comprehensive data analysis.