House Price Prediction

This project explores the Ames housing dataset using regression models to predict home sale prices. I apply XGBoost and ElasticNet after carefully cleaning and preprocessing the dataset.

Project Overview

The dataset includes 80+ features per home. I handled missing values, transformed skewed variables, and engineered new features to improve model performance.

Key Techniques

• Feature selection and categorical encoding
• Log transformation on sale price and predictors
• XGBoost for non-linear pattern modeling
• ElasticNet for regularized linear regression
• Cross-validation with RMSE evaluation

Code & Repository

You can explore the full code on GitHub.