House Sale Price Prediction Using Machine Learning

Developed a machine learning project to predict house sale prices based on property features using the Kaggle House Prices dataset. The project includes data cleaning, exploratory data analysis (EDA), feature engineering, and preprocessing of numerical and categorical data.

Implemented and compared multiple regression models, including Linear Regression, Ridge, Lasso, Polynomial Regression, Random Forest, and Extra Trees. Evaluated model performance using R², MAE, and RMSE to identify the best-performing model for accurate house price prediction.

Technologies: Python, Pandas, NumPy, Scikit-learn, Matplotlib, Seaborn.