House Price Prediction

Built a machine learning model to predict house prices based on various features such as location, size, number of rooms, and other relevant factors. The project involved data preprocessing, handling missing values, feature engineering, and training multiple regression models.

Evaluated model performance using metrics like Mean Absolute Error (MAE) and R² score, and selected the best-performing model. This project demonstrates strong skills in data analysis, regression techniques, and working with real-world datasets using Python libraries such as Pandas, NumPy, and Scikit-learn.