Boston Housing Price Prediction using Machine Learning

• Implemented regression models to predict housing prices based on socioeconomic and geographical features.
• Conducted data cleaning and exploratory data analysis to understand feature importance and distribution.
• Visualized relationships between variables such as RM (average rooms), LSTAT (% lower status population), and MEDV (median value).
• Applied and compared multiple ML models: Linear Regression, Decision Tree Regressor, and Random Forest Regressor.
• Evaluated model performance using metrics like RMSE, Cross Validation to determine the best-fit model.