In this project, I have used Linear Regression model and Random Forest Regression model to predict housing prices for regions in the USA, compared the accuracies of the models to find out the best suitable model.

The data set used is USA_Hosuing from Kaggle: https://www.kaggle.com/datasets/kanths028/usa-housing

The data contains the following columns:

• 'Avg. Area Income': Avg. Income of residents of the city house is located in.
• 'Avg. Area House Age': Avg Age of Houses in same city
• 'Avg. Area Number of Rooms': Avg Number of Rooms for Houses in same city
• 'Avg. Area Number of Bedrooms': Avg Number of Bedrooms for Houses in same city
• 'Area Population': Population of city house is located in
• 'Price': Price that the house sold at
• 'Address': Address for the house

1. MAE: 81257.5579585586
2. MSE: 10169125565.897497
3. RMSE: 100842.08231634994
4. R2 Score: 91.18925903400932