Random Forest regression for predicting housing prices, using the Ames dataset. Detailed information about dataset, and data download links, can be found here.
The objective is to predict sale prices based on a data sample of ~ 2900 homes. The model achieves an R^2 score of 0.9, which shows how a simple Random Forest approach can be fairly accurate. Some effort on feature engineering improved the score by ~ 20%.