Conducted feature analysis on 79 explanatory variables and used various supervised learning models to predict the final price of a home. The 10-fold cross-validation method was used to identify the best predictive model, which includes linear models, random forests, and gradient boosting. The best-performing regression model explained 92% of the variation of the response variable, and essential features were identified using an inference analysis.
