Predicting house prices in Iowa
This project was based on the Ames-Iowa Housing Dataset. The aim of the project was to apply different machine learning techniques to optimize house pricing predictions.
- Imputing missing values
- dummying ordinal and categorical variables.
- Stepwise Forward Feature Selection using BIC (Bayesian Information Criteria)
- Evaluation of RMSE (Root mean squared error) using Ridge,Lasso and Random Forest algorithm.