House-price Prediction Based on OLS Linear Regression and Random Forest

Based on a simple dataset with 20 features of houses, this paper builds two forecasting models to predict house prices. It primarily takes a series of data processes, including missing value process, outlier removal, new variables creation, and correlation analysis. Then this paper conducts the first theoretical model using OLS method. Next, it uses secondary cross-validation to optimize the parameters of random forest and gets a respectively realistic model. Finally based on the results, this paper analyzes and compares the advantages and disadvantages of the two models.