Comparative Research on House Price Forecast Model Based on Multiple Linear Regression and Random Forest Algorithm
To analyze the influencing factors of house price and select a better Machine Learning model for forecasting house price trend,two forecasting models are constructed using two Machine Learning algorithms and the forecasting results are compared.The Multiple Linear Regression forecasting model and Random Forest forecasting model are constructed by analyzing features of public datasets,preprocessing,and partitioning datasets.Mean Absolute Error(MAE),Root Mean Square Error(RMSE),and R-squared(R2)are used as indicators for evaluating the final model.The results indicate that the Random Forest model has lower MAE and RMSE values and a higher R-squared.The evaluation shows that prediction accuracy of the Random Forest model is better than that of the Multiple Linear Regression model.
Multiple Linear RegressionRandom Foresthouse price forecast
国家科技期刊平台
NSTL
万方数据
