Research on House Price Prediction based on Machine Learning

Accurately predicting house prices is of vital importance to individual home buyers and investment groups, which not only profoundly affects the formulation of home-buying strategies, but also is closely related to the smooth operation of the economy and the overall development of the society. In recent years, machine learning techniques have shown remarkable potential in house price prediction, as these models can mine the complex nonlinear correlations in large amounts of historical data to produce more detailed and accurate predictions. This study aims to evaluate and compare the performance of various machine learning models on the task of house price prediction. For the house price prediction task, Random Forests generally perform better than Linear Regression and Single Decision Tree because they can better capture complex patterns in the data and reduce the risk of overfitting. Linear regression models are simple and easy to interpret, but may not be accurate enough when dealing with nonlinear relationships and outliers. The advantages of random forests are reflected in higher predictive accuracy, robustness to outliers, and the ability to handle interactions between variables automatically.