Decision Trees for Objective House Price Prediction

Different people buy houses with the same value at different prices, which usually leads to dissatisfaction with housing prices and unfair housing prices. To solve this problem, we designed an objective housing price prediction scheme based on a decision tree. First, we selected 5 important features based on the decision tree for subsequent modeling. Then we designed a housing price prediction model based on a decision tree. To obtain the optimal parameters, we used grid search. The results showed that the number of houses is the most important factor affecting housing prices, followed by the local population's quality, geographic location, education, and crime rate. To verify the effectiveness of the decision tree scheme, we compared it with some other advanced machine learning models. The implementation results show that our scheme achieves the best results.