Predicting Housing Price Based on Ensemble Learning Algorithm

The housing prices are affected by many factors. Finding a practical prediction algorithm is becoming an important issue. Because the effect of a single classifier is limited. In this paper, we select a real estate transaction instance of a city in the United States, and employ the bagging algorithm and random forest to construct a model to predict housing price. Ensemble learning which combines multiple base learners, which can obtain excellent generalization performance and improves the prediction accuracy. The results show that ensemble learning has better prediction accuracy than the decision tree model.