House Price Prediction Using Optimal Regression Techniques

摘要

Humans are very thoughtful when they want to make some investments, especially when it is a house. When people need a house, they focus on various factors but the most important is the budget. The motivation of this research work is to predict the house prices for lower- and middle-class people based on their financial parameters. This paper compared the results of different regression techniques like Support vector regression, XGBoost, Decision tree regression, and Random forest regression, and based on their performances, we have taken the best among them for optimally predicting the house prices. To train the models, we have focused on various parameters that affect the costs of a house like physical condition, location, amenities, interest rates, etc.