Predicting Hanoi House Prices Using Machine Learning

Nowadays, predicting house prices has long been a fundamental challenge in the real estate industry and finance. Machine learning methods are utilized to uncover valuable models beneficial to both home buyers and sellers. This study is to predict house prices in Hanoi using data from the website batdongsan.com and applying different algorithms such as linear regression, Support Vector Machine, decision tree, and random forest. Many factors affect house prices, including physical factors such as area and location, as well as economic factors. In this paper, we use root mean squared error, mean absolute error, and R-squared as measures of model effectiveness. From there, we utilize and contrast these metrics to identify the model that exhibits the highest level of accuracy, contributing to a deeper understanding of the Hanoi real estate market.