House Price Prediction using Machine Learning

Abstract

Real estate values change daily, making it a un- predictable market. So our project is based on using machine learning to predict property prices. We sought to employ machine learning techniques in our study to predict more accurate housing values. We explore and analyze a variety of forecasting strategies that use the linear regression model because of its flexible housing table and probabilistic model selection techniques. Because of its model selection flexibility, we used lasso hindsight as our machine learning model. Machine learning evolved tremendously in recent years. Existing models have a variety of faults, including a lack of security and an inability to keep up with price fluctuations that occur often. It also had a major impact on the field of medicine and types of equipment. Our findings indicate that our approach to solving the problem is likely to be successful and that we can test theories against rising housing prices.