House Price Prediction Using Machine Learning

Abstract

Predicting house prices is an important research and application area in the fields of real estate economics and social sciences. This study uses statistics that include various characteristics of houses, such as location, size, age and quality, to create a method for estimating house prices. Accurate predictions are achieved through powerful data processing, feature selection and modeling techniques, including background analysis and machine learning algorithms. The results show that factors such as location, size, and neighborhood characteristics have a significant impact on home prices. Additionally, research shows that advanced techniques such as geographic analysis and economic analysis are used to improve forecast accuracy. The findings underscore the importance of using accurate statistics and analytical methods to predict house prices, providing valuable information to stakeholders in real estate investment, urban planning and policy making. This retrospective focuses on summarizing the methodology, key findings and conclusions of research in the field of house price forecasting. Adjustments may be made based on the specific results and methods used in a particular study Keyword: House Price Prediction, Machine Learning.