House Price Precision Assessor

House price prediction using machine learning entails the development of a model capable of estimating the market value of residential properties by considering various features, including size, location, number of bedrooms, and more. This technology offers practical applications in the real estate industry, assisting buyers, sellers, and investors in making well-informed decisions. Machine learning algorithms analyze historical data to provide accurate price predictions, thereby facilitating property valuation and informed investment strategies. For a number of stakeholders, including investors, legislators, real estate brokers, and homeowners, the ability to anticipate property values accurately is crucial. A thorough summary of the methods and strategies used to forecast home values is provided in this abstract, along with important conclusions and new information from recent research. The study examines the wide variety of variables that affect home values, such as market trends, economic indicators, size, amenities, and location. Many machine learning approaches, such as neural networks, decision trees, random forests, and linear regression, have been utilized to build prediction models. Everyone has pros and cons of their own. There have been uses for random forests, trees, and linear regression. Everyone has pros and cons of their own.