Residential Price Analysis Using Machine Learning

The main aim of the project is to gain insight into the decision-making process of housing prices using real estate data and machine learning techniques. This requires data analysis to identify key trends and trends regarding changes in house prices. The main goal is to create a reliable prediction model capable of predicting the future price of the house. This model will provide valuable advice to home buyers and sellers, enabling them to make informed decisions regarding real estate transactions. Finally, the successful program will enable individuals, real estate professionals and investors to tap into the complex real estate market. The project will help make more informed decisions by understanding the value of the house, develop investment strategies and contribute to business transparency. Analysing real estate prices is a difficult task because there are many factors that affect it. Factors such as location, property type, amenities and price are important to buyers. Accurate estimates are important to help people find housing within their budget without compromising their financial security. Machine learning algorithms can help you make informed decisions when choosing a home. Comparing regression methods such as linear, random forest, XGBoost and other regression, this model aims to predict high population housing price. The main success of this model is to accurately predict the price of the house based on the customer’s needs. In this study, various machine learning algorithms are tried to be used to predict real estate prices. The algorithm with the most accurate prediction will be selected for use.