House Price Prediction using Machine Learning

House price prediction is an essential task in the real estate sector, which proves to be helpful for buyers, sellers, and investors. The goal of this study is to forecast the price of a house using different parameters through Machine Learning (ML) methods. Here, we utilize regression models like Linear Regression, Decision Tree, and Random Forest to examine and forecast house prices. The used dataset has various features such as location, area, the number of bedrooms, amenities, and construction year. The accuracy level of the Random Forest model is higher than other models as indicated by the results. The suggested model offers a precise method of estimating house prices, thus supporting stakeholders in making the right decisions.