Predicting Real Estate Prices with Regression Models report

Regression models are used to predict real estate prices by estimating property values based on a variety of factors using historical data. Gathering pertinent information about the property’s size, location, number of bedrooms, age, and market circumstances is the first step in the process. A regression model—usually linear regression, decision trees, or more complex models like random forests or gradient boosting—is trained after the data has been preprocessed by handling missing values, encoding categorical variables, and normalising numerical characteristics. These models pick up on the connection between property values and input features.After training, the model can forecast a property’s price based on its attributes. Metrics that show how well the model fits the data, such as Mean Absolute Error (MAE), Root Mean Square Error (RMSE), or R-squared, are commonly used to assess performance. Realtors, investors, and appraisers frequently utilise regression models for real estate pricing in order to forecast the market and make data-driven judgements.