Property Price Optimization Using Machine Learning Techniques

As we in this era property price optimization Is a censorious aspect of the real estate industry, buyers, sellers, etc. Traditional models were often failing to account for the complex interactions between various datasets. This study explores the application of machine learning techniques, specifically Random Forest and Linear Regression. The dataset consist of real estate listings with different parameters like square footage, No. of bedrooms , pricing, furnishing status and so on. We have used different ML models such as Linear Regression is a fundamental tool for real estate valuation, providing insights into property trends whereas Random Forest is an learning method that builds multiple decision trees and help to improve accuracy and to handle complex relationships within variables. The models were assessed by using performance metrices such as Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE). The result shows that Random Forest model surpassed Linear Regression Model by handling all the errors effectively for future basis. The main motive of this paper is to conclude the models in such a way that it can be easily used by all the writers who are working on this topic. We have summarize all the research paper in one paper.