An Automated Property Pricing System using Machine Learning

The use of data mining in today's real estate industry is increasingly widespread, particularly for predicting property values, identifying key housing features, and extracting insights from raw data. This technology plays a critical role by providing information that aids various stakeholders in the real estate market. Fluctuations in property values can cause anxiety for homeowners and industry professionals alike, and the rising demand for buying and renting homes, driven by urbanization, further intensifies the need for accurate property valuations. As a result, developing more efficient methods to estimate home prices that reflect current market trends has become crucial. This study focuses on evaluating various supervised machine learning regressors for house price prediction. Algorithms such as Extra Trees Regressor, K-Nearest Neighbors (KNN), Decision Tree Regressor, Bagging Regressor, and Linear and Logistic Regression are tested to determine their efficacy in predicting home values. A comparative analysis is performed among these algorithms to identify which one best fits the dataset and model. Ultimately, the most effective algorithm is selected to create a prototype for forecasting house prices. Additionally, a website has been developed, using the selected model, to provide users with real-time predictions based on the algorithm that demonstrated the highest accuracy. This research highlights the importance of employing advanced machine learning techniques to address challenges in real estate, ensuring that property values are predicted more accurately to benefit both consumers and industry professionals.