Random Forest-based House Price Prediction

The precise prediction of house prices is highly significant for professionals in the real estate industry, homeowners, and potential buyers. This study primarily focuses on utilizing the random forest algorithm to accurately predict house prices. A comprehensive dataset containing various features such as location, size, rooms, amenities, and historical transaction data was collected. The investigation included steps of data conditioning, designing features, instruction of models, and oversight. To assess the outcome of the model along with avoiding the situation a random-forest stance was employed, and validated cross-valid techniques were employed. Lasso regression is employed to identify the key features that significantly influence house prices. Results showed promising predictive accuracy, surpassing other baseline algorithms. Feature importance analysis highlighted the significant influence of location, size, and the number of rooms on house prices, aligning with domain knowledge. This study illustrates the precision of random forests and Lasso-regression-based ML assertions, assisting housing specialists, homeowners, and potential purchasers in making accurate choices. Future research can explore integrating additional data sources and advanced techniques to enhance prediction accuracy and address housing market challenges.