Authors: Assistant Professor S.Venkateswara Rao, C. Nikitha
Abstract: The ever-changing real estate market and the ever-increasing density of metropolitan areas have made accurate home rent forecast a must-have tool for investors, renters, property owners, and real estate agents. Historical patterns, manual estimate, and expert opinion are the mainstays of conventional pricing approaches, yet they often overlook the intricate interrelationships between various property qualities. This research suggests a combination of the Random Forest and Extreme Gradient Boosting (XGBoost) regression models to overcome these shortcomings and provide reliable rental home predictions. With the help of thorough data preparation, feature engineering, normalisation, and categorical encoding, the suggested framework enhances prediction performance using the Housing Price in India dataset, which contains 193,011 property records. The prediction power of the regression models is increased by adding additional parameters like bedroom-to-bathroom ratio and price per square foot. Highly accurate rental price predictions are generated by combining the strengths of the Random Forest and XGBoost models, which are trained on the processed dataset. In addition to Accuracy, Precision, Recall, and F1-score, the following metrics are used to assess the model's performance: Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and R-squared (R²). Overall, the experimental findings show an impressive performance in terms of prediction accuracy (around 98.7%), precision (0.987), recall (0.985), and F1-score (0.986), with an MSE of 0.0001, RMSE of 0.01, MAE of 0.01, and R³ of 0.9998. While maintaining the integrity of the dataset, algorithms, methodology, and experimental results, the suggested ensemble learning framework offers a dependable, efficient, and scalable answer to the problem of intelligent home rent prediction.
