• Tarushi Vasishth
• Shruti Jain
• Anuradha Bhasin
• Dr. Arunima Kumari

The ever-changing nature of real estate rental prices makes it a prime candidate for applying machine learning in price prediction. In this paper, a novel prediction model, Voting Regressor is proposed to forecast rental values based on current market conditions. It combines the strengths of various algorithms like Ridge Regression, Decision Trees, SVM Regressor, and Random Forest. The proposed prediction model is tested and validated on a housing dataset having 4746 records and 12 features, including physical characteristics and geographical information. This data is sourced from a leading real estate website, www.magicbricks. com, through Kaggle. Further, the model benefits from a rigorous data preprocessing pipeline encompassing data cleaning, outlier removal, fea- ture engineering, scaling, and hyperparameter tuning. We have evaluated and compared the performance of different models employed to predict rental prices.

Ridge Regression, SVR, Decision Tree, Gradient Descent

"Comparison of different ML algorithms for prediction of House Rental Prices", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 7, page no.b367-b377, July-2024, Available :http://www.jetir.org/papers/JETIR2407138.pdf

"Comparison of different ML algorithms for prediction of House Rental Prices", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 7, page no. ppb367-b377, July-2024, Available at : http://www.jetir.org/papers/JETIR2407138.pdf