Abstract
Abstract: Forecasting rental trends is crucial for many individuals as rental rates in the real estate market tend to escalate annually. Some people want a house with low rent and a usable area, so our article furnishes valuable insights into rental expenses, catering to individuals seeking prospective tenants. Hence, considering our demographic profile, it's essential to employ a systematic approach for predicting future residential rental prices. Buyers who rent accommodation according to their specific needs will know the perfect duration for renting. Many factors, such as seller type, bedroom count, layout type, property type, locality, price, area, furnish type, and bathroom count, influence rent. This study explores and applies multiple regression models Including linear regression, Random Forest regression, and gradient boosting regression, and XG boosted regression algorithm in Machine learning is utilized for prediction of the rental rate of a residence
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