• P. Silpa Chaitanya
• Vadapalli Vaishnavi Devi
• Yedururu Siva Naga Jyothi
• Tommandru Durga Jaya Malleswari
• Patnapu Hema

My research project is focused on leveraging real-world data to forecast real estate prices. My goal in this situation is to base our decisions on every significant factor that goes into house pricing. In many real-world applications, it is more desirable and practical to predict a price variance rather than a definite figure. In this case, price prediction can be viewed as a classification problem. A popular method for calculating housing price fluctuations is the House Price Index (HPI). Given the strong correlation between property prices and other variables like predicting individual property values requires information beyond HPI, such as location, city, and population.HPI is useless as it is ineffective in estimating the price of a particular house.Random Forest is a classifier that increases a dataset's expected accuracy by averaging the outcomes of numerous decision trees applied on various dataset subsets. This research investigates the utilisation of Random Forest machine learning.

Price variance, Price prediction, Classification problem, House Price Index (HPI), Property prices, Random Forest

"House Price Prediction Using Random Forest Model", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 11, page no.457-464, November-2024, Available :http://www.jetir.org/papers/JETIRGO06048.pdf

"House Price Prediction Using Random Forest Model", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 11, page no. pp457-464, November-2024, Available at : http://www.jetir.org/papers/JETIRGO06048.pdf