House Price Prediction Using Machine Learning

• Author(s): Dipak Jadhav ; Aadesh Ghule ; Prof. M . D. Sarjare
• Paper ID: 1708798
• Page: 1828-1830
• Published Date: 29-05-2025
• Published In: Iconic Research And Engineering Journals
• Publisher: IRE Journals
• e-ISSN: 2456-8880
• Volume/Issue: Volume 8 Issue 11 May-2025

Abstract

House price prediction is an essential problem in the real estate industry. Accurately forecasting housing prices helps buyers, sellers, and investors make informed decisions. Traditional valuation methods lack precision and adaptability to dynamic market factors. This research paper explores machine learning techniques to predict house prices using multiple features like location, area, number of bedrooms, and amenities. Various regression algorithms, including Linear Regression, Decision Tree, and Random Forest, are implemented and compared. The Random Forest algorithm emerged as the best-performing model with high accuracy and low error rates.

Keywords

House Price Prediction, Machine Learning, Regression, Random Forest, Feature Engineering, Real Estate

Citations

IRE Journals:
Dipak Jadhav , Aadesh Ghule , Prof. M . D. Sarjare "House Price Prediction Using Machine Learning " Iconic Research And Engineering Journals Volume 8 Issue 11 2025 Page 1828-1830

IEEE:
Dipak Jadhav , Aadesh Ghule , Prof. M . D. Sarjare "House Price Prediction Using Machine Learning " Iconic Research And Engineering Journals, 8(11)