Copyright © 2026 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

@article{198346,
        author = {Sukanya Bangale and Yadnyesh Patil and Akanksha kathar and Isha Pawar},
        title = {House price prediction with regression model},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {11},
        pages = {8147-8150},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=198346},
        abstract = {This paper present a study on machine learning techniques for predicting house prices using regression models. House price prediction plays a significant role in the real estate sector, assisting buyers and sellers in making informed decisions. The Price of a house in influenced by various factors such as area, number of rooms, location, and available amenities. In this work, three regression models-Linear Regression, Decision Tree Regression, and Random Forest Regression-are implemented and compared. The models are trained on a house dataset and evaluated using performance metrics such as Mean Absolute Error (MAE), Mean Squared Error (MSE), and R2 score. The experimental results indicate that Random Forest Regression outperforms the other models in terms of accuracy and error reduction.},
        keywords = {Machine Learning, Regression, House Price Prediction, Linear Regression, Random Forest.},
        month = {April},
        }