Copyright © 2025 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{189072,
        author = {SIMRAH AHMAD and RISHU PATEL and SAGAR KUMAR and VARSHA KUMARI},
        title = {House Price Prediction using Machine Learning},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {7},
        pages = {4548-4552},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=189072},
        abstract = {The real estate market changes frequently, making it difficult for home buyers and investors to estimate the right price of a property. To address this challenge, this project uses Machine Learning to predict house prices accurately based on important factors such as location, size, and other property features. We use Linear Regression as the primary algorithm, along with data preprocessing techniques such as cleaning, handling outliers, and feature selection to improve model accuracy. The dataset used in this study is collected from trusted real-estate sources to ensure reliable results. We also apply techniques like cross-validation and basic hyperparameter tuning to make the model more effective. The goal of this system is to help users make informed decisions without depending on brokers or facing unexpected price changes. The results show that the proposed model can predict house prices with high accuracy, proving that Machine Learning can be a useful tool for real- estate price estimation.},
        keywords = {Linear regression model, Python, Machine Learning, Housing Price Prediction},
        month = {December},
        }