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@article{178634,
        author = {Aryan Hande and Shagun Pandey and Ayush Bhagwat and Lucky Ali and Swati Bhautekar and Mamta Navale},
        title = {Real Estate Price Forecast using Machine Learning},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {12},
        pages = {8747-8751},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=178634},
        abstract = {This study presents a machine learning-based approach to accurately forecast real estate prices using a combination of historical transaction data, property features, and location-based information. The primary objective is to develop a predictive model that assists buyers, sellers, and investors in making informed decisions. The dataset was preprocessed and analyzed to identify key features influencing property values. Several regression algorithms, including Linear Regression, Random Forest, and XGBoost, were applied and evaluated based on performance metrics such as RMSE and R² score. Among the models, XGBoost demonstrated the highest prediction accuracy. The results indicate that features such as location, number of bedrooms, and property age significantly impact pricing trends. This research contributes to data-driven decision-making in the real estate sector by demonstrating that machine learning models can provide reliable pricing insights. The study concludes by suggesting the integration of external factors like market trends and economic indicators to further improve prediction accuracy.},
        keywords = {Data analysis, Forecasting, Machine learning, Price prediction, Real estate, Regression},
        month = {May},
        }