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@article{198837,
        author = {K. Sakthi Priyan and B. Abinaya},
        title = {Big Data Analysis Techniques In Mathematical House Price Prediction Model},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {11},
        pages = {12267-12271},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=198837},
        abstract = {Accurate property price prediction remains one of the most pressing challenges in the modern real estate sector, where market values are shaped by a complex interplay of structural, locational, and socioeconomic factors. This paper presents a machine learning-based predictive framework that leverages big data analytics to estimate residential property prices with high accuracy. The system integrates four supervised learning algorithms—Random Forest, Linear Regression, Decision Tree, and Support Vector Machine (SVM)—trained on a multi-attribute real estate dataset. Input features include property area, number of bedrooms and bathrooms, number of stories, furnishing status, access to main road, guest room and basement availability, hot water heating, air conditioning, parking spaces, and preferred area classification. The raw dataset undergoes comprehensive preprocessing, including missing value imputation, categorical encoding, outlier treatment, and feature normalization, ensuring optimal model performance. Model accuracy is evaluated using Mean Absolute Error (MAE), Mean Squared Error (MSE), and R-squared (R²) metrics. Empirical results demonstrate that Random Forest consistently outperforms competing algorithms by effectively capturing non-linear feature interactions and mitigating overfitting. The proposed system is deployed as a Flask-based web application with a MySQL backend, enabling real-time price estimation for end users. This work contributes a reliable, data-driven decision-support tool for real estate buyers, sellers, and market analysts.},
        keywords = {House Price Prediction, Machine Learning, Random Forest, Support Vector Machine, Big Data Analytics, Feature Engineering, Real Estate, Flask, Python.},
        month = {April},
        }