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@article{170665,
        author = {Lilly Florence and Charu Prabha P and Arul Maria Agnes and Anandha Bhairavi and Geo},
        title = {Predicting House Prices in Emerging Markets: A Data-Driven Approach to Urban Growth in India},
        journal = {International Journal of Innovative Research in Technology},
        year = {2024},
        volume = {11},
        number = {7},
        pages = {1548-1555},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=170665},
        abstract = {The paper presents a data-driven approach to predicting house prices in emerging markets, with a focus on urban growth in India. Using a blend of historical housing data, socioeconomic factors, and machine learning models, we aim to identify key predictors of housing prices. The study explores various algorithms and evaluates their accuracy in forecasting price trends. Our findings offer insights into market dynamics and urbanization's role in price fluctuations. The results highlight the potential of predictive models in assisting stakeholders, from policymakers to investors, in making informed decisions.},
        keywords = {},
        month = {December},
        }