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@article{164173,
        author = {Shivam Parasram Kale  and Abhilash Anil  Kashid },
        title = {Indian Population Predictor},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {10},
        number = {12},
        pages = {1130-1133},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=164173},
        abstract = {Given that India is among the most populous nations on earth, planning and policy-making must take population dynamics into account. Using historical data from vital statistics and census records, this study creates a strong prediction model to estimate India's population increase. The research examines how changes in migration, birth rates, and other demographic factors affect population fluctuations. The project trains and improves models using a variety of machine learning algorithms, such as time-series and regression methods, to produce precise population projections for both short- and long-term periods. It also creates scenario models based on future assumptions and investigates how sensitive projections are to changes in certain variables. The project will produce accurate population forecasts, a list of the main factors influencing population growth, as well as reports and visualizations.},
        keywords = {},
        month = {},
        }