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@article{180504,
        author = {Rashmi Thakre and Tejas Sadapure and Abhay Rathod and Gajanan Kalmegh and Akshay Morkhade},
        title = {Olympics 2024 Dashboard Using Python and Power BI: Further Predictions and Study},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {1},
        pages = {2282-2286},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=180504},
        abstract = {The Olympic Games represent one of the most significant global sporting events, attracting attention from analysts, spectators, and policymakers worldwide. Leveraging data analytics and visualization tools, this study focuses on the development of a dynamic and predictive dashboard for the 2024 Olympics using Python and Power BI. The project integrates historical Olympic datasets, machine learning models for medal predictions, and interactive visual elements to provide insights into country performances, athlete statistics, and expected outcomes. This paper elaborates on the stages of data acquisition, transformation, visualization, model evaluation, and future enhancements while emphasizing the importance of predictive analytics in sports management.},
        keywords = {Olympics 2024, Sports Analytics, Power BI Dashboard, Python Data Analysis, Predictions, Olympic Medal Forecasting, Data Visualization, Predictive Modeling, Athlete Performance Analysis, Historical Sports Data, Interactive Dashboards.},
        month = {June},
        }