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@article{198521,
        author = {Mahendra Kachchhavah and Meet Patel and Moneesh Kadam and Nishva Patel and Tamanna Patel and Asmita Rajput and Rupali Attarde},
        title = {Student Scholarship and Placement Analytics with Machine Learning using Power BI},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {11},
        pages = {9280-9288},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=198521},
        abstract = {Schools and universities have access to numerous sources of information on their students, including student marks, institutions they attend, and modes of transport they use. However, such data are fragmented, and their potential is not explored due to the lack of integration analytics platforms. This research presents a holistic analytics approach designed as the result of Microsoft Power BI and Machine Learning integration. The application integrates various data sources and employs structured analytics techniques, such as data pre-processing, modeling, and visualization to extract valuable insights from the raw data. The solution consists of six different dashboards, namely Overview, Academic Performance, Placement Analysis, Scholarship Analysis, School Analysis, and Life Changes. Dashboards provide flexible data analysis capabilities and enable users to perform various transformations to the dataset via interactive visualizations, KPIs, and filters. Moreover, a predictive machine learning model based on linear regression is built in the application to predict future placement trends and scholarships allocation. The outcomes of the experiment demonstrate that the system facilitates decision-making process, improves transparency and reduces labor costs. Furthermore, the solution is scalable, self-learning and highly valuable for educational institutions as it leverages predictive analytics.},
        keywords = {Power BI, Machine Learning, Student Analytics, Dashboard, Predictive Analytics},
        month = {April},
        }