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@article{200733,
        author = {Kirti Jagtap and Yamini Koli and Kajal Kamble and Seema Chowhan},
        title = {Library Data Analysis Using Machine Learning},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {12},
        pages = {2040-2045},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=200733},
        abstract = {This work reveals insights from a library data analysis tool that employs several machine learning algorithms. The data samples that were analyzed in this work consisted of book, user, and rating data extracted from Kaggle, Baburaoji Gholap College Library, and self-generated data for the current research project. Several techniques of data preprocessing were applied, including handling missing data, data cleaning, and data transformation. The technique known as exploratory data analysis revealed some patterns concerning the distribution of books and the behavior of users. The machine learning algorithms used in this work included Decision Tree, ensemble machine learning algorithms like Random Forest, Gradient Boosting, and XGBoost, and K-Means Clustering for classifying data.},
        keywords = {Data Analytics, Machine Learning, Library Data Analysis, Clustering, Classification},
        month = {May},
        }