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@article{197331,
        author = {Megha Ambekar and Rashmi Kulkarni},
        title = {IMBD Movie rating analysis and Recommendation using varies ML Techniques},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {11},
        pages = {6582-6587},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=197331},
        abstract = {This research explores the application of machine learning in movie analytics and recommendation systems. Three methodologies are investigated: Random Forest Classification for rating prediction, Decision Tree Classification for rating categorization, and content-based recommendation using TF-IDF vectorization. Through hyperparameter tuning and evaluation, these approaches enhance user experience by accurately classifying ratings, providing insights into audience reception, and generating personalized recommendations based on user preferences. This research contributes to advancing movie analytics and recommendation systems, offering practical solutions to enhance user engagement.},
        keywords = {Random Forest Classification; Decision Tree Classification; TF-IDF Vectorization},
        month = {April},
        }