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@article{191246,
        author = {A. ANANDHI and M. MONICA},
        title = {Netflix Show Ratings Prediction Using Machine Learning Algorithms},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {8},
        pages = {6972-6975},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=191246},
        abstract = {The exponential growth of digital streaming services has produced massive datasets, offering a unique opportunity to apply machine learning for analyzing viewer-centric metrics. Since IMDb ratings are widely regarded as a benchmark for content quality and audience reception, predicting these scores has become a significant objective in data analytics. This research introduces a web-based application built on a Flask architecture that classifies Netflix shows into IMDb rating categories using an integrated dataset. By utilizing a built-in repository of over one hundred records, the system operates independently of external data uploads. The methodology involves rigorous preprocessing including data cleaning and normalization followed by the implementation of Decision Tree, Random Forest, and Artificial Neural Network (ANN) models. These algorithms categorize content into four tiers: Excellent, Good, Moderate, and Poor. Beyond prediction, the application features secure role-based access, interactive performance dashboards, and automated PDF report generation for comprehensive analytical insights.},
        keywords = {Machine Learning, Netflix, IMDb Ratings, Flask, Random Forest, ANN, Web Application.},
        month = {January},
        }