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@article{184152,
        author = {Meghana D. R and Umm E Asma and Asma Noorain and Sindu N},
        title = {Movie Recommendation System: A Personalized Approach Using Weighted Ratings and Linear Regression},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {4},
        pages = {304-306},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=184152},
        abstract = {With the exponential growth of online streaming platforms, choosing a movie that aligns with user preferences has become a challenging task due to the vast amount of content available. This paper presents a Movie Recommendation System that enhances the recommendation process by considering not only IMDb ratings but also the number of user votes. We introduce a weighted rating model, which improves the accuracy of the recommendations by factoring in both the quality and popularity of the movies. Additionally, a Linear Regression model is employed to predict the revenue of a movie based on its rating. The system utilizes Python libraries such as Pandas, Matplotlib, and Scikit-Learn for data processing, visualization, and predictive modelling. The experimental results demonstrate the effectiveness of the system in providing personalized movie recommendations and revenue predictions.},
        keywords = {},
        month = {September},
        }