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@article{167501,
        author = {Jashandeep Singh},
        title = {UNVEILING TRENDS: DATA CLUSTERING ANALYSIS OF NETFLIX TV SHOWS AND MOVIES THROUGH EDA },
        journal = {International Journal of Innovative Research in Technology},
        year = {2024},
        volume = {11},
        number = {3},
        pages = {1509-1513},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=167501},
        abstract = {This paper explores unsupervised clustering analysis of Netflix's extensive collection of movies and TV shows using advanced techniques such as K-means, Agglomerative Clustering, and Affinity Propagation. Leveraging technologies like Word2Vec for word embedding, the study focuses on optimizing clustering models through meticulous data preprocessing, text cleaning, and hyper-parameter tuning. Key criteria such as Silhouette Score, Elbow Method, and Dendrogram are employed to determine the optimal number of clusters. Insights from exploratory data analysis reveal Netflix's strategic shift towards emphasizing TV content over movies globally. The findings contribute to understanding content preferences across different regions and showcase the platform's effective use of machine learning and AI for personalized recommendations.},
        keywords = {},
        month = {August},
        }