Prime video insightful recommender : Unraveling patterns with TF-IDF and cosine similarity

Amazon Prime Video, a prominent on-demand broadcast platform, has achieved a remarkable global reach, extending its services to more than 200 countries and territories. This study delves into the dynamic landscape of Amazon Prime Video, utilizing a dataset of 9,668 records obtained from Kaggle. Our approach involves an exploratory analysis to distill essential insights into the available content on this prominent on-demand broadcast platform. In tandem, we implemented the recommendations using well-designed TF-IDF and cosine similarity algorithms in natural language processing (NLP).Our exploration unveils intriguing data on current content trends. Despite current limitations in the recommendation system, its potential shines through when considering the integration of additional features. This research endeavors to contribute nuanced perspectives on Amazon Prime Video’s content landscape, offering insights into emerging trends and the promise held by recommendation algorithms within the platform’s dynamic ecosystem.