International Journal for Interdisciplinary Sciences and Engineering Applications

Flixlens: A Data Driven Exploration of Netflix Content

M. Sreedevi¹, Miss N. Vasantha², Miss N. Sri Vyshnavi³, Mr. V. Srinivasa Anil⁴, Mr. Sk. Mastan Vali⁵
Professor¹, UG Students^2-5

Department of Computer Science and Engineering, Amrita Sai Institute of Science & Technology, Paritala, Andhra Pradesh, India

Emotion Detection, Deep Learning, CNN, Facial Recognition, Neural Networks

FlixLens is an intelligent data analysis system designed to examine Netflix’s vast content library and derive meaningful insights. This system leverages machine learning and TF-IDF (Term Frequency Inverse Document Frequency), a text-processing algorithm, to analyze metadata such as genres, descriptions, and ratings. By applying TF-IDF, the system efficiently ranks keywords based on their significance, enhancing the accuracy of content categorization and trend identification. The proposed system features a user-friendly interface, enabling users to explore Netflix data and gain real-time analytical insights. The system processes Netflix’s dataset using TF-IDF and machine learning techniques to identify patterns and correlations between genres, countries, and release years. It maintains a structured content database and employs data mining methods to enhance the precision of its insights. Additionally, the system ensures data integrity through validation mechanisms and secure storage protocols. To further support content exploration, the system provides AI-driven recommendations on trending genres, audience preferences, and content engagement metrics. By integrating TF-IDF for text analysis and machine learning for predictive modeling, FlixLens aims to bridge the gap between raw data and meaningful content insights. It enables trend analysis, reduces manual effort in content exploration, and enhances accessibility to data-driven entertainment insights. This approach promotes efficient content analysis and empowers users with structured, analytical perspectives on Netflix’s content catalog. Overall, the FlixLens system provides a reliable and efficient approach to content analysis by integrating TF-IDF for metadata extraction and machine learning for trend detection. By automating data exploration, it reduces the complexity of manual analysis while enabling users to uncover patterns in Netflix’s content. The system’s ability to process vast datasets ensures accurate insights, while its secure and interactive interface enhances usability. With its potential to improve content discovery and data-driven decision-making, this solution represents a significant step toward a smarter and more analytical approach to entertainment insights.

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