Comprehensive Exploratory Data Analysis of the Netflix Dataset: Uncovering Viewer Preferences and Content Trends
Keywords:
Leading Streaming Giants, Through Data Cleaning, Delivering Personalized Recommendations, Machine Learning Algorithms, Popular Streaming Platform
Abstract
This study focuses on a comprehensive Exploratory Data Analysis (EDA) of Netflix's dataset to uncover user preferences and content trends. The objective is to analyze viewer behaviors and provide personalized movie recommendations using machine learning algorithms. The methods involve data cleaning, visualization, and the development of a recommendation system combining collaborative and content-based filtering techniques. The study utilized Python's NumPy and Pandas libraries to manipulate and analyze the dataset. The EDA revealed patterns in movie ratings, genre popularity, and user preferences. The results showed that combining collaborative filtering with content-based filtering significantly improved the accuracy and relevance of movie recommendations. This research demonstrates the effectiveness of data-driven insights in enhancing user experience on streaming platforms.
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Published
2024-10-17
How to Cite
Regin, R., & Rajest, S. S. (2024). Comprehensive Exploratory Data Analysis of the Netflix Dataset: Uncovering Viewer Preferences and Content Trends. CENTRAL ASIAN JOURNAL OF MATHEMATICAL THEORY AND COMPUTER SCIENCES, 5(4), 388-400. Retrieved from https://cajmtcs.casjournal.org/index.php/CAJMTCS/article/view/673
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Articles
