Netflix Analysis
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Central Research Question: The central research question is to understand how the distribution of content on Netflix has evolved over time, particularly focusing on the types of content (TV shows vs. movies) available in different countries and factors that could improve Netflix's recommendation system. The work contributes to this problem by analyzing the trends in Netflix's content offerings, predicting content types based on features such as release dates and ratings, and providing insights on strategic content release and recommendation enhancements.
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Handling the Solution: The solution involved several steps: data cleaning to remove entries with missing information, encoding categorical data like content ratings and countries for model compatibility, and using machine learning algorithms to predict whether a title is a movie or a TV show. A challenge was handling the 'country' variable due to its complexity (multiple countries per title), which was solved by focusing on the first listed country and encoding it.
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Results: The analysis revealed that TV-MA is the most common content rating and that the most titles are added to Netflix on Fridays. The visual trend from the graph showed that the number of TV shows on Netflix has indeed been increasing compared to movies over recent years. The recommendation system could be improved by considering these insights, such as prioritizing TV-MA content and optimizing releases for Fridays to align with user engagement patterns.