The Netflix Dataset Analysis project delves into a dataset containing comprehensive information about the diverse array of movies and TV shows offered on the popular streaming platform, Netflix. In an age of digital media and streaming services, having insights into the content available on platforms like Netflix has become paramount for a wide spectrum of users, ranging from viewers seeking engaging content to content creators and strategists aiming to decipher trends and preferences in an ever-evolving landscape.
The dataset used for this analysis was obtained from Kaggle. It boasts an impressive collection of approximately 5500 entries, each providing details such as title, type (movie or TV show), release year, IMDb score, runtime, genres, age certification, production countries, seasons (for TV shows), and TMDB popularity scores.
The primary purpose and underlying motives for conducting this analysis encompass several key areas:
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Content Analysis: Understanding the distribution of movies and TV shows is crucial for content creators and strategists. By categorizing and analyzing content, this analysis aids in identifying trends and preferences in the types of content available on Netflix.
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Audience Targeting: Knowledge of age certifications for TV shows and movies assists in effective audience targeting. This information is invaluable for the creation and promotion of content suitable for different age groups.
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Quality Assessment: Calculating the average IMDb score for movies and identifying the top-rated movies is a vital tool for both users and Netflix to gauge the overall quality of the content available.
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Runtime and Popularity: The analysis of runtime for movies and the popularity of TV shows provides critical insights into user preferences and is fundamental in the decision-making process for creating new content and acquiring content licenses.
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Genre Insights: By understanding the most common genres on Netflix, content creators can make informed decisions about the development and promotion of specific genres of content.
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Content Production Analysis: Identifying the most common production countries aids in comprehending the global reach of content on Netflix and its impact on regional audiences.
The following analysis tasks were executed on the Netflix dataset:
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Number of Movies and TV Shows: Determined the total count of movies and TV shows available on Netflix.
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Release on Movies and TV Shows in Different Decades: Categorized releases into different decades to gain insights into the temporal distribution of content.
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Average IMDb Score for Movies: Calculated the average IMDb score for movies in the dataset, providing a measure of content quality.
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Top 10 Movies with the Highest IMDb Score: Identified the top-rated movies based on IMDb scores, assisting users in discovering critically acclaimed content.
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Bottom 10 Movies with the Lowest IMDb Score: Presented a list of movies with the lowest IMDb scores, guiding users away from low-rated content.
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Top 10 TV Shows with the Most Seasons: Listed TV shows with the most seasons, helping users identify long-running series.
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Top 10 Movies with the Longest Runtime: Highlighted movies with the longest runtimes, valuable for viewers who prefer lengthier content.
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Top 10 Most Popular TV Shows (by TMDB Popularity Score): Identified the TV shows with the highest TMDB popularity scores, helping users discover trending content.
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Top 10 Movies with the Highest IMDb Score in the 'Drama' Genre: Listed the best-rated drama movies, appealing to fans of this genre.
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Top 10 Most Common Production Countries: Identified the most common production countries, offering insights into the geographic diversity of content.
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Count of TV Shows by Age Certification: Provided a breakdown of TV shows by age certification, useful for users and content creators seeking to target specific age groups.
In conclusion, the Netflix Dataset Analysis project offers a comprehensive examination of the content available on the Netflix platform. It addresses a myriad of questions, ranging from content categorization and user preferences to quality assessment and content production. The insights derived from this analysis serve as valuable tools for content creators, administrators, and users who seek a deeper understanding of the Netflix library.
The SQL analysis code used for this project and the findings are included in this repository.