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Anime Series Ratings and Viewership Data
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About
Explore a dataset of anime titles sourced from MyAnimeList.net. This dataset includes key information such as unique identifiers, titles, ranks, stream types, episode counts, release dates, member counts, and user scores. It offers insights into the popularity, duration, and user ratings of various anime series, making it a rich source of structured data for statistical analysis, machine learning models, or exploring trends in anime viewership.
Columns
- UID: A unique identifier for each anime entry.
- Title: The title of the anime series.
- Rank: The ranking of the anime based on user ratings and popularity.
- Stream type: The type of streaming format for the anime (e.g., TV, Movie).
- Episodes: The total number of episodes in the series.
- Start date: The initial release date of the anime.
- End Date: The final release date of the anime.
- Members: The number of community members who have added the anime to their lists.
- Score: The weighted score used to evaluate and rank the anime.
Distribution
- Format: CSV
- Size: The dataset contains 8,650 records. The number of rows and columns can be confirmed upon file inspection.
- Structure: The data is structured with columns containing unique identifiers, text, numerical values, and dates.
Usage
Ideal applications for this dataset include:
- Statistical analysis of anime trends.
- Building recommendation engines.
- Developing machine learning models to predict anime popularity or scores.
- Exploring viewership patterns and genre popularity over time.
- Data visualisation projects.
Coverage
- Geographic: Global, as the data is from an international online platform (MyAnimeList.net).
- Time Range: The dataset covers anime released from August 1960 to July 2024.
- Demographic: The data reflects the user base of MyAnimeList.net, which is an international community of anime fans.
License
CC BY-NC-SA 4.0
Who Can Use It
- Data Analysts: To analyse trends in anime popularity, viewership, and ratings over several decades.
- Machine Learning Engineers: To create predictive models for anime success or build content-based recommendation systems.
- Academic Researchers: For studies on media trends, online communities, and popular culture.
- Anime Enthusiasts: To conduct personal projects, create visualisations, and discover viewing patterns.
Dataset Name Suggestions
- MyAnimeList Top Anime Rankings
- Anime Series Ratings and Viewership Data
- MyAnimeList.net Analytics Dataset
- Historical Anime Popularity and Score Data
Attributes
Original Data Source: Anime Series Ratings and Viewership Data
QUALITY