Context: This dataset provides comprehensive details about anime, catering to enthusiasts, researchers, and analysts who are interested in understanding trends and patterns in anime content. It includes essential information such as anime names, ratings, genres, popularity, and user recommendations.

Source: The data has been curated from MyAnimeList.

Inspiration: Anime has become a global phenomenon, transcending cultural barriers and gaining widespread popularity. This dataset is inspired by the need to analyze:

Key Features: - Name & English Name: Includes both the original and English-translated titles of anime. - Image Source: Links to visual representations of each anime. - Synopsis: A brief description or storyline for each anime. - Rating & Ranked by Users: Quantitative ratings and the number of users contributing to those ratings. - Popularity & Rank: Indicators of an anime's popularity in the community. - Producers, Studios, and Genres: Information about production houses, studios, and genre classifications. - Themes & Demographics: Target audience and recurring themes across anime. - Release Time & Episodes: Information on when the anime aired and its duration. - Anime Recommendations: Suggested similar anime based on user feedback and algorithms.

Context

This dataset contains information about Anime scraped from Anime Planet on 28/06/2023. It contains information about anime (episodes, aired date, rating, genre, etc.), and favorite anime based on the countries and top countries that watch the most anime.

• The scraped program of this dataset is in Anime.Quest GitHub repository.
• Tableau visualization of this dataset can also be found in Anime Quest: Visualization.

Content

The dataset contains 3 files:

📁 anime_data.csv: 1. Name: Full name of the anime 2. Media Type: TV, Web, Movie, etc. 3. Episodes: Total episodes of the anime 4. Studio: Name of the studios of the anime, from most recent to oldest. 5. Start Year: Release Year of the anime 6. End Year: Last year of the anime airing 7. Ongoing: Is the anime currently airing or not? True or False. 8. Release Season: Spring, Fall, Winter, and Summer 9. Rating: The global rating ranges from 0 to 5. 10. Rank: Global ranking of the anime 11. Members: Total members of the anime 12. Genre: The category of the anime 13. Creator: Creator of the anime

📁 anime_top_by_country_data.csv: 1. Country: Individual country name 2. Most Popular: The most popular anime in the country 3. 2nd Place: Second-most popular anime in the country 4. 3rd Place: Third-most popular anime in the country 5. 4th Place: Fourth-most popular anime in the country 6. 5th Place: The fifth-most popular anime in the country

📁 anime_watching_data.csv: 1. Rank: Ranking of countries based on the number of anime viewers 2. Country: Individual country name 3. Population: Total population of the country 4. Percentage of People Watching: Percentage of people watching anime in the country 5. Number of People Watching: Total number of people watching anime in the country

Acknowledgements

The website Anime Planet was used to scrape this dataset. Please include citations for this dataset if you use it in your own research.

Inspiration

This dataset can be used to find the factors determining an anime's rating and ranking. Additionally, it can be used to make anime recommendations. The pattern can be observed in anime.

This dataset captures insights into the most popular anime across various countries worldwide. It includes 100 records, each representing a top anime with relevant details such as: Anime Name 🎥 The title of the anime series or movie that is the most watched in a given country.

Most Watched in Country 🌍 The country where the specified anime has the highest viewership or popularity.

Ratings ⭐ The average viewer rating for the anime, typically on a scale of 1 to 10, indicating its overall reception.

Number of Episodes 🎬 The total number of episodes produced for the anime series.

Animation Studio Name 🏢 The name of the production studio responsible for creating the anime (e.g., Studio Ghibli, Toei Animation).

Budget (in Million USD) 💵 The estimated production budget for the anime, expressed in millions of U.S. dollars.

Release Year 📅 The year the anime was first released or aired.

Genre 🎭 The genre or category of the anime (e.g., action, fantasy, drama), describing the primary theme or style.

Duration per Episode (minutes) ⏱️ The average duration of each episode in minutes for the anime series.

This dataset offers a comprehensive overview of the top animes of 2024, and is useful for building recommendation systems, visualizing trends in anime popularity and score, predicting scores and popularity, and such.

Contents

The dataset contains 22 features:

• Score: The rating or score assigned to each anime title.
• Popularity: Measure of how popular each anime is among viewers.
• Rank: Ranking of each anime title within the dataset.
• Members: The number of members or viewers associated with each anime.
• Description: A brief overview or summary of the plot and themes of each anime.
• Synonyms: Alternative titles or synonyms used for each anime.
• Japanese Title: Original title of the anime in Japanese.
• English Title: English-translated title of the anime.
• Type: Classification of anime type (e.g., TV series, movie, OVA, etc.).
• Eps: Total number of episodes in each anime series.
• Status: Current status of the anime (e.g., ongoing, completed, etc.).
• Aired: Date range of when the anime was aired.
• Premiered: Date when the anime premiered for the first time.
• Broadcast: Information about the broadcasting platform or channel.
• Producers: Companies or studios involved in producing the anime.
• Licensors: Organizations or companies holding the licensing rights for the anime.
• Studios: Animation studios responsible for producing the anime.
• Source: Original source material for the anime (e.g., manga, novel, original).
• Genres: Categories or genres that the anime belongs to.
• Demographic: Target demographic audience for the anime (e.g., shounen, shoujo, seinen, josei).
• Duration: Duration of each episode or movie.
• Rating: Content rating assigned to each anime (e.g., G, PG, PG-13, R).

Acknowledgements

All of the information in this dataset has been gathered by scraping the MyAnimeList website, and is available under the Creative Commons License.

Cover Photo by: Playground.ai

Japan AE: Philippines: Shopping: Manga Comics, Anime,Character Merchandise data was reported at 5,646.558 JPY in Dec 2017. This records an increase from the previous number of 5,177.672 JPY for Sep 2017. Japan AE: Philippines: Shopping: Manga Comics, Anime,Character Merchandise data is updated quarterly, averaging 5,457.152 JPY from Mar 2014 (Median) to Dec 2017, with 16 observations. The data reached an all-time high of 26,138.861 JPY in Sep 2015 and a record low of 1,116.667 JPY in Dec 2014. Japan AE: Philippines: Shopping: Manga Comics, Anime,Character Merchandise data remains active status in CEIC and is reported by Ministry of Land, Infrastructure, Transport and Tourism. The data is categorized under Global Database’s Japan – Table JP.Q028: Tourism and Leisure: Average Expenditure per Purchaser by Nationality.

This dataset contains a comprehensive collection of anime titles spanning the years 1970 to 2024. The data was collected from MyAnimeList using web scraping techniques. It includes essential information about each anime, such as its unique ID, title, genre, description, studio, release year, and user ratings. The dataset offers a valuable resource for exploring the evolution of anime over the decades and understanding trends in the industry. Researchers, anime enthusiasts, and data analysts can use this dataset to analyze various aspects of anime production and consumption, including popular genres, top-rated studios, and changes in audience preferences over time. The dataset is presented in a CSV format and is suitable for a wide range of data analysis and machine learning applications.

Columns in the data.json :

mal_id: Unique identifier for the anime entry. titles: List of titles associated with the anime. In this case, "Attack No.1". type: Type of the anime, e.g., TV series. source: Source material of the anime, here it's based on a manga. episodes: Number of episodes in the anime (104 in this case). rating: Audience rating category, PG-13 in this example. score: Average score given to the anime by users. scored_by: Number of users who have scored the anime. rank: Ranking of the anime based on score or popularity. popularity: Popularity ranking of the anime. members: Number of members who have added this anime to their list. favorites: Number of users who have favorited this anime. synopsis: Plot summary or synopsis of the anime. studios: Production studio responsible for creating the anime. genres: List of genres the anime belongs to (e.g., Drama, Sports). themes: List of themes present in the anime (e.g., Team Sports).

Columns in the user_recommendation.csv :

mal_id: This column represents the ID of an anime that users have watched or interacted with. mal_id_recomm: This column lists the IDs of anime recommended by users for a specific mal_id. votes: The votes column indicates the number of votes or recommendations given by users for the recommendation of mal_id_recomm for mal_id.

The dataset is ready for exploration, analysis, and visualization to uncover insights into the world of anime and its dynamic landscape.

Japan TE: Taiwan: Visiting Film/Anime Settings data was reported at 16.000 Person in Mar 2018. This records a decrease from the previous number of 25.000 Person for Dec 2017. Japan TE: Taiwan: Visiting Film/Anime Settings data is updated quarterly, averaging 26.000 Person from Mar 2014 (Median) to Mar 2018, with 17 observations. The data reached an all-time high of 44.000 Person in Sep 2016 and a record low of 12.000 Person in Jun 2017. Japan TE: Taiwan: Visiting Film/Anime Settings data remains active status in CEIC and is reported by Ministry of Land, Infrastructure, Transport and Tourism. The data is categorized under Global Database’s Japan – Table JP.Q032: Tourism and Leisure: Satisfaction Rating Visiting to Japan.

Japan AE: Indonesia: Shopping: Manga Comics, Anime, Character Merchandise data was reported at 3,800.000 JPY in Dec 2017. This records an increase from the previous number of 2,842.593 JPY for Sep 2017. Japan AE: Indonesia: Shopping: Manga Comics, Anime, Character Merchandise data is updated quarterly, averaging 5,779.762 JPY from Mar 2014 (Median) to Dec 2017, with 16 observations. The data reached an all-time high of 19,758.319 JPY in Jun 2017 and a record low of 2,782.222 JPY in Sep 2016. Japan AE: Indonesia: Shopping: Manga Comics, Anime, Character Merchandise data remains active status in CEIC and is reported by Ministry of Land, Infrastructure, Transport and Tourism. The data is categorized under Global Database’s Japan – Table JP.Q028: Tourism and Leisure: Average Expenditure per Purchaser by Nationality.

Japan MSP: Taiwan: Manga Comics, Anime, Character Merchandise data was reported at 12.000 Person in Dec 2017. This records a decrease from the previous number of 13.000 Person for Sep 2017. Japan MSP: Taiwan: Manga Comics, Anime, Character Merchandise data is updated quarterly, averaging 19.500 Person from Mar 2015 (Median) to Dec 2017, with 12 observations. The data reached an all-time high of 34.000 Person in Sep 2016 and a record low of 12.000 Person in Dec 2017. Japan MSP: Taiwan: Manga Comics, Anime, Character Merchandise data remains active status in CEIC and is reported by Ministry of Land, Infrastructure, Transport and Tourism. The data is categorized under Global Database’s Japan – Table JP.Q032: Tourism and Leisure: Satisfaction Rating Visiting to Japan.

Ebitda Time Series for Gree Inc. GREE Holdings, Inc., together with its subsidiaries, engages in the gaming business in Japan and internationally. It operates in five segments: Game Business, Metaverse Business, IP Business, DX Business, and Investment Business. The company develops, operates, and distributes various smartphone and consumer games, and a social game platform under the GREE, WFS, GREE Studios, and GREE Entertainment brands. It also offers REALITY, a smartphone-oriented metaverse; VTuber, an agency that manages and produces talents; and Web3, which develops and publishes blockchain games. In addition, the company provides various anime and manga contents for consumer and corporate businesses; DADAN app for manga entertainment; and DEDEN, a cloud-based solution for e-book management operations, as well as engages in anime production and licensing; and planning, production, and sale of merchandise. Further, it is involved in the development of SaaS solutions, including app marketing, social marketing, and tourism DX, as well as influencer marketing platform and DX consulting services; and invests in the internet and information technology industries through fund and startup investments. The company was formerly known as GREE, Inc. and changed its name to GREE Holdings, Inc. in January 2025. GREE Holdings, Inc. was incorporated in 2004 and is headquartered in Minato, Japan. GREE Holdings, Inc is a subsidiary of Sequoia Co., Ltd.

Japan MSP: Germany: Manga Comics, Anime, Character Merchandise data was reported at 3.000 Person in Dec 2017. This records an increase from the previous number of 2.000 Person for Sep 2017. Japan MSP: Germany: Manga Comics, Anime, Character Merchandise data is updated quarterly, averaging 3.000 Person from Mar 2015 (Median) to Dec 2017, with 12 observations. The data reached an all-time high of 6.000 Person in Sep 2016 and a record low of 0.000 Person in Mar 2017. Japan MSP: Germany: Manga Comics, Anime, Character Merchandise data remains active status in CEIC and is reported by Ministry of Land, Infrastructure, Transport and Tourism. The data is categorized under Global Database’s Japan – Table JP.Q032: Tourism and Leisure: Satisfaction Rating Visiting to Japan.

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Context

Anime is a popular form of entertainment originating from Japan. It encompasses a wide range of animated TV series, movies, and OVAs (original video animations) that cater to various genres and target audiences. Anime is known for its distinctive art style, compelling storytelling, and diverse themes. Anime covers a vast array of genres, including action, adventure, comedy, drama, romance, fantasy, sci-fi, and many more. Each genre offers unique storytelling elements and appeals to different preferences and interests among anime enthusiasts. It has gained significant popularity worldwide and has developed a dedicated and passionate fanbase. Fans of anime often engage in discussions, reviews, and rankings, contributing to the vibrant community surrounding this form of entertainment. Due to the vast number of anime titles available, recommendations play a crucial role in helping enthusiasts discover new shows that align with their interests. Recommendation systems leverage user ratings, genres, and other factors to suggest anime series that users might enjoy based on their preferences.

Content

------------------------------------------ "anime-dataset-2023.csv" ----------------------------------------------

• anime_id: Unique ID for each anime.
• Name: The name of the anime in its original language.
• English name: The English name of the anime.
• Other name: Native name or title of the anime(can be in Japanese, Chinese or Korean).
• Score: The score or rating given to the anime.
• Genres: The genres of the anime, separated by commas.
• Synopsis: A brief description or summary of the anime's plot.
• Type: The type of the anime (e.g., TV series, movie, OVA, etc.).
• Episodes: The number of episodes in the anime.
• Aired: The dates when the anime was aired.
• Premiered: The season and year when the anime premiered.
• Status: The status of the anime (e.g., Finished Airing, Currently Airing, etc.).
• Producers: The production companies or producers of the anime.
• Licensors: The licensors of the anime (e.g., streaming platforms).
• Studios: The animation studios that worked on the anime.
• Source: The source material of the anime (e.g., manga, light novel, original).
• Duration: The duration of each episode.
• Rating: The age rating of the anime.
• Rank: The rank of the anime based on popularity or other criteria.
• Popularity: The popularity rank of the anime.
• Favorites: The number of times the anime was marked as a favorite by users.
• Scored By: The number of users who scored the anime.
• Members: The number of members who have added the anime to their list on the platform.
• Image URL: The URL of the anime's image or poster.

The dataset offers valuable information for analyzing and comprehending the characteristics, ratings, popularity, and viewership of various anime shows. By utilizing this dataset, one can conduct a wide range of analyses, including identifying the highest-rated anime, exploring the most popular genres, examining the distribution of ratings, and gaining insights into viewer preferences and trends. Additionally, the dataset facilitates the creation of recommendation systems, time series analysis, and clustering to delve deeper into anime trends and user behavior.

--------------------------------------------- "users-details-2023.csv" ------------------------------------------------

• Mal ID: Unique ID for each user.
• Username: The username of the user.
• Gender: The gender of the user.
• Birthday: The birthday of the user (in ISO format).
• Location: The location or country of the user.
• Joined: The date when the user joined the platform (in ISO format).
• Days Watched: The total number of days the user has spent watching anime.
• Mean Score: The average score given by the user to the anime they have watched.
• Watching: The number of anime currently being watched by the user.
• Completed: The number of anime completed by the user.
• On Hold: The number of anime on hold by the user.
• Dropped: The number of anime dropped by the user.
• Plan to Watch: The number of anime the user plans to watch in the future.
• Total Entries: The total number of anime entries in the user's list.
• Rewatched: The number of anime rewatched by the user.
• Episodes Watched: The total number of episodes watched by the user.

The User Details Dataset provides valuable information for analyzing user behavior and preferences on the anime platform. By examining mean scores and anime genres, you can gain insights into user preferences. Users can be segmented into different gro...

The Kaggle data set "Anime Comments Scrapped from https://myanimelist.net" is a valuable resource for anyone interested in exploring the world of anime. It is a collection of comments and reviews on various anime titles, sourced from the popular anime review website MyAnimeList. The data set was scraped using the Octoparse software, which is a powerful web scraping tool used to extract data from websites.

The data set contains five columns of information, namely S.no, Title, Date of comment, User name, and text. The S.no column contains a unique identifier for each comment in the data set, while the Title column contains the name of the anime being reviewed. The Date of comment column indicates the date when the comment was posted, while the User name column shows the username of the person who posted the comment. Finally, the text column contains the actual comment or review left by the user on the anime in question.

The data set is a great resource for anyone looking to analyze or explore anime-related content. Researchers and analysts can use the data set to gain insights into the opinions and sentiments of anime fans towards various titles. For example, one can use the data set to analyze which anime titles are the most popular or controversial among fans, and why. Similarly, researchers can analyze how the opinions and sentiments of anime fans have changed over time for specific anime titles.

Another potential use case for the data set is in building recommendation systems for anime fans. By analyzing the text column of the data set, one can extract information about what anime fans like or dislike about certain anime titles. This information can then be used to build recommendation systems that suggest new anime titles to fans based on their preferences.

The data set can also be used to build natural language processing (NLP) models for sentiment analysis. By training NLP models on the comments and reviews in the data set, researchers can build algorithms that automatically classify comments as positive, negative, or neutral. These models can then be used to analyze large volumes of comments and reviews quickly and efficiently.

Furthermore, the data set can be used to perform network analyses of the relationships between anime titles and users. By analyzing which anime titles are reviewed or commented on by which users, one can identify clusters of users with similar tastes in anime. These clusters can then be used to build communities of anime fans with similar tastes, and to facilitate discussions and recommendations between these users.

Another important point to note about the "Anime Comments Scrapped from https://myanimelist.net" data set is that it contains a large number of comments. Specifically, the data set includes over 30,000 comments on various anime titles. This makes the data set a rich source of information for anyone looking to perform large-scale analyses or build machine learning models.

Overall, the "Anime Comments Scrapped from https://myanimelist.net" data set is a valuable resource for anyone interested in exploring the world of anime. It contains a wealth of information on the opinions and sentiments of anime fans towards various titles, and can be used for a variety of research and analysis purposes. Whether you are an anime enthusiast, a data analyst, or a machine learning researcher, this data set has something to offer.

Description for Manga Dataset

The Manga Dataset is an extensive collection of metadata on various manga series, providing a comprehensive overview of their attributes and current status. This dataset is ideal for analysis, research, and understanding trends in the manga industry. Below are the detailed descriptions of the key features included in the dataset:

1. ID (id):

   • A unique identifier assigned to each manga series, ensuring distinct entries for each title.

2. Title (title):

   • The primary title of the manga series, which is the main identifier for readers and databases.

3. Sub-title (sub_title):

   • An alternative or additional title for the manga series, providing more context or localization of the title.

4. Status (status):

   • The current publication status of the manga series. Common statuses include:
     • Ongoing: The series is still being published with new chapters released periodically.
     • Completed: The series has concluded, and no new chapters will be released.
     • Hiatus: The series is temporarily paused, with plans to continue in the future.

5. Thumbnail (thumb):

   • A URL link to the thumbnail image of the manga series, providing a visual representation for quick identification.

6. Summary (summary):

   • A brief description or synopsis of the manga's plot, giving readers an overview of the story and main themes.

7. Authors (authors):

   • A list of authors or creators involved in the development of the manga series. This includes individual writers, illustrators, or collaborative teams.

8. Genres (genres):

   • A list of genres associated with the manga series, categorizing the series based on its themes and content. Examples include:
     • Action
     • Fantasy
     • Romance
     • Comedy
     • Drama
     • Harem
     • Shounen
     • Adventure

9. NSFW (nsfw):

   • An indicator of whether the manga contains Not Safe For Work content. This boolean field helps users identify series with mature themes or explicit content.

10. Type (type):

    • The origin or classification of the manga, indicating its cultural or production background. Common types include:
      • Korea (Korean manhwa)
      • China (Chinese manhua)
      • Japan (Japanese manga)

11. Total Chapters (total_chapter):

    • The total number of chapters released for the manga series, providing an idea of the series' length and publication history.

12. Creation Date (create_at):

    • A timestamp indicating when the manga entry was created in the dataset, formatted as milliseconds since the Unix epoch (1970-01-01).

13. Update Date (update_at):

    • A timestamp indicating the last update made to the manga entry, ensuring users have access to the most recent information.

Example Entries

• ID: 65a52ea8f64a55128b487e1b
• Title: "A World of Gold to You"
• Sub-title: N/A
• Status: Ongoing
• Thumbnail: Thumbnail URL
• Summary: "Once upon a time, the world had been divided into several kingdoms..."
• Authors: ['']
• Genres: ['Action', 'Fantasy', 'Manga', 'Adventure', 'Shounen']
• NSFW: True
• Type: Korea
• Total Chapters: 0
• Creation Date: 1705324200839
• Update Date: 1705324333619

This dataset provides a valuable resource for manga enthusiasts, researchers, and industry professionals looking to analyze and explore various aspects of manga series, from publication trends to genre popularity and author contributions.

Japan TE: Total: Visiting Film/Anime Settings data was reported at 236.000 Person in Mar 2018. This records a decrease from the previous number of 244.000 Person for Dec 2017. Japan TE: Total: Visiting Film/Anime Settings data is updated quarterly, averaging 251.000 Person from Mar 2014 (Median) to Mar 2018, with 17 observations. The data reached an all-time high of 373.000 Person in Sep 2016 and a record low of 126.000 Person in Dec 2014. Japan TE: Total: Visiting Film/Anime Settings data remains active status in CEIC and is reported by Ministry of Land, Infrastructure, Transport and Tourism. The data is categorized under Global Database’s Japan – Table JP.Q032: Tourism and Leisure: Satisfaction Rating Visiting to Japan.

Content

The original extracted versions (in .srt and .ass format) are also included in this release (which, idk why, but kaggle decompressed >:U)

This dataset contains 1,497,770 messages across 3,836 episodes of anime. The raw dataset contains 1,563,442 messages, some of which were removed during cleaning.

This version (V4) adapts the original (frankly, terrible) format into the newer format I developed, which is used in https://github.com/JEF1056/clean-discord. The Dataset folder contains compressed text files, which are compatable with tensorflow datasets. These can be streamed as a textlinedataset in the TSV format.

V4 also fixes many (but not all) issues that the original cleaning script was too simple to realistically take care of. It also uses the clean-discord cleaner algorithms to make sentences more natural language than formatting. The script has also been optimized to run on multi-core systems, allowing it to complete cleaning this entire dataset in under 30 seconds on a 4-core machine. See the new and impoved script here: https://github.com/JEF1056/clean-discord/blob/v1.2/misc/anime.py (no longer bundled in the dataset files)

Format

The files are now all compressed to save space, and are compatable with tensorflow datasets. You can initialize a dataset function as such: def dataset_fn_local(split, shuffle_files=False): global nq_tsv_path del shuffle_files # Load lines from the text file as examples. files_to_read=[os.path.join(nq_tsv_path[split],filename) for filename in os.listdir(nq_tsv_path[split]) if filename.startswith(split)] print(f"~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~Split {split} contains {len(files_to_read)} files. First 10: {files_to_read[0:10]}") ds = tf.data.TextLineDataset(files_to_read, compression_type="GZIP").filter(lambda line:tf.not_equal(tf.strings.length(line),0)) ds = ds.shuffle(buffer_size=600000) ds = ds.map(functools.partial(tf.io.decode_csv, record_defaults=["",""], field_delim="\t", use_quote_delim=False), num_parallel_calls=tf.data.experimental.AUTOTUNE) ds = ds.map(lambda *ex: dict(zip(["question", "answer"], ex))) return ds

Acknowledgements

A sincere thanks to all of my friends for helping me come up with anime titles, a shoutout to the talented and dedicated people translating Japanese anime, and an even bigger thanks to Leen Chan for compiling the actual subtitles.

This dataset is far from complete! I hope that people who are willing to find, add and clean the data are out there, and could do their best to try and help out in the effort to grow this data

SR: Vietnam: Visiting Film/Anime Settings data was reported at 2.000 Person in Mar 2018. This records an increase from the previous number of 0.000 Person for Dec 2017. SR: Vietnam: Visiting Film/Anime Settings data is updated quarterly, averaging 0.000 Person from Mar 2014 (Median) to Mar 2018, with 17 observations. The data reached an all-time high of 2.000 Person in Mar 2018 and a record low of 0.000 Person in Dec 2017. SR: Vietnam: Visiting Film/Anime Settings data remains active status in CEIC and is reported by Ministry of Land, Infrastructure, Transport and Tourism. The data is categorized under Global Database’s Japan – Table JP.Q032: Tourism and Leisure: Satisfaction Rating Visiting to Japan.

Japan MSP: United Kingdom: Manga Comics, Anime, Character Merchandise data was reported at 5.000 Person in Dec 2017. This records an increase from the previous number of 3.000 Person for Sep 2017. Japan MSP: United Kingdom: Manga Comics, Anime, Character Merchandise data is updated quarterly, averaging 3.000 Person from Mar 2015 (Median) to Dec 2017, with 12 observations. The data reached an all-time high of 8.000 Person in Sep 2016 and a record low of 0.000 Person in Mar 2015. Japan MSP: United Kingdom: Manga Comics, Anime, Character Merchandise data remains active status in CEIC and is reported by Ministry of Land, Infrastructure, Transport and Tourism. The data is categorized under Global Database’s Japan – Table JP.Q032: Tourism and Leisure: Satisfaction Rating Visiting to Japan.

Business Context

Streaming media services facilitate on-demand or real-time presentation and distribution of audio, video, and multimedia content across a communications route without downloading the files to their systems. This saves users time and storage, and at the same time provides the media owners with built-in copy protection. In today's digital space, streaming has become an influential medium for accessing information. Improved connectivity and advancement in technology have made streaming services accessible to almost everyone having an internet connection, and the surging demand for on-demand entertainment services such as entertainment programs and live matches is boosting the adoption of streaming media services globally.

Streamist is a streaming company that streams web series and movies to a worldwide audience. Every content on their portal is rated by the viewers, and the portal also provides other information for the content like the number of people who have watched it, the number of people who want to watch it, the number of episodes, duration of an episode, etc.

Objective

Streamist is currently focusing on the anime available in their portal and wants to identify the most important factors involved in rating an anime. As a data scientist at Streamist, you are tasked with analyzing the portal's anime data and identifying the important factors by building a predictive model to predict the rating of an anime.

Data Dictionary

Each record in the database provides a description of an anime. A detailed data dictionary can be found below.

title: title of the anime mediaType: format of publication eps: number of episodes (movies are considered 1 episode) duration: duration of an episode in minutes startYr: the year that airing started finishYr: the year that airing finished description: the synopsis of the plot contentWarn: content warning watched: number of users that completed it watching: number of users that are watching it rating: average user rating votes: number of votes that contribute to the rating studio_primary: studios responsible for creation studios_colab: whether there was a collaboration between studios for anime production genre: genre to which the anime belongs

Japan TE: Others: Visiting Film/Anime Settings data was reported at 15.000 Person in Mar 2018. This records an increase from the previous number of 4.000 Person for Dec 2017. Japan TE: Others: Visiting Film/Anime Settings data is updated quarterly, averaging 4.000 Person from Mar 2014 (Median) to Mar 2018, with 17 observations. The data reached an all-time high of 15.000 Person in Mar 2018 and a record low of 1.000 Person in Dec 2016. Japan TE: Others: Visiting Film/Anime Settings data remains active status in CEIC and is reported by Ministry of Land, Infrastructure, Transport and Tourism. The data is categorized under Global Database’s Japan – Table JP.Q032: Tourism and Leisure: Satisfaction Rating Visiting to Japan.