6a78a69e2ef0b4a2fe9ffbdda1c6a7b29b2d34690a453ea25db8c3bcdcca28842

**1. Anime-Recommendations-Database on Kaggle:** This dataset focuses on user ratings and preferences for over 12,000 anime, making it perfect for analyzing viewing patterns and building recommendation systems. It includes: * **User IDs and ratings:** This allows you to see how different users rate the same anime and identify trends in preferences. * **Anime information:** Each anime entry has details like title, genre, release year, and studio, enabling you to connect user preferences to specific anime characteristics. * **Temporal data:** The dataset includes timestamps for user ratings, allowing you to analyze how anime popularity and preferences change over time. **2. MyAnimeList Dataset on Kaggle:** This dataset combines information on around 12,000 anime titles with user reviews and profiles from MyAnimeList. It's a great resource for understanding not only anime metadata but also user sentiment and opinions. You can find: * **Anime details:** Similar to the Anime-Recommendations-Database, this dataset includes information like title, genre, studio, and episode count. * **User reviews:** Read what other anime fans have to say about their favorite shows and gain insights into the overall reception of different anime. * **User profiles:** Explore user demographics, preferences, and watchlists to understand the diverse anime fandom. **3. Anime Face Dataset on Kaggle:** This dataset is a bit different, focusing on images of anime faces instead of textual data. It contains over 63,000 high-quality anime faces, making it valuable for tasks like: * **Training machine learning models for facial recognition or image classification.** * **Analyzing anime art styles and character design trends.** * **Creating creative projects like anime face generators or deepfakes.** **4. Between Our Worlds datasets on Data.world:** These datasets explore the cultural impact of anime, offering information beyond the shows themselves. You can find data on: * **Anime references in video games, music, and other media:** This can help you understand how anime has influenced popular culture and spread across different forms of entertainment. * **Social media trends and discussions about anime:** Analyze online conversations to see how fans engage with anime and what aspects they find most interesting. * **Real-world events and activities inspired by anime:** Discover how anime has inspired cosplay communities, fan conventions, and even tourism to places featured in anime. These are just a few examples, and there are many other great anime datasets available online. Remember to consider your specific needs and interests when choosing a dataset to explore the fascinating world of anime!

6a78a69e2ef0b4a2fe9ffbdda1c6a7b29b2d34690a453ea25db8c3bcdcca28841

**General Anime Information:** * **Anime Dataset 2023 on Kaggle (dbdmobile):** This comprehensive dataset includes details on over 17,000 anime titles, covering aspects like release dates, genres, studios, ratings, and popularity. It also provides user data from a popular anime platform, allowing you to analyze preferences and trends. * **Anime DataSet 2022 on Kaggle (vishalmane10):** This dataset contains information on roughly 18,500 anime from Anime Planet, with details like titles, studios, genres, episodes, and release years. It's suitable for basic exploration of anime characteristics and trends. * **Anime Dataset with Reviews from MyAnimeList on Kaggle (marlesson):** This dataset combines information on around 12,000 anime titles with user reviews and profiles from MyAnimeList. It allows you to not only explore anime metadata but also understand user sentiment and opinions. **Specific areas of interest:** * **Anime Recommendations Database on Kaggle (CooperUnion):** This dataset focuses on user ratings and preferences for over 12,000 anime, enabling you to analyze viewing patterns and build recommendation systems. * **Between Our Worlds datasets on Data.world:** These datasets offer information on the cultural impact of anime, including anime references in video games, music, and other media. **Considerations:** * **Data size and format:** Choose a dataset with the appropriate size and format for your needs. Some datasets are massive, while others are smaller and easier to manipulate. * **Data source and methodology:** Consider the source and methodology used to collect the data. This can impact the accuracy and reliability of the information. * **Specific focus:** Choose a dataset that aligns with your specific research or analysis goals. Some datasets are broad, while others focus on particular aspects of anime, like recommendations or cultural impact. I hope this helps! Let me know if you have any questions about specific datasets or need further recommendations based on your interests.