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Simple Anime Recommendation system based on user input of anime category. Used Anime Recommendations Database from Kaggle used as main dataset and built recommendation system using Scikit-learn.

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ZacharySeymour/AnimeRecommender

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AnimeRecommender

Anime Recommendation System coded with Python in Jupyter Notebook. The recommendation system asks the user for a category of anime they want to watch and then recommends the top 10 anime in the selected category based on user ratings. Dataset taken from Kaggle, link can be found in the anime.csv file of this repository or at bottom of this README file.

How to run:

Here are the steps to run Jupyter notebook cells in command line:

  1. Open the Jupyter notebook you want to run in command line.

  2. In the Jupyter notebook interface, click on the File menu and select Download as > Notebook (.ipynb).

  3. Save the notebook file in a directory of your choice.

  4. Open a command line interface (CLI) and navigate to the directory where you saved the notebook file.

  5. Run the following command:

      - jupyter nbconvert --to script my_notebook.ipynb
    
  6. Replace my_notebook.ipynb with the name of your notebook file. This command will convert the notebook to a Python script.

  7. After the conversion is complete, you can run the Python script using the following command:

     - python my_notebook.py
    
  8. Replace my_notebook.py with the name of the Python script generated by nbconvert.

  9. The Python script will execute the code cells in the Jupyter notebook in the order they appear in the notebook.

Data:

Includes Top Twitch games from 2016 - 2023 and associated metrics.

You can download the dataset from here:

Source: https://www.kaggle.com/datasets/rankirsh/evolution-of-top-games-on-twitch?select=Twitch_game_data.csv

License

This project is licensed under the MIT License - see the LICENSE.md file for details.

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Simple Anime Recommendation system based on user input of anime category. Used Anime Recommendations Database from Kaggle used as main dataset and built recommendation system using Scikit-learn.

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