Anime Recommendation System

The Anime Recommendation System is a Python project that uses collaborative filtering to recommend anime to users based on their viewing history and ratings. It utilizes a dataset containing information from 73,516 users on 12,294 anime titles. This README file provides an in-depth overview of the project, including dataset details, requirements, usage instructions, data cleaning, visualization, recommendation system, and more.

Table of Contents

Dataset

Features

Requirements

Usage

Data Cleaning and Preprocessing

Data Visualization

Anime Genre Analysis

Recommendation System

Example Recommendations

Contributing

License

Dataset

The dataset used in this project can be found on Kaggle: Anime Recommendations Database. It consists of two main CSV files: anime.csv and rating.csv.

Features

anime.csv

anime_id: Unique ID for each anime on MyAnimeList.net.

name: Full name of the anime.

genre: Comma-separated list of genres for the anime.

type: Type of anime (e.g., movie, TV, OVA).

episodes: Number of episodes in the show (1 if it's a movie).

rating: Average rating out of 10 for the anime.

members: Number of community members associated with this anime.

rating.csv

user_id: Non-identifiable randomly generated user ID.

anime_id: The anime that the user has rated.

rating: Rating out of 10 assigned by the user (-1 if the user watched it but didn't assign a rating).

Requirements

To run the Anime Recommendation System, you'll need the following libraries and tools:

Python 3.x

Libraries: pandas, numpy, matplotlib, seaborn, scikit-learn, wordcloud, tabulate, tkinter (for file dialog)

You can install these libraries using pip:

pip install pandas numpy matplotlib seaborn scikit-learn wordcloud tabulate

git clone https://github.com/yourus...

cd anime-recommendation-system

python anime_recommendation.py

Data Cleaning and Preprocessing

The script performs data cleaning and preprocessing, handling missing values, duplicates, and text cleaning.

Data Visualization

Explore various data visualizations, including bar plots and histograms, to understand anime popularity and user ratings.

Anime Genre Analysis

Discover the most common anime genres using a word cloud visualization.

Recommendation System

The recommendation system uses collaborative filtering with Nearest Neighbors and cosine similarity to recommend anime titles to users based on their preferences.

Example Recommendations

See an example of how to find anime recommendations for a specific title.