animeMatch is a anime recommendation system built using machine learning concepts and deployed with the Streamlit library on Heroku. This project aims to provide personalized anime recommendations based on user preferences, leveraging both collaborative filtering and content-based filtering techniques.

The goal of animeMatch is to recommend animes to users based on their input anime choice. By analyzing anime data from The anime Database (TMDB), including genres, keywords, cast, and crew, the system aims to enhance the viewing experience for users by suggesting relevant and personalized content.

Dataset-> Link to Download dataset

• Source: TMDB
• Data: Consists of information for 5000 animes, including title, genres, keywords, overview, cast, crew, and more.
• Data Cleaning and Preparation: The data is cleaned, null values are handled, and relevant columns are selected for analysis. Textual data is processed using natural language processing techniques.

1. Data Preparation: Exploration of columns, handling null values, and merging datasets.
2. Data Cleaning: Text data is processed, including extracting genres, keywords, cast, and crew information.
3. Feature Extraction: Relevant features are extracted for analysis, and a combined dataset is created.
4. Vectorization: CountVectorizer is used to convert textual data into numeric data for further processing.
5. Similarity Calculation: Cosine similarity is employed to measure the similarity between animes based on their textual descriptions.
6. Model Deployment: The recommendation system is deployed on Heroku using Streamlit, providing users with a user-friendly interface to discover anime recommendations.

• Collaborative filtering and content-based filtering techniques
• User-friendly interface for inputting anime preferences
• Top 10 anime recommendations based on input anime choice
• Integration with TMDB to fetch additional anime information, including posters

Contributions to animeMatch are welcome! Whether it's bug fixes, feature enhancements, or documentation improvements, feel free to open an issue or submit a pull request.