Advance Anime Recommendation System

[Niraj1608]

Deep Learning Simplified Repository (Proposing new issue)

🔴 Project Title :Advance Anime Recommendation System

🔴 Aim :This project implements an Anime Recommendation System using both content-based and collaborative filtering techniques. It provides recommendations based on user preferences and anime content, leveraging deep learning for enhanced collaborative filtering.

🔴 Dataset : The dataset is available on Kaggle:

• Anime.csv and Rating.csv - Kaggle Dataset
  

🔴 Approach : using Nlp and DL techniques

Features

• Data Preprocessing: Cleans and preprocesses anime.csv and rating.csv datasets.
• Exploratory Data Analysis (EDA): Provides an overview of anime types, genres, ratings, and other insights.
• Content-Based Filtering: Generates recommendations based on anime genres, type, and average ratings.
• Collaborative Filtering: A deep learning-based recommendation model that learns user preferences.
• Genre-Based Recommendations: Ensures diverse recommendations by including various genres in top recommendations.
• New User and New Anime Recommendations: Customized recommendations for new users and newly added anime.

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📍 Follow the Guidelines to Contribute in the Project :

• You need to create a separate folder named as the Project Title.
• Inside that folder, there will be four main components.
  • Images - To store the required images.
  • Dataset - To store the dataset or, information/source about the dataset.
  • Model - To store the machine learning model you've created using the dataset.
  • requirements.txt - This file will contain the required packages/libraries to run the project in other machines.
• Inside the Model folder, the README.md file must be filled up properly, with proper visualizations and conclusions.

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🔴🟡 Points to Note :

• The issues will be assigned on a first come first serve basis, 1 Issue == 1 PR.
• "Issue Title" and "PR Title should be the same. Include issue number along with it.
• Follow Contributing Guidelines & Code of Conduct before start Contributing.

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✅ To be Mentioned while taking the issue :

• Full name : Parmar Niraj

• GitHub Profile Link : @Niraj1608

• Email ID : [email protected]

• Participant ID (if applicable):GSSOC

• What is your participant role? (Mention the Open Source program)GSSOC

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Happy Contributing 🚀

All the best. Enjoy your open source journey ahead. 😎

[github-actions]

Thank you for creating this issue! We'll look into it as soon as possible. Your contributions are highly appreciated! 😊

[Niraj1608]

@abhisheks008 assign me this issue ready to raise a pr

[abhisheks008]

Assigning this issue to you, @Niraj1608. Make sure you complete the previous PR first before pushing this PR.

[github-actions]

Hello @Niraj1608! Your issue #981 has been closed. Thank you for your contribution!