Recommendation Systems

This repository contains Jupyter notebooks for three recommendation systems: Anime Recommendation System, Manga Recommendation System, and Movies Recommendation System. These systems are built using the K-Nearest Neighbors (KNN) algorithm and include data cleaning, preprocessing, and feature extraction steps.

Table of Contents

• Introduction
• Repository Structure
• Usage
• Contributing

Introduction

Recommendation systems are widely used in various domains to provide personalized recommendations to users based on their preferences and behaviors. This repository focuses on building recommendation systems for anime, manga, and movies using the KNN algorithm.

The KNN algorithm is a simple but effective method for collaborative filtering, which is a common approach for building recommendation systems. It predicts the preferences of a user by finding similar users or items based on their features or ratings.Here I have build Recommendation system of three famous domains of entertainment namely for Animes, mangas and movies.

Repository Structure

The repository is organized as follows:

├── Anime Recommendation System.ipynb
├── Manga Recommendation System.ipynb
├── Movies Recommendation System.ipynb
└── README.md

• Anime Recommendation System.ipynb: Collab notebook containing the code and documentation for the Anime Recommendation System.
• Manga Recommendation System.ipynb: Collab notebook containing the code and documentation for the Manga Recommendation System.
• Movies Recommendation System.ipynb: Collab notebook containing the code and documentation for the Movies Recommendation System.
• README.md: This file, providing an overview and instructions for the repository.

Usage

To use the recommendation systems in this repository, follow these steps:

1. Clone the repository to your local machine:

git clone https://github.com/shivang2607/Recommendation-System-Machine-Learning-.git

2. Open the desired notebook (Anime Recommendation System.ipynb, Manga Recommendation System.ipynb, or Movies Recommendation System.ipynb) using Jupyter Notebook or JupyterLab or google Collab.
3. Execute the code cells in the notebook to run the recommendation system.

Contributing

Contributions to this repository are welcome. If you have any ideas, suggestions, or improvements, please open an issue or submit a pull request.