https://github.com/abhishekbagdiya01/movies-recommendation-system

This repository contains the code for a movie recommendation system built using Jupyter Notebook.
https://github.com/abhishekbagdiya01/movies-recommendation-system

aiml jupyter-notebook numpy pandas python scikit-learn

Last synced: about 1 month ago
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This repository contains the code for a movie recommendation system built using Jupyter Notebook.

• Host: GitHub
• URL: https://github.com/abhishekbagdiya01/movies-recommendation-system
• Owner: Abhishekbagdiya01
• Created: 2024-07-17T07:15:58.000Z (7 months ago)
• Default Branch: master
• Last Pushed: 2024-07-17T07:33:40.000Z (7 months ago)
• Last Synced: 2024-11-05T21:48:01.465Z (3 months ago)
• Topics: aiml, jupyter-notebook, numpy, pandas, python, scikit-learn
• Language: Jupyter Notebook
• Homepage:
• Size: 8.68 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
## Movie Recommendation System with Jupyter Notebook

This repository contains the code for a movie recommendation system built using Jupyter Notebook.

# Getting Started-

This project utilizes a conda environment for managing dependencies. Here's how to get started:

    #Clone the repository:

    git clone https://github.com/Abhishekbagdiya01/Movies-Recommendation-System.git

    #Create and activate the conda environment:

    conda create movies_recommendation_system  

    source activate movies_recommendation_system 

    conda install numpy pandas scikit-learn jupyter notebook

    #This will create and activate a conda environment named movies_recommendation_system with the required dependencies installed.

    #Run the Jupyter Notebook:

    jupyter notebook

    #This will open the Jupyter Notebook interface in your web browser. You can then open and run the notebook file (e.g., movie_recommendation.ipynb) to train and use the recommendation system.

# Project Overview

This project implements a movie recommendation system using a content-based filtering approach. Content-based filtering recommends movies to users based on the similarity of the movies' features to the user's preferences.

This project uses the following Python libraries (listed in the conda-env.yml file):

    Pandas

    NumPy

    Scikit-learn 

Make sure you have these libraries installed in your activated conda environment.

This project is licensed under the MIT License.