https://github.com/tss-sniper/movie-recommendation

An effective movie recommendation system that can provide personalized movie recommendations using user-based collaborative filtering.
https://github.com/tss-sniper/movie-recommendation

collaborative-filtering gradio-interface jupyter-notebook movie-recommendation python

Last synced: 2 days ago
JSON representation

An effective movie recommendation system that can provide personalized movie recommendations using user-based collaborative filtering.

• Host: GitHub
• URL: https://github.com/tss-sniper/movie-recommendation
• Owner: TSS-sniper
• Created: 2023-08-15T19:04:20.000Z (over 1 year ago)
• Default Branch: main
• Last Pushed: 2023-09-06T22:31:53.000Z (over 1 year ago)
• Last Synced: 2025-01-05T06:43:50.017Z (2 days ago)
• Topics: collaborative-filtering, gradio-interface, jupyter-notebook, movie-recommendation, python
• Language: Jupyter Notebook
• Homepage:
• Size: 2.14 MB
• Stars: 2
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

Awesome Lists containing this project

README

        
# Movie Recommendation System

### Introduction:

* The goal of this project is to develop an effective movie recommendation system that can provide personalized movie recommendations to the user. This system will leverage datasets containing information about movies and their ratings as provided by other users.

* By analyzing these datasets and employing User-based Collaborative Filtering, we aim to enhance the user experience.

### Methodology involved:

1. Loading the Datasets.

2. Cleaning the Data (Removing Punctuations using Regular expression Library).

3. Search for the movies which are rated more than 4 by other users who have also watched the same movie as our user.

4. Give Movie Recommendations to the user and display it on the gradio interface.

### Tools and Technologies involved:

* Pandas Library

* Numpy Library

* re (Regular Expression) Library

* Gradio

* scikit-learn library

* Jupyter Notebook

* Collaborative Filtering

### Dataset Link:

* Download Datasets (i.e. movies.csv and ratings.csv) from [MovieLens_Dataset.zip](https://files.grouplens.org/datasets/movielens/ml-25m.zip) :trollface:.