https://github.com/akashnegi-github/movie-recommendation-system

https://github.com/akashnegi-github/movie-recommendation-system

Last synced: about 1 month ago
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• Host: GitHub
• URL: https://github.com/akashnegi-github/movie-recommendation-system
• Owner: AKASHNEGI-github
• Created: 2024-08-06T07:10:54.000Z (almost 2 years ago)
• Default Branch: main
• Last Pushed: 2024-08-11T06:04:21.000Z (almost 2 years ago)
• Last Synced: 2025-01-26T08:17:47.300Z (over 1 year ago)
• Language: Jupyter Notebook
• Size: 13.7 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

          
# Movie Recommendation System

![Screenshot (310)](https://github.com/user-attachments/assets/e3e53e5c-428f-4b99-a349-3e513363d308)

- Movie Recommendation System

The Movie Recommendation System is a content-based recommendation engine developed using Python and machine learning techniques. The system leverages Natural Language Processing (NLP) to analyze and process movie descriptions. The core NLP techniques used include text vectorization via Bag of Words and word stemming using the Porter Stemmer algorithm. Cosine similarity is applied to measure the similarity between movie descriptions, allowing the system to recommend movies that are closely related in content to a user's preference.

The system was designed to handle a dataset of 5,000 movies, transforming the processed data into a serialized pickle file for efficient retrieval and display on the web interface. The frontend of the application is built using Streamlit, providing an interactive and user-friendly platform for users to receive movie recommendations based on their selected preferences.

This project demonstrates the integration of machine learning, NLP, and web development to create a practical, real-world application.