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https://github.com/shivshah19/movie-recommendation-system
This Movie Recommendation System is designed to provide personalized movie recommendations based on user preferences.
https://github.com/shivshah19/movie-recommendation-system
cosine-similarity data-analysis machine-learning pandas python streamlit
Last synced: 27 days ago
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This Movie Recommendation System is designed to provide personalized movie recommendations based on user preferences.
- Host: GitHub
- URL: https://github.com/shivshah19/movie-recommendation-system
- Owner: ShivShah19
- Created: 2024-02-21T07:51:11.000Z (11 months ago)
- Default Branch: main
- Last Pushed: 2024-02-21T08:31:36.000Z (11 months ago)
- Last Synced: 2024-02-22T09:23:41.618Z (11 months ago)
- Topics: cosine-similarity, data-analysis, machine-learning, pandas, python, streamlit
- Language: Jupyter Notebook
- Homepage:
- Size: 2.15 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# Movie Recommendation System
## Overview
This Movie Recommendation System is designed to provide personalized movie recommendations based on user preferences. The system analyzes a dataset of 5000 movies, employing cosine similarity and other techniques to generate accurate recommendations.
## Features
- **Cosine Similarity:** Utilizes cosine similarity to find movies similar to the user's preferences.
- **Interactive Frontend:** Built with Streamlit for an easy-to-use and visually appealing interface.
- **Data Analysis:** Prior to recommendation, the system analyzes a dataset of 5000 movies to extract relevant information.
## Steps
- Step1: First open and execute this Movie_py.ipynb
- step: download the analysed data (or csv file) and store it in the folder name movie-files.
- step3: cd movie-recommendation-system
- Step 4: To run this : streamlit run .\1_🎬_Home.py .py
- Access the application in your browser at [http://localhost:8501](http://localhost:8501).
## Usage
1. Upon running the application, you will be presented with a user-friendly interface.
2. Input your favorite movies, and click on recommendation.
3. There are sections like popular movie and genre wise movie recommendation.
## Technologies Used
- Python
- Cosine Similarity
- Streamlit
## Acknowledgments
- Special thanks to https://www.kaggle.com/ for providing the movie dataset.
- Special thank to https://www.themoviedb.org/ for providing the movie poster api.
## Screenshot
