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

This Movie Recommendation System is a Python-based application that leverages machine learning and natural language processing techniques to provide movie recommendations based on user preferences and movie characteristics.
https://github.com/thakurdiwakar/movie-recommendation-system

imdb-dataset jupyter-notebook machine-learning movies natural-language-processing pandas pycharm-ide pyhton streamlit

Last synced: 4 months ago
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This Movie Recommendation System is a Python-based application that leverages machine learning and natural language processing techniques to provide movie recommendations based on user preferences and movie characteristics.

• Host: GitHub
• URL: https://github.com/thakurdiwakar/movie-recommendation-system
• Owner: thakurdiwakar
• Created: 2023-10-07T03:58:48.000Z (over 1 year ago)
• Default Branch: main
• Last Pushed: 2023-10-07T04:09:12.000Z (over 1 year ago)
• Last Synced: 2024-10-09T19:02:24.052Z (4 months ago)
• Topics: imdb-dataset, jupyter-notebook, machine-learning, movies, natural-language-processing, pandas, pycharm-ide, pyhton, streamlit
• Language: Jupyter Notebook
• Homepage: https://movie-recommendation-system9-b468a0e34741.herokuapp.com/
• Size: 13.7 KB
• Stars: 5
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# 🎬 Movie Recommendation System

This Movie Recommendation System is a Python-based application that leverages machine learning and natural language processing techniques to provide movie recommendations based on user preferences and movie characteristics.

### 🔥 Key Features:

- Content-Based Recommendation: Recommends movies similar to the one you select, based on movie details such as genre, keywords, cast, and crew.

- Interactive User Interface: Use the Streamlit web app to search for movies, rate them, and receive personalized recommendations.

- Data Analysis: Explore movie data, including details like budget, genres, and keywords, to better understand the dataset.

### 🛠️ Technologies Used:

- Python

- Pandas

- NumPy

- Scikit-learn

- Streamlit

- IMDbPY

- The Movie Database (TMDb) API

### 📦 Installation:

To run the Movie Recommendation System on your local machine, follow these steps:

1. Clone this repository.

2. Install the required libraries using `pip install -r requirements.txt`.

3. Run the Streamlit app with `streamlit run app.py`.

### 🤝 Contributing:

We welcome contributions from the open-source community! 

### 📧 Contact:

For questions or feedback, you can reach out to us at [[email protected]].

### 🙏 Acknowledgments:

We extend our gratitude to the creators of the IMDbPY library and The Movie Database (TMDb) API for their invaluable data and resources.