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https://github.com/aymen016/film-recommendation-engine
A machine learning-powered movie recommender system designed to provide personalized recommendations based on user preferences and data analysis. This project includes a backend recommendation engine, a Streamlit-based interface, and a web-based frontend for an enhanced user experience.
https://github.com/aymen016/film-recommendation-engine
flask numpy pandas pickle python scikit-learn streamlit
Last synced: 21 days ago
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A machine learning-powered movie recommender system designed to provide personalized recommendations based on user preferences and data analysis. This project includes a backend recommendation engine, a Streamlit-based interface, and a web-based frontend for an enhanced user experience.
- Host: GitHub
- URL: https://github.com/aymen016/film-recommendation-engine
- Owner: Aymen016
- License: mit
- Created: 2024-12-06T16:48:53.000Z (about 1 month ago)
- Default Branch: main
- Last Pushed: 2024-12-25T11:01:44.000Z (22 days ago)
- Last Synced: 2024-12-25T11:28:31.019Z (22 days ago)
- Topics: flask, numpy, pandas, pickle, python, scikit-learn, streamlit
- Language: Jupyter Notebook
- Homepage:
- Size: 31.2 MB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# 🎥 Movie Recommender System
A machine learning-powered system for recommending movies based on user preferences and data analysis. This repository includes both a backend recommendation engine and a frontend interface for user interaction.
---
## ✨ Features
- 🔮 **Recommendation Engine**: Uses machine learning models to provide personalized movie recommendations.
- 🖥️ **Streamlit Application**: A user-friendly interface for interacting with the recommendation system.
- 🌐 **Frontend**: Includes the web-based frontend to provide a better user experience.
---
## 📂 Project Structure
- `frontend/` - Frontend application code
- `streamlit-app/` - Streamlit application files
- `.gitattributes` - Git LFS tracking for large files
- `Recommendation System.ipynb` - Jupyter notebook for the recommendation system
- `app.py` - Main file for running the application
- `dataset.csv` - Dataset used for building recommendations
- `main.py` - Core backend logic
- `movies_list.pkl` - Serialized movie list (Pickle format)
## 🛠️ Prerequisites
To run this project, ensure you have the following installed:
- 🐍 **Python 3.8+**
- 🧰 **Streamlit**
- 📦 **Required Python libraries** (listed in `requirements.txt`)
---
## 🚀 Installation
1. **Clone this repository**:
```bash
git clone https://github.com/Aymen016/Film-recommendation-engine.git
cd Film-recommendation-engine
```
2. **Install dependencies**:
```bash
pip install -r requirements.txt
```
3. **Set up Git LFS** for handling large files (if not already done):
```bash
git lfs install
```
## 🏃 Usage
### Run the Backend:
Start the backend logic by executing:
```bash
python main.py
```
### Run the Streamlit App:
Launch the Streamlit interface:
```bash
streamlit run app.py
```
### Access the Frontend:
Use the files in the `frontend/` folder to set up and serve the web-based frontend.
---
## 📊 Dataset
- 📁 The dataset (`dataset.csv`) is used for building movie recommendations.
- 🗂️ `movies_list.pkl` contains a preprocessed list of movies for faster recommendations.
---
## 🤝 Contributing
Contributions are welcome! Please follow these steps:
1. 🍴 Fork the repository.
2. 🌱 Create a new branch for your feature:
```bash
git checkout -b feature-name
```
3. ✍️ Commit your changes:
```bash
git commit -m "Add feature-name"
```
4. 🚀 Push to the branch:
```bash
git push origin feature-name
```
5. 🛠️ Create a pull request.
---
## 📜 License
This project is licensed under the MIT License - see the [LICENSE](./LICENSE) file for details.
---
## 👨💻 About the Author
**Aymen Baig**
A passionate developer and aspiring Data Scientist specializing in Machine Learning and Natural Language Processing. Experienced in building lightweight and efficient chatbot systems for small businesses. Always open to collaborations and learning new technologies.
- **GitHub**: [Aymen Baig](https://github.com/Aymen016/)
- **LinkedIn**: [Aymen Baig](https://www.linkedin.com/in/aymen-baig-700a06284/)