https://github.com/vamshikrishna779/cine-match
AI-powered movie recommendations using collaborative and content-based filtering. Built with Python and Streamlit.
https://github.com/vamshikrishna779/cine-match
html-css-javascript jupyter-notebook python streamlit
Last synced: 2 months ago
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AI-powered movie recommendations using collaborative and content-based filtering. Built with Python and Streamlit.
- Host: GitHub
- URL: https://github.com/vamshikrishna779/cine-match
- Owner: Vamshikrishna779
- Created: 2025-02-15T09:37:01.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2025-02-28T17:29:41.000Z (over 1 year ago)
- Last Synced: 2025-02-28T21:55:56.321Z (over 1 year ago)
- Topics: html-css-javascript, jupyter-notebook, python, streamlit
- Language: Jupyter Notebook
- Homepage:
- Size: 54.7 KB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# Cine Match - Movie Recommender Application
## Overview
The Cine Match is a machine learning-based system that provides personalized movie recommendations based on user preferences and interactions. It employs collaborative and content-based filtering techniques to enhance the user experience on movie platforms. Built using Python and Streamlit, this project processes movie metadata and user behavior to generate relevant suggestions.
## Features
- Search and browse movies from the dataset
- Generate personalized movie recommendations
- Machine learning-based filtering techniques
- Fast performance with precomputed similarity matrices
- Interactive web interface using Streamlit
## Technologies Used
- Python
- Streamlit
- Scikit-learn
- Pandas
- NumPy
- Pickle (for model persistence)
## Project Structure
```
├── .ipynb_checkpoints # Jupyter notebook checkpoints
├── .venv # Virtual environment (optional)
├── screenshots # Folder for UI screenshots
├── template # Template files
├── app.py # Main application
├── movie_dict.pkl # Serialized movie dataset
├── similarity.pkl # Serialized similarity matrix
├── model.pkl # Serialized recommendation model
├── requirements.txt # Dependencies
├── BDTProject # Additional project files
├── README.md # Project documentation
```
## Installation and Setup
1. Clone the repository:
```sh
git clone https://github.com/your-username/movie-recommender.git
cd movie-recommender
```
2. Create a virtual environment (optional but recommended):
```sh
python -m venv venv
source venv/bin/activate # On Windows use `venv\Scripts\activate`
```
3. Install dependencies:
```sh
pip install -r requirements.txt
```
4. Run the Streamlit app:
```sh
streamlit run app.py
```
5. Open the browser and go to **http://localhost:8501/** to access the app.
## User Interface
### Home Page
Displays an introduction and an overview of the app.
### Movies Page
- Allows users to browse and search for movies.
- Retrieves metadata and details for selected movies.
### Recommendations Page
- Users can select a movie they like.
- The system generates and displays recommendations based on machine learning models.
## Results and Evaluation
- High recommendation accuracy based on user interactions and preferences.
- Optimized performance using precomputed similarity matrices.
- Positive user feedback on relevant and engaging recommendations.
## Contact
For any queries, feedback, or collaboration opportunities, reach out to:
- GitHub: [Vamshikrishna779](https://github.com/Vamshikrishna779)
## License
This project is licensed under the MIT License.