https://github.com/alihassanml/movie-recommendation-system
This project implements a movie recommendation system using text-based similarity measures. It processes a dataset of movies, computes similarity scores using TF-IDF and cosine similarity, and provides recommendations based on movie titles.
https://github.com/alihassanml/movie-recommendation-system
Last synced: about 1 month ago
JSON representation
This project implements a movie recommendation system using text-based similarity measures. It processes a dataset of movies, computes similarity scores using TF-IDF and cosine similarity, and provides recommendations based on movie titles.
- Host: GitHub
- URL: https://github.com/alihassanml/movie-recommendation-system
- Owner: alihassanml
- License: mit
- Created: 2024-08-22T12:56:11.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2024-08-22T16:23:38.000Z (about 1 year ago)
- Last Synced: 2024-11-08T17:16:01.331Z (11 months ago)
- Language: Jupyter Notebook
- Size: 11.8 MB
- Stars: 2
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# Movie Recommendation System
This project implements a movie recommendation system using content-based filtering. It processes a dataset of movies, computes similarity scores using TF-IDF vectorization and cosine similarity, and provides recommendations based on a given movie title.
## Features
- **Data Processing**: Cleans and preprocesses movie data, including titles, overviews, and genres.
- **TF-IDF Vectorization**: Converts text data into numerical features.
- **Cosine Similarity**: Computes similarity scores between movies.
- **Recommendation System**: Provides movie recommendations based on similarity scores.
- **Streamlit App**: Interactive web app where users can input a movie title and receive recommendations.
## Installation
### Requirements
- Python 3.x
- Pandas
- NumPy
- Scikit-learn
- NLTK
- Streamlit
Install the required packages using:
```bash
pip install -r requirements.txt
```
## Usage
### Running the Streamlit App
To run the Streamlit app:
```bash
streamlit run app.py
```
The app will launch in your default web browser. You can enter a movie title to get recommendations.
### Example
To use the recommendation function directly:
```python
import pickle
import pandas as pd
# Load pickled files
with open('average_similarity.pkl', 'rb') as f:
average_similarity = pickle.load(f)
with open('indices.pkl', 'rb') as f:
indices = pickle.load(f)
combined_data = pd.read_pickle('combined_data.pkl')
# Get recommendations
recommendations = recommend_from_combined_similarity('The Matrix', combined_data, indices, average_similarity)
print(recommendations)
```
## Data
The dataset used is `movies_metadata.csv`, which contains information about movies, including titles, overviews, and genres.
## Pickling
The model, vectorizer, and similarity matrices are saved as pickle files for efficient reuse.
## Repository Structure
```plaintext
Movie-Recommendation-System/
│
├── app.py # Streamlit app
├── requirements.txt # Python dependencies
├── average_similarity.pkl # Pickled similarity matrix
├── indices.pkl # Pickled indices dictionary
├── combined_data.pkl # Pickled combined data DataFrame
└── README.md # Project documentation
```
## Contributing
Contributions are welcome! Please fork the repository and submit a pull request.
## License
This project is licensed under the MIT License.
## Contact
For any questions, please contact [alihassanml](https://github.com/alihassanml).
```
This `README.md` provides an overview of the project, instructions on setting up the environment, and how to use the app or the recommendation system directly.