Ecosyste.ms: Awesome
An open API service indexing awesome lists of open source software.
https://github.com/karapetianash/recsystembackend
Recommender system backend.
https://github.com/karapetianash/recsystembackend
cosine-similarity csv ml recommender-system sqlie3
Last synced: about 2 months ago
JSON representation
Recommender system backend.
- Host: GitHub
- URL: https://github.com/karapetianash/recsystembackend
- Owner: karapetianash
- Created: 2024-06-13T19:31:50.000Z (7 months ago)
- Default Branch: main
- Last Pushed: 2024-06-19T13:28:21.000Z (7 months ago)
- Last Synced: 2024-06-19T23:49:51.815Z (7 months ago)
- Topics: cosine-similarity, csv, ml, recommender-system, sqlie3
- Language: Python
- Homepage:
- Size: 24.4 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# Movie Recommendation System - Python Part
This part of the project handles data preprocessing, model training, recommendation generation, and evaluation.
## Table of Contents
- [Overview](#overview)
- [Structure](#structure)
- [Setup](#setup)
- [Usage](#usage)
- [License](#license)
## Overview
The Python part of the project reads movie and ratings data, preprocesses it, splits it into training and test sets, trains a collaborative filtering model, generates recommendations, and evaluates the model's performance.
## Structure
python/
├── Dockerfile
├── requirements.txt
├── preprocess.py
├── main.py
├── database.py
├── similarity.py
├── recommendations.py
└── tests/
├── test_database.py
├── test_integration.py
├── test_preprocess.py
├── test_recommendations.py
└── test_similarity.py
## Setup
### Prerequisites
- Python 3.x
- Install dependencies using `requirements.txt`:
```bash
pip install -r requirements.txt
```
## Usage
### Data Preprocessing
Run the preprocessing script to preprocess the data and split it into training and test sets:
```bash
python preprocess.py
```
### Model Training and Recommendation Generation
Run the main script to train the model, generate recommendations, and evaluate the model:
```bash
python main.py
```
### Running Tests
```bash
pytest -s tests
```
## License
This project is licensed under the MIT License.