Flask application for feature analysis involving fraud detection and a recommender system for fraud protection services. Incorporating the use of SOTA machine learning algorithms and deep learning techniques.
- Clone the repository -
git clone - Install the requirements -
pip install -r requirements.txt - Run the application -
python app.py
- Run the application -
python app.py - Navigate to the application's URL -
http://localhost:5000/ - Upload a CSV file -
transactions.csv - Select the target column -
Class - Select the features to analyze -
Amount,Is Fraud, etc. - Click the "Analyze" button -
Analyze
- Fork the repository - ``
- Create a new branch -
git checkout -b new-branch - Commit your changes -
git commit -m "New branch" - Push your changes -
git push origin new-branch - Submit a pull request - ``
- Flask
- Bootstrap
- jQuery
- Plotly
- Pandas
- Scikit-Learn
- NumPy
- SciPy
- Matplotlib
- Seaborn
- Jinja
- Python
- HTML
- CSS
Jason Robinson