Fraud Detection Project

Overview

This project aims to detect fraudulent transactions using machine learning techniques. It includes data preprocessing, feature engineering, model training, explainability, and deployment using Flask and Dash.

Folder Structure

• data/: Contains dataset files.
• notebooks/: Jupyter notebooks for EDA and preprocessing.
• src/: Source code for data processing, model training, and logging.
• app/: Flask application and Dockerfile for deployment.
• dashboard/: Dash app for visualizing fraud insights.
• mlflow/: MLflow configuration for experiment tracking.
• tests/: Unit tests for the project.
• logs/: Log files for monitoring the application.

Setup

1. Clone the repository.
2. Install the required packages listed in requirements.txt.
3. Set up the environment and run the Flask app.
4. Access the MLflow UI and Dash dashboard.

Running the Application

To run the application, execute the following command: