Fraud Detection System

Overview

This project aims to develop robust and accurate fraud detection models. he models are designed to enhance the detection of fraudulent transactions for e-commerce and bank credit transactions. The solution leverages machine learning models, geolocation analysis, and transaction pattern recognition to improve fraud detection accuracy.

Project Goals

1. Data Analysis and Preprocessing
2. Tracking file changes using DVC
3. Model Building and Training
4. Model Explainability analysis
5. Model Deployment and API Development
6. Build a Dashboard with Flask and Dash

Getting Started

Prerequisites

Make sure you have the following installed:

• Python 3.x
• Pip (Python package manager)

Installation

Clone the repository:

git clone https://github.com/Yosef-ft/FraudDetection.git
cd FraudDetection

Create a virtual environment and activate it:

python -m venv venv
source venv/bin/activate  # On Windows use `venv\Scripts\activate`

Install the required packages:

pip install -r requirements.txt

Viewing the Dashboard

To view the dashboard, follow these steps:

1. Run Notebooks: Run all the Jupyter notebooks in the repository.
2. Create a Report Folder: In the Flask_Dash directory, create a folder named report.
3. Download Experiment Plots from MLflow: Inside MLflow, download the experiment plots. Save them to the Flask_Dash/report directory.

Run Docker Compose: Use the following command to build and start the Docker containers:

docker compose up --build