andugu / credit-fraud

E2E implementation of a credit fraud detector with DVC, BentoML & XGBoost for Kaggles Credit Fraud Dataset

💳 Credit Fraud Detection

This project implements a comprehensive solution for detecting credit card fraud using machine learning. It leverages the Kaggle Credit Card Fraud Dataset to build an end-to-end pipeline for fraud detection.

Key Features

• Data Version Control (DVC): We use DVC to efficiently manage and version control our dataset and model files. This ensures reproducibility and easy collaboration.
• Serving with BentoML: Our model is deployed using BentoML, a powerful framework for serving machine learning models. This allows for seamless integration into production environments.
• XGBoost Binary Classifier: The heart of our fraud detection system is a binary classifier built using the XGBoost native library, known for its high performance and accuracy.

Getting Started

To run this project, follow these simple steps:

1. Clone the repository:

   git clone https://github.com/andugu/credit-fraud.git
   

2. Install the required dependencies:

   pip install -r requirements.txt
   

3. Use DVC to reproduce the pipeline:

   dvc repro
   

4. Start serving the model on the default port (8000) of localhost:

   bentoml serve bentoml/FraudClassifier --port 8000
   

   To run the service on a different port, simply replace 8000 with the desired port.

Project Workflow

The DAG of the project is as follows:

+---------+    
| Prepare |    
+---------+    
     |         
     |         
     |         
+----------------+ 
| Feature Engine | 
+----------------+ 
     |         
     |         
     |       
 +---------+ 
 |  Train  |
 +---------+
     |         
     |         
     |       
 +--------+      
 |  Pack  |      
 +--------+      
     |         
     |         
     |         
+---------+      
|  Serve  |      
+---------+