Content | Installation | Quickstart | Acknowledgements | References | Portfolio

Anomaly Detection in Transactions (Fraud Detection)

End to End Implementing MLOps (Machine Learning Operations) lifecycle from scratch practices

📖 Contents

• Model Overview
  • Introduction
  • Architecture
• Getting Started
  • Installation
  • QuickStart
  • Install Required Packages
  • Prepare the Training Data
  • Models
• Inference and Demo
  • Results
  • Deployment
• Acknowledgements
• Future Plans
• References

🧊 Model Overview

Introduction

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Architecture

🪸 Getting Started

🛡️ Installation

From release:

pip install ano-detection

Alternatively, from source:

git clone https://github.com/tph-kds/anomaly_detection_in_transactions.git

Or using docker container with our image, you can run:

    docker run -p 8000:8000 ano-detection/ano-detection

🔥 Quickstart

This is a small example program you can run to see ano-dection in action!

# Good Luck! And Thank you for your interesting. 

Note

You could also check step by step of this project's workflow such as Data Ingestion, Data Processing, and more... in the tests/integration folder .

Install Required Packages

(It is recommended that the dependencies be installed under the Conda environment.)

pip install -r requirements.txt

or run init_setup.sh file in the project's folder:

<!-- Run this command to give the script execution rights: -->

chmod +x init_setup.sh

<!-- Right now, you can execute the script by typing: -->

bash init_setup.sh

To be detailed requirements on Pypi Website

The required supportive environment uses hardware accelerator GPUs such as T4 of Colab, GPU A100, etc. as well as local CPU for machine-learning models

Prepare the Training Data

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Models

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✌️ Acknowledgements

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• Logo is generated by @tranphihung

⭐ Future Plans

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Stay tuned for future releases as we are continuously working on improving the model, expanding the dataset, and adding new features.

Thank you for your interest in my project. We hope you find it useful. If you have any questions, please feel free and don't hesitate to contact me at tranphihung8383@gmail.com

References

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Contribute to it🌱

To make contribution in this project:

• Clone the repository.
• Fork the repository.
• Make changes.
• Create a Pull request.
• Also, publish an issue!

Have a nice day, Good Luck! And Thank you for your interesting.