https://github.com/tph-kds/anomaly_detection_in_transactions
This project aims to detect fraudulent transactions by leveraging machine learning-based anomaly detection techniques, and to develop an automated system that can monitor transactions in real-time, identify anomalies, and flag potential fraudulent transactions for further investigation.
https://github.com/tph-kds/anomaly_detection_in_transactions
anomaly-detection deep-learning machine-learning mlops mlops-community mlops-project tutorials
Last synced: 4 months ago
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This project aims to detect fraudulent transactions by leveraging machine learning-based anomaly detection techniques, and to develop an automated system that can monitor transactions in real-time, identify anomalies, and flag potential fraudulent transactions for further investigation.
- Host: GitHub
- URL: https://github.com/tph-kds/anomaly_detection_in_transactions
- Owner: tph-kds
- License: apache-2.0
- Created: 2024-10-17T13:06:22.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2024-11-08T16:41:27.000Z (over 1 year ago)
- Last Synced: 2025-04-15T20:01:50.683Z (about 1 year ago)
- Topics: anomaly-detection, deep-learning, machine-learning, mlops, mlops-community, mlops-project, tutorials
- Language: Jupyter Notebook
- Homepage:
- Size: 6.77 MB
- Stars: 3
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
- Security: SECURITY.md
Awesome Lists containing this project
README
Content |
Installation |
Quickstart |
Acknowledgements |
References |
Portfolio
Anomaly Detection in Transactions (Fraud Detection)
#
End to End Implementing MLOps (Machine Learning Operations) lifecycle from scratch practices
## :book: Contents
* [Model Overview](#-model-overview)
* [Introduction](#introduction)
* [Architecture](#architecture)
* [Getting Started](#-getting-started)
* [Installation](#shield-installation)
* [QuickStart](#fire-quickstart)
* [Install Required Packages](#install-required-packages)
* [Prepare the Training Data](#prepare-the-training-data)
* [Models](#models)
* [Inference and Demo](#infer-and-demo)
* [Results](#results)
* [Deployment](#deployment)
* [Acknowledgements](#v-acknowledgements)
* [Future Plans](#star-future-plans)
* [References](#references)
## 🧊 Model Overview
### Introduction
---
### Architecture
## 🪸 Getting Started
### :shield: Installation
From release:
```bash
pip install ano-detection
```
Alternatively, from source:
```bash
git clone https://github.com/tph-kds/anomaly_detection_in_transactions.git
```
Or using docker container with our image, you can run:
``` bash
docker run -p 8000:8000 ano-detection/ano-detection
```
### :fire: Quickstart
This is a small example program you can run to see `ano-dection` in action!
```python
# 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:
```
chmod +x init_setup.sh
bash init_setup.sh
```
To be detailed requirements on [Pypi Website](https://pypi.org/project/trim-rag/)
**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
---
### Models
---
![]()
## :v: Acknowledgements
---
- Logo is generated by [@tranphihung](https://github.com/tph-kds)
## :star: Future Plans
-----
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](tranphihung8383@gmail.com)
## References
-----
## 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. ``