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https://github.com/touradbaba/fraud_det_app_mlops
Fraud Detection App integrating MLOps for seamless deployment and continuous enhancement.
https://github.com/touradbaba/fraud_det_app_mlops
azure ci-cd cicd data-science flask github-actions machine-learning mlflow mlops
Last synced: about 1 month ago
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Fraud Detection App integrating MLOps for seamless deployment and continuous enhancement.
- Host: GitHub
- URL: https://github.com/touradbaba/fraud_det_app_mlops
- Owner: TouradBaba
- License: mit
- Created: 2024-02-12T13:37:28.000Z (11 months ago)
- Default Branch: main
- Last Pushed: 2024-11-16T18:26:17.000Z (about 1 month ago)
- Last Synced: 2024-11-16T19:28:15.701Z (about 1 month ago)
- Topics: azure, ci-cd, cicd, data-science, flask, github-actions, machine-learning, mlflow, mlops
- Language: Python
- Homepage: https://frauddetectionmodel.azurewebsites.net/
- Size: 341 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# Fraud Detection App
This repository contains a Fraud Detection App, This application utilizes machine learning to predict potentially fraudulent activities in financial transactions. Whether you're accessing the app on the cloud or locally, Follow the guide below to navigate through the setup proces.
**Key Features:**
- **Predictive Power:** Machine learning algorithm to identify potential fraudulent activities in financial transactions.
- **Cloud Accessibility:** Access the app through the cloud using the URL: [https://frauddetectionmodel.azurewebsites.net](https://frauddetectionmodel.azurewebsites.net).
- **Local Deployment:** If preferred, set up and use the app locally by following the installation and usage instructions provided in this guide.
**Enhancements and Continuous Improvement:**
- **CI/CD Integration:** Continuous Integration/Continuous Deployment (CI/CD) pipeline.
- **Workflow for Data Drift Monitoring:** Workflow for monitoring data drift, which triggers model retraining and deployment in response to changes in the dataset, ensuring the app's accuracy over time.
- **Monthly Retraining:** The app undergoes monthly retraining, enhancing its predictive capabilities.
Follow the comprehensive guide below to navigate through the setup process.
## Table of Contents
1. [Prerequisites](#prerequisites)
2. [Installation](#installation)
3. [Usage on the Cloud](#usage-on-the-cloud)
4. [Usage Locally](#usage-locally)
5. [Endpoints](#endpoints)
6. [File Upload](#file-upload)
7. [Response](#response)
## Prerequisites
- Python 3.11
- Virtualenv (optional but recommended)
## Installation
1. Clone the repository:
```bash
git clone https://github.com/TouradBaba/Fraud_det_model_MLOps.git
```
2. Navigate to the app directory:
```bash
cd app
```
3. Create and activate a virtual environment (optional but recommended):
```bash
python -m venv venv
source venv/bin/activate # Linux/Mac
.\venv\Scripts\activate # Windows
```
4. Install dependencies:
```bash
pip install -r requirements.txt
```
5. Change the app port:
In the `app.py` file, change the port from:
```bash
app.run(host="0.0.0.0", port=8000)
```
to:
```bash
app.run(port=5001)
```
## Usage on the Cloud
1. Access the app through a web browser: [https://frauddetectionmodel.azurewebsites.net](https://frauddetectionmodel.azurewebsites.net)
2. Use the provided credentials for basic authentication:
- Username: admin
- Password: admin
## Usage Locally
1. Navigate to the app directory:
```bash
cd app
```
2. Run the Flask app:
```bash
python app.py
```
3. Access the app through a web browser: [http://localhost:5001/](http://localhost:5001/)
4. Use the provided credentials for basic authentication:
- Username: admin
- Password: admin
## Endpoints
- `/`: Home page with a form for making predictions.
- `/predict`: Endpoint for making predictions with JSON data.
- `/predict-file`: Endpoint for making predictions with a CSV file upload.
## File Upload
To make predictions using a CSV file, upload a CSV file containing transaction data. An Example file is provided in the repository called `test.csv`.
## Response
- For files, the app will add a new column called `Class` and assign 1 or 0 for each row:
- 1 --> Fraudulent
- 0 --> Normal
- For inserted data, the response will be either:
- "Potentially Fraudulent Activity Detected"
- "Transaction Appears Normal"
## Disclaimer
The machine learning model used in this application is trained on specific features from a particular dataset. Therefore, it is not suitable for detecting fraud in other systems that utilize different features.