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https://github.com/nneji123/credit-card-fraud-detection
Credit Card Fraud Detection App built with Streamlit, FastAPI and Docker.
https://github.com/nneji123/credit-card-fraud-detection
credit-card data-science deployment docker docker-compose fastapi fraud-detection machine-learning streamlit
Last synced: about 1 month ago
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Credit Card Fraud Detection App built with Streamlit, FastAPI and Docker.
- Host: GitHub
- URL: https://github.com/nneji123/credit-card-fraud-detection
- Owner: Nneji123
- License: mit
- Created: 2022-06-16T11:09:11.000Z (over 2 years ago)
- Default Branch: main
- Last Pushed: 2022-07-30T22:16:24.000Z (over 2 years ago)
- Last Synced: 2024-07-30T18:04:22.896Z (5 months ago)
- Topics: credit-card, data-science, deployment, docker, docker-compose, fastapi, fraud-detection, machine-learning, streamlit
- Language: Jupyter Notebook
- Homepage: https://share.streamlit.io/nneji123/credit-card-fraud-detection/main
- Size: 4.44 MB
- Stars: 25
- Watchers: 2
- Forks: 25
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# Credit Card Fraud Detection App built with Streamlit, FastAPI and Docker
[](https://www.python.org)
[](http://www.pytorch.org/news.html)
[](https://lung-cancer-api.herokuapp.com/docs)
[](https://share.streamlit.io/nneji123/lung-cancer-prediction/main)
An end-to-end Machine Learning Project carried out by Group 3 Zummit Africa AI/ML Team to detect fraudulent credit card transactions. Built with FastAPI, Streamlit and Docker.
## Contributors
- **NNEJI IFEANYI DANIEL**
- **IFEZUE TOONNAEMEKA HILARY**
- **SOMTOCHUKWU OGUCHIENTI**
- **KACHUKWU OKOH**
You can check out the article on Medium describing in detail how this project was carried out.
https://medium.com/mlearning-ai/credit-card-fraud-detection-2527ca04c3de
## Problem Statement
Credit card fraud is an inclusive term for fraud committed using a payment card, such as a credit card or debit card. The purpose may be to obtain goods or services or to make payment to another account, which is controlled by a criminal.
**This Streamlit App utilizes a Machine Learning model served as an API with FastAPI framework in order to detect fraudulent credit card transactions based on the following criteria: hours, type of transaction, amount, balance before and after transaction etc.**
The machine learning model used for this web application was deployed as an API using the FastAPI framework and then accessed through a frontend interface with Streamlit.
The App can be viewed [through this link](https://share.streamlit.io/nneji123/credit-card-fraud-detection/main)
The API and its documentation can be viewed [here](https://credit-fraud-ml-api.herokuapp.com/docs) or [here.](https://credit-fraud-ml-api.herokuapp.com/redoc)
## Data Preparation
Publicly accessible datasets on financial services are scarce, particularly in the newly growing field of mobile money transfers. Many scholars, like us who conduct research in the field of fraud detection, value financial datasets. Because financial transactions are inherently private, there are no publicly accessible datasets, which contributes to the problem.
A synthetic dataset generated using the simulator called PaySim was used as the dataset for building the model used in this project. PaySim uses aggregated data from the private dataset to generate a synthetic dataset that resembles the normal operation of transactions and injects malicious behaviour to later evaluate the performance of fraud detection methods.
[Dataset Link](https://www.kaggle.com/datasets/ealaxi/paysim1v)
### Modelling
In this project 2 different classification algorithms were tested namely:
- Logistic Regression
- Random Forest
The final model used for the API was the **Random Forest Classifier** model which had an accuracy score of 0.99 and an F1 score of 0.86.
## Preview
### API Demo
### Streamlit App Demo
## How to run API and Streamlit App on Google Colab:
💻 Running the API on Google Colab
To run a demo or carry out testing with the API it's best to do that with Google Colab. To run/test the API on Google Colab do the following:
1. Clone the repository to your Google Colab Instance.
```
!git clone https://github.com/Nneji123/Credit-Card-Fraud-Detection.git
```
2. Install the requirements by running the following codes:
```
%%writefile requirements.txt
colabcode
fastapi
uvicorn
pyngrok
```
```
!pip install -r requirements.txt
```
3. Change the working directory:
```
!cd /content/Credit-Card-Fraud-Detection
```
4. Install Ngrok to your Google Colab Instance:
```
!wget https://bin.equinox.io/c/bNyj1mQVY4c/ngrok-v3-stable-linux-amd64.tgz
!tar -xvzf ngrok-v3-stable-linux-amd64.tgz
!ngrok authtoken your_token
```
Replace "your_token" with your token which is available on [Ngrok](https://dashboard.ngrok.com/get-started/your-authtoken)
5. Copy the contents of the **app.py** file to an empty cell and then run the cell.
6. Instantiate ColabCode and run the FastAPI app by running the following code in a new cell:
```
from colabcode import ColabCode
cc = ColabCode(port=18000, code=False)
cc.run_app(app=app)
```
You should now be able to view the API by clicking on the generated link.
💻 Running the Streamlit App on Google Colab
The Streamlit App can also be viewed using Google Colab by doing the following:
1. Copy the contents of "streamlit_app.py" to an empty cell and at the top of cell write the following code and run the cell.
```
%%writefile streamlit_app.py
contents
```
2. Install the requirements by running the following codes in different cells:
```
%%writefile requirements.txt
numpy==1.21.6
requests==2.23.0
streamlit==1.10.0
pyngrok
```
```
!pip install -r requirements.txt
```
3. Run the following code in your instance:
```
from pyngrok import ngrok
public_url = ngrok.connect(port='8501')
public_url
```
4. You can then view the streamlit app on your Google Colab instance by running:
```
!streamlit run /content/streamlit_app.py & npx localtunnel --port 8501
```
## Running on Local Machine :computer:
Since we have multiple containers communcating with each other, A bridge network was created called AIservice. For testing, a **docker-compose.yml** file has been included so as to run both the API and Streamlit app simultaneously as docker containers. To run the API and the Streamlit app on your local machine do the following:
1. Clone the repository to your local machine
2. Install docker and docker-compose if you haven't
3. Open a bash/cmd in the directory and run:
```
docker network create AIservice
```
4. Then run this command
```docker
docker-compose up -d --build
```
5. After the above steps have been carried out you can now view the documentation of the API and also the Streamlit app.
To visit the FastAPI documentation go to http://localhost:8000 with a web browser.
To visit the Streamlit UI, visit http://localhost:8501.
Logs can be inspected via:
```
docker-compose logs
```
The **docker-compose** method can also be used to deploy the API and Streamlit app on Heroku(using Dockhero which is not free) or using cloud services such as Microsoft Azure, Amazon Web Services or Google Cloud Platform.
### Running in a Gitpod Cloud Environment
**Click the button below to start a new development environment:**
[](https://gitpod.io/#https://github.com/Nneji123/Credit-Card-Fraud-Detection.)
## Deployment
The API and Streamlit App have both been deployed using the dockerfile on heroku and Streamlit Cloud respectively.
💻 Deploying the API
Assuming you have git and heroku cli installed just carry out the following steps:
1. Clone the repository
```
git clone https://github.com/Nneji123/Credit-Card-Fraud-Detection.git
```
2. Change the working directory
```
cd Credit-Card-Fraud-Detection
```
3. Create the heroku app
```
heroku create your-app-name
```
Replace **your-app-name** with the name of your choosing.
4. Set the heroku cli git remote to that app
```
heroku git:remote your-app-name
```
5. Set the heroku stack setting to container
```
heroku stack:set container
```
6. Push to heroku
```
git push heroku main
```
💻 Deploying the Streamlit App to Streamlit Cloud
The Streamlit App was deployed using the streamlit cloud and accesses the API deployed on Heroku. To deploy the app using streamlit cloud share do the following:
1. Fork this repository to your Github account.
2. Create a Streamlit Account and then navigate to https://streamlit.io/cloud
3. Create a new app and then choose the repository you cloned and the **"streamlit_app.py"** and then click deploy.
After the app has been built on the cloud you should then be able to view your app right away!