Directory structure:

└── artifacts
│   └── artifacts.pkl                      <- ML artifacts (encoder, model, scaler)
│
├── data                                   <- Put switrs.sqlite and transactions_train.csv here
│
├── docs
|   └─── README.md                         <- Contains top-level information of this project
│
├── lib
|   ├── constants.py.example               <- Template for constants file storing values
|   ├── transaction.py                     <- Basic Model for transaction reqquest
│   └── utils.py                           <- Utility functions
│
├── notebooks                              <- Jupyter notebooks
│   ├── Exploratory Data Analysis.ipynb    <- EDA for transactions_train.csv dataset
│   ├── Screening Test.ipynb               <- EDA to answer SWITRS questions
│   └── Train.ipynb                        <- Train model for Fraud detection
│
├── test
│   └── Train.ipynb                        <- Test fraud detection API
│
├── app.py                                 <- FastApi application
|
├── dockerfile                             <- Build docker image
│
└── requirements.txt                       <- Dependencies

Install dependencies

pip install -r requirements.txt

Model

Cross-validation

CV F1Score and Accuracy

AUC

Test F1Score and Accuracy

F1Score: 0.8266
Accuracy: 0.9996

Run API locally

uvicorn app:app --host 0.0.0.0 --port 8080

Deployed API endpoint:

POST http://ec2-3-231-160-226.compute-1.amazonaws.com/is-fraud
Params:
    "step": 699,
    "type": "TRANSFER",
    "amount": 162326.52,
    "nameOrig": "C1557504343",
    "oldbalanceOrig": 162326.52,
    "newbalanceOrig": 0.00,
    "nameDest": "C404511346",
    "oldbalanceDest": 0.0,
    "newbalanceDest": 0.0