/ShieldML

Machine learning project for fraud detection and risk assessment in financial transactions.

Primary LanguagePython

ShieldML

This repository contains a machine learning project for fraud detection and risk assessment in financial transactions. The project uses a combination of supervised and unsupervised learning algorithms to classify transactions as either fraudulent or legitimate based on various features and risk factors. The models were trained on a large dataset of real-world financial transactions and evaluated using various metrics such as accuracy, precision, recall, and F1 score. The code is written in Python and uses popular machine learning libraries such as Scikit-learn, Pandas, and Numpy.

The repository also includes a detailed README file with instructions for running the code and reproducing the results.

Testing

Run all test with the following command:

python -m pytest -vvv -x

Structure


ShieldML/
├── data/
│   ├── raw/
│   ├── processed/
│   └── models/
├── docs/
├── src/
│   ├── controller/
│   │   ├── fraud_detection.py
│   │   └── risk_assessment.py
│   ├── model/
│   │   ├── preprocessing.py
│   │   ├── fraud_detection_model.py
│   │   └── risk_assessment_model.py
│   └── view/
│       ├── dashboard.py
│       └── results.py
├── tests/
│   ├── unit/
│   ├── integration/
│   └── e2e/
├── config/
│   ├── config.yaml
│   └── logging.yaml
├── requirements.txt
├── README.md
├── LICENSE
└── .gitignore

Directories

  • data/: This directory contains the raw and processed data, as well as the trained models. The raw/ directory contains the raw data files, the processed/ directory contains the preprocessed data files, and the models/ directory contains the trained machine learning models.

  • docs/: This directory contains the documentation for the project. This can include information on how to set up and run the project, as well as any technical documentation for the code.

  • src/: This directory contains the source code for the AI project. The controller/ directory contains the Flask app and the controllers for handling user input and updating the Model and View components. The model/ directory contains the code for preprocessing the data and building the machine learning models. The view/ directory contains the templates and HTML files for displaying the results to the user.

  • tests/: This directory contains the testing code for the AI project. The unit/ directory contains the unit tests for each module, the integration/ directory contains the integration tests for testing the interactions between the different modules, and the e2e/ directory contains the end-to-end tests for testing the entire system.

  • config/: This directory contains the configuration files for the project. The config.yaml file contains the configuration for the Flask app, and the logging.yaml file contains the configuration for the logging.

  • requirements.txt: This file contains the dependencies required by the project. This can include libraries such as Flask, pandas, scikit-learn, and matplotlib.

  • README.md: This file contains information about the project and its usage. This can include instructions on how to set up and run the project, as well as any other relevant information.

  • LICENSE: This file contains the license for the project. This can include information on how the code can be used and distributed.

  • .gitignore: This file lists the files and directories that should be ignored by Git. This can include files such as logs, configuration files, and temporary files.

Datasets

Fraud detection dataset

It has the following columns:

  • trans_date_trans_time: The date and time of the transaction.

  • cc_num: credit card number.

  • merchant: Merchant who was getting paid.

  • category: In what area does that merchant deal.

  • amt: Amount of money in American Dollars.

  • first: first name of the card holder.

  • last: last name of the card holder.

  • gender: Gender of the cardholder.

  • street:Street of card holder residence

  • city:city of card holder residence

  • state:state of card holder residence

  • zip:ZIP code of card holder residence

  • lat:latitude of card holder

  • long:longitude of card holder

  • city_pop:Population of the city

  • job:trade of the card holder

  • dob:Date of birth of the card holder

  • trans_num: Transaction ID

  • unix_time: Unix time which is the time calculated since 1970 to today.

  • merch_lat: latitude of the merchant

  • merch_long:longitude of the merchant

  • is_fraud: Whether the transaction is fraud(1) or not(0)

Tech stack

Tech used in this application