https://github.com/kurtispykes/fraud-detection-project

A mono-repository containing a packaged machine learning model and simple REST API.
https://github.com/kurtispykes/fraud-detection-project

feature-engineering gemfury machine-learning portfolio python random-forest rest-api

Last synced: 3 days ago
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A mono-repository containing a packaged machine learning model and simple REST API.

• Host: GitHub
• URL: https://github.com/kurtispykes/fraud-detection-project
• Owner: kurtispykes
• Created: 2021-10-26T08:42:36.000Z (about 3 years ago)
• Default Branch: main
• Last Pushed: 2021-12-22T11:13:49.000Z (almost 3 years ago)
• Last Synced: 2024-10-12T18:16:25.291Z (about 1 month ago)
• Topics: feature-engineering, gemfury, machine-learning, portfolio, python, random-forest, rest-api
• Language: Jupyter Notebook
• Homepage:
• Size: 130 KB
• Stars: 12
• Watchers: 3
• Forks: 6
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Fraud Detection Project

IEEE-CIS Fraud Detection challenge was first hosted by Kaggle in 2019. The idea was for competitors to develop a model 

to detect fraud from customer transactions. While IEEE-CIS already have a fraud prevention system in place, researchers

were looking for ways to improve the current figure being saved by the system, and improve the customer experience.

## Usage

Clone this repository to your computer. 

To view explorations navigate to the project directory cd IEEE-CIS Fraud Detection from 

your terminal then cd into the `notebooks` directory. This directory contains data analysis

and the pipeline we converted into a package. To run the notebooks, you'll have

to install the [data](https://www.kaggle.com/c/ieee-fraud-detection/data) into a directory

called data. The directory must live at the same level as the `notebooks` and `packages`

directory. 

To use the sample the deployed model locally through the API, navigate to the project 

directory from your terminal then cd into `packages/fraud_detection_api`. From here, 

run the following command: 

`py -m tox -e run`

This will create a localhost link, simply click it or copy and paste it into your 

browser. Then select the docs option and go to the `predict` heading. There is already

an example instance there, but you may play around with the values.

## Extending This Work

Some ideas to extend this work:

- Replace the model 

- Add monitoring