https://github.com/ac12644/fraud-detection-ai

Build predictive models on highly skewed data by selecting an example of fraudulent transactions in the financial institutions🚀
https://github.com/ac12644/fraud-detection-ai

analytics data-mining data-science data-visualisation fraud-detection fraud-prevention machine-learning machine-learning-algorithms sampling-methods skewed-data

Last synced: 6 days ago
JSON representation

Build predictive models on highly skewed data by selecting an example of fraudulent transactions in the financial institutions🚀

• Host: GitHub
• URL: https://github.com/ac12644/fraud-detection-ai
• Owner: ac12644
• Created: 2022-04-12T13:26:15.000Z (almost 3 years ago)
• Default Branch: main
• Last Pushed: 2022-06-01T18:41:29.000Z (over 2 years ago)
• Last Synced: 2024-11-14T12:51:55.719Z (2 months ago)
• Topics: analytics, data-mining, data-science, data-visualisation, fraud-detection, fraud-prevention, machine-learning, machine-learning-algorithms, sampling-methods, skewed-data
• Language: Jupyter Notebook
• Homepage:
• Size: 29.3 KB
• Stars: 1
• Watchers: 2
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

Awesome Lists containing this project

README

        






  

    

  


  
Fraud Detection AI


  


    Build predictive models on highly skewed data by selecting an example of fraudulent transactions in the financial institutions.

  



## Contributing

Contributions are what make the open source community such an amazing place to learn, inspire, and create. Any contributions you make are **greatly appreciated**.

If you have a suggestion that would make this better, please fork the repo and create a pull request. You can also simply open an issue with the tag "enhancement".

Don't forget to give the project a star! Thanks again!

1. Fork the Project

2. Create your Feature Branch (`git checkout -b feature/AmazingFeature`)

3. Commit your Changes (`git commit -m 'Add some AmazingFeature'`)

4. Push to the Branch (`git push origin feature/AmazingFeature`)

5. Open a Pull Request

(back to top)