https://github.com/germa89/financial_fraud_detection

Using decision trees to detect financial fraud in credit cards records.
https://github.com/germa89/financial_fraud_detection

Last synced: 4 days ago
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Using decision trees to detect financial fraud in credit cards records.

• Host: GitHub
• URL: https://github.com/germa89/financial_fraud_detection
• Owner: germa89
• Created: 2020-04-24T20:32:47.000Z (over 4 years ago)
• Default Branch: master
• Last Pushed: 2020-04-25T21:21:34.000Z (over 4 years ago)
• Last Synced: 2024-11-26T05:20:25.644Z (about 1 month ago)
• Language: Jupyter Notebook
• Size: 3.79 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Finance Fraud Detection using XGBoost

## Description

Nowadays having a credit or debit card for shopping online seems like mandatory, specially during quarantine. However everybody (at least me!) feels a bit unsecured when they input their card numbers on a website. There are many risks out there, identity fraud, theft of bank details, etc. Fortunately many banks are catching up with fraud detection applying machine learning techniques.

In this article/gist we are going to **demonstrate how we can use boosted decision trees to detect fraud** in credit cards records.

## Notebook

Link: [Notebook - Financial Fraud Detection](https://github.com/germa89/Finalcial_Fraud_Detection/blob/master/Finance_Fraud_Detection_using_Gradient_Decision_Boosted.ipynb)

## Disclaimer

The author do not assume any liability or provide any warranty implicit or explicit.