https://github.com/steviecurran/money-launder

Machine learning prediction of customers suspected of laundering money
https://github.com/steviecurran/money-launder

binary-classification decision-trees finance logistic-regression machine-learning nearest-neighbors prediction

Last synced: 5 days ago
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Machine learning prediction of customers suspected of laundering money

• Host: GitHub
• URL: https://github.com/steviecurran/money-launder
• Owner: steviecurran
• Created: 2023-04-05T04:44:29.000Z (over 2 years ago)
• Default Branch: main
• Last Pushed: 2025-02-25T04:30:41.000Z (8 months ago)
• Last Synced: 2025-02-25T05:26:38.812Z (8 months ago)
• Topics: binary-classification, decision-trees, finance, logistic-regression, machine-learning, nearest-neighbors, prediction
• Language: Jupyter Notebook
• Homepage:
• Size: 17.3 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

          
# Machine learning prediction of customers suspected of laundering money

The assignment is to build a quick model to demonstrate to the CEO of an online gambling platform 

the potential of machine learning in detecting  money laundering by customers. The CEO wants

a basic demonstration model within a week.

The analysis is summarised in [report.pdf](https://github.com/steviecurran/money-launder/blob/main/report.pdf),

were we find 80% accuracy for the machine learning models. Given the small sample (1752 suspect players), we find that

deep learning (artifical neural networks) are not suited to this task.

![](https://raw.githubusercontent.com/steviecurran/money-launder/refs/heads/main/accuracy.png)

Although all of the processing was done in the python scripts mentioned in the report, a simplified run-through is included as the Jupyter notebook PP+ML.ipynb