https://github.com/tsu2000/audit_risk

Machine learning web app in Streamlit about classifying fraudulent companies using various classification models.
https://github.com/tsu2000/audit_risk

machine-learning plotly python random-forest scikit-learn streamlit-webapp

Last synced: about 1 month ago
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Machine learning web app in Streamlit about classifying fraudulent companies using various classification models.

• Host: GitHub
• URL: https://github.com/tsu2000/audit_risk
• Owner: tsu2000
• License: mit
• Created: 2023-01-15T04:47:30.000Z (almost 2 years ago)
• Default Branch: main
• Last Pushed: 2024-03-13T01:07:20.000Z (10 months ago)
• Last Synced: 2024-05-11T05:53:16.581Z (8 months ago)
• Topics: machine-learning, plotly, python, random-forest, scikit-learn, streamlit-webapp
• Language: Python
• Homepage: https://audit-ml.streamlit.app
• Size: 114 KB
• Stars: 1
• Watchers: 2
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE
  • Audit: audit_risk.csv

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README

        
# audit_risk

A simple web application for exploring various classification models based on an audit risk dataset to identify fradulent firms based on present and historical risk factors. Users can further adjust the settings of these models to observe changes in the results, and view a basic exploratory data analysis (EDA) of the data provided.



Current machine learning algorithms available in the app include:

- K-Nearest Neighbours

- Naïve Bayes (Gaussian)

- Logistic Regression

- Support Vector Machine (SVM)

- Random Forest Classifier

**Link to Web App**:

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