This project is a Streamlit application designed to detect threats in fintech environments using various machine learning models. The application allows users to upload a CSV file, preprocess the data, and make predictions using pre-trained models. The results are displayed with visualizations and suggestions for mitigation actions based on the threat level.
- Upload CSV files for prediction
- Preprocess data (remove duplicates, handle missing values, label encoding, scaling)
- Load and use multiple machine learning models
- Display model performance with conditional formatting
- Identify the best model and provide mitigation suggestions
- Visualize model performance and feature importance
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Clone the repository:
git clone https://github.com/yourusername/your-repo-name.git cd your-repo-name -
Create a virtual environment and activate it:
python -m venv venv .\venv\Scripts\activate # On Windows # source venv/bin/activate # On macOS/Linux
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Install the required packages:
pip install -r requirements.txt
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Place your pre-trained model files in the project directory. The model file names should match those specified in the
model_filesdictionary in the code. -
Run the Streamlit application:
streamlit run main.py
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Open your web browser and go to
http://localhost:8501to access the application. -
Upload your test CSV file and view the predictions and visualizations.
app.py: Main application coderequirements.txt: List of required Python packagesREADME.md: Project documentation
- streamlit
- pandas
- joblib
- scikit-learn
- plotly
This project is licensed under the MIT License. See the LICENSE file for details.
Contributions are welcome! Please open an issue or submit a pull request for any changes or improvements.
For any questions or inquiries, please contact [[email protected]].