Simulated ML modeling of synthetic defense contracting data. Uses scikit-learn to explore patterns in overdue accounts receivable. Great for testing classification, regression, and time series on randomized financial scenarios.
Part of the Regulus fintech suite.
🔧 How to Use ar-forecast
- Clone the repository:
git clone https://github.com/Hamiltonius/AR-Forecast.git cd AR-Forecast
- (Optional, but recommended) Create a virtual environment and install dependencies:
python3 -m venv venv source venv/bin/activate pip install -r requirements.txt
- Load the included dataset: The repo comes with a synthetic dataset: 📄 defense_ar_synthetic_data.csv
If you’d like to use your own data, just replace this file and ensure the format matches what’s expected in dataload.py.
- Run the main script:
python main.py
This will: • Load the dataset • Train classification and regression models on overdue behavior • Output feature importance and forecast charts to interpret trends and AR risk