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Veteran Suicide Prediction Model

📌 Project Overview

Veteran suicide is a critical issue, and this project leverages machine learning to analyze historical trends and predict future suicide rates among veterans. Using data from 2001-2022, the model considers year, geographic region, and state to estimate the number of veteran suicides.

📂 Dataset

The dataset is sourced from the U.S. Department of Veterans Affairs (VA) and contains:

  • Year: 2001-2022
  • Geographic Region: (Northeastern, Southern, etc.)
  • State: All 50 U.S. states & territories
  • Number of Veteran Suicides

🛠️ Tech Stack

  • Python 🐍
  • TensorFlow / Keras 🤖
  • Scikit-Learn 📊
  • Pandas 🏷️
  • Matplotlib 📈

📑 Project Structure

/VAData
│── data/
│   ├── VA_State_Sheets_2001-2022_Appendix_508.xlsx   # Raw dataset
│── notebooks/
│   ├── veteran_suicide_analysis.ipynb   # Data analysis & model training
│── models/
│   ├── veteran_suicide_model.keras   # Saved trained model
│── src/
│   ├── train_model.py   # Train the model script
│   ├── predict.py   # Make predictions
│── README.md
│── requirements.txt

🚀 How to Run This Project

1️⃣ Set Up Environment

Clone the repository and install dependencies:

git clone https://github.com/yourusername/veteran-suicide-prediction.git
cd veteran-suicide-prediction
pip install -r requirements.txt

2️⃣ Train the Model

Run the training script to process the dataset and train the TensorFlow model:

python src/train_model.py

3️⃣ Make Predictions

After training, make predictions for a specific year and state:

python src/predict.py --year 2025 --state "Texas"

Example Output:

Predicted Veteran Suicides in Texas (2025): 105

📊 Model Performance

  • Mean Absolute Error (MAE): 232.97 (Average error in suicide predictions)
  • Mean Squared Error (MSE): 349,559.59 (Overall prediction accuracy measure)
  • Training Improvement: Loss decreased over 100 epochs, indicating learning.

🔍 Future Improvements

  • Feature Expansion: Incorporate factors like GDP, unemployment rates, and VA funding per state.
  • Model Optimization: Experiment with different architectures, layers, and optimizers.
  • Deployment: Implement a FastAPI endpoint for real-time predictions.
  • Dashboard Integration: Use Streamlit to visualize veteran suicide trends.

📜 License

This project is open-source under the MIT License.

👥 Contributing

Contributions are welcome! Open an issue or submit a pull request if you find bugs or have ideas to improve the model.

💡 Acknowledgment

This project is dedicated to supporting veterans and raising awareness about mental health challenges within the veteran community.