This project utilizes a Physics-Informed Neural Network (PINN) to evaluate and predict the demand satisfaction of various affordable housing options. By integrating a rating agency's perspective, the model assesses financial and operational risks, assigns rating grades, and forecasts daily contributions to meet housing demand, facilitating informed investment and policy decisions.
📊 Features Synthetic Data Generation: Simulates a diverse dataset of affordable housing options with relevant financial, environmental, and social indicators. Risk Assessment and Grading: Evaluates each housing option based on default risk, regulatory compliance, and liquidity risk, assigning rating grades from 'AAA' to 'CCC'. Model Development: Constructs separate regression and classification neural network models, incorporating constraints to ensure reliable predictions. Comprehensive Evaluation: Measures model performance using metrics such as Mean Squared Error (MSE), Mean Absolute Error (MAE), R² score, accuracy, and provides detailed classification reports. Visualization: Generates plots for training progress, actual vs. predicted demand satisfaction, residual distributions, and confusion matrices to facilitate result interpretation. Individual Contribution Analysis: Calculates and displays the daily contributions of each affordable housing option towards meeting the overall housing demand. 🚀 Getting Started 🛠 Installation Clone the Repository
bash Copy code git clone https://github.com/Affordable-Housing-Risk-Assessment-and-Demand-Prediction.git cd affordable-housing-risk-assessment Create a Virtual Environment (Optional but Recommended)
bash Copy code python -m venv env source env/bin/activate # On Windows: env\Scripts\activate Install Dependencies
bash Copy code pip install -r requirements.txt If requirements.txt is not provided, install the necessary libraries manually:
bash Copy code pip install pandas numpy tensorflow scikit-learn matplotlib 🎯 Usage Run the Python Script
bash Copy code python main.py Ensure that main.py contains the complete Python code provided in this project.
Review Outputs
Console Output: Displays statistical summaries, individual contributions, constraint satisfaction, and evaluation metrics. Visualizations: Generates plots for training/validation loss, actual vs. predicted demand satisfaction, residual distributions, and classification confusion matrices. 📂 Project Structure bash Copy code affordable-housing-risk-assessment/ │ ├── data/ │ └── synthetic_housing_data.csv # (Generated by the script) │ ├── notebooks/ │ └── analysis.ipynb # (Optional: Jupyter notebooks for exploration) │ ├── main.py # (Main Python script) ├── README.md # (Project description) ├── requirements.txt # (Python dependencies) └── LICENSE # (Project license) 🔍 Model Evaluation and Insights Regression Metrics: Evaluates the accuracy of demand satisfaction predictions using MSE, MAE, and R² score. Classification Metrics: Assesses the performance of risk grading with accuracy scores and detailed classification reports. Constraint Satisfaction: Ensures that the model adheres to predefined financial and scalability constraints. Individual Contributions: Analyzes the daily impact of each housing option in meeting the overall demand, aiding in strategic decision-making. 📈 Visualization Examples Training and Validation Loss
Actual vs. Predicted Demand Satisfaction
Residuals Distribution
Confusion Matrix
🤝 Contributing Contributions are welcome! Please open an issue or submit a pull request for any enhancements or bug fixes.
📜 License This project is licensed under the GNU General Public License v2.0.
📧 Contact For any questions or suggestions, please contact r02522318@gmail.com