HUD_data

Purpose

This repository provides data and visualizations to support various nonprofits in our community that serve individuals and families with housing needs.

By bringing together key metrics and trends, we aim to highlight the critical role these organizations play in ensuring housing stability and community well-being.

What We’ll Explore

We’ll work with datasets such as (placeholder for now till we get projects picked):

• Meals served – indicators of outreach and support services
• Rent assistance expenditures – tracking financial support over time
• Individuals and families served – measuring impact and reach
• Trends and patterns – identifying needs and resource gaps

How the Data Will Be Used

The insights and visual materials created from this project can be used for:

• Social media campaigns
• Community and stakeholder presentations
• Fundraising and grant proposals
• Strategic planning and resource allocation

Project Goals

• Collect and organize relevant data from partner nonprofits
• Build clear, easy-to-understand data visualizations
• Create shareable reports and presentation-ready materials
• Support nonprofits in communicating their impact and securing funding

Reference repo for advanced topics

https://github.com/dmorton714/CY_post_grad_data

Always Utilize a Virtual Environment

Virtual Environment Commands

Command	Linux/Mac	GitBash
Create	python3 -m venv venv	python -m venv venv
Activate	source venv/bin/activate	source venv/Scripts/activate
Install	pip install -r requirements.txt	pip install -r requirements.txt
Deactivate	deactivate	deactivate

Follow this to the best of our ability.

We might not have some of the parts like models etc.

data-project/
├── README.md                  # Project overview, instructions, documentation
├── requirements.txt           # Python dependencies (or environment.yml for Conda)
├── .gitignore                 # Files and folders to ignore in Git
│
├── data/
│   ├── raw/                   # Original, immutable datasets
│   ├── interim/               # Intermediate data (cleaned or partially processed)
│   └── processed/             # Final datasets ready for analysis or modeling
│
├── notebooks/
│   ├── name.ipynb   # Exploratory data analysis
│
├── src/
│   ├── __init__.py
│   ├── data/
│   │   └── load_data.py       # Data loading functions
│   ├── features/
│   │   └── build_features.py  # Feature engineering scripts
│   ├── models/
│   │   ├── train_model.py     # Training scripts
│   │   └── predict_model.py   # Inference scripts
│   └── visualization/
│       └── visualize.py       # Visualization utilities
│
├── models/
│   ├── trained_model.pkl      # Saved ML models
│   └── model_metrics.json     # Model performance tracking
│
├── reports/
│   ├── figures/               # Generated plots and images
│   └── final_report.md        # Project summary or results
│
├── tests/
│   └── test_load_data.py      # Unit tests for functions
│
└── scripts/
    ├── run_pipeline.py        # CLI or orchestration script
    └── preprocess_data.py