sfbrigade/datasci-sf-homeless-project

CTA-NorCal Homeless Program Outcomes Analysis

HMIS Analytics

Members of the Data Science Working Group at Code for San Francisco have been charged with answering the Community Technology Alliance’s prompt about homelessness programs.

Prompt

What variables best predict whether an individual is categorized as ‘in permanent housing’ as an outcome, by population segment:

• Veterans
• Chronically Homeless
• Continuously Homeless
• Has Disabling Condition
• Domestic Violence Victim
• Male/Female
• Latino/Non-Latino

Data

Data is in HMIS format, a data standard defined by the US Department of Housing and Urban Development

Results

View the HMIS Data Science Study Presentation for a summary of our findings

Featured Notebooks

• Load and clean the data
• Explore the data
• Feature engineering to prepare input variables
• Make outcome predictions with logistic regression model

Setup

Install Jupyter Notebook; this is most easily done by installing Anaconda: https://www.continuum.io/downloads

Install seaborn. To do this in a new conda environment:
conda create --name datasci seaborn

To deactivate/activate the environment:
source deactivate datasci
source activate datasci

Get Started

1. Fork this repository and clone it locally.
2. Locate the dataset (pinned in #datasci-homeless on Slack).
3. Run jupyter notebook
4. Navigate to notebooks/load_data_example_v2.ipynb to start exploring the data.

Additional information on completed and open items can be found in the pinned documents in #datasci-homeless.

datasci-sf-homeless-project's Issues

Need CSV files in repo or Dropbox

The load_data_example_v2 Jupyter notebook invokes homeless_dataset.py to load the data. This expects csv files but Dropbox only has xls

Test Proj

Converting to Issue