sfbrigade/datasci-sf-homeless-project

Name: datasci-sf-homeless-project

Owner: SFBrigade

Description: null

Created: 2016-06-30 03:53:30.0

Updated: 2018-01-13 00:12:33.0

Pushed: 2017-08-17 01:12:54.0

Homepage: null

Size: 21756

Language: Jupyter Notebook

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README

HMIS Data Science Study

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:

eactivate/activate the environment:  

et Started

ork this repository and clone it locally.
ocate the dataset (pinned in #datasci-homeless on Slack).
un ```jupyter notebook```
avigate to notebooks/load_data_example_v2.ipynb to start exploring the data.

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