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# TEMPLATE-base-repo
## Predictive Model Based on Homelessness

Create a new branch from dev, add changes on the new branch you just created.
Client Team:
Dr. Tom Byrne
Associate Professor, School of Social Work, BU
Dr. Molly Richard
Postdoctoral Associate, Center for Innovation in Social Science, BU

Open a Pull Request to dev. Add your PM and TPM as reviewers.
Instructor: Prof. Thomas Gardos

Technical Project Manager (TPM): Dhruv Shah
Project Manager (PM): Jasmine Dong

Team Members:
Syeda Shehrbano Aqeel (team lead)
Samritha Aadhi Ravikumar
Kunshu Yang
Renjie Fan
Shiheng Xu

## Project Overview:
Goal: Develop a predictive model of homelessness at the community level using data from 2007-2023.
Focuses on approximately 400 Continuums of Care (CoC) that receive federal homeless assistance funding from the U.S. Department of Housing and Urban Development (HUD).
Unique Focus: Unlike previous studies that predict homelessness at the individual level, this project centers on community-level factors.

## Data Sources:
Primary Data: Annual homelessness counts from HUD across CoC units.
Additional Data: Publicly available community-level factors such as rent rates, demographic and economic conditions, aggregated by CoC.
Timeframe: 2007 to 2023.

## Key Research Questions:
Moving beyond simply identifying associations between community-level factors and homelessness.
Objective: Predict the number or rate of homelessness in each CoC based on structural determinants like rent levels and economic conditions.

## Methodology and Tools:
Modeling Approach: Regression models or other predictive machine learning techniques.
Required Skills: Familiarity with regression models, feature engineering, and experience with the pandas and scikit-learn packages in Python.


# TEMPLATE-base-repo
Create a new branch from dev, add changes on the new branch you just created.
Open a Pull Request to dev. Add your PM and TPM as reviewers.
At the end of the semester during project wrap up open a final Pull Request to main from dev branch.

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