Applying Data Analytics & ML Methods to Decrease Homelessness

Challenge Background

According to recent statistics, there were 580,466 people experiencing homelessness in America. Most were individuals (70%), and the rest were people living in families with children. They lived in every state and territory, and they reflected the diversity of our country.

The Problem

Below some important socio-economic facts about people experiencing homelessness.

People experiencing homelessness are likely to be single adults. People experience chronic homelessness in the county at almost double the rate than the rest of the state and the United States.

Both nationally and in Texas, the inability to afford housing is one of the leading causes of homelessness. A lack of investment in affordable housing options combined with stagnant wages leaves stable housing out of reach for many. In Austin, a minimum wage worker would need to work 125 hours a week just to afford a one-bedroom apartment!

Ending Homelessness does not mean individuals and families will never again experience homelessness. Instead, it means that as a community we will have a systematic response that can address immediate needs, quickly connect people to housing, and provide services to ensure long-term stability.

Goal of the Project

• Determine the correlation between homelessness viz. housing, income, health, and domestic violence
• Build predictive modeling of basic needs through seasonal forecasting
• Create a tool that will allow the matching of homeless people to available resources (data set permitting)

Project Timeline

What you'll learn

1. Analyze datasets and perform EDA on homelessness in different regions

2. Apply ML models to come up with potential answers to the aforementioned project goals

3. Create a data visualization methodology using GIS, Streamlit, Matplotlib and come up with the meaningful proposal