NYC-Poverty-Studies

Requirements

• R
• General Linear Regression
• Random Forest

Introduction

This projects built General Linear Regression model and Random Forest to predict poverty rates in New York City. The studies of poverty is important because it can help decision makers better understand this ongoing problem to break the cycles of poverty and prioritize effective interventions for the most at-risk groups. The project findings show that Income, Unit Population, Family Type, Housing Type and Borough have the strongest predictive power for poverty status. (Shown as below)