Abstract
The goal of this research paper is to propose an approach to uncovering important and previously unknown indicators that could prevent suicide by analyzing data released by the Centers for Disease Control and Prevention (CDC). The CDC data includes 75 variables that identify characteristics and demographics of the deceased. This paper describes the process of exploring data science methods to build a predictive model that could lead to prevention of suicide rate in USA. The data was explored with the Python programming language, R scripting language, RStudio and in the Tableau environment. Preliminary analysis of the CDC 2013 deaths by suicide data indicate that education was one of the important factors that lead to suicide; further analysis of this and other data may provide a new set of objective risk factors from one or more of the variables in this data set.
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