Prediction of Guam’s Registered Voters Based on Data Analysis - A Case Study

Abstract

In various disciplines such as education, life science, meteorology, and environmental studies and as well as industries and businesses, there is a need to analyze large amounts of data (Big Data). Although, to find an effective and high-performance analytic algorithm that can predict future performance of selected data areas is still a challenge among Data Scientists, especially Computer Scientists and Mathematicians. In this paper, we will combine both a data visualization method and numeric analysis method in order to analyze large data (Big Data). This paper also includes a case study selected from the 2013 Guam Statistical Yearbook of Registered Voters as the original data, step by step, to show how to analyze a nonlinear data pattern and predict the future registered voters. Computational and mathematical efforts in this paper suggest that the proposed methods (or algorithms) are efficient in data analysis and data prediction using the nonlinear regression method.