Principal Component Analysis for Predicting the Party of the Legislators

Authors

Afsana Mimi, CUNY New York City College of TechnologyFollow

Document Type

Poster

Publication Date

12-3-2020

Abstract

In Spring 2020, I did a project, "Decision Tree Predicting the Party of Legislators," and construct a decision tree model to predict legislators' parties' based on their votes. We also use this model to identify legislators who frequently voted against their parties. We used the legislators' roll call votes, Office of Clerk U.S. House of Representatives Data Sets (Categorical values) collected in 2018 and 2019. In this new project, We study the 2018 and 2019 vote data using Principal Component Analysis (PCA). The goal is to find a (compressed) model using unsupervised learning to distinguish the legislators' parties, and PCA and Decision Tree have similar accuracy. We use R and Excel to handle the data analysis.

Comments

This poster, second place winner for STEM individual projects, was presented at the 33rd Semi-Annual Dr. Janet Liou-Mark Honors and Undergraduate Research Scholars Poster Presentation, Dec. 3, 2020. Mentor: Prof. Nan Li (Mathematics).