Document Type
Other
Publication Date
2024
Abstract
Principal Component Analysis (PCA) is a type of dimension reduction technique used in data analysis to process the data before making a model. In general, dimension reduction allows analysts to make conclusions about large data sets by reducing the number of variables while retaining as much information as possible. Using the numerical variables from a data set, PCA aims to compute a smaller set of uncorrelated variables, called principal components, that account for a majority of the variability from the data. The purpose of this paper is to understand PCA and determine which principal components should be kept from a sample credit card data set in order to draw conclusions about whether or not someone accepts a credit card offer. An overview of the credit card data set will be given. Then, a few pre-processing methods used to clean the data set will be discussed. PCA will be explained and performed on the credit card data set. Finally, our conclusions will be stated.
Publisher
South Dakota State University
Rights
Copyright © 2024 Eleanor Cain
Recommended Citation
Cain, Elenor, "Principal Component Analysis with Application to Credit Card Data" (2024). Schultz-Werth Award Papers. 67.
https://openprairie.sdstate.edu/schultz-werth/67
