Presentation Type
Poster
Student
Yes
Track
Other
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 poster is to understand PCA as well as perform PCA on a large sample credit card data set. PCA will be explained, and an overview of the data will be given. Then, we will examine the principal components computed after performing PCA. Finally, our conclusions and future work will be stated.
Start Date
2-6-2024 1:00 PM
End Date
2-6-2024 2:00 PM
Included in
Principal Component Analysis with Application to Credit Card Data
Volstorff A
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 poster is to understand PCA as well as perform PCA on a large sample credit card data set. PCA will be explained, and an overview of the data will be given. Then, we will examine the principal components computed after performing PCA. Finally, our conclusions and future work will be stated.