Comparison between classification algorithms using a multiple correspondence analysis with python.

  • Labdaoui Ahlam Constantine1 University
  • Mekki Soundes Constantine1 University

Abstract

To analyse the data of a survey questionnaire we apply multiple correspondence analysis “MCA” as a method to help us convert data into a scatter plot, but it is difficult to study it and get good results, so we need to make a classification to facilitate the study. Among the most usable classification methods, hierarchical ascending classification and k-means. To compare them, we conducted a questionnaire on distance studies during the Corona crisis, which included the opinions of 304 university professors from most universities in Algeria; in our application, we used the Python programming language.

Downloads

Download data is not yet available.

References

Richmond, B., Introduction to Data Analysis, Handbook-ERIC 2006.

Maheshwari, A., Data Analytics, 2014.

Green acre, M., Blass’s, J. Multiple Correspondence Analysis and Related Methods., CRC Press 2006.

Jacob Kegan, Introduction to Clustering Large and High-Dimensional Data, Cambridge University Press, Cambridge, 2007.

McQueen, J. Some Methods for Classification and Analysis of Multivariate Observations Dan’s Proc. 5th Berkeley Symposium on Mathematical Statistics and Probability, pages 281-297, 1967.

Python Data Analytics, Data Analysis and Science Using, Pandas, matplotlib, and the Python Programming Language 2012.

Dawson, M. Python Programming for the Absolute Beginner,3rd Edition, 2019.

Published
2024-05-02
Section
Research Articles