Extracting the Recommended Features from the Elementary School Student Dataset through Exploration Data Analysis (EDA)
- DOI
- 10.2991/978-94-6463-386-3_37How to use a DOI?
- Keywords
- Exploratory Data Analysis (EDA); Elementary School Student Dataset; Chi Square; T-test
- Abstract
Exploratory data analysis (EDA) is an important stage in a data science cycle. In this research, the EDA process is carried out on the elementary school student dataset derived from the student “interest” and “talent” questionnaires. The purpose of this research is to find recommended features that will be used in the modeling stage. The main methods used in the implementation of EDA are chi square and T-test on the dependent variable, “class” and fifteen dependent variables. The stages were carried out by (1) analyzing the documents, data, and participants; (2) developing the questionnaire; (3) implementing the Likert and Yes/No questions; (4) formatting the data into tabular data; (5) coding and exploratory data analysis; (6) interpreting the findings and conclusion. From the results of chi square testing, the highest value was obtained in the “excellent in acting” variable with a value of 17.79284731, while the lowest result was found in the “writing, reading or storytelling” variable with a value of 0.29389977. Through the T-test, 3 categories of variable influence were obtained, i.e., “strong”, “moderate”, and “weak”.
- Copyright
- © 2024 The Author(s)
- Open Access
- Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.
Cite this article
TY - CONF AU - Devi Sartika AU - Febie Elfaladonna AU - Indra Griha Tofik Isa AU - Andre Mariza Putra PY - 2024 DA - 2024/02/27 TI - Extracting the Recommended Features from the Elementary School Student Dataset through Exploration Data Analysis (EDA) BT - Proceedings of the 7th FIRST 2023 International Conference on Global Innovations (FIRST-ESCSI 2023) PB - Atlantis Press SP - 338 EP - 351 SN - 2352-5401 UR - https://doi.org/10.2991/978-94-6463-386-3_37 DO - 10.2991/978-94-6463-386-3_37 ID - Sartika2024 ER -