Using Data Analytics methods before using Machine Learning algorithms: prediction on mixed data

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Published: Apr 22, 2024
Keywords:
Multivariate data Multidimensional data Mixed-type data Principal Components Analysis Multiple Correspondence Analysis Machine learning Application of Machine Learning algorithms
Nikolaos Papafilippou
ARISTOTLE UNIVERSITY OF THESSALONIKI
https://orcid.org/0009-0003-3148-7229
Zacharenia Kyrana
ARISTOTLE UNIVERSITY OF THESSALONIKI
Emmanouil Pratsinakis
ARISTOTLE UNIVERSITY OF THESSALONIKI
Angelos Markos
DEMOKRITOUS UNIVERSITY OF THRACE
George Menexes
ARISTOTLE UNIVERSITY OF THESSALONIKI
Abstract

In this study, the possibility of using certain methods of Data Analysis as a preparatory stage of Machine Learning methods to improve their predictive ability, was investigated. Data Analysis methods examined were: Principal Component Analysis, Multiple Correspondence Analysis and Non-Linear - Categorical Principal Component Analysis with optimal scaling. Machine Learning methods examined were Support Vector Machine (SVM) and more specifically Support Vector Classifier (SVC), Stochastic Gradient Descent (SGDClassifier), Naïve Bayes (GaussianNB), K-Nearest Neighbor (KNN), Decision Tree Classifier, Random Forest Classifier and Multinomial Logistic Regression. Tests were performed using data collected in a nationwide survey. The total sample involved 42,593 teenagers, who were interviewed and answered more than 155 questions regarding their eating habits. Body Mass Index (BMI) was set as a dependent variable. BMI was measured and used in the analyses as a quantitative variable, but also as a qualitative one, where the values of the index were divided into classes, based on the recommendations of the World Health Organization. According to the tests results for this data set, the prediction is more secure when we use as dependent variable BMI as a qualitive ordinal variable with four classes. Disinging with a data analysis strategy contributes to saving time and also to the selection of the best prediction model, while dimensionality reduction, if it does not improve tre predictive ability of the modes, at least contributes to the “interpretability” of the results.

Article Details
  • Section
  • Empirical studies
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