Journal of Education, Technology and Computer Science nr 5(35)2024

URI dla tej Kolekcjihttps://repozytorium.ur.edu.pl/handle/item/11305

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Przeglądanie Journal of Education, Technology and Computer Science nr 5(35)2024 według Autor "Dymora, Paweł"

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  • Obrazek miniatury
    Pozycja
    A Comparative Analysis of Selected Data Mining Algorithms and Programming Languages
    (The University of Rzeszów Publishing House, 2024-12) Dymora, Paweł; Mazurek, Mirosław; Smyła, Łukasz
    This paper evaluates the performance of ten selected data mining algorithms in the context of classification and regression and the effectiveness between two popular programming languages used in data science: Python and R. The algorithms included in the study were Naive Bayes Classi fier, K-Nearest Neighbors (k-NN), Support Vector Machine (SVM), Decision Tree, Random Forest, Gradient Boosting Machine (GBM), Logistic Regression, Linear Regression, Ridge Re gression, and LASSO Regression. The study aimed to evaluate how the various algorithms per form in classification and regression tasks in the context of a specific problem, in this case fraud detection. The performance of the algorithms was evaluated based on key metrics such as accura cy, execution time, the difference between the best and worst results, and in terms of mean square error (MSE). Moreover, learning tools such as R and Python enable students not only to perform multidimensional data analysis, but also to predict future trends and changes. The ability to work with data, modelling and visualisation are key competences in the context of many areas of mo dern life and to support the making of accurate business decisions.
  • Obrazek miniatury
    Pozycja
    Forecasting the Arrival of the Next Pandemic Wave – Modeling and Tools
    (The University of Rzeszów Publishing House, 2024-12) Dymora, Paweł; Mazurek, Mirosław; Górniak, Paulina
    The scope of the paper is to review the literature on data analysis and visualization in the context of the COVID-19 pandemic and to describe the different tools and methods used in this type of analysis. Examples of the use of these tools in practice and their limitations will also be presented. The paper concludes with conclusions and recommendations for the use of data analysis and visualization to better understand the COVID-19 pandemic and to predict the arrival of future pandemic waves.An important feature of the article is the possibility of a broad overview of modelling possibilities and the selection of appropriate frameworks and tools which can be used in the educational process of data analysis for students for in-depth study and prediction of trends and data, in particular of such important issues as the evolution of pandemic.