ARDP: simplified machine learning predictor for missing unidimensional academic results dataset

• Autorzy: Folorunso Olufemi A. , Akinyede Olufemi R. , Agbele Kehinde K.

Identyfikatory

DOI

10.35784/acs-2023-04

• Języki publikacji: EN

Abstrakty

EN

In this paper, we present the Academic Results Datasets Predictor (ARDP), for missing academic results datasets, based on chi-squared expected calculation, positional clustering, progressive approximation of relative residuals, and positional averages of the data in a sampled population. Academic results datasets are data originating from inside academic institutions’ results repositories. It is a technique designed specifically for predicting missing academic results. Since the whole essence of data mining is to elicit useful information and gain knowledge-driven insights into datasets, ARDP positions data explorer at this advantageous perspective. ARDP is committed to solve missing academic results dataset problems more quickly over and above what currently obtains. PARD is computed by leveraging on the averages of neighbouring values. The predictor was implemented using Python, and the results show that it is admissible in a minimum of up to 85 percent accurate predictions of the sampled cases. It has been verified that ARDP shows a tendency toward greater precision in providing the best solution to the problems of predictions of missing academic results datasets in universities.

Słowa kluczowe

EN

missingness; predictor variable; training datasets heuristics; unidimensionality

• Wydawca: Polskie Towarzystwo Promocji Wiedzy ; Lublin University of Technology
• Czasopismo: Applied Computer Science
• Rocznik: 2023
• Tom: Vol. 19, no 1
• Strony: 47--63
• Opis fizyczny: Bibliogr. 27 poz., tab.

Twórcy

autor

Folorunso Olufemi A.

• Computer Department, Elizade University, Ilara Mokin, Nigeria

• https://orcid.org/0000-0002-0242-9316

autor

Akinyede Olufemi R.

• Information Systems Dept., Federal University of Technology, Akure, Nigeria

• https://orcid.org/0000-0002-5544-8529

autor

Agbele Kehinde K.

• Computer Department, Elizade University, Ilara Mokin, Nigeria

• https://orcid.org/0000-0002-4265-0314

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