ARDP: SIMPLIFIED MACHINE LEARNING PREDICTOR FOR MISSING UNIDIMENSIONAL ACADEMIC RESULTS DATASET
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 re...
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| Format: | Article |
| Language: | English |
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Polish Association for Knowledge Promotion
2023-03-01
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| Series: | Applied Computer Science |
| Subjects: | |
| Online Access: | http://www.acs.pollub.pl/pdf/v19n1/4.pdf |
| _version_ | 1797848825576030208 |
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| author | Olufemi A. FOLORUNSO Olufemi R. AKINYEDE Kehinde K. AGBELE |
| author_facet | Olufemi A. FOLORUNSO Olufemi R. AKINYEDE Kehinde K. AGBELE |
| author_sort | Olufemi A. FOLORUNSO |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-09T18:33:48Z |
| format | Article |
| id | doaj.art-215d8cbe2acf43f69356f7f3f9080c90 |
| institution | Directory Open Access Journal |
| issn | 1895-3735 2353-6977 |
| language | English |
| last_indexed | 2024-04-09T18:33:48Z |
| publishDate | 2023-03-01 |
| publisher | Polish Association for Knowledge Promotion |
| record_format | Article |
| series | Applied Computer Science |
| spelling | doaj.art-215d8cbe2acf43f69356f7f3f9080c902023-04-11T11:56:52ZengPolish Association for Knowledge PromotionApplied Computer Science1895-37352353-69772023-03-01191476310.35784/acs-2023-04ARDP: SIMPLIFIED MACHINE LEARNING PREDICTOR FOR MISSING UNIDIMENSIONAL ACADEMIC RESULTS DATASETOlufemi A. FOLORUNSO0https://orcid.org/0000-0002-0242-9316Olufemi R. AKINYEDE 1https://orcid.org/0000-0002-5544-8529Kehinde K. AGBELE 2https://orcid.org/0000-0002-4265-0314Computer Department, Elizade University, Ilara Mokin, NigeriaInformation Systems Dept., Federal University of Technology, Akure, NigeriaComputer Department, Elizade University, Ilara Mokin, NigeriaIn 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.http://www.acs.pollub.pl/pdf/v19n1/4.pdfmissingnesspredictor variabletraining datasetsheuristicsunidimensionality |
| spellingShingle | Olufemi A. FOLORUNSO Olufemi R. AKINYEDE Kehinde K. AGBELE ARDP: SIMPLIFIED MACHINE LEARNING PREDICTOR FOR MISSING UNIDIMENSIONAL ACADEMIC RESULTS DATASET Applied Computer Science missingness predictor variable training datasets heuristics unidimensionality |
| title | ARDP: SIMPLIFIED MACHINE LEARNING PREDICTOR FOR MISSING UNIDIMENSIONAL ACADEMIC RESULTS DATASET |
| title_full | ARDP: SIMPLIFIED MACHINE LEARNING PREDICTOR FOR MISSING UNIDIMENSIONAL ACADEMIC RESULTS DATASET |
| title_fullStr | ARDP: SIMPLIFIED MACHINE LEARNING PREDICTOR FOR MISSING UNIDIMENSIONAL ACADEMIC RESULTS DATASET |
| title_full_unstemmed | ARDP: SIMPLIFIED MACHINE LEARNING PREDICTOR FOR MISSING UNIDIMENSIONAL ACADEMIC RESULTS DATASET |
| title_short | ARDP: SIMPLIFIED MACHINE LEARNING PREDICTOR FOR MISSING UNIDIMENSIONAL ACADEMIC RESULTS DATASET |
| title_sort | ardp simplified machine learning predictor for missing unidimensional academic results dataset |
| topic | missingness predictor variable training datasets heuristics unidimensionality |
| url | http://www.acs.pollub.pl/pdf/v19n1/4.pdf |
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