Application of Data Mining to Prediction of New Students' Interested Departements With an Approach Naive Bayes Algorithm
Why this work is in the frame
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Bibliographic record
Abstract
This research aims to apply data mining techniques using the Naïve Bayes algorithm to predict new students' majors. Choosing a major is an important decision in college, and accurate predictions can help new students make better decisions. In this study, we collected historical data about past students, including information about academic values, interests, and other factors that influence major selection. The Naïve Bayes algorithm is used to process this data and produce a prediction model that can identify majors that best suit the characteristics of new students. The results of data processing for new students obtained accuracy values with the Naïve Bayes algorithm model of 98.55%, precision of 99.97%, and recall of 98.55%. The naive Bayes algorithm model obtained can be implemented in the form of an application designed to predict new students' majors in determining the study program they will take. The Naïve Bayes algorithm is able to provide fairly accurate predictions, which can be used as a guide for new students in choosing their major. This research makes a positive contribution to the development of data mining applications in the field of higher education, with the potential to help students and universities increase the efficiency of major selection. The Naïve Bayes algorithm is able to provide fairly accurate predictions, which can be used as a guide for new students in choosing their major. This research makes a positive contribution to the development of data mining applications in the field of higher education, with the potential to help students and universities increase the efficiency of major selection. The Naïve Bayes algorithm is able to provide fairly accurate predictions, which can be used as a guide for new students in choosing their major. This research makes a positive contribution to the development of data mining applications in the field of higher education, with the potential to help students and universities increase the efficiency of major selection.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it