A machine learning framework for predicting student placement outcomes
Bibliographic record
Abstract
The accurate prediction of student placement outcomes is an important task for academic institutions to improve career support services.One difficulty is that most of the traditional statistical methods are not equipped to handle the interplay between the academics, complex variables and demographics that often combine to produce a niched placement success.In this work, we present a robust Machine Learning (ML) model for predicting outcomes for student placements, based on a large publicly available Kaggle dataset.The pipeline includes the successful prepossessing of all machine learning models with systematic data pre-processing, exploratory data analysis (EDA), encoding of categorical feature and data scaling for better quality of data for input to the models.Several machine learning algorithms, including Decision Trees (DT), Logistic Regression (LR), Voting Classifier (VC), and other classifiers are trained and their performances are compared.The model's predictive performance is enhanced through hyperparameter optimization and cross validation.The proposed voting classifier outperforms the existing traditional ML models in terms of accuracy, precision, and computational efficiency.Our results show that machine learning models greatly improve predictability of a student placement and could be a valuable tool for data-driven career counselling and institutional planning.This research illustrates the value of artificial intelligence (AI) in the educational landscape and provides a stepping stone for further development such as real-time predictions and integrating a wider range of features.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".