A Hybrid Machine Learning Approach to IT Salary Prediction: Insights from Academic, Demographic, and Socio-Economic Factors
Bibliographic record
Abstract
IT professional’s first job after graduation is a significant milestone, marking the first step to financial independence. To explore the factors influencing compensation for IT graduates, a binary classification model using an ensemble machine learning approach that integrates Random Forest (RF), Neural Networks (NN), and LightGBM was developed. A multilevel strategy was adopted, beginning with training the data using RF and subsequently feeding the output leaf indices and the feature set into NN, culminating with LightGBM functioning as a meta-classifier. A cross-validation approach was employed to assess the model’s accuracy rigorously. The model achieved an 88% accuracy rate and an F1 score exceeding 80% across all categories. Utilizing SHAP analysis, key features per model were extracted and analyzed. Notable features highlighted by the two models are the mother’s educational level, IT experience, degree concentration, study frequency, accommodation, siblings and grades. Features such as holding a degree in cybersecurity and residing on dorms emerged as significant predictors of higher starting salaries. The model offers valuable insights for students, enabling them to enhance their qualifications and improve their compensation prospects after graduation
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".