Build notes · Goh Kun Ming
Singapore Graduate Employment, Salary & Labour-Market Analysis
Data · Public project
Jan 2025 – Feb 2025
Project overview
A graduate-employment and salary study using data-quality checks, exploratory modelling and transparent group comparisons.
A Singapore graduate-employment and labour-market analysis combining data cleaning, descriptive views, salary modelling and clustering. The project examines employment and income patterns across education and population groups.
Inside this build
- Prepared a workflow for seven source datasets, covering type conversion, missing values, duplicates and IQR or z-score outlier checks.
- Derived salary-spread and employment-rate-gap measures and mapped fields of study.
- Used random forests for salary imputation or prediction alongside clustering and descriptive analysis.
- Compared patterns across universities, degrees, years, industries, age, gender, education and population context.
- Checked seven notebooks without stored errors and added 17 tests with 80% coverage across Python 3.10–3.12.
- Documented missing raw source tables, fitted models, held-out metrics and uncertainty analysis; the results do not establish predictive performance.