AutoTrain MLCreator and reuse
Paulo Cortez
Review the provider license or terms before reuse.
Dataset reference
Student Performance
Student achievement records from two Portuguese schools with demographic, social, school, and grade attributes.
Dataset overview
Rows649
Features30+
TaskRegression / classification
TargetG3
Before training
Inspect missing values, verify target availability, remove leakage and identifiers, split before fitting transformations, and choose metrics that fit the intended decision.
Data quality checks
Confirm row meaning, duplicates, impossible values, category spelling, target balance, and whether the sample represents the population where the model will be used.
Validation planning
Use random holdout only when rows are independent. Prefer grouped, temporal, or geographic splits when related observations could cross the boundary.
Research context
Forecasting Students' Academic Performance in Educational Data Using Machine Learning Techniques (2026), International Journal of Information and Communication Technology Education.
Open journal article