This paper proposes to apply data mining techniques
to predict school failure and dropout. We use real data on
670 middle-school students from Zacatecas, México, and employ
white-box classification methods, such as induction rules and
decision trees. Experiments attempt to improve their accuracy for
predicting which students might fail or dropout by first, using all
the available attributes; next, selecting the best attributes; and
finally, rebalancing data and using cost sensitive classification.
The outcomes have been compared and the models with the best
results are shown.