Statlog - Identifying students at greater risk of dropout

Societal issues

Identifying students at greater risk of dropout


  • All high schools seek to increase their graduation rates.
  • Limited human resources and budgets require interventions to be efficiently targeted.
  • A school board wanted to assess the potential of machine-learning methods for identifying students who were most likely to drop out.
  • They also wanted to use econometric methods to understand what were the most important predictors of dropout.

Our approach

  • We engineered more than 450 features covering sociodemographic characteristics, earning difficulties, attendance, behaviour, and academic results.
  • Several machine learning models were trained and econometric models estimated.

Our results


accuracy in predicting dropout among students.

These tools allowed the school board to proactively allocate resources to students with the greatest needs and risks.