Student Performance/Drop out prediction

Business challenge

  • Student performance can be gauged only after exams
  • Teachers do not about which students are struggling with what lessons
  • Teachers do not know which students need special attention
  • Precautionary measures can’t be taken to improve an individual student Performance.
  • Not able to predict which student will Dropout


  • Run machine learning regression models on student data Sets such as e-learning log files (when they interact with digital learning systems), Attendance data, Historical data, demographic data, educator demographic, performance data, registration data and others.
  • Models Accurately predict the student performance, weak areas, strong areas, which students need special attention and on what subjects.
  • Our Models also predict the risk of student dropout
  • Our Models continuously learns by processing huge amounts of data and continuously improves its accuracy in prediction.

Business benefit

  • Teachers can identify the students who are at risk of dropping out, why they are struggling, as well as provide insight into the interventions needed to overcome their learning challenges: