Which kind of data will be required for creating a machine learning model for predicting whether a student will be successful in an exam or not ?

To create a machine learning model that predicts whether a student will be successful in an exam, we need a dataset containing information about students from the past. This data helps the model learn patterns and make predictions.

The required data can include:

1. Academic Performance Data

• Marks/grades in previous tests and exams
• Subjec-wise scores and overall percentage

2. Study and Practice Data

• Hours spent studying regularly
• Homework and assignment completion
• Number of practice papers/tests attempted

3. Attendance Data

• Attendance percentage
• Regularity in attending classes

4. Classwork/Behaviour Indicators

• Class participation
• Teacher feedback on performance and effort

5. Final Result (Target Data)

• The past final exam result of the student (e.g., Pass/Fail or Successful/Not successful)
• This is the output label that the model learns to predict.