Predicting student performance using multiple features: study hours, lecture attendance, lab participation, assignments, and more.
Our dataset contains 30 students with 5 features measuring their academic engagement. The goal is to predict whether a student will have Good or Poor performance.
| # | Hours Studied | Lectures (%) | Labs Attended | Assignments (%) | Sleep (hrs) | Performance |
|---|
Choose any two features to visualize how they relate to student performance. Notice how different feature combinations reveal different patterns.
Watch how the algorithm splits the data step by step. Each split is chosen to maximize information gain — creating the purest possible groups.
Not all features are equally useful for prediction. Feature importance shows how much each feature contributes to the decision tree's splits.
Enter a student's academic profile to see how the decision tree classifies them.
Decision trees are easy to understand and explain. You can trace exactly why a prediction was made.
The algorithm automatically identifies which features are most important for prediction.
Too deep trees memorize noise. Use pruning, max depth limits, or ensemble methods like Random Forests.