sentinelX Ai agent security platform

نموذج تعلم آلي لتصنيف والتنبؤ بالمستوى الأكاديمي للطلاب اعتمادًا على بيانات سلوكية وأكاديمية، باستخدام خوارزمية Random Forest.

Built a machine learning classification model to predict students' academic performance categories based on behavioral and academic features. The project involved preprocessing a real-world student dataset — encoding categorical variables, splitting data, and training a Random Forest Classifier — achieving strong accuracy on unseen test data.

What was done:

- Cleaned and encoded categorical features using Label Encoding

- Trained a Random Forest model with an 80/20 train-test split

- Evaluated performance using accuracy score and a full classification report

- Compared train vs. test accuracy to assess model generalization