Machine Learning Classifiers for Grade Prediction

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Overview

This project explores the use of Machine Learning classification models to predict student performance in an Artificial Intelligence course.

It covers a full ML workflow, including data preparation, EDA, feature engineering, and model evaluation using a variety of supervised classifiers. The project focuses on identifying key academic, demographic, and social factors that impact student grades.

Models

• Naive Bayes
• Decision Tree (scikit-learn)
• Random Forest
• XGBoost
• Decesion Tree (from Scratch)

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