Build predictive models starting with Logistic Regression and Decision Trees for baseline results, followed by ensemble methods like Random Forests and XGBoost, evaluating them with cross-validation and metrics such as accuracy, precision, recall, F1-score, and AUC-ROC. Finally, we will visualize and interpret the model outcomessuch as feature importance and confusion matricesto identify key factors influencing student success or dropout

# Load the dataset data 

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