Use Rstudio for analysis

Methodology: 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 outcomes?such as feature importance and confusion matrices?to identify key factors influencing student success or dropout

Guiding question: Which interventions can be implemented to improve students' retention and academic success?

Dataset info: https://archive.ics.uci.edu/dataset/697/predict+students+dropout+and+academic+success

Columns:

\fEPF log_model OL, singular. ok = singular . ok)) 2. eval (call(if (is. function(method) ) "method" else method, x = x, y = Y, weights = weights, start = start, etastart = etastart, mustart = mustart, offset = offset, family = family, control = control, intercept = attr (mt, "intercept") > OL, singular. ok = singular . ok)) 1. glm(DropoutBinary ~ . , data = train_data, family = binomial)\f\f>)> summary (log_model) Call: gim (formula = DropoutBinary ~ ., family = binomial, data = train_data) Coefficients: Estimate Std. Error z value Pr()| 2/ ) (Intercept) -0. 875 D. 123 -7. 11