Admission Prediction Using Decision Trees and Random Forests

This project builds a machine learning pipeline to predict graduate admission likelihood based on applicant profiles. Using Decision Tree and Random Forest classifiers, the dataset is preprocessed with imputation and scaling, followed by cross-validation and hyperparameter tuning via GridSearchCV. Model performance is evaluated through confusion matrices, ROC curves, and AUC scores, achieving strong predictive accuracy for admission outcomes.