Analysis of Campus Facility Availability and Academic Community Satisfaction Using Logistic Regression and Random Forest
Keywords:
campus facilities, student satisfaction, Binary Logistic Regression, Random Forest, feature selectionAbstract
Campus facilities play an important role in supporting academic activities and student satisfaction. This study aimed to classify student satisfaction with campus facilities using Binary Logistic Regression and Random Forest. Survey data from 150 student respondents were used, with overall satisfaction scores of 1–3 classified as lower satisfaction and scores of 4–5 as higher satisfaction. Five facility-availability predictors were selected using Pearson correlation with a threshold of (|r| 0.20). The dataset was divided into training and testing sets using an 80:20 stratified split. Class weighting, five-fold stratified cross-validation, and GridSearchCV were applied to address class imbalance and optimize model hyperparameters. On the testing set, the tuned Random Forest achieved 93.33% accuracy, 83.33% precision, 83.33% recall, an F1-score of 83.33%, and a ROC-AUC of 86.81%. Logistic Regression achieved 90.00% accuracy, 71.43% precision, 83.33% recall, an F1-score of 76.92%, and a ROC-AUC of 92.36%. Random Forest was selected as the final model because it achieved higher test-set accuracy, precision, and F1-score than Logistic Regression. Feature importance analysis identified parking availability as the most important predictor, with an importance value of 0.3899. These findings suggest that Random Forest can classify student satisfaction in this dataset, although further validation using larger samples is needed to assess model generalizability.
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