Abstract: This study applies machine learning (ML) algorithms within the Knowledge Discovery in Databases (KDD) framework to analyze Bachelor of Science in Information Systems (BSIS) graduate tracer study data. Two core tasks were addressed: (1) predicting structured graduate outcomes, such as employment status, and (2) classifying sentiments from textual curriculum feedback. Data selection, preprocessing, and transformation prepared structured and unstructured inputs for supervised ML experiments conducted in Python 3.x using Google Colab, with scikit-learn supporting classical algorithms and TensorFlow/Keras enabling deep learning deployment. Algorithms evaluated included Logistic Regression, Random Forest, Gradient Boosting, Support Vector Machines, K-Nearest Neighbors, Naive Bayes, and Long Short-Term Memory (LSTM) networks. Results showed that Logistic Regression achieved the best performance for predicting employment status, with 74.19% accuracy, precision of 0.82, and the highest Kappa score (0.54), outperforming all other models. Gradient Boosting emerged as the most effective approach for sentiment analysis, attaining balanced results across recall (0.497), F1-score (0.481), and Kappa (0.441). Insights from confusion matrices revealed that Logistic Regression excelled at distinguishing employed graduates but struggled with underemployed and unemployed categories, while Gradient Boosting strongly identified neutral sentiments but faced challenges in detecting negative feedback. The findings highlight that algorithm selection should be task-specific: Gradient Boosting is more effective for sentiment analysis, whereas classical models like Logistic Regression are better suited for structured prediction. A hybrid framework combining both is recommended to enhance tracer study systems, enabling richer, data-driven insights for curriculum evaluation, institutional planning, and graduate outcome monitoring.