Unsupervised Learning Lab
Introduction to Machine Learning in Pharmaceutical Sciences (BP301T) · Semester 3 · Unit 5: Unsupervised Learning & Practical Case Studies
Explore browser-native K-means-style grouping of fictional profiles, cluster-count choice, and careful cluster interpretation.
What this unit covers
Unsupervised learning finds structure without target labels. K-means clustering and cluster-count selection can support a fictional patient-segmentation or drug-property grouping lesson, but a cluster is not a diagnosis, a treatment recommendation, or a validated clinical subgroup.
Apply it
Choose a cluster count for a simulated segmentation or drug-grouping plot and explain what the clusters can and cannot represent.
How this lab works
A visible browser-native teaching interaction updates illustrative states, scores, or visual summaries from authored values.
Data used
Fictional or browser-generated teaching inputs; no patient, proprietary manufacturing, or clinical-record data.
Limits
Educational simulation only. Outputs are not clinical advice, patient-specific predictions, release specifications, regulatory decisions, or professional competency evidence.