Analyzing and Predicting Treatment Effects for Schizophrenia Patients

For this project, my STATS 202 class was tasked with using the array of statistical and machine learning tools we learned over the course of the quarter to answer questions about a real-life biomedical dataset concerning patients diagnosed with Schizophrenia. Specifically, my group utilized hypothesis testing to test the efficacy of a drug that was administered to a subgroup of patients, unsupervised learning techniques such as principal component analysis to define natural groupings of patients, and finally, supervised learning to predict future severity of patient symptoms and to predict when erroneous diagnoses might occur.