Assignmet 3 focused on classifying three orthopedic disease given various "biomechanical" values.
The Jupyter notebook in this repo is the notebook I submitted to be graded. This notebook was a guided application of ML techniques involving minimal prepocessing of the dataset, training and testing multi-class classifiers (Random Forest and PCA(Principal Component Analysis)), testing and plotting the impact of the number of estimators on Mean CV Score, identifying the most salient features for accurately predicting orthopedic disease class, identifying which disease was easiest to predict accurately, Cross-validation of the PCA model, exloring and plotting the optimal number of dimensions for the PCA model, determine which PC(principal component) correlates highly to which class, etc.
The end results was that I built two models, a Random Forest multi-class classifier and a PCA multi-class classifer, then optimized the number of estimators used in the RF model and optimzed the number of dimesions the PCA model used.