my implementations of the lecture and lab assignment materials
covered topics:
- dataframes from data files, websites, images
- data cleanup & feature conversion & data slicing & data normalization
- exploration: histograms, scatter plots, parallel coordinate plot, Andrews curve, correlation matrix
- dimensionality reduction through PCA & isomap
- linear regression
- clustering through KMeans
- classification through K-Nearest Neighbors, SVC, Decision Tree, Random Forest
- model evaluation (scores & reports)
- cross-validation
- optimize parameters: grid search, randomized search
- pipeline of estimators
presented in Jupyter notebook