This Kurs contains live coding examples for one selected descriptive and one predictive machine learning technique. To this end, data preparation milestones are presented before the machine learning methods are applied. As a preparatory measure, these milestones contain code examples for renowned data exploration, data cleansing and data transformation techniques. The machine learning processes are applied on the basis of this preparation.
On the descriptive side, the k-Means approach is presented as a clustering method including the dimensionality reduction technique named Principal Component Analysis (PCA), and various optimisation iterations including the evaluation of the clustering quality by using the Silhouette index. As a predictive approach, the prepared data is processed with the goal of predicting the salary interval using the CART decision tree through the application of various validation methods (as a representative of classification methods). The coding examples are part of the course ‘Machine Learning’, which is offered to Bachelor students at the University of Hohenheim as an elective module.
