The purpose of this personal assignment is to verify your level of understanding of the basic concepts of Machine Learning, in particular of classification and clustering.
The initial part, which is perhaps the most qualifying, will consist in choosing a dataset from the repositories that I will propose that has a meaning for you and on which you will act for the two main tasks mentioned above. Then you will consider a dataset with the various features and the target column (with the classification labels) that indicates the correct answer to the ML model during the training phase. Then you can create an instance to be classified (even by extracting it in advance from the original dataset, therefore not including it in the training phase), ensuring it is correctly classified. Choose at least two classification methods and evaluate the differences in performance. For the clustering phase, use the same dataset not including the target column, obtaining the clusters, and with the original dataset, evaluate to what extent the clusters correspond to the classification labels (not always the case as we have verified in the case of the Wine dataset ).
As deliverables, I expect a report on Google Docs and the related Orange 3 workflow.
List of dataset repositories:
Datasets from the Dataset widget in Orange 3 (https://orange3.readthedocs.io/projects/orange-visual-programming/en/latest/widgets/data/datasets.html)
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