Study the dataset for classification at the URL:
and download it.
2. Prepare the data by dropping the possible missing values.
3. Visualize the data by using the most suited visualization technique.
4. Analyze the data by indicating the most relevant information when solving classification tasks.
5. Apply a logictic regression, a MLP and a decision-tree technique on a training set, and compare the performance results on the same test set.
Which techniques is the best? Try to interpret the results.
6. Apply the K-Means, DBScan and Hierarchical Agglomerative on the dataset (same training and test sets as before) and compare the results.
Write down in a Word document every choice, remark and information and result you think is important…(Mandatory)
For steps 2 to 6, write Python codes.
(Code examples are provided for help)
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