Description
Tasks
In the class we talked about how to use k-Nearest Neighbours and SVM algorithms to classify
supervised data.
• Load the dataset and store it as a DataFrame.
• Formulate the problem as kNN and SVM (linear) problem and run using standard libraries
in your language or choice.
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• Create a few plots of your model on the test data two of the data dimensions indicating
the predicted elements of each class using different colours or shapes. You may need to try
plotting various pairs of dimensions to see which provide some interesting result. Be sure to
label your axis and legend.
• Provide a short explanation of the result you have shown and what it means.
• For fun : do the same for SVM with RBF kernal and compare the results for the same
dimensions.
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