You should perform the following steps: 1. Analyse the attributes in the data, and consider their relative importance with respect to the target class. You should explain what kind of classifier you...

You should perform the following steps:
1. Analyse the attributes in the data, and consider their relative importance with respect to the target class.  You should explain what kind of classifier you believe might be most suitable for this task, given the information about the attributes alone.      [20 marks]
2. Describe in brief the operation of the classification algorithms you intend to use – these algorithms should be taken from those described in the module.  Explain their main characteristics and parameters.  Additionally explain any other algorithms you intend to use (such as to modify the original dataset).           [25 marks]
3. Describe briefly (not with screenshots) the steps you will use in Weka to prepare the data (if necessary) and run your selected classification algorithms.  Construct a table and graph of classification performance against training set size for the classifiers.  What can you conclude from your results?          [25 marks]
4. Analyse the data structure/representation generated by at least three classifiers when trained on the complete dataset.  What does your analysis tell you about the data set?  [20 marks]
5. Combine the results from the previous steps and all your classifiers to develop a model of why instances fall into particular classes.  (Your answer to this question should be understandable by someone who is not a specialist in data mining; imagine you are making a strategic recommendation to the manager of a company.)       [10 marks]