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1. Analyse the attributes in the data, and consider their relative importance with respect to the target class. You should explain

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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.

                                                            Data Mining

A dataset in .ARFF format has been provided for you on Studynet. Analyse this dataset using the WEKA toolkit and tools introduced within this module. Produce a report explaining which tools you used and why, what results you obtained, and what this tells you about the data. Marks will be awarded for: variety of tools used, quality of analysis, and interpretation of the results. An extensive report is not required (at most 4000 words), nor is detailed explanation of the techniques employed, but any graphs or tables produced should be described and analysed in the text. A reasonable report could be achieved by doing a thorough analysis using three techniques. An excellent report would use at least four tools to analyse the dataset, and provide detailed comparisons between the results.

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]

 

[Total 100 marks]

Description of dataset:

The following describe the numeric attributes. All instances are for women aged at least 21. Values of 0 in fields like blood pressure represent missing values.

The output class indicates if the woman had diabetes (1) or not (0).

  1. Number of times pregnant
  1. Plasma glucose concentration a 2 hours in an oral glucose tolerance test
  1. Diastolic blood pressure (mm Hg)
  1. Triceps skin fold thickness (mm)
  1. 2-Hour serum insulin (mu U/ml)
  2. Body mass index (weight in kg/(height in m)^2)
  3. Diabetes pedigree function
  1. Age (years)
  1. Class variable (0 or 1)

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  • Title: 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.
  • Price: £ 299
  • Post Date: 2018-11-10T10:09:25+00:00
  • Category: Assignment
  • No Plagiarism Guarantee
  • 100% Custom Written

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