Tasks
1A. Initial data exploration
1. Identify the attribute type of each attribute in your dataset. If it's not clear, you may need to justify why you chose the type.
2. Identify the values of the summarising properties for the attributes, including frequency, location and spread (e.g. value ranges of the attributes, frequency of values, distributions, medians, means, variances, percentiles, etc. - the statistics that have been covered in the lectures and materials given). Note that not all of these summary statistics will make sense for all the attribute types, so use your judgement! Where necessary, use proper visualisations for the corresponding statistics.
3. Using KNIME or other tools, explore your dataset and identify any outliers, clusters of similar instances, "interesting" attributes and specific values of those attributes. Note that you may need to 'temporarily' recode attributes to numeric or from numeric to nominal. The report should include the corresponding snapshots from the tools and an explanation of what has been identified there.
Present your findings in the assignment report.
1B. Data preprocessing
Perform each of the following data preparation tasks (each task applies to the original data) using your choice of tool:
1. Use the following binning techniques to smooth the values of the following two attributes:
• - Departure Delay in Minutes
• - Arrival Delay in Minutes
For each attribute, you must apply:
I. Equi-width binning
II. Equi-depth binning
In the assignment report, for each of these techniques, you need to illustrate your steps. In your Excel workbook file place the results in separate columns in the corresponding spreadsheet. Use your judgement in choosing the appropriate number of bins - and justify this in the report.
2. Use the following techniques to normalise the following attribute:
• - Flight Distance
For this attribute, you must apply:
I. min-max normalization to transform the values onto the range [0.0-1.0].
II. z-score normalization to transform the values.
The assignment report provides an explanation of each of the applied techniques. In your Excel workbook file place the results in separate columns in the corresponding spreadsheet.
3. Discretise the AGE attribute into the following categories:
o Children and Adolescents (0-18 years)
o Young adults (18-35 years)
o Adults (35-65 years)
o Elderlies (65-100 years)
Provide the frequency of each category in your dataset.
Your assignment report should provide an explanation of each of the applied techniques. In your Excel workbook file place the results in a separate column in the corresponding spreadsheet.
4. Binarise the CLASS variable [with values "0" or "1"].
Your assignment report should provide an explanation of the applied binarisation technique. In your Excel workbook file place the results in separate columns in the corresponding spreadsheet.
1C. Summary
At the end of the report include a summary section in which you summarise your findings. The summary is not a narrative of what you have done, but a condensed informative section of what you have found about the data that you should report to the Head of the Analytics Unit. The summary may include the most important findings (specific characteristics (or values) of some attributes, important information about the distributions, some clusters identified visually that you propose to examine, associations found that should be investigated more rigorously, etc.).
Deliverables
The deliverables are:
• A report, for which the structure should follow the tasks of the assignment, and
• An Excel workbook file with individual spreadsheets for each task (spreadsheets should be labelled according to the task names, for example, "1A"). Each of the results of parts (a) to (d) in task 1B should be presented in a separate sheet (and respectively table in the assignment report).
In the report, include a section (starting with a section title) for each of the tasks in the assignment.
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