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Fundamentals of Data Analytics

· 案例展示

Scenario
You have just started working as a data miner/analyst in the Analytics Unit of a company. The Head of the Analytics Unit has brought you a data set [a welcome present ;-))]. The data set includes two files: a description of the attributes and a table with the actual values of these attributes. The Head of the Analytics Unit has mentioned to you that this is some sort of demographic data that a potential client has provided for analysis. The Head of the Analytics Unit would like to have a report with some insights about that data, that he/she could deliver to the client. Your tasks include:

understanding the specifics of the data set
extracting information about each of the attributes, possible associations between them and other specifics of the data set.
The tasks in the assignment are specified below.

Data sets
Each student is assigned an individual dataset with the actual values of the attributes. Please use the file that is linked to your name in this zipped file: Dataset_Assignment2.zip

The description of the attributes can be found here: Attribute_Description_Assignment2.pdfPreview the document

Tasks
1A. Initial data exploration
(a) Identify the type of all the attributes {BATHRM, HEAT_D, AC, ......., GIS_LAST_MOD_DTTM} (nominal, ordinal, interval or ratio). Please justify why you choose the type.

(b) Identify the values of the summarising properties for all 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.

(c) Using KNIME or other tools, explore your data set 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. In the report include the corresponding snapshots from the tools and 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:

(a) Use the following binning techniques to smooth the values of the PRICE attribute:

equi-width binning
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.

(b) Use the following techniques to normalise the attribute PRICE:

min-max normalization to transform the values onto the range [0.0-1.0].
z-score normalization to transform the values.
In the assignment report provide explanation about each of the applied techniques. In your Excel workbook file place the results in separate columns in the corresponding spreadsheet.

(c) Discretise the AYB attribute into the following categories: Very Old=0-1800; Old=1801-1950; New=1951-2000; Very New=2001 + . Provide the frequency of each category in your data set.

In the assignment report provide explanation about each of the applied techniques. In your Excel workbook file

corresponding spreadsheet.

(d) Binarise the CNDTN_D variable [with values "0" or "1"].

In the assignment report provide explanation about 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 and Submission Information
The deliverables include:

A report, which structure should follow the tasks of the assignment, and
An Excel workbook file with individual spreadsheets for each task (spreadsheets should be labeled according to the task names, for example, "1A"). Each of the results of parts (a) through (d) in task 1B should be presented in a separate spreadsheet (and respectively table in the assignment report).
Report: In the report include a section (starting with a section title) for each of the tasks in this assignment.

Your report will likely be between 20-25 pages in length using 11 or 12 point Times or Arial fonts, including title page and graphs. On average you will require between 15 and 23 hours to complete this assignment.

Use the filename fda_a2_xxxxxx.pdf or fda_a2_xxxxx.doc for the report, where xxxxx is your student id, and fda_a2_xxxxx.xls for the spreadsheet. You may need to zip files to submit to Canvas.

Sl. No.AttributeDescription
1BATHRMNumber of full bathrooms
2HEAT_DHeating description
3ACAir conditioning (Y/N)
4NUM_UNITSNumber of units
5ROOMSNumber of rooms
6BEDRMNumber of bedrooms
7AYBThe earliest time the main portion of the building was
built. It is not affected by subsequent construction.
8YR_RMDLLast year residence was remodeled
9SALEDATEDate of most recent sale
10PRICEPrice of most recent sale
11QUALIFIEDQualified (Q), unqualified (U)
12STYLEStyle code
13STRUCTStructure code
14GRADE_DGrade description
15CNDTN_DCondition description
16EXTWALL_DExterior wall description
17ROOFRoof type code
18INTWALL_DInterior wall description
19KITCHENSNumber of kitchens
20FIREPLACESNumber of fireplaces
21LANDAREALand area of property in square feet
22GIS_LAST_MOD_DTTMLast modified datetime
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