MIS772: Predictive Analytics – Mini Case Study – Report Writing Assessment Answer

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Code: MIS772

Predictive Analytics Mini Case Study Assessment Answer

Assignment Task:

This mini case study will be used in all workshops of module 1, i.e. M1T1-M1T4. All amendments, extensions and assumptions should be recorded in the final submission. Australian Wine Importers (AWI) asked you to develop a data mining method of classifying imported wines based on:
• Price category (as 5 equal size bins)
AWI provided you with a sample of 130,000 wine tasting results, which include:
? Taster name and twitter handle;
? Wine “title” (name + vintage);
? Country, Province and Region;
? Variety and Winery;
? Description and Designation (text data);
? Price (US$) and Points (taster’s rating).
As there are great many tasting results, AWI would like to get the preliminary insight into the wine’s origin and its marketability. The following questions are of interests to AWI:
A) What is the best source of wine in the optimum price-rating ratio? and,
B) What is the expected price range category of newly imported wines?
C) Can all wine tasters in the data set be trusted with their tasting results?
AWI wants you to cleanup and explore wine tasting data, develop and evaluate a classifier to determine the price range for new wines, and to minimize classification errors.
In technical terms:
Your project objectives form a learning portfolio. The first objective (LP1) is to acquire and explore the available data, visualise and report any significant characteristics of non-text data, as well as, prepare the data for further processing. The second objective (LP2) is to create a classification system able to answer management questions using only non-text data. Text processing will be featured in assignment A2. Reports in PDF format and models developed in LP1 and LP2 in ZIP archives are to be submitted via CloudDeakin by their respective deadlines.

You can use the above form to estimate the expected mark against the rubric (see the assignment “info” document). Be realistic and note that we will find many problems you may not be aware of. Assume that markers may be tired when assessing your work and they may miss some important aspects of your submission when not presented clearly, or when you deviate from the structure of this template, or if youn do not include them in your report. So be clear, number all tables, charts and screen shots used as evidence, describe all visuals, cross-reference your analysis with evidence. Submit this report in PDF format to avoid accidental reformatting of the content. Submit all RapidMiner processes (.RMP files) in a separate ZIP archive, so that if there is any doubt we could load your work and replicate your results (we will not do this to find missing report parts). Ensure that the report is readable and the font is no smaller than Arial 10 points. In the report include only the most significant results for your analysis and recommendations. You will be able to submit your work once only so make sure you get it right – check these before posting on CloudDeakin: Is this your document? Is this the correct unit, assignment, year and trimester? Is your name entered above? Is the group number included and is it correct? Are names of your group members entered as well? Are all pages included? Does it all fit into the required page limit? Have you zipped all RapidMiner files (.RMP files)? Is the report contents yours alone?

University: Deakin University

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