Instructions
• Use the data from the Final-WineData.xlsx file. This workbook is comprised of three sheets as described below.
• Be sure to begin by going through the process of examining the data and cleaning it up as needed, using ideas discussed throughout the course.
• Please organize your submission in the form of either a presentation or a memo. You should put your submission (including any/all graphs, tables, and other relevant numerical results) in a single file (PPT, PDF, or MS Word).
• Regardless of which of these formats you choose, be sure to tell a story that speaks clearly to the client’s specific situation and questions. Also, feel free to include other discoveries that you believe may be of interest and/or of value to the client and her team.
• You should feel free to provide some additional technical details (for a more statistically focused team member) - if you choose to do so, please put these in an Appendix.
• Any numerical information - even if “correct” - will lose points if presented without a clear and appropriate explanation.
As you know, I have been trying to take a more scientific approach to develop new wines ever since I went to that wine chemistry seminar a few years ago. We have managed to get our hands on a big database that describes the chemical properties of a few thousand as well as a measurement of how each of these wines was rated by a group of (sober) product testers.
We were hoping you’d be able to help myself and a few others on the Product Team by helping us make sense of what it is telling us, and to help us come up with a quality prediction for a new group of wines that we have in “beta” right now.
Attached to this memo is the dataset I mentioned above. The first tab (“info”)
includes a description of the fields, the second tab (“training”) contains the wine chemistry information and taster ratings for almost 5000 wines that are currently on the market, and the third tab (“test”) includes chemistry information about the new wines that we have in development.
Our goal is to use the data we have about wine chemistry and taster ratings to try to predict how consumers are going to like each of these new wines (we will not be able to bring all of them to market, so we want to focus our development efforts on the ones that we think consumers are going to like the best).
Based on the historical data, we would like you to put together a model that we can use to forecast the how tasters will rate each of our new wines. We know that there may be lots of other variables that we could (and should) use for a forecasting model but for now let’s just use these wine chemistry fields (there’s a description of each of the variables in one of the worksheets too).
We’re not even sure actually if all of these variables are helpful in a forecasting model. We are hoping you know some ways to determine which variables are most important and which ones are going to contribute to the most accurate predictions.
A few things we’re hoping your work addresses:
1. If you have to clean up the data to deal with things like missing values and
outliers, please give us a quick summary of what kinds of things you found
and what you did to deal with these issues.
2. We also expect to see a brief description of your overall modeling and prediction approach. You should also include a detailed description of your model, perhaps in an Appendix (you don’t want to lose us right in the middle of your summary).
3. Based on your analysis, which of the wine chemistry variables are most
helpful for predicting taster ratings, which ones are least important but still helpful for forecasting, and which ones are not useful at all?
4. Which of the wines under development do you predict will have the highest taster ratings? How confident are you about your prediction? What metrics do you have to support this assessment? Is your forecast any better than just guessing by taking the average rating for the whole database?
5. Are there other variables that you would like to capture in the future in order to improve your prediction accuracy? If so, please describe the data that you would hope to have access to in the future, and also explain why you think these particular variables might be helpful in predicting ratings.
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