EXAM 2<\/p>\n
Total: 425 points<\/p>\n
What to Submit<\/p>\n
You will submit 5 files. D2L will allow multiple submissions. Submit one file at a time. Details follow.<\/p>\n
One file. File Name: Exam 2_537_LastName_FirstName.docx or .pdf (no .zip). In this file, post screenshots for each part in sequence. If you cannot do a part, state that!<\/p>\n
Q1.R In this file submit the R-code only. (if .R extension is disallowed by D2L, try .zip or submit as .doc) <\/p>\n
Q2.R In this file submit the R-code only. <\/p>\n
Q3.R In this file submit the R-code only. <\/p>\n
Q4.R In this file submit the R-code only. <\/p>\n
Q5.R In this file submit the R-code only. <\/p>\n
Include comments in your code.<\/p>\n
Make sure your code is written nicely (i.e. adequate tabs, spaces, etc.)<\/p>\n
Failure to adhere to any of the above instructions will result in point-deductions. <\/p>\n
Q1 150 points<\/p>\n
File to Use: Travel.csv <\/p>\n
The Travel records contained in this file show the traveler miles accumulated by a flier, and how the miles were obtained (e.g. bought by a Credit card, etc.) or spent. There are more than 4000 records. Your goal is to find any commonalities in the flier data for targeted marketing. <\/p>\n
Apply hierarchical clustering on the given data (normalize the data first). How many clusters appear?<\/p>\n
If you don\u2019t normalize, what happens then? <\/p>\n
Now Remove 5% of the data randomly (i.e. take a random sample of 95% of the records), and repeat the analysis. Does the dendrogram look the same?<\/p>\n
Use k-means clustering with the number of clusters that you found in part A. Are the results comparable\/similar?<\/p>\n
Which clusters would you target for offers, and what types of offers would you target to fliers in that cluster?<\/p>\n
Q2 50 points<\/p>\n
File to Use: Transacts.csv <\/p>\n
The Transactions contained in this file show the records of items customers bought from a store over a time-period. There are more than 9,500 records of more than 150 items.<\/p>\n
Your task is to perform a market basket analysis with a support of 0.008, and confidence of 0.2. Use transactions that have a minimum of three items in them. <\/p>\n
What is the total number of rules you get? <\/p>\n
Now sort the rules by lift, and state the top most rule in Normal English. <\/p>\n
Q3 75 points<\/p>\n
File to Use: iPhone.csv <\/p>\n
A bank has partnered with Apple to identify whether a given customer will be a prospective iPhone buyer or not. The .csv file gives the info on the Income of the individuals and their bank savings (in $1000s) along with their ownership status. <\/p>\n
What type of regression is best suited in this case, and why?<\/p>\n
Create a colored scatter plot of Income vs. Savings and distinguish between iPhone owners and nonowners. Looking at the plot, owners have a higher avg. income, or nonowners?<\/p>\n
What are the chances that an individual with a $65,000 income and savings of $25,000 is an owner?<\/p>\n
Q4 100 points<\/p>\n
File to Use: ToyotaCorollaFinal.csv <\/p>\n
The file is an extension of the example you saw in the Primer posted on D2L. It includes the sales price and other information on the car, such as its age, odometer mileage, fuel type, horsepower, etc. There are more than 35 attributes in total, and more than 1400 records. <\/p>\n
Your goal is to predict the price of a used car from the given data and specifications. For this purpose, use 50% of the records for training, keep 30% for validation, and then use the remaining 20% of the records for testing. <\/p>\n
For the outcome variable Price, use the following predictor variable set: Car_Age, Odometer, Fuel_Type, HP, Automatic, Doors, Qtr_Tax, Build_Guarantee, Guarantee_Months, Cold_Air, Cold_Air_Auto, CD_Player, Powered_Windows, Sports, and Towing.<\/p>\n
From the above-mentioned predictors, which (say 3 or 4) are most important for predicting the price of the car. <\/p>\n
Now, the predictors that you deem most important, use them to assess the model performance (for predicting prices).<\/p>\n
Q5 50 points<\/p>\n
Nike makes two shoes AirMax 97 and AirGlide. <\/p>\n
The AirMax 97\u2019s rubber-sole thickness is computed to be normally distributed with a mean of 8 mm, and standard deviation 2 mm. There is a 100,000 steps warranty on AirMax 97. But to last that much, the rubber-sole should be \u2267 5.9mm; otherwise the shoe is sold as AirGlide (lower warranty on this product).<\/p>\n
What proportion of tires are expected to be sold as AirGlide.<\/p>\n
If Nike wants that 25% of the shoes produced should be sold as AirGlide (say per market demand), then what should be the rubber-sole thickness threshold set as (formerly set as 5.9mm)?<\/p>\n
NOTE:<\/p>\n
Due to the nature of questions, and choice of solutions, the chances of Your code matching another student\u2019s code are slim. Thus, please do NOT plagiarize!<\/p>\n","protected":false},"excerpt":{"rendered":"
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