Q1 100 points File to Use: ToyotaCorollaFinal.csv 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. 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. 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.
A. From the above-mentioned predictors, which (say 3 or 4) are most important for predicting the price of the car.
B. Now, the predictors that you deem most important, use them to assess the model performance (for predicting prices).
Q2 75 points File to Use: LawnMowers.csv A marketing company is tasked by a lawn mower manufacturer to identify whether a given household will be a prospective owner or not. The given file lists a sample of households that the company collected classifying the households on the basis of Income (in $1000s) and Lot Size (in 1000 sq. ft).
A. What type of regression is best suited in this case, and why?
B. Create a colored scatter plot of Income vs. Lot Size and distinguish between owners and nonowners. Looking at the plot, owners have a higher avg. income, or nonowners?
C. What are the chances that a household with a $50,000 income and a lot size of 25,000 sq. ft. is an owner?
Q3 150 points File to Use: Airlines.csv Frequent flier data for almost 4000 participants is listed in the file. It includes the participants’ mileage history, and how the miles were attained (e.g. using Credit card, etc.) or spent in the previous year. Our goal is to look for groups of passengers that may have some underlying commonalities for the purpose of targeted marketing.
A. Apply hierarchical clustering on the given data (normalize the data first). How many clusters appear?
B. If you don’t normalize, what happens then?
C. Cluster stability check: 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?
D. Use k-means clustering with the number of clusters that you found in part A. Are the results comparable/similar?
E. Which clusters would you target for offers, and what types of offers would you target to customers in that cluster?
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