For this chapter$$s exercise, you will compile your own data set based on people you know and the cars they drive, and then create a linear discriminant analysis, k$-$NN$,$ and Na$$ve Bayes of your data in order to predict categories for a scoring data set. Complete the following steps:

$1)$ Open a new blank spreadsheet in OpenOffice Calc or another spreadsheet program of your choice. At the bottom of the spreadsheet there will be three default tabs labeled Sheet$1,$ Sheet$2,$ Sheet$3.$ Rename the first one Training and the second one Scoring. You can rename the tabs by double clicking on their labels. You can delete or ignore the third default sheet.

$2)$ On the training sheet, starting in cell A$1$ and going across, create attribute labels for six attributes: Age, Gender, Marital$\_$Status, Employment, Housing, and Car$\_$Type.

$3)$ Copy each of these attribute names except Car$\_$Type into the Scoring sheet.

$4)$ On the Training sheet, enter values for each of these attributes for several people that you know who have a car. These could be family members, friends and neighbors, coworkers or fellow students, etc. Try to do at least $20$ observations; $30$ or more would be better. Enter husband and wife couples as two separate observations, so long as each spouse has a different vehicle. Use the following to guide your data entry:

a$.$ For Age, you could put the person$$s actual age in years, or you could put them in buckets. For example, you could put $10$ for people aged $10-19$; $20$ for people aged $20-29$; etc.

b$.$ For Gender, enter $0$ for female and $1$ for male.

c$.$ For Marital$\_$Status, use $0$ for single, $1$ for married, $2$ for divorced, and $3$ for widowed.

d$.$ For Employment, enter $0$ for student, $1$ for full$-$time, $2$ for part$-$time, and $3$ for retired.

e$.$ For Housing, use $0$ for lives rent$-$free with someone else, $1$ for rents housing, and $2$ for owns housing.

f$.$ For Car$\_$Type, you can record data in a number of ways. This will be your label, or the attribute you wish to predict. You could record each person$$s car by make $($e$.$g$.$ Toyota, Honda, Ford, etc.$),$ or you could record it by body style $($e$.$g$.$ Car, Truck, SUV, etc.$).$ Be consistent in assigning classifications, and note that depending on the size of the data set you create, you won$$t want to have too many possible classifications, or your predictions in the scoring data set will be spread out too much. With small data sets containing only $20-30$ observations, the number of categories should be limited to three or four. You might even consider using Japanese, American, European as your Car$\_$Types values.

$5)$ Once you$$ve compiled your Training data set, switch to the Scoring sheet in OpenOffice Calc. Repeat the data entry process for at least $20$ people $($more is better$)$ that you know who do not have a car. You will use the training set to try to predict the type of car each of these people would drive if they had one.

$6)$ Use the File $>$ Save As menu option in OpenOffice Calc to save your Training and Scoring sheets as CSV files.

$7)$ Either import your two CSV files into your RapidMiner respository, being sure to give them descriptive names, or read them into a new process using Read CSV$.$

$8)$ If you have prepared your data well in OpenOffice Calc, you shouldn$$t have any missing or inconsistent data to contend with, so data preparation should be minimal. Rename the two Retrieve operators $($or Read CSV operators$)$ so you can tell the difference between your training and scoring data sets.

$9)$ One necessary data preparation step is to add a Set Role operator and define the Car$\_$Type attribute as your label.

$10)$ Add a Linear Discriminant Analysis operator to your Training stream.

$11)$ Apply your LDA model to your scoring data and run your model. Evaluate and report your results. Did you get any confidence percentages? Do the predicted Car$\_$Types seem reasonable and consistent with your training data? Why or why not?

$12)$ Change your model operator to k$-$NN$,$ then to Na$$ve Bayes. Compare and contrast the results the outputs from the three modeling methodologies. Describe and discuss the differences.

Please turn in your training data, scoring data and answer document.