Problem Context for Q$5$: $(25$ points$)$

Ana Gomez, a data analyst at Cha$-$Ching Bank has compiled data on $500$ past customers to whom Cha$-$Ching Bank marketed its Home Equity Line of Credit $($HELOC$)$ product. The data includes the age, sex, income, and whether or not the customer responded to the HELOC offer. Ana would like to team up with you to accomplish two data mining tasks:

$($a$)$ Develop a k$-$NN model for predicting whether or not a bank customer will respond to a HELOC offer.

$($b$)$ Identify for each of the $20$ new customers if they are likely to respond to a HELOC offer.

Follow the k$-$NN optimization $($with normalization$)$ process as shown the example process $07-01-$RidingMowers k$-$NN Optimized Normalized.rmp with some changes as described below:

$$ Make a copy of the RidingMowers process mentioned above. Rename the process by right$-$clicking it$.$ Double$-$click and load this process on the RapidMiner canvas to start making changes to it$.$

$$ Import HELOC.csv and HELOC$-$score.csv data into RapidMiner repository.

$$ Load the files in the process appropriately $($connect them instead of the existing data files$).$

$$ Remove the Nominal to Binominal operator from the original process.

$$ Instead, use the Numerical to Binominal operator to convert HELOC outcome variable to a binominal attribute.

$$ Use the Set Role operator to set HELOC as the label role.

$$ In the Edit Parameter Settings panel of the Optimize Parameters $($Grid$)$ operator, change the range of k to vary from a minimum of $1$ to maximum of $50$ in $25$ steps $($linear scale$).$

$$ Inside the Optimize Parameters $($Grid$)$ operator, change the split ratio of the Validation $($Split Validation$)$ operator to $0\mathrm{.}75$ split ratio with stratified sampling.

$$ In the k$-$NN operator, change the measure types to MixedMeasures and mixed measure to MixedEuclideanDistance $($since we have $2$ numeric and $1$ categorical attribute $($Sex$)).$

$$ In the Performance $($Binominal Classification$)$ operator, set the positive class to true and main criterion for optimization to f$\_$measure.

Run the process. Report the following results and provide your interpretation $($important$).$

$1.$ What is the optimal k value obtained?

$2.$ What is the optimal $($f measure$)$ value for the validation partition?

$3.$ What is the AUC of your model?

$4.$ What is the precision, recall and accuracy of the model?

$5.$ Provide screenshots of the following:

a$.$ Confusion matrix obtained from the Performance operator

b$.$ Result from Optimize Parameters $($Grid$)$ showing the optimal k$-$value selected

c$.$ Result with table showing all the k$-$values and performance metrics. Sort by f$\_$measure in descending order.

d$.$ Show the $20$ new customer data, clearly showing the confidence$($true$),$ confidence$($false$),$ and the prediction$($HELOC$)$ columns.