Answer the following questions based on the city that is assigned to you by your professor. $($If it is not assigned to you, contact your professor$)$

Do not analyze the whole data$-$set

Filter the data$-$set and work on your own city

Question $1$

Divide the data set to Train and Test Data Sets

Question $2$a

Use the train data and create a Na$$ve Bayes Classifier for predicting whether the customer will buy electronics based on the combination of two variables family income and family size $.$

Question $2$b

Use the test data and your model and make predictions regarding whether the customer will buy electronics based on the combination of two variables family income and family size.

Question $3$a

Use the train data and K$-$Nearest Neighbor Classifier for predicting whether the customer will buy electronics based on the combination of two variables Educational Years and family size.

Question $3$b

Use the test data and your model and make predictions regarding whether the customer will buy electronics based on the combination of two variables Educational Years and family size.

Question $4$a

Compose the confusion matrix of Naive Bayes Classifier

Compose the confusion matrix of K$-$Nearest Neighbor

decide which model has better accuracy.

Question $4$b

Compose the ROC $,$ gain and lift charts of the Naive Bayes model

Compose the ROC $,$ gain and lift charts of the K$-$Nearest Neighbor model

which model is better? Argue why?

Question $5$

Use k$-$means clustering, what would be an optimum model that would cluster the buyers based on family income and family size? develop a K$-$Means model for clustering and Visualize your clusters

Question $6$

Using Hierarchical Clustering what is optimum number of clusters of customers do you detect based on EdYears and FamilySize? develop a Hierarchical clustering model and Visualize your clusters