The provided assignment content is about Principal Component Analysis (PCA) and Naive Bayes classification. The PCA part involves creating a pivot table for the training data with Online as a column variable, CC as a row variable, and Loan as a secondary row variable. The values inside the cells should convey the count (how many records are in that cell). Then, consider the task of classifying a customer who owns a bank credit card and is actively using online banking services. Analyze the pivot table and calculate the probability that this customer will accept the loan offer. Furthermore, design two separate pivot tables for the training data, one with Loan (rows) as a function of Online (columns) and the other with Loan (rows) as a function of CC. Compute the conditional probabilities P(A|B) for several events, such as P(CC=1|Loan=1), P(Online=1|Loan=1), and so on. Finally, use these quantities to compute the Naive Bayes probability P(Loan=1|CC=1, Online=1).
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