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A bank manager would like to predict whether a customer will apply for a loan or not based on the history of the customer activities. This information will help the manager to make decision on marketing planning towards the customer and allocation bratet for the loans. Low 3500 Yes Table 1: Customer Loan Application Customer Income Loan ID Level Applied C123 Middle No C124 High Yes C125 Yes C126 Middle Yes C127 Middle Yes C128 High C129 Low No C130 Low No C131 Middle Yes C132 Low C133 Middle No C134 High Yes C135 High Yes C136 Low No C137 Middle ? TTTTT Table 2: Customer Preferences on Smartphone Monthly Customer Income Work No ID (RM) Gender Status 1 C171 Male Fulltime 2 C172 4000 Male Fulltime 3 C173 2500 Female Parttime C174 3000 Female Parttime 5 C175 5500 Male Fulltime 6 C176 4050 Male Fulltime 7 C177 2000 Female Parttime 8 C178 1500 Male Parttime 9 C179 6000 Female Fulltime 10 C180 1500 Male Parttime 11 C181 1800 Female Parttime Loan Applied No Neutral Yes Yes Neutral Yes Neutral Yes No No ? Yes a) Conduct data mining analysis by using the Nave Bayes data mining algorithm for table 1. (7marks) b) Conclude the pattern or decision making derived from the analysis in Question 3(a). (8 marks) c) Conduct data mining analysis by using the kNN data mining algorithm for table 2. (12 marks) d) Conclude the pattern or decision making derived from the analysis in Question 3(c)