A bank wants to give credit card offers to its customers. Currently, they look at the details of each customer and based on this information, decide which offer should be given to which customer. The bank can potentially have millions of customers. Certainly, it does not make sense to look at the details of each customer separately and then make a decision. It is a manual process and will take a huge amount of time. The bank wants an intelligent system that can predict the potential customers eligible for receiving credit cards. One option to develop such system is to segment the bank customers into different groups based on their income, like High Income, Average Income, and Low Income. The bank can now make three different strategies or offers, one for each group. Here, instead of creating different strategies for individual customers, they only have to make 3 strategies. This will reduce the effort as well as the time. Keeping in view, the algorithms you have learned for machine learning and artificial intelligence answer the following questions.

a. What type of algorithm(s) can be used to segment the bank customers into different groups?

b. Will it be a supervised or unsupervised learning problem?

(e) When will the Breadth-First search be a special case of uniform-cost search?

(f) A relaxed question: In this hot summer with such a tough studies schedule and of course final exams! You deserve an easy question.

What is 1+1?

a. Not this one

b. Still not this one

c. 2

d. You have gone too far, go back to c