Personal Loan Acceptance.

 

Personal Loan Acceptance. Universal Bank is a relatively young bank growing rapidly in terms of overall customer acquisition. The majority of these customers are liability customers (depositors) with varying sizes of relationship with the bank. The customer base of asset customers (borrowers) is quite small, and the bank is interested in expanding this base rapidly to bring in more loan business. In particular, it wants to explore ways of converting its liability customers to personal loan customers (while retaining them as depositors).
A campaign that the bank ran last year for liability customers showed a healthy conversion rate of over 9% success. This has encouraged the retail marketing department to devise smarter campaigns with better target marketing. The goal is to use k-NN to predict whether a new customer will accept a loan offer.
This will serve as the basis for the design of a new campaign.

The file UniversalBank.xls contains data on 5000 customers. The data include customer demographic information (age, income, etc.), the customer’s relationship with the bank (mortgage, securities account, etc.), and the customer response to the last personal loan campaign (Personal Loan). Among these 5000 customers, only 480 (= 9.6%) accepted the personal loan that was offered to them in the earlier campaign.

Transform categorial predictors with more than two categories into dummy variables, then, partition the data into training (60%) and validation (40%) sets.

a. Consider the following customer:
Age=40, Experience = 10, Income = 84, Family = 2, CCAvg = 2, Education_1 = 0, Education_2 = 1, Education_3 = 0, Mortgage = 0, Securities Account = 0, CD Account = 0, Online = 1, and Credit Card = 1. Perform a k-NN classification with all predictors except ID and ZIP code using k = 1. Specify the success class as 1 (loan acceptance), and use the default cutoff value of 0.5. How would this customer be classified?

b. What is a choice of k that balances between overfitting and ignoring the predictor information?

c. Show the classification matrix for the validation data that results from using the best k.

d. Consider the following customer: Age = 40, Experience = 10, Income = 84, Family = 2, CCAvg = 2, Education_1 = 0, Education_2 = 1, Education_3 = 0, Mortgage = 0, Securities Account = 0, CD Account = 0, Online = 1 and Credit Card = 1. Classify the customer using the best k.

e. Repartition the data, this time into training, validation, and test sets (50% : 30% : 20%). Apply the k-NN method with the k chosen above. Compare the classification matrix of the test set with that of the training and validation sets. Comment on the differences and their reason.

Sample Solution

in any given instance. Our identities are comprised of what we know best of our relations to self, others and the world. The conclusive link between identity formation and music lies in the precise semiotic character of these activities. Thomas Turino’s theory of semiotics is a useful lens for looking at the unconscious political effects that music often has. American philosopher Charles Sanders Peirce created a theory of signs known as semiotics. A sign is anything that indicates something else. Every sign has three features: the sign or sign vehicle, the objector idea indicated by the sign, and the effect or meaning of the sign- object relation to the perceiver. The effect signs have can range from physical reactions to different thoughts, ideas or memories coming back to the mind of the perceiver. Turino identifies three different kinds of semiotic relationships in music: Icon, index and symbol. These three kinds of semiotic relationships create distinct and powerful responses to the listener.

An icon relationship is where people make connections based on resemblance. For example, resemblance in music can be recognising it belonging to a genre as it sounds like other songs that you’ve heard before; you can identify rap music through stylised rhythmic tunes. As Turino puts it, “icons can spur imaginative connections of resemblance between the signs perceived and the objects stood for in light of the internal context of the perceiver”. Whether intended by the artist or not; sounds or lyrics in music may resemble other ideas outside of music to the listener.

The second type of semiotic relationship is an index. Indexes are signs that point to objects or ideas they represent, this applies to music associated with a concept or occasion. For example, a national anthem at a sporting event becomes an index of patriotism. Indexical responses often happen when listening to music such as when advertisements play a jingle connected to a product, that jingle becomes an index to the product. Semantic snowballing happens when new indices are added to old ones, creating a variety of different meanings. One example is how the Civil Rights Movement used pre-existing tunes that indexed the church and progressive labour movements and set new lyrics about civil rights to these tunes. This combined old associations of religious righteousness with progressive politics, adding historical depth and power. Because indexes link a song with a personal event, indexes tend to be the most personal type of semiotic relationship and often evoke the most emotional and powerful responses.

The third sign is the symbol: language is a system of symbols, wherein each word or phrase has a definite and consistent meaning, albeit often contextually defined. Words are usually shortcuts for somet

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