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A Bank Wants to Understand Better the Details of Customers

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A bank wants to understand better the details of customers who are likely to default the loan. In order to analyze this, the data from a random sample of 200 customers are given below:
A bank wants to understand better the details of customers who are likely to default the loan. In order to analyze this, the data from a random sample of 200 customers are given below:                     In XLMiner's Partition with Oversampling procedure, partition the data so there is 50 percent successes (Loan default) in the training set and 40 percent of the validation data is taken away as test data. Classify the data using k-nearest neighbors with up to k = 10. Use Loan default as the output variable and all the other variables as input variables. In Step 2 of XLMiner's k-nearest neighbors Classification procedure, be sure to Normalize input data and to Score on best k between 1 and specified value. Generate lift charts for both the validation data and test data.  a. For the cutoff probability value 0.5, what value of k minimizes the overall error rate on the validation data? b. What is the overall error rate on the test data? Interpret this measure.  c. What are the Class 1 error rate and the Class 0 error rate on the test data?  d. Compute and interpret the sensitivity and specificity for the test data. e. Examine the decile-wise lift chart on the test data. What is the first decile lift on the test data? Interpret this value.
A bank wants to understand better the details of customers who are likely to default the loan. In order to analyze this, the data from a random sample of 200 customers are given below:                     In XLMiner's Partition with Oversampling procedure, partition the data so there is 50 percent successes (Loan default) in the training set and 40 percent of the validation data is taken away as test data. Classify the data using k-nearest neighbors with up to k = 10. Use Loan default as the output variable and all the other variables as input variables. In Step 2 of XLMiner's k-nearest neighbors Classification procedure, be sure to Normalize input data and to Score on best k between 1 and specified value. Generate lift charts for both the validation data and test data.  a. For the cutoff probability value 0.5, what value of k minimizes the overall error rate on the validation data? b. What is the overall error rate on the test data? Interpret this measure.  c. What are the Class 1 error rate and the Class 0 error rate on the test data?  d. Compute and interpret the sensitivity and specificity for the test data. e. Examine the decile-wise lift chart on the test data. What is the first decile lift on the test data? Interpret this value.
A bank wants to understand better the details of customers who are likely to default the loan. In order to analyze this, the data from a random sample of 200 customers are given below:                     In XLMiner's Partition with Oversampling procedure, partition the data so there is 50 percent successes (Loan default) in the training set and 40 percent of the validation data is taken away as test data. Classify the data using k-nearest neighbors with up to k = 10. Use Loan default as the output variable and all the other variables as input variables. In Step 2 of XLMiner's k-nearest neighbors Classification procedure, be sure to Normalize input data and to Score on best k between 1 and specified value. Generate lift charts for both the validation data and test data.  a. For the cutoff probability value 0.5, what value of k minimizes the overall error rate on the validation data? b. What is the overall error rate on the test data? Interpret this measure.  c. What are the Class 1 error rate and the Class 0 error rate on the test data?  d. Compute and interpret the sensitivity and specificity for the test data. e. Examine the decile-wise lift chart on the test data. What is the first decile lift on the test data? Interpret this value.
A bank wants to understand better the details of customers who are likely to default the loan. In order to analyze this, the data from a random sample of 200 customers are given below:                     In XLMiner's Partition with Oversampling procedure, partition the data so there is 50 percent successes (Loan default) in the training set and 40 percent of the validation data is taken away as test data. Classify the data using k-nearest neighbors with up to k = 10. Use Loan default as the output variable and all the other variables as input variables. In Step 2 of XLMiner's k-nearest neighbors Classification procedure, be sure to Normalize input data and to Score on best k between 1 and specified value. Generate lift charts for both the validation data and test data.  a. For the cutoff probability value 0.5, what value of k minimizes the overall error rate on the validation data? b. What is the overall error rate on the test data? Interpret this measure.  c. What are the Class 1 error rate and the Class 0 error rate on the test data?  d. Compute and interpret the sensitivity and specificity for the test data. e. Examine the decile-wise lift chart on the test data. What is the first decile lift on the test data? Interpret this value.
A bank wants to understand better the details of customers who are likely to default the loan. In order to analyze this, the data from a random sample of 200 customers are given below:                     In XLMiner's Partition with Oversampling procedure, partition the data so there is 50 percent successes (Loan default) in the training set and 40 percent of the validation data is taken away as test data. Classify the data using k-nearest neighbors with up to k = 10. Use Loan default as the output variable and all the other variables as input variables. In Step 2 of XLMiner's k-nearest neighbors Classification procedure, be sure to Normalize input data and to Score on best k between 1 and specified value. Generate lift charts for both the validation data and test data.  a. For the cutoff probability value 0.5, what value of k minimizes the overall error rate on the validation data? b. What is the overall error rate on the test data? Interpret this measure.  c. What are the Class 1 error rate and the Class 0 error rate on the test data?  d. Compute and interpret the sensitivity and specificity for the test data. e. Examine the decile-wise lift chart on the test data. What is the first decile lift on the test data? Interpret this value.
In XLMiner's Partition with Oversampling procedure, partition the data so there is 50 percent successes (Loan default) in the training set and 40 percent of the validation data is taken away as test data. Classify the data using k-nearest neighbors with up to k = 10. Use Loan default as the output variable and all the other variables as input variables. In Step 2 of XLMiner's k-nearest neighbors Classification procedure, be sure to Normalize input data and to Score on best k between 1 and specified value. Generate lift charts for both the validation data and test data.
a. For the cutoff probability value 0.5, what value of k minimizes the overall error rate on the validation data?
b. What is the overall error rate on the test data? Interpret this measure.
c. What are the Class 1 error rate and the Class 0 error rate on the test data?
d. Compute and interpret the sensitivity and specificity for the test data.
e. Examine the decile-wise lift chart on the test data. What is the first decile lift on the test data? Interpret this value.


Definitions:

Working Capital

The variance between a business's present assets and its present debts, which demonstrates the firm's liquidity.

Common Stock

A type of equity security that represents ownership in a corporation, with rights to vote on corporate matters and receive dividends.

Market Price

The existing rate for buying or selling a product or service in a market environment.

Working Capital

The difference between a company's current assets and current liabilities, indicating the short-term financial health.

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