Reference no: EM132400039
FI 4090 Introduction to Financial Data Analytics Assignment - Logistic Regression, Georgia State University, USA
Part 1 - Analyze the data in the CreditCard dataset in AER package. (Note that you have to install AER package and any other additional package that are required by AER)
The following variables are included in the dataset:
1. card: was the application for a card accepted? (Binary: 1/0) Response Variable
2. reports: Number of major derogatory reports
3. income: Yearly income (in USD 10,000)
4. Age: Age in years plus 12ths of a year
5. Owner: Does the individual own his/her home?
6. dependents: number of dependents
7. months: Months living at current address
8. share: ratio of monthly credit card expenditure to yearly income
9. selfemp: Is the individual self-employed?
10. majorcards: number of major credit cards held
11. active: number of active credit accounts
12. expenditure: average monthly credit card expenditure
Use variables 2 to 8 to determine which of the predictors influence the probability that an application is accepted.
A. Provide summary stat of the predictors.
B. There are some values of variable age under one year. Consider data with age>18 for your analysis for the rest of the questions.
C. Plot of income vs. reports (Number of major derogatory reports): mark individuals with card application accepted as blue, and not accepted as red.
D. Boxplots of income as a function of card acceptance status. Boxplots of reports as a function of card acceptance status (mark card application accepted as blue, and not accepted as red). (Display two boxplots in same page).
E. Construct the histogram for the predictors.
Note that share is highly right-skewed, so log(share) will be used in the analysis. reports is also extremely right skewed (most values of reports are 0 or 1, but the maximum value is 14. To reduce the skewness, log(reports+1) will be used for your analysis. Highly skewed predictors have high leverage points and are less likely to be linearly related to the response.
F. Use variables 2 to 8 to determine which of the predictors influence the probability that an application is accepted. Use the summary function to print the results.
G. To predict whether the application will be accepted or not, convert the predicted probabilities into class labels yes with the following condition: probs >.5="yes". Compute the confusion matrix and overall fraction of correct predictions.
Online Quiz will be based on the following and related questions: Explain what the confusion matrix is telling you about the types of mistakes made by logistic regression (false positive, false negative, overall correct predictions).
H. Now fit the logistic regression model using a training data for observations 1 to 1000. Compute the confusion matrix and the overall fraction of correct predictions for the test data (that is, the data for observations 1001 to end of data.)
Useful hints:
1. Run the glm() logistic regression on the training data.
2. Use test data in the predict() function to predict the card acceptance probability on the test observations on the basis of the predicted model.
3. To predict whether a card will be accepted (yes/no), convert the predicted probabilities into class labels "yes" or "no" on the test data.
4. Compute the accuracy of the model on the test observations based on predicted (yes/no) and actual (yes/no) of card acceptance.
Deliverables:
1. Submit R scripts.
2. Please submit one R program (one file) containing all the parts of the assignment. R code should provide comments on each sections of the assignment the code is intended for.
Attachment:- Financial Data Analytics Assignment File.rar