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How many variables are in German credit data?
The German Credit Data contains data on 20 variables and the classification whether an applicant is considered a Good or a Bad credit risk for 1000 loan applicants. Here is a link to the German Credit data ( right-click and “save as” ).
Where can I find the German credit data?
The German Credit Data contains data on 20 variables and the classification whether an applicant is considered a Good or a Bad credit risk for 1000 loan applicants. Here is a link to the German Credit data ( right-click and “save as” ). A predictive model developed on this data is expected to provide a bank manager guidance for making
How to analyze German credit card risk data?
Show from german credit card dataset on the basis of which they have been risk classified or no. The variable response in th e dataset corresponds to the risk label, 1 has been classified as bad and 2 has been classified as well. Label the variables to 1 and 0. 0 means corresponding to a well credit record and 1 means corresponding to a bad one.
What are the attributes of a German credit card?
Total of the attributes in German Credit Card dataset are 21 attributes. Status of existing checking account. 3. Credit history 4. Purpose 5. Credit amount 6. Savings account 7. Present employment since 8. Installment rate in percentage of disposable income 9. Personal Status and Sex (gender) 10. Other debtors / guarantors 11.
Which is the best way to analyze German credit data?
Logistic regression: The response is binary (Good credit risk or Bad) and several predictors are available. Before getting into any sophisticated analysis, the first step is to do an EDA and data cleaning. Since both categorical and continuous variables are included in the data set, appropriate tables and summary statistics are provided.
What is sample code for logistic regression in R?
Sample R code for Logistic Model building with Training data and assessing for Test data R output: Removing the nonsignificant variables a second logistic regression is fit to the data. R output: Need to remove another variable to come up with a model where all predictors are significant at 10% level.
How are significant predictors included in a logistic regression model?
Only significant predictors are to be included in the logistic regression model. Since there are 1000 observations 50:50 cross-validation scheme is tried: 1000 observations are randomly partitioned into two equal sized subsets – Training and Test data. A logistic model is fit to the Training set.