Reference no: EM133424172
Question 1: Load the dataset into a variable "mydata". Remove any rows that contain missing values on any of the variables.
Change the variable "loan_status" to binary (1 for Fully Paid and 0 for charged off). Change the variable "home_ownership" to a categorical variable (0 for RENT, 1 for MORTGAGE and 2 for OWN). Remove the word "months" in variable "term" and the symbol "%" in the variable "int_rate".
Question 2: Split the dataset into 70% training and 30% testing using the sample() function. We are tying to predict "loan_status" so store the variable value in "response.test" and set the value to null on the test dataset.
Question 3: Run the logistic regression on "trainData" using glm() command with "loan_amnt", "funded_amnt", "int_rate", "term", "annual_inc","dti" and "delinq_2yrs" as input variables. Explain what the coefficient values for the variables "int_rate" and "delinq_2yrs" mean in plain English.
Question 4: Now use the predict() function on the test dataset to predict the probability of outcome. Set the predicted outcome to be 1 if probability is greater than 0.5 or else 0. Use the table() command to compare the predicted outcome and the actual. What are the values of precision, recall and overall accuracy?
Question 5: Now use the trainControl() and train() methods on "mydata" to perform 10-fold cross validation and use logistic regression. What are the values of precision, recall, overall accuracy and AUC? (Hint : You need to install the libraries "caret" and "pROC" before you run the models)
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