Programming in R

(a) For this exercise, your goal is to build a model to identify inputs or predictors that differentiate risky customers from others (based on patterns pertaining to previous customers) and then use those inputs to predict new risky customers. This sample case is typical for this domain. The sample data to be used in this exercise is [login to view URL] . The data set has 425 cases and 15 variables pertaining to past and current customers who have borrowed from a bank for various reasons. The data set contains customer-related information such as financial standing, reason for the loan, employment, demographic information, and the outcome or dependent variable for credit standing, classifying each case as good or bad, based on the institution’s past experience. Take 400 of the cases as training cases and set aside the other 25 for testing. Build a decision tree model to learn the characteristics of the problem. Test its performance on the other 25 cases. Report on your model’s learning and testing performance. Prepare a report that identifies the decision tree model and training parameters, as well as the resulting performance on the test set. You can use either R Package rattle (GUI-based) or Weka. To use Weka go through Learning Resource for Weka decision tree To use rattle Learning resources for rattle for decision tree (b) Using the same dataset also develop a Neural Network (NN) model using either rattle or Weka (Multilayer Perceptron) (c) Compare and evaluate the model performances of decision tree and NN. (use 10-fold cross validation and Leave-one-out for classification assessment). Also generate ROC plots. Explain and discuss the results. Report everything in a .pdf file with descriptions of preprocessing steps, model development, model output interpretation and the explanations of the model significance. Q2. In this exercise you'll use R package tidyverse (see chapter 4 of Introduction to Data Science Data Analysis and Prediction Algorithms with R by Rafael A. Irizarry. You need to go through chapter 4 before attempting the following questions. Using dplyr functions (i.e., filter, mutate ,select, summarise, group_by etc. ) and "murder" dataset (available in dslab R package) write appropriate R syntax to answer the followings: a. Calculate regional total murder. b. Display the regional population and regional murder numbers. c. How many states are there in each region? (Hint: n ()) d. How many states have murder number greater than its regional average (Hint: nrow() ) e. Display 2 least populated states in each region (Hint: slice_min() ) Use pipe %>% operator for all the queries. Show all the output results.