ISYE 6501- Homework 1

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ISYE 6501- Homework 1 Michael Chapman August 23, 2018 Question 2.1 In my current job, I am responsible for understanding my company’s customer experience feedback when processing an insurance claim on their mobile device. This feedback is typically in the form of a survey. The main metric we use to gauge customer satisfaction is Net Promoter Score (NPS), which is an industry standard where based on a customers response to a particular question, we put them into one of three categories: • Promoter • Neutral • Detractor One of our challenges is understanding what factor or combination of factors would result in someone being a detractor. This way we can know what to avoid things to do or try to control in order to set up a customer to not be a detractor. Using classification models, we could try to identify what individuals would be a detractor based on a number of key predictors about their claim experience. Here are a few key predictors that I might use: 1. How long it took the customer to file the claim 2. Price of the insurance deductible 3. Premium Price 4. Whether or not we required additional documentation Question 2.2 In order to find a good classifier that will properly predict the “R1” variable, I will build a Support Vector Machine (SVM) model. The goal will be to find the most accurate classifier, so I will walk through various different steps on how to prepare and assess them model, and finally arrive at our final equation. Prepare Script/Load Data First I’ll load the necessary libraries that will allow us to perform the modeling, plotting, and assessment activities. I’m assuming that the following steps are performed up front: • Dataset is in the same directory as your R script • Dataset is the “credit_card_data-headers.txt” file • For some helpful tips on managing R Projects, please see the following articles: • https://www.tidyverse.org/articles/2017/12/workflow-vs-script/ • https://support.rstudio.com/hc/en-us/articles/200526207-Using-Projects # Initial Setup ----------------------------------------------------------- # install.packages("kernlab") # Uncomment this if you don't have this installed yet # install.packages("tidyverse") # Uncomment this if you don't have this installed yet # install.packages("skimr") # Uncomment this if you don't have this installed yet # install.packages("kknn") library(tidyverse) library(kernlab) library(skimr) library(ggplot2) credit_card_file <- "credit_card_data.txt" credit_card_file_headers <- "credit_card_data-headers.txt" # If anything is using randomization, setting the seed makes this repeatable set.seed(289) # Read in the data -----------------

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BUNDLED PAPERS (Multiple versions) FOR Georgia Institute Of Technology ISYE 6501 Homeworks 1 - 15, Midterm 1 & 2 + FINAL EXAM | ISYE6501x Courseware | edX - Complete Solutions - Introduction To Analytics Modeling - GTX ISYE 6501

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