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ISYE 6501 Introduction to Analytic Model Homework Latest Updated Georgia Institute of Technology.
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ISYE 6501 Introduction to Analytic Model Homework Latest Updated Georgia Institute of Technology.
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lOMoAR cPSD| 43283024ISYE 6501
Introduction to Analytic Model
Homework 2024-2025 Latest
Updated Georgia Institute of
Technology.
, lOMoAR cPSD| 43283024
ISYE 6501 - Homework 1
2024-01-15
Question 2.1 Describe a situation for which a classification model would be ap-
propriate. List some (up to 5) predictors that you might use.
Recently, I found myself in the process of shopping for new auto insurance and filled out an online intake
form on an insurance company’s website. What struck me was the commonality in the predictors used by
every auto insurance company. These factors play a pivotal role in shaping insurance premiums and coverage.
Here are five key aspects that stood out during the form-filling process:
1. Driving History: The questions about past driving records, encompassing accidents, traffic violations,
and claims history, were quite detailed. It was evident that this information significantly influences an
insurance company’s assessment of an applicant’s risk level.
2. Age and Gender: The intake form delved into specifics about age and gender. It appears that these
demographic factors consistently factor into insurers’ statistical evaluations of risk, potentially leading
to varied premiums for younger or male drivers.
3. Vehicle Type: Notably, there was a focus on the make and model of my vehicle. This suggests that
insurers use this data to gauge the overall risk associated with a particular vehicle, considering repair
costs and safety features.
4. Annual Mileage: Questions about the number of miles driven annually were prominent. The em-
phasis on mileage indicates that insurers view higher mileage as a potential risk factor, likely tied to
an increased likelihood of accidents and, subsequently, higher insurance rates.
5. Credit Score: Surprisingly, the intake form sought information on my credit score. It seems that
some insurers use credit history as a predictor, linking a higher credit score to responsible financial
behavior and, potentially, responsible driving habits.
Question 2.2
Q 2.2.1 Using the support vector machine function ksvm contained in the R package kernlab,
find a good classifier for this data. . .
Install and load “kernlab”
rm(list = ls())
#install.packages("kernlab")
if(!require(pacman)) install.packages("pacman")
## Loading required package: pacman
library(pacman)
p_load(kernlab, tinytex)
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