Medical Expense Data Analysis and Predictive Modeling

• author: Karanpal Singh, Sreejith Munthikodu, Sirine Chahma

About

Knowing the estimated medical expenses in advance is very critical for insurance companies to set their yearly premiums to beneficiaries. They should make sure that the amount they spent on beneficiaries is less than the total premium they receive. Using a predictive model that predicts the expected medical expense of a customer, the insurance companies get an estimate of how much premium to charge on each segment of customers such that the cost results in reasonable profit to the company. Using such predictive models, they can identify potentially high-risk customers and charge a higher premium from such customers. In this project, we attempt to build a regression model that will help us predict the medical expenses of a person given information such as age, sex, number of children, smoking habits, and the region where the person is from. In the process, we are also interested in figuring out if there is a significant difference in expenses between smokers and non-smokers, and between males and females. Our hypothesis tests suggest that at 0.05 significance level there is significant evidence to conclude that smokers incur more medical expenses than non-smokers. Also, there is statistically no significant evidence to conclude that the medical expenses of men and women are different. For our predictive model, after trying different regression models, we found that the decision tree regressor performs the best on our data. Using our final tuned model, we achieved satisfying results on the test data set, with a score of 0.826.

The Data we are using for this analysis is used in the book Machine Learning with R by Brett Lantz(Lantz 2013); which is a book that provides an introduction to machine learning using R. All of these datasets are in the public domain. The data explain the cost of a small sample of USA population Medical Insurance Cost based on attributes like age, sex, number of children etc. Additional information about this data can be found here.

Report

• The final report can be found here.
• Exploratory Data Analysis can be found here.

Usage

There are two suggested ways to run this analysis:

1. Using Docker

note - the instructions in this section also depends on running this in a unix shell (e.g., terminal or Git Bash)

To replicate the analysis, install Docker. Then clone this GitHub repository and run the following command at the command line/terminal from the root directory of this project:

docker run --rm -v "$(pwd):/home/rstudio/" singhkaranpal/milestone_4 make -C /home/rstudio/ all

To reset the repo to a clean state, with no intermediate or results files, run the following command at the command line/terminal from the root directory of this project:

docker run --rm -v "$(pwd):/home/rstudio/" singhkaranpal/milestone_4 make -C /home/rstudio/ clean

2. Without using Docker

To replicate the analysis, clone this GitHub repository, install the dependencies listed below, and run the following command at the command line/terminal from the root directory of this project:

make all

To reset the repo to a clean state (which means with no intermediate or results files), run the following command at the command line/terminal from the root directory of this project:

make clean

Dependency diagram of the Makefile

Dependencies

• Python 3.7.3 and Python packages:
  • docopt==0.6.2
  • pandas==0.24.2
  • feather-format==0.4.0
  • scikit-learn==0.22.1
  • altair==4.0.0
  • scipy==1.2.3
  • matplotlib==3.2.1
  • selenium –3.141.0
• R version 3.6.1 and R packages:
  • knitr==1.26
  • testthat==2.3.1
  • broom==0.5.3
  • feather==0.3.5
  • tidyverse==1.2.1
  • caret==6.0-84
  • ggridges==0.5.1
  • ggthemes==4.2.0
  • selenium 3.141.0
• ChromeDriver 79.0.3945.36

References