YInsurance™

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BMI
Income vs. State Average
Silver-Bronze Difference

Inspiration

Vitech's Life Insurance challenge appealed to our group's interest in learning about data mining and machine learning.

What it does

A user inputs information such as their age, gender, height, weight and medical conditions into the web form, which will return a recommended plan for the consumer. We also created a document that has graphs and statistics that are useful for the life insurance company to understand their customers' purchasing habits.

How we built it

Using a Node.js application, we scraped Vitech's API to get data, which was parsed and stored in a CSV. This CSV was then imported into RStudio, where we used Generalized Boosted Regression (GBM) model to predict the insurance plan mostly likely to be purchased by a particular customer. The web form was built using Shiny, a web framework for R projects.

Challenges we ran into

Getting the data from the API was a struggle due to the slow Internet speeds
Figuring out how to use RStudio and Shiny

Accomplishments that we're proud of

Learning how to do basic data mining and classification with RStudio
Brainstorming how to parse complex data for information that produced useful models