Title: Coupon Data Exploration
Project Description: For Module 5, we were provided a dataset of customer & coupon attributes and a binary result of whether the customer accepted the coupon.
Our goal is to explore the dataset and highlight the differences between customers who did and did not accept the coupons

Approach: I followed two routes to explore the data set: 
1) Build a Plotly Dash App to adjust filters and graph selection criteria
2) In Jupyter, create a one-hot dataframe to explore the acceptance ratio of specific values 

Overview (from project file): The goal of this project is to use what you know about visualizations and probability distributions to 
distinguish between customers who accepted a driving coupon versus those that did not.

Data (from project file): This data comes to us from the UCI Machine Learning repository and was collected via a survey on Amazon Mechanical Turk. 
The survey describes different driving scenarios including the destination, current time, weather, passenger, etc., and 
then ask the person whether he will accept the coupon if he is the driver. Answers that the user will drive there ‘right away’ or ‘later 
before the coupon expires’ are labeled as ‘Y = 1’ and answers ‘no, I do not want the coupon’ are labeled as ‘Y = 0’. 
There are five different types of coupons -- less expensive restaurants (under $20), coffee houses, carry out & take away, bar, 
and more expensive restaurants ($20 - $50).