Intent: Determine whether a Customer will accept the Coupon or not.

Context:

Imagine driving through town and a coupon is delivered to your cell phone for a restaraunt near where you are driving. Would you accept that coupon and take a short detour to the restaraunt? Would you accept the coupon but use it on a sunbsequent trip? Would you ignore the coupon entirely? What if the coupon was for a bar instead of a restaraunt? What about a coffee house? Would you accept a bar coupon with a minor passenger in the car? What about if it was just you and your partner in the car? Would weather impact the rate of acceptance? What about the time of day?

Obviously, proximity to the business is a factor on whether the coupon is delivered to the driver or not, but what are the factors that determine whether a driver accepts the coupon once it is delivered to them? How would you determine whether a driver is likely to accept a coupon?

Overview:

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:

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).

Goal:

The goal of the final product is to provide a brief report that highlights the differences between customers who did and did not accept the coupons and to explore the data that will be utilized to provide visual and statistical summaries.

Setup:

1. Run Jypyter Notebook from your local machine using the below command: jupyter-notebook

   or

   jupyter-lab (if you have installed jupyter lab)

   Please see installation guidelines here - https://jupyter.org/install

2. Open coupon_analysis.ipynb in Jupyter Notebook and run it to see the response.

Contributing to Repo:

1. All branches should have a summary of the change

2. Make sure all commits has details about the commits in the commit message

3. Pull Request title should have the summary of the change