Introduction A customer receives a drink from Starbucks barista millions of times each week, but each interaction is unique. This is just a moment in time, but nevertheless a connection. How does this customer behave, and what prompts them to make a purchase? To find out, we can use a simulated dataset that mimics customer behaviour on the Starbucks rewards mobile app. Once every few days, Starbucks sends out an offer to users of the mobile app. An offer can be merely an advertisement for a drink or an actual offer such as a discount or BOGO (buy one get one free). Some users might not receive any offers during certain weeks. Not all users receive the same offer, and that is the challenge to solve with this data set. Our task is to combine transactions, demographics and offer data to determine which demographic groups respond best to which offer…

Introduction Airbnb is a popular way for homeowners to make money by renting out their properties or even spare rooms in their own home. More people are considering joining Airbnb to profit by investing in new properties to transform into Airbnbs. However, how will they know what to consider to make their property an attractive proposition for customers? How will they identify which variables can increase their listing price and profit? There is a problem though, you see. Hosts remove their listings for various reasons such as a lack of bookings or if the property is currently occupied. This means we must find a way to predict that data and recommend them a reasonable price so they can attract more guests. Before we can answer those questions, we need to find relevant variables to use. Since 2008, guests and hosts have used Airbnb to travel in a more unique, personalized…