Introduction

Consider a small food-delivery business that is looking to optimize their product. An analyst might look at the appropriate data and determine what type of food people are enjoying most. Perhaps they find a large amount of people are ordering the spiciest food options, indicating the business might be losing out on customers who desire something even more spicy. This is quite basic, or as some might say, "vanilla" analytics.

In a separate task, the analyst could employ predictive analytics by modeling the order volumes over time. With enough data, they could predict the future order volumes and therefore guide the restaurant as to how many staff are required each day. This model could take factors such as the weather into account to make the best predictions. For instance, a heavy rainstorm could be an indicator to staff more delivery personnel to make up for slow travel times. With historical weather data, that type of signal could be encoded into the model. This prediction...