In this chapter, we will use a deep feedforward neural network to predict taxi fares in New York City (NYC), given inputs such as the pickup and drop off locations.
In the previous chapter, Chapter 2, Predicting Diabetes with Multilayer Perceptrons, we saw how we can use a MLP with two hidden layers to perform a classification task (whether the patient is at risk of diabetes or not). In this chapter, we will build a deep neural network to perform a regression task of estimating taxi fares. As we shall see, we will need a deeper (that is, more complex) neural network to achieve this goal.
In this chapter, we will cover the following topics:
- The motivation for the problem that we're trying to tackle—making accurate predictions of taxi fares
- Classification versus regression problems in machine learning
- In-depth analysis...
