In this chapter, we designed and implemented a deep feedforward neural network capable of predicting taxi fares in NYC within an error of ~$3.50. We first performed exploratory data analysis, where we gained important insights on the factors that affect taxi fares. With these insights, we then performed feature engineering, which is the process of using your domain knowledge of the problem to create new features. We also introduced the concept of modularizing our functions in machine learning projects, which allowed us to keep our main code relatively short and neat.
We created our deep feedforward neural network in Keras, and trained it using the preprocessed data. Our results show that the neural network is able to make highly accurate predictions for both short and long distance trips. Even for fixed-rate trips, our neural network was able to produce highly accurate...
