Loading the data

The dependent variable that we are interested in predicting is the housing price per location, which is denoted as a continuous variable that denotes house prices in thousands of dollars.

Hence, each of our observations can be represented as a vector of dimension 13, with a corresponding scalar label. In the following code, we are plotting out the second observation in our training set, along with its corresponding label:

import keras
from keras.datasets import boston_housing.load_data()
(x_train, y_train),(x_test,y_test)=boston_housing.load_data()
x_train[1], y_train[1]