Supervised learning in neural networks

As previously mentioned, supervised learning is a learning method where there is a part of training data which acts as a teacher to the algorithm to determine the model. In the following section, an example of a regression predictive modeling problem is proposed to understand how to solve it with neural networks.

Boston dataset

The dataset describes 13 numerical properties of houses in Boston suburbs, and is concerned with modeling the price of houses in those suburbs in thousands of dollars. As such, this is a regression predictive modeling problem. Input attributes include things like crime rate, proportion of non-retail business acres, chemical concentrations, and more. In the following list are shown all the variables followed by a brief description: 

• Number of instances: 506
• Number of attributes: 13 continuous attributes (including class attribute MEDV), and one binary-valued attribute

Each of the attributes is detailed as follows:

1. crim per capita crime...