In traditional programming, the programmer will input data along with rules. From the combination of the two, the model will predict the answers. However, machine learning is a reorientation, as the programmer inputs data and answers, and the model instead figures out the rules. For example, it can figure out the relationship and other rules between variables.
When I ran the code twice to predict the corresponding value of an input of 7.0, I got 2 different outputs. There was a small difference between the two numbers, but both numbers were very close to 22.0. I got different answers because neural networks deal with probabilities, so there is a high probability the answer is 22.0, but with such few data points to go off of, the model can’t be 100% certain.
The Hudgins house presents a great deal, because my neural network estimated that the price of a 3 bedroom house should be around $233,000, but the house is only priced at $97,000. One of the worst deals is the church house, because the network determined that a 4 bedroom house should be priced at around $300,000, but the house is isntead priced at $399,000.