Creating a structured data regressor to predict Boston house prices
In the following example, we will try to predict the median home price in a Boston suburb in the mid-1970s, given data features about the suburb at that time, such as the crime rate, tax rate of the property, local property, and so on.
We will create a model that will find out the house price of a specific suburb based on its features. For this, we will train the model with the boston_housing dataset, which we must add to our repository (https://github.com/PacktPublishing/Automated-Machine-Learning-with-AutoKeras/blob/main/boston.csv). The dataset we will use is relatively small – 506 samples divided between 404 training samples and 102 test samples. Note that the dataset isn't normalized, which means that each characteristic in the input data applies a different scale to its values. For example, some columns have values in the 0 to 1 range, while others are between 1 and 12, 0 and 100, and so on. So...
