Preparing feature variables for the logistic regression model

In the previous section, we identified our target variable that will be used as our predictor for fire calls in our logistic regression model. This section will focus on identifying all of the features that will best help the model identify what the target should be. This is known as feature selection.

Getting ready

This section will require importing StringIndexer from pyspark.ml.feature. In order to ensure proper feature selection, we will need to map string columns to columns of indices. This will help generate distinct numeric values for categorical variables that will provide ease of computation for the machine learning model to ingest the independent...