Building a baseline model

As a result of our data analysis, we were able to identify some of the features with predictive value. We can now build a model by using this knowledge to select relevant features. We will start with a model that will use just two out of the many features we investigated. This is called a baseline model and it is used as a starting point for the incremental refinement of the solution.

For the baseline model, we chose a RandomForestClassifier model. The model is simple to use, gives good results with the default parameters, and can be interpreted easily, using feature importance.

Let’s begin with the following code block to implement the model. First, we import a few libraries that are needed to prepare the model. Then, we convert the categorical data to numerical. We need to do this since the model we chose deals with numbers only. The operation of converting the categorical feature values to numbers is called label encoding. Then, we split...