Feature engineering for the baseline model

In this section, you will learn how to select features that are important in order to develop the predictive model. So right now, just to begin with, we won't focus much on deriving new features at this stage because first, we need to know which input variables / columns / data attributes / features give us at least baseline accuracy. So, in this first iteration, our focus is on the selection of features from the available training dataset.

Finding out Feature importance

We need to know which the important features are. In order to find that out, we are going to train the model using the Random Forest classifier. After that, we will have a rough idea about the important features for us. So let's get straight ...

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