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 into the code. You can refer to the code snippet in the following figure:

In this code, we are using Random Forest Classifier from scikit-learn. We use the fit() function to perform training, and then, in order to generate the importance of the features, we will use the feature_importances_ function, which is available in the scikit-learn library. Then, we will print the features with the highest importance value to the lowest importance value.

Let's draw a graph of this to get a better understanding of the most important features. You can find the code snippet in the following figure:

In this code snippet, we are using the matplotlib library to draw the graph. Here, we use a bar graph and feed in the values of all the data attributes and their importance values, which we previously derived. You can refer to the graph in the following figure:

For the first iteration, we did this quite some work on the feature engineering front. We will surely revisit feature engineering in the upcoming sections. Now it's time to implement machine learning algorithms to generate the baseline predictive model, which will give us an idea of whether a person will default on a loan in the next 2 years or not. So let's jump to the next section.