Visualization separation of classes with PCA I

A common question you may receive in a machine learning interview is visualizing dimensionality after PCA. In this exercise, you will do just that by plotting the first 2 principal components of loan_data in order to visualize the class separation between both components on whether the loan status has been fully paid or charged off.

The loan_data dataset has been scaled and one-hot encoded, meaning categorical variables were turned into binary indicators, since features should be on the same scale as well as numeric prior to PCA.

A PCA model with 2 PCs and setting up a plot with x and y labels and title has already been taken care of for you. You'll use a DataFrame called loan_data_PCA in the exercises. The possible values for the target variable Loan Status are 0 and 1. You'll be plotting PC1 on the x-axis and PC2 on the y-axis.

Already imported for you are matplotlib.pyplot as plt, seaborn as sns, PCA from sklearn.decomposition.

Assign the target variable values to the list targets.
Pass the lists just created to the zip() function inside the for loop.
Pass the instances where Loan Status is equal to target to indicesToKeep.

Exercise

Visualization separation of classes with PCA I

Instructions 1/3