Include the principal components plot here. How would you interpret the cumulative variance? Is that a good/bad/high/low value? What could be the reason for this specific value?

Check the PCA plot. The cumulative variance of the first two principal components is how much?

Make a feature pairplot of the top feature in PC 1 and PC2 respectively (full dataset). Compare it to the PCA plot, how do you interpret the two plots? Are they similar, yes/no? Why?

Check the probabilities of the misclassified samples especially of the actual cancer cases that the model missed. Was the decision close or quite clear for the model? Do these probabilities make sense also when you check the decision boundary plot with your top 2 features?

Create a decision boundary plot with the two top important features that you determined and attach it here. Also upload the ROC plot and the PR plot.

Interpret the Precision-Recall Plot: which precision-recall value pair from the plot would you select in this case of breast cancer data and which threshold was used for that? Why would you go for that value?