Supervised Learning - Classification Week 8 Challenge

1.Apply knn to the “Surface defects in stainless steel plates” and identify the differences
Accuracy for SVM with train test = 70% : 30% is 73%.
Accuracy for KNN with train test = 70% : 30% is 72%.
So here in KNN we are losing our accuracy by 1% compare to SVM.
The file for 1 question has been attached below

2.What are the pros and cons of knn
Advantages of KNN
No Training Period: KNN is called Lazy Learner (Instance based learning). It does not learn anything in the training period.
New data can be added seamlessly which will not impact the accuracy of the algorithm.
KNN is very easy to implement.
Disadvantages of KNN
Does not work well with large dataset.
Sensitive to noisy data, missing values and outliers.