Supervised Learning - Classification Week 7 Challenge

1) Pros and cons of SVM

Pros :

• Works well for high dimensional problems
• Since it calculates using support vectors the computational memory is low

Cons :

• Does not work well for a large amount of data
• Does not work well if the target classes are overlapped

2) Apply SVM to the “Surface defects in stainless steel plates” dataset and evaluate it

Aim :

To apply SVM algorithm on the surface defects in stainless steel plates dataset and evaluate it