Supervised Learning - Classification Week 7 Challenge

1.) Pros and cons of SVM

Ans::

pros	cons
it works efficiently with unstructed and semi structed data like text, images and trees	it is very difficult to have a good kernal functions
people who have zero knowledge of data can also operate SVM	it requires long training time for large databases
the risk of overfitting is less in SVM because svm models are very generalized. it perform well in case of categorical	hyper parameters like code - C and gamma are verydifficult to fine tune which makes it hard to visualize

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

Ans:: sending steel plate .ipynb file..