Supervised Learning - Classification Week 7 Challenge

Q1. Pros of Support Vector Machine:

1. It works comparably well when there is an understandable margin of dissociation between classes.
2. It is more productive in high-dimensional spaces.
3. It is effective in instances where the number of dimensions is larger than the number of specimens.
4. Regularization : SVMs can be regularized, which means that the algorithm can be modified to avoid overfitting.

Cons of Support Vector Machine:

1. This algorithm is not accptable for large datasets.
2. It does not execute very well when the data set has more sound. i.e, target classes are overlapping.
3. In cases where the number of properties for each point outstrips the number of training data specimens, the support vector machine will underperform.
4. Computationally expensive: SVMs can be computationally expensive for large datasets, as the algorithm requires solving a quadratic optimization problem.

Q2. Answer in Week_7_Challenges.py file a attached below.