Supervised Learning - Classification Week 7 Challenge

Answer no.1

Pros of SVM:

• Effective in high-dimensional spaces.
• Robust to overfitting.
• Versatile kernels.
• Effective in handling nonlinear data.
• Memory efficient.

Cons of SVM:

• Computationally intensive.
• Sensitivity to parameter tuning.
• Lack of probabilistic output.
• Difficulty in handling large datasets.
• Black box nature.

Answer no.2 Submitted in .ipynb form