Supervised Learning - Classification Week 8 Challenge

2.Pros:

• It is a simple Manchine Learing algorithm and easy to understand.
• kNN can be used for classification and regression.
• It can be used for multi-class problem.
• It provides a variety of distance criteria to choose from:Euclidean Distance Hamming Distance Manhattan Distance Minkowski Distance.

Cons:

• It is difficult to choose the optimal number of neighbours to be consider while classifying the new data entry.
• K-NN inherently has no capability of dealing with missing value problem.
• K Nearest Neighbour has calculate the proximity between each neighbour for every single value in the dataset.For large datasets its computation cost increases.