Supervised Learning - Classification Week 8 Challenge

Pros:

• It is a simple Machine Learning 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 neighbors to be consider while classifying the new data entry.
• K-NN inherently has no capability of dealing with missing value problem.
• k Nearest Neighbor has calculate the proximity between each neighbors for every single value in the dataset. For large daasets its computation cost increases.

KNN: K-Nearest Neighbors is a supervised machine learning algorithm that classifies a data point based on how its neighbors are classified. It classifies new cases based on feature similarity measures.

KNN algorithm

k in the KNN algorithm is a parameter that refers to the number of nearest neighbors to include in the majority voting process. It is important to choose the right value of k in order to attain high model accuracy and this is achieved by parameter tuning.

KNN is used for data that is labeled, small in size, and noise-free.