K-Nearest Neighbors Algorithm in Machine Learning [With Examples]

K-Nearest Neighbors Algorithm in Machine Learning [With Examples]

### **Introduction** Machine Learning is undoubtedly one of the most happening and powerful technologies in today’s data driven world where we are collecting more amount of data every single second. This is one of the [rapid growing...

Introduction

Machine Learning is undoubtedly one of the most happening and powerful technologies in today’s data driven world where we are collecting more amount of data every single second. This is one of the rapid growing technology where every domain and every sector has its own use cases and projects.

Machine Learning or Model Development is one of the phases in a Data Science Project Life Cycle which seems to be one of the most important on as well. This article is designed as an introduction to KNN (K-Nearest Neighbors) in Machine Learning.

K-Nearest Neighbors

 If you’re familiar with machine learning or have been a part of Data Science or AI team, then you’ve probably heard of the k-Nearest Neighbors algorithm, or simple called as KNN. This algorithm is one of the go to algorithms used in machine learning because it is easy-to-implement, non-parametric, lazy learning and has low calculation time.

Another advantage of k-Nearest Neighbors algorithm is that it can be used for both Classification and Regression type of Problems. If you are unaware of the difference between these two then let me make it clear to you, the main difference between Classification and Regression is that the output variable in regression is numerical(Continuous) while that for classification is categorical(Discrete).

How does k-Nearest Neighbors work?

K-nearest neighbors (KNN) algorithm uses the technique ‘feature similarity’ or ‘nearest neighbors’ to predict the cluster that a new data point fall into. Below are the few steps based on which we can understand the working of this algorithm better

Step 1 − For implementing any algorithm in Machine learning, we need a cleaned data set ready for modelling. Let’s assume that we already have a cleaned dataset which has been split into training and testing data set.

Step 2 − As we already have the data sets ready, we need to choose the value of K (integer) which tells us how many nearest data points we need to take into consideration to implement the algorithm. We can get to know how to determine the k value in the later stages of the article.

Step 3 − This step is an iterative one and needs to be applied for each data point in the dataset

I. Calculate the distance between test data and each row of training data using any of the distance metric

a. Euclidean distance

b. Manhattan distance

c. Minkowski distance

d. Hamming distance.

 Many data scientists tend to use the Euclidean distance, but we can get to know the significance of each one in the later stage of this article.

II. We need to sort the data based on the distance metric that we have used in the above step.

III. Choose the top K rows in the transformed sorted data.

IV. Then it will assign a class to the test point based on most frequent class of these rows.

Step 4 − End

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