KNN is a machine learning algorithm that can be used for both regression and classification tasks. This algorithm is non-parametric, meaning that it doesn’t make any assumptions about the data.

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## Introduction

Machine learning is a method of data analysis that automates analytical model building. It is a branch of artificial intelligence based on the idea that systems can learn from data, identify patterns and make predictions with minimal human intervention.

The k-nearest neighbor algorithm (k-NN) is a simple machine learning technique used for both classification and regression. In classification, the aim is to predict the class (or label) of a given data point. In regression, the aim is to predict the value of a given data point.

The k-NN algorithm works by comparing an unseen data point with all other training data points. The distance between these points is then used to assign labels or values to the new data point. The main advantage of k-NN is that it is a very simple algorithm to understand and implement. It can also be used for non-linear problems.

The main disadvantage of k-NN is that it can be very computationally expensive when working with large datasets. It also doesn’t work well with high-dimensional data (data with many features).

## What is KNN?

KNN is a supervised learning algorithm that can be used for both regression and classification problems. The algorithm works by finding the K nearest neighbors of a data point and then taking the mean (for regression) or mode (for classification) of the known values for those neighbors.

The K in KNN can be any integer, but is typically set to a small value like 3 or 5. The number of neighbors used has a big impact on the performance of the algorithm, so it is important to choose a value that strikes a balance between accuracy and computational efficiency.

KNN is a non-parametric model, which means that it does not make any assumptions about the underlying data distribution. This makes it well suited for working with data that does not have a linear structure.

The KNN algorithm is relatively simple to implement and can be easily extended to deal with multiple classes and missing values. However, the computational cost of finding the nearest neighbors increases with the number of data points, which can make the algorithm very slow when working with large datasets.

## How does KNN work?

K nearest neighbor is a simple machine learning algorithm that is used for both classification and regression tasks. The algorithm works by calculating the distances between a point (or data point) and all of the other points in the data set. The point is then assigned a class label (for classification tasks) or a value (for regression tasks) based on the majority of points around it.

For example, say we have a data set of animals, and we want to use KNN to classify them into two groups: mammals and non-mammals. We start by calculating the distance between each animal and all of the other animals in the data set. We then look at the K nearest neighbors (for example, K=3) and see what group they belong to. If most of the nearest neighbors are mammals, then we classify the animal as a mammal. If most of them are not mammals, then we classify the animal as not a mammal.

The KNN algorithm is also used for regression tasks, such as predicting house prices based on features like number of bedrooms, square footage, etc. In this case, instead of assigning a class label, we predict a value (such as the price).

KNN is a non-parametric algorithm, which means that it does not make any assumptions about the distribution of the data. This makes it very versatile and robust to different types of data sets.

## The advantages of KNN

There are many advantages of using the K nearest neighbor machine learning algorithm including its simplicity, flexibility, and accuracy.

KNN is a non-parametric model which means that it does not make any assumptions about the underlying data. This is advantageous as it allows the model to be used on a wide variety of data sets. The algorithm is also very flexible and can be used for both regression and classification problems.

One of the main advantages of KNN is that it is an accurate classifier. Studies have shown that KNN outperforms other machine learning algorithms such as decision trees and logistic regression.

## The disadvantages of KNN

KNN has a number of disadvantages, some of which are listed below.

-KNN can be computationally expensive, especially when working with large datasets.

-KNN can be susceptible to overfitting, particularly when the data is “noisy” or there are outliers present.

-KNN requires careful selection of the hyperparameter k, as a value that is too high or too low can lead to poor results.

## KNN in action

Now let’s see the KNN algorithm in action. We will use the iris dataset for this purpose. This dataset contains information about different types of irises (a type of flower). There are three different types of irises in this dataset, and we can use the KNN algorithm to try to predict which type of iris a new flower will be based on its measurements.

First, we need to split our data into a training set and a test set. We will use the training set to train our KNN model, and the test set to evaluate its performance. For this example, we will use a 80/20 split, which means that 80% of our data will be used for training and 20% will be used for testing.

Next, we need to choose a value for K. This is the number of “nearest neighbors” that our KNN model will use when making predictions. For this example, we will use a value of 5.

Once we have our training data and our value for K, we can train our KNN model. To do this, we simply need to compute the distance between each observation in the training set and every other observation in the training set. Once we have these distances, we can find the K nearest neighbors for each observation in the training set (i.e., the observation’s “K nearest neighbors”).

Once we have found the K nearest neighbors for each observation in the training set, we can make predictions about which type of iris a new flower will be by looking at the most common type of iris among its K nearest neighbors (i.e., its “majority class”). For example, if an observation has 4 nearest neighbors that belong to the “Iris-setosa” class and 1 nearest neighbor that belongs to the “Iris-versicolor” class, then our model would predict that the new flower is an “Iris-setosa”.

Finally, we can evaluate our model’s performance by seeing how many predictions it got right on our test data. For this example, our model was able to correctly predict the type of iris 95% of time!

## KNN applications

KNN is a versatilemachine learning algorithm that can be used for a variety of purposes, including classification and regression. In this article, we’ll focus on KNN’s application in classification.

Classification is the process of assigning a label to an input data point. For instance, we might want to classify an email as spam or not spam. KNN can be used for binary classification (labeling an instance as either 0 or 1) or for multiclass classification (labeling an instance as one of several class labels).

The KNN algorithm works by comparing an input data point to the points in the training data set. The “K” in KNN refers to the number of nearest neighbors that are considered when making the prediction. For example, if K = 3, then we would consider the 3 nearest neighbors when making the prediction.

The prediction is made by majority vote: whichever class label is most common among the K nearest neighbors will be assigned to the input data point.

KNN is a simple algorithm, but it can be very powerful. In many cases, it outperforms more sophisticated machine learning algorithms such as support vector machines (SVMs) and neural networks.

## KNN algorithm

KNN is a supervised learning algorithm that can be used for both classification and regression problems. The algorithm works by finding the K nearest neighbors of a data point, and using those neighbors to determine the class or value of the data point.

KNN is a non-parametric algorithm, which means that it does not make any assumptions about the underlying data. This is both a strength and a weakness of the algorithm; because KNN makes no assumptions about the data, it is very versatile and can be used for a variety of problems. However, this also means that KNN can be more computationally expensive than other algorithms, and may not scale well to very large datasets.

The main parameter to tune in KNN is K, the number of nearest neighbors to use when making predictions. A larger K will result in more smooth predictions, while a smaller K will result in more jagged predictions. In general, it is best to choose a value for K that is between 3 and 10.

## KNN pseudocode

Pseudocode for the KNN algorithm is as follows:

1. Select the number of nearest neighbors, k.

2. Calculate the distance between the new data point and all existing data points.

3. Select the k nearest neighbors based on their distance from the new data point.

4. Determine the class of the new data point by majority vote (i.e., if two classes are equally represented, choose the class with the higher frequency).

## KNN in Python

The K Nearest Neighbor Machine Learning Algorithm (KNN) is a supervised learning algorithm that can be used for both regression and classification problems. The algorithm is trained using a training dataset and then makes predictions using the test dataset. In this blog post, we will implement the KNN algorithm in Python using the scikit-learn library.

KNN is a non-parametric and lazy learning algorithm. Non-parametric means that the model does not make any assumptions about the data distribution. Lazy means that the model does not learn a model until it is asked to make a prediction.

The KNN algorithm works by storing all of the training data points in an n-dimensional space. When a new data point comes in, the algorithm calculates the distance from that point to all of the other points in space. The K nearest neighbors are then found and their class labels are combined to form a prediction. The value of K can be any value from 1 to N, where N is the number of training data points.

Keyword: The K Nearest Neighbor Machine Learning Algorithm (KNN)