K_nearest neighbor (KNN) is a supervised algorithm, that can solve both classification and regression tasks. Since it does not have a specialized training phase, it is called a **lazy learning algorithm**. It uses all the data for training while classifying a new data point.

**Why do we need a K-NN Algorithm?**

Let’s assume that we have two categories, Category A and Category B, & we have a new data point x1, so the question here is in which of these categories will the data point lie? To solve this kind of problem, we need a K-nearest neighbor algorithm. With the help of K-NN, the category or class of a particular dataset can be identified easily. Let’s consider the below figure:

**How does K-NN work?**

The working of KNN for classification and regression.

**K-NN for Classification**

- Calculate the number of nearest neighbors.
- Calculate the distance of testing observations with all training data using Euclidean distance.
- Select 5 shortest distance observations from the testing point.
- Calculate the probability of all shortest observations.
- Assign testing observation with the highest priority.

Refer this article to know Support Vector Machine Algorithm (SVM) – Understanding Kernel Trick

**K-NN for Regression**

- Calculate the number of nearest neighbors.
- Calculate the distance of testing observations with all training data using Euclidean distance.
- Select 5 shortest distance observations from the testing point.
- Calculate the average distance of the nearest neighbor to testing observations.
- Assign average distance as the predicted value.

KNN Algorithm Explained | K- Nearest Neighbours | Machine Learning

**How to select the value of K in the K-NN Algorithm?**

The points to be remembered for selecting the value for K in KNN:

- To find the value of K we don’t have a specific predefined method.
- K value is initialized randomly & starts computing.
- If you choose a small value of K, the decision boundaries will be unstable.
- Derive a plot between error rate & K denoting values in a defined range.
- Then choose the value for K which has less error rate.

**Take a look at the Pros and Cons of KNN:**

**Pros:**

- The implementation is very easy.
- As said earlier, it is a lazy learning algorithm & therefore requires no training prior to making real-time predictions. This makes the KNN algorithm much faster than other algorithms that require training ex. SVM, linear regression, etc.
- New data can be added easily because the algorithm requires no training before making predictions.
- To implement KNN there are only two parameters.
- The math behind this algorithm is very easy to understand.
- Hyperparameter tuning is not required.

**Cons:**

- KNN does not work well with the large dataset, it will also not work well with high dimensions.
- It requires feature scaling (standardization and normalization).
- KNN is sensitive toward missing values and outliers.
- It requires lots of space because we need to store the whole training set for every test set.

Refer this article to know A Complete Guide to Naive Bayes Algorithm in Python

**Distance measures on KNN**

There are several distance measures techniques but wwe is only one among them.

**Applications of KNN Algorithm**

**Recommending systems:**Recommending ads for youtube and social media users, recommending products on any E-commerce websites. For example, let’s say you buy a laptop from any E-commerce site, it recommends you to buy a portable mouse, keyboard, or laptop cover with it.- KNN is used in
**politics**whether the voter will vote or will not vote candidate. - Other applications of KNN include
**video recognition, image recognition,**and**handwriting detection**.

**Python Implementation of KNN**

**Business Case:-To predicts whether a person will have diabetes or not.**

**Importing all required libraries**

- import pandas as pd
- import numpy as np
- from sklearn.neighbors import KNeighborsClassifier
- from sklearn.neighbors import KNeighborsRegressor
- from sklearn.preprocessing import StandardScaler
- from sklearn.model_selection import train_test_split
- from sklearn.metrics import accuracy_score, confusion_matrix,classification_report
- import matplotlib.pyplot as plt
- import seaborn as sns
- import warnings
- warnings.filterwarnings(‘ignore’)

Measure purity of a node in Decision Tree Algorithm – Machine Learning

**Reading the data**

data = pd.read_csv(“diabetes.csv”)

data.head()

**Output:**

**Get the insights from Exploratory Data Analysis**

**Check for missing values, categorical variables and outliers**

**Splitting X and y**

X = data.drop(columns = [‘Outcome’]) # Independent variables

y = data[‘Outcome’] # Dependent or target variable.

**Checking the balance of the target**

sns.catplot(x=’Outcome’,data=data,kind=’count’) # Imbalanced dataset

Output:

The above plot says that the output is an imbalance, now let’s see how to balance an imbalance data.

!pip install imblearn

#Apply SMOTE to balance the data

from imblearn.over_sampling import SMOTE

smote = SMOTE() ## object creation

X_train_smote, y_train_smote = smote.fit_resample(X_train.astype(‘float’),

y_train)

from collections import Counter

print(“Actual Classes”,Counter(y_train))

print(“SMOTE Classes”,Counter(y_train_smote))

**Output:**

Actual Classes Counter({0: 996, 1: 504})

SMOTE Classes Counter({0: 996, 1: 996})

Also read: A Guide to Principal Component Analysis (PCA) for Machine Learning

**Scaling the data**

scalar = StandardScaler()

X_scaled = scalar.fit_transform(X)

**Splitting the training and the testing data**

X_train,X_test,y_train,y_test=train_test_split(X_scaled,y,random_state=42)

**Fitting the data into KNN model**

knn1 = KNeighborsClassifier(n_neighbors=5)

knn1.fit(X_train,y_train)

**Output:** KNeighborsClassifier()

**Predicting**

y_pred = knn1.predict(X_test)

**Evaluation**

print(“The accuracy score is : “, accuracy_score(y_test,y_pred))

**Output:** The accuracy score is : 0.814

print(classification_report(y_test,y_pred))

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