K-Nearest Neighbor (KNN) Algorithm in Machine Learning using Python

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’)

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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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