Support Vector Machine (SVM) is a popular supervised learning algorithm used for classification and regression problems in **Machine Learning**. The basic idea behind SVM is to find a **decision boundary** that separates data points of different classes with the maximum margin. So, if you are new to Machine Learning and want to know how the SVM algorithm works, this article is for you. In this article, I will introduce how the SVM algorithm works and how to implement it using Python.

## Here’s How SVM Algorithm Works

The basic idea behind SVM is to find a decision boundary that separates data points of different classes with the maximum margin. It means that SVM aims to find the best line or curve that separates data points of different classes with the greatest possible distance. The data points closest to the decision boundary are called support vectors. Let’s understand how the SVM algorithm works by taking an example of a real-time business problem.

Suppose a company wants to predict whether a customer will default on a loan based on their credit score and income. The business can use SVM to find the best decision boundary that separates defaulting customers from non-defaulting customers based on their credit score and income. The SVM algorithm would analyze the historical data of customers who failed to repay their loans and those who did not, then find the best decision boundary that maximizes the margin between the two classes.

Once the decision boundary is found, it will predict whether a new customer will default on their loan based on their credit score and income. If a new customer falls on the default side of the decision boundary, the business can take appropriate action to mitigate the risk of default, such as declining the loan or raising the interest rate.

## Implementation of SVM using Python

Now let’s see how to implement the SVM algorithm using Python. To implement it using Python, we can use the scikit-learn library in Python, which provides the functionality of implementing all Machine Learning algorithms and concepts using Python.

Let’s first import the necessary Python libraries and create a sample data based on the example we discussed above:

import numpy as np n = 100 credit_scores = np.random.normal(loc=650, scale=100, size=n) income = np.random.normal(loc=50000, scale=10000, size=n) default = np.zeros(n) default_idx = np.random.choice(range(n), size=20, replace=False) default[default_idx] = 1 dataset = np.column_stack((credit_scores, income, default))

Now here’s how to train a Machine Learning model using the SVM algorithm:

from sklearn.model_selection import train_test_split # Split the dataset into training and testing sets X_train, X_test, y_train, y_test = train_test_split(dataset[:, :2], dataset[:, 2], test_size=0.2, random_state=42) from sklearn.svm import SVC # Train an SVM model on the training set model = SVC(kernel='linear', C=1) model.fit(X_train, y_train)

So this is how the SVM algorithm works.

### Summary

Support Vector Machine (SVM) is a popular supervised learning algorithm used for classification and regression problems in Machine Learning. The basic idea behind SVM is to find a decision boundary that separates data points of different classes with the maximum margin. I hope you liked this article on how the SVM algorithm works and how to implement it using Python. Feel free to ask valuable questions in the comments section below.