“Support Vector Machine” (SVM) is a supervised machine learning algorithm. It is used for both classification or regression challenges. But in most of cases it used to solve the classification problems, here we plot each data item as a point in n-dimensional space ( n is number of features).

The goal of the SVM algorithm is to create the best line or decision boundary that can segregate n-dimensional space into classes so that we can easily put the new data point in the correct category in the future. This best decision boundary is called a hyperplane.

Example Using Sklearn:

# importing required libraries
import pandas as pd
from sklearn.svm import SVC
from sklearn.metrics import accuracy_score
 
# read the train and test dataset
train_data = pd.read_csv('train-data.csv')
test_data = pd.read_csv('test-data.csv')
 
# shape of the dataset
print('Shape of training data :',train_data.shape)
print('Shape of testing data :',test_data.shape)
 
# seperate the independent and target variable on training data
train_x = train_data.drop(columns=['Income'],axis=1)
train_y = train_data['Income']
 
# seperate the independent and target variable on testing data
x_test = test_data.drop(columns=['Income'],axis=1)
y_test = test_data['Income']
 
model = SVC()
 
# fit the model with the training data
model.fit(x_train,y_train)
 
# predict the target on the train dataset
predict_train = model.predict(x_train)
print('Target on train data',predict_train) 
 
# Accuray Score on train dataset
accuracy_train = accuracy_score(y_train,predict_train)
print('accuracy_score on train dataset : ', accuracy_train)
 
# predict the target on the test dataset
predict_test = model.predict(x_test)
print('Target on test data',predict_test) 
 
# Accuracy Score on test dataset
accuracy_test = accuracy_score(test_y,predict_test)
print('accuracy_score on test dataset : ', accuracy_test)