Support Vector Machine (SVM) is a supervised machine learning algorithm widely used for classification problems. It finds an optimal hyperplane that separates data points belonging to different classes. The hyperplane is selected in such a way that the margin between the nearest data points of different classes is maximized. These nearest points are called support vectors. SVM is particularly effective for datasets having a clear separation between classes and can also handle non-linear classification using kernel functions. For this implementation, the Iris dataset is selected. It is a commonly used dataset containing measurements of iris flowers. It has four features: sepal length, sepal width, petal length, and petal width. The dataset contains three classes: Setosa, Versicolor, and Virginica. The following Python program implements SVM classification using the scikit-learn library: ```python # Import required libraries import numpy as np import matplotlib.pyplot as plt from sklearn import datasets from sklearn.model_selection import train_test_split from sklearn.preprocessing import StandardScaler from sklearn.svm import SVC from sklearn.metrics import accuracy_score, classification_report, confusion_matrix # Load Iris dataset iris = datasets.load_iris() # Features and target X = iris.data y = iris.target #