SVMclassifier.py

import numpy as np
import matplotlib.pyplot as plt
from sklearn.datasets import make_classification
from sklearn.model_selection import train_test_split
from sklearn.svm import SVC
from sklearn.metrics import accuracy_score

# Generate sample data for binary classification
X, y = make_classification(n_samples=200, n_features=2, n_informative=2, n_redundant=0, random_state=42)

# Split the data into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Create and train the SVM classifier
model = SVC(kernel='linear')
model.fit(X_train, y_train)

# Make predictions on the test set
y_pred = model.predict(X_test)

# Calculate accuracy
accuracy = accuracy_score(y_test, y_pred)

print(f"Model Accuracy: {accuracy:.2f}")

# Visualize the decision boundary and margin
plt.figure(figsize=(10, 6))
plt.scatter(X[:, 0], X[:, 1], c=y, cmap=plt.cm.Paired, s=30)
ax = plt.gca()
xlim = ax.get_xlim()
ylim = ax.get_ylim()

# Create grid to plot decision boundary
xx, yy = np.meshgrid(np.linspace(xlim[0], xlim[1], 100), np.linspace(ylim[0], ylim[1], 100))
Z = model.decision_function(np.c_[xx.ravel(), yy.ravel()])
Z = Z.reshape(xx.shape)

# Plot decision boundary and margins
plt.contour(xx, yy, Z, colors='k', levels=[-1, 0, 1], alpha=0.5,
            linestyles=['--', '-', '--'])
plt.scatter(model.support_vectors_[:, 0], model.support_vectors_[:, 1], s=100,
            linewidth=1, facecolors='none', edgecolors='k')
plt.xlabel("Feature 1")
plt.ylabel("Feature 2")
plt.title("Support Vector Machine Decision Boundary and Margins")
plt.show()