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Enhanced SVM: Added accuracy report, flexible kernel, and visualization improvements #2

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Enhanced SVM: Added accuracy report, flexible kernel, and visualization improvements #2

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19 changes: 15 additions & 4 deletions support_vector_machine.py
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Original file line number Diff line number Diff line change
Expand Up @@ -26,7 +26,9 @@
# Fitting the classifier into the Training set

from sklearn.svm import SVC
classifier = SVC(kernel = 'linear', random_state = 0)
kernel_type = 'rbf' # options: 'linear', 'rbf', 'poly', 'sigmoid'
classifier = SVC(kernel=kernel_type, random_state=0)

classifier.fit(X_Train, Y_Train)

# Predicting the test set results
Expand All @@ -37,7 +39,14 @@

from sklearn.metrics import confusion_matrix
cm = confusion_matrix(Y_Test, Y_Pred)
print("Confusion Matrix:\n", cm)

# Add accuracy and classification report
from sklearn.metrics import accuracy_score, classification_report
print("Accuracy:", accuracy_score(Y_Test, Y_Pred))
print("\nClassification Report:\n", classification_report(Y_Test, Y_Pred))

# Step 7: Visualizing the Training Results
# Visualising the Training set results

from matplotlib.colors import ListedColormap
Expand All @@ -51,9 +60,11 @@
for i, j in enumerate(np.unique(Y_Set)):
plt.scatter(X_Set[Y_Set == j, 0], X_Set[Y_Set == j, 1],
c = ListedColormap(('red', 'green'))(i), label = j)
plt.title('Support Vector Machine (Training set)')
plt.xlabel('Age')
plt.ylabel('Estimated Salary')
plt.title(f'Support Vector Machine ({kernel_type.capitalize()} Kernel) - Training set')

plt.xlabel('Age (years)')
plt.ylabel('Estimated Salary ($)')

plt.legend()
plt.show()

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