From 3da65944e03c534afe2d56d6e0a6b41436aad0e5 Mon Sep 17 00:00:00 2001
From: zain-cs <mzainulabidin.cs@gmail.com>
Date: Wed, 22 Oct 2025 21:12:41 +0500
Subject: [PATCH] Enhanced SVM: added accuracy report, flexible kernel, and
 visualization improvements

---
 support_vector_machine.py | 19 +++++++++++++++----
 1 file changed, 15 insertions(+), 4 deletions(-)

diff --git a/support_vector_machine.py b/support_vector_machine.py
index 01e4e6e..ddbfe7d 100644
--- a/support_vector_machine.py
+++ b/support_vector_machine.py
@@ -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
@@ -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
@@ -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()