diff --git a/Code/Day 6_Logistic_Regression.py b/Code/Day 6_Logistic_Regression.py
index 6aa54ce..97388dc 100644
--- a/Code/Day 6_Logistic_Regression.py	
+++ b/Code/Day 6_Logistic_Regression.py	
@@ -33,3 +33,39 @@ cm = confusion_matrix(y_test, y_pred)
 print(cm)  # print confusion_matrix
 print(classification_report(y_test, y_pred))   # print classification report
 
+#Visualization
+from matplotlib.colors import ListedColormap
+X_set,y_set=X_train,y_train
+X1,X2=np. meshgrid(np. arange(start=X_set[:,0].min()-1, stop=X_set[:, 0].max()+1, step=0.01),
+                   np. arange(start=X_set[:,1].min()-1, stop=X_set[:,1].max()+1, step=0.01))
+plt.contourf(X1, X2, classifier.predict(np.array([X1.ravel(),X2.ravel()]).T).reshape(X1.shape),
+             alpha = 0.75, cmap = ListedColormap(('red', 'green')))
+plt.xlim(X1.min(),X1.max())
+plt.ylim(X2.min(),X2.max())
+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(' LOGISTIC(Training set)')
+plt. xlabel(' Age')
+plt. ylabel(' Estimated Salary')
+plt. legend()
+plt. show()
+
+X_set,y_set=X_test,y_test
+X1,X2=np. meshgrid(np. arange(start=X_set[:,0].min()-1, stop=X_set[:, 0].max()+1, step=0.01),
+                   np. arange(start=X_set[:,1].min()-1, stop=X_set[:,1].max()+1, step=0.01))
+
+plt.contourf(X1, X2, classifier.predict(np.array([X1.ravel(),X2.ravel()]).T).reshape(X1.shape),
+             alpha = 0.75, cmap = ListedColormap(('red', 'green')))
+plt.xlim(X1.min(),X1.max())
+plt.ylim(X2.min(),X2.max())
+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(' LOGISTIC(Test set)')
+plt. xlabel(' Age')
+plt. ylabel(' Estimated Salary')
+plt. legend()
+plt. show()