Support Vector Machines Algorithm added

Naive_Bayes_Guide.m

@@ -30,7 +30,7 @@
 
 %---------------Visualizing Training Results
 labels = unique(data.Purchased);
-classifier_name = 'Naive Bayesain (Training Results)';
+classifier_name = 'Naive Bayes (Training Results)';
 
 Age_range = min(data.Age(training(cv)))-1:0.01:max(data.Age(training(cv)))+1;
 Estimated_salary_range = min(data.EstimatedSalary(training(cv)))-1:0.01:max(data.EstimatedSalary(training(cv)))+1;
@@ -60,7 +60,7 @@
 
 %---------------Visualizing Test Results 
 labels = unique(data.Purchased);
-classifier_name = 'K-Nearest Neigbor (Testing Results)';
+classifier_name = 'Naive Bayes (Testing Results)';
 
 Age_range = min(data.Age(training(cv)))-1:0.01:max(data.Age(training(cv)))+1;
 Estimated_salary_range = min(data.EstimatedSalary(training(cv)))-1:0.01:max(data.EstimatedSalary(training(cv)))+1;

Support_Vector_Machines_Guide.m

@@ -0,0 +1,89 @@
+%%%-------------Support Vector Machines (SVM)
+
+%---------------Importing Dataset
+data = readtable('C:\Users\Asus\Desktop\necessary\Naive Bayes\Social_Network_Ads.csv');
+
+%---------------Feature Scaling (Standardization Method)
+stand_age = (data.Age - mean(data.Age))/std(data.Age);
+data.Age = stand_age; 
+
+stand_estimted_salary = (data.EstimatedSalary - mean(data.EstimatedSalary))/std(data.EstimatedSalary);
+data.EstimatedSalary = stand_estimted_salary; 
+
+%---------------Classifying Data  
+classification_model = fitcsvm(data,'Purchased~Age+EstimatedSalary');
+% Uses normal distribution as default
+% classification_model = fitcnb(normalized_data, 'Survived~Age+Fare+Parch+SibSp+female+male+Pclass','Distribution','kernel');
+
+%---------------Partitioning
+cv = cvpartition(classification_model.NumObservations, 'HoldOut', 0.2);
+cross_validated_model = crossval(classification_model,'cvpartition',cv); 
+
+%---------------Predictions
+Predictions = predict(cross_validated_model.Trained{1},data(test(cv),1:end-1));
+
+%---------------Analyzing the Results
+Results = confusionmat(cross_validated_model.Y(test(cv)),Predictions);
+
+%---------------Visualizing Training Results
+labels = unique(data.Purchased);
+classifier_name = 'SVM (Training Results)';
+
+Age_range = min(data.Age(training(cv)))-1:0.01:max(data.Age(training(cv)))+1;
+Estimated_salary_range = min(data.EstimatedSalary(training(cv)))-1:0.01:max(data.EstimatedSalary(training(cv)))+1;
+
+[xx1, xx2] = meshgrid(Age_range,Estimated_salary_range);
+XGrid = [xx1(:) xx2(:)];
+
+predictions_meshgrid = predict(cross_validated_model.Trained{1},XGrid);
+
+gscatter(xx1(:), xx2(:), predictions_meshgrid,'rgb');
+
+hold on
+
+training_data = data(training(cv),:);
+Y = ismember(training_data.Purchased,labels{1});
+
+scatter(training_data.Age(Y),training_data.EstimatedSalary(Y), 'o' , 'MarkerEdgeColor', 'black', 'MarkerFaceColor', 'red');
+scatter(training_data.Age(~Y),training_data.EstimatedSalary(~Y) , 'o' , 'MarkerEdgeColor', 'black', 'MarkerFaceColor', 'green');
+
+xlabel('Age');
+ylabel('Estimated Salary');
+
+title(classifier_name);
+legend off, axis tight
+
+legend(labels,'Location',[0.45,0.01,0.45,0.05],'Orientation','Horizontal');
+
+%---------------Visualizing Test Results 
+labels = unique(data.Purchased);
+classifier_name = 'SVM  (Testing Results)';
+
+Age_range = min(data.Age(training(cv)))-1:0.01:max(data.Age(training(cv)))+1;
+Estimated_salary_range = min(data.EstimatedSalary(training(cv)))-1:0.01:max(data.EstimatedSalary(training(cv)))+1;
+
+[xx1, xx2] = meshgrid(Age_range,Estimated_salary_range);
+XGrid = [xx1(:) xx2(:)];
+
+predictions_meshgrid = predict(cross_validated_model.Trained{1},XGrid);
+
+figure
+
+gscatter(xx1(:), xx2(:), predictions_meshgrid,'rgb');
+
+hold on
+
+testing_data =  data(test(cv),:);
+Y = ismember(testing_data.Purchased,labels{1});
+
+scatter(testing_data.Age(Y),testing_data.EstimatedSalary(Y), 'o' , 'MarkerEdgeColor', 'black', 'MarkerFaceColor', 'red');
+scatter(testing_data.Age(~Y),testing_data.EstimatedSalary(~Y) , 'o' , 'MarkerEdgeColor', 'black', 'MarkerFaceColor', 'green');
+
+xlabel('Age');
+ylabel('Estimated Salary');
+
+title(classifier_name);
+legend off, axis tight
+
+legend(labels,'Location',[0.45,0.01,0.45,0.05],'Orientation','Horizontal');
+