Create NN2.m

NN2.m

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+clear all 
+clc
+close all
+% Generate synthetic data for male and female
+numPoints = 100;  % Number of points per class
+
+% Generate random data for males (e.g., height, weight)
+maleHeight = randn(numPoints, 1) * 5 + 175;  % Mean height around 175 cm
+maleWeight = randn(numPoints, 1) * 10 + 75;   % Mean weight around 75 kg
+maleData = [maleHeight, maleWeight];
+
+% Generate random data for females (e.g., height, weight)
+femaleHeight = randn(numPoints, 1) * 5 + 165;  % Mean height around 165 cm
+femaleWeight = randn(numPoints, 1) * 10 + 65;   % Mean weight around 65 kg
+femaleData = [femaleHeight, femaleWeight];
+
+% Combine data into input and target matrices
+inputs = [maleData; femaleData]';  % Transpose to get features in columns
+targets = [zeros(1, numPoints), ones(1, numPoints)];  % 0 for male, 1 for female
+
+% Create and configure the feedforward neural network
+hiddenLayerSize = 10;  % Number of neurons in the hidden layer
+net = feedforwardnet(hiddenLayerSize);
+
+% Train the network
+net = train(net, inputs, targets);
+
+% Test the network
+outputs = net(inputs);
+predictions = outputs > 0.5;  % Convert probabilities to binary predictions
+
+% Calculate accuracy
+accuracy = sum(predictions == targets) / length(targets) * 100;
+
+% Display accuracy
+fprintf('Accuracy: %.2f%%\n', accuracy);
+
+% Visualize the results
+figure;
+hold on;
+scatter(maleData(:,1), maleData(:,2), 'b', 'filled');  % Male points
+scatter(femaleData(:,1), femaleData(:,2), 'r', 'filled');  % Female points
+
+% Create a grid for visualizing the decision boundary
+[x1Grid, x2Grid] = meshgrid(140:1:200, 40:1:100);  % Grid for height and weight
+gridInputs = [x1Grid(:)'; x2Grid(:)'];
+gridOutputs = net(gridInputs);
+decisionBoundary = reshape(gridOutputs, size(x1Grid));
+
+% Plot the decision boundary
+contour(x1Grid, x2Grid, decisionBoundary, [0.5 0.5], 'k', 'LineWidth', 2);
+title('Feedforward Neural Network for Male vs. Female Classification');
+xlabel('Height (cm)');
+ylabel('Weight (kg)');
+legend('Male', 'Female', 'Decision Boundary');
+hold off;