diff --git a/ex5/ex5.m b/ex5/ex5.m
index 8ca6c23..9a8698c 100644
--- a/ex5/ex5.m
+++ b/ex5/ex5.m
@@ -3,7 +3,7 @@
 %
 %  Instructions
 %  ------------
-% 
+%
 %  This file contains code that helps you get started on the
 %  exercise. You will need to complete the following functions:
 %
@@ -19,7 +19,7 @@
 clear ; close all; clc
 
 %% =========== Part 1: Loading and Visualizing Data =============
-%  We start the exercise by first loading and visualizing the dataset. 
+%  We start the exercise by first loading and visualizing the dataset.
 %  The following code will load the dataset into your environment and plot
 %  the data.
 %
@@ -27,7 +27,7 @@ clear ; close all; clc
 % Load Training Data
 fprintf('Loading and Visualizing Data ...\n')
 
-% Load from ex5data1: 
+% Load from ex5data1:
 % You will have X, y, Xval, yval, Xtest, ytest in your environment
 load ('ex5data1.mat');
 
@@ -43,8 +43,8 @@ fprintf('Program paused. Press enter to continue.\n');
 pause;
 
 %% =========== Part 2: Regularized Linear Regression Cost =============
-%  You should now implement the cost function for regularized linear 
-%  regression. 
+%  You should now implement the cost function for regularized linear
+%  regression.
 %
 
 theta = [1 ; 1];
@@ -57,7 +57,7 @@ fprintf('Program paused. Press enter to continue.\n');
 pause;
 
 %% =========== Part 3: Regularized Linear Regression Gradient =============
-%  You should now implement the gradient for regularized linear 
+%  You should now implement the gradient for regularized linear
 %  regression.
 %
 
@@ -74,10 +74,10 @@ pause;
 
 %% =========== Part 4: Train Linear Regression =============
 %  Once you have implemented the cost and gradient correctly, the
-%  trainLinearReg function will use your cost function to train 
+%  trainLinearReg function will use your cost function to train
 %  regularized linear regression.
-% 
-%  Write Up Note: The data is non-linear, so this will not give a great 
+%
+%  Write Up Note: The data is non-linear, so this will not give a great
 %                 fit.
 %
 
@@ -98,10 +98,10 @@ pause;
 
 
 %% =========== Part 5: Learning Curve for Linear Regression =============
-%  Next, you should implement the learningCurve function. 
+%  Next, you should implement the learningCurve function.
 %
 %  Write Up Note: Since the model is underfitting the data, we expect to
-%                 see a graph with "high bias" -- slide 8 in ML-advice.pdf 
+%                 see a graph with "high bias" -- slide 8 in ML-advice.pdf
 %
 
 lambda = 0;
@@ -159,7 +159,7 @@ pause;
 
 %% =========== Part 7: Learning Curve for Polynomial Regression =============
 %  Now, you will get to experiment with polynomial regression with multiple
-%  values of lambda. The code below runs polynomial regression with 
+%  values of lambda. The code below runs polynomial regression with
 %  lambda = 0. You should try running the code with different values of
 %  lambda to see how the fit and learning curve change.
 %
@@ -196,7 +196,7 @@ fprintf('Program paused. Press enter to continue.\n');
 pause;
 
 %% =========== Part 8: Validation for Selecting Lambda =============
-%  You will now implement validationCurve to test various values of 
+%  You will now implement validationCurve to test various values of
 %  lambda on a validation set. You will then use this to select the
 %  "best" lambda value.
 %
@@ -218,3 +218,11 @@ end
 
 fprintf('Program paused. Press enter to continue.\n');
 pause;
+
+% Computing test set error
+[~, best_i] = min(error_val, [], 1);
+lambda_best = lambda_vec(best_i);
+theta_best = trainLinearReg(X_poly, y, lambda_best);
+error_test = linearRegCostFunction(X_poly_test, ytest, theta_best, 0);
+fprintf('Test set error for best lambda = %f: %f\n', ...
+    lambda_best, error_test);