Predict House Price based on Area in feet, Locality, No of Bed Rooms. I have created this demo project while completing my ML course

ex1TrainingSet.txt

850,3,3,4500000
1000,4,4,3500000
585,3,3,3100000
801,4,4,5500000
729,4,3,4200000
776,4,3,4500000
700,2,3,7100000
800,4,3,4400000
1500,3,5,10000000
1800,3,4,7500000

predict_house_price.m

% This program runs with Octave 3.8+ software

clear ; close all; clc

data = load('ex1TrainingSet.txt');
X = data(:, 1:3);
y = data(:, 4);
m = length(y);
dd = [810 3 3]
ddd = [1 dd]

[X mu sigma] = featureNormalize(X);
X = [ones(m, 1) X];
alpha = 0.01;
num_iters = 400;

theta = zeros(4, 1);
[theta, J_history] = gradientDescentMulti(X, y, theta, alpha, num_iters);

figure;
plot(1:numel(J_history), J_history, '-b', 'LineWidth', 2);
xlabel('Number of iterations');
ylabel('Cost J');

d = dd;
d = (d - mu) ./ sigma;
d = [ones(1, 1) d];
price = d * theta;

fprintf(['Predicted price of a %d sq-ft, %d locality and %d bed room house ' ...
         '(using gradient descent):\n $%f\n'], d(1), d(2), d(3), price);

data = csvread('ex1TrainingSet.txt');
X = data(:, 1:3);
y = data(:, 4);
m = length(y);

X = [ones(m, 1) X];
theta = normalEqn(X, y);

d = ddd;
price = d * theta; % You should change this

fprintf(['Predicted price of a %d sq-ft, %d locality and %d bed room house ' ...
         '(using gradient descent):\n $%f\n'], d(2), d(3), d(4), price);

function [X_norm, mu, sigma] = featureNormalize(X)
  X_norm = X;
  mu = zeros(1, size(X, 2));
  sigma = zeros(1, size(X, 2));

  mu = mean(X_norm);
  sigma = std(X_norm);

  tf_mu = X_norm - repmat(mu,length(X_norm),1);
  tf_std = repmat(sigma,length(X_norm),1);

  X_norm = tf_mu ./ tf_std;
end

function [theta, J_history] = gradientDescentMulti(X, y, theta, alpha, num_iters)
  m = length(y);
  J_history = zeros(num_iters, 1);

  for iter = 1:num_iters
    delta = (1/m)*sum(X.*repmat((X*theta - y), 1, size(X,2)));
    theta = (theta' - (alpha * delta))';
    J_history(iter) = computeCostMulti(X, y, theta);
  end
end

function [theta] = normalEqn(X, y)
  theta = zeros(size(X, 2), 1);
  theta = pinv(X'*X)*X'*y;
end

function J = computeCostMulti(X, y, theta)
  m = length(y); % number of training examples
  J = 0;
  J = (1/(2*m))*sum(power((X*theta - y),2));
end