Please help me solve this problem.
Linear Algebra using Octave with some more knowledge of machine learning
Here is the assignment:
You will investigate linear regression using gradient descent and the normal equations
The training set of housing prices in Portland, Oregon is in files ex3x and ex3y, where
$y are the prices and the inputs $x are the living area and the number of bedrooms. We will use only living area in this assignment
First part of the assignment is to calculate linear estimator using normal equation, and then use the parameters to calculate price of the house with 1650sf,
File to modify: gradinet3student_neq
Second part of the assignment is to calculate linear estimator using gradient descent, and then use the parameters to calculate price of the house with 1650sf,
File to modify: gradinet3student_GD
Note: Recall when you are applying GD, it is good practice to normalize the input data (subtract the average and scale by standard deviation), to bring all data in the similar range. During prediction you need to do the same.
Send me house price estimate and weights for part one and two, in following format:
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