Let the first three columns of the data set be separate explanatory variables X₁, X2, X3. Again, let the fourth column be the dependent variable y. Run linear regression simultaneously using all three explanatory variables. Report the linear model you found by running the gradient descent algorithm. Predict the value of y for new (X₁, X2, X3) values (1, 1, 1), for (2, 0, 4), and for (3, 2, 1). (Note: You cannot use built-in function from ML libraries for gradient descent, you have to implement it yourself.) Attach your implementation code as well! For example, assume I code two functions to solve this problem (this is only an example, you don't have to follow this strictly):
Let the first three columns of the data set be separate explanatory variables X₁, X2, X3. Again, let the fourth column be the dependent variable y. Run linear regression simultaneously using all three explanatory variables. Report the linear model you found by running the gradient descent algorithm. Predict the value of y for new (X₁, X2, X3) values (1, 1, 1), for (2, 0, 4), and for (3, 2, 1). (Note: You cannot use built-in function from ML libraries for gradient descent, you have to implement it yourself.) Attach your implementation code as well! For example, assume I code two functions to solve this problem (this is only an example, you don't have to follow this strictly):
Database System Concepts
7th Edition
ISBN:9780078022159
Author:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan
Publisher:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan
Chapter1: Introduction
Section: Chapter Questions
Problem 1PE
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matlab, use dummy data
Transcribed Image Text:We will use a dataset provided: "D3.csv"
Let the first three columns of the data set be separate explanatory variables X₁, X₂, X3. Again, let
the fourth column be the dependent variable y.
Run linear regression simultaneously using all three explanatory variables. Report the linear model
you found by running the gradient descent algorithm. Predict the value of y for new (X₁, X2, X3)
values (1, 1, 1), for (2, 0, 4), and for (3, 2, 1). (Note: You cannot use built-in function from ML
libraries for gradient descent, you have to implement it yourself.)
Attach your implementation code as well! For example, assume I code two functions to solve this
problem (this is only an example, you don't have to follow this strictly):
Function 1.m
function run_linear regression ()
% load data
XXXX
% gradient decent
XXXXX
end
Function2.m
function gradient_decent ()
% this is an implementation of gradient decent
XXXXXXX
end
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