# Gradient Descent: Linear regression with two variables

# In this Assignment you are going to solve a linear regression problem with $2$ variables and $1000$ observations.

# Let us create and load the data.

from sklearn.linear$\_$model import LinearRegression

from sklearn.datasets import make$\_$regression

import numpy as np

X$,$Y $=$ make$\_$regression$($n$\_$samples $=1000,$ n$\_$features$=2,$ n$\_$informative$=2,$ n$\_$targets$=1,$ bias$=10,$ coef$=$ False, noise $=20,$ random$\_$state$=0)$

# without scaling

theta$0=0$

theta$1=0$

theta$2=0$

n$\_$iters $=1000$ #Maximum number of iterations

precision $=0\mathrm{.}0001$ # The precision value to stop the algorithm

# Continue from HERE $.........$

Solve the given linear regression problem using gradient descent without scaling.