Implementation of Gradient Descent Linear Regression Algorithm

The format of the dataset is as follows-

x1 \tab y1
x1 \tab y1
x1 \tab y1
x1 \tab y1
..
..
..
..
and so on.

The task is to build a model that has a very good (as less as possible) mean squared error.

Once the implementation is done and we run the Gradient Descent algorithm correctly, we should get the values of w0 and w1, for the equation y = w0 + w1 * x

Using these two weight values we should be able to calculate the mean squared error for each of the data points in the dataset.