In this problem, we write a program to find the coefficients for a linear regression model for the dataset provided (data2.txt). Assume a linear model: y = w0 + w1*x. You need to
1) Plot the data (i.e., x-axis for 1 st column, y-axis for 2 nd column), and use Python to implement the following methods to find the coefficients:
2) Normal equation, and
3) Gradient Descent using batch AND stochastic modes respectively:
a) Determine an appropriate termination condition (e.g., when cost function is less than a threshold, and/or after a given number of iterations).
b) Print the cost function vs. iterations for each mode; compare and discuss batch and stochastic modes in terms of the accuracy and the speed of convergence.
c) Choose a best learning rate. For example, you can plot cost function vs. learning rate to determine the best learning rate.
Please implement the algorithms by yoursef and do NOT use the fit() function of the library