Goal: In this assignment, we will experiment with three different algorithms to train a linear regression models: solving normal equatians, batch gradient descent, stochastic gradient descent. Input Format: The datasets are given in tvs (tab-separated tormat. The file format is: 1st row: the numer of data points N. . 2nd row: the number of features D. rd row: the tirst column is the label, and following columns are teature names. . N following rows: each has (D columns where the the first column is the label and following D columns are features. An example file can he found here. There are two dataset that we wil work with in this assignment. 1. data 1 tic 100.tsv:1 his dataset contains 1U,000 pnints, each with 100 features. 2. dlala 100k 300 lsv: This dalasel conlins 100,000 poinls, each wilh 300 ledtures. The dataset can be downloaded fron here Output Format: output must be given in ts format, with (D-1) columns and two rows: The first 'is the coeffic ent names of the inear regression model The first D columns tontain 1, w up to Ly . The second row contains values corresponding to the coefficents of the regression model her is the coefficient of the ' th feature. The bas term named o is in the last column. The sample output for the sample dataset above can be downloaded here There are three questions in this assigment. The first and second question are worth 10 points each where the third question is worth 30 points, all of 50 points total, lineaeessi 90k 300 tion value han nt data loss Questlon 3 (30 polnts): Implement the stochastic gracdient descent algorithm w 1. T-20 epochs, learning rate -0.000001 this is 10 and batch size m-1 on the dataset data 10k 100 tsv For full score, your algorithm must run in less than 1 minutes with loss value less than 30. 2. T-12 epochs, learning rate -00000001 this is 10-7 and batch size m-1 on the dataset data 100k 300 tsv For full score. your algorithm must run in less than 10 minutes with loss value less than 70 Each part in question 3 is worth 15 points