Question:

Implement Linear regression using NumPy. You should implement your code in the provided linear$\_$regression.py file. This python file takes a csv file and a list of features as input. From these list of features, the last feature will be the target label and the remaining ones will be your training inputs. For example, in the following usage scenario, $$linear$.$csv$$ is the csv file and $$Writing$$ is the target label, while $$Math$$ and $$Reading$$ are training inputs.

$python$3$ linear$\_$regression.py linear.csv Math Reading Writing

Output the RMSE score of prediction.

$-$provided linear$\_$regression.py file,is as follow:

import numpy as np

import pandas as pd

import math

import sys

import os

#Todo : define necessary functions

def linear$\_$regression$($data$)$:

$"""$

data: input data matrix

return: rmse value

$"""$

#Todo : fill code here

return $-1$

# do not modify this function

def load$\_$data$()$:

filename $=$ sys$.$argv$[1]$

feature$\_$matrix $=$ pd$.$read$\_$csv$($filename$)$

feature$\_$matrix $=$ feature$\_$matrix.dropna$()$

features $=$ sys$.$argv$[2$:$]$

#print$($feature$\_$matrix$[$features$])$

return feature$\_$matrix

if $\_\_$name$\_\_=="\_\_$main$\_\_"$:

data $=$ load$\_$data$()$

RMSE$\_$SCORE $=$ linear$\_$regression$($data$)$

print$("$RMSE score is : $",$ RMSE$\_$SCORE$)$

$-$

$$linear$.$csv$$contains in the following format: Math, Reading, Writing. There are $1001$ lines of data structured in this way.also the image of inear.csv has been attched