Problem

Construct a module by the name of $$DataAnalysis$.$py$,$ which has a class LinearRegression,

and two functions split data$()$ and pairwiseplot$().$ Use this module in a different file to

fit linear regression on california housing data. Use split data to split the data into two

sets namely, training and test. Use training data to fit the regression and evaluate quality

of the fit using test data. See below for the required details related to functions and the class.

class LinearRegression

$$ constructor init $()$ function$[$this function instantiates an object of the class and takes

fit intercept$=$True as an argument$].$ fit intercept takes True as a default argument,

which indicates that intercept needs to be calculated for linear regression.

$$ fit$()[$it takes X$($features$)$ and y$($targets$)$ to fit a linear regression$]$

$$ predict$()[$it takes the test data and predicts the target values.$]$

$$ mean squared error$()[$it takes predicted and actual target values to calculate the mean

squared error.$]$

$$ coeff $[$attribute which returns the coefficients of regression$]$

$$ intercept $[$attribute which returns the intercept of the regression$]$

$$ plot$()[$plots the predicted and actual target values on same graph$]$

split data$()$

$$ it should take X$,$ y and the size of test data$($test size$)$ as the arguments$($test size$)$ to

split the data into training and test datasets.

pairwiseplot$()$

$$ This function takes X$($set of features$)$ as an argument and plot the pairwise scatter

plots of all the features.

Instructions

$$ Use matplotlib, numpy and pandas modules.

$$ Do not use any function from sklearn and seaborn libraries except for loading the

fetch california housing data.

$$ All graphs should be nice looking. Use proper labels, titles, markers, sizes, colors, etc

to finish your graphs.

$$ Search the internet to find the formula you need to cod multi linear regression.