machien learning - linear regration

We consider the following data set concerning House pricing. The table presents the values of two features (Area and Number of rooms) and the house price. 1- One variable Linear Regression We consider only the Area feature for estimating the price of house. We suggest to use a Linear Regression model the relationship between the Area (x) - House price (y). (y=wx+b) a) Compute w and b the parameters of the Linear regression b) Compute the obtained cost function J(w,b)=i=1m(yiy^i)2 c) Compute the corresponding R2 d) For computing the values of w,b, we suggest using the Gradient Descent, with a learning rate =0.1. Compute the update of the parameters for three iterations of the GD. 2- Multivariable Linear Regression In this case both features re considered to build a Linear Regression. y=w2x2+w1x1+w0 Where x1 and x2 correspond respectively to the features Area and number of rooms. a) Scaling of the data according to the two following steps - Subtract the mean value of each feature from the dataset. - After subtracting the mean, additionally scale (divide) the feature values by their respective standard deviations. b) Give the normal equations that compute the linear regression parameter vector W=(w0,w1,w2)T (fill the matrix Xb and the vector Y by the appropriate values)