in [ ] shape out[ ]( lin [ ] [: out[ ]array([ - - - - - ][- - - - - - - - - ] - - - - - - ][- - - - - - ] - - - - - ]in [ ] [: out[ ]array( ]since we are using the data in the form of numpy arrayswe don' get the name of the features in the data itself but we will keep the reference to the variable names as they may be needed later in our process or just for future reference in [ ]feature_names=['age''sex''bmi''bp'' '' '' '' '' '' 'for prediction of the response variable herewe will learn lasso model lasso model is an extension of the normal linear regression model which allows us to apply regularization to the model simply puta lasso regression will try to minimize the number of independent variables in the final model this will give us the model with the most important variables only (feature selectionin [ ]from sklearn import datasets from sklearn linear_model import lasso import numpy as np from sklearn import linear_modeldatasets from sklearn model_selection import gridsearchcv we will split our data into separate test and train sets of data (train is used to train the model and test is used for model performance testing and evaluationin [ ]diabetes datasets load_diabetes(x_train diabetes data[: y_train diabetes target[: x_test diabetes data[ :y_test diabetes data[ :