# Import the libraries
from random
from sklearn.linear_model import LinearRegression# Create an empty list for the feature data set 'X' and the target data set 'y'
feature_set = []
target_set= []# get the number of rows wanted for the data set
number_of_rows = 200# limit the possible values in the data set
random_number_limit = 2000#Create the training data set
#Create and append a randomly generated data set to the input and output
for i in range(0,number_of_rows):
x = random.randint(0, random_number_limit)
y = random.randint(0, random_number_limit)
z = random.randint(0, random_number_limit)#Create a linear function for the target data set 'y'
function = (10*x) + (2*y) + (3*z)
feature_set.append([x,y,z])
target_set.append(function)