Preliminaries
import pandas as pd
from sklearn.datasets import make_regression
Create Simulated Data
# Generate fetures, outputs, and true coefficient of 100 samples,
features, output, coef = make_regression(n_samples = 100,
# three features
n_features = 3,
# where only two features are useful,
n_informative = 2,
# a single target value per observation
n_targets = 1,
# 0.0 standard deviation of the guassian noise
noise = 0.0,
# show the true coefficient used to generated the data
coef = True)
View Simulated Data
# View the features of the first five rows
pd.DataFrame(features, columns=['Store 1', 'Store 2', 'Store 3']).head()

Store 1 
Store 2 
Store 3 
0 
0.166697 
0.177142 
2.329568 
1 
0.093566 
0.544292 
0.685165 
2 
0.625958 
0.193049 
1.168012 
3 
0.843925 
0.567444 
0.193631 
4 
1.079227 
0.819236 
1.609171 
# View the output of the first five rows
pd.DataFrame(output, columns=['Sales']).head()

Sales 
0 
149.387162 
1 
4.164344 
2 
52.166904 
3 
56.996180 
4 
27.246575 
# View the actual, true coefficients used to generate the data
pd.DataFrame(coef, columns=['True Coefficient Values'])

True Coefficient Values 
0 
0.000000 
1 
80.654346 
2 
57.993548 