block3.python

# linear regression feature importance
from sklearn.linear_model import LinearRegression
from matplotlib import pyplot as plt 
# define the model
model = LinearRegression()
# fit the model
model.fit(X_reg, y_reg)
# get importance
importance = model.coef_
# summarize feature importance
for i,v in enumerate(importance):
	print('Feature: %0d, Score: %.5f' % (i,v))
# plot feature importance
plt.figure(figsize = (10,6))
plt.bar([x for x in range(len(importance))], importance)
plt.show()