plt.ylabel("CO2 EMISSION")
plt.legend(title='CYLINDERS')
plt.show()
for name, group in groups:
plt.plot(group['FUELCONSUMPTION_COMB_MPG'],
group['CO2EMISSIONS'],
marker="o",
linestyle="",
label=name)
plt.xlabel("FUEL CONSUMPTION COMBINED")
plt.ylabel("CO2 EMISSION")
plt.legend(title='CYLINDERS')
plt.show()
#create test and train datasets
ds = Transform.createTrainTest(Transform, df, 0.8)
X_train = ds['X_train'][['ENGINESIZE', 'CYLINDERS', 'FUELCONSUMPTION_COMB']]
X_test = ds['X_test'][['ENGINESIZE', 'CYLINDERS', 'FUELCONSUMPTION_COMB']]
Y_train = ds['Y_train'][['CO2EMISSIONS']]
Y_test = ds['Y_test'][['CO2EMISSIONS']]
#Identify Top performing models
simple = linear_model.LinearRegression()
ridge = linear_model.Ridge()
lasso = linear_model.Lasso()
elastic = linear_model.ElasticNet()
lasso_lars = linear_model.LassoLars()
bayesian_ridge = linear_model.BayesianRidge()
models = [ridge, lasso, elastic, lasso_lars, bayesian_ridge]
