multiple linear regression example data

Example 1: plot multiplr linear regression model python

import matplotlib.pyplot as plt

plt.style.use('default')
plt.style.use('ggplot')

fig, ax = plt.subplots(figsize=(7, 3.5))

ax.plot(x_pred, y_pred, color='k', label='Regression model')
ax.scatter(X, y, edgecolor='k', facecolor='grey', alpha=0.7, label='Sample data')
ax.set_ylabel('Gas production (Mcf/day)', fontsize=14)
ax.set_xlabel('Porosity (%)', fontsize=14)
ax.legend(facecolor='white', fontsize=11)
ax.text(0.55, 0.15, '$y = %.2f x_1 - %.2f $' % (model.coef_[0], abs(model.intercept_)), fontsize=17, transform=ax.transAxes)

fig.tight_layout()

Example 2: Multiple Regression

from sklearn import linear_model
regr = linear_model.LinearRegression()
x = np.asanyarray(train[['ENGINESIZE','CYLINDERS','FUELCONSUMPTION_COMB']])
y = np.asanyarray(train[['CO2EMISSIONS']])
regr.fit (x, y)
# The coefficients
print ('Coefficients: ', regr.coef_)

Example 3: plot multiplr linear regression model python

from sklearn import linear_model

ols = linear_model.LinearRegression()
model = ols.fit(X, y)