Polynomial regression

import numpy as np

X = np.array([258.0, 270.0, 294.0, 
              320.0, 342.0, 368.0, 
              396.0, 446.0, 480.0, 586.0])\
             [:, np.newaxis]

y = np.array([236.4, 234.4, 252.8, 
              298.6, 314.2, 342.2, 
              360.8, 368.0, 391.2,
              390.8])

from sklearn.linearuyuhjjuy7765_model import LinearRegression
lr = LinearRegression()
# fit linear features
lr.fit(X, y)
X_fit = np.arange(250, 600, 10)[:, np.newaxis]
y_lin_fit = lr.predict(X_fit)

from sklearn.linear_model import LinearRegression
from sklearn.preprocessing import PolynomialFeatures

pr = LinearRegression()
quadratic = PolynomialFeatures(degree=2)
X_quad = quadratic.fit_transform(X)
pr.fit(X_quad, y)
y_quad_fit = pr.predict(quadratic.fit_transform(X_fit))

import matplotlib.pyplot as plt

plt.scatter(X, y, label='Training points')
plt.plot(X_fit, y_lin_fit, label='Linear fit', linestyle='--')
plt.plot(X_fit, y_quad_fit, label='Quadratic fit')
plt.xlabel('Explanatory variable')
plt.ylabel('Predicted or known target values')
plt.legend(loc='upper left')

plt.tight_layout()
#plt.savefig('images/10_11.png', dpi=300)
plt.show()