Example 1: plynomial regression implementation python
poly = PolynomialFeatures(degree=2)
X_F1_poly = poly.fit_transform(X_F1)
X_train, X_test, y_train, y_test = train_test_split(X_F1_poly, y_F1,
random_state = 0)
linreg = LinearRegression().fit(X_train, y_train)
Example 2: numpy method to make polynomial model
import numpy as np
import matplotlib.pyplot as plt
x = [1,2,3,5,6,7,8,9,10,12,13,14,15,16,18,19,21,22]
y = [100,90,80,60,60,55,60,65,70,70,75,76,78,79,90,99,99,100]
model = np.poly1d(np.polyfit(x,y,3))
line = np.linspace(1, 22, 100)
plt.scatter(x, y)
plt.plot(line, model(line))
plt.show()