polynomial_example_poly_plot.py

import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
sns.set_style('whitegrid')
% matplotlib inline
from sklearn.preprocessing import PolynomialFeatures

n_samples = 100
X = np.linspace(0, 10, 100)
y = X ** 3 + np.random.randn(n_samples) * 100 + 100


poly_reg = PolynomialFeatures(degree=2)
X_poly = poly_reg.fit_transform(X.reshape(-1, 1))
lin_reg_2 = LinearRegression()
lin_reg_2.fit(X_poly, y.reshape(-1, 1))
y_pred = lin_reg_2.predict(X_poly)
plt.figure(figsize=(10,8))
plt.scatter(X, y)
plt.plot(X, y_pred)