Linear Regression on non linear data.py

import numpy as np
import matplotlib.pyplot as plt

from sklearn.linear_model import LinearRegression

np.random.seed(0)
x = 2 - 3 * np.random.normal(0, 1, 20)
y = x - 2 * (x ** 2) + 0.5 * (x ** 3) + np.random.normal(-3, 3, 20)

# transforming the data to include another axis
x = x[:, np.newaxis]
y = y[:, np.newaxis]

model = LinearRegression()
model.fit(x, y)
y_pred = model.predict(x)

plt.scatter(x, y, s=10)
plt.plot(x, y_pred, color='r')
plt.show()