Polynomial Regression

import numpy as np
from scipy import stats
import matplotlib.pyplot as plt
import sklearn.metrics

def main():
    xs = np.random.normal(4.0, 1.2, 1000)
    ys = np.random.normal(40.0, 1.2, 1000) / xs

    f2 = np.poly1d(np.polyfit(np.array(xs), np.array(ys), 2))
    f3 = np.poly1d(np.polyfit(np.array(xs), np.array(ys), 3))
    f4 = np.poly1d(np.polyfit(np.array(xs), np.array(ys), 4))
    f5 = np.poly1d(np.polyfit(np.array(xs), np.array(ys), 5))
    xp = np.linspace(0, 7, 100)

    plt.title("Polynomial Regression")
    plt.scatter(xs, ys)
    p2, = plt.plot(xp, f2(xp), c = 'r')
    p3, = plt.plot(xp, f3(xp), c = 'g')
    p4, = plt.plot(xp, f4(xp), c = 'y')
    p5, = plt.plot(xp, f5(xp), c = 'b')
    plt.legend([p2, p3, p4, p5], [label + " degree" for label in ["2nd", "3rd", "4th", "5th"]])
    plt.show()

if __name__ == '__main__':
    main()