Polynomial regression python

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]

#a method that lets us make a polynomial model:
model = np.poly1d(np.polyfit(x,y,3))

#Then specify how the line will display, we start 
#at position 1, and end at position 22
line = np.linspace(1, 22, 100)


plt.scatter(x, y)
plt.plot(line, model(line))
plt.show()

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)

Polynomial regression python

# pip install polynomial-regression-model
from polynomial_regression.main import regress
dataset1 = [1, 2, 3, 4, 5]
dataset2 = [2, 3, 4, 5, 6]
regression = regress(dataset1, dataset2)

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