How to Build and Visualize Polynomial Regression in Python

Import libraries

import numpy as np
import matplotlib.pyplot as plt
from sklearn.preprocessing import PolynomialFeatures
from sklearn.linear_model import LinearRegression

Load and visualize data

data = np.genfromtxt("job.csv", delimiter=",")
x_data = data[1:, 1]
y_data = data[1:, 2]
plt.scatter(x_data, y_data)
plt.show()

Reshape for scikit-learn

x_data = x_data[:, np.newaxis]
y_data = y_data[:, np.newaxis]

Linear regression

model = LinearRegression()
model.fit(x_data, y_data)

plt.plot(x_data, y_data, 'b.')
plt.plot(x_data, model.predict(x_data), 'r')
plt.show()

Polynomial regression (degree 6)

poly_reg = PolynomialFeatures(degree=6)
x_poly = poly_reg.fit_transform(x_data)
lin_reg = LinearRegression()
lin_reg.fit(x_poly, y_data)

plt.plot(x_data, y_data, 'b.')
plt.plot(x_data, lin_reg.predict(poly_reg.fit_transform(x_data)), c='r')
plt.title('Truth or Bluff (Polynomial Regression)')
plt.xlabel('Position level')
plt.ylabel('Salary')
plt.show()

Prediction on a dense test grid

x_test = np.linspace(1, 10, 100)
x_test = x_test[:, np.newaxis]
plt.plot(x_data, y_data, 'b.')
plt.plot(x_test, lin_reg.predict(poly_reg.fit_transform(x_test)), c='r')
plt.title('Truth or Bluff (Polynomial Regression)')
plt.xlabel('Position level')
plt.ylabel('Salary')
plt.show()