Polynorminal Linear Regression

preprocessing

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt

dataset = pd.read_csv('_data/04.csv')
x = dataset.iloc[:, 1:-1].values
y = dataset.iloc[:, -1].values

Linear Regression Model

from sklearn.linear_model import LinearRegression

regressor = LinearRegression()
regressor.fit(x, y)

LinearRegression()

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Polynorminal Linear Regression

from sklearn.preprocessing import PolynomialFeatures

poly = PolynomialFeatures(degree=4)
x_poly = poly.fit_transform(x)
regressor2 = LinearRegression()
regressor2.fit(x_poly, y)

LinearRegression()

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Visualize Linear Regression

plt.scatter(x, y, color='red')
plt.plot(x, regressor.predict(x), color='blue')
plt.title('Linear Regression Model')
plt.xlabel('Position Level')
plt.ylabel('Salary')
plt.show()

Visualize Poly Linear Regression

plt.scatter(x, y, color='red')
plt.plot(x, regressor2.predict(x_poly), color='blue')
plt.title('Poly Linear Regression Model')
plt.xlabel('Position Level')
plt.ylabel('Salary')
plt.show()