Polynomial Regression
A tutorial on How to use Polynomial Regression.
- 0. Data Preprocessing
- Training the Linear Regression model on the whole dataset
- Training the Polynomial Regression model on the whole dataset
- Visualising the Linear Regression results
- Visualising the Polynomial Regression results
- Predicting a new result with Linear Regression
- Predicting a new result with Polynomial Regression
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
dataset = pd.read_csv('Position_Salaries.csv')
dataset
X = dataset.iloc[:, 1:-1].values
y = dataset.iloc[:, -1].values
from sklearn.linear_model import LinearRegression
lin_reg = LinearRegression()
lin_reg.fit(X, y)
from sklearn.preprocessing import PolynomialFeatures
poly_reg = PolynomialFeatures(degree = 4)
X_poly = poly_reg.fit_transform(X)
lin_reg_2 = LinearRegression()
lin_reg_2.fit(X_poly, y)
plt.scatter(X, y, color = 'red')
plt.plot(X, lin_reg.predict(X), color = 'blue')
plt.title('Truth or Bluff (Linear Regression)')
plt.xlabel('Position Level')
plt.ylabel('Salary')
plt.show()
plt.scatter(X, y, color = 'red')
plt.plot(X, lin_reg_2.predict(poly_reg.fit_transform(X)), color = 'blue')
plt.title('Truth or Bluff (Polynomial Regression)')
plt.xlabel('Position level')
plt.ylabel('Salary')
plt.show()
lin_reg.predict([[6.5]])
lin_reg_2.predict(poly_reg.fit_transform([[6.5]]))