Python多项式回归的实现方法

# Importing the libraries 
import numpy as np 
import matplotlib.pyplot as plt 
import pandas as pd 

# Importing the dataset 
datas = pd.read_csv('data.csv') 
datas 

X = datas.iloc[:, 1:2].values 
y = datas.iloc[:, 2].values 

# Fitting Linear Regression to the dataset 
from sklearn.linear_model import LinearRegression 
lin = LinearRegression() 

lin.fit(X, y) 

# Fitting Polynomial Regression to the dataset 
from sklearn.preprocessing import PolynomialFeatures 

poly = PolynomialFeatures(degree = 4) 
X_poly = poly.fit_transform(X) 

poly.fit(X_poly, y) 
lin2 = LinearRegression() 
lin2.fit(X_poly, y) 

# Visualising the Linear Regression results 
plt.scatter(X, y, color = 'blue') 

plt.plot(X, lin.predict(X), color = 'red') 
plt.title('Linear Regression') 
plt.xlabel('Temperature') 
plt.ylabel('Pressure') 

plt.show() 

# Visualising the Polynomial Regression results 
plt.scatter(X, y, color = 'blue') 

plt.plot(X, lin2.predict(poly.fit_transform(X)), color = 'red') 
plt.title('Polynomial Regression') 
plt.xlabel('Temperature') 
plt.ylabel('Pressure') 

plt.show() 

# Predicting a new result with Linear Regression 
lin.predict(110.0) 

# Predicting a new result with Polynomial Regression 
lin2.predict(poly.fit_transform(110.0))