Write a python program to implement polynomial regression for given dataset.

# Importing the libraries

import numpy as np

import matplotlib.pyplot as plt

import pandas as pd

 

#Importing the dataset

datas=pd.read_csv('H:\\ML_Practical\\CSV Files\\data1.csv')

print(datas)

print(datas.head())

# Dividing the dataset into 2 components

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

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

# Fitting Linear Regression to the dataset

from sklearn.linear_model import LinearRegression

line = LinearRegression()

 

line.fit(X, y)

# Visualising the Linear Regression results

plt.scatter(X, y, color = 'blue')

 

plt.plot(X,line.predict(X), color = 'red')

plt.title('Linear Regression')

plt.xlabel('Temperature')

plt.ylabel('Pressure')

 

plt.show()

# Fitting Polynomial Regression to the dataset

from sklearn.preprocessing import PolynomialFeatures

 

poly = PolynomialFeatures(degree = 8)

X_poly = poly.fit_transform(X)

lin2 = LinearRegression()

lin2.fit(X_poly, y)

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()