Machine-Learning--Python/Polynomial regression at master · sudheera96/Machine-Learning--Python · GitHub

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#reading the data
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd

dataset=pd.read_csv('Position_Salaries.csv')
X=dataset.iloc[:,1:2].values
Y=dataset.iloc[:,2].values

#fitting linear regressor
from sklearn.linear_model import LinearRegression
lin_reg=LinearRegression()
lin_reg.fit(X,Y)

#fitting polynomial regression
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)

#plotting the linear regression
plt.scatter(X,Y,color='red')
plt.plot(X,lin_reg.predict(X),color='blue')
plt.xlabel('levels')
plt.ylabel('salary')
plt.title('truth or bluff(linear regresson)')
plt.show()

#plotting polynomial model
plt.scatter(X,Y,color='red')
plt.plot(X,lin_reg_2.predict(X_poly),color='blue')
plt.xlabel('levels')
plt.ylabel('salary')
plt.title('truth or bluff(polinomial regresson)')
plt.show()

#predict with linear regression
lin_reg.predict(6.5)

#predict with polynomial regression
lin_reg_2.predict(poly_reg.fit_transform(6.5))