

In [24]:

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



In [25]:

    
np.set_printoptions(precision=3, suppress=True)

Importing a dataset



In [26]:

    
dataset = pd.read_csv('Position_Salaries.csv')



In [27]:

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

Fitting Linear Regression to the dataset



In [28]:

    
from sklearn.linear_model import LinearRegression



In [29]:

    
lin_reg = LinearRegression()
lin_reg.fit(X, y)









    Out[29]:





LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)

Fitting Polynomial Regression to the dataset



In [30]:

    
from sklearn.preprocessing import PolynomialFeatures



In [31]:

    
poly_reg = PolynomialFeatures(degree=4)
X_poly = poly_reg.fit_transform(X)
poly_reg.fit(X_poly, y)









    Out[31]:





PolynomialFeatures(degree=4, include_bias=True, interaction_only=False)



In [32]:

    
lin_reg_2 = LinearRegression()
lin_reg_2.fit(X_poly, y)









    Out[32]:





LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)

Visualising the Linear Regression results



In [33]:

    
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()

Visualising the Polynomial Regression results



In [34]:

    
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()

Visualising the Polynomial Regression results (for higher resolution and smoother curve)



In [35]:

    
X_grid = np.arange(min(X), max(X), 0.1)
X_grid = X_grid.reshape((len(X_grid), 1))
plt.scatter(X, y, color='red')
plt.plot(X_grid, lin_reg_2.predict(poly_reg.fit_transform(X_grid)), color='blue')
plt.title('Truth or Bluff (Polynomial Regression)')
plt.xlabel('Position level')
plt.ylabel('Salary')
plt.show()

Predicting a new result with Linear Regression



In [36]:

    
lin_reg.predict(6.5)









    Out[36]:





array([ 330378.788])

Predicting a new result with Polynomial Regression



In [37]:

    
lin_reg_2.predict(poly_reg.fit_transform(6.5))









    Out[37]:





array([ 158862.453])