Python for Machine Learning | Line of Best Fit in Linear Regression | Cost Function - P86

Опубликовано: 10 Октябрь 2024
на канале: technologyCult

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Python for Machine Learning | Line of Best Fit in Linear Regression | Cost Function - P86

Python for Machine Learning - Session # 86

Topic to be covered - Minimize Error in Linear Regression

Code Starts Here
==============

import numpy as np
import matplotlib.pyplot as plt

X = np.array([0,0.5,1,1.1,1,5,2.5,3,3.5,4,4.5,5,5.5,6,6.5,7,7.5,8,8.5,9,9.5,10])

Y = np.array([2,2.3,2.5,3.2,2.1,3.3,4.5,2.9,6.4,4.8,5,5.5,6.9,6.3,6.2,7.4,7.5,7.8,8.8,10.4,9.6,9.9,])

plt.scatter(X,Y)
plt.show()

m = (Y.dot(X) - Y.mean()*X.sum()) / ( X.dot(X) - X.mean()* X.sum() )

c = ( Y.mean() * X.dot(X) - X.mean() * Y.dot(X)) / ( X.dot(X) - X.mean()* X.sum() )

Y_hat = m*X + c

plt.scatter(X,Y)
plt.plot(X,Y_hat)
plt.show()

'''R2 = 1 - (SSR / SSM)'''

SSR = Y - Y_hat

SSM = Y - Y.mean()

R2 = 1 - (SSR.dot(SSR)) / (SSM.dot(SSM))
print('R Sqaure :', R2)

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