Python for Machine Learning | Polynomial Linear Regression with Numpy - P8

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Polynomial Linear Regression with Numpy - P8

Topics to be covered - Polynomial Regression without sklearn

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

import numpy as np

'''
Degree 1 - y = 0.3321x + 0.475

Degree 2 - y = -0.0179x2 + 0.4393x + 0.3857

Degree 3 - y = 0.0194x3 - 0.1929x2 + 0.8282x + 0.269

Degree 4 - y = -0.0087x4 + 0.124x3 - 0.5799x2 + 1.2688x + 0.2242

Degree 5 - y = 0.0129x5 - 0.2025x4 + 1.1358x3 - 2.7112x2 + 2.7536x + 0.1873

'''

print(coefint1)
print(coefint2)
print(coefint3)
print(coefint4)
print(coefint5)

for i in range(1,6):
print('Degree is ', i)

ypred = coefint2[0]*x*x + coefint2[1]*x + coefint2[2]

print('Predicted Values:', ypred)

print('Actual Values :', y)

ypred = coefint1[0]*x + coefint1[1]
print(ypred)

print(y)

yresidual = y - ypred

Sumofresidual = sum(pow(yresidual,2))
Rsquare = 1 - Sumofresidual/SumofTotal

print(Rsquare)

slope, intercept, r_value, p_value, str_err = linregress(x,y)
print(pow(r_value,2))

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