Interactive Linear and Polynomial Regression In Jupyter Notebook Python

Interactive Linear and Polynomial Regression In Jupyter Notebook Python


 In this we will create an interactive gui  for curve fitting  using linear and polynomial regression this is a great way to see what polynomial order degree will give you best results

Linear Regression Formula 

Y = mx + c 

m = slope  , c  = constant , x = variable which changes output Just you know linear Regression always give  straight line and not much useful in most of the real life scenarios


Polynomial Regression


Y =  b1X^n +c

b1= slope  , c  = constant , x = variable which changes output Just you know linear Regression always give  straight line and not much useful in most of the real life scenarios

n = degree of polynomial regression


import numpy

import matplotlib.pyplot as plt

from ipywidgets import interactive

def f(n,t):

    x = [1,2,3,4,5,6,7,8,9,10,11,12,15,17,22,23,25,30]

    y = [100,90,80,60,60,55,60,65,70,70,75,76,78,79,90,99,99,100]


    curve_model = numpy.poly1d(numpy.polyfit(x, y, n))


    curve_fitting_line = numpy.linspace(1, t, 100)


    plt.scatter(x, y)

    plt.plot(curve_fitting_line, curve_model(curve_fitting_line))

    plt.show()



interactive_plot= interactive(f,n=(1,20),t = (1,30))

interactive_plot





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