Fitting polynomial regression

Polynomial regression is a type of regression analysis that is used to adapt the nonlinear relationships between dependent and independent variables. In this type of regression, variables are modeled as the nth polynomial degree. It is used to understand the growth rate of various phenomena, such as epidemic outbreaks and growth in sales. Let's understand the equation of polynomial regression:

Here, is the independent variable and is a dependent variable. The intercepts, ..., are a coefficient of x and (the Greek letter pronounced as epsilon) is an error term that will act as a random variable.

Let's see an example to understand the polynomial concept in detail:

# import libraries
import matplotlib.pyplot as plt
import numpy as np

# Create X and Y lists
X=[1,2,3,4,5,6,7,8,9,10]
y=[9,10,12,16,22,28,40,58,102,200]

# Plot scatter diagram
plt.scatter(X,y, color = 'red')
plt.title('Polynomial Regression...