Polynomials are mathematical expressions with non-negative strategies. Examples of polynomial functions are linear, quadratic, cubic, and quartic functions. NumPy offers the polyfit() function to generate polynomials using least squares. This function takes x-coordinate, y-coordinate, and degree as parameters, and returns a list of polynomial coefficients.
NumPy also offers polyval() to evaluate the polynomial at given values. This function takes coefficients of polynomials and arrays of points and returns resultant values of polynomials. Another function is linspace(), which generates a sequence of equally separated values. It takes the start, stop, and the number of values between the start-stop range and returns equally separated values in the closed interval.
Let's see an example to generate and evaluate polynomials using NumPy, as follows:
# Import required libraries NumPy, polynomial and matplotlib
import numpy as np
import matplotlib.pyplot...
