Python: Fitting Polynomial Curves to Data for Brute Force, Backtrack, and Branch & Bound Algorithms

The code snippet fits a polynomial curve to data points representing the performance of brute force, backtrack, and branch and bound algorithms. It uses the 'np.polyfit' function to find the polynomial coefficients that best fit the data. Then, it generates a smooth curve by evaluating the fitted polynomial at 100 evenly spaced points between the minimum and maximum values of 'N'. The resulting smooth curves represent the estimated times for each algorithm as a function of 'N'.