1 Merge Sort and InsertionSort Although merge sort runs in Θ(n lg n) worst-case time and insertion sort runs in Θ(n 2 ) worst-case time, the constant factors in insertion sort can make it faster in practice for small problem sizes on many machines. Thus, it makes sense to coarsen the leaves of the recursion by using insertion sort within merge sort when subproblems become sufficiently small.1.1 Experiment: Merge Sort vs. Insertion SortThe goal of this first experiment is to compare empirically time complexities of Insertionsort vs. Merge sort. To do so, after coding these two algorithms, you will need to calculate the time com-plexities of the two algorithms for different values of n and plot the obtained complexityvalues. The time complexities will be approximated only by counting the numbers of tests(like : if (A[i] > 1), and simple instructions (like: A[i] = A[i − 1] + 1).To draw plots, you will need to calculate time complexities for different values of n. For this consider the following ones: 5, 10, 15, ... , 90, 95, 100. For each of these values, you will need to generate an array of random integer values between 0 and 1000, whichsize is equal to the value of n. To avoid the effect of sampling you will need to repeat thecalculations for 10 different arrays of the same size and find the time complexity for thevalue of n as the average of the ten calculated complexity values. Required Work 1provide the code for both algorithms and show your counters used for calculatingtime complexity.2. Plot the time complexity graphs for both algorithms on the same figure.3. What is the biggest value of n after which merge sort is better than insertion sort?