Binomial distribution models the number of repeated trials with the same probability on each trial. Here, each trial is independent and has two possible outcomes—success and failure—that can occur on each client. The following formula represents the binomial distribution:
Here, p and q are the probabilities of success and failure, n is the number of trials, and X is the number of the desired output.
The numpy.random subpackage provides a binomial() function that generates samples based on the binomial distribution for certain parameters, number of trials, and the probability of success.
Let's consider a 17th-century gambling house where you can bet on eight tossing pieces and nine coins being flipped. If you get five or more heads then you win, otherwise you will lose. Let's write code for this simulation for 1,000 coins using the binomial() function, as follows:
# Import required libraries
import...
