FIU Data Science Bootcamp Application Deadline is Oct. 3 -Apply Now
What is a Monte Carlo Simulation? (Part 3)
By Zachariah Miller • February 12, 2018
This is part 3 of a series by Metis Sr. Data Scientist Zach Miller dedicated to investigating how Monte Carlo can be a great tool.Part 1introduces the concept of Monte Carlo, and in part 2, he goes over how to implement it using Python. _____
Spoiler alert: No, you can't. If you could, the games wouldn't be in the casino. However, meta logic aside, how definite is it that the casino will always beat you? We're going to use Monte Carlo to explore that concept. The methodology will go a little bit like this: build out a Python representation of a card deck, then use MC methodology to determine the best possible play you can make given your hand, then make that play and track your money over time.
The MC part will come into play when we select which cards to hold in the game. (If you aren't familiar with video poker, try checking this out.) We'll see what cards we have, then simulate the results of choosing to hold the cards and see what method gives us the best possible payout. Since this is a lot of work, I've put together a fairly long notebook that shows you all the code it takes to build a card deck, set up the poker game (we have to do the hand scoring), and then actually run the Monte Carlo simulations to see what happens. Throughout the Video Poker Monte Carlo Notebook there are lots of comments and text blurbs to explain the methodology.
So what did we learn?
As you can see from the notebook, even if you play poker perfectly by always making the move that gives you the best-expected return, the casino pretty much always comes out on top if you play long enough. We built out some pretty nifty machinery to be able to let Python play cards with itself over and over to figure what that best move is and it's still all for naught. However, along the way we've learned about using combinations of cards, multiple randomized simulations, and expected returns to make better choices. We also see that by playing smartly, we can WAY outperform playing randomly. In our next installment, we'll investigate how we can use these same principles to figure out how particles interact with matter by building our own particle simulation package in Python (coming soon)!
If you haven't already, read Part 1 and part 2 of Zach's series.
During a recent webinar with Course Report, Metis Sr. Data Scientist Kimberly Fessel discusses how Python is used for data science, how much Python you should know before starting a data science bootcamp, and more. Watch it here.
I came across a question on Quora that boiled down to: "How can I learn machine learning in six months?" I started to write up a short answer, but it quickly snowballed into a huge discussion of the pedagogical approach I used and how I made the transition from physics nerd to physics-nerd-with-machine-learning-in-his-toolbelt to data scientist. Here's a roadmap highlighting major points along the way.