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This project analyzes data created by training an RL machine learning algorithm to play a simulation of blackjack. The website will allow the user to play blackjack, provide statistics for win probability of the current hand, and provide a recommendation for what move should be made next. There is further analysis documenting the optimal strategies based on the algorithm, win percentage tracked across 1 million simulations, and more.

Python 1.30% JavaScript 0.65% HTML 1.59% CSS 0.29% Jupyter Notebook 96.17%

blackjackbot5000's Introduction

Black Jack & Machine Learning

Application & Presentation Website:

https://blackjackbot5000.herokuapp.com/

Repositories:

https://github.com/Austin-Potts/Blackjackbot5000

https://github.com/jpicca/blackjackML

Group team members:

Austin Potts
Chris Armstrong
Joey Picca
Lance Westlake
Ming Gao
Shane Gatenby

Key Files:

Reinforcement Learning:
- blackJackReinforcement_final_v1.ipynb
  - Important Instructions: Instructional README.md
  - Outputs:
    - Q_policy
    - win_pct
    - my_csv.csv
    - Q_values.csv
    - blackJackGameData.csv
    - blackJackGameTrackerData.csv
Tableau/Visualization:
- BlackJack Outcome Viz.twb
- RL_models_v3.twb
- EDA Desktop View.twbx
Webpage/Presentation/Application:
- html: /templates/index.html
- css: /static/style.css
- javascript: /static/controller.js
- javascript: /static/chart.js

Please see the Instructional README.md file located in the /final_RLandEDA/BlackJackRL/ directory for notes and instruction for using the blackJackReinforcement jupyter notebook file

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