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In our data-driven project, we unlock BattleBots tournament predictions through machine learning and historical data analysis. By evaluating their match performance, we craft an accurate model to anticipate competition outcomes. Connect with us to discover how we're enriching fans' experiences by empowering them to foresee tournament winners.

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Battle-Bots-Project/battle-bots

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Battle Bots, Roll Out!

Python Pandas NumPy Matplotlib seaborn sklearn SciPy Beautifulsoup

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Project Description:

A data-driven project that focuses on unlocking Battlebot Tournament predictions through the power of Machine Learning and data analysis. By evaluating their match performance, we craft an accurate model to anticipate competition outcomes. With the prediction model, we look to enrich fans' experiences by empowering them to foresee tournament winners.

Project Goals:

Executive Summary:

Initial Hypothesis:

Project Plan:

  • Acquire the data:

  • Prepare data for exploration:

    • Observations that had special characters were removed from analysis

      • special characters (%, -)
    • Initial rows per season:

      • World Championship VII:
      • Season 2021/2022:
      • Season 2020:
      • Season 2019:
  • Separate into train, validate, and test datasets

  • Explore data to develop an understanding of what features affect a robot's chances at winning.

    • Initial questions:
  • Prepare the data for modeling:

  • **Develop a model to predict if a robot will win or lose.

    • Classification models were used to predict a robot's chance to win
      • Decision Tree
      • Random Forest
      • K Nearest Neighbor
      • Logistic Regression
      • XGBoost
    • Evaluate models on train and validate data**
    • Select the best model without overfitting.

Data Dictionary:

Feature Definition
robot_name name of robot
total_matches total number of matches
win_percentage percent of matches won
total_wins total wins
losses number of losses
ko number of knockouts
ko_percentage percentage of knockouts for matches
avg_ko_time average time taken to knock an opponent out
ko_against opponent that got knockedout
ko_against_percentage percentage of knockout wins
decision_wins number of wins by judges' decision

Steps to Reproduce

  1. Clone this repo
  2. Use the function from wrangle.py to scrape the data from the battlebots website
    • May take a few hours to web scrape.
  3. Use the functions from prepare.py to prepare the data for exploration
  4. Run the explore and modeling notebook
  5. Run final report notebook

About

In our data-driven project, we unlock BattleBots tournament predictions through machine learning and historical data analysis. By evaluating their match performance, we craft an accurate model to anticipate competition outcomes. Connect with us to discover how we're enriching fans' experiences by empowering them to foresee tournament winners.

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