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Use supervised machine learning to analyze key performance indicators of a player's strengths and weaknesses. The process involved data gathering from API, data cleaning, data storage in SQL and CSV files, multiple machine learning models like Random Forest, Logistic linear regression classifiers.

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Scouter

Here I decided to tackle an specific problem which affects the community as well as the teams who compete in tournaments.

Scouting.

Scouting is the ability to recognize strengths and weaknesses, in this case of each individual player.

Knowing strengths and weaknesses can help individuals become better players.

But it also helps teams find that valuable piece that they need to compete in their own league or that person who they can develop for the future.

That being said, there are some tools online that show you match history and ingame statistics.

But they are not easy to digest, or they don’t even make it to compare a player to another as they only show raw data.

Workflow

  • Get matches data of high elo players (Because of the limited time).
  • Group the data by role in game (TOP, JUNGLER, MID, BOTTOM, SUPPORT).
  • Check player's stats and compare them with the average and give tips to improve.
  • Compare against another player of choose.
  • Compare against best player.

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Use supervised machine learning to analyze key performance indicators of a player's strengths and weaknesses. The process involved data gathering from API, data cleaning, data storage in SQL and CSV files, multiple machine learning models like Random Forest, Logistic linear regression classifiers.

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