Guess the rank lol-Complete Details

Guess the rank lolGuess the rank lolGuess the rank lolGuess the rank lolGuess the rank lolGuess the rank lolGuess the rank lol: Millions of people play the multiplayer online game League of Legends on both a casual and professional basis. It’s obvious that if you choose to play the most overpowered (“OP”) champions.

which are characters you play, you’ll have a higher chance of winning games against players of your skill level in a competitive 5 against 5 game where players battle to destroy the other’s home base.

So, to make sure you make the most statistically sound decisions in your next game, how about trying an ML analysis if you’re hoping to get better at the challenging League of Legends ranked game mode? How about learning more about “machine learning (ML),” a term in the tech sector, while taking in stuff related to your interests?

We at Mage consider ML as a tool that can be used by anybody and for anything, not just a select group of data scientists who are excellent mathematicians and programmers. This is similar to Hextech in Arcane. Both of these are not necessary; all you need to know is what a ranking model is Guess the rank lol.

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Guess the rank lol

What is ranking?

A machine learning application called ranking arranges data according to a parameter (like whether you win or lose in a League of Legends game). We can forecast the results of fresh data base on the order in which the data is sorte.

In other words, the model will rate the champion Teemo as “low” on relevance to “wins” if you take a list of Teemo games and rank them according to whether he wins or loses.

As a result, Teemo receives a poor rating for his significance to wins given that his participation in the game has little to no impact on whether you win or lose. Later on, when you ask the model to forecast your chances of winning this new Teemo game .


The majority of data scientists are aware that the best models start with a reliable and appropriate dataset. For the purpose of creating this guide, we painstakingly created 5000 rows and 14 columns of actual platinum and diamond-tier ranked game data from parsing Riot Games API answers. Wish upon this star, download it, and perform your own data studies.

More information about how we created our dataset using League of Legends ranked game data is accessible on my Github repository.

This dataset appears to have 5000 rows, each of which contains details about a player’s matches from games with platinum- or diamond-level rankings.

A player’s KDA, champion they selected, ban, if a teammate went AFK, gold at 10 minutes, gold at the end of the game.

Whether they won or lost are among the information found in the 14 columns. Our next steps will be to prepare the data for the model to train now that we have it .

Removing duplicates:

By selecting a random plat or diamond-level player named “A” and producing their 20 most recent ranked games, we acquired data from the Riot Games API. However, there will be games when the teammates and opponents of players A and B are recorded twice if our list of 25 players, from whom we collected the data, also included player A’s party member, “B.”

By giving the model the impression that an event occurs more frequently, duplicate data might distort prediction results. If we maintained copies of games where the one-trick Yasuo main dominated, just think how inaccurately a model would predict a Yasuo player’s success.

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