What an exciting career path - I’ve learned a lot and I’ve had lots of fun along the way!
Here is a link to the Jupyter Notebook of my following portfolio project:
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"source": [
"# Building a Random Forest Classifier to Predict League of Legends Wins"
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"League of Legends (LoL) is an internationally popular and highly competitive online multiplayer team game. Two teams of five players play against each other on a symmetrical map: one side is team 1 side, the other side is team 2 side. In order to win teams must destroy towers and inhibitor(s) to reach the other side of the map and destroy the enemy team's nexus. Each nexus is protected by 2 towers, these towers are invincible until AT LEAST 1 inhibitor has been destroyed. Inhibitors remain invincible until the 3 towers in the lane leading up to the inhibitor have been sequentially destroyed. Thus, in order to secure a win, a team must destroy AT LEAST 5 towers (3 lane towers leading up to the inhibitor, as well as the 2 towers guarding the nexus) and 1 inhibitor.\n",
"\n",
"Although it is necessary to destroy the nexus (which means destroying at least 5 towers and an inhibitor) in order to secure a win, there are many factors that contribute to how quickly players are able to destroy towers and inhibitor(s). Players often argue about which factors (besides towers and inhibitors) to focus on in a game (factors such as player, dragon, baron, rift herald kills). Therefore, in this project I will attempt to answer what factors are most important for winning a LoL game.\n",
"\n",
"I will be using data collected from over 50,000 randomly selected ranked (rank unspecified) games of LoL from the EUW (Europe West) server. Obtained from https://www.kaggle.com/datasets/datasnaek/league-of-legends?resource=download\n",
show original
In this project I use a Random Forest classifier to predict the outcome of League of Legends games. I also use data visualization to identify what components of a game might be useful in order to secure a win.
The project took me approximately 30hrs or so to complete. I feel much more confident going out and doing my own projects after tackling this project. It was great to be able to utilize old and new skills that I’ve learned.
Your feedback is highly appreciated, thank you!
Best,
I liked it It was pretty neat and the visualizations are pretty good