If you’re like most people starting their data science journey on their own, you probably struggled at the beginning trying to find what to do after completing courses on Data Science/Machine Learning. Well, almost everyone (even myself) suggests starting to work on personal projects to get familiarized with everything you learned from the aforementioned courses.
Let’s take a look at what an end to end project consists of:
A data scientist’s guide to winning most games.
This is a prequel to my previous article on League of Legends Win Prediction where we created several models to predict a game’s outcome based on several in-game features. This article and the previous one are based on a dataset from Kaggle with 9800+ high ranked games.
If you’ve ever played League of Legends, you can tell how a lot of games feel like a coin toss, but in most cases, you can actually do something about it and win it! …
Imagine, if just for a minute, we live in a world where you order a Lyft and it doesn’t immediately tell you the surge pricing multiplier. How much will the ride be?
Now you are stuck in the rain wondering how much the ride will be. Well, I got the answer! Or, I can at least show you how to get the answer.
First, we need to get the dataset from Kaggle. This dataset contains two CSVs; one for distance, cab type (Uber or Lyft), price surge, price, etc…, and the other one for the weather. …
Let’s take a look at how you can predict if your team will win or lose after the 10th minute!
If you’ve played this game before, you should know how much of a coin toss most games are. Whether you should continue playing, or should you forfeit (FF 15) it’s mostly up to you and your team, but this model can give you a better insight on who will win!
A brief summary of League of Legends
Mechanical Engineer | Data Scientist looking to learn more about data science every day!