Kaggle r

Kaggle R Tutorial on Machine Learning

16 Exercises 1 hour 28,800 Participants 1,450 XP

Open Course Description

Always wanted to compete in a Kaggle machine learning competition but not sure you have the right skillset? This interactive tutorial by Kaggle and DataCamp on Machine Learning data sets offers the solution. Step-by-step you will learn through fun coding exercises how to predict survival rate for Kaggle's Titanic competition using R Machine Learning packages and techniques. Upload your results and see your ranking go up!

New to R? Give our Introduction to R course a try.

Chapter 1: Raising anchor

In this first chapter you will be introduced to DataCamp's interactive interface and the Titanic data set. Once you're familiar with the Kaggle data sets, you make your first predictions using survival rate, gender data, as well as age data.

Chapter 2: From icebergs to trees

After making your first predictions in the previous chapter, it's time to bring you to the next level. In chapter 2 you will be introduced to a fundamental concept in machine learning: decision trees.

Chapter 3: Improving your predictions through Random Forests

What techniques can you use to improve your predictions even more? One possible way is by making use of the machine learning method Random Forest. Namely, a forest is just a collection of trees...

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The DataCamp Team
DataCamp is a young team of data analytics enthusiasts that provide affordable interactive data science and statistics education to the world. We do not believe in an educational framework that centers on passively reading books, or on watching YouTube videos that put a focus on the instructor, and not the scholar. We provide courses for both the novice and the experienced data scientist, and even allow passionate users to freely use the learning platform to create their own interactive courses.

This interactive course was based on the excellent tutorial by Trevor Stephens. He tackles other techniques and improvements as well, so make sure to check it out.