Sign In Start Free Trial
Account

Add to playlist

Create a Playlist

Modal Close icon
You need to login to use this feature.
  • Book Overview & Buying R Data Science Essentials
  • Table Of Contents Toc
R Data Science Essentials

R Data Science Essentials

By : Raja B. Koushik, Sharan Kumar Ravindran
3 (3)
close
close
R Data Science Essentials

R Data Science Essentials

3 (3)
By: Raja B. Koushik, Sharan Kumar Ravindran

Overview of this book

With organizations increasingly embedding data science across their enterprise and with management becoming more data-driven it is an urgent requirement for analysts and managers to understand the key concept of data science. The data science concepts discussed in this book will help you make key decisions and solve the complex problems you will inevitably face in this new world. R Data Science Essentials will introduce you to various important concepts in the field of data science using R. We start by reading data from multiple sources, then move on to processing the data, extracting hidden patterns, building predictive and forecasting models, building a recommendation engine, and communicating to the user through stunning visualizations and dashboards. By the end of this book, you will have an understanding of some very important techniques in data science, be able to implement them using R, understand and interpret the outcomes, and know how they helps businesses make a decision.
Table of Contents (10 chapters)
close
close
9
Index

Logistic regression


In order to build a logistic regression in R, we generally use the glm function, which is nothing but a generalized linear model on the binary dependent variable. In the following section, you will learn to build the model to predict if the life expectancy for a country is more than 70 based on the other parameters, which we call the independent variables. These independent variables can either be continuous or categorical:

model<- glm(as.factor(life_expectancy_morethan_70)~., traindata, family=binomial(link = "logit"))

In the preceding glm function, the first parameter that we pass is the dependent variable column that has to be predicted for the dataset. We will predict the life_expectancy_morethan_70 column and the dot followed by the ~ symbol represents that we are considering all the others variables present in the dataset as independent variables. The next parameter that we mentioned is the name of the dataset and, in this case, the traindata data frame is the...

CONTINUE READING
83
Tech Concepts
36
Programming languages
73
Tech Tools
Icon Unlimited access to the largest independent learning library in tech of over 8,000 expert-authored tech books and videos.
Icon Innovative learning tools, including AI book assistants, code context explainers, and text-to-speech.
Icon 50+ new titles added per month and exclusive early access to books as they are being written.
R Data Science Essentials
notes
bookmark Notes and Bookmarks search Search in title playlist Add to playlist font-size Font size

Change the font size

margin-width Margin width

Change margin width

day-mode Day/Sepia/Night Modes

Change background colour

Close icon Search
Country selected

Close icon Your notes and bookmarks

Confirmation

Modal Close icon
claim successful

Buy this book with your credits?

Modal Close icon
Are you sure you want to buy this book with one of your credits?
Close
YES, BUY

Submit Your Feedback

Modal Close icon
Modal Close icon
Modal Close icon