In this example, an XGBoost model is built in R to predict incidences of customers cancelling their hotel booking. The analysis is based on data from Antonio, Almeida and Nunes (2019): Hotel booking demand datasets.
The H1 dataset is used for training and validation, while H2 is used for testing purposes.
In order to predict customers that will cancel their booking (where variable IsCanceled = 1 means a cancellation, and IsCanceled = 0 means the customer follows through with the booking), an XGBoost model is built in R with the following features:
In order to make the data suitable for analysis with the XGBoost model in R — some data manipulation procedures are required.
Firstly, the **xgboost **and Matrix libraries are loaded:
A data frame of features is formed through defining the variables as.numeric, and also defining in factor format where appropriate. The data frame is then converted into **Matrix **format.
leadtime<-as.numeric(H1$LeadTime) country<-as.numeric(factor(H1$Country)) marketsegment<-as.numeric(factor(H1$MarketSegment)) deposittype<-as.numeric(factor(H1$DepositType)) customertype<-as.numeric(factor(H1$CustomerType)) rcps<-as.numeric(H1$RequiredCarParkingSpaces) week<-as.numeric(H1$ArrivalDateWeekNumber) df<-data.frame(leadtime,country,marketsegment,deposittype,customertype,rcps,week) attach(df) df<-as.matrix(df)
#machine-learning #data-science #boosting #rstats #r-programming
In this blog post, we’ll look at how to use R Markdown. By the end, you’ll have the skills you need to produce a document or presentation using R Mardown, from scratch!
We’ll show you how to convert the default R Markdown document into a useful reference guide of your own. We encourage you to follow along by building out your own R Markdown guide, but if you prefer to just read along, that works, too!
R Markdown is an open-source tool for producing reproducible reports in R. It enables you to keep all of your code, results, plots, and writing in one place. R Markdown is particularly useful when you are producing a document for an audience that is interested in the results from your analysis, but not your code.
R Markdown is powerful because it can be used for data analysis and data science, collaborating with others, and communicating results to decision makers. With R Markdown, you have the option to export your work to numerous formats including PDF, Microsoft Word, a slideshow, or an HTML document for use in a website.
Turn your data analysis into pretty documents with R Markdown.
We’ll use the RStudio integrated development environment (IDE) to produce our R Markdown reference guide. If you’d like to learn more about RStudio, check out our list of 23 awesome RStudio tips and tricks!
Here at Dataquest, we love using R Markdown for coding in R and authoring content. In fact, we wrote this blog post in R Markdown! Also, learners on the Dataquest platform use R Markdown for completing their R projects.
We included fully-reproducible code examples in this blog post. When you’ve mastered the content in this post, check out our other blog post on R Markdown tips, tricks, and shortcuts.
Okay, let’s get started with building our very own R Markdown reference document!
R Markdown is a free, open source tool that is installed like any other R package. Use the following command to install R Markdown:
Now that R Markdown is installed, open a new R Markdown file in RStudio by navigating to
File > New File > R Markdown…. R Markdown files have the file extension “.Rmd”.
When you open a new R Markdown file in RStudio, a pop-up window appears that prompts you to select output format to use for the document.
The default output format is HTML. With HTML, you can easily view it in a web browser.
We recommend selecting the default HTML setting for now — it can save you time! Why? Because compiling an HTML document is generally faster than generating a PDF or other format. When you near a finished product, you change the output to the format of your choosing and then make the final touches.
One final thing to note is that the title you give your document in the pop-up above is not the file name! Navigate to
File > Save As.. to name, and save, the document.
#data science tutorials #beginner #r #r markdown #r tutorial #r tutorials #rstats #rstudio #tutorial #tutorials
I currently lead a research group with data scientists who use both R and Python. I have been in this field for over 14 years. I have witnessed the growth of both languages over the years and there is now a thriving community behind both.
I did not have a straightforward journey and learned many things the hard way. However, you can avoid making the mistakes I made and lead a more focussed, more rewarding journey and reach your goals quicker than others.
Before I dive in, let’s get something out of the way. R and Python are just tools to do the same thing. Data Science. Neither of the tools is inherently better than the other. Both the tools have been evolving over years (and will likely continue to do so).
Therefore, the short answer on whether you should learn Python or R is: it depends.
The longer answer, if you can spare a few minutes, will help you focus on what really matters and avoid the most common mistakes most enthusiastic beginners aspiring to become expert data scientists make.
#r-programming #python #perspective #r vs python: what should beginners learn? #r vs python #r
Generalized Linear Model (GLM) is popular because it can deal with a wide range of data with different response variable types (such as binomial_, Poisson, or _multinomial).Comparing to the non-linear models, such as the neural networks or tree-based models, the linear models may not be that powerful in terms of prediction. But the easiness in interpretation makes it still attractive, especially when we need to understand how each of the predictors is influencing the outcome.The shortcomings of GLM are as obvious as its advantages. The linear relationship may not always hold and it is really sensitive to outliers. Therefore, it’s not wise to fit a GLM without diagnosing.In this post, I am going to briefly talk about how to diagnose a generalized linear model. The implementation will be shown in R codes.There are mainly two types of diagnostic methods. One is outliers detection, and the other one is model assumptions checking.
Before diving into the diagnoses, we need to be familiar with several types of residuals because we will use them throughout the post. In the Gaussian linear model, the concept of residual is very straight forward which basically describes the difference between the predicted value (by the fitted model) and the data.
In the GLM, it is called “response” residuals, which is just a notation to be differentiated from other types of residuals.The variance of the response is no more constant in GLM, which leads us to make some modifications to the residuals.If we rescale the response residual by the standard error of the estimates, it becomes the Pearson residual.
#data-science #linear-models #model #regression #r
In this R Tutorial For Beginners video, you will learn r programming language from scratch to advance concepts. This R training video also covers hands-on demo and interview questions. This R Programming Course is a must-watch video for everyone who wishes to learn the R language and make a career in the data science domain.
#r programming course #r programming course #r tutorial for beginners #learn r language
The Association of Data Scientists (AdaSci), the premier global professional body of data science and ML practitioners, has announced a hands-on workshop on deep learning model deployment on February 6, Saturday.
Over the last few years, the applications of deep learning models have increased exponentially, with use cases ranging from automated driving, fraud detection, healthcare, voice assistants, machine translation and text generation.
Typically, when data scientists start machine learning model development, they mostly focus on the algorithms to use, feature engineering process, and hyperparameters to make the model more accurate. However, model deployment is the most critical step in the machine learning pipeline. As a matter of fact, models can only be beneficial to a business if deployed and managed correctly. Model deployment or management is probably the most under discussed topic.
In this workshop, the attendees get to learn about ML lifecycle, from gathering data to the deployment of models. Researchers and data scientists can build a pipeline to log and deploy machine learning models. Alongside, they will be able to learn about the challenges associated with machine learning models in production and handling different toolkits to track and monitor these models once deployed.
#hands on deep learning #machine learning model deployment #machine learning models #model deployment #model deployment workshop