Blogdown is a R package to generate static websites based on R Markdown and Hugo, a static web engine. To be honost: It’s fantastic! I’ve been using Wordpress for a while, with all that hassle of copy-pasting html into the wp text editor, adjusting images… Not to mention all the fuzz that goes along when using htmlwidgets. That blog-agony is all over with blogdown. My take-ways Must read: Blogdown online book: definitely read the first 3 chapters and the appendix!
Makeovermonday is a weekly social data project with the intention to rework some random chosen chart. A new challenge is posted every week on the data set page. Although it is more focused to the Tableau community, I took the challenge to rework the chart with R.
In most cases, you’ll write a R script that pulls data, manipulates it and dumps the output to a database or you’ll create a beautiful report in rmarkdown. Suppose you want to run this script or report every day, week, day, etc. Well, there are a few possibilities for automating these procedures on Windows machine. Windows Task Scheduler: You can use the default windows task scheduler. To do so, you can create a cmd file including the path to your Rscript.
Refining the credit model(s) To continue with the creditworthiness case, I want to explore this case a little bit more by adding more meta algorithms such as boosting, winnowing, cross validation etc. Additionally, I’ll use randomforest as classifier algorithm. I’m still using the same german credit data as in the previous post. I’m also using the same train/testest. Each model is stored into one object models. # object that will store all the models in a list models <- list() I start with three different models, which are all generated with the C5.
While looking for a interesting Machine Learning exercise I decided to go along with credit scoring exercise. I want to know what kind of information influences the decision for giving someone credit (or not). Typically, a bank would ask you to fill in some kind of assesment form with question about demographics, purpose of the loan, your status of employment and salary. Today, this is not a standard proces.
From now this will be an experimental environment to write things about my field of interest, mostly related to data science. As this will be the kick-off it must be clear what my starting-point is. I know a little of R compared to our R-master Hadley Wickham but do well on average. Not long ago – it must be two years – i started playing on code academy with a basic Java course just for fun.