R 3.4.2 (codename “Short Summer”) was released yesterday. You can get the latest binaries version from here. (or the .tar.gz source code from here). As mentioned by David Smith, R 3.4.2 includes a performance improvement for names: c() and unlist() are now more efficient in constructing the names(.) of their return ...

Motivation Before trying a new restaurant, we frequently consult with review platforms, such as Yelp, Zomato, or Google, where we can read comments from previous diners. […] The post Real time Yelp reviews analysis and response solutions for restaurant owners appeared first on NYC Data Science Academy Blog.

One of the assumptions of Classical Linear Regression Model is that there is no exact collinearity between the explanatory variables. If the explanatory variables are perfectly correlated, you will face with these problems: Parameters of the model become indeterminate Standard errors of the estimates become infinitely large However, the case ...

In my recent post, My travels through the realms of Data Science, Machine Learning, Deep Learning and (AI), I had recounted my journey in the domains of of Data Science, Machine Learning (ML), and more recently Deep Learning (DL) all of which are useful while analyzing data. Of late, I ...

August was a relatively slow month for new R packages; “only” 180 new packages stuck to CRAN. Here are my “Top 40” picks organized into seven categories: Data, Machine Learning, Miscellaneous, Science, Statistics, Utilities and Visualizations. Although they have been written for specialized audiences, I have included the three “Science” packages because, ...

Banaue rice terraces. Photo: Jon Rawlinson In a previous article, we showed the use of partial pooling, or hierarchical/multilevel models, for level coding high-cardinality categorical variables in vtreat. In this article, we will discuss a little more about the how and why of partial pooling in R. We will ...

Recently, I saw a reference to an interesting piece from 2013 by Peter Grogono, a computer scientist now retired from Concordia University. It's to do with checking the "quality" of a (pseudo-) random number generator. Specifically, Peter discusses what he calls "The Pickover Test". This refers to the following suggestion that ...

Please read the first part published at DataScience+, if you haven’t. Effect sizes and the strength of our prediction One relatively common question in statistics or data science is, how “big” is the difference or the effect? At this point we can state with some statistical confidence that tire ...