## Data discretization made easy with funModeling

tl;dr: Convert numerical variables into categorical, as it is shown in the next image. ⏳ Reading time ~ 6 min. Let's start! The package funModeling (from version »

tl;dr: Convert numerical variables into categorical, as it is shown in the next image. ⏳ Reading time ~ 6 min. Let's start! The package funModeling (from version »

This package lets you analyze the variables of a dataset, to evaluate how the data is shaped. Consider this the first step when you have your »

Well after some time, and +300 commits, this is the biggest release of the Data Science Live Book! (open source), after the first publication more than »

Hi! Well finally there is the first release of this project: A open source book which will hopefully contain some useful resources for those who want »

Introduction Time series have maximum and minimum points as general patterns. Sometimes the noise present on it causes problems to spot general behavior. In this post, »

POST UPDATE 09/24/2016 Good news! funModeling documentation evolved into an open source book! Please follow the link below Jump to the book... This release »

These systems are used in cross-selling industries, and they measure correlated items as well as their user rate. This last point wasn't included the apriori algorithm »

Introduction This is an excellent resource to understand 2 types of data frame format: Long and Wide. Just take a look at figure 1 inside the »

Introduction "I want to develop a model that automatically learns over time", a really challenging objective. We'll develop in this post a procedure that loads data, »

Introduction In clustering you let data to be grouped according to their similarity. A cluster model is a group of segments -clusters- containing cases (such as »

Treating outliers Introduction Outliers are the extreme values that a variable has, depending on the model or requirement, it could be necessary to treat them, either »