data science Data Science Live Book (open source) ~ new big release! 200-pages 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 1 year ago :) tl;dr: Hi there!
clustering Playing with dimensions: from Clustering, PCA, t-SNE... to Carl Sagan! Playing with dimensions Hi there! This post is an experiment combining the result of t-SNE with two well known clustering techniques: k-means and hierarchical. This will be the practical section, in R. But
data science Model Performance in Data Science Live Book Hi there! I decided to almost re-write the model validation section since it didn't reflect real case scenarios. Hopefully in the two new chapters you will gain a deeper knowledge on methodological aspects
data science Data Science Live Book - Scoring, Model Performance & profiling - Update! This update contains a new chapter -scoring- which is related to model performance and model deployment, used when predicting a binary outcome. Link to the scoring chapter. Important: To use following updates please
R Time Series Analysis Using Max/Min... and some Neuroscience. 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, we will smooth time series -reducing noise-
R Anomaly Detection in R Introduction Inspired by this Netflix post, I decided to write a post based on this topic using R. There are several nice packages to achieve this goal, the one we´re going to
R Text Mining Analysis: some theory and practice in R Introduction Big Data help us to analyze unstructred data (aka "text" ), with many techniques, in this post it is presented one: Cosine Similarity. There are also other analysts work, who scraped
R Recommendation Systems in R 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 (or association rules), used in market basket
R {Long Vs. Wide} Data Frames 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 article Long format: ggplot2 needs in certain
R Introduction to automatic machine learning 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, build a model, make predictions
R Data Science - Short lesson on cluster analysis 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 clients, patients, cars, etc.). Once a cluster
EU Life Quality Geo Report Living longer, living better? It's equally important to measure the longer living as well as its quality. Analyzing data from [eurostat](http://ec.europa.eu/eurostat/publications/recently-published?p_auth=ZKofrOKp&p_p_
R Dynamic analysis on outliers 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 transforming or deleting. Variable “Income”
