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Ridge and Lasso in R

18/02/2020

A common and very challenging problem in machine learning is overfitting, and it comes in many different appearances. It is one of the major aspects of training the model. Overfitting occurs when the model is capturing too much noise in the training data set which leads to bad predication accuracy when applying the model to new data. One of the ways to avoid overfitting is regularization technique. In this tutorial, we will examine...

Random Forest in R: An Example

19/09/2019

Random Forest is a powerful ensemble learning method that can be applied to various prediction tasks, in particular classification and regression. The method uses an ensemble of decision trees as a basis and therefore has all advantages of decision trees, such as high accuracy, easy usage, and no necessity of scaling data. Moreover, it also has a very important additional benefit, namely perseverance to overfitting (unlike simple...

Building a simple sentiment classifier in R using Trump's tweets

08/07/2019

For the past few years, tasks involving text and speech processing have become really hot-trendy. Among the various researches belonging to the fields of Natural Language Processing and Machine Learning, sentiment analysis ranks really high. Sentiment analysis allows identifying and getting subjective information from the source data using data analysis and visualization, ML models for classification, text mining and analysis. This helps to...

Connect to PostgreSQL with R: A step-by-step example

07/06/2019

Among the variety of open source relational databases, PostgreSQL is probably one of the most popular due to its functional capacities. That is why it is frequently used among all the areas of work where databases are involved. In this article, we will go through connection and usage of PostgreSQL in R. R is an open source language for statistical and graphics data analysis providing scientists, statisticians, and academics powerful tools...

Exploratory Data Analysis in R

20/05/2019

Introduction Exploratory data analysis (EDA) is an approach to data analysis to summarize the main characteristics of data. It can be performed using various methods, among which data visualization takes a great place. The idea of EDA is to recognize what information can data give us beyond the formal modeling or hypothesis testing task. In other words, if initially we don’t have at all or there are not enough priori ideas about the...

Top R Data Visualization Libraries for Data Science

20/03/2019

The more carefully you process the data and go into details, the more valuable information you can get for your benefit. Data visualization is an efficient and handy tool for gaining insights from data. Moreover, you can make the data far more understandable, colorful and pleasant with the help of visualization tools. As data is changing every second, it is an urgent task to investigate it carefully and get the insights as fast as possible....

Top 10 R Pakete für Data Science

11/12/2018

Das Open-Source-Projekt R gehört zu den führenden Tools für datenwissenschaftliche und maschinelle Lernaufgaben. Aufgrund des Open-Source-Frameworks gibt es kontinuierliche Beiträge, und Paketbibliotheken mit neuen Funktionen werden häufig angezeigt. Derzeit verfügt das CRAN-Paket-Repository über 12'525 verfügbare Pakete. Dieser Beitrag wirft einen Blick auf die beliebtesten und nützlichsten Pakete, die die Standards für die Lösung von...

Analyzing Google Trends with R: Retrieve and plot with gtrendsR

02/09/2018

Google became the main starting point for our online activities. Processing more than 40,000 search queries every second, Google captures a lot of what we’re thinking and worrying about all the time. Hidden racism, sexual orientation or ad returns - check out the work by Seth Stephens-Davidowitz to get some inspiration for the huge potential of Google Trends data. While the Google Trends cockpit offers a user-friendly...

Data Science: Welche Programmiersprachen für den deutschen Arbeitsmarkt?

24/07/2018

Das Erlernen neuer Programmiersprachen ist eine Investition ins Humankapital. Die Ermittlung des Return on Investment kann daher sehr aussagekräftig sein. Die Anforderungen für jede Branche und jeden spezifischen Job sind sehr spezifisch – eine verallgemeinerbare Antwort auf diese Frage zu finden, ist deshalb schwierig. Ein Ansatz könnte aber darin bestehen, die erforderlichen Softwarekenntnisse bei Stellenausschreibungen zu analysieren,...

Data Science Competitions: 4 Platforms to Train Your Skills

07/06/2018

Are you looking for real world data science problems to sharpen your skills? In this post, we introduce you to four platforms hosting data science competitions. Data science competitions can be a great way for gaining practical experience with real world data, and for boosting your motivation through the competitive environment they provide. Check them out, competitions are a lot of fun! Kaggle Kaggle is the best known platform for...

Top 10 R Packages for Data Science

15/04/2018

The open-source project R is among the leading tools for data science and machine learning tasks. Given its open-source framework, there are continuous contributions, and package libraries with new features pop up frequently. Currently, the CRAN package repository features 12,525 available packages. This post takes a look at the most popular and useful packages that have set the standards for solving data manipulation, visualization, and...

Data Science and the Success of Python and R: Evidence from Google Trends

10/01/2018

Currently, Python and R are the dominating data science tools and Python will probably even be taking the lead (at least based on the latest KDNuggets survey ). When did the two open source players manage to become the leading platforms for analytics, data science, and machine learning, leaving behind established players such as Matlab or SAS? Here are some insights from Google Trends. Looking at the years 2009 - 2013 in the first...

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