Zillow's typical US home value reached another record in August 2026: $368,697, the highest since its series begins in 2000, and 17 of Zillow's 50 largest metro areas set a…

The AUC* or concordance statistic c is the most commonly used measure for diagnostic accuracy of quantitative tests. It is a discrimination measure which tells us how well we can…

Suppose as a business manager you have the responsibility for testing and comparing the lifetimes of four brands (Apollo, Bridgestone, CEAT and Falken) of automobile tyres. The lifetime of these…

Extreme Gradient Boosting is amongst the excited R and Python libraries in machine learning these times. Previously, I have written a tutorial on how to use Extreme Gradient Boosting with…

I have been using keras and TensorFlow for a while now - and love its simplicity and straight-forward way to modeling. As part of the latest update to my workshop…

In this post we will show how to import data from other sources into the R workspace. Import data from SPSS R can import datasets from SPSS with the function…

In Data Science, As much as it is important to find patterns that repeat, It is also equally important to find anomalies that break those. This is actually very important…

In 2016 a developer pulled an 11-line JavaScript package called left-pad from npm, and builds broke for projects as large as React and Babel. I wondered how exposed R is…

In this post, we’ll explore how Monte Carlo simulations can be applied in practice. In particular, we will see how we can run a simulation when trying to predict the…

In this post, I am going to run an exploratory analysis of the plant leaf dataset as made available by UCI Machine Learning repository at this link. The dataset is…

The dplyr is one of the most popular r-packages and also part of tidyverse that's been developed by Hadley Wickham. The mere fact that dplyr package is very famous means,…

This is a quick, short, and concise tutorial on how to impute missing data. Previously, we published an extensive tutorial on imputing missing values with the MICE package. The current…

Note, at the time of this writing using the packages ggplot2 and ggmap from CRAN will result in an error. To avoid the error be sure to install both packages…

The dplyr package, which is one of my favorite R packages, works with in-memory data and with data stored in databases. In this extensive and comprehensive post, I will share…

What share of students who start at a US four-year college finish a degree there? The Department of Education's College Scorecard has a six-year graduation rate for every college, so…

It can happen that your dataset is not complete; when a piece of information is not available, we call it a missing value. In R, missing values are coded with…

A convolutional neural network is a type of Deep neural network which has got great success in image classification problems, it is primarily used in object recognition by taking images…

This is the continuation of my first post published here. Similar to the other article, it will be simple and easy to follow tutorial. Housing dataset in which you have…

Sentiment analysis is a method of analyzing a piece of text and deciding whether the writing is positive, negative or neutral. It is commonly used to understand how people feel…

Presenting data results in the most informative and compelling manner is part of the role of the data scientist. It's all well and good to master the arcana of some…

A few colleagues of mine and I from codecentric.ai are currently working on developing a free online course about machine learning and deep learning. As part of this course, I…

Extreme Gradient Boosting is among the hottest libraries in supervised machine learning these days. It supports various objective functions, including regression, classification, and ranking. It has gained much popularity and…

When the price of gasoline goes up, the pump seems to follow overnight; when it comes down, the pump seems to take its time. Economists call this "rockets and feathers",…

Tensorflow is an open-source software library for numerical computation using data flow graphs that enables machine learning practitioners to do more data-intensive computing. It provides a robust implementation of some…

This tutorial is an attempt to explain beginners how to install, run, and use RStudio. Unlike many of the beginners' assumption, Both R and RStudio are two different applications/software. One…

We are continuing on with our NYC bus breakdown problem. When we left off, we had constructed a rule-based Cubist regression model with our expanded pool of predictors; but we…

New York City grades its restaurants from the points an inspector hands out: 0 to 13 points earns an A, 14 to 27 a B, 28 or more a C,…

Principal Component Analysis (PCA) is unsupervised learning technique and it is used to reduce the dimension of the data with minimum loss of information. PCA is used in an application…

One of the advanced algorithms in the field of computer science is Genetic Algorithm inspired by the Human genetic process of passing genes from one generation to another.It is generally…

Before going into complex model building, looking at data relation is a sensible step to understand how your different variable interact together. Correlation look at trends shared between two variables,…

Since I learned Markdown and use R notebook in R Studio to produce high-quality PDF report, I hoped that I am not going to use Microsoft Office anymore. In fact,…

This short post is exactly what it seems: a showcase of all ggplot2 themes available within the ggplot2 package. I was doing such a list for myself (you know that…

In the age of smartphones, Notifications have become an integral part of life that Smart watches have started popping up to handle our notifications. Desktop Notification is a very good…

With the BMuCaret Shiny app post you can perform Caret-models comparison and find the best one, which achieves the best performance for a given data set. But isn't it true…

All data needs to be clean before you can explore and create models. Common sense, right. Cleaning data can be tedious but I created a function that will help. The…

Wikipedia is where a lot of people meet the standard deviation, the p-value and the t-test for the first time. I wondered whether they still do, now that "what is…

OpenCV is a library of programming functions mainly aimed at real-time computer vision. Originally developed by Intel, it was later supported by Willow Garage then Itseez. The library is cross-platform…

In the previous part of this tutorial, we build several models based on logistic regression. One aspect to be further considered is the decision threshold tuning that may help in…

This article is showing a geometric and intuitive explanation of the covariance matrix and the way it describes the shape of a data set. We will describe the geometric relationship…

In this article, I will show you how to use decision trees to predict whether the birth weights of infants will be low or not. We will use the birthwt…

Card payments in a New York yellow cab end on a screen of tip buttons, usually 20%, 25% and 30%. I wanted to know what the 20% button actually charges.…

This is the fifth tutorial on the Spark RDDs Vs DataFrames vs SparkSQL blog post series. The first one is available here. In the first part, we saw how to…

When you download data from the World Bank, you get a wide dataset: one row per country and one column per year, from 1960 to 2025. This shape is not…

Note, at the time this writing the package gganimate requires the package cowplot. Be sure to install and load the package before continuing. install.packages("cowplot") library(cowplot) Introduction In Part 1 and…

Natural Language Processing (NLP) has been seen as one of the blackboxes of Data Analytics. The aim of this post is to introduce this simple-to-use but effective R package udpipe…

In my last post had answers to some of the common questions in R that a person who has just begun exploring the language, needs to know. As we advance…

After scraping reviews from Goodreads in the first installment of this series, we are now ready to do some exploratory data analysis to get a better sense of the data…

This is a pretty short post on an issue that popped at some point in the past, at that time I found a way around it but as it arose…

This is the fourth part of our post series about the exploratory analysis of a publicly available dataset reporting earthquakes and similar events within a specific 30 days time span.…

I wanted to know which US counties have the deadliest roads, measured as deaths per resident. The ranking takes a few lines of R: count deaths per county, divide by…