Blog Archives

How to Aggregate Data in R

July 12, 2018
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The process involves two stages. First, collate individual cases of raw data together with a grouping variable. Second, perform which calculation you want on each...

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Predict Customer Churn with Gradient Boosting

July 2, 2018
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Predict Customer Churn with Gradient Boosting

Customer churn is a key predictor of the long term success or failure of a business. But when it comes to all this data, what’s...

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How to Format Numbers, Dates, and Time Using in D3 HTMLWidgets in R

November 26, 2017
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If you have ever spent any time investigating where the cool kids play in the data visualization world you will know it is all about...

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How to Build a Geographic Dashboard with Real-Time Data

November 20, 2017
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How to Build a Geographic Dashboard with Real-Time Data

In this post, I show how to build an interactive geographic dashboard using Displayr, Plotly and R. It is particularly fascinating in that it tracks...

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Linear Discriminant Analysis in R: An Introduction

October 11, 2017
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How does Linear Discriminant Analysis work and how do you use it in R? This post answers these questions and provides an introduction to Linear...

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Goodness of Fit in MDS and t-SNE with Shepard Diagrams

September 28, 2017
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The goodness of fit for data reduction techniques such as MDS and t-SNE can be easily assessed with Shepard diagrams. A Shepard diagram compares how far apart your data points are before and after you transform...

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Analyzing Google Trends Data in R

September 4, 2017
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Analyzing Google Trends Data in R

Google Trends shows the changes in the popularity of search terms over a given time (i.e., number of hits over time). It can be used to find search terms with growing or decreasing popularity or to review periodic variations from the past such as seasonality. Google Trends search data can be added to other analyses, Related Post Gradient boosting in...

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Analyzing Google Trends Data in R

August 23, 2017
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Google Trends shows the changes in the popularity of search terms over a given time (i.e., number of hits over time). It can be used to find search terms with growing or decreasing popularity or...

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Automatically Fitting the Support Vector Machine Cost Parameter

July 17, 2017
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Automatically Fitting the Support Vector Machine Cost Parameter

In an earlier post I discussed how to avoid overfitting when using Support Vector Machines. This was achieved using cross validation. In cross validation, prediction accuracy is maximized by varying the cost parameter. Importantly, prediction accuracy is...

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Using Partial Least Squares to Conduct Relative Importance analysis in R

June 19, 2017
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Using Partial Least Squares to Conduct Relative Importance analysis in R

Partial Least Squares (PLS) is a popular method for relative importance analysis in fields where the data typically includes more predictors than observations. Relative importance analysis is a general term applied to any technique used for...

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