Articles by Perceptive Analytics

How to Perform Ordinal Logistic Regression in R

June 18, 2019 | Perceptive Analytics

In this article, we discuss the basics of ordinal logistic regression and its implementation in R. Ordinal logistic regression is a widely used classification method, with applications in variety of domains. This method is the go-to tool when there is a natural ordering in the dependent variable. For example, dependent ...
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Simple Steps to Create Treemap in R

September 21, 2018 | Perceptive Analytics

The following document details how to create a treemap in R using the treemap package. What are they & when do we use them In the most basic terms a treemap is generally used when we want to visualize proportions. It can be thought of a pie map where the slices ... [Read more...]

Using Control Charts in R

August 30, 2018 | Perceptive Analytics

I am sure you must have heard of Six Sigma quality standard or Six Sigma experts. But, what is Six Sigma? Six Sigma is a set of techniques used by organizations to improve their processes and optimize operations. Six Sigma was popularized by manufacturing organizations and Jack Welch, former CEO ...
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Anomaly Detection in R

June 11, 2018 | Perceptive Analytics

The World of Anomalies Imagine you are a credit card selling company and you know about a particular customer who makes a purchase of 25$ every week. You guessed this purchase is his fixed weekly rations but one day, this customer makes a different purchase of 700$. This development will not just ...
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Exploratory Factor Analysis in R

May 10, 2018 | Perceptive Analytics

Changing Your Viewpoint for Factors In real life, data tends to follow some patterns but the reasons are not apparent right from the start of the data analysis. Taking a common example of a demographics based survey, many people will answer questions in a particular ‘way’. For example, all married ...
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Discriminant Analysis: Statistics All The Way

March 27, 2018 | Perceptive Analytics

Discriminant analysis is used when the variable to be predicted is categorical in nature. This analysis requires that the way to define data points to the respective categories is known which makes it different from cluster analysis where the classification criteria is not know. It works by calculating a score ...
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Steps to Perform Survival Analysis in R

March 26, 2018 | Perceptive Analytics

Another way of analysis? When there are so many tools and techniques of prediction modelling, why do we have another field known as survival analysis? As one of the most popular branch of statistics, Survival analysis is a way of prediction at various points in time. This is to say, ...
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Understanding Naïve Bayes Classifier Using R

January 22, 2018 | Perceptive Analytics

The Best Algorithms are the Simplest The field of data science has progressed from simple linear regression models to complex ensembling techniques but the most preferred models are still the simplest and most interpretable. Among them are regression, logistic, trees and naive bayes techniques. Naive Bayes algorithm, in particular is ... [Read more...]

How to Perform Hierarchical Clustering using R

December 18, 2017 | Perceptive Analytics

What is Hierarchical Clustering? Clustering is a technique to club similar data points into one group and separate out dissimilar observations into different groups or clusters. In Hierarchical Clustering, clusters are created such that they have a predetermined ordering i.e. a hierarchy. For example, consider the concept hierarchy of ...
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Implementing Parallel Processing in R

August 7, 2017 | Perceptive Analytics

If something takes less time if done through parallel processing, why not do it and save time? Modern laptops and PCs today have multi core processors with sufficient amount of memory available and one can use it to generate outputs quickly. Parallelizing your codes has its own numerous advantages. Instead ... [Read more...]

Machine Learning Using Support Vector Machines

April 19, 2017 | Perceptive Analytics

Support Vector Machines (SVM) is a data classification method that separates data using hyperplanes. The concept of SVM is very intuitive and easily understandable. If we have labeled data, SVM can be used to generate multiple separating hyperplanes such that the data space is divided into segments and each segment ...
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