Like it or not, data-driven artificial intelligence algorithms and other high-tech robotic applications are coming to fill our jobs. An analysis by PwC estimated that up to 38 percent of current American jobs could be taken over by machines within the next 15 years.

Even white-collar jobs aren’t safe, since algorithms are capable of governing sophisticated tasks for machines in ways that previously were unthinkable, such as writing or distributing pharmaceuticals. The transition has…

Multicollinearity (Collinearity) is not a new term especially when dealing with multiple regression models. This phenomenon of relationship in between one response variable with the set of predictor variables also include models like classification and regression trees as well as neural networks. Collinearity is infamously famous for inflating the variance of at least one estimated regression coefficient, which can cause the model to predict erroneously and in a business setup it can have an…

Linear Model better known as linear regression is one of the most common and flexible analysis framework to identify relationship between two or more variables. The widely used linear model is represented by drawing the best fit line through a series of data points represented on a scatter plot. 

For any budding business analyst this should be the starting point to understand how model works at the very core of its design.

Selecting the Variables in Deducer…

As we all know CRISP DM stands for Cross Industry Standard Process for Data Mining is a process model that outlines the most common approach to tackle data driven problems. Per the poll conducted by KDNuggets in 2014 this was and “is” one of the most popular and widest used methodology. This method of gleaning insights out of the data is very dear to the industry experts and data miners.

As the title suggest I will align some of the most useful R packages with this most popular and…

Six Sigma is a quantitative approach to problem solving - to solve certain types of problems. At the root of Six Sigma is an improvement methodology that can be described by the acronym DMAIC: define, measure, analyze, improve, and control [1]. Those interested in reading up on Six Sigma might consider the book for dummies, which I found fairly succinct. Those wondering what I mean by "certain types of problems" should consider how to apply the approach to their own business circumstances. I…

As per the largest market research firm MarketsandMarkets the speech analytics industry will grow to USD 1.60 billion by 2020 at a Compound Annual Growth Rate (CAGR) of 22% from 2015 to 2020. Today the omnichannel world consists of voice, email, chat, social channels, and surveys, and each channel has its own importance.

Therefore, it becomes inevitable for any customer centric organization to ignore the information that can be glean…

As the world is getting more tech savvy and advancements made in the information technology especially in the healthcare industry has opened areas in data mining and machine learning. Within the area of data mining one technique which has gained a lot of popularity as well as skepticism among the auditors and fraud detectives is Benford’s Law or “The Law of First digit.

In the past some researchers in Canada used the Benford’s Law distribution to detect anomalies within the claims…

If you haven’t started using artificial intelligence in your business, you’re falling behind on the curve. Many business owners today are leveraging AI, whether they are aware of it or not. This is done through everyday business software suites that integrate machine learning and automation to carry out such functions as email communications, voice recognition and response and predictive analysis.

The extent to which businesses employ AI solutions needs to be increased if they are to…

Best Subset Regression method can be used to create a best-fitting regression model. This technique of model building helps to identify which predictor (independent) variables should be included in a multiple regression model(MLR).

This method comprises of scrutinizing all of the models created from all possible permutation combination of predictor variables. This technique uses the R Squared value to check for the best model. Considering the level of complexity involved in creating…

I had some magical moments in my life. Perhaps the most magical was the summer I went to work wearing shorts and running shoes. Captain C. said to me, as I sat in our shared office, “If you want to work during the weekend, you can borrow the keys to the building.” It didn’t seem like a big deal at the time. I had already borrowed a military vehicle that, as per Captain C., I could park anywhere in Canada without receiving a parking ticket. So I borrowed the keys to the building. I went…

This post covers the following tasks using R programming:

• cleans the texts,
• sorts and aggregates by publisher names
• creates word clouds and word…

Introduction

There’s a lot of buzzword around the term “Sentiment Analysis” and the various ways of doing it. Great! So you report with reasonable accuracies what the sentiment about a particular brand or product is.

Opinion Mining and Sentiment Analysis

After publishing this report, your client comes back to you and…

This is an article which attempts to detect dependable variables with non-linear method.

I'm going to apply a method for checking variable dependency which was introduced in my previous post. Because the "dependency" I get with this rule is not true dependency as defined in Probability then I will call variables practically dependent at a confidence level…

(This post originally appeared on recurrentnull.wordpress.com, as first part in a series on sentiment analysis of movie reviews.)

Imagine I show you a book review, on amazon.com, say. Imagine I hide the number of stars, – all you get to see is the number of stars. And now I’m asking you, that review, is it good or bad?…

The different tasks that data scientists may hold are very diverse, but no matter what niche a data scientist fills in their line of work, being specialized in certain technical areas of software development is extremely important. The following skills are some of the most important techniques that a data scientist will need to have in order to perform software development properly.



Statistics knowledge



Before anything else, a data…

This is the most comprehensive guide to Ratio Analysis / Financial Statement Analysis

This expert-written guide goes beyond the usual gibberish and explore practical Financial Statement Analysis as used by investment bankers and equity research analysts. 

Table of Content:…

More and more organizations today are moving to unified communications (UC) platforms for better communications within their organization, with their customers and with their partners. These platforms combine voice, email, chat and web into a seamless Omni-channel experience for its users. They today boost of a number of features, but most of them provide either static or rule based experiences. Given that these platforms generate tons of data, can this data be used to improve user…

How many times have you heard managers and colleagues complain about the quality of the data in a particular report, system or database? People often describe poor quality data as unreliable or not trustworthy. Defining exactly what high or low quality data is, why it is a certain quality level and how to manage and improve it is often a trickier…

By Matt Holzapfel, procurement/sourcing product lead at Tamr

Sourcing managers often pride themselves on their deep understanding of their suppliers, but as supplier count grows, it becomes almost impossible for managers to stay up-to-date on the health and activities of all their suppliers. Compounding the problem is the constantly growing amount of information being pushed to managers. A new approach is needed that allows managers to focus their energy on…

By Matt Holzapfel, procurement/sourcing product lead at Tamr

The business case for almost every merger and acquisition includes an assumption of significant cost savings. Unfortunately, achieving these cost savings is often harder than anticipated, which is one reason why 70-90% of mergers and acquisitions fail.

These numbers highlight the challenge of merging the…