Boire Filler Group
As I have stated in previous articles, the most difficult challenge in building predictive models is the creation of the analytical file. Typically, this comprises between 80%-90% of the data scientist’s time with 10%-20% comprising the actual run or runs of the different mathematical/statistical algorithms. In the creation of the analytical file, the two elements
In my last article, I discussed the increasing impact of automation on business and the displacement of jobs. With artificial intelligence looming as the ultimate disruptor, the overall theme of job displacement has shifted more towards knowledge-intensive...
Artificial intelligence seems to be the latest term which is capturing the not only the predictive analytics discipline’s attention but the general public as well as seen by the many articles in much of the mainstream media....
Customer Retention models are arguably the most valuable models that organizations can develop in improving overall customer profitability. The ability to target high value customers who are most likely to defect or become inactive allows organizations to...
As technology continues to empower our ability to conduct analytics with “Big Data”, the “Internet of Things” has arisen as an area where devices themselves capture and transmit data albeit machine-level type data. Let’s discuss some of...
One of the more recent topics gaining traction in Big Data Analytics is the notion of machine learning. Many people think that this is a recent development or phenomenon occurring as a result of newer Big Data...
Much work in predictive analytics and data science has been primarily focused around the business to consumer sector (B2C). Certainly predictive analytics solutions have been applied to the B2B sector but it pales in comparison to what...
Data,data,data everywhere and what do I do with it. How do I make sense of it that is useful to the business? More importantly, how do I tell the story now that the solution is built? VISUALIZATION....
Last month, I discussed the importance of variable selection as a key component of the modelling process. We examined three techniques: factor analysis, correlation analysis, and clustering. This month, we will explore CHAID/Decision Tree, Exploratory Data Analysis(EDA),...
