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What are the Innovative Techniques Emerging for Data Analytics?

The ideal analytic technique relies on the data analytics project ready at hand. Many projects have a clear and defined objective—prediction of imminent outcome with reliability. Other projects at are just putting an effort to derive insights from a huge chunk of data—understanding the historical data by analysis. Where the outcome is prone to be known, for instance, credit card fraud, customer profitability or responsiveness to offers, we utilize that outcome to direct the search for terms like words or phrases that possess the genuine signal strength. In other words, we locate the terms that are most strong, reliable and correlate to one of those outcomes.

  1. A term document matrix

It enlists all the unique terms in the text we are working on ranging from all the cases or documents in the analysis. This easy, however, very large intermediate outcome delivers the foundation for further analysis. How to differentiate between consumers who purchased products A and who didn’t purchased before.  This leads us to a reduction step, where the words or phrases are formally sorted to be modelled from the weakest to the strongest, on the basis of their signal strength. The presence and frequency of these filtered words and phrases can be a clear indication in a numerical way, in the new column within the modelling dataset, and incorporated directly into the search for an optimal new predictive model. This approach utilizes conventional scorecard method augmented with unstructured information.

  1. Named entity extraction or NEE

It is based on natural language processing which works on the disciplines of computer science, artificial intelligence and linguistics. With the structural analysis of the text, named entity extraction ascertains which parts of it are more likely to associate entities like persons, locations, organizations, job titles, products, monetary amounts, percentages, dates and times. One reason this method is compatible with the scorecards is because both the methods readily let for engineering. For every entity identified, the NEE algorithm delivers a score that indicates the probability that the identification s accurate. And therefore, our data scientists can engineer the probability thresholds that accepts only those entities with a score above 80 percent. For instance, in the development of the structured feature and the inclusion of that feature in a predictive model.

  1. Latent Dirichlet Allocation or LDA

Another analytic method which has been proven effective for segmentation and for detecting changes in customer behaviors as well is the Latent Dirichlet Allocation or LDA and other related methods for allocating similarities in data that let you classify and group them together. LDA is a statistical method that is unsupervised when it comes to extracting topics, concepts and other types of meaning from unstructured data. It cannot comprehend any syntax or any other aspect of human language. It only looks for patterns ad it does that equally good no matter in what language the text is written in.