Analytics

Whether you love it or hate it, predictive analytics has already helped elect presidents, discover new energy sources, score consumer credit, assess health risks, detect fraud, and target prospective buyers. It is here to stay, and technology advances ranging from faster hardware to software that analyzes increasingly vast quantities of data are making the use of predictive analytics more creative and efficient than ever before.

Predictive analytics (or supervised learning) is the use of statistics, machine learning, data mining, and modeling to analyze current and historical facts to make predictions about future events. Said another way, it gives mere mortals the ability to predict the future like Nostradamus. In recent years, data-mining has become one of the most valuable tools for extracting and manipulating data and for establishing patterns in order to produce useful information for decision-making.

   

 

Whether you love it or hate it, predictive analytics has already helped elect presidents, discover new energy sources, score consumer credit, assess health risks, detect fraud, and target prospective buyers. It is here to stay, and technology advances ranging from faster hardware to software that analyzes increasingly vast quantities of data are making the use of predictive analytics more creative and efficient than ever before.

Predictive analytics is an area of data mining that deals with extracting information from data and using it to predict trends and behavioral patterns. Often the unknown event of interest is in the future, but predictive analytics can be applied to any type of unknown, whether that is in the past, the present, or the future.

Predictive analytics uses many techniques from data mining to analyze current data to make predictions about the future, including statistics, modeling, machine learning, and artificial intelligence. For example, logistic regression can be used to turn a market basket analysis into a predictor so that a casino can understand what items are usually purchased together. Of course, the old beer and diapers story market basket wouldn’t fit for a casino, but gleaning data from the casino floor could reveal second favorite games that patrons like to play. This could be useful information when a patron is having a run of bad luck on his or her favorite game. Perhaps a marketing offer for a game he or she sometimes plays would be appreciated rather than an offer on his or her favorite game, as that might not be seen in such a positive light while the patron is in the midst of a losing run.

For a casino, predictive analytics can also be used for CRM, collection analysis, cross-sell, customer retention, direct marketing, fraud detection, product prediction, project risk management, amongst many other things.

Prescriptive analytics tries to optimize a key metric, such as profit, by not only anticipating what will happen, but also when it will happen and why it happens. Wikipedia states that, “Prescriptive analytics suggests decision options on how to take advantage of a future opportunity or mitigate a future risk and shows the implication of each decision option. Prescriptive analytics can continually take in new data to re-predict and re-prescribe, thus automatically improving prediction accuracy and prescribing better decision options.”

Prescriptive analytics can ingest a mixture of structured, unstructured, and semi-structured data, and utilize business rules that can predict what lies ahead, as well as advise how to exploit this predicted future without compromising other priorities. Stream processing can add an entirely new component to prescriptive analytics for this.

Intelligencia can help customers build the following analytical models on their data:

  • Regression
  • Linear regression
  • Discrete choice models
  • Logistic regression
  • Multinomial logistic regression
  • Probit regression
  • Time series models
  • Survival or duration analysis
  • Classification and regression tress
  • Multivariate adaptive regression splines
  • Machine learning
  • Neural networks
  • Naïve Bayes
  • K-nearest neighbors

 

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