Analytics in a Big Data World. The Essential Guide to Data by Bart Baesens

By Bart Baesens

The consultant to focusing on and leveraging enterprise possibilities utilizing substantial information & analytics

By leveraging substantial information & analytics, companies create the aptitude to higher comprehend, deal with, and strategically exploiting the complicated dynamics of shopper habit. Analytics in an enormous information World finds find out how to faucet into the strong software of information analytics to create a strategic virtue and determine new company possibilities. Designed to be an obtainable source, this crucial publication doesn't contain exhaustive insurance of all analytical strategies, in its place targeting analytics innovations that truly offer further price in company environments.

The publication attracts on writer Bart Baesens' services at the subject matters of huge information, analytics and its purposes in e.g. credits chance, advertising and marketing, and fraud to supply a transparent roadmap for corporations that are looking to use info analytics to their virtue, yet want a solid start line. Baesens has carried out huge examine on colossal information, analytics, shopper courting administration, internet analytics, fraud detection, and credits hazard administration, and makes use of this event to carry readability to a posh topic.

  • Includes a number of case stories on possibility administration, fraud detection, patron courting administration, and internet analytics
  • Offers the result of examine and the author's own event in banking, retail, and government
  • Contains an summary of the visionary rules and present advancements at the strategic use of analytics for business
  • Covers the subject of information analytics in easy-to-understand phrases with no an undo emphasis on arithmetic and the trivialities of statistical analysis

For companies seeking to improve their functions through info analytics, this source is the go-to reference for leveraging info to augment company capabilities.

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Extra resources for Analytics in a Big Data World. The Essential Guide to Data Science and its Applications

Example text

By categorizing the variable into ranges, part of the nonmonotonicity can be taken into account in the regression. Hence, categorization of continuous variables can be useful to model nonlinear effects into linear models. Various methods can be used to do categorization. Two very basic methods are equal interval binning and equal frequency binning. Consider, for example, the income values 1,000, 1,200, 1,300, 2,000, 1,800, and 1,400. Equal interval binning would create two bins with the same range—Bin 1: 1,000, 1,500 and Bin 2: 1,500, 2,000—whereas equal frequency binning would create two bins with the same number of observations—Bin 1: 1,000, 1,200, 1,300; Bin 2: 1,400, 1,800, 2,000.

The training sample will be used to make the splitting decision. The validation sample is an independent sample, set aside to monitor the misclassification error (or any other performance metric). 8. The error on the training sample keeps on decreasing as the splits become more and more specific toward it. On the validation sample, the error will initially decrease, but at some point it will increase back again since the splits become too specific for the training sample as the tree starts to memorize it.

Different types of predictive analytics techniques have been suggested in the literature. In what follows, we will discuss a selection of techniques with a particular focus on the practitioner’s perspective. TARGET DEFINITION Because the target variable plays an important role in the learning process, it is of key importance that it is appropriately defined. In what follows, we will give some examples. In a customer attrition setting, churn can be defined in various ways. Active churn implies that the customer stops the relationship with the firm.

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