Data Science

Abstract

Many problems of recent interest in the banks are detection of fraud analysis as well as insurance analysis that can be put in the framework of convex optimization. Due to the explosion in size and complexity of modern datasets, it is increasingly important to be able to solve problems with a very large number of fea- tures or training examples. As a result, both the decentralized collection or storage of these datasets as well as accompanying distributed solution methods are either necessary or at least highly desirable.

H2O makes it possible for anyone to easily apply math and predictive analytics to solve today’s most challenging business problems. Combine the power of highly advanced algorithms, the freedom of open source, and the capacity of truly scalable in-memory processing for big data on one or many nodes. These capabilities make it faster, easier, and more cost effective to harness big data to maximum benefit for the business. Some Key features of using H2O are

  • Easy-to-use WebUI and Familiar Interfaces – Set up and get started quickly using either H2O’s intuitive Web-based user interface or familiar programming environ- ments like R, Java, Scala, Python, JSON, and through our powerful APIs.
  • Massively Scalable Big Data Analysis – Train a model on complete data sets, not just small samples, and iterate and develop models in real-time with H2O’s rapid in-memory distributed parallel processing.
  • Real-time Data Scoring – Use the Nanofast Scoring Engine to score data against models for accurate predictions in just nanoseconds in any environment. Enjoy 10X faster scoring.

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