Thursday, March 2, 2017

Predictive Analytics using R and Java

Ever wondered how Star Sports is able to show the prediction of cricket match winner? You must have seen some fancy analysis like "India wins the match batting second 80% of the time when Virat Kohli scores 30+ runs in first 10 overs". Recently I delivered a webinar on Predictive Analytics using R and Java on Techgig which has an answer to these questions.

Predictive analytics is about predicting future outcomes based on currently available or historical data using several machine learning algorithms and statistical techniques.

R - a language and environment for statistical computing, makes it really easy to quickly try out several algorithms like Decision Trees, Random Forests, Neural Networks etc. on top of your existing datasets. R packages, like Rattle, can quickly allow you to interactively create machine learning models which can be trained and exported to a PMML file format.

The Predictive Model Markup Language (PMML) is a platform agnostic, XML-based predictive model interchange format which can be used to exchange the trained machine learning models across different technology platforms. Any machine learning model exported in PMML can be imported in your Java program by using libraries like JPPML and can be used to predict future outcomes based on the existing data.

As part of this webinar, you will understand how to quickly create trained machine learning model based on an existing dataset, export it to a PMML file and import into a Java Project.

You will learn about
  1. Basics of Predictive Analytics, brief explanation of Decision Trees, Random Forests and Neural Networks.
  2. How to use R and Rattle to create machine learning models which can be used to predict outcomes based on existing data.
  3. How to train, validate and test machine learning models in R using Rattle.
  4. How to export a trained machine learning model in PMML format using R.
  5. How to use the exported model in PMML format in your Java Program using JPMML

You can find the webinar recording below.

Code Samples related to this webinar can be found below

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