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Data is big
&amp;amp;quot;The future is here. It's just not evenly distributed yet.&amp;amp;quot; - William Gibson     :::: Follow this topic for fresh resources and ideas related to Data Science, Machine Learning, Algorithms and #bigdata :::: <a href="http://www.dataisbig.co" rel="nofollow">http://www.dataisbig.co</a>/
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Techniques to improve the accuracy of your Predictive Models

Code from talk: http://anotherdataminingblog.blogspot.com.au/2013/10/techniques-to-improve-accuracy-of-your_17.html The Heritage Health Prize was a two year ...

ukituki's insight:

The Heritage Health Prize was a two year predictive analytics competition that recently concluded, and Melbourne based Phil Brierley was the P in POWERDOT, the 'winning' team. In this talk Phil will share some* of the tips and tricks for building accurate predictive models (hopefully with some 'live' demonstrations using R) and tell the POWERDOT story. 


*Note: The competition is ongoing - so don't expect to learn everything ;-)


Code from talk: http://anotherdataminingblog.blogspot.com.au/2013/10/techniques-to-improve-accuracy-of-your_17.html  

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Togaware: One Page R: A Survival Guide to Data Science with R

ukituki's insight:

Welcome to One Page R. This compendium of modules weaves together a collection of tools for the data miner, data scientist, and decision scientist. The tools are all part of the RStatistical Software Suite.

 

Each module begins with a Lecture on the topic, followed by a OnePageR oriented introduction to the topic. The OnePageR consists of multiple, one page recipes that cover many aspects of the topic. It can be worked through by the student and then used as a reference guide. Each page aims to be a bite sized chunk for hands-on learning, building on what has gone before. A laboratory session then follows, where the student is epxected to complete the tasks and submit material for assessment.

 

The R code sitting behind each OnePageR module is also provided and can be run standalone to replicate the material presented in the module.

T

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Forecasting time series using R

Forecasting time series using R | Data is big | Scoop.it

Melbourne R Users' Group

 

ukituki's insight:

"I will look at the various facilities for time series forecasting available in R, concentrating on the forecast package. This package implements several automatic methods for forecasting time series including forecasts from ARIMA models, ARFIMA models and exponential smoothing models. I will also look more generally at how to go about forecasting non-seasonal data, seasonal data, seasonal data with high frequency, and seasonal data with multiple frequencies. Examples will be taken from my own consulting experience. I will give an overview of what’s possible and available and where it is useful, rather than give the mathematical details of any specific time series methods."

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