Tag: Data set

Heritage Health Prize- Data Mining Contest for 3mill USD

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If Netflix was about 1 mill USD to better online video choices, here is a chance to earn serious money, write great code, and save lives!

From http://www.heritagehealthprize.com/

Heritage Health Prize
Launching April 4

Laptop

More than 71 Million individuals in the United States are admitted to
hospitals each year, according to the latest survey from the American
Hospital Association. Studies have concluded that in 2006 well over
$30 billion was spent on unnecessary hospital admissions. Each of
these unnecessary admissions took away one hospital bed from someone
else who needed it more.

Prize Goal & Participation

The goal of the prize is to develop a predictive algorithm that can identify patients who will be admitted to the hospital within the next year, using historical claims data.

Official registration will open in 2011, after the launch of the prize. At that time, pre-registered teams will be notified to officially register for the competition. Teams must consent to be bound by final competition rules.

Registered teams will develop and test their algorithms. The winning algorithm will be able to predict patients at risk for an unplanned hospital admission with a high rate of accuracy. The first team to reach the accuracy threshold will have their algorithms confirmed by a judging panel. If confirmed, a winner will be declared.

The competition is expected to run for approximately two years. Registration will be open throughout the competition.

Data Sets

Registered teams will be granted access to two separate datasets of de-identified patient claims data for developing and testing algorithms: a training dataset and a quiz/test dataset. The datasets will be comprised of de-identified patient data. The datasets will include:

  • Outpatient encounter data
  • Hospitalization encounter data
  • Medication dispensing claims data, including medications
  • Outpatient laboratory data, including test outcome values

The data for each de-identified patient will be organized into two sections: “Historical Data” and “Admission Data.” Historical Data will represent three years of past claims data. This section of the dataset will be used to predict if that patient is going to be admitted during the Admission Data period. Admission Data represents previous claims data and will contain whether or not a hospital admission occurred for that patient; it will be a binary flag.

DataThe training dataset includes several thousand anonymized patients and will be made available, securely and in full, to any registered team for the purpose of developing effective screening algorithms.

The quiz/test dataset is a smaller set of anonymized patients. Teams will only receive the Historical Data section of these datasets and the two datasets will be mixed together so that teams will not be aware of which de-identified patients are in which set. Teams will make predictions based on these data sets and submit their predictions to HPN through the official Heritage Health Prize web site. HPN will use the Quiz Dataset for the initial assessment of the Team’s algorithms. HPN will evaluate and report back scores to the teams through the prize website’s leader board.

Scores from the final Test Dataset will not be made available to teams until the accuracy thresholds are passed. The test dataset will be used in the final judging and results will be kept hidden. These scores are used to preserve the integrity of scoring and to help validate the predictive algorithms.

Teams can begin developing and testing their algorithms as soon as they are registered and ready. Teams will log onto the official Heritage Health Prize website and submit their predictions online. Comparisons will be run automatically and team accuracy scores will be posted on the leader board. This score will be only on a portion of the predictions submitted (the Quiz Dataset), the additional results will be kept back (the Test Dataset).

Form

Once a team successfully scores above the accuracy thresholds on the online testing (quiz dataset), final judging will occur. There will be three parts to this judging. First, the judges will confirm that the potential winning team’s algorithm accurately predicts patient admissions in the Test Dataset (again, above the thresholds for accuracy).

Next, the judging panel will confirm that the algorithm does not identify patients and use external data sources to derive its predictions. Lastly, the panel will confirm that the team’s algorithm is authentic and derives its predictive power from the datasets, not from hand-coding results to improve scores. If the algorithm meets these three criteria, it will be declared the winner.

Failure to meet any one of these three parts will disqualify the team and the contest will continue. The judges reserve the right to award second and third place prizes if deemed applicable.

 

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Input Data in R using the top 3 R GUI

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One area where clearly GUI methods are preferable to command line methods in R, is data input. There is no need of learning read.csv or read.table when these options are only two clicks away in any R GUI. For academics/students there is a definite need to easily access

datasets from attached packages just as it is a need for business analysts to access databases with a few clicks than learn or read pages of pdf on RODBC. However some GUI (like Rattle) need data only in data frames, rather than list or arrays-this limits R’s flexibility. These are my views but you can see and compare views of data input in R Commander, Rattle and Deducer.



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WPS Version 2.5.1 Released – can still run SAS language/data and R

However this is what Phil Rack the reseller is quoting on http://www.minequest.com/Pricing.html

Windows Desktop Price: $884 on 32-bit Windows and $1,149 on 64-bit Windows.

The Bridge to R is available on the Windows platforms and is available for free to customers who
license WPS through MineQuest,LLC. Companies and organizations outside of North America
may purchase a license for the Bridge to R which starts at $199 per desktop or $599 per server

Windows Server Price: $1,903 per logical CPU for 32-bit and $2,474 for 64-bit.

Note that Linux server versions are available but do not yet support the Eclipse IDE and are
command line only

WPS sure seems going well-but their pricing is no longer fixed and on the home website, you gotta fill a form. Ditt0 for the 30 day free evaluation

http://www.teamwpc.co.uk/products/wps/modules/core

Data File Formats

The table below provides a summary of data formats presently supported by the WPS Core module.

Data File Format Un-Compressed
Data		 Compressed
Data
Read Write Read Write
SD2 (SAS version 6 data set)
SAS7BDAT (SAS version 7 data set)
SAS7BDAT (SAS version 8 data set)
SAS7BDAT (SAS version 9 data set)
SASSEQ (SAS version 8/9 sequential file)
V8SEQ (SAS version 8 sequential file)
V9SEQ (SAS version 9 sequential file)
WPD (WPS native data set)
WPDSEQ (WPS native sequential file)
XPORT (transport format)

Additional access to EXCEL, SPSS and dBASE files is supported by utilising the WPS Engine for DB Filesmodule.

and they have a new product release on Valentine Day 2011 (oh these Europeans!)

From the press release at http://www.teamwpc.co.uk/press/wps2_5_1_released

WPS Version 2.5.1 Released 

New language support, new data engines, larger datasets, improved scalability

LONDON, UK – 14 February 2011 – World Programming today released version 2.5.1 of their WPS software for workstations, servers and mainframes.

WPS is a competitively priced, high performance, highly scalable data processing and analytics software product that allows users to execute programs written in the language of SAS. WPS is supported on a wide variety of hardware and operating system platforms and can connect to and work with many types of data with ease. The WPS user interface (Workbench) is frequently praised for its ease of use and flexibility, with the option to include numerous third-party extensions.

This latest version of the software has the ability to manipulate even greater volumes of data, removing the previous 2^31 (2 billion) limit on number of observations.

Complimenting extended data processing capabilities, World Programming has worked hard to boost the performance, scalability and reliability of the WPS software to give users the confidence they need to run heavy workloads whilst delivering maximum value from available computer power.

WPS version 2.5.1 offers additional flexibility with the release of two new data engines for accessing Greenplum and SAND databases. WPS now comes with eleven data engines and can access a huge range of commonly used and industry-standard file-formats and databases.

Support in WPS for the language of SAS continues to expand with more statistical procedures, data step functions, graphing controls and many other language items and options.

WPS version 2.5.1 is available as a free upgrade to all licensed users of WPS.

Summary of Main New Features:

  • Supporting Even Larger Datasets
    WPS is now able to process very large data sets by lifting completely the previous size limit of 2^31 observations.
  • Performance and Scalability Boosted
    Performance and scalability improvements across the board combine to ensure even the most demanding large and concurrent workloads are processed efficiently and reliably.
  • More Language Support
    WPS 2.5.1 continues the expansion of it’s language support with over 70 new language items, including new Procedures, Data Step functions and many other language items and options.
  • Statistical Analysis
    The procedure support in WPS Statistics has been expanded to include PROC CLUSTER and PROC TREE.
  • Graphical Output
    The graphical output from WPS Graphing has been expanded to accommodate more configurable graphics.
  • Hash Tables
    Support is now provided for hash tables.
  • Greenplum®
    A new WPS Engine for Greenplum provides dedicated support for accessing the Greenplum database.
  • SAND®
    A new WPS Engine for SAND provides dedicated support for accessing the SAND database.
  • Oracle®
    Bulk loading support now available in the WPS Engine for Oracle.
  • SQL Server®
    To enhance existing SQL Server database access, a new SQLSERVR (please note spelling) facility in the ODBC engine.

More Information:

Existing Users should visit www.teamwpc.co.uk/support/wps/release where you can download a readme file containing more information about all the new features and fixes in WPS 2.5.1.

New Users should visit www.teamwpc.co.uk/products/wps where you can explore in more detail all the features available in WPS or request a free evaluation.

and from http://www.teamwpc.co.uk/products/wps/data it seems they are going on the BIG DATA submarine as well-

Data Support 

Extremely Large Data Size Handling

WPS is now able to handle extremely large data sets now that the previous limit of 2^31 observations has been lifted.

Access Standard Databases

Use I/O Features in WPS Core

  • CLIPBOARD (Windows only)
  • DDE (Windows only)
  • EMAIL (via SMTP or MAPI)
  • FTP
  • HTTP
  • PIPE (Windows and UNIX only)
  • SOCKET
  • STDIO
  • URL

Use Standard Data File Formats

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Common Analytical Tasks

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Some common analytical tasks from the diary of the glamorous life of a business analyst-

1) removing duplicates from a dataset based on certain key values/variables
2) merging two datasets based on a common key/variable/s
3) creating a subset based on a conditional value of a variable
4) creating a subset based on a conditional value of a time-date variable
5) changing format from one date time variable to another
6) doing a means grouped or classified at a level of aggregation
7) creating a new variable based on if then condition
8) creating a macro to run same program with different parameters
9) creating a logistic regression model, scoring dataset,
10) transforming variables
11) checking roc curves of model
12) splitting a dataset for a random sample (repeatable with random seed)
13) creating a cross tab of all variables in a dataset with one response variable
14) creating bins or ranks from a certain variable value
15) graphically examine cross tabs
16) histograms
17) plot(density())
18)creating a pie chart
19) creating a line graph, creating a bar graph
20) creating a bubbles chart
21) running a goal seek kind of simulation/optimization
22) creating a tabular report for multiple metrics grouped for one time/variable
23) creating a basic time series forecast

and some case studies I could think of-

 

As the Director, Analytics you have to examine current marketing efficiency as well as help optimize sales force efficiency across various channels. In addition you have to examine multiple sales channels including inbound telephone, outgoing direct mail, internet email campaigns. The datawarehouse is an RDBMS but it has multiple data quality issues to be checked for. In addition you need to submit your budget estimates for next year’s annual marketing budget to maximize sales return on investment.

As the Director, Risk you have to examine the overdue mortgages book that your predecessor left you. You need to optimize collections and minimize fraud and write-offs, and your efforts would be measured in maximizing profits from your department.

As a social media consultant you have been asked to maximize social media analytics and social media exposure to your client. You need to create a mechanism to report particular brand keywords, as well as automated triggers between unusual web activity, and statistical analysis of the website analytics metrics. Above all it needs to be set up in an automated reporting dashboard .

As a consultant to a telecommunication company you are asked to monitor churn and review the existing churn models. Also you need to maximize advertising spend on various channels. The problem is there are a large number of promotions always going on, some of the data is either incorrectly coded or there are interaction effects between the various promotions.

As a modeller you need to do the following-
1) Check ROC and H-L curves for existing model
2) Divide dataset in random splits of 40:60
3) Create multiple aggregated variables from the basic variables

4) run regression again and again
5) evaluate statistical robustness and fit of model
6) display results graphically
All these steps can be broken down in little little pieces of code- something which i am putting down a list of.
Are there any common data analysis tasks that you think I am missing out- any common case studies ? let me know.

 

 

 

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Challenges of Analyzing a dataset (with R)

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Analyzing data can have many challenges associated with it. In the case of business analytics data, these challenges or constraints can have a marked effect on the quality and timeliness of the analysis as well as the expected versus actual payoff from the analytical results.

Challenges of Analytical Data Processing-

1) Data Formats- Reading in complete data, without losing any part (or meta data), or adding in superfluous details (that increase the scope). Technical constraints of data formats are relatively easy to navigate thanks to ODBC and well documented and easily search-able syntax and language.

The costs of additional data augmentation (should we pay for additional credit bureau data to be appended) , time of storing and processing the data (every column needed for analysis can add in as many rows as whole dataset, which can be a time enhancing problem if you are considering an extra 100 variables with a few million rows), but above all that of business relevance and quality guidelines will ensure basic data input and massaging are considerable parts of whole analytical project timeline.

2) Data Quality-Perfect data exists in a perfect world. The price of perfect information is one business will mostly never budget or wait for. To deliver inferences and results based on summaries of data which has missing, invalid, outlier data embedded within it makes the role of an analyst just as important as which ever tool is chosen to remove outliers, replace missing values, or treat invalid data.

3) Project Scope-

How much data? How much Analytical detail versus High Level Summary? Timelines for delivery as well as refresh of data analysis? Checks (statistical as well as business)?

How easy is it to load and implement the new analysis in existing Information Technology Infrastructure? These are some of the outer parameters that can limit both your analytical project scope, your analytical tool choice, and your processing methodology.
4) Output Results vis a vis stakeholder expectation management-

Stakeholders like to see results, not constraints, hypothesis ,assumptions , p-value, or chi -square value. Output results need to be streamlined to a decision management process to justify the investment of human time and effort in an analytical project, choice,training and navigating analytical tool complexities and constraints are subset of it. Optimum use of graphical display is a part of aligning results to a more palatable form to stakeholders, provided graphics are done nicely.

Eg Marketing wants to get more sales so they need a clear campaign, to target certain customers via specific channels with specified collateral. In order to base their business judgement, business analytics needs to validate , cross validate and sometimes invalidate this business decision making with clear transparent methods and processes.

Given a dataset- the basic analytical steps that an analyst will do with R are as follows. This is meant as a note for analysts at a beginner level with R.

Package -specific syntax

update.packages() #This updates all packages
install.packages(package1) #This installs a package locally, a one time event
library(package1) #This loads a specified package in the current R session, which needs to be done every R session

CRAN________LOCAL HARD DISK_________R SESSION is the top to bottom hierarchy of package storage and invocation.

ls() #This lists all objects or datasets currently active in the R session

> names(assetsCorr)  #This gives the names of variables within a dataframe
[1] “AssetClass”            “LargeStocksUS”         “SmallStocksUS”
[4] “CorporateBondsUS”      “TreasuryBondsUS”       “RealEstateUS”
[7] “StocksCanada”          “StocksUK”              “StocksGermany”
[10] “StocksSwitzerland”     “StocksEmergingMarkets”

> str(assetsCorr) #gives complete structure of dataset
‘data.frame’:    12 obs. of  11 variables:
$ AssetClass           : Factor w/ 12 levels “CorporateBondsUS”,..: 4 5 2 6 1 12 3 7 11 9 …
$ LargeStocksUS        : num  15.3 16.4 1 0 0 …
$ SmallStocksUS        : num  13.49 16.64 0.66 1 0 …
$ CorporateBondsUS     : num  9.26 6.74 0.38 0.46 1 0 0 0 0 0 …
$ TreasuryBondsUS      : num  8.44 6.26 0.33 0.27 0.95 1 0 0 0 0 …
$ RealEstateUS         : num  10.6 17.32 0.08 0.59 0.35 …
$ StocksCanada         : num  10.25 19.78 0.56 0.53 -0.12 …
$ StocksUK             : num  10.66 13.63 0.81 0.41 0.24 …
$ StocksGermany        : num  12.1 20.32 0.76 0.39 0.15 …
$ StocksSwitzerland    : num  15.01 20.8 0.64 0.43 0.55 …
$ StocksEmergingMarkets: num  16.5 36.92 0.3 0.6 0.12 …

> dim(assetsCorr) #gives dimensions observations and variable number
[1] 12 11

str(Dataset) – This gives the structure of the dataset (note structure gives both the names of variables within dataset as well as dimensions of the dataset)

head(dataset,n1) gives the first n1 rows of dataset while
tail(dataset,n2) gives the last n2 rows of a dataset where n1,n2 are numbers and dataset is the name of the object (here a data frame that is being considered)

summary(dataset) gives you a brief summary of all variables while

library(Hmisc)
describe(dataset) gives a detailed description on the variables

simple graphics can be given by

hist(Dataset1)
and
plot(Dataset1)

As you can see in above cases, there are multiple ways to get even basic analysis about data in R- however most of the syntax commands are intutively understood (like hist for histogram, t.test for t test, plot for plot).

For detailed analysis throughout the scope of analysis, for a business analytics user it is recommended to using multiple GUI, and multiple packages. Even for highly specific and specialized analytical tasks it is recommended to check for a GUI that incorporates the required package.

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Interview Luis Torgo Author Data Mining with R

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Here is an interview with Prof Luis Torgo, author of the recent best seller “Data Mining with R-learning with case studies”.

Ajay- Describe your career in science. How do you think can more young people be made interested in science.

Luis- My interest in science only started after I’ve finished my degree. I’ve entered a research lab at the University of Porto and started working on Machine Learning, around 1990. Since then I’ve been involved generally in data analysis topics both from a research perspective as well as from a more applied point of view through interactions with industry partners on several projects. I’ve spent most of my career at the Faculty of Economics of the University of Porto, but since 2008 I’m at the department of Computer Science of the Faculty of Sciences of the same university. At the same time I’ve been a researcher at LIAAD / Inesc Porto LA (www.liaad.up.pt).

I like a lot what I do and like science and the “scientific way of thinking”, but I cannot say that I’ve always thought of this area as my “place”. Most of all I like solving challenging problems through data analysis. If that translates into some scientific outcome than I’m more satisfied but that is not my main goal, though I’m kind of “forced” to think about that because of the constraints of an academic career.

That does not mean I’m not passionate about science, I just think there are many more ways of “doing science” than what is reflected in the usual “scientific indicators” that most institutions seem to be more and more obsessed about.

Regards interesting young people in science that is a hard question that I’m not sure I’m qualified to answer. I do tend to think that young people are more sensible to concrete examples of problems they think are interesting and that science helps in solving, as a way of finding a motivation for facing the hard work they will encounter in a scientific career. I do believe in case studies as a nice way to learn and motivate, and thus my book 😉

Ajay- Describe your new book “Data Mining with R, learning with case studies” Why did you choose a case study based approach? who is the target audience? What is your favorite case study from the book

Luis- This book is about learning how to use R for data mining. The book follows a “learn by doing it” approach to data mining instead of the more common theoretical description of the available techniques in this discipline. This is accomplished by presenting a series of illustrative case studies for which all necessary steps, code and data are provided to the reader. Moreover, the book has an associated web page (www.liaad.up.pt/~ltorgo/DataMiningWithR) where all code inside the book is given so that easy copy-paste is possible for the more lazy readers.

The language used in the book is very informal without many theoretical details on the used data mining techniques. For obtaining these theoretical insights there are already many good data mining books some of which are referred in “further readings” sections given throughout the book. The decision of following this writing style had to do with the intended target audience of the book.

In effect, the objective was to write a monograph that could be used as a supplemental book for practical classes on data mining that exist in several courses, but at the same time that could be attractive to professionals working on data mining in non-academic environments, and thus the choice of this more practically oriented approach.

Regards my favorite case study that is a hard question for an author… still I would probably choose the “Predicting Stock Market Returns” case study (Chapter 3). Not only because I like this challenging problem, but mainly because the case study addresses all aspects of knowledge discovery in a real world scenario and not only the construction of predictive models. It tackles data collection, data pre-processing, model construction, transforming predictions into actions using different trading policies, using business-related performance metrics, implementing a trading simulator for “real-world” evaluation, and laying out grounds for constructing an online trading system.

Obviously, for all these steps there are far too many options to be possible to describe/evaluate all of them in a chapter, still I do believe that for the reader it is important to see the overall picture, and read about the relevant questions on this problem and some possible paths that can be followed at these different steps.

In other words: do not expect to become rich with the solution I describe in the chapter !

Ajay- Apart from R, what other data mining software do you use or have used in the past. How would you compare their advantages and disadvantages with R

Luis- I’ve played around with Clementine, Weka, RapidMiner and Knime, but really only playing with teaching goals, and no serious use/evaluation in the context of data mining projects. For the latter I mainly use R or software developed by myself (either in R or other languages). In this context, I do not think it is fair to compare R with these or other tools as I lack serious experience with them. I can however, tell you about what I see as the main pros and cons of R. The main reason for using R is really not only the power of the tool that does not stop surprising me in terms of what already exists and keeps appearing as contributions of an ever growing community, but mainly the ability of rapidly transforming ideas into prototypes. Regards some of its drawbacks I would probably mention the lack of efficiency when compared to other alternatives and the problem of data set sizes being limited by main memory.

I know that there are several efforts around for solving this latter issue not only from the community (e.g. http://cran.at.r-project.org/web/views/HighPerformanceComputing.html), but also from the industry (e.g. Revolution Analytics), but I would prefer that at this stage this would be a standard feature of the language so the the “normal” user need not worry about it. But then this is a community effort and if I’m not happy with the current status instead of complaining I should do something about it!

Ajay- Describe your writing habit- How do you set about writing the book- did you write a fixed amount daily or do you write in bursts etc

Luis- Unfortunately, I write in bursts whenever I find some time for it. This is much more tiring and time consuming as I need to read back material far too often, but I cannot afford dedicating too much consecutive time to a single task. Actually, I frequently tease my PhD students when they “complain” about the lack of time for doing what they have to, that they should learn to appreciate the luxury of having a single task to complete because it will probably be the last time in their professional life!

Ajay- What do you do to relax or unwind when not working?

Luis- For me, the best way to relax from work is by playing sports. When I’m involved in some game I reset my mind and forget about all other things and this is very relaxing for me. A part from sports I enjoy a lot spending time with my family and friends. A good and long dinner with friends over a good bottle of wine can do miracles when I’m too stressed with work! Finally,I do love traveling around with my family.

Luis Torgo

Short Bio: Luis Torgo has a degree in Systems and Informatics Engineering and a PhD in Computer Science. He is an Associate Professor of the Department of Computer Science of the Faculty of Sciences of the University of Porto. He is also a researcher of the Laboratory of Artificial Intelligence and Data Analysis (LIAAD) belonging to INESC Porto LA. Luis Torgo has been an active researcher in Machine Learning and Data Mining for more than 20 years. He has lead several academic and industrial Data Mining research projects. Luis Torgo accompanies the R project almost since its beginning, using it on his research activities. He teaches R at different levels and has given several courses in different countries.

For reading “Data Mining with R” – you can visit this site, also to avail of a 20% discount the publishers have generously given (message below)-

For more information and to place an order, visit us at http://www.crcpress.com.  Order online and apply 20% Off discount code 907HM at checkout.  CRC is pleased to offer free standard shipping on all online orders!

link to the book page  http://www.crcpress.com/product/isbn/9781439810187

Price: $79.95
Cat. #: K10510
ISBN: 9781439810187
ISBN 10: 1439810184
Publication Date: November 09, 2010
Number of Pages: 305
Availability: In Stock
Binding(s): Hardback 

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Dataists shake up R community with a rocking contest

Flipboard
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Newly created Dataists are creating waves on Hacker News and beyond with their innovative contest- A Recommendation Engine for R Packages.

Not only is the contest useful, it is likely to teach R Users some data hacking skills, as well as the basics of creating a GitHub Project.

Read more here-http://www.dataists.com/2010/10/using-data-tools-to-find-data-tools-the-yo-dawg-of-data-hacking/

For that reason, we’ve settled on the more manageable question, “which packages are most often installed by normal R users?”

This last question could potentially be answered in a variety of ways. Our current approach uses a convenience sample of installation data that we’ve collected from volunteers in the R community, who kindly agreed to send us a list of the packages they have on their systems. We’ve anonymized this data and compiled a set of metadata-based predictors that allow us to predict the installation probabilities quite well. We’re releasing all of our current work, including the data we have and all of the code we’ve used so far for our exploratory analyses. The contest itself will go live on Kaggle on Sunday and will end four months from Sunday on February 10, 2011. The rules, prizes and official data sets are all described below.

Rules and Prizes

To win the contest, you need to predict the probability that a user U has a package P installed on their system for every pair, (U, P). We’ll assess your performance using ROC methods, which will be evaluated against a held out test data set. The winning team will receive 3 UseR! books of their choosing. In order to win the contest, you’ll have to provide your analysis code to us by creating a fork of our GitHub repository. You’ll also be required to provide a written description of your approach. We’re asking for so much openness from the winning team because we want this contest to serve as a stepping stone for the R community. We’re also hoping that enterprising data hackers will extend the lessons learned through this contest to other programming languages.

Extract from-http://www.dataists.com/2010/10/using-data-tools-to-find-data-tools-the-yo-dawg-of-data-hacking/

Read the full article there