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==Resources==
==Resources==
* [[Machine Learning]]
* [https://pslcdatashop.web.cmu.edu/KDDCup/rules_data_format.jsp KDD Rules and Data Format]
* [https://pslcdatashop.web.cmu.edu/KDDCup/rules_data_format.jsp KDD Rules and Data Format]
* [http://cran.r-project.org/ R language]
* [http://cran.r-project.org/ R language]
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* [https://www.noisebridge.net/mailman/listinfo/ml Our mailing list]
* [https://www.noisebridge.net/mailman/listinfo/ml Our mailing list]
* [http://www.s3fox.net/ S3Fox]
* [http://www.s3fox.net/ S3Fox]
* [[Machine_Learning/SqliteImport | Importing data into Sqlite]] for SQL'ing the data
* [https://www.noisebridge.net/wiki/Machine_Learning/SVM Thomas' great libSVM writeup]
* [[Machine_Learning/OmniscopeVisualization | Visualizing Sqlite data in Omniscope]] for understanding the data
 
* [http://swarmfinancial.com/ec2mapping.zip Chance mapping dataset for Vikram's EC2 presentation]
==TODOs==
 
* Vikram -- will create a guide for Mahout setup
* Thomas -- will get libsvm working on the data and put together a "how to" guide for doing so
** put together a [[Machine_Learning/kdd_sample | perl script]] which will take random samples from the data, for working on smaller instances
** put together a [[Machine_Learning/kdd_r | simple R script]] for loading the data
* Andy --
* Erin -- Will put meeting notes of 5/19 on https://www.noisebridge.net/wiki/Machine_Learning; will work on data transformations and ways to create better representations of the data; will provide the orthogonalized data sets
 
 


== Notes ==
== Notes ==
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== How to run Weka (quick 'n very dirty tutorial) ==  
== How to run Weka (quick 'n dirty tutorial) ==  
* Download and install Weka
* Download and install Weka
* Get your KDD data & preprocess your data:  
* Get your KDD data
this command takes 1000 lines from the given training data set and converts it into .csv file
* preprocess your data: this command takes 1000 lines from the given training data set and converts it into .csv file
attention, in the last sed command you need to replace the long whitespace with a tab.  In OSX terminal, you do that by pressing CONTROL+V and then tab. (Copying and pasting the command below won't work, since it interprets the whitespace as spaces)
* attention, in the last sed command you need to replace the long whitespace with a tab.  In OSX terminal, you do that by pressing CONTROL+V and then tab. (Copying and pasting the command below won't work, since it interprets the whitespace as spaces)
  head -n 1000 algebra_2006_2007_train.txt | sed -e 's/[",]/ /g' | sed 's/      /,/g' > algebra_2006_2007_train_1kFormatted.csv
  head -n 1000 algebra_2006_2007_train.txt | sed -e 's/[",]/ /g' | sed 's/      /,/g' > algebra_2006_2007_train_1kFormatted.csv
* The following screencast shows you how to do these steps:  
* The following screencast shows you how to do these steps:  
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* [http://swarmfinancial.com/screencasts/nb/kddWekaUsage2.swf Screencast2]
* [http://swarmfinancial.com/screencasts/nb/kddWekaUsage2.swf Screencast2]


== A more step-by-step weka example ==
== How to run SVM ==
* [[Machine Learning/weka]]
 
== How to run libSVM ==
* See the notes at [[Machine Learning/SVM]]
* See the notes at [[Machine Learning/SVM]]
== How to run MOA ==
* See the notes at [[Machine Learning/moa]]
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