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Using R to win Kaggle Data Mining Competitions Chris Raimondi November 1, 2012

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Overview of talk What I hope you get out of this talk Life before R Simple model example R programming language Background/Stats/Info How to get started Kaggle

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Overview of talk Individual Kaggle competitions HIV Progression Chess Mapping Dark Matter Dunnhumbys Shoppers Challenge Online Product Sales

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What I want you to leave with Belief that you dont need to be a statistician to use R - NOR do you need to fully understand Machine Learning in order to use it Motivation to use Kaggle competitions to learn R Knowledge on how to start

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My life before R Lots of Excel Had tried programming in the past got frustrated Read NY Times article in January 2009 about R & Google Installed R, but gave up after a couple minutes Months later

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My life before R Using Excel to run PageRank calculations that took hours and was very messy Was experimenting with Pajek a windows based Network/Link analysis program Was looking for a similar program that did PageRank calculations Revisited R as a possibility

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My life before R Came across R Graph Gallery Saw this graph

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Addicted to R in one line of code pairs(iris[1:4], main="Edgar Anderson's Iris Data", pch=21, bg=c("red", "green3", "blue")[unclass(iris$Species)]) pairs = function iris = dataframe

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What do we want to do with R? Machine learning a.k.a. or more specifically Making models We want to TRAIN a set of data with KNOWN answers/outcomes In order to PREDICT the answer/outcome to similar data where the answer is not known

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How to train a model R allows for the training of models using probably over 100 different machine learning methods To train a model you need to provide 1.Name of the function which machine learning method 2.Name of Dataset 3.What is your response variable and what features are you going to use

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Example machine learning methods available in R BaggingPartial Least Squares Boosted TreesPrincipal Component Regression Elastic NetProjection Pursuit Regression Gaussian ProcessesQuadratic Discriminant Analysis Generalized additive modelRandom Forests Generalized linear modelRecursive Partitioning K Nearest NeighborRule-Based Models Linear RegressionSelf-Organizing Maps Nearest Shrunken CentroidsSparse Linear Discriminant Analysis Neural NetworksSupport Vector Machines

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Code used to train decision tree library(party) irisct