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Demographics and Weblog Hackathon – Case Study 5.3% of Motley Fool visitors are subscribers. Design a classificaiton model for insight into which variables are important for strategies to increase the subscription rate Learn by Doing http://www.meetup.com/HandsOnPro grammingEvents/ Data Mining Hackathon Funded by Rapleaf • • • • • With Motley Fool’s data App note for Rapleaf/Motley Fool Template for other hackathons Did not use AWS. R on individual PCs Logisics: Rapleaf funded prizes and food for 2 weekends for ~20-50. Venue was free Getting more subscribers Headline Data, Weblog Demographics Cleaning Data • training.csv(201,000), headlines.tsv(811MB), entry.tsv(100k), demographics.tsv • Feature Engineering • Github: Ensemble Methods • Bagging, Boosting, randomForests • Overfitting • Stability (small changes make large prediction changes) • Previously none of these work at scale • Small scale results using R, large scale exist in proprietary implementations(google, amazon, etc..) ROC Curves Binary Classifier Only! Paid Subscriber ROC curve, ~61% Boosted Regression Trees Performance • training data ROC score = 0.745 • cv ROC score = 0.737 ; se = 0.002 • 5.5% less performance than the winning score without doing any data processing • Random is 50% or .50. We are .737-.50 better than random by 23.7% Contribution of predictor variables Predictive Importance • Friedman, number of times a variable is selected for splitting weighted by squared error or improvement to model. Measure of sparsity in data • Fit plots remove averages of model variables • 1 pageV 74.0567852 • 2 loc 11.0801383 • 3 income 4.1565597 • 4 age 3.1426519 • 5 residlen 3.0813927 • 6 home 2.3308287 • 7 marital 0.6560258 • 8 sex 0.6476549 • 9 prop 0.3817017 • 10 child 0.2632598 • 11 own 0.2030012 Behavioral vs. Demographics • Demographics are sparse • Behavioral weblogs are the best source. Most sites aren’t using this information correctly. There is no single correct answer. Trial and Error on features. The features are more important than the algorithm • Linear vs. Nonlinear Fitted Values (Crappy) Fitted Values Better Predictor Variable Interaction • Adjusting variable interactions Variable Interactions Plot Interactions age, loc Trees vs. other methods • Can see multiple levels good for trees. Do other variables match this? Simplify model or add more features. Iterate to a better model • No Math. Analyst Number of Trees Data Set Number of Trees Hackathon Results Weblogs only 68.15%, 18% better than random Demographics add 1% AWS Advantages • Running multiple instances with different algorithms and parameters using R • Add tutorial, install Screen, R GUI bugs • http://amazonlabs.pbworks.com/w/page/280 36646/FrontPage Conclusion • Data Mining at scale requires more development in visualization, MR algorithms, MR data preprocessing. • Tuning using visualization. Tune 3 parameters, tc, lr, #trees. Didn’t cover 2/3. • This isn’t reproducable in Hadoop/Mahout or any open source code I know of • Other use cases, i.e. predicting which item will sell(eBay), search engine ranking. • Careful with MR paradigms, Hadoop MR != Couchbase MR