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 rateLearn by Doing

http://www.meetup.com/HandsOnProgrammingEvents/

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