Mark Davey & Rich Brosius Kaggle Competition Outbrain Mark Davey & Rich Brosius

What is Kaggle? A place for aspiring Data Scientist Learn Share Compete  Check it out: https://mldavidson.wordpress.com/2016/08/31/getting-started-with-kaggle-become-a-contributor/

Outbrain is an advertising company using behavioral targeting to recommend articles. The sites with the recommended articles pay Outbrain for this service. Outbrain's promoted articles are found on more than 35,000 websites, Serves over 250 billion recommendations and 15 billion page views per month Outbrain's recommendations reach over 87% of US Internet users

Competition – Dataset Large Relational Dataset – More than 100 Gigabytes of data! Goal - Predict which content each user will predict Data – sorted into 11 files detailing click history and add layout spit into training and test sets Highly suggest doing the Kaggle servers which host the data Currently 207 teams competing for $25000 prize https://www.kaggle.com/c/outbrain-click-prediction/kernels/fork/395654

Visualizing relationships in Data

Tools and Specifications Communication through Notebooks shared via slack Notebooks have data and support R & Pythons Build our first recommender engine using Notebooks Load data into AWS for processing full scale machine learning (Mysql & batch processing, may be Hadoop) Submit data.

Accepting new team members Goals (Learn, Participate) Learn about participation through notebooks and slack. Improve our Python and R programming skills Learning about large data (The files unzipped are over 1TB) Learn about how kaggle competitions work Learn recommender systems Milestones Data Analysis - Understand the data (Through end of october) Learn Recommender Engines (November) Build first models in notebooks on Kaggle (November) Learn about big data techniques to scale notebooks to full data using AWS Test Models Submit models, improve and resubmit (December)