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

2. What is Kaggle? A place for aspiring Data Scientist Learn Share
Compete 
Check it out:

3. 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

4. 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

5. Visualizing relationships in Data

6. 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.

7. 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)