The document introduces a Kaggle competition focused on predicting survival on the Titanic using public domain data, suitable for participants of all expertise levels. It outlines the structure of the competition, classification methods such as decision trees and logistic regression, and provides initial steps for newcomers. Additionally, it highlights opportunities for collaboration, learning, and discussion regarding data modeling and analytics.

1. Research Triangle Analysts
rtpanalysts.org
• Intro to Kaggle.com
• Titantic Getting Started Competition
• Prediction Problem with two outcome Levels
• Opportunity for an extended Data Shootout with Kaggle.com
providing data, scoring, tutorials, forums.
• Public domain data allows for detailed discussion of modeling
issues and solutions without client data confidentiality concerns.
• A common ground for in depth learning and debates on
analytics topics.
• Participants of all levels of expertise welcome
• You influence the direction of this effort by your
participation. Post questions and thoughts on
rtpanalysts.org .
• Welcome!
Slides by Linda Schumacher. Contact via Research Triangle Analysts LinkedIn group member list 1

2. Classification Problems
• 2- levels or outcomes
• Data Model Predictions
• Examples
– Find customers who are likely to buy product
– Id patients likely to be admitted to hospital
– Categorize cells as cancerous or benign
– Who survives the Titanic disaster?
Slides by Linda Schumacher. Contact via Research Triangle Analysts LinkedIn group member
2
list

3. Classifier - Trees
• Decision Trees
All
Passengers
Female] Male
Second
First Class Age < 16 Age >= 16
Third Class
Slides by Linda Schumacher. Contact via
Research Triangle Analysts LinkedIn group 3
member list

4. Classifier - Logistic Regression
• Equation – Logistic Regression
• F(x) = sigmoid(age+class-embarked+gender)
Slides by Linda Schumacher. Contact via
Research Triangle Analysts LinkedIn group 4
member list

5. Titanic Data
• Passenger List
– Name, class, fare, embarked, family
members, age, cabin, etc
– Survival
• Training Set of 891 Passengers
• Test Set of 418
Slides by Linda Schumacher. Contact via
Research Triangle Analysts LinkedIn group 5
member list

6. Kaggle.com
• Data
• Tutorials
– Tools – Excel, Python
– Models – Trees, Random Forests
• Submission
• Leaderboard
Slides by Linda Schumacher. Contact via
Research Triangle Analysts LinkedIn group 6
member list

7. Where to Start
• create a Kaggle account
http://www.kaggle.com/account/register
• read and agree to the rules if you choose to continue
• enter the Kaggle Titantic Competition
http://www.kaggle.com/c/titanic-gettingStarted
• download train.csv and test.csv
• If you choose to use R, obtain-download R from
http://www.r-project.org/ You will have to choose a
‘mirror’ or site – usually a university or research site
• If you share code or data outside of your Kaggle
team, be sure to post a copy on Kaggle Titanic Forum
see http://www.kaggle.com/c/titanic-
gettingStarted/details/rules
Slides by Linda Schumacher. Contact via
Research Triangle Analysts LinkedIn group 7
member list

8. Benefits
• Extended Data Shoot-Out
• Tailor participation
• Opportunities
- New classifiers
- New tools, languages
- Training vs test error
- Round Table Discussion of Solutions
- Compare model results
Slides by Linda Schumacher. Contact via
Research Triangle Analysts LinkedIn group 8
member list