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Titanic prediction
Titanic prediction
Titanic prediction
Titanic prediction
Titanic prediction
Titanic prediction
Titanic prediction
Titanic prediction
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Titanic prediction

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Info from rtpanalysts.org lunch discussion. …

Info from rtpanalysts.org lunch discussion.
I am not affliated with Kaggle. See kaggle.com for info about their competitions.

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  • 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 SecondFirst 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

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