1. Classification of Titanic Passenger Data and Chances of Surviving the Disaster Data Mining with Weka and Kaggle Competition Data
Shawn Cicoria, John Sherlock, Manoj Muniswamaiah, Lauren Clarke
Seidenberg School of Computer Science and Information Systems
Pace University
White Plains, NY, US

2. Background Titanic Disaster – April 15, 1912
1,502 passengers and crew perished out of 2,224[2]
Researchers still try to identify chance of survival[2,3]
[2] “Titanic: Machine Learning from Disaster,” Kaggle.com. [Online]. Available: [Accessed: 13-Dec-2013].
[3] Wiki, “Titanic.” [Online]. Available: [Accessed: 13-Dec-2013].

3. Kaggle.com
Crowd sourcing and competition for Analytics and Data mining
Online Presence
Example Competition
General Electric (GE) offering $200,000

4. Weka Waikato Environment for Knowledge Analysis
Open Source tool
Collection of machine learning algorithms and analytical tools
Cross Platform – Java based
Primary authors – Researchers at University of Waikato NZ

5. Basic Premise
What classes of passengers impacted the survivability for the Titanic Disaster?
Sex
Cabin Class
Point of Departure
Age

6. Source Data Kaggle (Kaggle.com) Titanic Disaster Competition
Used Test Data set

7. Data Set – Coaxing for Weka
Original Data
Data Modifications

8. Final Data Format
Final CSV
ARFF Format

9. J48 Classifier C.45 Based 81% correct classification
42nd in Kaggle if submitted !!
ID3 (1979)
C4.5(1993)
C4.8 (1996?)  J48
C5.0 (commercial)
J48: “top‐down induction of decision trees”
Information gain
 Amount of information gained by knowing the value of the attribute
 (Entropy of distribution before the split) –(entropy of distribution after it)
Claude Shannon, American mathematician and scientist 1916–2001

10. J48 Tree Diagram
Sex largest impact
Cabin Class
Departure point

11. Simple K Means Clustering
Sex had clear clustering impact

12. Simple K Means Clustering
Cabin Class showed significant clustering
3rd class not so great

13. Simple K Means Clustering
Age Group
Hard to distinguish if any
Lowest influencer in J48

14. Simple K Means Point of Departure Southampton seems significant
We didn’t identify if departure was associated with Cabin Class – another study needed.

15. Simple K Means Point of Departure vs. Survived
Instance Colored by Class (1st, 2nd, 3rd)
Show’s strong association between embark and class

16. Conclusions and Summary
Sex clearly had the most significant impact on the survival rate
J48 classifier ~ 81% correctly classified instances
Kaggle competition 43rd place.

17. Finally Weka is powerful, however;
Requires significant coaxing of the data into a more amiable format
At first, we had chosen baseball statistics
Became overwhelmed
Baseball statistics were tossed out – very late in our project.
Kaggle to the rescue
Stumbled upon this dataset
Simple manipulation had compatible ARFF format
Demonstrated which classes of passengers had the greatest impact on survivability.

18. References
[1] GE, “Flight Quest Challenge,” Kaggle.com. [Online]. Available: [Accessed: 13-Dec-2013]. [2] “Titanic: Machine Learning from Disaster,” Kaggle.com. [Online]. Available: [Accessed: 13-Dec- 2013]. [3] Wiki, “Titanic.” [Online]. Available: [Accessed: 13-Dec-2013]. [4] Kaggle, Data Science Community, [Online]. Available: [Accessed: 13-Dec-2013] [5] Weka 3: Data Mining Software in Java, [Online]. Available: [Accessed: 13-Dec-2013] [6] C4.5 Algorithm, Wikipedia, Wikimedia Foundation, [Online]. Available: [Accessed: 13-Dec-2013]