IntroductionThe Model
Results
Predicting survival on the Titanic.
Moderator: C. Fodya.
Group Members:
M. Durojaye, R. Rakotonirainy, S. Shabalala, A. Akinyelu, D.Raphulu, S. Simelane.
Graduate Student Workshop.
January 11, 2014
Titanic MISG 2014
IntroductionThe Model
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Outline
1 Introduction
2 The ModelApproachAnalysis
3 Results
Titanic MISG 2014
IntroductionThe Model
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Figure: The sinking Titanic (Photo: D. Paris)
Titanic MISG 2014
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Introduction
On April 15, 1912, during her maiden voyage, the Titanicsank after colliding with an iceberg.1502 out of 2224 passengers and crew died.This sensational tragedy led to better safety regulations forships.
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Introduction cont...
One of the reasons that the shipwreck led to such loss oflife was that there were not enough lifeboats for thepassengers and crew.Some groups of people were more likely to survive thanothers, such as women, children, and the upper-class.
Titanic MISG 2014
IntroductionThe Model
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Figure: Schematic of the Titanic (Photo: D. Raphulu)
Titanic MISG 2014
IntroductionThe Model
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ApproachAnalysis
Material
We deal with two datasets, training data and testing data.For the training set, all information for each passenger isgiven.
Titanic MISG 2014
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ApproachAnalysis
Below we have our test data set with the empty survivalcolumn.
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Main Purpose
Our main aim is to fill up the survival column of the testdata set.
How?finding patterns and building models from the training data.prediction
Tools and algorithmsPython, Excel and C#
Random forest is the machine learning algorithm used.Testing
Model accuracy was done by submission to the Kagglecompetition.
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Variable Rank
We ranked each variable according to the correlationbetween itself and survival.
Variable Corr − CoefGender 0.5434PClass 0.3385Cabin 0.3196Fare 0.257
Embark 0.1018Parch 0.0816Age 0.0772
SibSp 0.0353
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Gender
PClass
Fare
Cabin
Embark
M
F
1st
2nd
3rd
a
b
cd
yes no
S
CQ
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Single Variable
Predicting survival using gender.
Gender SurvivedWomen 0.74
Men 0.18
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Three variables combination
Female
0 − 9 10 − 19 20 − 29 > 301st class 0 0 0.8 0.972nd class 0 0.91 0.9 13rd class 0.59 0.58 0.3 0.12
Male
0 − 9 10 − 19 20 − 29 > 301st class 0 0 0.4 0.382nd class 0 0.15 0.16 0.213rd class 0.11 0.23 0.13 0.24
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Four variables combination
Female + Cabin Crew
0 − 9 10 − 19 20 − 29 > 301st class 0 0 0 1.2nd class 0 0.92 0.9 1.3rd class 0.59 0.57 0.3 0.125
Female + Not Cabin Crew
0 − 9 10 − 19 20 − 29 > 301st class 0 0 0.83 0.972nd class 0 0.88 0 13rd class 0 0.6 1 0.
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Cont...
Male + Cabin crew
0 − 9 10 − 19 20 − 29 > 301st class 0 0 0.33 0.152nd class 0 0.14 0.09 0.153rd class 0.11 0.21 0.125 0.24
Male + Non Cabin crew
0 − 9 10 − 19 20 − 29 > 301st class 0 0 0.44 0.422nd class 0 0.5 0.66 13rd class 0.2 1 0 0.
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Results
Measuring the model accuracy
Variables Model accuracy Random forestGender 77.1 % 77.14 %
Gender + Pclass 76.0 % 76.02 %3 77.9 % 77.93 %4 76.5 % 76.51 %5 69.1 % 69.17 %
3 = Gender + Pclass + Fare4 = Gender + Pclass + Fare + Cabin5 = Gender + Pclass + Fare + Cabin + Embark
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Discussion
A simple model is not always a bad model.Building a sophisticated model (by adding too manyvariables) might not improve the prediction accuracy of themodel.A moderate model (not too simple and not too complex) issufficient for developing a robust prediction system.
Titanic MISG 2014
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Thank you!!!!Any questions are most
welcome!!!!
Titanic MISG 2014
IntroductionThe Model
Results
Thank you!!!!Any questions are most
welcome!!!!
Titanic MISG 2014