big data: data analysis boot camp titanic dataset · a summary of all personnel on the rms titanic...

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Introduction Background Classification problem Techniques Hands-on Q & A Conclusion References Files

Big Data: Data Analysis Boot CampTitanic Dataset

Chuck Cartledge, PhDChuck Cartledge, PhDChuck Cartledge, PhDChuck Cartledge, PhDChuck Cartledge, PhDChuck Cartledge, PhDChuck Cartledge, PhDChuck Cartledge, PhDChuck Cartledge, PhDChuck Cartledge, PhDChuck Cartledge, PhDChuck Cartledge, PhDChuck Cartledge, PhDChuck Cartledge, PhDChuck Cartledge, PhDChuck Cartledge, PhDChuck Cartledge, PhDChuck Cartledge, PhDChuck Cartledge, PhDChuck Cartledge, PhDChuck Cartledge, PhD

22 September 201722 September 201722 September 201722 September 201722 September 201722 September 201722 September 201722 September 201722 September 201722 September 201722 September 201722 September 201722 September 201722 September 201722 September 201722 September 201722 September 201722 September 201722 September 201722 September 201722 September 2017

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Table of contents (1 of 1)

1 Introduction

2 Background

3 Classification problem

4 Techniques

5 Hands-on

6 Q & A

7 Conclusion

8 References

9 Files

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What are we going to cover?

We’re going to talk about:

R’s RMS Titanic dataset.

Other Titanic datasets that containdifferent data.

Modeling the datasets to see whowill live and who will die.

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“Well” settled data

Basic information

Ordered: 17 Sep. 1908

Completed: 2 Apr. 1912

Maiden voyage: 10 Apr.1912

Sank: 14 Apr. 1912

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Where she was damaged

Red are water tightbulkheads

Green is where the iceberghit

As the bow settled, wateroverflowed the bulkheads Image from [11].

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Same image.

Image from [11].

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How many died and why?

Sailing capacity (passengersand crew): 3,372

Lifeboat capacity: 1,178

Number of people on board(accounts vary): 2,201

Number of people whosurvived: ˜706 - 712 (Rthinks 711)

Passengers, crew, builder’s men,and others. Image from [8].

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Data from diverse places

Expected first class passengers

Lots of lists of 1st classpassengers. Even, some of 2nd,and 3rd class passengers[10].Lists of non-passengers (ship’screw, and builder’s technicians)are more challenging[6].R has a built-in Titanic dataset:Titanic

Image from [7].

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A crew list

A reasonable collection of crewand builder’s representatives isavailable.

Name, job, status (lost ornot)

Age, place of birth

Image from [9].

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titanic3 dataset from PASWR[14]

Part of the PASWR library

Thomas Cason of UVa hasgreatly updated andimproved the titanic dataframe using theEncyclopedia Titanic.

Focuses and expands thepassenger data.

Image from [2].

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titanic3 attributes/variables

Name Explanation

Pclass Passenger Class (1 = 1st; 2 = 2nd; 3 = 3rd)survival Survival (0 = No; 1 = Yes)name Namesex Sexage Agesibsp Number of Siblings/Spouses Aboardparch Number of Parents/Children Aboardticket Ticket Numberfare Passenger Fare (British pound)cabin Cabinembarked Port of Embarkation (C = Cherbourg; Q = Queenstown; S = Southampton)boat Lifeboatbody Body Identification Numberhome.dest Home/Destination

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Bringing the pieces together

Combining:

Passenger data fromtitanic3

Crew data fromSouthampton

Not all data in both datasets

Get a reasonable estimation ofwho survived, or not when theRMS Titanic went down.

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What is it?

A definition

“Classification is the task of learning a targetfunction f that maps each attribute set x to one of thepredefined class labels y.”

Tan, et al. [12]

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What is it?

As a picture

1 A collection of correctlylabeled data (training data)is available.

2 The supervised data isprocessed by some sort ofmachine learning algorithm(there are many) to create amodel (or classifier).

3 Unlabeled (test or new)data, is processed by themodel and predictions aremade.

Image from [5].

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What is it?

Same image.

Image from [5].

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What is it?

Supervised vs. Unsupervised learning

Supervised learning

A training dataset with correct answers(labels) is “mined” to create a model

Unsupervised learning

Data are provided with no aprioriknowledge of labels or patterns. Thegoal is to discover labels and patterns.

Semi-supervised learning

Knowledge from one dataset is appliedto another dataset to help withmining, analysis, classification, andinterpretation.

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What is it?

Supervised vs. Unsupervised learning techniques

With the Titanic dataset, we will be focusing on classification.

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Training and testing

Working with data

Supervised learning requires:

Training data – usuallyabout 70% of available data

Testing data – usually about30% of available data

Training data can also bepartitioned into validation data

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Training and testing

Lots of different things can be done with training data

Use as one monolithicentity

Randomly sample data(with and withoutreplacement)

Divide original training

data into training andvalidation subsets to createmultiple models

With multiple models:

Choose best one,Use all and vote on theoutcome

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Types of errors

Sample problem space

1,000 data points between+/- 1

Two classes of data points

color =

red , if 0.5 ≤√

x2 + y2 ≤ 1.0black, otherwise

(See attached file.)

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Types of errors

A decision tree based on sample data

A decision tree to classify thecircular data problem.

All nodes are labeled

Each mode shows thepercentage of the problemspace they address

Attached file.

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Types of errors

Same image.

Attached file.

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Types of errors

Errors in machine learning

Total sample divided into training(70%) and testing (30%) datasets

Training dataset was partitionedinto different sized decision trees(models)

Training and testing datasetswere classified using each model

Results were compared to theoriginal data

Initially models under-fitted untilaround 6 nodes

Finally models over-fitted beyond25 nodes Training and testing errors

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Types of errors

Same image.

Training and testing errors

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A collection of decision tree techniques

rpart from the rpart library. “Recursive partitioning for classification, regression and survival trees. Animplementation of most of the functionality of the 1984 book by Breiman, Friedman, Olshen andStone.”[13]

C50 from the C50 library. “C5.0 decision trees and rule-based models for pattern recognition.” [4]

Random Forest from the randomForest library. “Classification and regression based on a forest of trees using randominputs.”[1]

J48 from the RWeka library. “An R interface to Weka (Version 3.9.1). Weka is a collection of machine learningalgorithms for data mining tasks written in Java, containing tools for data pre-processing, classification,regression, clustering, association rules, and visualization.”[3] The J48 algorithm is run in a pruned andunpruned mode.

These and additional techniques to be covered in detail later.

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Results

C50 decision tree

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Results

Random Forest decision tree

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Results

rpart decision tree

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Results

J48 (unpruned) decision tree

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Results

J48 (pruned) decision tree

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Results

Accuracy based on training percentage

The horizontal line at 50%represents the accuracy thatwould be achieved based onusing an unbiased coin to decidethe likelihood of survival.

Using training percentages fromabout 10 to 60 result in all

algorithms having nearly identicalaccuracies. Below 10%, the

Random Forest approach appearsbest.

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Results

Same image.

Using training percentages from about 10 to 60 result in allalgorithms having nearly identical accuracies. Below 10%, the

Random Forest approach appears best.

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Results

Simple mosaic from the titanic package

Sometimes you don’t need a lotof code.library(titanic)

library(graphics)

mosaicplot(Titanic, main =

"Survival on the Titanic")

A summary of all personnel on the RMS Titanic brokendown by gender, by survival or not, and class. It is

interesting to look at the data and consider the adage:“women and children first.”

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Results

Same image.

A summary of all personnel on the RMS Titanic broken down by gender, by survival or not, and class. It isinteresting to look at the data and consider the adage: “women and children first.”

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Some simple exercises to get familiar with data analysis

1 Using the Titanic report asa guide, create a recursivepartition decision treemodeling survival based onsex and number of siblings

2 Create a recursive partitiondecision tree modelingsurvival based on allavailable data

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Q & A time.

Q: How many Harvard MBA’sdoes it take to screw in a lightbulb?A: Just one. He grasps it firmlyand the universe revolves aroundhim.

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What have we covered?

All of the decision tree algorithmstested had comparable results(˜76% accuracy) when the trainingdataset was between 10 and 60%of the entire dataset.Random forest performed mostconsistently over the widest rangeof training percentages of all testedalgorithms.

Next: LPAR Chapter 2, basic data visualization

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References (1 of 5)

[1] Leo Breiman,randomForest: Breiman and Cutlers random forests for classification and regression,http://stat-www.berkeley.edu/users/breiman/

RandomForests, 2006.

[2] Jr. Frank E. Harrell, Titanic Data,http://biostat.mc.vanderbilt.edu/wiki/pub/Main/

DataSets/titanic.html, 2002.

[3] K Hornik, A Zeileis, T Hothorn, and C Buchta,RWeka: an R interface to Weka, R package version 0.4-32(2017).

http://stat-www.berkeley.edu/users/breiman/RandomForestshttp://stat-www.berkeley.edu/users/breiman/RandomForestshttp://biostat.mc.vanderbilt.edu/wiki/pub/Main/DataSets/titanic.htmlhttp://biostat.mc.vanderbilt.edu/wiki/pub/Main/DataSets/titanic.html

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References (2 of 5)

[4] M Kuhn, S Weston, N Coulter, M Culp, and R Quinlan,C5.0 Decision Trees and Rule-Based Models, R PackageVersion 0.1. 0 24 (2015).

[5] Sebastian Raschka,Predictive modeling, supervised machine learning, and pattern classification,http://sebastianraschka.com/Articles/2014_intro_

supervised_learning.html, 2014.

[6] Encyclopedia Titanica Staff,Encyclopedia Titanica, Titanic Facts, History and Biography,https://www.encyclopedia-titanica.org/, 2017.

http://sebastianraschka.com/Articles/2014_intro_supervised_learning.htmlhttp://sebastianraschka.com/Articles/2014_intro_supervised_learning.htmlhttps://www.encyclopedia-titanica.org/

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References (3 of 5)

[7] ISM Staff, Titanic Survivor, Titanic Passenger List Booklet,http://www.phillyseaport.org/web_exhibits/mini_

exhibits/titanic_passenger_list/titanic_

passenger_list-object-passenger_list.html, 2017.

[8] OceanGate Staff, Titanic Survey Expedition: 2018,http://www.oceangate.com/expeditions/titanic-

survey-2018.html, 2017.

[9] Southampton Staff, Titanic crew list,http://www.plimsoll.org/Southampton/Titanic/

titaniccrewlist/Default.asp, 2017.

http://www.phillyseaport.org/web_exhibits/mini_exhibits/titanic_passenger_list/titanic_passenger_list-object-passenger_list.htmlhttp://www.phillyseaport.org/web_exhibits/mini_exhibits/titanic_passenger_list/titanic_passenger_list-object-passenger_list.htmlhttp://www.phillyseaport.org/web_exhibits/mini_exhibits/titanic_passenger_list/titanic_passenger_list-object-passenger_list.htmlhttp://www.oceangate.com/expeditions/titanic-survey-2018.htmlhttp://www.oceangate.com/expeditions/titanic-survey-2018.htmlhttp://www.plimsoll.org/Southampton/Titanic/titaniccrewlist/Default.asphttp://www.plimsoll.org/Southampton/Titanic/titaniccrewlist/Default.asp

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References (4 of 5)

[10] Titanic Facts Staff, Titanic Passenger List, http://www.titanic-facts.com/titanic-passenger-list.html,2017.

[11] Wikipedia Staff, Sinking of the RMS Titanic, https://en.wikipedia.org/wiki/Sinking_of_the_RMS_Titanic,2017.

[12] Pang-Ning Tan, Michael Steinbach, and Vipin Kumar,Introduction to Data Mining, Pearson Education India, 2006.

[13] Terry Therneau, Beth Atkinson, and Brian Ripley, rpart,Available at CRAN. R-project. org/package= rpart. AccessedMay (2015).

http://www.titanic-facts.com/titanic-passenger-list.htmlhttp://www.titanic-facts.com/titanic-passenger-list.htmlhttps://en.wikipedia.org/wiki/Sinking_of_the_RMS_Titanichttps://en.wikipedia.org/wiki/Sinking_of_the_RMS_Titanic

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References (5 of 5)

[14] Maria Dolores Ugarte, Ana F Militino, and Alan T Arnholt,Probability and Statistics with R, CRC Press, 2008.

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Files of interest

1 A short report about the

Titanic

2 titanic3 data

3 titanic3 meta data

4 Example circular binary

classifier

In Search of the Royal Mail Ship (RMS) Titanic

Chuck Cartledge

July 29, 2017

Contents

1 Introduction 1

2 Discussion 12.1 Sources of data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

2.1.1 R . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12.1.2 R package “titanic” . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22.1.3 R package “vcdExtra” . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22.1.4 The crew . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6

2.2 Questions that can be asked . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62.3 Presentation of results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10

3 Conclusion 13

A Plotting commands 20

B Files 21

List of Tables

1 Command line, or pass parameters. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 102 Cross comparison of different partitioning algorithms. . . . . . . . . . . . . . . . . . . . . . . 13

List of Figures

1 The RMS Titanic. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22 Titanic’s Collapsible Boat D approaches RMS Carpathia. . . . . . . . . . . . . . . . . . . . . 33 Commemorative sailing booklet. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44 A Mosaic plot of R Titanic data. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55 vcdExtra Titanic data. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76 vcdExtra Titanic data decision tree based on passenger class, age, and number and type of

traveling companions. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87 vcdExtra Titanic data decision tree based on all available data. . . . . . . . . . . . . . . . . . 98 Missing data map for the “internal” dataset. . . . . . . . . . . . . . . . . . . . . . . . . . . . 119 Missing data map for the “titanic3” dataset. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1210 C50 decision tree. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15

i

11 Random Forest decision tree. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1612 rpart decision tree. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1713 J48 (unpruned) decision tree. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1714 J48 (pruned) decision tree. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1815 Accuracy based on training dataset size. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19

1 Introduction

The sinking of the Royal Mail Ship (RMS) Titanic on her maiden voyage is a source of constant mystery andromance. Now after more than a century, there are still unanswered questions about the disaster that madeher a part of the English lexicon. Perhaps the simplest question is: how many people (passengers and crew)were on board when she sank, and how many survived? Surprisingly, there is no definitive answer to these,the most simple of questions. Neither from the White Star Line (her owner), nor from the British WreckCommissioner assigned to inquiry into her sinking. In this report, we will enumerate some of the disparatesources, and look at some data that has made its way into the R programming language.

2 Discussion

The RMS Titanic (see Figure 1 on the following page), set sail from South Hampton England for New York,New York on 10 April 1912. She had a fire in her coal stores that started almost 10 days before she sailed,and continued after leaving South Hampton[9]. She had 16 lifeboats, and four Engelhardt “collapsibles” (seeFigure 2 on page 3) that could collectively hold 1,178 people (aka, souls)[5]. When the Titanic set sail,she had approximately 2,224 passengers and crew. Titanic could had a maximum capacity of 3,327 souls.The number of survivors differs between sources; ranging from 706[7, 8] to 712[4]. 711 are reported to havesurvived according to the R library titanic.

Titanic’s sailing was such a special event, that special booklets were created (see Figure 3 on page 4),complete with list of first class passengers2.

2.1 Sources of data

One of the many things that is unusual about the sinking of the Titanic, is that there does not seem to bea definitive list of passengers and crew, let alone a list that identifies those who survived and those who didnot[1]. In the following sections, we will investigate a few of these sources.

2.1.1 R

A summary of the classes of people on the Titanic, and whether or not they survived, is part of the standardR installation in the table Titanic (see Figure 4 on page 5).

“This data set provides information on the fate of passengers on the fatal maiden voyage of theocean liner ’Titanic’, summarized according to economic status (class), sex, age and survival.”

R Staff [6]

2http://www.phillyseaport.org/web_exhibits/mini_exhibits/titanic_passenger_list/titanic_passenger_

list-object-passenger_list.html

http://www.phillyseaport.org/web_exhibits/mini_exhibits/titanic_passenger_list/titanic_passenger_list-object-passenger_list.html

http://www.phillyseaport.org/web_exhibits/mini_exhibits/titanic_passenger_list/titanic_passenger_list-object-passenger_list.html

Figure 1: The RMS Titanic. Photographed 10 April 19121. The Titanic sank 15 April 1912.

2.1.2 R package “titanic”

A collection of 1,309 passenger records organized to support the Kaggle competition3. The total data isdivided into training and testing portions to support machine learning. The data does not have any crewdata.

This data set provides information on the fate of passengers on the fatal maiden voyage of theocean liner “Titanic”, summarized according to economic status (class), sex, age and survival.Whereas the base R Titanic data found by calling data(“Titanic”) is an array resulting fromcross-tabulating 2201 observations, these data sets are the individual non-aggregated observationsand formatted in a machine learning context with a training sample, a testing sample, and twoadditional data sets that can be used for deeper machine learning analysis.

Hendricks [3]

The library has copies of the training and testing dataset used by the Kaggle competition to validate andtest various machine learning algorithms. While the titanic train data set has which passenger survivedor not, the titanic test data set does not. So the only practical way to see how well your approachworked was to submit your R script to Kaggle and the await results. There is sufficient information in thetitanic test data set to reconstruct who lived or died, it may not be worth the effort.

2.1.3 R package “vcdExtra”

A collection of 1,309 passenger records.

Provides additional data sets, methods and documentation to complement the ’vcd’ packagefor Visualizing Categorical Data and the ’gnm’ package for Generalized Nonlinear Models.

3https://www.kaggle.com/c/titanic

2

https://www.kaggle.com/c/titanic

Figure 2: Titanic’s Collapsible Boat D approaches RMS Carpathia.

3

Figure 3: Commemorative sailing booklet.4

Figure 4: A Mosaic plot of R Titanic data. A summary of all personnel on the RMS Titanic broken down bygender, by survival or not, and class. It is interesting to look at the data and consider the adage: “womenand children first.”

5

Friendly et al. [2]

The vcdExtra::Titanicp is a data frame with 1,309 observations on the following 6 variables (see Figure 5on the following page):

• class a factor with levels 1st, 2nd, and 3rd

• survived a factor with levels died, and survived (1 and 2 respectively)

• sex a factor with levels female, and male

• age passenger age in years (or fractions of a year, for children), a numeric vector; age is missing for263 of the passengers

• sibsp number of siblings or spouses aboard, integer: 0:8

• parch number of parents or children aboard, integer: 0:6

In many ways, “vcdExtra::Titanicp” is a union of the “titanic::titanic train” and “titanic::titanic test”datasets, less some of the columnular values. The vcdExtra::Titanicp data supports looking at the data indifferent ways[2] (see Figure 6 on page 8). If we use all the data columns, then the decision tree becomesmuch more interesting

2.1.4 The crew

The previous sections focused on Titanic’s passengers, but the ship also had crew and almost all Titanic listsignore them. I was able to find one site that claimed to have a list of crew members, their job, and whetherthey survived or not. Port Cities Southampton4 claims to be a digital archive of maritime activities for thePort of Southampton, including the departure of the Titanic5. The crew list is broken into several partsbased on last names, and is available for download as PDF files.

An R script was written to parse the crew list files and put the data into a data frame compatible withthe other Titanic datasets.

2.2 Questions that can be asked

Every person that sailed on the Titanic had a long list of attributes that could be used to describe them.These attributes include:

• Class (1st, 2nd, 3rd, crew, other)

• Age,

• Fare,

• Gender,

• Number of traveling family members,

• Number of siblings,

• Place of embarkation, or4http://www.plimsoll.org/StartHere/AboutUs/default.asp5http://www.plimsoll.org/Southampton/Titanic/titaniccrewlist/Default.asp

6

http://www.plimsoll.org/StartHere/AboutUs/default.asp

http://www.plimsoll.org/Southampton/Titanic/titaniccrewlist/Default.asp

Figure 5: vcdExtra Titanic data. The data shows that only three classes of data are present (no crewmembers), that everyone survived or not, that there were only 2 genders identified, and that the attributesof age, sibsp, and parch have the most variability.

7

Figure 6: vcdExtra Titanic data decision tree based on passenger class, age, and number and type of travelingcompanions. Based on the data; if you were not in 3rd class, were, under 16, and in 2nd class, then 157 ofthe 249 people like you died.

8

Figure 7: vcdExtra Titanic data decision tree based on all available data.

9

Table 1: Command line, or pass parameters. The predictions.R R script supports a variety of passparameters, or command line arguments.

Name Switch Default MeaningsourceSelection s internal Which dataset to use.trainingPercentage t 30 Percentage of data (num-

ber of people) to use in thetraining set. The remain-der will be used as the testset.

dataCollection d FALSE Should a long run be madeto collect data based on thedata set selected.

verbose v FALSE Control lots of debugginginformation.

• Nationality of each person (there have been comments that percentage wise Americans survived thannon-Americans because the Americans believed the announcements to abandon ship),

Not all of the datasets included in predictions.R have all attributes. The internal dataset is missing theattribute “embarkation” (see Figure 8 on the following page). While the optional dataset, has all values (seeFigure 9 on page 12).

Each of these attributes can be used to form the question as to who survived and who did not. Not alldatasets have all attributes.

For our investigation, the question is:What is the likelihood that someone survived based on their gender, the number of siblings on board,

the number of people in the traveling family unit, and the group’s traveling class (1st, 2nd, 3rd, and crew).

2.3 Presentation of results

An R script was written to compare the different datasets and to use different partitioning approaches onthe datasets. The script supports a collection of arguments either via the command line, or by passingarguments via the main() function (see Table 1).

predictions.R incorporates four different partitioning algorithms (one algorithm has two variants).They are:

rpart from the rpart library. “Recursive partitioning for classification, regression and survival trees. Animplementation of most of the functionality of the 1984 book by Breiman, Friedman, Olshen andStone.”[?]

C50 from the C50 library. “C5.0 decision trees and rule-based models for pattern recognition.” [?]

Random Forest from the randomForest library. “Classification and regression based on a forest of treesusing random inputs.”[?]

J48 from the RWeka library. “An R interface to Weka (Version 3.9.1). Weka is a collection of machinelearning algorithms for data mining tasks written in Java, containing tools for data pre-processing,classification, regression, clustering, association rules, and visualization.”[?] The J48 algorithm is runin a pruned and unpruned mode.

10

Figure 8: Missing data map for the “internal” dataset.

11

Figure 9: Missing data map for the “titanic3” dataset.

12

Table 2: Cross comparison of different partitioning algorithms. The row names correspond to the cells in aconfusion matrix, with TRUE positives and negatives, and FALSE positives and negatives.

C50 Random Forest rpart J48 (unpruned) J48 (pruned)True + 1031 1031 1031 1031 1031True - 175 175 175 175 175False + 12 12 12 12 12False - 323 323 323 323 323Accuracy 0.7826087 0.7826087 0.7826087 0.7826087 0.7826087Kappa 0.4061712 0.4061712 0.4061712 0.4061712 0.4061712

(see Figure 10on page 15)





Each algorithm was run using the default program values and verbose = TRUE so that cross algorithmcomparisons can be made (see Table 2). Data on each algorithm is reported:

True + from a confusion matrix, this is the TRUE Positive.

True - from a confusion matrix, this is the TRUE Negative.

False + from a confusion matrix, this is the FALSE Positive.

False - from a confusion matrix, this is the FALSE Negative.

Accuracy the accuracy based on data in the confusion matrix Accuracy = truePositive+trueNegativeallPositive+allNegative

Kappa

While the figures created by the default values are interesting (see Figures 10 on page 15 through 14on page 18), they only provide a snapshot of how their respective algorithms perform based on a singlepartitioning of the data into training and testing subsets. It might be more informative to see how theaccuracy of the algorithms vary as a function of the size of the training dataset. To answer that question,predictions.R was run with dataCollection = TRUE and trainingPercentage = 1. This causes thescript to vary trainingPercentage from 1 to 90%, in 10% steps. The accuracy for each algorithm wascaptured and plotted (see Figure 15 on page 19). Based on the collected data, it appears that trainingpercentages from about 10 to 60 result in all algorithms having nearly identical accuracies. Below 10%, theRandom Forest approach appears best. Above 70%, they all seem to be an overfit the data.

3 Conclusion

A number of interesting things that came out of this investigation, including:

1. The Titanic carried more lifeboats than law required, but far too few to save all personnel (1,178 versus3,327).

2. There does not appear to be an official and agreed to number of people who sailed on the RMS Titanic,and of those who survived or died.

3. The values from in built in R data set (datasets:Titanic)6 is a reasonable approximation, althoughit has limited attributes.

6At the R command prompt: library(help=datasets)

13

4. There are many Titanic people data sets, but some do not contain all classes of people (1st, 2nd, 3rd,and crew).

5. Various passenger and crew lists (with more attributes than the built in Titanic dataset) are available,and can be consolidated into a dataset with more attributes, and still have approximately the samenumber of people.

6. All of the decision tree algorithms tested had comparable results (˜76% accuracy) when the trainingdataset was between 10 and 60% of the entire dataset.

7. Random forest performed most consistently over the widest range of training percentages of all testedalgorithms.

In summary: most of the Titanic personnel datasets are very comparable, and the random forest decisiontree consistently worked the best.

References

[1] RJM Dawson, The ”Unusual Episode” Data Revisited, Journal of Statistics Education 3 (1995), no. 3,1–7.

[2] Michael Friendly, Heather Turner, Achim Zeileis, and Maintainer Michael Friendly, Package ’vcdExtra’,(2016).

[3] Paul Hendricks, Titanic Passenger Survival Data Set, https://github.com/paulhendricks/titanic,2015.

[4] Encyclopedia Titanica Staff, Titanic Survivors, https://www.encyclopedia-titanica.org/titanic-survivors/, 2017.

[5] History Staff, Titanic, http://www.history.com/topics/titanic, 2017.

[6] R Staff, Survival of passengers on the Titanic, package:datasets, 2017.

[7] Titanic Facts Staff, Titanic Survivors, http://www.titanicfacts.net/titanic-survivors.html,2017.

[8] Titanic Universe Staff, How Many People Survived the Titanic Disaster?, http://www.titanicuniverse.com/how-many-people-survived-the-titanic-disaster/1253, 2017.

[9] Wikipedia Staff, British Wreck Commissioner’s inquiry into the sinking of the RMS Titanic,https://en.wikipedia.org/wiki/British_Wreck_Commissioner%27s_inquiry_into_the_sinking_

of_the_RMS_Titanic, 2017.

14

https://github.com/paulhendricks/titanic

https://www.encyclopedia-titanica.org/titanic-survivors/

https://www.encyclopedia-titanica.org/titanic-survivors/

http://www.history.com/topics/titanic

http://www.titanicfacts.net/titanic-survivors.html

http://www.titanicuniverse.com/how-many-people-survived-the-titanic-disaster/1253

http://www.titanicuniverse.com/how-many-people-survived-the-titanic-disaster/1253

https://en.wikipedia.org/wiki/British_Wreck_Commissioner%27s_inquiry_into_the_sinking_of_the_RMS_Titanic

https://en.wikipedia.org/wiki/British_Wreck_Commissioner%27s_inquiry_into_the_sinking_of_the_RMS_Titanic

Figure 10: C50 decision tree.

15

Figure 11: Random Forest decision tree.

16

Figure 12: rpart decision tree.

Figure 13: J48 (unpruned) decision tree.

17

Figure 14: J48 (pruned) decision tree.

18

Figure 15: Accuracy based on training dataset size. The horizontal line at 50% represents the accuracy thatwould be achieved based on using an unbiased coin to decide the likelihood of survival.

19

A Plotting commands

A collection of commands to create the images in this report.

‘A Mosaic plot of R Titanic data’ on page 5 –

library(titanic)

library(graphics)

mosaicplot(Titanic, main = "Survival on the Titanic")

‘vcdExtra Titanic data’ on page 7 –

library(vcdExtra)

data(Titanicp)

plot(Titanicp)

‘vcdExtra Titanic data decision tree based on passenger class, age, and number and type of traveling companions’ on page 8–

library(rpart)

library(rpart.plot)

data(Titanicp, package="vcdExtra")

rp0

B Files

A collection of miscellaneous files mentioned in the report.

• titanic3.xls – A consolidated list of passengers.

• predictions.R – An R script to demonstrate the different partitioning approaches and their results.

21

titanic3

pclasssurvivednamesexagesibspparchticketfarecabinembarkedboatbodyhome.dest

11Allen, Miss. Elisabeth Waltonfemale290024160211.3375B5S2St Louis, MO

11Allison, Master. Hudson Trevormale0.916712113781151.5500C22 C26S11Montreal, PQ / Chesterville, ON

10Allison, Miss. Helen Lorainefemale212113781151.5500C22 C26SMontreal, PQ / Chesterville, ON

10Allison, Mr. Hudson Joshua Creightonmale3012113781151.5500C22 C26S135Montreal, PQ / Chesterville, ON

10Allison, Mrs. Hudson J C (Bessie Waldo Daniels)female2512113781151.5500C22 C26SMontreal, PQ / Chesterville, ON

11Anderson, Mr. Harrymale48001995226.5500E12S3New York, NY

11Andrews, Miss. Kornelia Theodosiafemale63101350277.9583D7S10Hudson, NY

10Andrews, Mr. Thomas Jrmale39001120500.0000A36SBelfast, NI

11Appleton, Mrs. Edward Dale (Charlotte Lamson)female53201176951.4792C101SDBayside, Queens, NY

10Artagaveytia, Mr. Ramonmale7100PC 1760949.5042C22Montevideo, Uruguay

10Astor, Col. John Jacobmale4710PC 17757227.5250C62 C64C124New York, NY

11Astor, Mrs. John Jacob (Madeleine Talmadge Force)female1810PC 17757227.5250C62 C64C4New York, NY

11Aubart, Mme. Leontine Paulinefemale2400PC 1747769.3000B35C9Paris, France

11Barber, Miss. Ellen "Nellie"female26001987778.8500S6

11Barkworth, Mr. Algernon Henry Wilsonmale80002704230.0000A23SBHessle, Yorks

10Baumann, Mr. John Dmale00PC 1731825.9250SNew York, NY

10Baxter, Mr. Quigg Edmondmale2401PC 17558247.5208B58 B60CMontreal, PQ

11Baxter, Mrs. James (Helene DeLaudeniere Chaput)female5001PC 17558247.5208B58 B60C6Montreal, PQ

11Bazzani, Miss. Albinafemale32001181376.2917D15C8

10Beattie, Mr. Thomsonmale36001305075.2417C6CAWinnipeg, MN

11Beckwith, Mr. Richard Leonardmale37111175152.5542D35S5New York, NY

11Beckwith, Mrs. Richard Leonard (Sallie Monypeny)female47111175152.5542D35S5New York, NY

11Behr, Mr. Karl Howellmale260011136930.0000C148C5New York, NY

11Bidois, Miss. Rosaliefemale4200PC 17757227.5250C4

11Bird, Miss. Ellenfemale2900PC 17483221.7792C97S8

10Birnbaum, Mr. Jakobmale25001390526.0000C148San Francisco, CA

11Bishop, Mr. Dickinson Hmale25101196791.0792B49C7Dowagiac, MI

11Bishop, Mrs. Dickinson H (Helen Walton)female19101196791.0792B49C7Dowagiac, MI

11Bissette, Miss. Ameliafemale3500PC 17760135.6333C99S8

11Bjornstrom-Steffansson, Mr. Mauritz Hakanmale280011056426.5500C52SDStockholm, Sweden / Washington, DC

10Blackwell, Mr. Stephen Weartmale450011378435.5000TSTrenton, NJ

11Blank, Mr. Henrymale400011227731.0000A31C7Glen Ridge, NJ

11Bonnell, Miss. Carolinefemale300036928164.8667C7S8Youngstown, OH

11Bonnell, Miss. Elizabethfemale580011378326.5500C103S8Birkdale, England Cleveland, Ohio

10Borebank, Mr. John Jamesmale420011048926.5500D22SLondon / Winnipeg, MB

11Bowen, Miss. Grace Scottfemale4500PC 17608262.3750C4Cooperstown, NY

11Bowerman, Miss. Elsie Edithfemale220111350555.0000E33S6St Leonards-on-Sea, England Ohio

11Bradley, Mr. George ("George Arthur Brayton")male0011142726.5500S9Los Angeles, CA

10Brady, Mr. John Bertrammale410011305430.5000A21SPomeroy, WA

10Brandeis, Mr. Emilmale4800PC 1759150.4958B10C208Omaha, NE

10Brewe, Dr. Arthur Jacksonmale0011237939.6000CPhiladelphia, PA

11Brown, Mrs. James Joseph (Margaret Tobin)female4400PC 1761027.7208B4C6Denver, CO

11Brown, Mrs. John Murray (Caroline Lane Lamson)female59201176951.4792C101SDBelmont, MA

11Bucknell, Mrs. William Robert (Emma Eliza Ward)female60001181376.2917D15C8Philadelphia, PA

11Burns, Miss. Elizabeth Margaretfemale410016966134.5000E40C3

10Butt, Major. Archibald Willinghammale450011305026.5500B38SWashington, DC

10Cairns, Mr. Alexandermale0011379831.0000S

11Calderhead, Mr. Edward Penningtonmale4200PC 1747626.2875E24S5New York, NY

11Candee, Mrs. Edward (Helen Churchill Hungerford)female5300PC 1760627.4458C6Washington, DC

11Cardeza, Mr. Thomas Drake Martinezmale3601PC 17755512.3292B51 B53 B55C3Austria-Hungary / Germantown, Philadelphia, PA

11Cardeza, Mrs. James Warburton Martinez (Charlotte Wardle Drake)female5801PC 17755512.3292B51 B53 B55C3Germantown, Philadelphia, PA

10Carlsson, Mr. Frans Olofmale33006955.0000B51 B53 B55SNew York, NY

10Carrau, Mr. Francisco Mmale280011305947.1000SMontevideo, Uruguay

10Carrau, Mr. Jose Pedromale170011305947.1000SMontevideo, Uruguay

11Carter, Master. William Thornton IImale1112113760120.0000B96 B98S4Bryn Mawr, PA

11Carter, Miss. Lucile Polkfemale1412113760120.0000B96 B98S4Bryn Mawr, PA

11Carter, Mr. William Ernestmale3612113760120.0000B96 B98SCBryn Mawr, PA

11Carter, Mrs. William Ernest (Lucile Polk)female3612113760120.0000B96 B98S4Bryn Mawr, PA

10Case, Mr. Howard Brownmale49001992426.0000SAscot, Berkshire / Rochester, NY

11Cassebeer, Mrs. Henry Arthur Jr (Eleanor Genevieve Fosdick)female001777027.7208C5New York, NY

10Cavendish, Mr. Tyrell Williammale36101987778.8500C46S172Little Onn Hall, Staffs

11Cavendish, Mrs. Tyrell William (Julia Florence Siegel)female76101987778.8500C46S6Little Onn Hall, Staffs

10Chaffee, Mr. Herbert Fullermale4610W.E.P. 573461.1750E31SAmenia, ND

11Chaffee, Mrs. Herbert Fuller (Carrie Constance Toogood)female4710W.E.P. 573461.1750E31S4Amenia, ND

11Chambers, Mr. Norman Campbellmale271011380653.1000E8S5New York, NY / Ithaca, NY

11Chambers, Mrs. Norman Campbell (Bertha Griggs)female331011380653.1000E8S5New York, NY / Ithaca, NY

11Chaudanson, Miss. Victorinefemale3600PC 17608262.3750B61C4

11Cherry, Miss. Gladysfemale300011015286.5000B77S8London, England

11Chevre, Mr. Paul Romainemale4500PC 1759429.7000A9C7Paris, France

11Chibnall, Mrs. (Edith Martha Bowerman)female0111350555.0000E33S6St Leonards-on-Sea, England Ohio

10Chisholm, Mr. Roderick Robert Crispinmale001120510.0000SLiverpool, England / Belfast

10Clark, Mr. Walter Millermale271013508136.7792C89CLos Angeles, CA

11Clark, Mrs. Walter Miller (Virginia McDowell)female261013508136.7792C89C4Los Angeles, CA

11Cleaver, Miss. Alicefemale2200113781151.5500S11

10Clifford, Mr. George Quincymale0011046552.0000A14SStoughton, MA

10Colley, Mr. Edward Pomeroymale4700572725.5875E58SVictoria, BC

11Compton, Miss. Sara Rebeccafemale3911PC 1775683.1583E49C14Lakewood, NJ

10Compton, Mr. Alexander Taylor Jrmale3711PC 1775683.1583E52CLakewood, NJ

11Compton, Mrs. Alexander Taylor (Mary Eliza Ingersoll)female6402PC 1775683.1583E45C14Lakewood, NJ

11Cornell, Mrs. Robert Clifford (Malvina Helen Lamson)female55201177025.7000C101S2New York, NY

10Crafton, Mr. John Bertrammale0011379126.5500SRoachdale, IN

10Crosby, Capt. Edward Giffordmale7011WE/P 573571.0000B22S269Milwaukee, WI

11Crosby, Miss. Harriet Rfemale3602WE/P 573571.0000B22S7Milwaukee, WI

11Crosby, Mrs. Edward Gifford (Catherine Elizabeth Halstead)female641111290126.5500B26S7Milwaukee, WI

10Cumings, Mr. John Bradleymale3910PC 1759971.2833C85CNew York, NY

11Cumings, Mrs. John Bradley (Florence Briggs Thayer)female3810PC 1759971.2833C85C4New York, NY

11Daly, Mr. Peter Denismale510011305526.5500E17S5 9Lima, Peru

11Daniel, Mr. Robert Williamsmale270011380430.5000S3Philadelphia, PA

11Daniels, Miss. Sarahfemale3300113781151.5500S8

10Davidson, Mr. Thorntonmale3110F.C. 1275052.0000B71SMontreal, PQ

11Davidson, Mrs. Thornton (Orian Hays)female2712F.C. 1275052.0000B71S3Montreal, PQ

11Dick, Mr. Albert Adrianmale31101747457.0000B20S3Calgary, AB

11Dick, Mrs. Albert Adrian (Vera Gillespie)female17101747457.0000B20S3Calgary, AB

11Dodge, Dr. Washingtonmale53113363881.8583A34S13San Francisco, CA

11Dodge, Master. Washingtonmale4023363881.8583A34S5San Francisco, CA

11Dodge, Mrs. Washington (Ruth Vidaver)female54113363881.8583A34S5San Francisco, CA

10Douglas, Mr. Walter Donaldmale5010PC 17761106.4250C86C62Deephaven, MN / Cedar Rapids, IA

11Douglas, Mrs. Frederick Charles (Mary Helene Baxter)female2711PC 17558247.5208B58 B60C6Montreal, PQ

11Douglas, Mrs. Walter Donald (Mahala Dutton)female4810PC 17761106.4250C86C2Deephaven, MN / Cedar Rapids, IA

11Duff Gordon, Lady. (Lucille Christiana Sutherland) ("Mrs Morgan")female48101175539.6000A16C1London / Paris

11Duff Gordon, Sir. Cosmo Edmund ("Mr Morgan")male4910PC 1748556.9292A20C1London / Paris

10Dulles, Mr. William Crothersmale3900PC 1758029.7000A18C133Philadelphia, PA

11Earnshaw, Mrs. Boulton (Olive Potter)female23011176783.1583C54C7Mt Airy, Philadelphia, PA

11Endres, Miss. Caroline Louisefemale3800PC 17757227.5250C45C4New York, NY

11Eustis, Miss. Elizabeth Musseyfemale54103694778.2667D20C4Brookline, MA

10Evans, Miss. Edith Corsefemale3600PC 1753131.6792A29CNew York, NY

10Farthing, Mr. Johnmale00PC 17483221.7792C95S

11Flegenheim, Mrs. Alfred (Antoinette)female00PC 1759831.6833S7New York, NY

11Fleming, Miss. Margaretfemale0017421110.8833C4

11Flynn, Mr. John Irwin ("Irving")male3600PC 1747426.3875E25S5Brooklyn, NY

10Foreman, Mr. Benjamin Laventallmale300011305127.7500C111CNew York, NY

11Fortune, Miss. Alice Elizabethfemale243219950263.0000C23 C25 C27S10Winnipeg, MB

11Fortune, Miss. Ethel Florafemale283219950263.0000C23 C25 C27S10Winnipeg, MB

11Fortune, Miss. Mabel Helenfemale233219950263.0000C23 C25 C27S10Winnipeg, MB

10Fortune, Mr. Charles Alexandermale193219950263.0000C23 C25 C27SWinnipeg, MB

10Fortune, Mr. Markmale641419950263.0000C23 C25 C27SWinnipeg, MB

11Fortune, Mrs. Mark (Mary McDougald)female601419950263.0000C23 C25 C27S10Winnipeg, MB

11Francatelli, Miss. Laura Mabelfemale3000PC 1748556.9292E36C1

10Franklin, Mr. Thomas Parhammale0011377826.5500D34SWestcliff-on-Sea, Essex

11Frauenthal, Dr. Henry Williammale5020PC 17611133.6500S5New York, NY

11Frauenthal, Mr. Isaac Geraldmale43101776527.7208D40C5New York, NY

11Frauenthal, Mrs. Henry William (Clara Heinsheimer)female10PC 17611133.6500S5New York, NY

11Frolicher, Miss. Hedwig Margarithafemale22021356849.5000B39C5Zurich, Switzerland

11Frolicher-Stehli, Mr. Maxmillianmale60111356779.2000B41C5Zurich, Switzerland

11Frolicher-Stehli, Mrs. Maxmillian (Margaretha Emerentia Stehli)female48111356779.2000B41C5Zurich, Switzerland

10Fry, Mr. Richardmale001120580.0000B102S

10Futrelle, Mr. Jacques Heathmale371011380353.1000C123SScituate, MA

11Futrelle, Mrs. Jacques Heath (Lily May Peel)female351011380353.1000C123SDScituate, MA

10Gee, Mr. Arthur Hmale470011132038.5000E63S275St Anne's-on-Sea, Lancashire

11Geiger, Miss. Amaliefemale3500113503211.5000C130C4

11Gibson, Miss. Dorothy Winifredfemale220111237859.4000C7New York, NY

11Gibson, Mrs. Leonard (Pauline C Boeson)female450111237859.4000C7New York, NY

10Giglio, Mr. Victormale2400PC 1759379.2000B86C

11Goldenberg, Mr. Samuel Lmale49101745389.1042C92C5Paris, France / New York, NY

11Goldenberg, Mrs. Samuel L (Edwiga Grabowska)female101745389.1042C92C5Paris, France / New York, NY

10Goldschmidt, Mr. George Bmale7100PC 1775434.6542A5CNew York, NY

11Gracie, Col. Archibald IVmale530011378028.5000C51CBWashington, DC

11Graham, Miss. Margaret Edithfemale190011205330.0000B42S3Greenwich, CT

10Graham, Mr. George Edwardmale3801PC 17582153.4625C91S147Winnipeg, MB

11Graham, Mrs. William Thompson (Edith Junkins)female5801PC 17582153.4625C125S3Greenwich, CT

11Greenfield, Mr. William Bertrammale2301PC 1775963.3583D10 D12C7New York, NY

11Greenfield, Mrs. Leo David (Blanche Strouse)female4501PC 1775963.3583D10 D12C7New York, NY

10Guggenheim, Mr. Benjaminmale4600PC 1759379.2000B82 B84CNew York, NY

11Harder, Mr. George Achillesmale25101176555.4417E50C5Brooklyn, NY

11Harder, Mrs. George Achilles (Dorothy Annan)female25101176555.4417E50C5Brooklyn, NY

11Harper, Mr. Henry Sleepermale4810PC 1757276.7292D33C3New York, NY

11Harper, Mrs. Henry Sleeper (Myna Haxtun)female4910PC 1757276.7292D33C3New York, NY

10Harrington, Mr. Charles Hmale0011379642.4000S

10Harris, Mr. Henry Birkhardtmale45103697383.4750C83SNew York, NY

11Harris, Mrs. Henry Birkhardt (Irene Wallach)female35103697383.4750C83SDNew York, NY

10Harrison, Mr. Williammale40001120590.0000B94S110

11Hassab, Mr. Hammadmale2700PC 1757276.7292D49C3

11Hawksford, Mr. Walter Jamesmale001698830.0000D45S3Kingston, Surrey

11Hays, Miss. Margaret Bechsteinfemale24001176783.1583C54C7New York, NY

10Hays, Mr. Charles Melvillemale55111274993.5000B69S307Montreal, PQ

11Hays, Mrs. Charles Melville (Clara Jennings Gregg)female52111274993.5000B69S3Montreal, PQ

10Head, Mr. Christophermale420011303842.5000B11SLondon / Middlesex

10Hilliard, Mr. Herbert Henrymale001746351.8625E46SBrighton, MA

10Hipkins, Mr. William Edwardmale550068050.0000C39SLondon / Birmingham

11Hippach, Miss. Jean Gertrudefemale160111136157.9792B18C4Chicago, IL

11Hippach, Mrs. Louis Albert (Ida Sophia Fischer)female440111136157.9792B18C4Chicago, IL

11Hogeboom, Mrs. John C (Anna Andrews)female51101350277.9583D11S10Hudson, NY

10Holverson, Mr. Alexander Oskarmale421011378952.0000S38New York, NY

11Holverson, Mrs. Alexander Oskar (Mary Aline Towner)female351011378952.0000S8New York, NY

11Homer, Mr. Harry ("Mr E Haven")male350011142626.5500C15Indianapolis, IN

11Hoyt, Mr. Frederick Maxfieldmale38101994390.0000C93SDNew York, NY / Stamford CT

10Hoyt, Mr. William Fishermale00PC 1760030.6958C14New York, NY

11Hoyt, Mrs. Frederick Maxfield (Jane Anne Forby)female35101994390.0000C93SDNew York, NY / Stamford CT

11Icard, Miss. Ameliefemale380011357280.0000B286

10Isham, Miss. Ann Elizabethfemale5000PC 1759528.7125C49CParis, France New York, NY

11Ismay, Mr. Joseph Brucemale49001120580.0000B52 B54 B56SCLiverpool

10Jones, Mr. Charles Cressonmale460069426.0000S80Bennington, VT

10Julian, Mr. Henry Forbesmale500011304426.0000E60SLondon

10Keeping, Mr. Edwinmale32.500113503211.5000C132C45

10Kent, Mr. Edward Austinmale58001177129.7000B37C258Buffalo, NY

10Kenyon, Mr. Frederick Rmale41101746451.8625D21SSouthington / Noank, CT

11Kenyon, Mrs. Frederick R (Marion)female101746451.8625D21S8Southington / Noank, CT

11Kimball, Mr. Edwin Nelson Jrmale42101175352.5542D19S5Boston, MA

11Kimball, Mrs. Edwin Nelson Jr (Gertrude Parsons)female45101175352.5542D19S5Boston, MA

10Klaber, Mr. Hermanmale0011302826.5500C124SPortland, OR

11Kreuchen, Miss. Emiliefemale390024160211.3375S2

11Leader, Dr. Alice (Farnham)female49001746525.9292D17S8New York, NY

11LeRoy, Miss. Berthafemale3000PC 17761106.4250C2

11Lesurer, Mr. Gustave Jmale3500PC 17755512.3292B101C3

10Lewy, Mr. Ervin Gmale00PC 1761227.7208CChicago, IL

10Lindeberg-Lind, Mr. Erik Gustaf ("Mr Edward Lingrey")male42001747526.5500SStockholm, Sweden

11Lindstrom, Mrs. Carl Johan (Sigrid Posse)female550011237727.7208C6Stockholm, Sweden

11Lines, Miss. Mary Conoverfemale1601PC 1759239.4000D28S9Paris, France

11Lines, Mrs. Ernest H (Elizabeth Lindsey James)female5101PC 1759239.4000D28S9Paris, France

10Long, Mr. Milton Clydemale290011350130.0000D6S126Springfield, MA

11Longley, Miss. Gretchen Fiskefemale21001350277.9583D9S10Hudson, NY

10Loring, Mr. Joseph Hollandmale300011380145.5000SLondon / New York, NY

11Lurette, Miss. Elisefemale5800PC 17569146.5208B80C

11Madill, Miss. Georgette Alexandrafemale150124160211.3375B5S2St Louis, MO

10Maguire, Mr. John Edwardmale300011046926.0000C106SBrockton, MA

11Maioni, Miss. Robertafemale160011015286.5000B79S8

11Marechal, Mr. Pierremale001177429.7000C47C7Paris, France

10Marvin, Mr. Daniel Warnermale191011377353.1000D30SNew York, NY

11Marvin, Mrs. Daniel Warner (Mary Graham Carmichael Farquarson)female181011377353.1000D30S10New York, NY

11Mayne, Mlle. Berthe Antonine ("Mrs de Villiers")female2400PC 1748249.5042C90C6Belgium Montreal, PQ

10McCaffry, Mr. Thomas Francismale46001305075.2417C6C292Vancouver, BC

10McCarthy, Mr. Timothy Jmale54001746351.8625E46S175Dorchester, MA

11McGough, Mr. James Robertmale3600PC 1747326.2875E25S7Philadelphia, PA

10Meyer, Mr. Edgar Josephmale2810PC 1760482.1708CNew York, NY

11Meyer, Mrs. Edgar Joseph (Leila Saks)female10PC 1760482.1708C6New York, NY

10Millet, Mr. Francis Davismale65001350926.5500E38S249East Bridgewater, MA

10Minahan, Dr. William Edwardmale44201992890.0000C78Q230Fond du Lac, WI

11Minahan, Miss. Daisy Efemale33101992890.0000C78Q14Green Bay, WI

11Minahan, Mrs. William Edward (Lillian E Thorpe)female37101992890.0000C78Q14Fond du Lac, WI

11Mock, Mr. Philipp Edmundmale30101323657.7500C78C11New York, NY

10Molson, Mr. Harry Marklandmale550011378730.5000C30SMontreal, PQ

10Moore, Mr. Clarence Bloomfieldmale470011379642.4000SWashington, DC

10Natsch, Mr. Charles Hmale3701PC 1759629.7000C118CBrooklyn, NY

11Newell, Miss. Madeleinefemale311035273113.2750D36C6Lexington, MA

11Newell, Miss. Marjoriefemale231035273113.2750D36C6Lexington, MA

10Newell, Mr. Arthur Webstermale580235273113.2750D48C122Lexington, MA

11Newsom, Miss. Helen Monypenyfemale19021175226.2833D47S5New York, NY

10Nicholson, Mr. Arthur Ernestmale640069326.0000S263Isle of Wight, England

11Oliva y Ocana, Dona. Ferminafemale3900PC 17758108.9000C105C8

11Omont, Mr. Alfred Fernandmale00F.C. 1299825.7417C7Paris, France

11Ostby, Miss. Helene Ragnhildfemale220111350961.9792B36C5Providence, RI

10Ostby, Mr. Engelhart Corneliusmale650111350961.9792B30C234Providence, RI

10Ovies y Rodriguez, Mr. Servandomale28.500PC 1756227.7208D43C189?Havana, Cuba

10Parr, Mr. William Henry Marshmale001120520.0000SBelfast

10Partner, Mr. Austenmale45.50011304328.5000C124S166Surbiton Hill, Surrey

10Payne, Mr. Vivian Ponsonbymale23001274993.5000B24SMontreal, PQ

10Pears, Mr. Thomas Clintonmale291011377666.6000C2SIsleworth, England

11Pears, Mrs. Thomas (Edith Wearne)female221011377666.6000C2S8Isleworth, England

10Penasco y Castellana, Mr. Victor de Satodemale1810PC 17758108.9000C65CMadrid, Spain

11Penasco y Castellana, Mrs. Victor de Satode (Maria Josefa Perez de Soto y Vallejo)female1710PC 17758108.9000C65C8Madrid, Spain

11Perreault, Miss. Annefemale30001274993.5000B73S3

11Peuchen, Major. Arthur Godfreymale520011378630.5000C104S6Toronto, ON

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20Morley, Mr. Henry Samuel ("Mr Henry Marshall")male390025065526.0000S

20Mudd, Mr. Thomas Charlesmale1600S.O./P.P. 310.5000SHalesworth, England

20Myles, Mr. Thomas Francismale62002402769.6875QCambridge, MA

20Nasser, Mr. Nicholasmale32.51023773630.0708C43New York, NY

21Nasser, Mrs. Nicholas (Adele Achem)female141023773630.0708CNew York, NY

21Navratil, Master. Edmond Rogermale21123008026.0000F2SDNice, France

21Navratil, Master. Michel Mmale31123008026.0000F2SDNice, France

20Navratil, Mr. Michel ("Louis M Hoffman")male36.50223008026.0000F2S15Nice, France

20Nesson, Mr. Israelmale260024436813.0000F2SBoston, MA

20Nicholls, Mr. Joseph Charlesmale1911C.A. 3311236.7500S101Cornwall / Hancock, MI

20Norman, Mr. Robert Douglasmale280021862913.5000S287Glasgow

21Nourney, Mr. Alfred ("Baron von Drachstedt")male2000SC/PARIS 216613.8625D38C7Cologne, Germany

21Nye, Mrs. (Elizabeth Ramell)female2900C.A. 2939510.5000F33S11Folkstone, Kent / New York, NY

20Otter, Mr. Richardmale39002821313.0000SMiddleburg Heights, OH

21Oxenham, Mr. Percy Thomasmale2200W./C. 1426010.5000S13Pondersend, England / New Durham, NJ

21Padro y Manent, Mr. Julianmale00SC/PARIS 214613.8625C9Spain / Havana, Cuba

20Pain, Dr. Alfredmale230024427810.5000SHamilton, ON

21Pallas y Castello, Mr. Emiliomale2900SC/PARIS 214713.8583C9Spain / Havana, Cuba

20Parker, Mr. Clifford Richardmale2800SC 1488810.5000SSt Andrews, Guernsey

20Parkes, Mr. Francis "Frank"male002398530.0000SBelfast

21Parrish, Mrs. (Lutie Davis)female500123043326.0000S12Woodford County, KY

20Pengelly, Mr. Frederick Williammale19002866510.5000SGunnislake, England / Butte, MT

20Pernot, Mr. Renemale00SC/PARIS 213115.0500C

20Peruschitz, Rev. Joseph Mariamale410023739313.0000S

21Phillips, Miss. Alice Frances Louisafemale2101S.O./P.P. 221.0000S12Ilfracombe, Devon

21Phillips, Miss. Kate Florence ("Mrs Kate Louise Phillips Marshall")female190025065526.0000S11Worcester, England

20Phillips, Mr. Escott Robertmale4301S.O./P.P. 221.0000SIlfracombe, Devon

21Pinsky, Mrs. (Rosa)female320023460413.0000S9Russia

20Ponesell, Mr. Martinmale340025064713.0000SDenmark / New York, NY

21Portaluppi, Mr. Emilio Ilario Giuseppemale3000C.A. 3464412.7375C14Milford, NH

20Pulbaum, Mr. Franzmale2700SC/PARIS 216815.0333CParis

21Quick, Miss. Phyllis Mayfemale2112636026.0000S11Plymouth, Devon / Detroit, MI

21Quick, Miss. Winifred Verafemale8112636026.0000S11Plymouth, Devon / Detroit, MI

21Quick, Mrs. Frederick Charles (Jane Richards)female33022636026.0000S11Plymouth, Devon / Detroit, MI

20Reeves, Mr. Davidmale3600C.A. 1724810.5000SBrighton, Sussex

20Renouf, Mr. Peter Henrymale34103102721.0000S12Elizabeth, NJ

21Renouf, Mrs. Peter Henry (Lillian Jefferys)female30303102721.0000SElizabeth, NJ

21Reynaldo, Ms. Encarnacionfemale280023043413.0000S9Spain

20Richard, Mr. Emilemale2300SC/PARIS 213315.0458CParis / Montreal, PQ

21Richards, Master. George Sibleymale0.8333112910618.7500S4Cornwall / Akron, OH

21Richards, Master. William Rowemale3112910618.7500S4Cornwall / Akron, OH

21Richards, Mrs. Sidney (Emily Hocking)female24232910618.7500S4Cornwall / Akron, OH

21Ridsdale, Miss. Lucyfemale5000W./C. 1425810.5000S13London, England / Marietta, Ohio and Milwaukee, WI

20Rogers, Mr. Reginald Harrymale19002800410.5000S

21Rugg, Miss. Emilyfemale2100C.A. 3102610.5000S12Guernsey / Wilmington, DE

20Schmidt, Mr. Augustmale260024865913.0000SNewark, NJ

20Sedgwick, Mr. Charles Frederick Waddingtonmale250024436113.0000SLiverpool

20Sharp, Mr. Percival James Rmale270024435826.0000SHornsey, England

21Shelley, Mrs. William (Imanita Parrish Hall)female250123043326.0000S12Deer Lodge, MT

21Silven, Miss. Lyyli Karoliinafemale180225065213.0000S16Finland / Minneapolis, MN

21Sincock, Miss. Maudefemale2000C.A. 3311236.7500S11Cornwall / Hancock, MI

21Sinkkonen, Miss. Annafemale300025064813.0000S10Finland / Washington, DC

20Sjostedt, Mr. Ernst Adolfmale590023744213.5000SSault St Marie, ON

21Slayter, Miss. Hilda Maryfemale300023481812.3500Q13Halifax, NS

20Slemen, Mr. Richard Jamesmale35002820610.5000SCornwall

21Smith, Miss. Marion Elsiefemale40003141813.0000S9

20Sobey, Mr. Samuel James Haydenmale2500C.A. 2917813.0000SCornwall / Houghton, MI

20Stanton, Mr. Samuel Wardmale410023773415.0458CNew York, NY

20Stokes, Mr. Philip Josephmale2500F.C.C. 1354010.5000S81Catford, Kent / Detroit, MI

20Swane, Mr. Georgemale18.50024873413.0000FS294

20Sweet, Mr. George Frederickmale140022084565.0000SSomerset / Bernardsville, NJ

21Toomey, Miss. Ellenfemale5000F.C.C. 1353110.5000S9Indianapolis, IN

20Troupiansky, Mr. Moses Aaronmale230023363913.0000S

21Trout, Mrs. William H (Jessie L)female280024092912.6500SColumbus, OH

21Troutt, Miss. Edwina Celia "Winnie"female27003421810.5000E101S16Bath, England / Massachusetts

20Turpin, Mr. William John Robertmale29101166821.0000SPlymouth, England

20Turpin, Mrs. William John Robert (Dorothy Ann Wonnacott)female27101166821.0000SPlymouth, England

20Veal, Mr. Jamesmale40002822113.0000SBarre, Co Washington, VT

21Walcroft, Miss. Nelliefemale3100F.C.C. 1352821.0000S14Mamaroneck, NY

20Ware, Mr. John Jamesmale3010CA 3135221.0000SBristol, England / New Britain, CT

20Ware, Mr. William Jefferymale23102866610.5000S

21Ware, Mrs. John James (Florence Louise Long)female3100CA 3135221.0000S10Bristol, England / New Britain, CT

20Watson, Mr. Ennis Hastingsmale002398560.0000SBelfast

21Watt, Miss. Bertha Jfemale1200C.A. 3359515.7500S9Aberdeen / Portland, OR

21Watt, Mrs. James (Elizabeth "Bessie" Inglis Milne)female4000C.A. 3359515.7500S9Aberdeen / Portland, OR

21Webber, Miss. Susanfemale32.5002726713.0000E101S12England / Hartford, CT

20Weisz, Mr. Leopoldmale271022841426.0000S293Bromsgrove, England / Montreal, PQ

21Weisz, Mrs. Leopold (Mathilde Francoise Pede)female291022841426.0000S10Bromsgrove, England / Montreal, PQ

21Wells, Master. Ralph Lestermale2112910323.0000S14Cornwall / Akron, OH

21Wells, Miss. Joanfemale4112910323.0000S14Cornwall / Akron, OH

21Wells, Mrs. Arthur Henry ("Addie" Dart Trevaskis)female29022910323.0000S14Cornwall / Akron, OH

21West, Miss. Barbara Jfemale0.916712C.A. 3465127.7500S10Bournmouth, England

21West, Miss. Constance Miriumfemale512C.A. 3465127.7500S10Bournmouth, England

20West, Mr. Edwy Arthurmale3612C.A. 3465127.7500SBournmouth, England

21West, Mrs. Edwy Arthur (Ada Mary Worth)female3312C.A. 3465127.7500S10Bournmouth, England

20Wheadon, Mr. Edward Hmale6600C.A. 2457910.5000SGuernsey, England / Edgewood, RI

20Wheeler, Mr. Edwin "Frederick"male00SC/PARIS 215912.8750S

21Wilhelms, Mr. Charlesmale310024427013.0000S9London, England

21Williams, Mr. Charles Eugenemale0024437313.0000S14Harrow, England

21Wright, Miss. Marionfemale260022084413.5000S9Yoevil, England / Cottage Grove, OR

20Yrois, Miss. Henriette ("Mrs Harbeck")female240024874713.0000SParis

30Abbing, Mr. Anthonymale4200C.A. 55477.5500S

30Abbott, Master. Eugene Josephmale1302C.A. 267320.2500SEast Providence, RI

30Abbott, Mr. Rossmore Edwardmale1611C.A. 267320.2500S190East Providence, RI

31Abbott, Mrs. Stanton (Rosa Hunt)female3511C.A. 267320.2500SAEast Providence, RI

31Abelseth, Miss. Karen Mariefemale16003481257.6500S16Norway Los Angeles, CA

31Abelseth, Mr. Olaus Jorgensenmale25003481227.6500F G63SAPerkins County, SD

31Abrahamsson, Mr. Abraham August Johannesmale2000SOTON/O2 31012847.9250S15Taalintehdas, Finland Hoboken, NJ

31Abrahim, Mrs. Joseph (Sophie Halaut Easu)female180026577.2292CCGreensburg, PA

30Adahl, Mr. Mauritz Nils Martinmale3000C 70767.2500S72Asarum, Sweden Brooklyn, NY

30Adams, Mr. Johnmale26003418268.0500S103Bournemouth, England

30Ahlin, Mrs. Johan (Johanna Persdotter Larsson)female401075469.4750SSweden Akeley, MN

31Aks, Master. Philip Frankmale0.8333013920919.3500S11London, England Norfolk, VA

31Aks, Mrs. Sam (Leah Rosen)female18013920919.3500S13London, England Norfolk, VA

31Albimona, Mr. Nassef Cassemmale2600269918.7875C15Syria Fredericksburg, VA

30Alexander, Mr. Williammale260034747.8875SEngland Albion, NY

30Alhomaki, Mr. Ilmari Rudolfmale2000SOTON/O2 31012877.9250SSalo, Finland Astoria, OR

30Ali, Mr. Ahmedmale2400SOTON/O.Q. 31013117.0500S

30Ali, Mr. Williammale2500

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