big data: data analysis boot camp titanic dataset

43
1/43 Introduction Background Classification problem Techniques Hands-on Q&A Conclusion References Files Big Data: Data Analysis Boot Camp Titanic Dataset Chuck Cartledge, PhD Chuck Cartledge, PhD Chuck Cartledge, PhD Chuck Cartledge, PhD Chuck Cartledge, PhD Chuck Cartledge, PhD Chuck Cartledge, PhD Chuck Cartledge, PhD Chuck Cartledge, PhD Chuck Cartledge, PhD Chuck Cartledge, PhD Chuck Cartledge, PhD Chuck Cartledge, PhD Chuck Cartledge, PhD Chuck Cartledge, PhD Chuck Cartledge, PhD Chuck Cartledge, PhD Chuck Cartledge, PhD Chuck Cartledge, PhD Chuck Cartledge, PhD Chuck Cartledge, PhD 22 September 2017 22 September 2017 22 September 2017 22 September 2017 22 September 2017 22 September 2017 22 September 2017 22 September 2017 22 September 2017 22 September 2017 22 September 2017 22 September 2017 22 September 2017 22 September 2017 22 September 2017 22 September 2017 22 September 2017 22 September 2017 22 September 2017 22 September 2017 22 September 2017

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Page 1: Big Data: Data Analysis Boot Camp Titanic Dataset

143

Introduction Background Classification problem Techniques Hands-on Q amp 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

243

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

Table of contents (1 of 1)

1 Introduction

2 Background

3 Classification problem

4 Techniques

5 Hands-on

6 Q amp A

7 Conclusion

8 References

9 Files

343

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

What are we going to cover

Wersquore going to talk about

Rrsquos RMS Titanic dataset

Other Titanic datasets that containdifferent data

Modeling the datasets to see whowill live and who will die

443

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

ldquoWellrdquo settled data

Basic information

Ordered 17 Sep 1908

Completed 2 Apr 1912

Maiden voyage 10 Apr1912

Sank 14 Apr 1912

543

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

ldquoWellrdquo settled data

Where she was damaged

Red are water tightbulkheads

Green is where the iceberghit

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

643

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

ldquoWellrdquo settled data

Same image

Image from [11]

743

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

ldquoWellrdquo settled data

How many died and why

Sailing capacity (passengersand crew) 3372

Lifeboat capacity 1178

Number of people on board(accounts vary) 2201

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

Passengers crew builderrsquos menand others Image from [8]

843

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

Data from diverse places

Expected first class passengers

Lots of lists of 1st classpassengers Even some of 2ndand 3rd class passengers[10]Lists of non-passengers (shiprsquoscrew and builderrsquos technicians)are more challenging[6]R has a built-in Titanic datasetTitanic

Image from [7]

943

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

Data from diverse places

A crew list

A reasonable collection of crewand builderrsquos representatives isavailable

Name job status (lost ornot)

Age place of birth

Image from [9]

1043

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

Data from diverse places

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]

1143

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

Data from diverse places

titanic3 attributesvariables

Name Explanation

Pclass Passenger Class (1 = 1st 2 = 2nd 3 = 3rd)survival Survival (0 = No 1 = Yes)name Namesex Sexage Agesibsp Number of SiblingsSpouses Aboardparch Number of ParentsChildren Aboardticket Ticket Numberfare Passenger Fare (British pound)cabin Cabinembarked Port of Embarkation (C = Cherbourg Q = Queenstown S = Southampton)boat Lifeboatbody Body Identification Numberhomedest HomeDestination

1243

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

Data from diverse places

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

1343

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

What is it

A definition

ldquoClassification is the task of learning a targetfunction f that maps each attribute set x to one of thepredefined class labels yrdquo

Tan et al [12]

1443

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

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]

1543

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

What is it

Same image

Image from [5]

1643

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

What is it

Supervised vs Unsupervised learning

Supervised learning

A training dataset with correct answers(labels) is ldquominedrdquo 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

1743

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

What is it

Supervised vs Unsupervised learning techniques

With the Titanic dataset we will be focusing on classification

1843

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

Training and testing

Working with data

Supervised learning requires

Training data ndash usuallyabout 70 of available data

Testing data ndash usually about30 of available data

Training data can also bepartitioned into validation data

1943

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

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 oneUse all and vote on theoutcome

2043

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

Types of errors

Sample problem space

1000 data points between+- 1

Two classes of data points

color =

red if 05 leradic

x2 + y2 le 10

black otherwise

(See attached file)

2143

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

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

2243

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

Types of errors

Same image

Attached file

2343

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

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

2443

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

Types of errors

Same image

Training and testing errors

2543

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

A collection of decision tree techniques

rpart from the rpart library ldquoRecursive partitioning for classification regression and survival trees Animplementation of most of the functionality of the 1984 book by Breiman Friedman Olshen andStonerdquo[13]

C50 from the C50 library ldquoC50 decision trees and rule-based models for pattern recognitionrdquo [4]

Random Forest from the randomForest library ldquoClassification and regression based on a forest of trees using randominputsrdquo[1]

J48 from the RWeka library ldquoAn R interface to Weka (Version 391) Weka is a collection of machine learningalgorithms for data mining tasks written in Java containing tools for data pre-processing classificationregression clustering association rules and visualizationrdquo[3] The J48 algorithm is run in a pruned andunpruned mode

These and additional techniques to be covered in detail later

2643

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

Results

C50 decision tree

2743

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

Results

Random Forest decision tree

2843

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

Results

rpart decision tree

2943

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

Results

J48 (unpruned) decision tree

3043

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

Results

J48 (pruned) decision tree

3143

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

Results

Accuracy based on training percentage

The horizontal line at 50represents 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

3243

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

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

3343

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

Results

Simple mosaic from the titanic package

Sometimes you donrsquot need a lotof codelibrary(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 adageldquowomen and children firstrdquo

3443

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

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 ldquowomen and children firstrdquo

3543

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

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

3643

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

Q amp A time

Q How many Harvard MBArsquosdoes it take to screw in a lightbulbA Just one He grasps it firmlyand the universe revolves aroundhim

3743

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

What have we covered

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

Next LPAR Chapter 2 basic data visualization

3843

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

References (1 of 5)

[1] Leo BreimanrandomForest Breiman and Cutlers random forests for classification and regressionhttpstat-wwwberkeleyeduusersbreiman

RandomForests 2006

[2] Jr Frank E Harrell Titanic DatahttpbiostatmcvanderbilteduwikipubMain

DataSetstitanichtml 2002

[3] K Hornik A Zeileis T Hothorn and C BuchtaRWeka an R interface to Weka R package version 04-32(2017)

3943

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

References (2 of 5)

[4] M Kuhn S Weston N Coulter M Culp and R QuinlanC50 Decision Trees and Rule-Based Models R PackageVersion 01 0 24 (2015)

[5] Sebastian RaschkaPredictive modeling supervised machine learning and pattern classificationhttpsebastianraschkacomArticles2014_intro_

supervised_learninghtml 2014

[6] Encyclopedia Titanica StaffEncyclopedia Titanica Titanic Facts History and Biographyhttpswwwencyclopedia-titanicaorg 2017

4043

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

References (3 of 5)

[7] ISM Staff Titanic Survivor Titanic Passenger List Booklethttpwwwphillyseaportorgweb_exhibitsmini_

exhibitstitanic_passenger_listtitanic_

passenger_list-object-passenger_listhtml 2017

[8] OceanGate Staff Titanic Survey Expedition 2018httpwwwoceangatecomexpeditionstitanic-

survey-2018html 2017

[9] Southampton Staff Titanic crew listhttpwwwplimsollorgSouthamptonTitanic

titaniccrewlistDefaultasp 2017

4143

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

References (4 of 5)

[10] Titanic Facts Staff Titanic Passenger List httpwwwtitanic-factscomtitanic-passenger-listhtml2017

[11] Wikipedia Staff Sinking of the RMS Titanic httpsenwikipediaorgwikiSinking_of_the_RMS_Titanic2017

[12] Pang-Ning Tan Michael Steinbach and Vipin KumarIntroduction to Data Mining Pearson Education India 2006

[13] Terry Therneau Beth Atkinson and Brian Ripley rpartAvailable at CRAN R-project orgpackage= rpart AccessedMay (2015)

4243

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

References (5 of 5)

[14] Maria Dolores Ugarte Ana F Militino and Alan T ArnholtProbability and Statistics with R CRC Press 2008

4343

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

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 121 Sources of data 1

211 R 1212 R package ldquotitanicrdquo 2213 R package ldquovcdExtrardquo 2214 The crew 6

22 Questions that can be asked 623 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 Titanicrsquos 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 ldquointernalrdquo dataset 119 Missing data map for the ldquotitanic3rdquo 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 thesethe 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 YorkNew York on 10 April 1912 She had a fire in her coal stores that started almost 10 days before she sailedand continued after leaving South Hampton[9] She had 16 lifeboats and four Engelhardt ldquocollapsiblesrdquo (seeFigure 2 on page 3) that could collectively hold 1178 people (aka souls)[5] When the Titanic set sailshe had approximately 2224 passengers and crew Titanic could had a maximum capacity of 3327 soulsThe 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

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

21 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

211 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)

ldquoThis data set provides information on the fate of passengers on the fatal maiden voyage of theocean liner rsquoTitanicrsquo summarized according to economic status (class) sex age and survivalrdquo

R Staff [6]

2httpwwwphillyseaportorgweb_exhibitsmini_exhibitstitanic_passenger_listtitanic_passenger_

list-object-passenger_listhtml

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

212 R package ldquotitanicrdquo

A collection of 1309 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 ldquoTitanicrdquo summarized according to economic status (class) sex age and survivalWhereas the base R Titanic data found by calling data(ldquoTitanicrdquo) 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

213 R package ldquovcdExtrardquo

A collection of 1309 passenger records

Provides additional data sets methods and documentation to complement the rsquovcdrsquo packagefor Visualizing Categorical Data and the rsquognmrsquo package for Generalized Nonlinear Models

3httpswwwkagglecomctitanic

2

Figure 2 Titanicrsquos Collapsible Boat D approaches RMS Carpathia

3

Figure 3 Commemorative sailing booklet4

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 ldquowomenand children firstrdquo

5

Friendly et al [2]

The vcdExtraTitanicp is a data frame with 1309 observations on the following 6 variables (see Figure 5on the following page)

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

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

bull sex a factor with levels female and male

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

bull sibsp number of siblings or spouses aboard integer 08

bull parch number of parents or children aboard integer 06

In many ways ldquovcdExtraTitanicprdquo is a union of the ldquotitanictitanic trainrdquo and ldquotitanictitanic testrdquodatasets less some of the columnular values The vcdExtraTitanicp 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

214 The crew

The previous sections focused on Titanicrsquos 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

22 Questions that can be asked

Every person that sailed on the Titanic had a long list of attributes that could be used to describe themThese attributes include

bull Class (1st 2nd 3rd crew other)

bull Age

bull Fare

bull Gender

bull Number of traveling family members

bull Number of siblings

bull Place of embarkation or

4httpwwwplimsollorgStartHereAboutUsdefaultasp5httpwwwplimsollorgSouthamptonTitanictitaniccrewlistDefaultasp

6

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 predictionsR R script supports a variety of passparameters or command line arguments

Name Switch Default MeaningsourceSelection s internal Which dataset to usetrainingPercentage 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

bull 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 predictionsR have all attributes The internal dataset is missing theattribute ldquoembarkationrdquo (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 isWhat 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 grouprsquos traveling class (1st 2nd 3rd and crew)

23 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)

predictionsR incorporates four different partitioning algorithms (one algorithm has two variants)They are

rpart from the rpart library ldquoRecursive partitioning for classification regression and survival trees Animplementation of most of the functionality of the 1984 book by Breiman Friedman Olshen andStonerdquo[]

C50 from the C50 library ldquoC50 decision trees and rule-based models for pattern recognitionrdquo []

Random Forest from the randomForest library ldquoClassification and regression based on a forest of treesusing random inputsrdquo[]

J48 from the RWeka library ldquoAn R interface to Weka (Version 391) Weka is a collection of machinelearning algorithms for data mining tasks written in Java containing tools for data pre-processingclassification regression clustering association rules and visualizationrdquo[] The J48 algorithm is runin a pruned and unpruned mode

10

Figure 8 Missing data map for the ldquointernalrdquo dataset

11

Figure 9 Missing data map for the ldquotitanic3rdquo 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 07826087 07826087 07826087 07826087 07826087Kappa 04061712 04061712 04061712 04061712 04061712

(see Figure 10on page 15)

(see Figure 11on page 16)

(see Figure 12on page 17)

(see Figure 13on page 17)

(see Figure 14on page 18)

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 questionpredictionsR 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 (1178 versus3327)

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

3 The values from in built in R data set (datasetsTitanic)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 3rdand crew)

5 Various passenger and crew lists (with more attributes than the built in Titanic dataset) are availableand 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 rdquoUnusual Episoderdquo Data Revisited Journal of Statistics Education 3 (1995) no 31ndash7

[2] Michael Friendly Heather Turner Achim Zeileis and Maintainer Michael Friendly Package rsquovcdExtrarsquo(2016)

[3] Paul Hendricks Titanic Passenger Survival Data Set httpsgithubcompaulhendrickstitanic2015

[4] Encyclopedia Titanica Staff Titanic Survivors httpswwwencyclopedia-titanicaorg

titanic-survivors 2017

[5] History Staff Titanic httpwwwhistorycomtopicstitanic 2017

[6] R Staff Survival of passengers on the Titanic packagedatasets 2017

[7] Titanic Facts Staff Titanic Survivors httpwwwtitanicfactsnettitanic-survivorshtml2017

[8] Titanic Universe Staff How Many People Survived the Titanic Disaster httpwww

titanicuniversecomhow-many-people-survived-the-titanic-disaster1253 2017

[9] Wikipedia Staff British Wreck Commissionerrsquos inquiry into the sinking of the RMS TitanichttpsenwikipediaorgwikiBritish_Wreck_Commissioner27s_inquiry_into_the_sinking_

of_the_RMS_Titanic 2017

14

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

lsquoA Mosaic plot of R Titanic datarsquo on page 5 ndash

library(titanic)

library(graphics)

mosaicplot(Titanic main = Survival on the Titanic)

lsquovcdExtra Titanic datarsquo on page 7 ndash

library(vcdExtra)

data(Titanicp)

plot(Titanicp)

lsquovcdExtra Titanic data decision tree based on passenger class age and number and type of traveling companionsrsquo on page 8ndash

library(rpart)

library(rpartplot)

data(Titanicp package=vcdExtra)

rp0 lt- rpart(survived ~ pclass + age data=Titanicp)

rpartplot(rp0 type=0 extra=2 cex=15)

lsquovcdExtra Titanic data decision tree based on all available datarsquo on page 9 ndash

library(rpart)

library(rpartplot)

data(Titanicp package=vcdExtra)

rp0 lt- rpart(survived ~ data=Titanicp)

rpartplot(rp0 type=0 extra=2 cex=15)

lsquoMissing data map for the ldquointernalrdquo datasetrsquo on page 11 ndash

source(predictionsR)

library(Amelia)

missmap(d$titanicPeople)

lsquoMissing data map for the ldquotitanic3rdquo datasetrsquo on page 12 ndash

source(predictionsR)

d lt- main(sourceSelection=titanic3)

library(Amelia)

missmap(d$titanicPeople)

20

B Files

A collection of miscellaneous files mentioned in the report

bull titanic3xls ndash A consolidated list of passengers

bull predictionsR ndash An R script to demonstrate the different partitioning approaches and their results

21

titanic3

Chuck Cartledge

rm(list=ls()) httpsrstudio-pubs-statics3amazonawscom123908_3df6d25c8ad04030a2a429661125a59bhtmllibrary(lattice)library(tree)library(plotrix)library(sandwich)library(grid)library(vcd)library(gnm)library(stats4)library(zoo)library(mvtnorm)library(modeltools)library(strucchange)library(sandwich)library(gplots)library(psych)library(readr)library(dplyr)library(party)library(rpart)library(rpartplot)library(ROCR)library(RWeka)library(C50)library(randomForest)library(reprtree)library(cwhmisc) library(stringr) library(gdata)library(getopt)showMissingData lt- function(d title=Dataframe of interest) missmap(d main=title)plotDataCollection lt- function(data) dataCollectionDataFrame lt- data$dataCollectionDataFrame percentages lt- data$percentages cols lt- 1ncol(dataCollectionDataFrame) pchs lt- cols plot(00 type=n xlim=c(min(percentages) max(percentages)) ylim=c(min(04 min(dataCollectionDataFrame)) max(1 max(dataCollectionDataFrame))) xlab=Training percentage ylab=Accuracy ) legend(topleftnames(dataCollectionDataFrame) col=cols pch=pchs) for(i in cols) points(percentages dataCollectionDataFrame[i] col=i pch=pchs[i] type=b) abline(h=05 col=red )setValueBasedOnCommandLine lt- function(input default) returnValue lt- setValue(input default) setValue lt- function(inputValue default) returnValue lt- default if (typeof(default) == logical) if (isnull(inputValue) == FALSE) returnValue lt- default else if (isnull(inputValue) == FALSE) returnValue lt- inputValue returnValueloadTitanic3 lt- function() fileName lt- Librarytitanic3xls d lt- readxls(fileName header=TRUE sheet = 1 stringsAsFactors = FALSE) d --------------------------------------------------------- Function C50graphviz Version 220 Date 26092014 Author Athanasios Tsakonas This code implements C50graphviz conversion routine ---------------------------------------------------------C50graphviz lt- function( C50model filename fontname =Arialcoldraw =black colfont =bluecolconclusion =lightpinkcolquestion = grey78 shapeconclusion =box3dshapequestion =diamond boolsubstitute = None prefix=FALSE vertical=TRUE ) httpr-project-thanosblogspotcom201409plot-c50-decision-trees-in-rhtml treeout lt- C50model$output treeoutlt- substr(treeout cpos(treeout Decision tree start=1)+14nchar(treeout)) treeoutlt- substr(treeout 1cpos(treeout Evaluation on training data start=1)-2) variables lt- dataframe(matrix(nrow=500 ncol=4)) names(variables) lt- c(SYMBOLTOKEN TYPE QUERY) connectors lt- dataframe(matrix(nrow=500 ncol=3)) names(connectors) lt- c(TOKEN STARTEND) theStack lt- dataframe(matrix(nrow=500 ncol=1)) names(theStack) lt- c(ITEM) theStackIndex lt- 1 currentvar lt- 1 currentcon lt- 1 open_connection lt- TRUE previousindent lt- -1 firstindent lt- 4 substitutes lt- dataframe(None=c(= 0= 1) yesno=c(noyes) truefalse=c(false true)TF=c(FT)) dtreestringlt-unlist( scan(text= treeout sep=n what =list(character))) for (linecount in c(1length(dtreestring))) lineindentlt-0 shortstring lt- str_trim(dtreestring[linecount] side=left) leadingspaces lt- nchar(dtreestring[linecount]) - nchar(shortstring) lineindent lt- leadingspaces4 dtreestring[linecount]lt-str_trim(dtreestring[linecount] side=left) while (isna(cpos(dtreestring[linecount] start=1)) ) lineindentlt-lineindent + 1 dtreestring[linecount]lt-substr(dtreestring[linecount] ifelse(isna(cpos(dtreestring[linecount] start=1)) 1 cpos(dtreestring[linecount] start=1)+4) nchar(dtreestring[linecount]) ) shortstring lt- str_trim(dtreestring[linecount] side=left) leadingspaces lt- nchar(dtreestring[linecount]) - nchar(shortstring) lineindent lt- lineindent + leadingspaces4 dtreestring[linecount]lt-str_trim(dtreestring[linecount] side=left) if (isna(cpos(dtreestring[linecount] start=1))) lineindentlt- lineindent + 1 dtreestring[linecount]lt-substr(dtreestring[linecount] ifelse(isna(cpos(dtreestring[linecount] start=1)) 1 cpos(dtreestring[linecount] start=1)+4) nchar(dtreestring[linecount]) ) dtreestring[linecount]lt-str_trim(dtreestring[linecount]) stringlist lt- strsplit(dtreestring[linecount]) stringpart lt- strsplit(unlist(stringlist)[1]s) if (open_connection==TRUE) variables[currentvarTOKEN] lt- unlist(stringpart)[1] variables[currentvarSYMBOL] lt- paste(nodeascharacter(currentvar) sep=) variables[currentvarTYPE] lt- shapequestion variables[currentvarQUERY] lt- 1 theStack[theStackIndexITEM]lt-variables[currentvarSYMBOL] theStack[theStackIndexINDENT] lt-firstindent theStackIndexlt-theStackIndex+1 currentvar lt- currentvar + 1 if(currentvargt2) connectors[currentcon - 1END] lt- variables[currentvar - 1 SYMBOL] connectors[currentconTOKEN] lt- paste(unlist(stringpart)[2]unlist(stringpart)[3]) if (connectors[currentconTOKEN]=== 0) connectors[currentconTOKEN] lt- ascharacter(substitutes[1boolsubstitute]) if (connectors[currentconTOKEN]=== 1) connectors[currentconTOKEN] lt- ascharacter(substitutes[2boolsubstitute]) if (open_connection==TRUE) if (lineindentltpreviousindent) theStackIndex lt- theStackIndex-(( previousindent- lineindent) +1 ) currentsymbol lt-theStack[theStackIndexITEM] else currentsymbol lt-variables[currentvar - 1SYMBOL] else currentsymbol lt-theStack[theStackIndex-((previousindent -lineindent ) +1 )ITEM] theStackIndex lt- theStackIndex-(( previousindent- lineindent) ) connectors[currentcon START] lt- currentsymbol currentcon lt- currentcon + 1 open_connection lt- TRUE if (length(unlist(stringlist))==2) stringpart2 lt- strsplit(unlist(stringlist)[2]s) variables[currentvarTOKEN] lt- paste(ifelse((prefix==FALSE)Class) unlist(stringpart2)[2]) variables[currentvarSYMBOL] lt- paste(nodeascharacter(currentvar) sep=) variables[currentvarTYPE] lt- shapeconclusion variables[currentvarQUERY] lt- 0 currentvar lt- currentvar + 1 connectors[currentcon - 1END] lt- variables[currentvar - 1SYMBOL] open_connection lt- FALSE previousindentlt-lineindent runningstring lt- paste(digraph g graph sep=n) runningstring lt- paste(runningstring [rankdir= sep=) runningstring lt- paste(runningstring ifelse(vertical==TRUETBLR) sep= ) runningstring lt- paste(runningstring ] sep=) for (lines in c(1(currentvar-1))) runningline lt- paste(variables[linesSYMBOL] [shape=) runningline lt- paste(runninglinevariables[linesTYPE] sep= ) runningline lt- paste(runningline label = sep= ) runningline lt- paste(runninglinevariables[linesTOKEN] sep= ) runningline lt- paste(runningline style=filled fontcolor= sep=) runningline lt- paste(runningline colfont) runningline lt- paste(runningline color= ) runningline lt- paste(runningline coldraw) runningline lt- paste(runningline fontname=) runningline lt- paste(runningline fontname) runningline lt- paste(runningline fillcolor=) runningline lt- paste(runningline ifelse(variables[linesQUERY]== 0 colconclusioncolquestion)) runningline lt- paste(runningline]) runningstring lt- paste(runningstring runningline sep=n) for (lines in c(1(currentcon-1))) runningline lt- paste (connectors[linesSTART] -gt) runningline lt- paste (runningline connectors[linesEND]) runningline lt- paste (runningline[label=) runningline lt- paste (runninglineconnectors[linesTOKEN] sep=) runningline lt- paste (runningline fontname= sep=) runningline lt- paste (runningline fontname) runningline lt- paste (runningline]) runningstring lt- paste(runningstring runningline sep=n) runningstring lt- paste(runningstring) cat(runningstring) write(runningstring file=filename) sink(filename split=TRUE) cat(runningstring) sink()dumpObject lt- function(object comment= verbose=TRUE) if(verbose == TRUE) print(sprintf(s -- Dumping the object s (of type s) comment deparse(substitute(object)) typeof(object)) ) print(object) predictionResults lt- function(models testData verbose=FALSE) Examining the predictions on the testing set from p 211 values = dataframe(matrix(ncol = length(models) nrow = 6)) rownames(values) = c(True + True - False + False - Accuracy Kappa) names(values) = names(models) i lt- 1 for (n in names(models)) temp lt- predict(models[[n]] newdata=testData type=class) dumpObject(temp verbose=verbose) tab = table(testData$survivedtemp) dumpObject(tab verbose=verbose) values[1i] = tab[11] values[2i] = tab[22] values[3i] = tab[12] values[4i] = tab[21] values[5i] = sum(diag(tab))sum(tab) values[6i] = cohenkappa(tab)[1] i lt- i + 1 dumpObject(values Should have values verbose=verbose) valuesgetTitanicPeople lt- function(selection) titanicPeople lt- NULL print(sprintf(Getting Titanic people using selection s selection)) switch(selection vcdExtra = titanicPeople=Titanicp titanicPeople$sex lt- paste0(toupper(substr(ascharacter(titanicPeople$sex) 1 1)) substr(ascharacter(titanicPeople$sex) 2 nchar(ascharacter(titanicPeople$sex)))) temp lt- c() for (i in 1length(titanicPeople$pclass)) pclass lt- NA if (titanicPeople$pclass[i] == 1st) pclass lt- 1 else if (ascharacter(titanicPeople$pclass[i]) == 2nd) pclass lt- 2 else if (ascharacter(titanicPeople$pclass[i]) == 3rd) pclass lt- 3 else pclass lt- 0 titanicPeople$pclass[i] lt- pclass temp lt- c(temp pclass) titanicPeople$pclass lt- temp titanicPeople$survived lt- ifelse(titanicPeople$survived == survivedyes = Saved no =Lost) titanicPeople$embarked lt- NA titanicPeople$fare lt- NA titanic3 = fileName lt- Librarytitanic3xls titanicPeople lt- readxls(fileName header=TRUE sheet = 1 stringsAsFactors = FALSE) titanicPeople$sex lt- paste0(toupper(substr(ascharacter(titanicPeople$sex) 1 1)) substr(ascharacter(titanicPeople$sex) 2 nchar(ascharacter(titanicPeople$sex)))) internal = temp lt- dataframe(Titanic) titanicPeople lt- dataframe(survived=ascharacter() embarked=ascharacter() sex=ascharacter() sibsp=asnumeric() parch=asnumeric() fare=asnumeric() pclass=asnumeric() ) for (i in 1nrow(temp)) if (temp$Freq[i] gt 0) for (j in 1temp$Freq[i]) terminus lt- ifelse(temp$Survived[i] == Yesyes = Saved no =Lost) gender lt- paste0(toupper(substr(ascharacter(temp$Sex[i]) 1 1)) substr(ascharacter(temp$Sex[i]) 2 nchar(ascharacter(temp$Sex[i])))) pclass lt- ascharacter(temp$Class[i]) if (ascharacter(temp$Class[i]) == 1st) pclass lt- 1 else if (ascharacter(temp$Class[i]) == 2nd) pclass lt- 2 else if (ascharacter(temp$Class[i]) == 3rd) pclass lt- 3 else pclass lt- 0 also lt- dataframe( survived=terminus embarked=NA sex=gender sibsp=0 parch=0 fare=0 pclass=pclass ) titanicPeople lt- rbind(titanicPeople also) ) titanicPeople lt- select(titanicPeople survived embarked sex sibsp parch fare pclass) titanicPeople lt- mutate(titanicPeople survived=factor(survived)) titanicPeople lt- mutate(titanicPeople sex=factor(sex)) titanicPeople main lt- function(sourceSelection = internal trainingPercentage = 30 dataCollection = FALSE verbose = FALSE ) setseed(100) predictions lt- list() textFileName lt- tempfile() c50ImageFileName lt- Imagesdatacollectionc50png datacollectionImageFileName lt- Imagesdatacollectionpng trainingPercentageMin lt- NA trainingPercentageMax lt- NA trainingPercentageStep lt- NA dataCollectionDataFrame lt- NULL spec lt- matrix(c( sourceSelection s 1 character trainingPercentage t 1 double dataCollection d 0 logical verbose v 0 logical ) ncol = 4 byrow = TRUE) opt lt- getopt(spec) sourceSelection lt- setValueBasedOnCommandLine(opt$sourceSelection sourceSelection) trainingPercentage lt- setValueBasedOnCommandLine(opt$trainingPercentage trainingPercentage) dataCollection lt- setValueBasedOnCommandLine(opt$dataCollection dataCollection) verbose lt- setValueBasedOnCommandLine(opt$verbose verbose) titanicPeople lt- getTitanicPeople(sourceSelection) dumpObject(names(titanicPeople) verbose=verbose) dumpObject(head(titanicPeople) verbose=verbose) dumpObject(table(titanicPeople$pclass) verbose=verbose) dumpObject(table(titanicPeople$survived) verbose=verbose) if (dataCollection == FALSE) trainingPercentageMin lt- trainingPercentage trainingPercentageMax lt- trainingPercentage trainingPercentageStep lt- 10 else trainingPercentageMin lt- trainingPercentage trainingPercentageStep lt- 10 trainingPercentageMax lt- min(999999 100 - trainingPercentageStep + trainingPercentageMin) percentages lt- seq(trainingPercentageMin trainingPercentageMax trainingPercentageStep) numberOfPeople lt- nrow(titanicPeople) for (percentage in percentages) indices lt- sample(1numberOfPeople size=numberOfPeoplepercentage100) data lt- NULL data$training lt- titanicPeople[indices] data$testing lt- titanicPeople[-indices] print(sprintf(Total number of rows = 0f training is f numberOfPeople percentage)) print(sprintf(There are 0f rows in the training set nrow(data$training))) print(sprintf(There are 0f rows in the test set nrow(data$test))) formula lt- survived ~ sex + sibsp + parch + pclass formula lt- survived ~ sex + sibsp + parch predictions[[C50]] lt- C50formula(asformula(formula) data=data$training) predictions[[Random Forest]] lt- randomForest(asformula(formula) data=data$training keepforest=TRUE proximity=TRUE ntree=100 importance=TRUE) predictions[[rpart]] lt- rpart(asformula(formula) data=data$training method=class cp=00001) predictions[[J48 (unpruned)]] lt- J48(asformula(formula) data=data$training control=Weka_control(U=TRUE)) predictions[[J48 (pruned)]] lt- J48(asformula(formula) data=data$training control=Weka_control(U=FALSE C=00001)) results lt- predictionResults(predictions data$test verbose) dumpObject(results verbose=verbose) if (dataCollection == TRUE) if (isnull(dataCollectionDataFrame) == TRUE) dataCollectionDataFrame lt- asdataframe(results[Accuracy]) else dataCollectionDataFrame lt- rbind(dataCollectionDataFrame asdataframe(results[Accuracy])) else for (n in names(predictions)) print(sprintf(Plotting data from s n)) switch(class(predictions[[n]])[1] randomForestformula = httpstatsstackexchangecomquestions41443how-to-actually-plot-a-sample-tree-from-randomforestgettree reprtreeplotgetTree(predictions[[n]]) rpart = rpartplot(predictions[[n]]) J48 = plot(predictions[[n]]) C50 = C50graphviz(predictions[[n]] filename=textFileName) command lt- sprintf(dot -Tpng s gt s textFileName c50ImageFileName) system(command) print(Default case) plot(predictions[[n]]) ) unlink(textFileName) returnValue lt- list(dataCollectionDataFrame=asdataframe(dataCollectionDataFrame) percentages=asvector(percentages) titanicPeople=titanicPeople ) if (dataCollection == TRUE) png(filename=datacollectionImageFileName width=960 height=480 type=c(cairo)) plotDataCollection(returnValue) devoff() returnValued lt- main()

Chuck Cartledge
Chuck Cartledge

titanic3

Chuck Cartledge

Bio150 ML Frank franklakeforestedu Page 1 of 3 [httpbiostatmcvanderbilteduwikipubMainDataSetstitanichtml]

Titanic Datasets The titanic and titanic2 data frames describe the survival status of individual passengers on the Titanic The titanic data frame does not contain information from the crew but it does contain actual ages of half of the passengers The principal source for data about Titanic passengers is the Encyclopedia Titanica The datasets used here were begun by a variety of researchers One of the original sources is Eaton amp Haas (1994) Titanic Triumph and Tragedy Patrick Stephens Ltd which includes a passenger list created by many researchers and edited by Michael A Findlay

The variables on our extracted dataset are pclass survived name age embarked homedest room ticket boat and sex pclass refers to passenger class (1st 2nd 3rd) and is a proxy for socio-economic class Age is in years and some infants had fractional values The titanic2 data frame has no missing data and includes records for the crew but age is dichotomized at adult vs child These data were obtained from Robert Dawson Saint Marys University E-mail The variables are pclass age sex survived These data frames are useful for demonstrating many of the functions in Hmisc as well as demonstrating binary logistic regression analysis using the Design library For more details and references see Simonoff Jeffrey S (1997) The unusual episode and a second statistics course J Statistics Education Vol 5 No 1 Thomas Cason of UVa has greatly updated and improved the titanic data frame using the Encyclopedia Titanica and created a new dataset called titanic3 These datasets reflects the state of data available as of 2 August 1999 Some duplicate passengers have been dropped many errors corrected many missing ages filled in and new variables created Click here for information about how this datatset was constructed

DATASET specs

NAME titanic3 TYPE Census SIZE 1309 Passengers 14 Variables DESCRIPTIVE ABSTRACT

The titanic3 data frame describes the survival status of individual passengers on the Titanic The titanic3 data frame does not contain information for the crew but it does contain actual and estimated ages for almost 80 of the passengers SOURCES

Hind Philip Encyclopedia Titanica Online-only resource Retrieved 01Feb2012 from httpwwwencyclopedia-titanicaorg

Bio150 ML Frank franklakeforestedu Page 2 of 3 VARIABLE DESCRIPTIONS Pclass Passenger Class (1 = 1st 2 = 2nd 3 = 3rd) survival Survival (0 = No 1 = Yes) name Name sex Sex age Age sibsp Number of SiblingsSpouses Aboard parch Number of ParentsChildren Aboard ticket Ticket Number fare Passenger Fare (British pound) cabin Cabin embarked Port of Embarkation (C = Cherbourg Q = Queenstown S = Southampton) boat Lifeboat body Body Identification Number homedest HomeDestination SPECIAL NOTES Pclass is a proxy for socio-economic status (SES) 1st ~ Upper 2nd ~ Middle 3rd ~ Lower Age is in Years Fractional if Age less than One (1) If the Age is estimated it is in the form xx5 Fare is in Pre-1970 British Pounds () Conversion Factors 1 = 12s = 240d and 1s = 20d With respect to the family relation variables (ie sibsp and parch) some relations were ignored The following are the definitions used for sibsp and parch

Sibling Brother Sister Stepbrother or Stepsister of Passenger Aboard Titanic Spouse Husband or Wife of Passenger Aboard Titanic (Mistresses and Fiances

Ignored) Parent Mother or Father of Passenger Aboard Titanic Child Son Daughter Stepson or Stepdaughter of Passenger Aboard Titanic

Other family relatives excluded from this study include cousins nephewsnieces auntsuncles and in-laws Some children travelled only with a nanny therefore parch=0 for them As well some travelled with very close friends or neighbors in a village however the definitions do not support such relations

Bio150 ML Frank franklakeforestedu Page 3 of 3 An interesting result may be obtained using functions from the Hmisc library attach (titanic3) plsmo (age survived group=sex datadensity=T) or group=pclass plot (naclus (titanic3)) study patterns of missing values summary (survived ~ age + sex + pclass + sibsp + parch data=titanic3) SUBMITTED BY Thomas E Cason tcasonvirginiaedu From MLF Prior to February 15 1971 (Decimal day or D-day) monetary amounts in the UK were expressed as pounds (pound) shillings (s) and pence (d) where pound1 = 20s = 240d After 1970 there were 100 pennies in a pound so one (new) penny = 24 old pence All numbers should be entered in decimal rather than fractional form (for example 15 rather than 1 12)

Lawrence H Officer Five Ways to Compute the Relative Value of a UK Pound Amount 1830 to Present MeasuringWorthcom Retrieved 2011Sep06 from httpwwwmeasuringworthcomukcompare

Chuck Cartledge

httpswww-userscsumnedu~kumardmbookch4pdf httpswwwyoutubecomwatchv=GOJN9SKl_OErm(list=ls())library(grid)library(mvtnorm)library(modeltools)library(stats4)library(strucchange)library(zoo)library(sandwich)library(gplots)library(readr)library(dplyr)library(party)library(rpart)library(rpartplot)library(ROCR)plotRawData lt- function(d title=) x=23 plot(x=c(-11) y = c(-1 1) type=n xlab=X values ylab=Y values main=paste(red = expression(05 lt= sqrt(x^2+y^2) ampamp sqrt(x^2+y^2) lt= 10) sep=) ) for (j in 12) points(d[which(d[3]==j)c(12)] col=j) plotErrors lt- function(data) plot(x=c(0 max(data[[nodes]])) y=c(01) type=n ylab=Model error xlab=Number of internal nodes ) cols=c(blue red) pchs=c(12) points(data[[nodes]] data[[training]] type=b col=cols[1] pch=pchs[1]) points(data[[nodes]] data[[testing]] type=b col=cols[2] pch=pchs[2]) legend(topright legend=c(Training error Testing error) pch=pchs col=cols) points(spline(data[[nodes]] data[[testing]]))decisionTree lt- function(dLocal cp=01) dTree lt- rpart(classification ~ x1 + x2 data = dLocal method=class control=c(cp=cp minsplit=1 minbucket=1) ) rpartplot(dTree type = 4 extra = 106)computeAccuracy lt- function(model data) performance lt- predict(model data type=class) temp lt- table(data$classification performance) accuracy lt- sum(diag(temp))sum(temp) accuracymain lt- function() setseed(123) numberOfDataPoints lt- 1000 trainingPercentage lt- 70 cps lt- c(01 005 0025 001 0005 0001 00001 000001) x1 lt- runif(numberOfDataPoints min = -1 max = 1) x2 lt- runif(numberOfDataPoints min = -1 max = 1) x lt- cbind(x1 x2) classification lt- c() tempList lt- c() for (i in 1nrow(x)) temp lt- sqrt(x[i1]^2 + x[i2]^2) cl lt- 1 if ((05 lt= temp) ampamp (temp lt= 10)) cl lt- 2 classification lt- c(classification cl) tempList lt- c(tempList temp) x lt- cbind(x classification tempList) plotRawData(x) s lt- sample(numberOfDataPoints numberOfDataPoints trainingPercentage 100) xtraining lt- asdataframe(x[s]) xtesting lt- asdataframe(x[-s]) returnValue lt- list() for (cp in cps) ml lt- decisionTree(xtraining cp = cp) print(ml) trainingError lt- 1 - computeAccuracy(ml xtraining) testingError lt- 1 - computeAccuracy(ml xtesting) nodes lt- tail(ml$cptable[nsplit] n=1) print(sprintf(cp = f internal nodes= 0f training error = 2f testing error = 2f cp nodes trainingError testingError ) ) returnValue[[nodes]] lt- c(returnValue[[nodes]] nodes) returnValue[[testing]] lt- c(returnValue[[testing]] testingError) returnValue[[training]] lt- c(returnValue[[training]] trainingError) print(The program has ended) returnValued lt- main()

Chuck Cartledge
  • Introduction
  • Background
    • ``Well settled data
    • Data from diverse places
      • Classification problem
        • What is it
        • Training and testing
        • Types of errors
          • Techniques
            • Results
              • Hands-on
              • Q amp A
              • Conclusion
              • References
              • Files
pclass survived name sex age sibsp parch ticket fare cabin embarked boat body homedest
1 1 Allen Miss Elisabeth Walton female 29 0 0 24160 2113375 B5 S 2 St Louis MO
1 1 Allison Master Hudson Trevor male 09167 1 2 113781 1515500 C22 C26 S 11 Montreal PQ Chesterville ON
1 0 Allison Miss Helen Loraine female 2 1 2 113781 1515500 C22 C26 S Montreal PQ Chesterville ON
1 0 Allison Mr Hudson Joshua Creighton male 30 1 2 113781 1515500 C22 C26 S 135 Montreal PQ Chesterville ON
1 0 Allison Mrs Hudson J C (Bessie Waldo Daniels) female 25 1 2 113781 1515500 C22 C26 S Montreal PQ Chesterville ON
1 1 Anderson Mr Harry male 48 0 0 19952 265500 E12 S 3 New York NY
1 1 Andrews Miss Kornelia Theodosia female 63 1 0 13502 779583 D7 S 10 Hudson NY
1 0 Andrews Mr Thomas Jr male 39 0 0 112050 00000 A36 S Belfast NI
1 1 Appleton Mrs Edward Dale (Charlotte Lamson) female 53 2 0 11769 514792 C101 S D Bayside Queens NY
1 0 Artagaveytia Mr Ramon male 71 0 0 PC 17609 495042 C 22 Montevideo Uruguay
1 0 Astor Col John Jacob male 47 1 0 PC 17757 2275250 C62 C64 C 124 New York NY
1 1 Astor Mrs John Jacob (Madeleine Talmadge Force) female 18 1 0 PC 17757 2275250 C62 C64 C 4 New York NY
1 1 Aubart Mme Leontine Pauline female 24 0 0 PC 17477 693000 B35 C 9 Paris France
1 1 Barber Miss Ellen Nellie female 26 0 0 19877 788500 S 6
1 1 Barkworth Mr Algernon Henry Wilson male 80 0 0 27042 300000 A23 S B Hessle Yorks
1 0 Baumann Mr John D male 0 0 PC 17318 259250 S New York NY
1 0 Baxter Mr Quigg Edmond male 24 0 1 PC 17558 2475208 B58 B60 C Montreal PQ
1 1 Baxter Mrs James (Helene DeLaudeniere Chaput) female 50 0 1 PC 17558 2475208 B58 B60 C 6 Montreal PQ
1 1 Bazzani Miss Albina female 32 0 0 11813 762917 D15 C 8
1 0 Beattie Mr Thomson male 36 0 0 13050 752417 C6 C A Winnipeg MN
1 1 Beckwith Mr Richard Leonard male 37 1 1 11751 525542 D35 S 5 New York NY
1 1 Beckwith Mrs Richard Leonard (Sallie Monypeny) female 47 1 1 11751 525542 D35 S 5 New York NY
1 1 Behr Mr Karl Howell male 26 0 0 111369 300000 C148 C 5 New York NY
1 1 Bidois Miss Rosalie female 42 0 0 PC 17757 2275250 C 4
1 1 Bird Miss Ellen female 29 0 0 PC 17483 2217792 C97 S 8
1 0 Birnbaum Mr Jakob male 25 0 0 13905 260000 C 148 San Francisco CA
1 1 Bishop Mr Dickinson H male 25 1 0 11967 910792 B49 C 7 Dowagiac MI
1 1 Bishop Mrs Dickinson H (Helen Walton) female 19 1 0 11967 910792 B49 C 7 Dowagiac MI
1 1 Bissette Miss Amelia female 35 0 0 PC 17760 1356333 C99 S 8
1 1 Bjornstrom-Steffansson Mr Mauritz Hakan male 28 0 0 110564 265500 C52 S D Stockholm Sweden Washington DC
1 0 Blackwell Mr Stephen Weart male 45 0 0 113784 355000 T S Trenton NJ
1 1 Blank Mr Henry male 40 0 0 112277 310000 A31 C 7 Glen Ridge NJ
1 1 Bonnell Miss Caroline female 30 0 0 36928 1648667 C7 S 8 Youngstown OH
1 1 Bonnell Miss Elizabeth female 58 0 0 113783 265500 C103 S 8 Birkdale England Cleveland Ohio
1 0 Borebank Mr John James male 42 0 0 110489 265500 D22 S London Winnipeg MB
1 1 Bowen Miss Grace Scott female 45 0 0 PC 17608 2623750 C 4 Cooperstown NY
1 1 Bowerman Miss Elsie Edith female 22 0 1 113505 550000 E33 S 6 St Leonards-on-Sea England Ohio
1 1 Bradley Mr George (George Arthur Brayton) male 0 0 111427 265500 S 9 Los Angeles CA
1 0 Brady Mr John Bertram male 41 0 0 113054 305000 A21 S Pomeroy WA
1 0 Brandeis Mr Emil male 48 0 0 PC 17591 504958 B10 C 208 Omaha NE
1 0 Brewe Dr Arthur Jackson male 0 0 112379 396000 C Philadelphia PA
1 1 Brown Mrs James Joseph (Margaret Tobin) female 44 0 0 PC 17610 277208 B4 C 6 Denver CO
1 1 Brown Mrs John Murray (Caroline Lane Lamson) female 59 2 0 11769 514792 C101 S D Belmont MA
1 1 Bucknell Mrs William Robert (Emma Eliza Ward) female 60 0 0 11813 762917 D15 C 8 Philadelphia PA
1 1 Burns Miss Elizabeth Margaret female 41 0 0 16966 1345000 E40 C 3
1 0 Butt Major Archibald Willingham male 45 0 0 113050 265500 B38 S Washington DC
1 0 Cairns Mr Alexander male 0 0 113798 310000 S
1 1 Calderhead Mr Edward Pennington male 42 0 0 PC 17476 262875 E24 S 5 New York NY
1 1 Candee Mrs Edward (Helen Churchill Hungerford) female 53 0 0 PC 17606 274458 C 6 Washington DC
1 1 Cardeza Mr Thomas Drake Martinez male 36 0 1 PC 17755 5123292 B51 B53 B55 C 3 Austria-Hungary Germantown Philadelphia PA
1 1 Cardeza Mrs James Warburton Martinez (Charlotte Wardle Drake) female 58 0 1 PC 17755 5123292 B51 B53 B55 C 3 Germantown Philadelphia PA
1 0 Carlsson Mr Frans Olof male 33 0 0 695 50000 B51 B53 B55 S New York NY
1 0 Carrau Mr Francisco M male 28 0 0 113059 471000 S Montevideo Uruguay
1 0 Carrau Mr Jose Pedro male 17 0 0 113059 471000 S Montevideo Uruguay
1 1 Carter Master William Thornton II male 11 1 2 113760 1200000 B96 B98 S 4 Bryn Mawr PA
1 1 Carter Miss Lucile Polk female 14 1 2 113760 1200000 B96 B98 S 4 Bryn Mawr PA
1 1 Carter Mr William Ernest male 36 1 2 113760 1200000 B96 B98 S C Bryn Mawr PA
1 1 Carter Mrs William Ernest (Lucile Polk) female 36 1 2 113760 1200000 B96 B98 S 4 Bryn Mawr PA
1 0 Case Mr Howard Brown male 49 0 0 19924 260000 S Ascot Berkshire Rochester NY
1 1 Cassebeer Mrs Henry Arthur Jr (Eleanor Genevieve Fosdick) female 0 0 17770 277208 C 5 New York NY
1 0 Cavendish Mr Tyrell William male 36 1 0 19877 788500 C46 S 172 Little Onn Hall Staffs
1 1 Cavendish Mrs Tyrell William (Julia Florence Siegel) female 76 1 0 19877 788500 C46 S 6 Little Onn Hall Staffs
1 0 Chaffee Mr Herbert Fuller male 46 1 0 WEP 5734 611750 E31 S Amenia ND
1 1 Chaffee Mrs Herbert Fuller (Carrie Constance Toogood) female 47 1 0 WEP 5734 611750 E31 S 4 Amenia ND
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3 0 Panula Mr Ernesti Arvid male 16 4 1 3101295 396875 S
3 0 Panula Mr Jaako Arnold male 14 4 1 3101295 396875 S
3 0 Panula Mrs Juha (Maria Emilia Ojala) female 41 0 5 3101295 396875 S
3 0 Pasic Mr Jakob male 21 0 0 315097 86625 S
3 0 Patchett Mr George male 19 0 0 358585 145000 S
3 0 Paulner Mr Uscher male 0 0 3411 87125 C
3 0 Pavlovic Mr Stefo male 32 0 0 349242 78958 S
3 0 Peacock Master Alfred Edward male 075 1 1 SOTONOQ 3101315 137750 S
3 0 Peacock Miss Treasteall female 3 1 1 SOTONOQ 3101315 137750 S
3 0 Peacock Mrs Benjamin (Edith Nile) female 26 0 2 SOTONOQ 3101315 137750 S
3 0 Pearce Mr Ernest male 0 0 343271 70000 S
3 0 Pedersen Mr Olaf male 0 0 345498 77750 S
3 0 Peduzzi Mr Joseph male 0 0 A5 2817 80500 S
3 0 Pekoniemi Mr Edvard male 21 0 0 STONO 2 3101294 79250 S
3 0 Peltomaki Mr Nikolai Johannes male 25 0 0 STONO 2 3101291 79250 S
3 0 Perkin Mr John Henry male 22 0 0 A5 21174 72500 S
3 1 Persson Mr Ernst Ulrik male 25 1 0 347083 77750 S 15
3 1 Peter Master Michael J male 1 1 2668 223583 C C
3 1 Peter Miss Anna female 1 1 2668 223583 F E69 C D
3 1 Peter Mrs Catherine (Catherine Rizk) female 0 2 2668 223583 C D
3 0 Peters Miss Katie female 0 0 330935 81375 Q
3 0 Petersen Mr Marius male 24 0 0 342441 80500 S
3 0 Petranec Miss Matilda female 28 0 0 349245 78958 S
3 0 Petroff Mr Nedelio male 19 0 0 349212 78958 S
3 0 Petroff Mr Pastcho (Pentcho) male 0 0 349215 78958 S
3 0 Petterson Mr Johan Emil male 25 1 0 347076 77750 S
3 0 Pettersson Miss Ellen Natalia female 18 0 0 347087 77750 S
3 1 Pickard Mr Berk (Berk Trembisky) male 32 0 0 SOTONOQ 392078 80500 E10 S 9
3 0 Plotcharsky Mr Vasil male 0 0 349227 78958 S
3 0 Pokrnic Mr Mate male 17 0 0 315095 86625 S
3 0 Pokrnic Mr Tome male 24 0 0 315092 86625 S
3 0 Radeff Mr Alexander male 0 0 349223 78958 S
3 0 Rasmussen Mrs (Lena Jacobsen Solvang) female 0 0 65305 81125 S
3 0 Razi Mr Raihed male 0 0 2629 72292 C
3 0 Reed Mr James George male 0 0 362316 72500 S
3 0 Rekic Mr Tido male 38 0 0 349249 78958 S
3 0 Reynolds Mr Harold J male 21 0 0 342684 80500 S
3 0 Rice Master Albert male 10 4 1 382652 291250 Q
3 0 Rice Master Arthur male 4 4 1 382652 291250 Q
3 0 Rice Master Eric male 7 4 1 382652 291250 Q
3 0 Rice Master Eugene male 2 4 1 382652 291250 Q
3 0 Rice Master George Hugh male 8 4 1 382652 291250 Q
3 0 Rice Mrs William (Margaret Norton) female 39 0 5 382652 291250 Q 327
3 0 Riihivouri Miss Susanna Juhantytar Sanni female 22 0 0 3101295 396875 S
3 0 Rintamaki Mr Matti male 35 0 0 STONO 2 3101273 71250 S
3 1 Riordan Miss Johanna Hannah female 0 0 334915 77208 Q 13
3 0 Risien Mr Samuel Beard male 0 0 364498 145000 S
3 0 Risien Mrs Samuel (Emma) female 0 0 364498 145000 S
3 0 Robins Mr Alexander A male 50 1 0 A5 3337 145000 S 119
3 0 Robins Mrs Alexander A (Grace Charity Laury) female 47 1 0 A5 3337 145000 S 7
3 0 Rogers Mr William John male 0 0 SCA4 23567 80500 S
3 0 Rommetvedt Mr Knud Paust male 0 0 312993 77750 S
3 0 Rosblom Miss Salli Helena female 2 1 1 370129 202125 S
3 0 Rosblom Mr Viktor Richard male 18 1 1 370129 202125 S
3 0 Rosblom Mrs Viktor (Helena Wilhelmina) female 41 0 2 370129 202125 S
3 1 Roth Miss Sarah A female 0 0 342712 80500 S C
3 0 Rouse Mr Richard Henry male 50 0 0 A5 3594 80500 S
3 0 Rush Mr Alfred George John male 16 0 0 A4 20589 80500 S
3 1 Ryan Mr Edward male 0 0 383162 77500 Q 14
3 0 Ryan Mr Patrick male 0 0 371110 241500 Q
3 0 Saad Mr Amin male 0 0 2671 72292 C
3 0 Saad Mr Khalil male 25 0 0 2672 72250 C
3 0 Saade Mr Jean Nassr male 0 0 2676 72250 C
3 0 Sadlier Mr Matthew male 0 0 367655 77292 Q
3 0 Sadowitz Mr Harry male 0 0 LP 1588 75750 S
3 0 Saether Mr Simon Sivertsen male 385 0 0 SOTONOQ 3101262 72500 S 32
3 0 Sage Master Thomas Henry male 8 2 CA 2343 695500 S
3 0 Sage Master William Henry male 145 8 2 CA 2343 695500 S 67
3 0 Sage Miss Ada female 8 2 CA 2343 695500 S
3 0 Sage Miss Constance Gladys female 8 2 CA 2343 695500 S
3 0 Sage Miss Dorothy Edith Dolly female 8 2 CA 2343 695500 S
3 0 Sage Miss Stella Anna female 8 2 CA 2343 695500 S
3 0 Sage Mr Douglas Bullen male 8 2 CA 2343 695500 S
3 0 Sage Mr Frederick male 8 2 CA 2343 695500 S
3 0 Sage Mr George John Jr male 8 2 CA 2343 695500 S
3 0 Sage Mr John George male 1 9 CA 2343 695500 S
3 0 Sage Mrs John (Annie Bullen) female 1 9 CA 2343 695500 S
3 0 Salander Mr Karl Johan male 24 0 0 7266 93250 S
3 1 Salkjelsvik Miss Anna Kristine female 21 0 0 343120 76500 S C
3 0 Salonen Mr Johan Werner male 39 0 0 3101296 79250 S
3 0 Samaan Mr Elias male 2 0 2662 216792 C
3 0 Samaan Mr Hanna male 2 0 2662 216792 C
3 0 Samaan Mr Youssef male 2 0 2662 216792 C
3 1 Sandstrom Miss Beatrice Irene female 1 1 1 PP 9549 167000 G6 S 13
3 1 Sandstrom Mrs Hjalmar (Agnes Charlotta Bengtsson) female 24 0 2 PP 9549 167000 G6 S 13
3 1 Sandstrom Miss Marguerite Rut female 4 1 1 PP 9549 167000 G6 S 13
3 1 Sap Mr Julius male 25 0 0 345768 95000 S 11
3 0 Saundercock Mr William Henry male 20 0 0 A5 2151 80500 S
3 0 Sawyer Mr Frederick Charles male 245 0 0 342826 80500 S 284
3 0 Scanlan Mr James male 0 0 36209 77250 Q
3 0 Sdycoff Mr Todor male 0 0 349222 78958 S
3 0 Shaughnessy Mr Patrick male 0 0 370374 77500 Q
3 1 Sheerlinck Mr Jan Baptist male 29 0 0 345779 95000 S 11
3 0 Shellard Mr Frederick William male 0 0 CA 6212 151000 S
3 1 Shine Miss Ellen Natalia female 0 0 330968 77792 Q
3 0 Shorney Mr Charles Joseph male 0 0 374910 80500 S
3 0 Simmons Mr John male 0 0 SOTONOQ 392082 80500 S
3 0 Sirayanian Mr Orsen male 22 0 0 2669 72292 C
3 0 Sirota Mr Maurice male 0 0 392092 80500 S
3 0 Sivic Mr Husein male 40 0 0 349251 78958 S
3 0 Sivola Mr Antti Wilhelm male 21 0 0 STONO 2 3101280 79250 S
3 1 Sjoblom Miss Anna Sofia female 18 0 0 3101265 74958 S 16
3 0 Skoog Master Harald male 4 3 2 347088 279000 S
3 0 Skoog Master Karl Thorsten male 10 3 2 347088 279000 S
3 0 Skoog Miss Mabel female 9 3 2 347088 279000 S
3 0 Skoog Miss Margit Elizabeth female 2 3 2 347088 279000 S
3 0 Skoog Mr Wilhelm male 40 1 4 347088 279000 S
3 0 Skoog Mrs William (Anna Bernhardina Karlsson) female 45 1 4 347088 279000 S
3 0 Slabenoff Mr Petco male 0 0 349214 78958 S
3 0 Slocovski Mr Selman Francis male 0 0 SOTONOQ 392086 80500 S
3 0 Smiljanic Mr Mile male 0 0 315037 86625 S
3 0 Smith Mr Thomas male 0 0 384461 77500 Q
3 1 Smyth Miss Julia female 0 0 335432 77333 Q 13
3 0 Soholt Mr Peter Andreas Lauritz Andersen male 19 0 0 348124 76500 F G73 S
3 0 Somerton Mr Francis William male 30 0 0 A5 18509 80500 S
3 0 Spector Mr Woolf male 0 0 A5 3236 80500 S
3 0 Spinner Mr Henry John male 32 0 0 STONOQ 369943 80500 S
3 0 Staneff Mr Ivan male 0 0 349208 78958 S
3 0 Stankovic Mr Ivan male 33 0 0 349239 86625 C
3 1 Stanley Miss Amy Zillah Elsie female 23 0 0 CA 2314 75500 S C
3 0 Stanley Mr Edward Roland male 21 0 0 A4 45380 80500 S
3 0 Storey Mr Thomas male 605 0 0 3701 S 261
3 0 Stoytcheff Mr Ilia male 19 0 0 349205 78958 S
3 0 Strandberg Miss Ida Sofia female 22 0 0 7553 98375 S
3 1 Stranden Mr Juho male 31 0 0 STONO 2 3101288 79250 S 9
3 0 Strilic Mr Ivan male 27 0 0 315083 86625 S
3 0 Strom Miss Telma Matilda female 2 0 1 347054 104625 G6 S
3 0 Strom Mrs Wilhelm (Elna Matilda Persson) female 29 1 1 347054 104625 G6 S
3 1 Sunderland Mr Victor Francis male 16 0 0 SOTONOQ 392089 80500 S B
3 1 Sundman Mr Johan Julian male 44 0 0 STONO 2 3101269 79250 S 15
3 0 Sutehall Mr Henry Jr male 25 0 0 SOTONOQ 392076 70500 S
3 0 Svensson Mr Johan male 74 0 0 347060 77750 S
3 1 Svensson Mr Johan Cervin male 14 0 0 7538 92250 S 13
3 0 Svensson Mr Olof male 24 0 0 350035 77958 S
3 1 Tenglin Mr Gunnar Isidor male 25 0 0 350033 77958 S 13 15
3 0 Theobald Mr Thomas Leonard male 34 0 0 363294 80500 S 176
3 1 Thomas Master Assad Alexander male 04167 0 1 2625 85167 C 16
3 0 Thomas Mr Charles P male 1 0 2621 64375 C
3 0 Thomas Mr John male 0 0 2681 64375 C
3 0 Thomas Mr Tannous male 0 0 2684 72250 C
3 1 Thomas Mrs Alexander (Thamine Thelma) female 16 1 1 2625 85167 C 14
3 0 Thomson Mr Alexander Morrison male 0 0 32302 80500 S
3 0 Thorneycroft Mr Percival male 1 0 376564 161000 S
3 1 Thorneycroft Mrs Percival (Florence Kate White) female 1 0 376564 161000 S 10
3 0 Tikkanen Mr Juho male 32 0 0 STONO 2 3101293 79250 S
3 0 Tobin Mr Roger male 0 0 383121 77500 F38 Q
3 0 Todoroff Mr Lalio male 0 0 349216 78958 S
3 0 Tomlin Mr Ernest Portage male 305 0 0 364499 80500 S 50
3 0 Torber Mr Ernst William male 44 0 0 364511 80500 S
3 0 Torfa Mr Assad male 0 0 2673 72292 C
3 1 Tornquist Mr William Henry male 25 0 0 LINE 00000 S 15
3 0 Toufik Mr Nakli male 0 0 2641 72292 C
3 1 Touma Master Georges Youssef male 7 1 1 2650 152458 C C
3 1 Touma Miss Maria Youssef female 9 1 1 2650 152458 C C
3 1 Touma Mrs Darwis (Hanne Youssef Razi) female 29 0 2 2650 152458 C C
3 0 Turcin Mr Stjepan male 36 0 0 349247 78958 S
3 1 Turja Miss Anna Sofia female 18 0 0 4138 98417 S 15
3 1 Turkula Mrs (Hedwig) female 63 0 0 4134 95875 S 15
3 0 van Billiard Master James William male 1 1 A5 851 145000 S
3 0 van Billiard Master Walter John male 115 1 1 A5 851 145000 S 1
3 0 van Billiard Mr Austin Blyler male 405 0 2 A5 851 145000 S 255
3 0 Van Impe Miss Catharina female 10 0 2 345773 241500 S
3 0 Van Impe Mr Jean Baptiste male 36 1 1 345773 241500 S
3 0 Van Impe Mrs Jean Baptiste (Rosalie Paula Govaert) female 30 1 1 345773 241500 S
3 0 van Melkebeke Mr Philemon male 0 0 345777 95000 S
3 0 Vande Velde Mr Johannes Joseph male 33 0 0 345780 95000 S
3 0 Vande Walle Mr Nestor Cyriel male 28 0 0 345770 95000 S
3 0 Vanden Steen Mr Leo Peter male 28 0 0 345783 95000 S
3 0 Vander Cruyssen Mr Victor male 47 0 0 345765 90000 S
3 0 Vander Planke Miss Augusta Maria female 18 2 0 345764 180000 S
3 0 Vander Planke Mr Julius male 31 3 0 345763 180000 S
3 0 Vander Planke Mr Leo Edmondus male 16 2 0 345764 180000 S
3 0 Vander Planke Mrs Julius (Emelia Maria Vandemoortele) female 31 1 0 345763 180000 S
3 1 Vartanian Mr David male 22 0 0 2658 72250 C 13 15
3 0 Vendel Mr Olof Edvin male 20 0 0 350416 78542 S
3 0 Vestrom Miss Hulda Amanda Adolfina female 14 0 0 350406 78542 S
3 0 Vovk Mr Janko male 22 0 0 349252 78958 S
3 0 Waelens Mr Achille male 22 0 0 345767 90000 S Antwerp Belgium Stanton OH
3 0 Ware Mr Frederick male 0 0 359309 80500 S
3 0 Warren Mr Charles William male 0 0 CA 49867 75500 S
3 0 Webber Mr James male 0 0 SOTONOQ 3101316 80500 S
3 0 Wenzel Mr Linhart male 325 0 0 345775 95000 S 298
3 1 Whabee Mrs George Joseph (Shawneene Abi-Saab) female 38 0 0 2688 72292 C C
3 0 Widegren Mr CarlCharles Peter male 51 0 0 347064 77500 S
3 0 Wiklund Mr Jakob Alfred male 18 1 0 3101267 64958 S 314
3 0 Wiklund Mr Karl Johan male 21 1 0 3101266 64958 S
3 1 Wilkes Mrs James (Ellen Needs) female 47 1 0 363272 70000 S
3 0 Willer Mr Aaron (Abi Weller) male 0 0 3410 87125 S
3 0 Willey Mr Edward male 0 0 SOPP 751 75500 S
3 0 Williams Mr Howard Hugh Harry male 0 0 A5 2466 80500 S
3 0 Williams Mr Leslie male 285 0 0 54636 161000 S 14
3 0 Windelov Mr Einar male 21 0 0 SOTONOQ 3101317 72500 S
3 0 Wirz Mr Albert male 27 0 0 315154 86625 S 131
3 0 Wiseman Mr Phillippe male 0 0 A4 34244 72500 S
3 0 Wittevrongel Mr Camille male 36 0 0 345771 95000 S
3 0 Yasbeck Mr Antoni male 27 1 0 2659 144542 C C
3 1 Yasbeck Mrs Antoni (Selini Alexander) female 15 1 0 2659 144542 C
3 0 Youseff Mr Gerious male 455 0 0 2628 72250 C 312
3 0 Yousif Mr Wazli male 0 0 2647 72250 C
3 0 Yousseff Mr Gerious male 0 0 2627 144583 C
3 0 Zabour Miss Hileni female 145 1 0 2665 144542 C 328
3 0 Zabour Miss Thamine female 1 0 2665 144542 C
3 0 Zakarian Mr Mapriededer male 265 0 0 2656 72250 C 304
3 0 Zakarian Mr Ortin male 27 0 0 2670 72250 C
3 0 Zimmerman Mr Leo male 29 0 0 315082 78750 S
Page 2: Big Data: Data Analysis Boot Camp Titanic Dataset

243

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

Table of contents (1 of 1)

1 Introduction

2 Background

3 Classification problem

4 Techniques

5 Hands-on

6 Q amp A

7 Conclusion

8 References

9 Files

343

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

What are we going to cover

Wersquore going to talk about

Rrsquos RMS Titanic dataset

Other Titanic datasets that containdifferent data

Modeling the datasets to see whowill live and who will die

443

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

ldquoWellrdquo settled data

Basic information

Ordered 17 Sep 1908

Completed 2 Apr 1912

Maiden voyage 10 Apr1912

Sank 14 Apr 1912

543

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

ldquoWellrdquo settled data

Where she was damaged

Red are water tightbulkheads

Green is where the iceberghit

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

643

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

ldquoWellrdquo settled data

Same image

Image from [11]

743

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

ldquoWellrdquo settled data

How many died and why

Sailing capacity (passengersand crew) 3372

Lifeboat capacity 1178

Number of people on board(accounts vary) 2201

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

Passengers crew builderrsquos menand others Image from [8]

843

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

Data from diverse places

Expected first class passengers

Lots of lists of 1st classpassengers Even some of 2ndand 3rd class passengers[10]Lists of non-passengers (shiprsquoscrew and builderrsquos technicians)are more challenging[6]R has a built-in Titanic datasetTitanic

Image from [7]

943

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

Data from diverse places

A crew list

A reasonable collection of crewand builderrsquos representatives isavailable

Name job status (lost ornot)

Age place of birth

Image from [9]

1043

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

Data from diverse places

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]

1143

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

Data from diverse places

titanic3 attributesvariables

Name Explanation

Pclass Passenger Class (1 = 1st 2 = 2nd 3 = 3rd)survival Survival (0 = No 1 = Yes)name Namesex Sexage Agesibsp Number of SiblingsSpouses Aboardparch Number of ParentsChildren Aboardticket Ticket Numberfare Passenger Fare (British pound)cabin Cabinembarked Port of Embarkation (C = Cherbourg Q = Queenstown S = Southampton)boat Lifeboatbody Body Identification Numberhomedest HomeDestination

1243

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

Data from diverse places

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

1343

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

What is it

A definition

ldquoClassification is the task of learning a targetfunction f that maps each attribute set x to one of thepredefined class labels yrdquo

Tan et al [12]

1443

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

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]

1543

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

What is it

Same image

Image from [5]

1643

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

What is it

Supervised vs Unsupervised learning

Supervised learning

A training dataset with correct answers(labels) is ldquominedrdquo 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

1743

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

What is it

Supervised vs Unsupervised learning techniques

With the Titanic dataset we will be focusing on classification

1843

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

Training and testing

Working with data

Supervised learning requires

Training data ndash usuallyabout 70 of available data

Testing data ndash usually about30 of available data

Training data can also bepartitioned into validation data

1943

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

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 oneUse all and vote on theoutcome

2043

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

Types of errors

Sample problem space

1000 data points between+- 1

Two classes of data points

color =

red if 05 leradic

x2 + y2 le 10

black otherwise

(See attached file)

2143

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

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

2243

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

Types of errors

Same image

Attached file

2343

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

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

2443

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

Types of errors

Same image

Training and testing errors

2543

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

A collection of decision tree techniques

rpart from the rpart library ldquoRecursive partitioning for classification regression and survival trees Animplementation of most of the functionality of the 1984 book by Breiman Friedman Olshen andStonerdquo[13]

C50 from the C50 library ldquoC50 decision trees and rule-based models for pattern recognitionrdquo [4]

Random Forest from the randomForest library ldquoClassification and regression based on a forest of trees using randominputsrdquo[1]

J48 from the RWeka library ldquoAn R interface to Weka (Version 391) Weka is a collection of machine learningalgorithms for data mining tasks written in Java containing tools for data pre-processing classificationregression clustering association rules and visualizationrdquo[3] The J48 algorithm is run in a pruned andunpruned mode

These and additional techniques to be covered in detail later

2643

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

Results

C50 decision tree

2743

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

Results

Random Forest decision tree

2843

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

Results

rpart decision tree

2943

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

Results

J48 (unpruned) decision tree

3043

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

Results

J48 (pruned) decision tree

3143

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

Results

Accuracy based on training percentage

The horizontal line at 50represents 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

3243

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

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

3343

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

Results

Simple mosaic from the titanic package

Sometimes you donrsquot need a lotof codelibrary(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 adageldquowomen and children firstrdquo

3443

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

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 ldquowomen and children firstrdquo

3543

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

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

3643

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

Q amp A time

Q How many Harvard MBArsquosdoes it take to screw in a lightbulbA Just one He grasps it firmlyand the universe revolves aroundhim

3743

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

What have we covered

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

Next LPAR Chapter 2 basic data visualization

3843

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

References (1 of 5)

[1] Leo BreimanrandomForest Breiman and Cutlers random forests for classification and regressionhttpstat-wwwberkeleyeduusersbreiman

RandomForests 2006

[2] Jr Frank E Harrell Titanic DatahttpbiostatmcvanderbilteduwikipubMain

DataSetstitanichtml 2002

[3] K Hornik A Zeileis T Hothorn and C BuchtaRWeka an R interface to Weka R package version 04-32(2017)

3943

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

References (2 of 5)

[4] M Kuhn S Weston N Coulter M Culp and R QuinlanC50 Decision Trees and Rule-Based Models R PackageVersion 01 0 24 (2015)

[5] Sebastian RaschkaPredictive modeling supervised machine learning and pattern classificationhttpsebastianraschkacomArticles2014_intro_

supervised_learninghtml 2014

[6] Encyclopedia Titanica StaffEncyclopedia Titanica Titanic Facts History and Biographyhttpswwwencyclopedia-titanicaorg 2017

4043

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

References (3 of 5)

[7] ISM Staff Titanic Survivor Titanic Passenger List Booklethttpwwwphillyseaportorgweb_exhibitsmini_

exhibitstitanic_passenger_listtitanic_

passenger_list-object-passenger_listhtml 2017

[8] OceanGate Staff Titanic Survey Expedition 2018httpwwwoceangatecomexpeditionstitanic-

survey-2018html 2017

[9] Southampton Staff Titanic crew listhttpwwwplimsollorgSouthamptonTitanic

titaniccrewlistDefaultasp 2017

4143

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

References (4 of 5)

[10] Titanic Facts Staff Titanic Passenger List httpwwwtitanic-factscomtitanic-passenger-listhtml2017

[11] Wikipedia Staff Sinking of the RMS Titanic httpsenwikipediaorgwikiSinking_of_the_RMS_Titanic2017

[12] Pang-Ning Tan Michael Steinbach and Vipin KumarIntroduction to Data Mining Pearson Education India 2006

[13] Terry Therneau Beth Atkinson and Brian Ripley rpartAvailable at CRAN R-project orgpackage= rpart AccessedMay (2015)

4243

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

References (5 of 5)

[14] Maria Dolores Ugarte Ana F Militino and Alan T ArnholtProbability and Statistics with R CRC Press 2008

4343

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

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 121 Sources of data 1

211 R 1212 R package ldquotitanicrdquo 2213 R package ldquovcdExtrardquo 2214 The crew 6

22 Questions that can be asked 623 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 Titanicrsquos 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 ldquointernalrdquo dataset 119 Missing data map for the ldquotitanic3rdquo 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 thesethe 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 YorkNew York on 10 April 1912 She had a fire in her coal stores that started almost 10 days before she sailedand continued after leaving South Hampton[9] She had 16 lifeboats and four Engelhardt ldquocollapsiblesrdquo (seeFigure 2 on page 3) that could collectively hold 1178 people (aka souls)[5] When the Titanic set sailshe had approximately 2224 passengers and crew Titanic could had a maximum capacity of 3327 soulsThe 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

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

21 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

211 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)

ldquoThis data set provides information on the fate of passengers on the fatal maiden voyage of theocean liner rsquoTitanicrsquo summarized according to economic status (class) sex age and survivalrdquo

R Staff [6]

2httpwwwphillyseaportorgweb_exhibitsmini_exhibitstitanic_passenger_listtitanic_passenger_

list-object-passenger_listhtml

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

212 R package ldquotitanicrdquo

A collection of 1309 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 ldquoTitanicrdquo summarized according to economic status (class) sex age and survivalWhereas the base R Titanic data found by calling data(ldquoTitanicrdquo) 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

213 R package ldquovcdExtrardquo

A collection of 1309 passenger records

Provides additional data sets methods and documentation to complement the rsquovcdrsquo packagefor Visualizing Categorical Data and the rsquognmrsquo package for Generalized Nonlinear Models

3httpswwwkagglecomctitanic

2

Figure 2 Titanicrsquos Collapsible Boat D approaches RMS Carpathia

3

Figure 3 Commemorative sailing booklet4

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 ldquowomenand children firstrdquo

5

Friendly et al [2]

The vcdExtraTitanicp is a data frame with 1309 observations on the following 6 variables (see Figure 5on the following page)

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

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

bull sex a factor with levels female and male

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

bull sibsp number of siblings or spouses aboard integer 08

bull parch number of parents or children aboard integer 06

In many ways ldquovcdExtraTitanicprdquo is a union of the ldquotitanictitanic trainrdquo and ldquotitanictitanic testrdquodatasets less some of the columnular values The vcdExtraTitanicp 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

214 The crew

The previous sections focused on Titanicrsquos 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

22 Questions that can be asked

Every person that sailed on the Titanic had a long list of attributes that could be used to describe themThese attributes include

bull Class (1st 2nd 3rd crew other)

bull Age

bull Fare

bull Gender

bull Number of traveling family members

bull Number of siblings

bull Place of embarkation or

4httpwwwplimsollorgStartHereAboutUsdefaultasp5httpwwwplimsollorgSouthamptonTitanictitaniccrewlistDefaultasp

6

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 predictionsR R script supports a variety of passparameters or command line arguments

Name Switch Default MeaningsourceSelection s internal Which dataset to usetrainingPercentage 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

bull 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 predictionsR have all attributes The internal dataset is missing theattribute ldquoembarkationrdquo (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 isWhat 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 grouprsquos traveling class (1st 2nd 3rd and crew)

23 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)

predictionsR incorporates four different partitioning algorithms (one algorithm has two variants)They are

rpart from the rpart library ldquoRecursive partitioning for classification regression and survival trees Animplementation of most of the functionality of the 1984 book by Breiman Friedman Olshen andStonerdquo[]

C50 from the C50 library ldquoC50 decision trees and rule-based models for pattern recognitionrdquo []

Random Forest from the randomForest library ldquoClassification and regression based on a forest of treesusing random inputsrdquo[]

J48 from the RWeka library ldquoAn R interface to Weka (Version 391) Weka is a collection of machinelearning algorithms for data mining tasks written in Java containing tools for data pre-processingclassification regression clustering association rules and visualizationrdquo[] The J48 algorithm is runin a pruned and unpruned mode

10

Figure 8 Missing data map for the ldquointernalrdquo dataset

11

Figure 9 Missing data map for the ldquotitanic3rdquo 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 07826087 07826087 07826087 07826087 07826087Kappa 04061712 04061712 04061712 04061712 04061712

(see Figure 10on page 15)

(see Figure 11on page 16)

(see Figure 12on page 17)

(see Figure 13on page 17)

(see Figure 14on page 18)

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 questionpredictionsR 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 (1178 versus3327)

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

3 The values from in built in R data set (datasetsTitanic)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 3rdand crew)

5 Various passenger and crew lists (with more attributes than the built in Titanic dataset) are availableand 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 rdquoUnusual Episoderdquo Data Revisited Journal of Statistics Education 3 (1995) no 31ndash7

[2] Michael Friendly Heather Turner Achim Zeileis and Maintainer Michael Friendly Package rsquovcdExtrarsquo(2016)

[3] Paul Hendricks Titanic Passenger Survival Data Set httpsgithubcompaulhendrickstitanic2015

[4] Encyclopedia Titanica Staff Titanic Survivors httpswwwencyclopedia-titanicaorg

titanic-survivors 2017

[5] History Staff Titanic httpwwwhistorycomtopicstitanic 2017

[6] R Staff Survival of passengers on the Titanic packagedatasets 2017

[7] Titanic Facts Staff Titanic Survivors httpwwwtitanicfactsnettitanic-survivorshtml2017

[8] Titanic Universe Staff How Many People Survived the Titanic Disaster httpwww

titanicuniversecomhow-many-people-survived-the-titanic-disaster1253 2017

[9] Wikipedia Staff British Wreck Commissionerrsquos inquiry into the sinking of the RMS TitanichttpsenwikipediaorgwikiBritish_Wreck_Commissioner27s_inquiry_into_the_sinking_

of_the_RMS_Titanic 2017

14

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

lsquoA Mosaic plot of R Titanic datarsquo on page 5 ndash

library(titanic)

library(graphics)

mosaicplot(Titanic main = Survival on the Titanic)

lsquovcdExtra Titanic datarsquo on page 7 ndash

library(vcdExtra)

data(Titanicp)

plot(Titanicp)

lsquovcdExtra Titanic data decision tree based on passenger class age and number and type of traveling companionsrsquo on page 8ndash

library(rpart)

library(rpartplot)

data(Titanicp package=vcdExtra)

rp0 lt- rpart(survived ~ pclass + age data=Titanicp)

rpartplot(rp0 type=0 extra=2 cex=15)

lsquovcdExtra Titanic data decision tree based on all available datarsquo on page 9 ndash

library(rpart)

library(rpartplot)

data(Titanicp package=vcdExtra)

rp0 lt- rpart(survived ~ data=Titanicp)

rpartplot(rp0 type=0 extra=2 cex=15)

lsquoMissing data map for the ldquointernalrdquo datasetrsquo on page 11 ndash

source(predictionsR)

library(Amelia)

missmap(d$titanicPeople)

lsquoMissing data map for the ldquotitanic3rdquo datasetrsquo on page 12 ndash

source(predictionsR)

d lt- main(sourceSelection=titanic3)

library(Amelia)

missmap(d$titanicPeople)

20

B Files

A collection of miscellaneous files mentioned in the report

bull titanic3xls ndash A consolidated list of passengers

bull predictionsR ndash An R script to demonstrate the different partitioning approaches and their results

21

titanic3

Chuck Cartledge

rm(list=ls()) httpsrstudio-pubs-statics3amazonawscom123908_3df6d25c8ad04030a2a429661125a59bhtmllibrary(lattice)library(tree)library(plotrix)library(sandwich)library(grid)library(vcd)library(gnm)library(stats4)library(zoo)library(mvtnorm)library(modeltools)library(strucchange)library(sandwich)library(gplots)library(psych)library(readr)library(dplyr)library(party)library(rpart)library(rpartplot)library(ROCR)library(RWeka)library(C50)library(randomForest)library(reprtree)library(cwhmisc) library(stringr) library(gdata)library(getopt)showMissingData lt- function(d title=Dataframe of interest) missmap(d main=title)plotDataCollection lt- function(data) dataCollectionDataFrame lt- data$dataCollectionDataFrame percentages lt- data$percentages cols lt- 1ncol(dataCollectionDataFrame) pchs lt- cols plot(00 type=n xlim=c(min(percentages) max(percentages)) ylim=c(min(04 min(dataCollectionDataFrame)) max(1 max(dataCollectionDataFrame))) xlab=Training percentage ylab=Accuracy ) legend(topleftnames(dataCollectionDataFrame) col=cols pch=pchs) for(i in cols) points(percentages dataCollectionDataFrame[i] col=i pch=pchs[i] type=b) abline(h=05 col=red )setValueBasedOnCommandLine lt- function(input default) returnValue lt- setValue(input default) setValue lt- function(inputValue default) returnValue lt- default if (typeof(default) == logical) if (isnull(inputValue) == FALSE) returnValue lt- default else if (isnull(inputValue) == FALSE) returnValue lt- inputValue returnValueloadTitanic3 lt- function() fileName lt- Librarytitanic3xls d lt- readxls(fileName header=TRUE sheet = 1 stringsAsFactors = FALSE) d --------------------------------------------------------- Function C50graphviz Version 220 Date 26092014 Author Athanasios Tsakonas This code implements C50graphviz conversion routine ---------------------------------------------------------C50graphviz lt- function( C50model filename fontname =Arialcoldraw =black colfont =bluecolconclusion =lightpinkcolquestion = grey78 shapeconclusion =box3dshapequestion =diamond boolsubstitute = None prefix=FALSE vertical=TRUE ) httpr-project-thanosblogspotcom201409plot-c50-decision-trees-in-rhtml treeout lt- C50model$output treeoutlt- substr(treeout cpos(treeout Decision tree start=1)+14nchar(treeout)) treeoutlt- substr(treeout 1cpos(treeout Evaluation on training data start=1)-2) variables lt- dataframe(matrix(nrow=500 ncol=4)) names(variables) lt- c(SYMBOLTOKEN TYPE QUERY) connectors lt- dataframe(matrix(nrow=500 ncol=3)) names(connectors) lt- c(TOKEN STARTEND) theStack lt- dataframe(matrix(nrow=500 ncol=1)) names(theStack) lt- c(ITEM) theStackIndex lt- 1 currentvar lt- 1 currentcon lt- 1 open_connection lt- TRUE previousindent lt- -1 firstindent lt- 4 substitutes lt- dataframe(None=c(= 0= 1) yesno=c(noyes) truefalse=c(false true)TF=c(FT)) dtreestringlt-unlist( scan(text= treeout sep=n what =list(character))) for (linecount in c(1length(dtreestring))) lineindentlt-0 shortstring lt- str_trim(dtreestring[linecount] side=left) leadingspaces lt- nchar(dtreestring[linecount]) - nchar(shortstring) lineindent lt- leadingspaces4 dtreestring[linecount]lt-str_trim(dtreestring[linecount] side=left) while (isna(cpos(dtreestring[linecount] start=1)) ) lineindentlt-lineindent + 1 dtreestring[linecount]lt-substr(dtreestring[linecount] ifelse(isna(cpos(dtreestring[linecount] start=1)) 1 cpos(dtreestring[linecount] start=1)+4) nchar(dtreestring[linecount]) ) shortstring lt- str_trim(dtreestring[linecount] side=left) leadingspaces lt- nchar(dtreestring[linecount]) - nchar(shortstring) lineindent lt- lineindent + leadingspaces4 dtreestring[linecount]lt-str_trim(dtreestring[linecount] side=left) if (isna(cpos(dtreestring[linecount] start=1))) lineindentlt- lineindent + 1 dtreestring[linecount]lt-substr(dtreestring[linecount] ifelse(isna(cpos(dtreestring[linecount] start=1)) 1 cpos(dtreestring[linecount] start=1)+4) nchar(dtreestring[linecount]) ) dtreestring[linecount]lt-str_trim(dtreestring[linecount]) stringlist lt- strsplit(dtreestring[linecount]) stringpart lt- strsplit(unlist(stringlist)[1]s) if (open_connection==TRUE) variables[currentvarTOKEN] lt- unlist(stringpart)[1] variables[currentvarSYMBOL] lt- paste(nodeascharacter(currentvar) sep=) variables[currentvarTYPE] lt- shapequestion variables[currentvarQUERY] lt- 1 theStack[theStackIndexITEM]lt-variables[currentvarSYMBOL] theStack[theStackIndexINDENT] lt-firstindent theStackIndexlt-theStackIndex+1 currentvar lt- currentvar + 1 if(currentvargt2) connectors[currentcon - 1END] lt- variables[currentvar - 1 SYMBOL] connectors[currentconTOKEN] lt- paste(unlist(stringpart)[2]unlist(stringpart)[3]) if (connectors[currentconTOKEN]=== 0) connectors[currentconTOKEN] lt- ascharacter(substitutes[1boolsubstitute]) if (connectors[currentconTOKEN]=== 1) connectors[currentconTOKEN] lt- ascharacter(substitutes[2boolsubstitute]) if (open_connection==TRUE) if (lineindentltpreviousindent) theStackIndex lt- theStackIndex-(( previousindent- lineindent) +1 ) currentsymbol lt-theStack[theStackIndexITEM] else currentsymbol lt-variables[currentvar - 1SYMBOL] else currentsymbol lt-theStack[theStackIndex-((previousindent -lineindent ) +1 )ITEM] theStackIndex lt- theStackIndex-(( previousindent- lineindent) ) connectors[currentcon START] lt- currentsymbol currentcon lt- currentcon + 1 open_connection lt- TRUE if (length(unlist(stringlist))==2) stringpart2 lt- strsplit(unlist(stringlist)[2]s) variables[currentvarTOKEN] lt- paste(ifelse((prefix==FALSE)Class) unlist(stringpart2)[2]) variables[currentvarSYMBOL] lt- paste(nodeascharacter(currentvar) sep=) variables[currentvarTYPE] lt- shapeconclusion variables[currentvarQUERY] lt- 0 currentvar lt- currentvar + 1 connectors[currentcon - 1END] lt- variables[currentvar - 1SYMBOL] open_connection lt- FALSE previousindentlt-lineindent runningstring lt- paste(digraph g graph sep=n) runningstring lt- paste(runningstring [rankdir= sep=) runningstring lt- paste(runningstring ifelse(vertical==TRUETBLR) sep= ) runningstring lt- paste(runningstring ] sep=) for (lines in c(1(currentvar-1))) runningline lt- paste(variables[linesSYMBOL] [shape=) runningline lt- paste(runninglinevariables[linesTYPE] sep= ) runningline lt- paste(runningline label = sep= ) runningline lt- paste(runninglinevariables[linesTOKEN] sep= ) runningline lt- paste(runningline style=filled fontcolor= sep=) runningline lt- paste(runningline colfont) runningline lt- paste(runningline color= ) runningline lt- paste(runningline coldraw) runningline lt- paste(runningline fontname=) runningline lt- paste(runningline fontname) runningline lt- paste(runningline fillcolor=) runningline lt- paste(runningline ifelse(variables[linesQUERY]== 0 colconclusioncolquestion)) runningline lt- paste(runningline]) runningstring lt- paste(runningstring runningline sep=n) for (lines in c(1(currentcon-1))) runningline lt- paste (connectors[linesSTART] -gt) runningline lt- paste (runningline connectors[linesEND]) runningline lt- paste (runningline[label=) runningline lt- paste (runninglineconnectors[linesTOKEN] sep=) runningline lt- paste (runningline fontname= sep=) runningline lt- paste (runningline fontname) runningline lt- paste (runningline]) runningstring lt- paste(runningstring runningline sep=n) runningstring lt- paste(runningstring) cat(runningstring) write(runningstring file=filename) sink(filename split=TRUE) cat(runningstring) sink()dumpObject lt- function(object comment= verbose=TRUE) if(verbose == TRUE) print(sprintf(s -- Dumping the object s (of type s) comment deparse(substitute(object)) typeof(object)) ) print(object) predictionResults lt- function(models testData verbose=FALSE) Examining the predictions on the testing set from p 211 values = dataframe(matrix(ncol = length(models) nrow = 6)) rownames(values) = c(True + True - False + False - Accuracy Kappa) names(values) = names(models) i lt- 1 for (n in names(models)) temp lt- predict(models[[n]] newdata=testData type=class) dumpObject(temp verbose=verbose) tab = table(testData$survivedtemp) dumpObject(tab verbose=verbose) values[1i] = tab[11] values[2i] = tab[22] values[3i] = tab[12] values[4i] = tab[21] values[5i] = sum(diag(tab))sum(tab) values[6i] = cohenkappa(tab)[1] i lt- i + 1 dumpObject(values Should have values verbose=verbose) valuesgetTitanicPeople lt- function(selection) titanicPeople lt- NULL print(sprintf(Getting Titanic people using selection s selection)) switch(selection vcdExtra = titanicPeople=Titanicp titanicPeople$sex lt- paste0(toupper(substr(ascharacter(titanicPeople$sex) 1 1)) substr(ascharacter(titanicPeople$sex) 2 nchar(ascharacter(titanicPeople$sex)))) temp lt- c() for (i in 1length(titanicPeople$pclass)) pclass lt- NA if (titanicPeople$pclass[i] == 1st) pclass lt- 1 else if (ascharacter(titanicPeople$pclass[i]) == 2nd) pclass lt- 2 else if (ascharacter(titanicPeople$pclass[i]) == 3rd) pclass lt- 3 else pclass lt- 0 titanicPeople$pclass[i] lt- pclass temp lt- c(temp pclass) titanicPeople$pclass lt- temp titanicPeople$survived lt- ifelse(titanicPeople$survived == survivedyes = Saved no =Lost) titanicPeople$embarked lt- NA titanicPeople$fare lt- NA titanic3 = fileName lt- Librarytitanic3xls titanicPeople lt- readxls(fileName header=TRUE sheet = 1 stringsAsFactors = FALSE) titanicPeople$sex lt- paste0(toupper(substr(ascharacter(titanicPeople$sex) 1 1)) substr(ascharacter(titanicPeople$sex) 2 nchar(ascharacter(titanicPeople$sex)))) internal = temp lt- dataframe(Titanic) titanicPeople lt- dataframe(survived=ascharacter() embarked=ascharacter() sex=ascharacter() sibsp=asnumeric() parch=asnumeric() fare=asnumeric() pclass=asnumeric() ) for (i in 1nrow(temp)) if (temp$Freq[i] gt 0) for (j in 1temp$Freq[i]) terminus lt- ifelse(temp$Survived[i] == Yesyes = Saved no =Lost) gender lt- paste0(toupper(substr(ascharacter(temp$Sex[i]) 1 1)) substr(ascharacter(temp$Sex[i]) 2 nchar(ascharacter(temp$Sex[i])))) pclass lt- ascharacter(temp$Class[i]) if (ascharacter(temp$Class[i]) == 1st) pclass lt- 1 else if (ascharacter(temp$Class[i]) == 2nd) pclass lt- 2 else if (ascharacter(temp$Class[i]) == 3rd) pclass lt- 3 else pclass lt- 0 also lt- dataframe( survived=terminus embarked=NA sex=gender sibsp=0 parch=0 fare=0 pclass=pclass ) titanicPeople lt- rbind(titanicPeople also) ) titanicPeople lt- select(titanicPeople survived embarked sex sibsp parch fare pclass) titanicPeople lt- mutate(titanicPeople survived=factor(survived)) titanicPeople lt- mutate(titanicPeople sex=factor(sex)) titanicPeople main lt- function(sourceSelection = internal trainingPercentage = 30 dataCollection = FALSE verbose = FALSE ) setseed(100) predictions lt- list() textFileName lt- tempfile() c50ImageFileName lt- Imagesdatacollectionc50png datacollectionImageFileName lt- Imagesdatacollectionpng trainingPercentageMin lt- NA trainingPercentageMax lt- NA trainingPercentageStep lt- NA dataCollectionDataFrame lt- NULL spec lt- matrix(c( sourceSelection s 1 character trainingPercentage t 1 double dataCollection d 0 logical verbose v 0 logical ) ncol = 4 byrow = TRUE) opt lt- getopt(spec) sourceSelection lt- setValueBasedOnCommandLine(opt$sourceSelection sourceSelection) trainingPercentage lt- setValueBasedOnCommandLine(opt$trainingPercentage trainingPercentage) dataCollection lt- setValueBasedOnCommandLine(opt$dataCollection dataCollection) verbose lt- setValueBasedOnCommandLine(opt$verbose verbose) titanicPeople lt- getTitanicPeople(sourceSelection) dumpObject(names(titanicPeople) verbose=verbose) dumpObject(head(titanicPeople) verbose=verbose) dumpObject(table(titanicPeople$pclass) verbose=verbose) dumpObject(table(titanicPeople$survived) verbose=verbose) if (dataCollection == FALSE) trainingPercentageMin lt- trainingPercentage trainingPercentageMax lt- trainingPercentage trainingPercentageStep lt- 10 else trainingPercentageMin lt- trainingPercentage trainingPercentageStep lt- 10 trainingPercentageMax lt- min(999999 100 - trainingPercentageStep + trainingPercentageMin) percentages lt- seq(trainingPercentageMin trainingPercentageMax trainingPercentageStep) numberOfPeople lt- nrow(titanicPeople) for (percentage in percentages) indices lt- sample(1numberOfPeople size=numberOfPeoplepercentage100) data lt- NULL data$training lt- titanicPeople[indices] data$testing lt- titanicPeople[-indices] print(sprintf(Total number of rows = 0f training is f numberOfPeople percentage)) print(sprintf(There are 0f rows in the training set nrow(data$training))) print(sprintf(There are 0f rows in the test set nrow(data$test))) formula lt- survived ~ sex + sibsp + parch + pclass formula lt- survived ~ sex + sibsp + parch predictions[[C50]] lt- C50formula(asformula(formula) data=data$training) predictions[[Random Forest]] lt- randomForest(asformula(formula) data=data$training keepforest=TRUE proximity=TRUE ntree=100 importance=TRUE) predictions[[rpart]] lt- rpart(asformula(formula) data=data$training method=class cp=00001) predictions[[J48 (unpruned)]] lt- J48(asformula(formula) data=data$training control=Weka_control(U=TRUE)) predictions[[J48 (pruned)]] lt- J48(asformula(formula) data=data$training control=Weka_control(U=FALSE C=00001)) results lt- predictionResults(predictions data$test verbose) dumpObject(results verbose=verbose) if (dataCollection == TRUE) if (isnull(dataCollectionDataFrame) == TRUE) dataCollectionDataFrame lt- asdataframe(results[Accuracy]) else dataCollectionDataFrame lt- rbind(dataCollectionDataFrame asdataframe(results[Accuracy])) else for (n in names(predictions)) print(sprintf(Plotting data from s n)) switch(class(predictions[[n]])[1] randomForestformula = httpstatsstackexchangecomquestions41443how-to-actually-plot-a-sample-tree-from-randomforestgettree reprtreeplotgetTree(predictions[[n]]) rpart = rpartplot(predictions[[n]]) J48 = plot(predictions[[n]]) C50 = C50graphviz(predictions[[n]] filename=textFileName) command lt- sprintf(dot -Tpng s gt s textFileName c50ImageFileName) system(command) print(Default case) plot(predictions[[n]]) ) unlink(textFileName) returnValue lt- list(dataCollectionDataFrame=asdataframe(dataCollectionDataFrame) percentages=asvector(percentages) titanicPeople=titanicPeople ) if (dataCollection == TRUE) png(filename=datacollectionImageFileName width=960 height=480 type=c(cairo)) plotDataCollection(returnValue) devoff() returnValued lt- main()

Chuck Cartledge
Chuck Cartledge

titanic3

Chuck Cartledge

Bio150 ML Frank franklakeforestedu Page 1 of 3 [httpbiostatmcvanderbilteduwikipubMainDataSetstitanichtml]

Titanic Datasets The titanic and titanic2 data frames describe the survival status of individual passengers on the Titanic The titanic data frame does not contain information from the crew but it does contain actual ages of half of the passengers The principal source for data about Titanic passengers is the Encyclopedia Titanica The datasets used here were begun by a variety of researchers One of the original sources is Eaton amp Haas (1994) Titanic Triumph and Tragedy Patrick Stephens Ltd which includes a passenger list created by many researchers and edited by Michael A Findlay

The variables on our extracted dataset are pclass survived name age embarked homedest room ticket boat and sex pclass refers to passenger class (1st 2nd 3rd) and is a proxy for socio-economic class Age is in years and some infants had fractional values The titanic2 data frame has no missing data and includes records for the crew but age is dichotomized at adult vs child These data were obtained from Robert Dawson Saint Marys University E-mail The variables are pclass age sex survived These data frames are useful for demonstrating many of the functions in Hmisc as well as demonstrating binary logistic regression analysis using the Design library For more details and references see Simonoff Jeffrey S (1997) The unusual episode and a second statistics course J Statistics Education Vol 5 No 1 Thomas Cason of UVa has greatly updated and improved the titanic data frame using the Encyclopedia Titanica and created a new dataset called titanic3 These datasets reflects the state of data available as of 2 August 1999 Some duplicate passengers have been dropped many errors corrected many missing ages filled in and new variables created Click here for information about how this datatset was constructed

DATASET specs

NAME titanic3 TYPE Census SIZE 1309 Passengers 14 Variables DESCRIPTIVE ABSTRACT

The titanic3 data frame describes the survival status of individual passengers on the Titanic The titanic3 data frame does not contain information for the crew but it does contain actual and estimated ages for almost 80 of the passengers SOURCES

Hind Philip Encyclopedia Titanica Online-only resource Retrieved 01Feb2012 from httpwwwencyclopedia-titanicaorg

Bio150 ML Frank franklakeforestedu Page 2 of 3 VARIABLE DESCRIPTIONS Pclass Passenger Class (1 = 1st 2 = 2nd 3 = 3rd) survival Survival (0 = No 1 = Yes) name Name sex Sex age Age sibsp Number of SiblingsSpouses Aboard parch Number of ParentsChildren Aboard ticket Ticket Number fare Passenger Fare (British pound) cabin Cabin embarked Port of Embarkation (C = Cherbourg Q = Queenstown S = Southampton) boat Lifeboat body Body Identification Number homedest HomeDestination SPECIAL NOTES Pclass is a proxy for socio-economic status (SES) 1st ~ Upper 2nd ~ Middle 3rd ~ Lower Age is in Years Fractional if Age less than One (1) If the Age is estimated it is in the form xx5 Fare is in Pre-1970 British Pounds () Conversion Factors 1 = 12s = 240d and 1s = 20d With respect to the family relation variables (ie sibsp and parch) some relations were ignored The following are the definitions used for sibsp and parch

Sibling Brother Sister Stepbrother or Stepsister of Passenger Aboard Titanic Spouse Husband or Wife of Passenger Aboard Titanic (Mistresses and Fiances

Ignored) Parent Mother or Father of Passenger Aboard Titanic Child Son Daughter Stepson or Stepdaughter of Passenger Aboard Titanic

Other family relatives excluded from this study include cousins nephewsnieces auntsuncles and in-laws Some children travelled only with a nanny therefore parch=0 for them As well some travelled with very close friends or neighbors in a village however the definitions do not support such relations

Bio150 ML Frank franklakeforestedu Page 3 of 3 An interesting result may be obtained using functions from the Hmisc library attach (titanic3) plsmo (age survived group=sex datadensity=T) or group=pclass plot (naclus (titanic3)) study patterns of missing values summary (survived ~ age + sex + pclass + sibsp + parch data=titanic3) SUBMITTED BY Thomas E Cason tcasonvirginiaedu From MLF Prior to February 15 1971 (Decimal day or D-day) monetary amounts in the UK were expressed as pounds (pound) shillings (s) and pence (d) where pound1 = 20s = 240d After 1970 there were 100 pennies in a pound so one (new) penny = 24 old pence All numbers should be entered in decimal rather than fractional form (for example 15 rather than 1 12)

Lawrence H Officer Five Ways to Compute the Relative Value of a UK Pound Amount 1830 to Present MeasuringWorthcom Retrieved 2011Sep06 from httpwwwmeasuringworthcomukcompare

Chuck Cartledge

httpswww-userscsumnedu~kumardmbookch4pdf httpswwwyoutubecomwatchv=GOJN9SKl_OErm(list=ls())library(grid)library(mvtnorm)library(modeltools)library(stats4)library(strucchange)library(zoo)library(sandwich)library(gplots)library(readr)library(dplyr)library(party)library(rpart)library(rpartplot)library(ROCR)plotRawData lt- function(d title=) x=23 plot(x=c(-11) y = c(-1 1) type=n xlab=X values ylab=Y values main=paste(red = expression(05 lt= sqrt(x^2+y^2) ampamp sqrt(x^2+y^2) lt= 10) sep=) ) for (j in 12) points(d[which(d[3]==j)c(12)] col=j) plotErrors lt- function(data) plot(x=c(0 max(data[[nodes]])) y=c(01) type=n ylab=Model error xlab=Number of internal nodes ) cols=c(blue red) pchs=c(12) points(data[[nodes]] data[[training]] type=b col=cols[1] pch=pchs[1]) points(data[[nodes]] data[[testing]] type=b col=cols[2] pch=pchs[2]) legend(topright legend=c(Training error Testing error) pch=pchs col=cols) points(spline(data[[nodes]] data[[testing]]))decisionTree lt- function(dLocal cp=01) dTree lt- rpart(classification ~ x1 + x2 data = dLocal method=class control=c(cp=cp minsplit=1 minbucket=1) ) rpartplot(dTree type = 4 extra = 106)computeAccuracy lt- function(model data) performance lt- predict(model data type=class) temp lt- table(data$classification performance) accuracy lt- sum(diag(temp))sum(temp) accuracymain lt- function() setseed(123) numberOfDataPoints lt- 1000 trainingPercentage lt- 70 cps lt- c(01 005 0025 001 0005 0001 00001 000001) x1 lt- runif(numberOfDataPoints min = -1 max = 1) x2 lt- runif(numberOfDataPoints min = -1 max = 1) x lt- cbind(x1 x2) classification lt- c() tempList lt- c() for (i in 1nrow(x)) temp lt- sqrt(x[i1]^2 + x[i2]^2) cl lt- 1 if ((05 lt= temp) ampamp (temp lt= 10)) cl lt- 2 classification lt- c(classification cl) tempList lt- c(tempList temp) x lt- cbind(x classification tempList) plotRawData(x) s lt- sample(numberOfDataPoints numberOfDataPoints trainingPercentage 100) xtraining lt- asdataframe(x[s]) xtesting lt- asdataframe(x[-s]) returnValue lt- list() for (cp in cps) ml lt- decisionTree(xtraining cp = cp) print(ml) trainingError lt- 1 - computeAccuracy(ml xtraining) testingError lt- 1 - computeAccuracy(ml xtesting) nodes lt- tail(ml$cptable[nsplit] n=1) print(sprintf(cp = f internal nodes= 0f training error = 2f testing error = 2f cp nodes trainingError testingError ) ) returnValue[[nodes]] lt- c(returnValue[[nodes]] nodes) returnValue[[testing]] lt- c(returnValue[[testing]] testingError) returnValue[[training]] lt- c(returnValue[[training]] trainingError) print(The program has ended) returnValued lt- main()

Chuck Cartledge
  • Introduction
  • Background
    • ``Well settled data
    • Data from diverse places
      • Classification problem
        • What is it
        • Training and testing
        • Types of errors
          • Techniques
            • Results
              • Hands-on
              • Q amp A
              • Conclusion
              • References
              • Files
pclass survived name sex age sibsp parch ticket fare cabin embarked boat body homedest
1 1 Allen Miss Elisabeth Walton female 29 0 0 24160 2113375 B5 S 2 St Louis MO
1 1 Allison Master Hudson Trevor male 09167 1 2 113781 1515500 C22 C26 S 11 Montreal PQ Chesterville ON
1 0 Allison Miss Helen Loraine female 2 1 2 113781 1515500 C22 C26 S Montreal PQ Chesterville ON
1 0 Allison Mr Hudson Joshua Creighton male 30 1 2 113781 1515500 C22 C26 S 135 Montreal PQ Chesterville ON
1 0 Allison Mrs Hudson J C (Bessie Waldo Daniels) female 25 1 2 113781 1515500 C22 C26 S Montreal PQ Chesterville ON
1 1 Anderson Mr Harry male 48 0 0 19952 265500 E12 S 3 New York NY
1 1 Andrews Miss Kornelia Theodosia female 63 1 0 13502 779583 D7 S 10 Hudson NY
1 0 Andrews Mr Thomas Jr male 39 0 0 112050 00000 A36 S Belfast NI
1 1 Appleton Mrs Edward Dale (Charlotte Lamson) female 53 2 0 11769 514792 C101 S D Bayside Queens NY
1 0 Artagaveytia Mr Ramon male 71 0 0 PC 17609 495042 C 22 Montevideo Uruguay
1 0 Astor Col John Jacob male 47 1 0 PC 17757 2275250 C62 C64 C 124 New York NY
1 1 Astor Mrs John Jacob (Madeleine Talmadge Force) female 18 1 0 PC 17757 2275250 C62 C64 C 4 New York NY
1 1 Aubart Mme Leontine Pauline female 24 0 0 PC 17477 693000 B35 C 9 Paris France
1 1 Barber Miss Ellen Nellie female 26 0 0 19877 788500 S 6
1 1 Barkworth Mr Algernon Henry Wilson male 80 0 0 27042 300000 A23 S B Hessle Yorks
1 0 Baumann Mr John D male 0 0 PC 17318 259250 S New York NY
1 0 Baxter Mr Quigg Edmond male 24 0 1 PC 17558 2475208 B58 B60 C Montreal PQ
1 1 Baxter Mrs James (Helene DeLaudeniere Chaput) female 50 0 1 PC 17558 2475208 B58 B60 C 6 Montreal PQ
1 1 Bazzani Miss Albina female 32 0 0 11813 762917 D15 C 8
1 0 Beattie Mr Thomson male 36 0 0 13050 752417 C6 C A Winnipeg MN
1 1 Beckwith Mr Richard Leonard male 37 1 1 11751 525542 D35 S 5 New York NY
1 1 Beckwith Mrs Richard Leonard (Sallie Monypeny) female 47 1 1 11751 525542 D35 S 5 New York NY
1 1 Behr Mr Karl Howell male 26 0 0 111369 300000 C148 C 5 New York NY
1 1 Bidois Miss Rosalie female 42 0 0 PC 17757 2275250 C 4
1 1 Bird Miss Ellen female 29 0 0 PC 17483 2217792 C97 S 8
1 0 Birnbaum Mr Jakob male 25 0 0 13905 260000 C 148 San Francisco CA
1 1 Bishop Mr Dickinson H male 25 1 0 11967 910792 B49 C 7 Dowagiac MI
1 1 Bishop Mrs Dickinson H (Helen Walton) female 19 1 0 11967 910792 B49 C 7 Dowagiac MI
1 1 Bissette Miss Amelia female 35 0 0 PC 17760 1356333 C99 S 8
1 1 Bjornstrom-Steffansson Mr Mauritz Hakan male 28 0 0 110564 265500 C52 S D Stockholm Sweden Washington DC
1 0 Blackwell Mr Stephen Weart male 45 0 0 113784 355000 T S Trenton NJ
1 1 Blank Mr Henry male 40 0 0 112277 310000 A31 C 7 Glen Ridge NJ
1 1 Bonnell Miss Caroline female 30 0 0 36928 1648667 C7 S 8 Youngstown OH
1 1 Bonnell Miss Elizabeth female 58 0 0 113783 265500 C103 S 8 Birkdale England Cleveland Ohio
1 0 Borebank Mr John James male 42 0 0 110489 265500 D22 S London Winnipeg MB
1 1 Bowen Miss Grace Scott female 45 0 0 PC 17608 2623750 C 4 Cooperstown NY
1 1 Bowerman Miss Elsie Edith female 22 0 1 113505 550000 E33 S 6 St Leonards-on-Sea England Ohio
1 1 Bradley Mr George (George Arthur Brayton) male 0 0 111427 265500 S 9 Los Angeles CA
1 0 Brady Mr John Bertram male 41 0 0 113054 305000 A21 S Pomeroy WA
1 0 Brandeis Mr Emil male 48 0 0 PC 17591 504958 B10 C 208 Omaha NE
1 0 Brewe Dr Arthur Jackson male 0 0 112379 396000 C Philadelphia PA
1 1 Brown Mrs James Joseph (Margaret Tobin) female 44 0 0 PC 17610 277208 B4 C 6 Denver CO
1 1 Brown Mrs John Murray (Caroline Lane Lamson) female 59 2 0 11769 514792 C101 S D Belmont MA
1 1 Bucknell Mrs William Robert (Emma Eliza Ward) female 60 0 0 11813 762917 D15 C 8 Philadelphia PA
1 1 Burns Miss Elizabeth Margaret female 41 0 0 16966 1345000 E40 C 3
1 0 Butt Major Archibald Willingham male 45 0 0 113050 265500 B38 S Washington DC
1 0 Cairns Mr Alexander male 0 0 113798 310000 S
1 1 Calderhead Mr Edward Pennington male 42 0 0 PC 17476 262875 E24 S 5 New York NY
1 1 Candee Mrs Edward (Helen Churchill Hungerford) female 53 0 0 PC 17606 274458 C 6 Washington DC
1 1 Cardeza Mr Thomas Drake Martinez male 36 0 1 PC 17755 5123292 B51 B53 B55 C 3 Austria-Hungary Germantown Philadelphia PA
1 1 Cardeza Mrs James Warburton Martinez (Charlotte Wardle Drake) female 58 0 1 PC 17755 5123292 B51 B53 B55 C 3 Germantown Philadelphia PA
1 0 Carlsson Mr Frans Olof male 33 0 0 695 50000 B51 B53 B55 S New York NY
1 0 Carrau Mr Francisco M male 28 0 0 113059 471000 S Montevideo Uruguay
1 0 Carrau Mr Jose Pedro male 17 0 0 113059 471000 S Montevideo Uruguay
1 1 Carter Master William Thornton II male 11 1 2 113760 1200000 B96 B98 S 4 Bryn Mawr PA
1 1 Carter Miss Lucile Polk female 14 1 2 113760 1200000 B96 B98 S 4 Bryn Mawr PA
1 1 Carter Mr William Ernest male 36 1 2 113760 1200000 B96 B98 S C Bryn Mawr PA
1 1 Carter Mrs William Ernest (Lucile Polk) female 36 1 2 113760 1200000 B96 B98 S 4 Bryn Mawr PA
1 0 Case Mr Howard Brown male 49 0 0 19924 260000 S Ascot Berkshire Rochester NY
1 1 Cassebeer Mrs Henry Arthur Jr (Eleanor Genevieve Fosdick) female 0 0 17770 277208 C 5 New York NY
1 0 Cavendish Mr Tyrell William male 36 1 0 19877 788500 C46 S 172 Little Onn Hall Staffs
1 1 Cavendish Mrs Tyrell William (Julia Florence Siegel) female 76 1 0 19877 788500 C46 S 6 Little Onn Hall Staffs
1 0 Chaffee Mr Herbert Fuller male 46 1 0 WEP 5734 611750 E31 S Amenia ND
1 1 Chaffee Mrs Herbert Fuller (Carrie Constance Toogood) female 47 1 0 WEP 5734 611750 E31 S 4 Amenia ND
1 1 Chambers Mr Norman Campbell male 27 1 0 113806 531000 E8 S 5 New York NY Ithaca NY
1 1 Chambers Mrs Norman Campbell (Bertha Griggs) female 33 1 0 113806 531000 E8 S 5 New York NY Ithaca NY
1 1 Chaudanson Miss Victorine female 36 0 0 PC 17608 2623750 B61 C 4
1 1 Cherry Miss Gladys female 30 0 0 110152 865000 B77 S 8 London England
1 1 Chevre Mr Paul Romaine male 45 0 0 PC 17594 297000 A9 C 7 Paris France
1 1 Chibnall Mrs (Edith Martha Bowerman) female 0 1 113505 550000 E33 S 6 St Leonards-on-Sea England Ohio
1 0 Chisholm Mr Roderick Robert Crispin male 0 0 112051 00000 S Liverpool England Belfast
1 0 Clark Mr Walter Miller male 27 1 0 13508 1367792 C89 C Los Angeles CA
1 1 Clark Mrs Walter Miller (Virginia McDowell) female 26 1 0 13508 1367792 C89 C 4 Los Angeles CA
1 1 Cleaver Miss Alice female 22 0 0 113781 1515500 S 11
1 0 Clifford Mr George Quincy male 0 0 110465 520000 A14 S Stoughton MA
1 0 Colley Mr Edward Pomeroy male 47 0 0 5727 255875 E58 S Victoria BC
1 1 Compton Miss Sara Rebecca female 39 1 1 PC 17756 831583 E49 C 14 Lakewood NJ
1 0 Compton Mr Alexander Taylor Jr male 37 1 1 PC 17756 831583 E52 C Lakewood NJ
1 1 Compton Mrs Alexander Taylor (Mary Eliza Ingersoll) female 64 0 2 PC 17756 831583 E45 C 14 Lakewood NJ
1 1 Cornell Mrs Robert Clifford (Malvina Helen Lamson) female 55 2 0 11770 257000 C101 S 2 New York NY
1 0 Crafton Mr John Bertram male 0 0 113791 265500 S Roachdale IN
1 0 Crosby Capt Edward Gifford male 70 1 1 WEP 5735 710000 B22 S 269 Milwaukee WI
1 1 Crosby Miss Harriet R female 36 0 2 WEP 5735 710000 B22 S 7 Milwaukee WI
1 1 Crosby Mrs Edward Gifford (Catherine Elizabeth Halstead) female 64 1 1 112901 265500 B26 S 7 Milwaukee WI
1 0 Cumings Mr John Bradley male 39 1 0 PC 17599 712833 C85 C New York NY
1 1 Cumings Mrs John Bradley (Florence Briggs Thayer) female 38 1 0 PC 17599 712833 C85 C 4 New York NY
1 1 Daly Mr Peter Denis male 51 0 0 113055 265500 E17 S 5 9 Lima Peru
1 1 Daniel Mr Robert Williams male 27 0 0 113804 305000 S 3 Philadelphia PA
1 1 Daniels Miss Sarah female 33 0 0 113781 1515500 S 8
1 0 Davidson Mr Thornton male 31 1 0 FC 12750 520000 B71 S Montreal PQ
1 1 Davidson Mrs Thornton (Orian Hays) female 27 1 2 FC 12750 520000 B71 S 3 Montreal PQ
1 1 Dick Mr Albert Adrian male 31 1 0 17474 570000 B20 S 3 Calgary AB
1 1 Dick Mrs Albert Adrian (Vera Gillespie) female 17 1 0 17474 570000 B20 S 3 Calgary AB
1 1 Dodge Dr Washington male 53 1 1 33638 818583 A34 S 13 San Francisco CA
1 1 Dodge Master Washington male 4 0 2 33638 818583 A34 S 5 San Francisco CA
1 1 Dodge Mrs Washington (Ruth Vidaver) female 54 1 1 33638 818583 A34 S 5 San Francisco CA
1 0 Douglas Mr Walter Donald male 50 1 0 PC 17761 1064250 C86 C 62 Deephaven MN Cedar Rapids IA
1 1 Douglas Mrs Frederick Charles (Mary Helene Baxter) female 27 1 1 PC 17558 2475208 B58 B60 C 6 Montreal PQ
1 1 Douglas Mrs Walter Donald (Mahala Dutton) female 48 1 0 PC 17761 1064250 C86 C 2 Deephaven MN Cedar Rapids IA
1 1 Duff Gordon Lady (Lucille Christiana Sutherland) (Mrs Morgan) female 48 1 0 11755 396000 A16 C 1 London Paris
1 1 Duff Gordon Sir Cosmo Edmund (Mr Morgan) male 49 1 0 PC 17485 569292 A20 C 1 London Paris
1 0 Dulles Mr William Crothers male 39 0 0 PC 17580 297000 A18 C 133 Philadelphia PA
1 1 Earnshaw Mrs Boulton (Olive Potter) female 23 0 1 11767 831583 C54 C 7 Mt Airy Philadelphia PA
1 1 Endres Miss Caroline Louise female 38 0 0 PC 17757 2275250 C45 C 4 New York NY
1 1 Eustis Miss Elizabeth Mussey female 54 1 0 36947 782667 D20 C 4 Brookline MA
1 0 Evans Miss Edith Corse female 36 0 0 PC 17531 316792 A29 C New York NY
1 0 Farthing Mr John male 0 0 PC 17483 2217792 C95 S
1 1 Flegenheim Mrs Alfred (Antoinette) female 0 0 PC 17598 316833 S 7 New York NY
1 1 Fleming Miss Margaret female 0 0 17421 1108833 C 4
1 1 Flynn Mr John Irwin (Irving) male 36 0 0 PC 17474 263875 E25 S 5 Brooklyn NY
1 0 Foreman Mr Benjamin Laventall male 30 0 0 113051 277500 C111 C New York NY
1 1 Fortune Miss Alice Elizabeth female 24 3 2 19950 2630000 C23 C25 C27 S 10 Winnipeg MB
1 1 Fortune Miss Ethel Flora female 28 3 2 19950 2630000 C23 C25 C27 S 10 Winnipeg MB
1 1 Fortune Miss Mabel Helen female 23 3 2 19950 2630000 C23 C25 C27 S 10 Winnipeg MB
1 0 Fortune Mr Charles Alexander male 19 3 2 19950 2630000 C23 C25 C27 S Winnipeg MB
1 0 Fortune Mr Mark male 64 1 4 19950 2630000 C23 C25 C27 S Winnipeg MB
1 1 Fortune Mrs Mark (Mary McDougald) female 60 1 4 19950 2630000 C23 C25 C27 S 10 Winnipeg MB
1 1 Francatelli Miss Laura Mabel female 30 0 0 PC 17485 569292 E36 C 1
1 0 Franklin Mr Thomas Parham male 0 0 113778 265500 D34 S Westcliff-on-Sea Essex
1 1 Frauenthal Dr Henry William male 50 2 0 PC 17611 1336500 S 5 New York NY
1 1 Frauenthal Mr Isaac Gerald male 43 1 0 17765 277208 D40 C 5 New York NY
1 1 Frauenthal Mrs Henry William (Clara Heinsheimer) female 1 0 PC 17611 1336500 S 5 New York NY
1 1 Frolicher Miss Hedwig Margaritha female 22 0 2 13568 495000 B39 C 5 Zurich Switzerland
1 1 Frolicher-Stehli Mr Maxmillian male 60 1 1 13567 792000 B41 C 5 Zurich Switzerland
1 1 Frolicher-Stehli Mrs Maxmillian (Margaretha Emerentia Stehli) female 48 1 1 13567 792000 B41 C 5 Zurich Switzerland
1 0 Fry Mr Richard male 0 0 112058 00000 B102 S
1 0 Futrelle Mr Jacques Heath male 37 1 0 113803 531000 C123 S Scituate MA
1 1 Futrelle Mrs Jacques Heath (Lily May Peel) female 35 1 0 113803 531000 C123 S D Scituate MA
1 0 Gee Mr Arthur H male 47 0 0 111320 385000 E63 S 275 St Annes-on-Sea Lancashire
1 1 Geiger Miss Amalie female 35 0 0 113503 2115000 C130 C 4
1 1 Gibson Miss Dorothy Winifred female 22 0 1 112378 594000 C 7 New York NY
1 1 Gibson Mrs Leonard (Pauline C Boeson) female 45 0 1 112378 594000 C 7 New York NY
1 0 Giglio Mr Victor male 24 0 0 PC 17593 792000 B86 C
1 1 Goldenberg Mr Samuel L male 49 1 0 17453 891042 C92 C 5 Paris France New York NY
1 1 Goldenberg Mrs Samuel L (Edwiga Grabowska) female 1 0 17453 891042 C92 C 5 Paris France New York NY
1 0 Goldschmidt Mr George B male 71 0 0 PC 17754 346542 A5 C New York NY
1 1 Gracie Col Archibald IV male 53 0 0 113780 285000 C51 C B Washington DC
1 1 Graham Miss Margaret Edith female 19 0 0 112053 300000 B42 S 3 Greenwich CT
1 0 Graham Mr George Edward male 38 0 1 PC 17582 1534625 C91 S 147 Winnipeg MB
1 1 Graham Mrs William Thompson (Edith Junkins) female 58 0 1 PC 17582 1534625 C125 S 3 Greenwich CT
1 1 Greenfield Mr William Bertram male 23 0 1 PC 17759 633583 D10 D12 C 7 New York NY
1 1 Greenfield Mrs Leo David (Blanche Strouse) female 45 0 1 PC 17759 633583 D10 D12 C 7 New York NY
1 0 Guggenheim Mr Benjamin male 46 0 0 PC 17593 792000 B82 B84 C New York NY
1 1 Harder Mr George Achilles male 25 1 0 11765 554417 E50 C 5 Brooklyn NY
1 1 Harder Mrs George Achilles (Dorothy Annan) female 25 1 0 11765 554417 E50 C 5 Brooklyn NY
1 1 Harper Mr Henry Sleeper male 48 1 0 PC 17572 767292 D33 C 3 New York NY
1 1 Harper Mrs Henry Sleeper (Myna Haxtun) female 49 1 0 PC 17572 767292 D33 C 3 New York NY
1 0 Harrington Mr Charles H male 0 0 113796 424000 S
1 0 Harris Mr Henry Birkhardt male 45 1 0 36973 834750 C83 S New York NY
1 1 Harris Mrs Henry Birkhardt (Irene Wallach) female 35 1 0 36973 834750 C83 S D New York NY
1 0 Harrison Mr William male 40 0 0 112059 00000 B94 S 110
1 1 Hassab Mr Hammad male 27 0 0 PC 17572 767292 D49 C 3
1 1 Hawksford Mr Walter James male 0 0 16988 300000 D45 S 3 Kingston Surrey
1 1 Hays Miss Margaret Bechstein female 24 0 0 11767 831583 C54 C 7 New York NY
1 0 Hays Mr Charles Melville male 55 1 1 12749 935000 B69 S 307 Montreal PQ
1 1 Hays Mrs Charles Melville (Clara Jennings Gregg) female 52 1 1 12749 935000 B69 S 3 Montreal PQ
1 0 Head Mr Christopher male 42 0 0 113038 425000 B11 S London Middlesex
1 0 Hilliard Mr Herbert Henry male 0 0 17463 518625 E46 S Brighton MA
1 0 Hipkins Mr William Edward male 55 0 0 680 500000 C39 S London Birmingham
1 1 Hippach Miss Jean Gertrude female 16 0 1 111361 579792 B18 C 4 Chicago IL
1 1 Hippach Mrs Louis Albert (Ida Sophia Fischer) female 44 0 1 111361 579792 B18 C 4 Chicago IL
1 1 Hogeboom Mrs John C (Anna Andrews) female 51 1 0 13502 779583 D11 S 10 Hudson NY
1 0 Holverson Mr Alexander Oskar male 42 1 0 113789 520000 S 38 New York NY
1 1 Holverson Mrs Alexander Oskar (Mary Aline Towner) female 35 1 0 113789 520000 S 8 New York NY
1 1 Homer Mr Harry (Mr E Haven) male 35 0 0 111426 265500 C 15 Indianapolis IN
1 1 Hoyt Mr Frederick Maxfield male 38 1 0 19943 900000 C93 S D New York NY Stamford CT
1 0 Hoyt Mr William Fisher male 0 0 PC 17600 306958 C 14 New York NY
1 1 Hoyt Mrs Frederick Maxfield (Jane Anne Forby) female 35 1 0 19943 900000 C93 S D New York NY Stamford CT
1 1 Icard Miss Amelie female 38 0 0 113572 800000 B28 6
1 0 Isham Miss Ann Elizabeth female 50 0 0 PC 17595 287125 C49 C Paris France New York NY
1 1 Ismay Mr Joseph Bruce male 49 0 0 112058 00000 B52 B54 B56 S C Liverpool
1 0 Jones Mr Charles Cresson male 46 0 0 694 260000 S 80 Bennington VT
1 0 Julian Mr Henry Forbes male 50 0 0 113044 260000 E60 S London
1 0 Keeping Mr Edwin male 325 0 0 113503 2115000 C132 C 45
1 0 Kent Mr Edward Austin male 58 0 0 11771 297000 B37 C 258 Buffalo NY
1 0 Kenyon Mr Frederick R male 41 1 0 17464 518625 D21 S Southington Noank CT
1 1 Kenyon Mrs Frederick R (Marion) female 1 0 17464 518625 D21 S 8 Southington Noank CT
1 1 Kimball Mr Edwin Nelson Jr male 42 1 0 11753 525542 D19 S 5 Boston MA
1 1 Kimball Mrs Edwin Nelson Jr (Gertrude Parsons) female 45 1 0 11753 525542 D19 S 5 Boston MA
1 0 Klaber Mr Herman male 0 0 113028 265500 C124 S Portland OR
1 1 Kreuchen Miss Emilie female 39 0 0 24160 2113375 S 2
1 1 Leader Dr Alice (Farnham) female 49 0 0 17465 259292 D17 S 8 New York NY
1 1 LeRoy Miss Bertha female 30 0 0 PC 17761 1064250 C 2
1 1 Lesurer Mr Gustave J male 35 0 0 PC 17755 5123292 B101 C 3
1 0 Lewy Mr Ervin G male 0 0 PC 17612 277208 C Chicago IL
1 0 Lindeberg-Lind Mr Erik Gustaf (Mr Edward Lingrey) male 42 0 0 17475 265500 S Stockholm Sweden
1 1 Lindstrom Mrs Carl Johan (Sigrid Posse) female 55 0 0 112377 277208 C 6 Stockholm Sweden
1 1 Lines Miss Mary Conover female 16 0 1 PC 17592 394000 D28 S 9 Paris France
1 1 Lines Mrs Ernest H (Elizabeth Lindsey James) female 51 0 1 PC 17592 394000 D28 S 9 Paris France
1 0 Long Mr Milton Clyde male 29 0 0 113501 300000 D6 S 126 Springfield MA
1 1 Longley Miss Gretchen Fiske female 21 0 0 13502 779583 D9 S 10 Hudson NY
1 0 Loring Mr Joseph Holland male 30 0 0 113801 455000 S London New York NY
1 1 Lurette Miss Elise female 58 0 0 PC 17569 1465208 B80 C
1 1 Madill Miss Georgette Alexandra female 15 0 1 24160 2113375 B5 S 2 St Louis MO
1 0 Maguire Mr John Edward male 30 0 0 110469 260000 C106 S Brockton MA
1 1 Maioni Miss Roberta female 16 0 0 110152 865000 B79 S 8
1 1 Marechal Mr Pierre male 0 0 11774 297000 C47 C 7 Paris France
1 0 Marvin Mr Daniel Warner male 19 1 0 113773 531000 D30 S New York NY
1 1 Marvin Mrs Daniel Warner (Mary Graham Carmichael Farquarson) female 18 1 0 113773 531000 D30 S 10 New York NY
1 1 Mayne Mlle Berthe Antonine (Mrs de Villiers) female 24 0 0 PC 17482 495042 C90 C 6 Belgium Montreal PQ
1 0 McCaffry Mr Thomas Francis male 46 0 0 13050 752417 C6 C 292 Vancouver BC
1 0 McCarthy Mr Timothy J male 54 0 0 17463 518625 E46 S 175 Dorchester MA
1 1 McGough Mr James Robert male 36 0 0 PC 17473 262875 E25 S 7 Philadelphia PA
1 0 Meyer Mr Edgar Joseph male 28 1 0 PC 17604 821708 C New York NY
1 1 Meyer Mrs Edgar Joseph (Leila Saks) female 1 0 PC 17604 821708 C 6 New York NY
1 0 Millet Mr Francis Davis male 65 0 0 13509 265500 E38 S 249 East Bridgewater MA
1 0 Minahan Dr William Edward male 44 2 0 19928 900000 C78 Q 230 Fond du Lac WI
1 1 Minahan Miss Daisy E female 33 1 0 19928 900000 C78 Q 14 Green Bay WI
1 1 Minahan Mrs William Edward (Lillian E Thorpe) female 37 1 0 19928 900000 C78 Q 14 Fond du Lac WI
1 1 Mock Mr Philipp Edmund male 30 1 0 13236 577500 C78 C 11 New York NY
1 0 Molson Mr Harry Markland male 55 0 0 113787 305000 C30 S Montreal PQ
1 0 Moore Mr Clarence Bloomfield male 47 0 0 113796 424000 S Washington DC
1 0 Natsch Mr Charles H male 37 0 1 PC 17596 297000 C118 C Brooklyn NY
1 1 Newell Miss Madeleine female 31 1 0 35273 1132750 D36 C 6 Lexington MA
1 1 Newell Miss Marjorie female 23 1 0 35273 1132750 D36 C 6 Lexington MA
1 0 Newell Mr Arthur Webster male 58 0 2 35273 1132750 D48 C 122 Lexington MA
1 1 Newsom Miss Helen Monypeny female 19 0 2 11752 262833 D47 S 5 New York NY
1 0 Nicholson Mr Arthur Ernest male 64 0 0 693 260000 S 263 Isle of Wight England
1 1 Oliva y Ocana Dona Fermina female 39 0 0 PC 17758 1089000 C105 C 8
1 1 Omont Mr Alfred Fernand male 0 0 FC 12998 257417 C 7 Paris France
1 1 Ostby Miss Helene Ragnhild female 22 0 1 113509 619792 B36 C 5 Providence RI
1 0 Ostby Mr Engelhart Cornelius male 65 0 1 113509 619792 B30 C 234 Providence RI
1 0 Ovies y Rodriguez Mr Servando male 285 0 0 PC 17562 277208 D43 C 189 Havana Cuba
1 0 Parr Mr William Henry Marsh male 0 0 112052 00000 S Belfast
1 0 Partner Mr Austen male 455 0 0 113043 285000 C124 S 166 Surbiton Hill Surrey
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3 0 Rice Master George Hugh male 8 4 1 382652 291250 Q
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3 0 Risien Mrs Samuel (Emma) female 0 0 364498 145000 S
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3 0 Robins Mrs Alexander A (Grace Charity Laury) female 47 1 0 A5 3337 145000 S 7
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3 0 Ryan Mr Patrick male 0 0 371110 241500 Q
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3 0 Saade Mr Jean Nassr male 0 0 2676 72250 C
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3 0 Sivola Mr Antti Wilhelm male 21 0 0 STONO 2 3101280 79250 S
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3 0 Skoog Mrs William (Anna Bernhardina Karlsson) female 45 1 4 347088 279000 S
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3 0 Smiljanic Mr Mile male 0 0 315037 86625 S
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3 1 Smyth Miss Julia female 0 0 335432 77333 Q 13
3 0 Soholt Mr Peter Andreas Lauritz Andersen male 19 0 0 348124 76500 F G73 S
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3 0 Strom Miss Telma Matilda female 2 0 1 347054 104625 G6 S
3 0 Strom Mrs Wilhelm (Elna Matilda Persson) female 29 1 1 347054 104625 G6 S
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3 0 Tobin Mr Roger male 0 0 383121 77500 F38 Q
3 0 Todoroff Mr Lalio male 0 0 349216 78958 S
3 0 Tomlin Mr Ernest Portage male 305 0 0 364499 80500 S 50
3 0 Torber Mr Ernst William male 44 0 0 364511 80500 S
3 0 Torfa Mr Assad male 0 0 2673 72292 C
3 1 Tornquist Mr William Henry male 25 0 0 LINE 00000 S 15
3 0 Toufik Mr Nakli male 0 0 2641 72292 C
3 1 Touma Master Georges Youssef male 7 1 1 2650 152458 C C
3 1 Touma Miss Maria Youssef female 9 1 1 2650 152458 C C
3 1 Touma Mrs Darwis (Hanne Youssef Razi) female 29 0 2 2650 152458 C C
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3 1 Turja Miss Anna Sofia female 18 0 0 4138 98417 S 15
3 1 Turkula Mrs (Hedwig) female 63 0 0 4134 95875 S 15
3 0 van Billiard Master James William male 1 1 A5 851 145000 S
3 0 van Billiard Master Walter John male 115 1 1 A5 851 145000 S 1
3 0 van Billiard Mr Austin Blyler male 405 0 2 A5 851 145000 S 255
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3 0 Van Impe Mr Jean Baptiste male 36 1 1 345773 241500 S
3 0 Van Impe Mrs Jean Baptiste (Rosalie Paula Govaert) female 30 1 1 345773 241500 S
3 0 van Melkebeke Mr Philemon male 0 0 345777 95000 S
3 0 Vande Velde Mr Johannes Joseph male 33 0 0 345780 95000 S
3 0 Vande Walle Mr Nestor Cyriel male 28 0 0 345770 95000 S
3 0 Vanden Steen Mr Leo Peter male 28 0 0 345783 95000 S
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3 0 Vander Planke Mr Leo Edmondus male 16 2 0 345764 180000 S
3 0 Vander Planke Mrs Julius (Emelia Maria Vandemoortele) female 31 1 0 345763 180000 S
3 1 Vartanian Mr David male 22 0 0 2658 72250 C 13 15
3 0 Vendel Mr Olof Edvin male 20 0 0 350416 78542 S
3 0 Vestrom Miss Hulda Amanda Adolfina female 14 0 0 350406 78542 S
3 0 Vovk Mr Janko male 22 0 0 349252 78958 S
3 0 Waelens Mr Achille male 22 0 0 345767 90000 S Antwerp Belgium Stanton OH
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3 0 Warren Mr Charles William male 0 0 CA 49867 75500 S
3 0 Webber Mr James male 0 0 SOTONOQ 3101316 80500 S
3 0 Wenzel Mr Linhart male 325 0 0 345775 95000 S 298
3 1 Whabee Mrs George Joseph (Shawneene Abi-Saab) female 38 0 0 2688 72292 C C
3 0 Widegren Mr CarlCharles Peter male 51 0 0 347064 77500 S
3 0 Wiklund Mr Jakob Alfred male 18 1 0 3101267 64958 S 314
3 0 Wiklund Mr Karl Johan male 21 1 0 3101266 64958 S
3 1 Wilkes Mrs James (Ellen Needs) female 47 1 0 363272 70000 S
3 0 Willer Mr Aaron (Abi Weller) male 0 0 3410 87125 S
3 0 Willey Mr Edward male 0 0 SOPP 751 75500 S
3 0 Williams Mr Howard Hugh Harry male 0 0 A5 2466 80500 S
3 0 Williams Mr Leslie male 285 0 0 54636 161000 S 14
3 0 Windelov Mr Einar male 21 0 0 SOTONOQ 3101317 72500 S
3 0 Wirz Mr Albert male 27 0 0 315154 86625 S 131
3 0 Wiseman Mr Phillippe male 0 0 A4 34244 72500 S
3 0 Wittevrongel Mr Camille male 36 0 0 345771 95000 S
3 0 Yasbeck Mr Antoni male 27 1 0 2659 144542 C C
3 1 Yasbeck Mrs Antoni (Selini Alexander) female 15 1 0 2659 144542 C
3 0 Youseff Mr Gerious male 455 0 0 2628 72250 C 312
3 0 Yousif Mr Wazli male 0 0 2647 72250 C
3 0 Yousseff Mr Gerious male 0 0 2627 144583 C
3 0 Zabour Miss Hileni female 145 1 0 2665 144542 C 328
3 0 Zabour Miss Thamine female 1 0 2665 144542 C
3 0 Zakarian Mr Mapriededer male 265 0 0 2656 72250 C 304
3 0 Zakarian Mr Ortin male 27 0 0 2670 72250 C
3 0 Zimmerman Mr Leo male 29 0 0 315082 78750 S
Page 3: Big Data: Data Analysis Boot Camp Titanic Dataset

343

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

What are we going to cover

Wersquore going to talk about

Rrsquos RMS Titanic dataset

Other Titanic datasets that containdifferent data

Modeling the datasets to see whowill live and who will die

443

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

ldquoWellrdquo settled data

Basic information

Ordered 17 Sep 1908

Completed 2 Apr 1912

Maiden voyage 10 Apr1912

Sank 14 Apr 1912

543

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

ldquoWellrdquo settled data

Where she was damaged

Red are water tightbulkheads

Green is where the iceberghit

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

643

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

ldquoWellrdquo settled data

Same image

Image from [11]

743

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

ldquoWellrdquo settled data

How many died and why

Sailing capacity (passengersand crew) 3372

Lifeboat capacity 1178

Number of people on board(accounts vary) 2201

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

Passengers crew builderrsquos menand others Image from [8]

843

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

Data from diverse places

Expected first class passengers

Lots of lists of 1st classpassengers Even some of 2ndand 3rd class passengers[10]Lists of non-passengers (shiprsquoscrew and builderrsquos technicians)are more challenging[6]R has a built-in Titanic datasetTitanic

Image from [7]

943

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

Data from diverse places

A crew list

A reasonable collection of crewand builderrsquos representatives isavailable

Name job status (lost ornot)

Age place of birth

Image from [9]

1043

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

Data from diverse places

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]

1143

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

Data from diverse places

titanic3 attributesvariables

Name Explanation

Pclass Passenger Class (1 = 1st 2 = 2nd 3 = 3rd)survival Survival (0 = No 1 = Yes)name Namesex Sexage Agesibsp Number of SiblingsSpouses Aboardparch Number of ParentsChildren Aboardticket Ticket Numberfare Passenger Fare (British pound)cabin Cabinembarked Port of Embarkation (C = Cherbourg Q = Queenstown S = Southampton)boat Lifeboatbody Body Identification Numberhomedest HomeDestination

1243

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

Data from diverse places

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

1343

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

What is it

A definition

ldquoClassification is the task of learning a targetfunction f that maps each attribute set x to one of thepredefined class labels yrdquo

Tan et al [12]

1443

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

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]

1543

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

What is it

Same image

Image from [5]

1643

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

What is it

Supervised vs Unsupervised learning

Supervised learning

A training dataset with correct answers(labels) is ldquominedrdquo 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

1743

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

What is it

Supervised vs Unsupervised learning techniques

With the Titanic dataset we will be focusing on classification

1843

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

Training and testing

Working with data

Supervised learning requires

Training data ndash usuallyabout 70 of available data

Testing data ndash usually about30 of available data

Training data can also bepartitioned into validation data

1943

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

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 oneUse all and vote on theoutcome

2043

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

Types of errors

Sample problem space

1000 data points between+- 1

Two classes of data points

color =

red if 05 leradic

x2 + y2 le 10

black otherwise

(See attached file)

2143

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

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

2243

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

Types of errors

Same image

Attached file

2343

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

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

2443

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

Types of errors

Same image

Training and testing errors

2543

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

A collection of decision tree techniques

rpart from the rpart library ldquoRecursive partitioning for classification regression and survival trees Animplementation of most of the functionality of the 1984 book by Breiman Friedman Olshen andStonerdquo[13]

C50 from the C50 library ldquoC50 decision trees and rule-based models for pattern recognitionrdquo [4]

Random Forest from the randomForest library ldquoClassification and regression based on a forest of trees using randominputsrdquo[1]

J48 from the RWeka library ldquoAn R interface to Weka (Version 391) Weka is a collection of machine learningalgorithms for data mining tasks written in Java containing tools for data pre-processing classificationregression clustering association rules and visualizationrdquo[3] The J48 algorithm is run in a pruned andunpruned mode

These and additional techniques to be covered in detail later

2643

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

Results

C50 decision tree

2743

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

Results

Random Forest decision tree

2843

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

Results

rpart decision tree

2943

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

Results

J48 (unpruned) decision tree

3043

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

Results

J48 (pruned) decision tree

3143

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

Results

Accuracy based on training percentage

The horizontal line at 50represents 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

3243

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

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

3343

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

Results

Simple mosaic from the titanic package

Sometimes you donrsquot need a lotof codelibrary(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 adageldquowomen and children firstrdquo

3443

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

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 ldquowomen and children firstrdquo

3543

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

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

3643

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

Q amp A time

Q How many Harvard MBArsquosdoes it take to screw in a lightbulbA Just one He grasps it firmlyand the universe revolves aroundhim

3743

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

What have we covered

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

Next LPAR Chapter 2 basic data visualization

3843

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

References (1 of 5)

[1] Leo BreimanrandomForest Breiman and Cutlers random forests for classification and regressionhttpstat-wwwberkeleyeduusersbreiman

RandomForests 2006

[2] Jr Frank E Harrell Titanic DatahttpbiostatmcvanderbilteduwikipubMain

DataSetstitanichtml 2002

[3] K Hornik A Zeileis T Hothorn and C BuchtaRWeka an R interface to Weka R package version 04-32(2017)

3943

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

References (2 of 5)

[4] M Kuhn S Weston N Coulter M Culp and R QuinlanC50 Decision Trees and Rule-Based Models R PackageVersion 01 0 24 (2015)

[5] Sebastian RaschkaPredictive modeling supervised machine learning and pattern classificationhttpsebastianraschkacomArticles2014_intro_

supervised_learninghtml 2014

[6] Encyclopedia Titanica StaffEncyclopedia Titanica Titanic Facts History and Biographyhttpswwwencyclopedia-titanicaorg 2017

4043

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

References (3 of 5)

[7] ISM Staff Titanic Survivor Titanic Passenger List Booklethttpwwwphillyseaportorgweb_exhibitsmini_

exhibitstitanic_passenger_listtitanic_

passenger_list-object-passenger_listhtml 2017

[8] OceanGate Staff Titanic Survey Expedition 2018httpwwwoceangatecomexpeditionstitanic-

survey-2018html 2017

[9] Southampton Staff Titanic crew listhttpwwwplimsollorgSouthamptonTitanic

titaniccrewlistDefaultasp 2017

4143

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

References (4 of 5)

[10] Titanic Facts Staff Titanic Passenger List httpwwwtitanic-factscomtitanic-passenger-listhtml2017

[11] Wikipedia Staff Sinking of the RMS Titanic httpsenwikipediaorgwikiSinking_of_the_RMS_Titanic2017

[12] Pang-Ning Tan Michael Steinbach and Vipin KumarIntroduction to Data Mining Pearson Education India 2006

[13] Terry Therneau Beth Atkinson and Brian Ripley rpartAvailable at CRAN R-project orgpackage= rpart AccessedMay (2015)

4243

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

References (5 of 5)

[14] Maria Dolores Ugarte Ana F Militino and Alan T ArnholtProbability and Statistics with R CRC Press 2008

4343

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

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 121 Sources of data 1

211 R 1212 R package ldquotitanicrdquo 2213 R package ldquovcdExtrardquo 2214 The crew 6

22 Questions that can be asked 623 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 Titanicrsquos 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 ldquointernalrdquo dataset 119 Missing data map for the ldquotitanic3rdquo 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 thesethe 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 YorkNew York on 10 April 1912 She had a fire in her coal stores that started almost 10 days before she sailedand continued after leaving South Hampton[9] She had 16 lifeboats and four Engelhardt ldquocollapsiblesrdquo (seeFigure 2 on page 3) that could collectively hold 1178 people (aka souls)[5] When the Titanic set sailshe had approximately 2224 passengers and crew Titanic could had a maximum capacity of 3327 soulsThe 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

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

21 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

211 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)

ldquoThis data set provides information on the fate of passengers on the fatal maiden voyage of theocean liner rsquoTitanicrsquo summarized according to economic status (class) sex age and survivalrdquo

R Staff [6]

2httpwwwphillyseaportorgweb_exhibitsmini_exhibitstitanic_passenger_listtitanic_passenger_

list-object-passenger_listhtml

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

212 R package ldquotitanicrdquo

A collection of 1309 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 ldquoTitanicrdquo summarized according to economic status (class) sex age and survivalWhereas the base R Titanic data found by calling data(ldquoTitanicrdquo) 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

213 R package ldquovcdExtrardquo

A collection of 1309 passenger records

Provides additional data sets methods and documentation to complement the rsquovcdrsquo packagefor Visualizing Categorical Data and the rsquognmrsquo package for Generalized Nonlinear Models

3httpswwwkagglecomctitanic

2

Figure 2 Titanicrsquos Collapsible Boat D approaches RMS Carpathia

3

Figure 3 Commemorative sailing booklet4

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 ldquowomenand children firstrdquo

5

Friendly et al [2]

The vcdExtraTitanicp is a data frame with 1309 observations on the following 6 variables (see Figure 5on the following page)

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

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

bull sex a factor with levels female and male

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

bull sibsp number of siblings or spouses aboard integer 08

bull parch number of parents or children aboard integer 06

In many ways ldquovcdExtraTitanicprdquo is a union of the ldquotitanictitanic trainrdquo and ldquotitanictitanic testrdquodatasets less some of the columnular values The vcdExtraTitanicp 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

214 The crew

The previous sections focused on Titanicrsquos 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

22 Questions that can be asked

Every person that sailed on the Titanic had a long list of attributes that could be used to describe themThese attributes include

bull Class (1st 2nd 3rd crew other)

bull Age

bull Fare

bull Gender

bull Number of traveling family members

bull Number of siblings

bull Place of embarkation or

4httpwwwplimsollorgStartHereAboutUsdefaultasp5httpwwwplimsollorgSouthamptonTitanictitaniccrewlistDefaultasp

6

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 predictionsR R script supports a variety of passparameters or command line arguments

Name Switch Default MeaningsourceSelection s internal Which dataset to usetrainingPercentage 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

bull 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 predictionsR have all attributes The internal dataset is missing theattribute ldquoembarkationrdquo (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 isWhat 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 grouprsquos traveling class (1st 2nd 3rd and crew)

23 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)

predictionsR incorporates four different partitioning algorithms (one algorithm has two variants)They are

rpart from the rpart library ldquoRecursive partitioning for classification regression and survival trees Animplementation of most of the functionality of the 1984 book by Breiman Friedman Olshen andStonerdquo[]

C50 from the C50 library ldquoC50 decision trees and rule-based models for pattern recognitionrdquo []

Random Forest from the randomForest library ldquoClassification and regression based on a forest of treesusing random inputsrdquo[]

J48 from the RWeka library ldquoAn R interface to Weka (Version 391) Weka is a collection of machinelearning algorithms for data mining tasks written in Java containing tools for data pre-processingclassification regression clustering association rules and visualizationrdquo[] The J48 algorithm is runin a pruned and unpruned mode

10

Figure 8 Missing data map for the ldquointernalrdquo dataset

11

Figure 9 Missing data map for the ldquotitanic3rdquo 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 07826087 07826087 07826087 07826087 07826087Kappa 04061712 04061712 04061712 04061712 04061712

(see Figure 10on page 15)

(see Figure 11on page 16)

(see Figure 12on page 17)

(see Figure 13on page 17)

(see Figure 14on page 18)

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 questionpredictionsR 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 (1178 versus3327)

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

3 The values from in built in R data set (datasetsTitanic)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 3rdand crew)

5 Various passenger and crew lists (with more attributes than the built in Titanic dataset) are availableand 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 rdquoUnusual Episoderdquo Data Revisited Journal of Statistics Education 3 (1995) no 31ndash7

[2] Michael Friendly Heather Turner Achim Zeileis and Maintainer Michael Friendly Package rsquovcdExtrarsquo(2016)

[3] Paul Hendricks Titanic Passenger Survival Data Set httpsgithubcompaulhendrickstitanic2015

[4] Encyclopedia Titanica Staff Titanic Survivors httpswwwencyclopedia-titanicaorg

titanic-survivors 2017

[5] History Staff Titanic httpwwwhistorycomtopicstitanic 2017

[6] R Staff Survival of passengers on the Titanic packagedatasets 2017

[7] Titanic Facts Staff Titanic Survivors httpwwwtitanicfactsnettitanic-survivorshtml2017

[8] Titanic Universe Staff How Many People Survived the Titanic Disaster httpwww

titanicuniversecomhow-many-people-survived-the-titanic-disaster1253 2017

[9] Wikipedia Staff British Wreck Commissionerrsquos inquiry into the sinking of the RMS TitanichttpsenwikipediaorgwikiBritish_Wreck_Commissioner27s_inquiry_into_the_sinking_

of_the_RMS_Titanic 2017

14

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

lsquoA Mosaic plot of R Titanic datarsquo on page 5 ndash

library(titanic)

library(graphics)

mosaicplot(Titanic main = Survival on the Titanic)

lsquovcdExtra Titanic datarsquo on page 7 ndash

library(vcdExtra)

data(Titanicp)

plot(Titanicp)

lsquovcdExtra Titanic data decision tree based on passenger class age and number and type of traveling companionsrsquo on page 8ndash

library(rpart)

library(rpartplot)

data(Titanicp package=vcdExtra)

rp0 lt- rpart(survived ~ pclass + age data=Titanicp)

rpartplot(rp0 type=0 extra=2 cex=15)

lsquovcdExtra Titanic data decision tree based on all available datarsquo on page 9 ndash

library(rpart)

library(rpartplot)

data(Titanicp package=vcdExtra)

rp0 lt- rpart(survived ~ data=Titanicp)

rpartplot(rp0 type=0 extra=2 cex=15)

lsquoMissing data map for the ldquointernalrdquo datasetrsquo on page 11 ndash

source(predictionsR)

library(Amelia)

missmap(d$titanicPeople)

lsquoMissing data map for the ldquotitanic3rdquo datasetrsquo on page 12 ndash

source(predictionsR)

d lt- main(sourceSelection=titanic3)

library(Amelia)

missmap(d$titanicPeople)

20

B Files

A collection of miscellaneous files mentioned in the report

bull titanic3xls ndash A consolidated list of passengers

bull predictionsR ndash An R script to demonstrate the different partitioning approaches and their results

21

titanic3

Chuck Cartledge

rm(list=ls()) httpsrstudio-pubs-statics3amazonawscom123908_3df6d25c8ad04030a2a429661125a59bhtmllibrary(lattice)library(tree)library(plotrix)library(sandwich)library(grid)library(vcd)library(gnm)library(stats4)library(zoo)library(mvtnorm)library(modeltools)library(strucchange)library(sandwich)library(gplots)library(psych)library(readr)library(dplyr)library(party)library(rpart)library(rpartplot)library(ROCR)library(RWeka)library(C50)library(randomForest)library(reprtree)library(cwhmisc) library(stringr) library(gdata)library(getopt)showMissingData lt- function(d title=Dataframe of interest) missmap(d main=title)plotDataCollection lt- function(data) dataCollectionDataFrame lt- data$dataCollectionDataFrame percentages lt- data$percentages cols lt- 1ncol(dataCollectionDataFrame) pchs lt- cols plot(00 type=n xlim=c(min(percentages) max(percentages)) ylim=c(min(04 min(dataCollectionDataFrame)) max(1 max(dataCollectionDataFrame))) xlab=Training percentage ylab=Accuracy ) legend(topleftnames(dataCollectionDataFrame) col=cols pch=pchs) for(i in cols) points(percentages dataCollectionDataFrame[i] col=i pch=pchs[i] type=b) abline(h=05 col=red )setValueBasedOnCommandLine lt- function(input default) returnValue lt- setValue(input default) setValue lt- function(inputValue default) returnValue lt- default if (typeof(default) == logical) if (isnull(inputValue) == FALSE) returnValue lt- default else if (isnull(inputValue) == FALSE) returnValue lt- inputValue returnValueloadTitanic3 lt- function() fileName lt- Librarytitanic3xls d lt- readxls(fileName header=TRUE sheet = 1 stringsAsFactors = FALSE) d --------------------------------------------------------- Function C50graphviz Version 220 Date 26092014 Author Athanasios Tsakonas This code implements C50graphviz conversion routine ---------------------------------------------------------C50graphviz lt- function( C50model filename fontname =Arialcoldraw =black colfont =bluecolconclusion =lightpinkcolquestion = grey78 shapeconclusion =box3dshapequestion =diamond boolsubstitute = None prefix=FALSE vertical=TRUE ) httpr-project-thanosblogspotcom201409plot-c50-decision-trees-in-rhtml treeout lt- C50model$output treeoutlt- substr(treeout cpos(treeout Decision tree start=1)+14nchar(treeout)) treeoutlt- substr(treeout 1cpos(treeout Evaluation on training data start=1)-2) variables lt- dataframe(matrix(nrow=500 ncol=4)) names(variables) lt- c(SYMBOLTOKEN TYPE QUERY) connectors lt- dataframe(matrix(nrow=500 ncol=3)) names(connectors) lt- c(TOKEN STARTEND) theStack lt- dataframe(matrix(nrow=500 ncol=1)) names(theStack) lt- c(ITEM) theStackIndex lt- 1 currentvar lt- 1 currentcon lt- 1 open_connection lt- TRUE previousindent lt- -1 firstindent lt- 4 substitutes lt- dataframe(None=c(= 0= 1) yesno=c(noyes) truefalse=c(false true)TF=c(FT)) dtreestringlt-unlist( scan(text= treeout sep=n what =list(character))) for (linecount in c(1length(dtreestring))) lineindentlt-0 shortstring lt- str_trim(dtreestring[linecount] side=left) leadingspaces lt- nchar(dtreestring[linecount]) - nchar(shortstring) lineindent lt- leadingspaces4 dtreestring[linecount]lt-str_trim(dtreestring[linecount] side=left) while (isna(cpos(dtreestring[linecount] start=1)) ) lineindentlt-lineindent + 1 dtreestring[linecount]lt-substr(dtreestring[linecount] ifelse(isna(cpos(dtreestring[linecount] start=1)) 1 cpos(dtreestring[linecount] start=1)+4) nchar(dtreestring[linecount]) ) shortstring lt- str_trim(dtreestring[linecount] side=left) leadingspaces lt- nchar(dtreestring[linecount]) - nchar(shortstring) lineindent lt- lineindent + leadingspaces4 dtreestring[linecount]lt-str_trim(dtreestring[linecount] side=left) if (isna(cpos(dtreestring[linecount] start=1))) lineindentlt- lineindent + 1 dtreestring[linecount]lt-substr(dtreestring[linecount] ifelse(isna(cpos(dtreestring[linecount] start=1)) 1 cpos(dtreestring[linecount] start=1)+4) nchar(dtreestring[linecount]) ) dtreestring[linecount]lt-str_trim(dtreestring[linecount]) stringlist lt- strsplit(dtreestring[linecount]) stringpart lt- strsplit(unlist(stringlist)[1]s) if (open_connection==TRUE) variables[currentvarTOKEN] lt- unlist(stringpart)[1] variables[currentvarSYMBOL] lt- paste(nodeascharacter(currentvar) sep=) variables[currentvarTYPE] lt- shapequestion variables[currentvarQUERY] lt- 1 theStack[theStackIndexITEM]lt-variables[currentvarSYMBOL] theStack[theStackIndexINDENT] lt-firstindent theStackIndexlt-theStackIndex+1 currentvar lt- currentvar + 1 if(currentvargt2) connectors[currentcon - 1END] lt- variables[currentvar - 1 SYMBOL] connectors[currentconTOKEN] lt- paste(unlist(stringpart)[2]unlist(stringpart)[3]) if (connectors[currentconTOKEN]=== 0) connectors[currentconTOKEN] lt- ascharacter(substitutes[1boolsubstitute]) if (connectors[currentconTOKEN]=== 1) connectors[currentconTOKEN] lt- ascharacter(substitutes[2boolsubstitute]) if (open_connection==TRUE) if (lineindentltpreviousindent) theStackIndex lt- theStackIndex-(( previousindent- lineindent) +1 ) currentsymbol lt-theStack[theStackIndexITEM] else currentsymbol lt-variables[currentvar - 1SYMBOL] else currentsymbol lt-theStack[theStackIndex-((previousindent -lineindent ) +1 )ITEM] theStackIndex lt- theStackIndex-(( previousindent- lineindent) ) connectors[currentcon START] lt- currentsymbol currentcon lt- currentcon + 1 open_connection lt- TRUE if (length(unlist(stringlist))==2) stringpart2 lt- strsplit(unlist(stringlist)[2]s) variables[currentvarTOKEN] lt- paste(ifelse((prefix==FALSE)Class) unlist(stringpart2)[2]) variables[currentvarSYMBOL] lt- paste(nodeascharacter(currentvar) sep=) variables[currentvarTYPE] lt- shapeconclusion variables[currentvarQUERY] lt- 0 currentvar lt- currentvar + 1 connectors[currentcon - 1END] lt- variables[currentvar - 1SYMBOL] open_connection lt- FALSE previousindentlt-lineindent runningstring lt- paste(digraph g graph sep=n) runningstring lt- paste(runningstring [rankdir= sep=) runningstring lt- paste(runningstring ifelse(vertical==TRUETBLR) sep= ) runningstring lt- paste(runningstring ] sep=) for (lines in c(1(currentvar-1))) runningline lt- paste(variables[linesSYMBOL] [shape=) runningline lt- paste(runninglinevariables[linesTYPE] sep= ) runningline lt- paste(runningline label = sep= ) runningline lt- paste(runninglinevariables[linesTOKEN] sep= ) runningline lt- paste(runningline style=filled fontcolor= sep=) runningline lt- paste(runningline colfont) runningline lt- paste(runningline color= ) runningline lt- paste(runningline coldraw) runningline lt- paste(runningline fontname=) runningline lt- paste(runningline fontname) runningline lt- paste(runningline fillcolor=) runningline lt- paste(runningline ifelse(variables[linesQUERY]== 0 colconclusioncolquestion)) runningline lt- paste(runningline]) runningstring lt- paste(runningstring runningline sep=n) for (lines in c(1(currentcon-1))) runningline lt- paste (connectors[linesSTART] -gt) runningline lt- paste (runningline connectors[linesEND]) runningline lt- paste (runningline[label=) runningline lt- paste (runninglineconnectors[linesTOKEN] sep=) runningline lt- paste (runningline fontname= sep=) runningline lt- paste (runningline fontname) runningline lt- paste (runningline]) runningstring lt- paste(runningstring runningline sep=n) runningstring lt- paste(runningstring) cat(runningstring) write(runningstring file=filename) sink(filename split=TRUE) cat(runningstring) sink()dumpObject lt- function(object comment= verbose=TRUE) if(verbose == TRUE) print(sprintf(s -- Dumping the object s (of type s) comment deparse(substitute(object)) typeof(object)) ) print(object) predictionResults lt- function(models testData verbose=FALSE) Examining the predictions on the testing set from p 211 values = dataframe(matrix(ncol = length(models) nrow = 6)) rownames(values) = c(True + True - False + False - Accuracy Kappa) names(values) = names(models) i lt- 1 for (n in names(models)) temp lt- predict(models[[n]] newdata=testData type=class) dumpObject(temp verbose=verbose) tab = table(testData$survivedtemp) dumpObject(tab verbose=verbose) values[1i] = tab[11] values[2i] = tab[22] values[3i] = tab[12] values[4i] = tab[21] values[5i] = sum(diag(tab))sum(tab) values[6i] = cohenkappa(tab)[1] i lt- i + 1 dumpObject(values Should have values verbose=verbose) valuesgetTitanicPeople lt- function(selection) titanicPeople lt- NULL print(sprintf(Getting Titanic people using selection s selection)) switch(selection vcdExtra = titanicPeople=Titanicp titanicPeople$sex lt- paste0(toupper(substr(ascharacter(titanicPeople$sex) 1 1)) substr(ascharacter(titanicPeople$sex) 2 nchar(ascharacter(titanicPeople$sex)))) temp lt- c() for (i in 1length(titanicPeople$pclass)) pclass lt- NA if (titanicPeople$pclass[i] == 1st) pclass lt- 1 else if (ascharacter(titanicPeople$pclass[i]) == 2nd) pclass lt- 2 else if (ascharacter(titanicPeople$pclass[i]) == 3rd) pclass lt- 3 else pclass lt- 0 titanicPeople$pclass[i] lt- pclass temp lt- c(temp pclass) titanicPeople$pclass lt- temp titanicPeople$survived lt- ifelse(titanicPeople$survived == survivedyes = Saved no =Lost) titanicPeople$embarked lt- NA titanicPeople$fare lt- NA titanic3 = fileName lt- Librarytitanic3xls titanicPeople lt- readxls(fileName header=TRUE sheet = 1 stringsAsFactors = FALSE) titanicPeople$sex lt- paste0(toupper(substr(ascharacter(titanicPeople$sex) 1 1)) substr(ascharacter(titanicPeople$sex) 2 nchar(ascharacter(titanicPeople$sex)))) internal = temp lt- dataframe(Titanic) titanicPeople lt- dataframe(survived=ascharacter() embarked=ascharacter() sex=ascharacter() sibsp=asnumeric() parch=asnumeric() fare=asnumeric() pclass=asnumeric() ) for (i in 1nrow(temp)) if (temp$Freq[i] gt 0) for (j in 1temp$Freq[i]) terminus lt- ifelse(temp$Survived[i] == Yesyes = Saved no =Lost) gender lt- paste0(toupper(substr(ascharacter(temp$Sex[i]) 1 1)) substr(ascharacter(temp$Sex[i]) 2 nchar(ascharacter(temp$Sex[i])))) pclass lt- ascharacter(temp$Class[i]) if (ascharacter(temp$Class[i]) == 1st) pclass lt- 1 else if (ascharacter(temp$Class[i]) == 2nd) pclass lt- 2 else if (ascharacter(temp$Class[i]) == 3rd) pclass lt- 3 else pclass lt- 0 also lt- dataframe( survived=terminus embarked=NA sex=gender sibsp=0 parch=0 fare=0 pclass=pclass ) titanicPeople lt- rbind(titanicPeople also) ) titanicPeople lt- select(titanicPeople survived embarked sex sibsp parch fare pclass) titanicPeople lt- mutate(titanicPeople survived=factor(survived)) titanicPeople lt- mutate(titanicPeople sex=factor(sex)) titanicPeople main lt- function(sourceSelection = internal trainingPercentage = 30 dataCollection = FALSE verbose = FALSE ) setseed(100) predictions lt- list() textFileName lt- tempfile() c50ImageFileName lt- Imagesdatacollectionc50png datacollectionImageFileName lt- Imagesdatacollectionpng trainingPercentageMin lt- NA trainingPercentageMax lt- NA trainingPercentageStep lt- NA dataCollectionDataFrame lt- NULL spec lt- matrix(c( sourceSelection s 1 character trainingPercentage t 1 double dataCollection d 0 logical verbose v 0 logical ) ncol = 4 byrow = TRUE) opt lt- getopt(spec) sourceSelection lt- setValueBasedOnCommandLine(opt$sourceSelection sourceSelection) trainingPercentage lt- setValueBasedOnCommandLine(opt$trainingPercentage trainingPercentage) dataCollection lt- setValueBasedOnCommandLine(opt$dataCollection dataCollection) verbose lt- setValueBasedOnCommandLine(opt$verbose verbose) titanicPeople lt- getTitanicPeople(sourceSelection) dumpObject(names(titanicPeople) verbose=verbose) dumpObject(head(titanicPeople) verbose=verbose) dumpObject(table(titanicPeople$pclass) verbose=verbose) dumpObject(table(titanicPeople$survived) verbose=verbose) if (dataCollection == FALSE) trainingPercentageMin lt- trainingPercentage trainingPercentageMax lt- trainingPercentage trainingPercentageStep lt- 10 else trainingPercentageMin lt- trainingPercentage trainingPercentageStep lt- 10 trainingPercentageMax lt- min(999999 100 - trainingPercentageStep + trainingPercentageMin) percentages lt- seq(trainingPercentageMin trainingPercentageMax trainingPercentageStep) numberOfPeople lt- nrow(titanicPeople) for (percentage in percentages) indices lt- sample(1numberOfPeople size=numberOfPeoplepercentage100) data lt- NULL data$training lt- titanicPeople[indices] data$testing lt- titanicPeople[-indices] print(sprintf(Total number of rows = 0f training is f numberOfPeople percentage)) print(sprintf(There are 0f rows in the training set nrow(data$training))) print(sprintf(There are 0f rows in the test set nrow(data$test))) formula lt- survived ~ sex + sibsp + parch + pclass formula lt- survived ~ sex + sibsp + parch predictions[[C50]] lt- C50formula(asformula(formula) data=data$training) predictions[[Random Forest]] lt- randomForest(asformula(formula) data=data$training keepforest=TRUE proximity=TRUE ntree=100 importance=TRUE) predictions[[rpart]] lt- rpart(asformula(formula) data=data$training method=class cp=00001) predictions[[J48 (unpruned)]] lt- J48(asformula(formula) data=data$training control=Weka_control(U=TRUE)) predictions[[J48 (pruned)]] lt- J48(asformula(formula) data=data$training control=Weka_control(U=FALSE C=00001)) results lt- predictionResults(predictions data$test verbose) dumpObject(results verbose=verbose) if (dataCollection == TRUE) if (isnull(dataCollectionDataFrame) == TRUE) dataCollectionDataFrame lt- asdataframe(results[Accuracy]) else dataCollectionDataFrame lt- rbind(dataCollectionDataFrame asdataframe(results[Accuracy])) else for (n in names(predictions)) print(sprintf(Plotting data from s n)) switch(class(predictions[[n]])[1] randomForestformula = httpstatsstackexchangecomquestions41443how-to-actually-plot-a-sample-tree-from-randomforestgettree reprtreeplotgetTree(predictions[[n]]) rpart = rpartplot(predictions[[n]]) J48 = plot(predictions[[n]]) C50 = C50graphviz(predictions[[n]] filename=textFileName) command lt- sprintf(dot -Tpng s gt s textFileName c50ImageFileName) system(command) print(Default case) plot(predictions[[n]]) ) unlink(textFileName) returnValue lt- list(dataCollectionDataFrame=asdataframe(dataCollectionDataFrame) percentages=asvector(percentages) titanicPeople=titanicPeople ) if (dataCollection == TRUE) png(filename=datacollectionImageFileName width=960 height=480 type=c(cairo)) plotDataCollection(returnValue) devoff() returnValued lt- main()

Chuck Cartledge
Chuck Cartledge

titanic3

Chuck Cartledge

Bio150 ML Frank franklakeforestedu Page 1 of 3 [httpbiostatmcvanderbilteduwikipubMainDataSetstitanichtml]

Titanic Datasets The titanic and titanic2 data frames describe the survival status of individual passengers on the Titanic The titanic data frame does not contain information from the crew but it does contain actual ages of half of the passengers The principal source for data about Titanic passengers is the Encyclopedia Titanica The datasets used here were begun by a variety of researchers One of the original sources is Eaton amp Haas (1994) Titanic Triumph and Tragedy Patrick Stephens Ltd which includes a passenger list created by many researchers and edited by Michael A Findlay

The variables on our extracted dataset are pclass survived name age embarked homedest room ticket boat and sex pclass refers to passenger class (1st 2nd 3rd) and is a proxy for socio-economic class Age is in years and some infants had fractional values The titanic2 data frame has no missing data and includes records for the crew but age is dichotomized at adult vs child These data were obtained from Robert Dawson Saint Marys University E-mail The variables are pclass age sex survived These data frames are useful for demonstrating many of the functions in Hmisc as well as demonstrating binary logistic regression analysis using the Design library For more details and references see Simonoff Jeffrey S (1997) The unusual episode and a second statistics course J Statistics Education Vol 5 No 1 Thomas Cason of UVa has greatly updated and improved the titanic data frame using the Encyclopedia Titanica and created a new dataset called titanic3 These datasets reflects the state of data available as of 2 August 1999 Some duplicate passengers have been dropped many errors corrected many missing ages filled in and new variables created Click here for information about how this datatset was constructed

DATASET specs

NAME titanic3 TYPE Census SIZE 1309 Passengers 14 Variables DESCRIPTIVE ABSTRACT

The titanic3 data frame describes the survival status of individual passengers on the Titanic The titanic3 data frame does not contain information for the crew but it does contain actual and estimated ages for almost 80 of the passengers SOURCES

Hind Philip Encyclopedia Titanica Online-only resource Retrieved 01Feb2012 from httpwwwencyclopedia-titanicaorg

Bio150 ML Frank franklakeforestedu Page 2 of 3 VARIABLE DESCRIPTIONS Pclass Passenger Class (1 = 1st 2 = 2nd 3 = 3rd) survival Survival (0 = No 1 = Yes) name Name sex Sex age Age sibsp Number of SiblingsSpouses Aboard parch Number of ParentsChildren Aboard ticket Ticket Number fare Passenger Fare (British pound) cabin Cabin embarked Port of Embarkation (C = Cherbourg Q = Queenstown S = Southampton) boat Lifeboat body Body Identification Number homedest HomeDestination SPECIAL NOTES Pclass is a proxy for socio-economic status (SES) 1st ~ Upper 2nd ~ Middle 3rd ~ Lower Age is in Years Fractional if Age less than One (1) If the Age is estimated it is in the form xx5 Fare is in Pre-1970 British Pounds () Conversion Factors 1 = 12s = 240d and 1s = 20d With respect to the family relation variables (ie sibsp and parch) some relations were ignored The following are the definitions used for sibsp and parch

Sibling Brother Sister Stepbrother or Stepsister of Passenger Aboard Titanic Spouse Husband or Wife of Passenger Aboard Titanic (Mistresses and Fiances

Ignored) Parent Mother or Father of Passenger Aboard Titanic Child Son Daughter Stepson or Stepdaughter of Passenger Aboard Titanic

Other family relatives excluded from this study include cousins nephewsnieces auntsuncles and in-laws Some children travelled only with a nanny therefore parch=0 for them As well some travelled with very close friends or neighbors in a village however the definitions do not support such relations

Bio150 ML Frank franklakeforestedu Page 3 of 3 An interesting result may be obtained using functions from the Hmisc library attach (titanic3) plsmo (age survived group=sex datadensity=T) or group=pclass plot (naclus (titanic3)) study patterns of missing values summary (survived ~ age + sex + pclass + sibsp + parch data=titanic3) SUBMITTED BY Thomas E Cason tcasonvirginiaedu From MLF Prior to February 15 1971 (Decimal day or D-day) monetary amounts in the UK were expressed as pounds (pound) shillings (s) and pence (d) where pound1 = 20s = 240d After 1970 there were 100 pennies in a pound so one (new) penny = 24 old pence All numbers should be entered in decimal rather than fractional form (for example 15 rather than 1 12)

Lawrence H Officer Five Ways to Compute the Relative Value of a UK Pound Amount 1830 to Present MeasuringWorthcom Retrieved 2011Sep06 from httpwwwmeasuringworthcomukcompare

Chuck Cartledge

httpswww-userscsumnedu~kumardmbookch4pdf httpswwwyoutubecomwatchv=GOJN9SKl_OErm(list=ls())library(grid)library(mvtnorm)library(modeltools)library(stats4)library(strucchange)library(zoo)library(sandwich)library(gplots)library(readr)library(dplyr)library(party)library(rpart)library(rpartplot)library(ROCR)plotRawData lt- function(d title=) x=23 plot(x=c(-11) y = c(-1 1) type=n xlab=X values ylab=Y values main=paste(red = expression(05 lt= sqrt(x^2+y^2) ampamp sqrt(x^2+y^2) lt= 10) sep=) ) for (j in 12) points(d[which(d[3]==j)c(12)] col=j) plotErrors lt- function(data) plot(x=c(0 max(data[[nodes]])) y=c(01) type=n ylab=Model error xlab=Number of internal nodes ) cols=c(blue red) pchs=c(12) points(data[[nodes]] data[[training]] type=b col=cols[1] pch=pchs[1]) points(data[[nodes]] data[[testing]] type=b col=cols[2] pch=pchs[2]) legend(topright legend=c(Training error Testing error) pch=pchs col=cols) points(spline(data[[nodes]] data[[testing]]))decisionTree lt- function(dLocal cp=01) dTree lt- rpart(classification ~ x1 + x2 data = dLocal method=class control=c(cp=cp minsplit=1 minbucket=1) ) rpartplot(dTree type = 4 extra = 106)computeAccuracy lt- function(model data) performance lt- predict(model data type=class) temp lt- table(data$classification performance) accuracy lt- sum(diag(temp))sum(temp) accuracymain lt- function() setseed(123) numberOfDataPoints lt- 1000 trainingPercentage lt- 70 cps lt- c(01 005 0025 001 0005 0001 00001 000001) x1 lt- runif(numberOfDataPoints min = -1 max = 1) x2 lt- runif(numberOfDataPoints min = -1 max = 1) x lt- cbind(x1 x2) classification lt- c() tempList lt- c() for (i in 1nrow(x)) temp lt- sqrt(x[i1]^2 + x[i2]^2) cl lt- 1 if ((05 lt= temp) ampamp (temp lt= 10)) cl lt- 2 classification lt- c(classification cl) tempList lt- c(tempList temp) x lt- cbind(x classification tempList) plotRawData(x) s lt- sample(numberOfDataPoints numberOfDataPoints trainingPercentage 100) xtraining lt- asdataframe(x[s]) xtesting lt- asdataframe(x[-s]) returnValue lt- list() for (cp in cps) ml lt- decisionTree(xtraining cp = cp) print(ml) trainingError lt- 1 - computeAccuracy(ml xtraining) testingError lt- 1 - computeAccuracy(ml xtesting) nodes lt- tail(ml$cptable[nsplit] n=1) print(sprintf(cp = f internal nodes= 0f training error = 2f testing error = 2f cp nodes trainingError testingError ) ) returnValue[[nodes]] lt- c(returnValue[[nodes]] nodes) returnValue[[testing]] lt- c(returnValue[[testing]] testingError) returnValue[[training]] lt- c(returnValue[[training]] trainingError) print(The program has ended) returnValued lt- main()

Chuck Cartledge
  • Introduction
  • Background
    • ``Well settled data
    • Data from diverse places
      • Classification problem
        • What is it
        • Training and testing
        • Types of errors
          • Techniques
            • Results
              • Hands-on
              • Q amp A
              • Conclusion
              • References
              • Files
pclass survived name sex age sibsp parch ticket fare cabin embarked boat body homedest
1 1 Allen Miss Elisabeth Walton female 29 0 0 24160 2113375 B5 S 2 St Louis MO
1 1 Allison Master Hudson Trevor male 09167 1 2 113781 1515500 C22 C26 S 11 Montreal PQ Chesterville ON
1 0 Allison Miss Helen Loraine female 2 1 2 113781 1515500 C22 C26 S Montreal PQ Chesterville ON
1 0 Allison Mr Hudson Joshua Creighton male 30 1 2 113781 1515500 C22 C26 S 135 Montreal PQ Chesterville ON
1 0 Allison Mrs Hudson J C (Bessie Waldo Daniels) female 25 1 2 113781 1515500 C22 C26 S Montreal PQ Chesterville ON
1 1 Anderson Mr Harry male 48 0 0 19952 265500 E12 S 3 New York NY
1 1 Andrews Miss Kornelia Theodosia female 63 1 0 13502 779583 D7 S 10 Hudson NY
1 0 Andrews Mr Thomas Jr male 39 0 0 112050 00000 A36 S Belfast NI
1 1 Appleton Mrs Edward Dale (Charlotte Lamson) female 53 2 0 11769 514792 C101 S D Bayside Queens NY
1 0 Artagaveytia Mr Ramon male 71 0 0 PC 17609 495042 C 22 Montevideo Uruguay
1 0 Astor Col John Jacob male 47 1 0 PC 17757 2275250 C62 C64 C 124 New York NY
1 1 Astor Mrs John Jacob (Madeleine Talmadge Force) female 18 1 0 PC 17757 2275250 C62 C64 C 4 New York NY
1 1 Aubart Mme Leontine Pauline female 24 0 0 PC 17477 693000 B35 C 9 Paris France
1 1 Barber Miss Ellen Nellie female 26 0 0 19877 788500 S 6
1 1 Barkworth Mr Algernon Henry Wilson male 80 0 0 27042 300000 A23 S B Hessle Yorks
1 0 Baumann Mr John D male 0 0 PC 17318 259250 S New York NY
1 0 Baxter Mr Quigg Edmond male 24 0 1 PC 17558 2475208 B58 B60 C Montreal PQ
1 1 Baxter Mrs James (Helene DeLaudeniere Chaput) female 50 0 1 PC 17558 2475208 B58 B60 C 6 Montreal PQ
1 1 Bazzani Miss Albina female 32 0 0 11813 762917 D15 C 8
1 0 Beattie Mr Thomson male 36 0 0 13050 752417 C6 C A Winnipeg MN
1 1 Beckwith Mr Richard Leonard male 37 1 1 11751 525542 D35 S 5 New York NY
1 1 Beckwith Mrs Richard Leonard (Sallie Monypeny) female 47 1 1 11751 525542 D35 S 5 New York NY
1 1 Behr Mr Karl Howell male 26 0 0 111369 300000 C148 C 5 New York NY
1 1 Bidois Miss Rosalie female 42 0 0 PC 17757 2275250 C 4
1 1 Bird Miss Ellen female 29 0 0 PC 17483 2217792 C97 S 8
1 0 Birnbaum Mr Jakob male 25 0 0 13905 260000 C 148 San Francisco CA
1 1 Bishop Mr Dickinson H male 25 1 0 11967 910792 B49 C 7 Dowagiac MI
1 1 Bishop Mrs Dickinson H (Helen Walton) female 19 1 0 11967 910792 B49 C 7 Dowagiac MI
1 1 Bissette Miss Amelia female 35 0 0 PC 17760 1356333 C99 S 8
1 1 Bjornstrom-Steffansson Mr Mauritz Hakan male 28 0 0 110564 265500 C52 S D Stockholm Sweden Washington DC
1 0 Blackwell Mr Stephen Weart male 45 0 0 113784 355000 T S Trenton NJ
1 1 Blank Mr Henry male 40 0 0 112277 310000 A31 C 7 Glen Ridge NJ
1 1 Bonnell Miss Caroline female 30 0 0 36928 1648667 C7 S 8 Youngstown OH
1 1 Bonnell Miss Elizabeth female 58 0 0 113783 265500 C103 S 8 Birkdale England Cleveland Ohio
1 0 Borebank Mr John James male 42 0 0 110489 265500 D22 S London Winnipeg MB
1 1 Bowen Miss Grace Scott female 45 0 0 PC 17608 2623750 C 4 Cooperstown NY
1 1 Bowerman Miss Elsie Edith female 22 0 1 113505 550000 E33 S 6 St Leonards-on-Sea England Ohio
1 1 Bradley Mr George (George Arthur Brayton) male 0 0 111427 265500 S 9 Los Angeles CA
1 0 Brady Mr John Bertram male 41 0 0 113054 305000 A21 S Pomeroy WA
1 0 Brandeis Mr Emil male 48 0 0 PC 17591 504958 B10 C 208 Omaha NE
1 0 Brewe Dr Arthur Jackson male 0 0 112379 396000 C Philadelphia PA
1 1 Brown Mrs James Joseph (Margaret Tobin) female 44 0 0 PC 17610 277208 B4 C 6 Denver CO
1 1 Brown Mrs John Murray (Caroline Lane Lamson) female 59 2 0 11769 514792 C101 S D Belmont MA
1 1 Bucknell Mrs William Robert (Emma Eliza Ward) female 60 0 0 11813 762917 D15 C 8 Philadelphia PA
1 1 Burns Miss Elizabeth Margaret female 41 0 0 16966 1345000 E40 C 3
1 0 Butt Major Archibald Willingham male 45 0 0 113050 265500 B38 S Washington DC
1 0 Cairns Mr Alexander male 0 0 113798 310000 S
1 1 Calderhead Mr Edward Pennington male 42 0 0 PC 17476 262875 E24 S 5 New York NY
1 1 Candee Mrs Edward (Helen Churchill Hungerford) female 53 0 0 PC 17606 274458 C 6 Washington DC
1 1 Cardeza Mr Thomas Drake Martinez male 36 0 1 PC 17755 5123292 B51 B53 B55 C 3 Austria-Hungary Germantown Philadelphia PA
1 1 Cardeza Mrs James Warburton Martinez (Charlotte Wardle Drake) female 58 0 1 PC 17755 5123292 B51 B53 B55 C 3 Germantown Philadelphia PA
1 0 Carlsson Mr Frans Olof male 33 0 0 695 50000 B51 B53 B55 S New York NY
1 0 Carrau Mr Francisco M male 28 0 0 113059 471000 S Montevideo Uruguay
1 0 Carrau Mr Jose Pedro male 17 0 0 113059 471000 S Montevideo Uruguay
1 1 Carter Master William Thornton II male 11 1 2 113760 1200000 B96 B98 S 4 Bryn Mawr PA
1 1 Carter Miss Lucile Polk female 14 1 2 113760 1200000 B96 B98 S 4 Bryn Mawr PA
1 1 Carter Mr William Ernest male 36 1 2 113760 1200000 B96 B98 S C Bryn Mawr PA
1 1 Carter Mrs William Ernest (Lucile Polk) female 36 1 2 113760 1200000 B96 B98 S 4 Bryn Mawr PA
1 0 Case Mr Howard Brown male 49 0 0 19924 260000 S Ascot Berkshire Rochester NY
1 1 Cassebeer Mrs Henry Arthur Jr (Eleanor Genevieve Fosdick) female 0 0 17770 277208 C 5 New York NY
1 0 Cavendish Mr Tyrell William male 36 1 0 19877 788500 C46 S 172 Little Onn Hall Staffs
1 1 Cavendish Mrs Tyrell William (Julia Florence Siegel) female 76 1 0 19877 788500 C46 S 6 Little Onn Hall Staffs
1 0 Chaffee Mr Herbert Fuller male 46 1 0 WEP 5734 611750 E31 S Amenia ND
1 1 Chaffee Mrs Herbert Fuller (Carrie Constance Toogood) female 47 1 0 WEP 5734 611750 E31 S 4 Amenia ND
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3 0 Panula Mr Jaako Arnold male 14 4 1 3101295 396875 S
3 0 Panula Mrs Juha (Maria Emilia Ojala) female 41 0 5 3101295 396875 S
3 0 Pasic Mr Jakob male 21 0 0 315097 86625 S
3 0 Patchett Mr George male 19 0 0 358585 145000 S
3 0 Paulner Mr Uscher male 0 0 3411 87125 C
3 0 Pavlovic Mr Stefo male 32 0 0 349242 78958 S
3 0 Peacock Master Alfred Edward male 075 1 1 SOTONOQ 3101315 137750 S
3 0 Peacock Miss Treasteall female 3 1 1 SOTONOQ 3101315 137750 S
3 0 Peacock Mrs Benjamin (Edith Nile) female 26 0 2 SOTONOQ 3101315 137750 S
3 0 Pearce Mr Ernest male 0 0 343271 70000 S
3 0 Pedersen Mr Olaf male 0 0 345498 77750 S
3 0 Peduzzi Mr Joseph male 0 0 A5 2817 80500 S
3 0 Pekoniemi Mr Edvard male 21 0 0 STONO 2 3101294 79250 S
3 0 Peltomaki Mr Nikolai Johannes male 25 0 0 STONO 2 3101291 79250 S
3 0 Perkin Mr John Henry male 22 0 0 A5 21174 72500 S
3 1 Persson Mr Ernst Ulrik male 25 1 0 347083 77750 S 15
3 1 Peter Master Michael J male 1 1 2668 223583 C C
3 1 Peter Miss Anna female 1 1 2668 223583 F E69 C D
3 1 Peter Mrs Catherine (Catherine Rizk) female 0 2 2668 223583 C D
3 0 Peters Miss Katie female 0 0 330935 81375 Q
3 0 Petersen Mr Marius male 24 0 0 342441 80500 S
3 0 Petranec Miss Matilda female 28 0 0 349245 78958 S
3 0 Petroff Mr Nedelio male 19 0 0 349212 78958 S
3 0 Petroff Mr Pastcho (Pentcho) male 0 0 349215 78958 S
3 0 Petterson Mr Johan Emil male 25 1 0 347076 77750 S
3 0 Pettersson Miss Ellen Natalia female 18 0 0 347087 77750 S
3 1 Pickard Mr Berk (Berk Trembisky) male 32 0 0 SOTONOQ 392078 80500 E10 S 9
3 0 Plotcharsky Mr Vasil male 0 0 349227 78958 S
3 0 Pokrnic Mr Mate male 17 0 0 315095 86625 S
3 0 Pokrnic Mr Tome male 24 0 0 315092 86625 S
3 0 Radeff Mr Alexander male 0 0 349223 78958 S
3 0 Rasmussen Mrs (Lena Jacobsen Solvang) female 0 0 65305 81125 S
3 0 Razi Mr Raihed male 0 0 2629 72292 C
3 0 Reed Mr James George male 0 0 362316 72500 S
3 0 Rekic Mr Tido male 38 0 0 349249 78958 S
3 0 Reynolds Mr Harold J male 21 0 0 342684 80500 S
3 0 Rice Master Albert male 10 4 1 382652 291250 Q
3 0 Rice Master Arthur male 4 4 1 382652 291250 Q
3 0 Rice Master Eric male 7 4 1 382652 291250 Q
3 0 Rice Master Eugene male 2 4 1 382652 291250 Q
3 0 Rice Master George Hugh male 8 4 1 382652 291250 Q
3 0 Rice Mrs William (Margaret Norton) female 39 0 5 382652 291250 Q 327
3 0 Riihivouri Miss Susanna Juhantytar Sanni female 22 0 0 3101295 396875 S
3 0 Rintamaki Mr Matti male 35 0 0 STONO 2 3101273 71250 S
3 1 Riordan Miss Johanna Hannah female 0 0 334915 77208 Q 13
3 0 Risien Mr Samuel Beard male 0 0 364498 145000 S
3 0 Risien Mrs Samuel (Emma) female 0 0 364498 145000 S
3 0 Robins Mr Alexander A male 50 1 0 A5 3337 145000 S 119
3 0 Robins Mrs Alexander A (Grace Charity Laury) female 47 1 0 A5 3337 145000 S 7
3 0 Rogers Mr William John male 0 0 SCA4 23567 80500 S
3 0 Rommetvedt Mr Knud Paust male 0 0 312993 77750 S
3 0 Rosblom Miss Salli Helena female 2 1 1 370129 202125 S
3 0 Rosblom Mr Viktor Richard male 18 1 1 370129 202125 S
3 0 Rosblom Mrs Viktor (Helena Wilhelmina) female 41 0 2 370129 202125 S
3 1 Roth Miss Sarah A female 0 0 342712 80500 S C
3 0 Rouse Mr Richard Henry male 50 0 0 A5 3594 80500 S
3 0 Rush Mr Alfred George John male 16 0 0 A4 20589 80500 S
3 1 Ryan Mr Edward male 0 0 383162 77500 Q 14
3 0 Ryan Mr Patrick male 0 0 371110 241500 Q
3 0 Saad Mr Amin male 0 0 2671 72292 C
3 0 Saad Mr Khalil male 25 0 0 2672 72250 C
3 0 Saade Mr Jean Nassr male 0 0 2676 72250 C
3 0 Sadlier Mr Matthew male 0 0 367655 77292 Q
3 0 Sadowitz Mr Harry male 0 0 LP 1588 75750 S
3 0 Saether Mr Simon Sivertsen male 385 0 0 SOTONOQ 3101262 72500 S 32
3 0 Sage Master Thomas Henry male 8 2 CA 2343 695500 S
3 0 Sage Master William Henry male 145 8 2 CA 2343 695500 S 67
3 0 Sage Miss Ada female 8 2 CA 2343 695500 S
3 0 Sage Miss Constance Gladys female 8 2 CA 2343 695500 S
3 0 Sage Miss Dorothy Edith Dolly female 8 2 CA 2343 695500 S
3 0 Sage Miss Stella Anna female 8 2 CA 2343 695500 S
3 0 Sage Mr Douglas Bullen male 8 2 CA 2343 695500 S
3 0 Sage Mr Frederick male 8 2 CA 2343 695500 S
3 0 Sage Mr George John Jr male 8 2 CA 2343 695500 S
3 0 Sage Mr John George male 1 9 CA 2343 695500 S
3 0 Sage Mrs John (Annie Bullen) female 1 9 CA 2343 695500 S
3 0 Salander Mr Karl Johan male 24 0 0 7266 93250 S
3 1 Salkjelsvik Miss Anna Kristine female 21 0 0 343120 76500 S C
3 0 Salonen Mr Johan Werner male 39 0 0 3101296 79250 S
3 0 Samaan Mr Elias male 2 0 2662 216792 C
3 0 Samaan Mr Hanna male 2 0 2662 216792 C
3 0 Samaan Mr Youssef male 2 0 2662 216792 C
3 1 Sandstrom Miss Beatrice Irene female 1 1 1 PP 9549 167000 G6 S 13
3 1 Sandstrom Mrs Hjalmar (Agnes Charlotta Bengtsson) female 24 0 2 PP 9549 167000 G6 S 13
3 1 Sandstrom Miss Marguerite Rut female 4 1 1 PP 9549 167000 G6 S 13
3 1 Sap Mr Julius male 25 0 0 345768 95000 S 11
3 0 Saundercock Mr William Henry male 20 0 0 A5 2151 80500 S
3 0 Sawyer Mr Frederick Charles male 245 0 0 342826 80500 S 284
3 0 Scanlan Mr James male 0 0 36209 77250 Q
3 0 Sdycoff Mr Todor male 0 0 349222 78958 S
3 0 Shaughnessy Mr Patrick male 0 0 370374 77500 Q
3 1 Sheerlinck Mr Jan Baptist male 29 0 0 345779 95000 S 11
3 0 Shellard Mr Frederick William male 0 0 CA 6212 151000 S
3 1 Shine Miss Ellen Natalia female 0 0 330968 77792 Q
3 0 Shorney Mr Charles Joseph male 0 0 374910 80500 S
3 0 Simmons Mr John male 0 0 SOTONOQ 392082 80500 S
3 0 Sirayanian Mr Orsen male 22 0 0 2669 72292 C
3 0 Sirota Mr Maurice male 0 0 392092 80500 S
3 0 Sivic Mr Husein male 40 0 0 349251 78958 S
3 0 Sivola Mr Antti Wilhelm male 21 0 0 STONO 2 3101280 79250 S
3 1 Sjoblom Miss Anna Sofia female 18 0 0 3101265 74958 S 16
3 0 Skoog Master Harald male 4 3 2 347088 279000 S
3 0 Skoog Master Karl Thorsten male 10 3 2 347088 279000 S
3 0 Skoog Miss Mabel female 9 3 2 347088 279000 S
3 0 Skoog Miss Margit Elizabeth female 2 3 2 347088 279000 S
3 0 Skoog Mr Wilhelm male 40 1 4 347088 279000 S
3 0 Skoog Mrs William (Anna Bernhardina Karlsson) female 45 1 4 347088 279000 S
3 0 Slabenoff Mr Petco male 0 0 349214 78958 S
3 0 Slocovski Mr Selman Francis male 0 0 SOTONOQ 392086 80500 S
3 0 Smiljanic Mr Mile male 0 0 315037 86625 S
3 0 Smith Mr Thomas male 0 0 384461 77500 Q
3 1 Smyth Miss Julia female 0 0 335432 77333 Q 13
3 0 Soholt Mr Peter Andreas Lauritz Andersen male 19 0 0 348124 76500 F G73 S
3 0 Somerton Mr Francis William male 30 0 0 A5 18509 80500 S
3 0 Spector Mr Woolf male 0 0 A5 3236 80500 S
3 0 Spinner Mr Henry John male 32 0 0 STONOQ 369943 80500 S
3 0 Staneff Mr Ivan male 0 0 349208 78958 S
3 0 Stankovic Mr Ivan male 33 0 0 349239 86625 C
3 1 Stanley Miss Amy Zillah Elsie female 23 0 0 CA 2314 75500 S C
3 0 Stanley Mr Edward Roland male 21 0 0 A4 45380 80500 S
3 0 Storey Mr Thomas male 605 0 0 3701 S 261
3 0 Stoytcheff Mr Ilia male 19 0 0 349205 78958 S
3 0 Strandberg Miss Ida Sofia female 22 0 0 7553 98375 S
3 1 Stranden Mr Juho male 31 0 0 STONO 2 3101288 79250 S 9
3 0 Strilic Mr Ivan male 27 0 0 315083 86625 S
3 0 Strom Miss Telma Matilda female 2 0 1 347054 104625 G6 S
3 0 Strom Mrs Wilhelm (Elna Matilda Persson) female 29 1 1 347054 104625 G6 S
3 1 Sunderland Mr Victor Francis male 16 0 0 SOTONOQ 392089 80500 S B
3 1 Sundman Mr Johan Julian male 44 0 0 STONO 2 3101269 79250 S 15
3 0 Sutehall Mr Henry Jr male 25 0 0 SOTONOQ 392076 70500 S
3 0 Svensson Mr Johan male 74 0 0 347060 77750 S
3 1 Svensson Mr Johan Cervin male 14 0 0 7538 92250 S 13
3 0 Svensson Mr Olof male 24 0 0 350035 77958 S
3 1 Tenglin Mr Gunnar Isidor male 25 0 0 350033 77958 S 13 15
3 0 Theobald Mr Thomas Leonard male 34 0 0 363294 80500 S 176
3 1 Thomas Master Assad Alexander male 04167 0 1 2625 85167 C 16
3 0 Thomas Mr Charles P male 1 0 2621 64375 C
3 0 Thomas Mr John male 0 0 2681 64375 C
3 0 Thomas Mr Tannous male 0 0 2684 72250 C
3 1 Thomas Mrs Alexander (Thamine Thelma) female 16 1 1 2625 85167 C 14
3 0 Thomson Mr Alexander Morrison male 0 0 32302 80500 S
3 0 Thorneycroft Mr Percival male 1 0 376564 161000 S
3 1 Thorneycroft Mrs Percival (Florence Kate White) female 1 0 376564 161000 S 10
3 0 Tikkanen Mr Juho male 32 0 0 STONO 2 3101293 79250 S
3 0 Tobin Mr Roger male 0 0 383121 77500 F38 Q
3 0 Todoroff Mr Lalio male 0 0 349216 78958 S
3 0 Tomlin Mr Ernest Portage male 305 0 0 364499 80500 S 50
3 0 Torber Mr Ernst William male 44 0 0 364511 80500 S
3 0 Torfa Mr Assad male 0 0 2673 72292 C
3 1 Tornquist Mr William Henry male 25 0 0 LINE 00000 S 15
3 0 Toufik Mr Nakli male 0 0 2641 72292 C
3 1 Touma Master Georges Youssef male 7 1 1 2650 152458 C C
3 1 Touma Miss Maria Youssef female 9 1 1 2650 152458 C C
3 1 Touma Mrs Darwis (Hanne Youssef Razi) female 29 0 2 2650 152458 C C
3 0 Turcin Mr Stjepan male 36 0 0 349247 78958 S
3 1 Turja Miss Anna Sofia female 18 0 0 4138 98417 S 15
3 1 Turkula Mrs (Hedwig) female 63 0 0 4134 95875 S 15
3 0 van Billiard Master James William male 1 1 A5 851 145000 S
3 0 van Billiard Master Walter John male 115 1 1 A5 851 145000 S 1
3 0 van Billiard Mr Austin Blyler male 405 0 2 A5 851 145000 S 255
3 0 Van Impe Miss Catharina female 10 0 2 345773 241500 S
3 0 Van Impe Mr Jean Baptiste male 36 1 1 345773 241500 S
3 0 Van Impe Mrs Jean Baptiste (Rosalie Paula Govaert) female 30 1 1 345773 241500 S
3 0 van Melkebeke Mr Philemon male 0 0 345777 95000 S
3 0 Vande Velde Mr Johannes Joseph male 33 0 0 345780 95000 S
3 0 Vande Walle Mr Nestor Cyriel male 28 0 0 345770 95000 S
3 0 Vanden Steen Mr Leo Peter male 28 0 0 345783 95000 S
3 0 Vander Cruyssen Mr Victor male 47 0 0 345765 90000 S
3 0 Vander Planke Miss Augusta Maria female 18 2 0 345764 180000 S
3 0 Vander Planke Mr Julius male 31 3 0 345763 180000 S
3 0 Vander Planke Mr Leo Edmondus male 16 2 0 345764 180000 S
3 0 Vander Planke Mrs Julius (Emelia Maria Vandemoortele) female 31 1 0 345763 180000 S
3 1 Vartanian Mr David male 22 0 0 2658 72250 C 13 15
3 0 Vendel Mr Olof Edvin male 20 0 0 350416 78542 S
3 0 Vestrom Miss Hulda Amanda Adolfina female 14 0 0 350406 78542 S
3 0 Vovk Mr Janko male 22 0 0 349252 78958 S
3 0 Waelens Mr Achille male 22 0 0 345767 90000 S Antwerp Belgium Stanton OH
3 0 Ware Mr Frederick male 0 0 359309 80500 S
3 0 Warren Mr Charles William male 0 0 CA 49867 75500 S
3 0 Webber Mr James male 0 0 SOTONOQ 3101316 80500 S
3 0 Wenzel Mr Linhart male 325 0 0 345775 95000 S 298
3 1 Whabee Mrs George Joseph (Shawneene Abi-Saab) female 38 0 0 2688 72292 C C
3 0 Widegren Mr CarlCharles Peter male 51 0 0 347064 77500 S
3 0 Wiklund Mr Jakob Alfred male 18 1 0 3101267 64958 S 314
3 0 Wiklund Mr Karl Johan male 21 1 0 3101266 64958 S
3 1 Wilkes Mrs James (Ellen Needs) female 47 1 0 363272 70000 S
3 0 Willer Mr Aaron (Abi Weller) male 0 0 3410 87125 S
3 0 Willey Mr Edward male 0 0 SOPP 751 75500 S
3 0 Williams Mr Howard Hugh Harry male 0 0 A5 2466 80500 S
3 0 Williams Mr Leslie male 285 0 0 54636 161000 S 14
3 0 Windelov Mr Einar male 21 0 0 SOTONOQ 3101317 72500 S
3 0 Wirz Mr Albert male 27 0 0 315154 86625 S 131
3 0 Wiseman Mr Phillippe male 0 0 A4 34244 72500 S
3 0 Wittevrongel Mr Camille male 36 0 0 345771 95000 S
3 0 Yasbeck Mr Antoni male 27 1 0 2659 144542 C C
3 1 Yasbeck Mrs Antoni (Selini Alexander) female 15 1 0 2659 144542 C
3 0 Youseff Mr Gerious male 455 0 0 2628 72250 C 312
3 0 Yousif Mr Wazli male 0 0 2647 72250 C
3 0 Yousseff Mr Gerious male 0 0 2627 144583 C
3 0 Zabour Miss Hileni female 145 1 0 2665 144542 C 328
3 0 Zabour Miss Thamine female 1 0 2665 144542 C
3 0 Zakarian Mr Mapriededer male 265 0 0 2656 72250 C 304
3 0 Zakarian Mr Ortin male 27 0 0 2670 72250 C
3 0 Zimmerman Mr Leo male 29 0 0 315082 78750 S
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