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Titanic Survival Prediction Using Machine Learning

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Md. Rana Mahmud
Md. Rana Mahmud

Titanic survival passenger survival analysis and predicting using machine learning.Software used for analysis Sas and R Programming Language. The data set used from kaggle.com. It was done as part of my Statistical Programmer Internee at the Shafi Consultancy Bangladesh Limited.

Data & Analytics
1 of 33
By MD. RANA MAHMUD Predicting Titanic Passenger Survival Using Machine Learning 1
Table of Context • Introduction • Overview of the problem • Questions to be answered • Data Description • Descriptive Analysis • Methodology • Data Processing • Predicting Survival • Summary Finding 2
Introduction I figure life’s a gift and I don’t intend on wasting it. -Jack; Titanic Movie 3
Data Description • Training Data • 891 passenger data • 12 variables • Test Data • 418 passenger data • 11 variables 4
Data Dictionary Variable Definition Key survival Survival 0 = No, 1 = Yes pclass Ticket class 1 = 1st, 2 = 2nd, 3 = 3rd sex Sex Age Age in years sibsp # of siblings / spouses aboard the Titanic parch # of parents / children aboard the Titanic ticket Ticket number fare Passenger fare cabin Cabin number embarked Port of Embarkation C = Cherbourg, Q = Queenstown, S = Southampton 5
Data Sample PassengerIdSurvived Pclass Name Sex Age SibSp Parch Ticket Fare Cabin Embarked 1 0 3 Braund, Mr. Owen Harrismale 22 1 0 A/5 21171 7.25 S 2 1 1 Cumings, Mrs. John Bradley (Florence Briggs Thayer)female 38 1 0 PC 17599 71.2833 C85 C 3 1 3 Heikkinen, Miss. Lainafemale 26 0 0 STON/O2. 31012827.925 S 4 1 1 Futrelle, Mrs. Jacques Heath (Lily May Peel)female 35 1 0 113803 53.1 C123 S 5 0 3 Allen, Mr. William Henrymale 35 0 0 373450 8.05 S 6 0 3 Moran, Mr. Jamesmale 0 0 330877 8.4583 Q 7 0 1 McCarthy, Mr. Timothy Jmale 54 0 0 17463 51.8625 E46 S 8 0 3 Palsson, Master. Gosta Leonardmale 2 3 1 349909 21.075 S 9 1 3 Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)female 27 0 2 347742 11.1333 S 10 1 2 Nasser, Mrs. Nicholas (Adele Achem)female 14 1 0 237736 30.0708 C 11 1 3 Sandstrom, Miss. Marguerite Rutfemale 4 1 1 PP 9549 16.7 G6 S 12 1 1 Bonnell, Miss. Elizabethfemale 58 0 0 113783 26.55 C103 S 13 0 3 Saundercock, Mr. William Henrymale 20 0 0 A/5. 2151 8.05 S 14 0 3 Andersson, Mr. Anders Johanmale 39 1 5 347082 31.275 S 15 0 3 Vestrom, Miss. Hulda Amanda Adolfinafemale 14 0 0 350406 7.8542 S 16 1 2 Hewlett, Mrs. (Mary D Kingcome)female 55 0 0 248706 16 S 17 0 3 Rice, Master. Eugenemale 2 4 1 382652 29.125 Q 18 1 2 Williams, Mr. Charles Eugenemale 0 0 244373 13 S 19 0 3 Vander Planke, Mrs. Julius (Emelia Maria Vandemoortele)female 31 1 0 345763 18 S 6
Titanic Cross Section 7
Overview of the problem? • Analyzing survival pattern • Predicting passenger survival using existing information 8
Descriptive Analysis 9
10
11
12
13
14
15
16
17
18
19
Missing Map in Training Data 20
Methodology • Decision Tree • Random Forest • Analysis of Variance 21
Decision Tree 22
Random Forest 23
Data Processing • Children – Age < 14 • Mother – Sex = Female & Parch 0 & Age > 18 • Title - First Part of Name before , • Deck - Frist Letter of Cabin • Ticket Group • Fare < 50 = Low • Fare > 50 & Fare <= 100 = med1 • Fare > 100 & Fare <= 150 = med2 • Fare >= 150 & Fare <= 500 = high • Fare > 500 = vhigh • Family member = SibSp + Parch + 1 • Family Type • Family Size == 1 = Single • Family Size > 1 & Family Size <5 = Smaller • Family Size > 4 = Large 24
Missing Value Replacement • Age = Pclass + Mother + FamilySize + Sex + SibSp + Parch + Deck + Fare + Embarked + Title + FamilyID + FamilySizeGroup + FamilySize • Embarked == “” by “S” • Fare by Median Fare 25
26
Prediction Call: randomForest(x = rftrain01, y = rflabel, ntree = 1000, importance = TRUE) Type of random forest: classification Number of trees: 1000 No. of variables tried at each split: 3 OOB estimate of error rate: 17.4% Confusion matrix: 0 1 class.error 0 491 58 0.1056466 1 97 245 0.2836257 27
28
library(party) set.seed(291) fit2 <- cforest(as.factor(Survived) ~ Pclass + Sex + Age + SibSp + Parch + Fare + Embarked + Title + FamilySize + FamilyID, data = train, controls=cforest_unbiased(ntree=2000, mtry=3)) MyPredict <- predict(fit2, test, OOB=TRUE, type = "response") predict7th <- data.frame(PassengerId = test$PassengerId, Survived = MyPredict) 29
Model Survived = Pclass + Sex + Age + SibSp + Parch + Fare + Embarked + Title + FamilySize + FamilyID Ntree = 2000 mtry = 3 30
Summary Accuracy 81.3% 31
Questions 32
Thank You 33

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Titanic Survival Prediction Using Machine Learning

  • 2. Table of Context • Introduction • Overview of the problem • Questions to be answered • Data Description • Descriptive Analysis • Methodology • Data Processing • Predicting Survival • Summary Finding 2
  • 3. Introduction I figure life’s a gift and I don’t intend on wasting it. -Jack; Titanic Movie 3
  • 4. Data Description • Training Data • 891 passenger data • 12 variables • Test Data • 418 passenger data • 11 variables 4
  • 5. Data Dictionary Variable Definition Key survival Survival 0 = No, 1 = Yes pclass Ticket class 1 = 1st, 2 = 2nd, 3 = 3rd sex Sex Age Age in years sibsp # of siblings / spouses aboard the Titanic parch # of parents / children aboard the Titanic ticket Ticket number fare Passenger fare cabin Cabin number embarked Port of Embarkation C = Cherbourg, Q = Queenstown, S = Southampton 5
  • 6. Data Sample PassengerIdSurvived Pclass Name Sex Age SibSp Parch Ticket Fare Cabin Embarked 1 0 3 Braund, Mr. Owen Harrismale 22 1 0 A/5 21171 7.25 S 2 1 1 Cumings, Mrs. John Bradley (Florence Briggs Thayer)female 38 1 0 PC 17599 71.2833 C85 C 3 1 3 Heikkinen, Miss. Lainafemale 26 0 0 STON/O2. 31012827.925 S 4 1 1 Futrelle, Mrs. Jacques Heath (Lily May Peel)female 35 1 0 113803 53.1 C123 S 5 0 3 Allen, Mr. William Henrymale 35 0 0 373450 8.05 S 6 0 3 Moran, Mr. Jamesmale 0 0 330877 8.4583 Q 7 0 1 McCarthy, Mr. Timothy Jmale 54 0 0 17463 51.8625 E46 S 8 0 3 Palsson, Master. Gosta Leonardmale 2 3 1 349909 21.075 S 9 1 3 Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)female 27 0 2 347742 11.1333 S 10 1 2 Nasser, Mrs. Nicholas (Adele Achem)female 14 1 0 237736 30.0708 C 11 1 3 Sandstrom, Miss. Marguerite Rutfemale 4 1 1 PP 9549 16.7 G6 S 12 1 1 Bonnell, Miss. Elizabethfemale 58 0 0 113783 26.55 C103 S 13 0 3 Saundercock, Mr. William Henrymale 20 0 0 A/5. 2151 8.05 S 14 0 3 Andersson, Mr. Anders Johanmale 39 1 5 347082 31.275 S 15 0 3 Vestrom, Miss. Hulda Amanda Adolfinafemale 14 0 0 350406 7.8542 S 16 1 2 Hewlett, Mrs. (Mary D Kingcome)female 55 0 0 248706 16 S 17 0 3 Rice, Master. Eugenemale 2 4 1 382652 29.125 Q 18 1 2 Williams, Mr. Charles Eugenemale 0 0 244373 13 S 19 0 3 Vander Planke, Mrs. Julius (Emelia Maria Vandemoortele)female 31 1 0 345763 18 S 6
  • 8. Overview of the problem? • Analyzing survival pattern • Predicting passenger survival using existing information 8
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  • 20. Missing Map in Training Data 20
  • 21. Methodology • Decision Tree • Random Forest • Analysis of Variance 21
  • 24. Data Processing • Children – Age < 14 • Mother – Sex = Female & Parch 0 & Age > 18 • Title - First Part of Name before , • Deck - Frist Letter of Cabin • Ticket Group • Fare < 50 = Low • Fare > 50 & Fare <= 100 = med1 • Fare > 100 & Fare <= 150 = med2 • Fare >= 150 & Fare <= 500 = high • Fare > 500 = vhigh • Family member = SibSp + Parch + 1 • Family Type • Family Size == 1 = Single • Family Size > 1 & Family Size <5 = Smaller • Family Size > 4 = Large 24
  • 25. Missing Value Replacement • Age = Pclass + Mother + FamilySize + Sex + SibSp + Parch + Deck + Fare + Embarked + Title + FamilyID + FamilySizeGroup + FamilySize • Embarked == “” by “S” • Fare by Median Fare 25
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  • 27. Prediction Call: randomForest(x = rftrain01, y = rflabel, ntree = 1000, importance = TRUE) Type of random forest: classification Number of trees: 1000 No. of variables tried at each split: 3 OOB estimate of error rate: 17.4% Confusion matrix: 0 1 class.error 0 491 58 0.1056466 1 97 245 0.2836257 27
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  • 29. library(party) set.seed(291) fit2 <- cforest(as.factor(Survived) ~ Pclass + Sex + Age + SibSp + Parch + Fare + Embarked + Title + FamilySize + FamilyID, data = train, controls=cforest_unbiased(ntree=2000, mtry=3)) MyPredict <- predict(fit2, test, OOB=TRUE, type = "response") predict7th <- data.frame(PassengerId = test$PassengerId, Survived = MyPredict) 29
  • 30. Model Survived = Pclass + Sex + Age + SibSp + Parch + Fare + Embarked + Title + FamilySize + FamilyID Ntree = 2000 mtry = 3 30