Titanic Survivability

Introduction

On 15 April 1912, Titanic, the largest ship of its time, sank after hitting an iceberg in the North Atlantic Ocean. Of the 2,224 people estimated on board, only 705 survived. Although limited, there were enough lifeboats to save 1,178 people and yet fewer made it.

Questions

How likely would a passenger survive the tragedy?

• If you are rich, would you most likely be prioritized?
• "Women and Children First". Does your age or gender influence your chances of survivability?

Objectives

This study analyzes the likelihood of survivability of passengers on board of the Titanic. The analysis is divided according to Demographics and Social Economic Status. The former will be based on Gender and Age and the latter will be based on Ticket Class and Fare.

Variables

Dependent Variable: If the passenger survived or not.
Independent Variables: 1. Gender 2. Age 3. Ticket Class 4. Fare.
Null Hypothesis: The likelihood of surviving the event are not influenced by demographics and socio economic status.
Hypothesis: The likelihood of survival is influenced by the demographics and socio economic status of the passengers.

Data Wrangling

Data Acquisition

The data provided is a list of names of 891 of the 2,224 passengers with the corresponding information for each on board. Below is the Data Dictionary of the data set from [Kaggle](https://www.kaggle.com/c/titanic/data).

• survival: Survival (0 = No, 1 = Yes)
• pclass: Ticket class (1st = Upper, 2nd = Middle, 3rd = Lower)
• sex: Sex
• Age: Age in years (Age is fractional if less than 1. If the age is estimated, is it in the form of xx.5)
• sibsp: # of siblings / spouse aboard the Titanic (Sibling = brother, sister, stepbrother, stepsister, Spouse = husband, wife (mistresses and fiancés were ignored))
• parch: # of parents / children aboard the Titanic (Parent = mother, father, Child = daughter, son, stepdaughter, stepson, Some children travelled only with a nanny, therefore parch=0 for them.)
• ticket: Ticket number
• fare: Passenger fare
• cabin: Cabin number
• embarked: Port of Embarkation (C = Cherbourg, Q = Queenstown, S = Southampton)

import pandas as pd
import numpy as np
# Graphing
import seaborn as sns
import matplotlib.pyplot as plt
sns.set_style("darkgrid")
titanic_df = pd.read_csv('titanic-data.csv') # Read CSV and stores in to titanic_df variable.

Data Cleaning

Once my file is loaded, I check if there are duplicate values in any of the column that could affect the analysis. I am also looking for inconsistencies in values, data type or missing values that may affect the investigation.

# PassengerId and Name must be unique. I check if there are any duplicate values in each col.
# There are no duplicates on the data.
print titanic_df.duplicated('PassengerId').sum()
print titanic_df.duplicated('Name').sum()
# I also check if the Ticket # is unique. Turns out that the Ticket isn't unique for each passenger.
# Seems odd and I will take note and come back to it if needed.
print titanic_df.duplicated('Ticket').sum()

0
0
210

# I check the data type of each column for any inconsistencies. Seems odd to have Age as an float64.
# I investigate and print out a couple of rows with non-whole number age.
titanic_df.dtypes

PassengerId      int64
Survived         int64
Pclass           int64
Name            object
Sex             object
Age            float64
SibSp            int64
Parch            int64
Ticket          object
Fare           float64
Cabin           object
Embarked        object
dtype: object

# Saw 7 entries that are less than 1. Looking at their names, I see prefix "Master",
# which is what is given to children. In these case, these were babies below the age of 1.
non_whole = titanic_df['Age'] < 1
titanic_df[non_whole]

	PassengerId	Survived	Pclass	Name	Sex	Age	SibSp	Parch	Ticket	Fare	Cabin	Embarked
78	79	1	2	Caldwell, Master. Alden Gates	male	0.83	0	2	248738	29.0000	NaN	S
305	306	1	1	Allison, Master. Hudson Trevor	male	0.92	1	2	113781	151.5500	C22 C26	S
469	470	1	3	Baclini, Miss. Helene Barbara	female	0.75	2	1	2666	19.2583	NaN	C
644	645	1	3	Baclini, Miss. Eugenie	female	0.75	2	1	2666	19.2583	NaN	C
755	756	1	2	Hamalainen, Master. Viljo	male	0.67	1	1	250649	14.5000	NaN	S
803	804	1	3	Thomas, Master. Assad Alexander	male	0.42	0	1	2625	8.5167	NaN	C
831	832	1	2	Richards, Master. George Sibley	male	0.83	1	1	29106	18.7500	NaN	S

# I check the head of the data set.
titanic_df.head()

	PassengerId	Survived	Pclass	Name	Sex	Age	SibSp	Parch	Ticket	Fare	Cabin	Embarked
0	1	0	3	Braund, Mr. Owen Harris	male	22.0	1	0	A/5 21171	7.2500	NaN	S
1	2	1	1	Cumings, Mrs. John Bradley (Florence Briggs Th...	female	38.0	1	0	PC 17599	71.2833	C85	C
2	3	1	3	Heikkinen, Miss. Laina	female	26.0	0	0	STON/O2. 3101282	7.9250	NaN	S
3	4	1	1	Futrelle, Mrs. Jacques Heath (Lily May Peel)	female	35.0	1	0	113803	53.1000	C123	S
4	5	0	3	Allen, Mr. William Henry	male	35.0	0	0	373450	8.0500	NaN	S

# and the tail.
titanic_df.tail()

	PassengerId	Survived	Pclass	Name	Sex	Age	SibSp	Parch	Ticket	Fare	Cabin	Embarked
886	887	0	2	Montvila, Rev. Juozas	male	27.0	0	0	211536	13.00	NaN	S
887	888	1	1	Graham, Miss. Margaret Edith	female	19.0	0	0	112053	30.00	B42	S
888	889	0	3	Johnston, Miss. Catherine Helen "Carrie"	female	NaN	1	2	W./C. 6607	23.45	NaN	S
889	890	1	1	Behr, Mr. Karl Howell	male	26.0	0	0	111369	30.00	C148	C
890	891	0	3	Dooley, Mr. Patrick	male	32.0	0	0	370376	7.75	NaN	Q

titanic_df.describe()

	PassengerId	Survived	Pclass	Age	SibSp	Parch	Fare
count	891.000000	891.000000	891.000000	714.000000	891.000000	891.000000	891.000000
mean	446.000000	0.383838	2.308642	29.699118	0.523008	0.381594	32.204208
std	257.353842	0.486592	0.836071	14.526497	1.102743	0.806057	49.693429
min	1.000000	0.000000	1.000000	0.420000	0.000000	0.000000	0.000000
25%	223.500000	0.000000	2.000000	20.125000	0.000000	0.000000	7.910400
50%	446.000000	0.000000	3.000000	28.000000	0.000000	0.000000	14.454200
75%	668.500000	1.000000	3.000000	38.000000	1.000000	0.000000	31.000000
max	891.000000	1.000000	3.000000	80.000000	8.000000	6.000000	512.329200

# Looking at Age col, I see that there are some empty fields. I check how many there are.
missing_age = titanic_df['Age'].isnull()
print 'There are {} logs with their Age not specified.'.format(missing_age.sum())

There are 177 logs with their Age not specified.

Exploratory

Women and Children First

The 177 logs that have missing age will affect the analysis. I attempt to limit the descrepency by rephrasing my question to distinguish the survivability between women and children vs Male adult passengers.

titanic_df['womChil'] = 0 # Created a column that groups women and children.
women = titanic_df['Sex'] == 'female' # Criteria - all female passengers.
child = titanic_df['Age'] < 19 # Criteria - all children under the age of 18 years.
# For passengers with missing age, I identify the children from the group of male passengers by looking
# for the title 'Master' in their Names, which are titles given to minors on board without their
# parents.
masters = titanic_df['Name'].str.contains('Master') # Criteria - all male children.
titanic_df.loc[(women | child | masters),'womChil'] = 1 # Add 1 (yes) that fits the criterias
women_children = titanic_df['womChil'] == 1 # Criteria - all Women and Children.
survived = titanic_df['Survived'] == 1 # Criteria - all passengers that survived.

# Extracted all women and children that sruvived.
women_children_survived = titanic_df[women_children & survived]
women_children_survived['PassengerId'].count() # Counted the women and children survivors.
print "Of the {} passengers that survived from the sample of 891 on the data provided, {} are \
women and children.".format(titanic_df[survived]['PassengerId'].count(), \
                            women_children_survived['PassengerId'].count())

Of the 342 passengers that survived from the sample of 891 on the data provided, 259 are women and children.

# Knowing that 259 of the passengers fit the criteria of women and children, I double check on
# the remaining survivors that did not fit the criteria and whose age were missing from the records
# and try find any other clue that might distinguish the the male passengers as children.
non_womChil = titanic_df['womChil'] == 0
print titanic_df[non_womChil & survived & missing_age]['Age'].isnull().sum()
titanic_df[non_womChil & survived & missing_age].sort_values('Pclass')

14

	PassengerId	Survived	Pclass	Name	Sex	Age	SibSp	Parch	Ticket	Fare	Cabin	Embarked	womChil
55	56	1	1	Woolner, Mr. Hugh	male	NaN	0	0	19947	35.5000	C52	S	0
298	299	1	1	Saalfeld, Mr. Adolphe	male	NaN	0	0	19988	30.5000	C106	S	0
507	508	1	1	Bradley, Mr. George ("George Arthur Brayton")	male	NaN	0	0	111427	26.5500	NaN	S	0
740	741	1	1	Hawksford, Mr. Walter James	male	NaN	0	0	16988	30.0000	D45	S	0
839	840	1	1	Marechal, Mr. Pierre	male	NaN	0	0	11774	29.7000	C47	C	0
17	18	1	2	Williams, Mr. Charles Eugene	male	NaN	0	0	244373	13.0000	NaN	S	0
547	548	1	2	Padro y Manent, Mr. Julian	male	NaN	0	0	SC/PARIS 2146	13.8625	NaN	C	0
36	37	1	3	Mamee, Mr. Hanna	male	NaN	0	0	2677	7.2292	NaN	C	0
107	108	1	3	Moss, Mr. Albert Johan	male	NaN	0	0	312991	7.7750	NaN	S	0
301	302	1	3	McCoy, Mr. Bernard	male	NaN	2	0	367226	23.2500	NaN	Q	0
444	445	1	3	Johannesen-Bratthammer, Mr. Bernt	male	NaN	0	0	65306	8.1125	NaN	S	0
643	644	1	3	Foo, Mr. Choong	male	NaN	0	0	1601	56.4958	NaN	S	0
692	693	1	3	Lam, Mr. Ali	male	NaN	0	0	1601	56.4958	NaN	S	0
828	829	1	3	McCormack, Mr. Thomas Joseph	male	NaN	0	0	367228	7.7500	NaN	Q	0

def survivability(data, criteria):
    yes = data[criteria & survived]['PassengerId'].size
    whole = data[criteria]['PassengerId'].size
    chances = (float(yes) / float(whole))* 100
    return chances

survive_womenAndChild = survivability(titanic_df, women_children)
survive_men = survivability(titanic_df, non_womChil)

## There is no more criteria we can use to identify the remaining surviving male passengers with
## no specified age as children. We then move on and draw our conclusions.
ax = sns.countplot(x='Survived', hue='womChil', data=titanic_df)
sns.plt.show()

print "Women and Children had a {0:.2f}% chance of surviving the tragedy".format(survive_womenAndChild)
print "Men only had a {0:.2f}% chances of surviving the tragedy".format(survive_men)

Women and Children had a 66.58% chance of surviving the tragedy
Men only had a 16.53% chances of surviving the tragedy

Preferential Treatment

We move on to the next question, would rich people have a higher chances of surviving the tragedy than the rest of the people on board Titanic?

# To answer the question, we group the passengers according to Pclass and see the describe info.
# There were 216 Passengers in Upper Class. Their Fare price has a maximum value of 512.33.
# There are 184 Passengers in the Middle Class. Max Fare is 73.50
# There are 491 Passengers in the Lowest Class. Max Fare is 69.50
passengers_by_class = titanic_df.groupby(['Pclass'])
passengers_by_class.describe()

		Age	Fare	Parch	PassengerId	SibSp	Survived	womChil
Pclass
1	count	186.000000	216.000000	216.000000	216.000000	216.000000	216.000000	216.000000
	mean	38.233441	84.154687	0.356481	461.597222	0.416667	0.629630	0.458333
	std	14.802856	78.380373	0.693997	246.737616	0.611898	0.484026	0.499418
	min	0.920000	0.000000	0.000000	2.000000	0.000000	0.000000	0.000000
	25%	27.000000	30.923950	0.000000	270.750000	0.000000	0.000000	0.000000
	50%	37.000000	60.287500	0.000000	472.000000	0.000000	1.000000	0.000000
	75%	49.000000	93.500000	0.000000	670.500000	1.000000	1.000000	1.000000
	max	80.000000	512.329200	4.000000	890.000000	3.000000	1.000000	1.000000
2	count	173.000000	184.000000	184.000000	184.000000	184.000000	184.000000	184.000000
	mean	29.877630	20.662183	0.380435	445.956522	0.402174	0.472826	0.494565
	std	14.001077	13.417399	0.690963	250.852161	0.601633	0.500623	0.501335
	min	0.670000	0.000000	0.000000	10.000000	0.000000	0.000000	0.000000
	25%	23.000000	13.000000	0.000000	234.500000	0.000000	0.000000	0.000000
	50%	29.000000	14.250000	0.000000	435.500000	0.000000	0.000000	0.000000
	75%	36.000000	26.000000	1.000000	668.000000	1.000000	1.000000	1.000000
	max	70.000000	73.500000	3.000000	887.000000	3.000000	1.000000	1.000000
3	count	355.000000	491.000000	491.000000	491.000000	491.000000	491.000000	491.000000
	mean	25.140620	13.675550	0.393075	439.154786	0.615071	0.242363	0.405295
	std	12.495398	11.778142	0.888861	264.441453	1.374883	0.428949	0.491450
	min	0.420000	0.000000	0.000000	1.000000	0.000000	0.000000	0.000000
	25%	18.000000	7.750000	0.000000	200.000000	0.000000	0.000000	0.000000
	50%	24.000000	8.050000	0.000000	432.000000	0.000000	0.000000	0.000000
	75%	32.000000	15.500000	0.000000	666.500000	1.000000	0.000000	1.000000
	max	74.000000	69.550000	6.000000	891.000000	8.000000	1.000000	1.000000

# Seems odd that on the 3 classes, the minimum Fare is 0, which is free.
# I check the Fare priced at 0.
free = titanic_df['Fare'] == 0
freeloaders = titanic_df[free]
freeloaders # Looks like some of these are crew of the titanic but I can't be sure.
# Might be too complicated to include the Fare in my analysis. I ignore it and move on.

	PassengerId	Survived	Pclass	Name	Sex	Age	SibSp	Parch	Ticket	Fare	Cabin	Embarked	womChil
179	180	0	3	Leonard, Mr. Lionel	male	36.0	0	0	LINE	0.0	NaN	S	0
263	264	0	1	Harrison, Mr. William	male	40.0	0	0	112059	0.0	B94	S	0
271	272	1	3	Tornquist, Mr. William Henry	male	25.0	0	0	LINE	0.0	NaN	S	0
277	278	0	2	Parkes, Mr. Francis "Frank"	male	NaN	0	0	239853	0.0	NaN	S	0
302	303	0	3	Johnson, Mr. William Cahoone Jr	male	19.0	0	0	LINE	0.0	NaN	S	0
413	414	0	2	Cunningham, Mr. Alfred Fleming	male	NaN	0	0	239853	0.0	NaN	S	0
466	467	0	2	Campbell, Mr. William	male	NaN	0	0	239853	0.0	NaN	S	0
481	482	0	2	Frost, Mr. Anthony Wood "Archie"	male	NaN	0	0	239854	0.0	NaN	S	0
597	598	0	3	Johnson, Mr. Alfred	male	49.0	0	0	LINE	0.0	NaN	S	0
633	634	0	1	Parr, Mr. William Henry Marsh	male	NaN	0	0	112052	0.0	NaN	S	0
674	675	0	2	Watson, Mr. Ennis Hastings	male	NaN	0	0	239856	0.0	NaN	S	0
732	733	0	2	Knight, Mr. Robert J	male	NaN	0	0	239855	0.0	NaN	S	0
806	807	0	1	Andrews, Mr. Thomas Jr	male	39.0	0	0	112050	0.0	A36	S	0
815	816	0	1	Fry, Mr. Richard	male	NaN	0	0	112058	0.0	B102	S	0
822	823	0	1	Reuchlin, Jonkheer. John George	male	38.0	0	0	19972	0.0	NaN	S	0

# I create criteria for each of the class and see how many survived for each.
first = titanic_df['Pclass'] == 1
second = titanic_df['Pclass'] == 2
third = titanic_df['Pclass'] == 3
non_survived = titanic_df['Survived'] == 0

survive_first = survivability(titanic_df, first)
survive_second = survivability(titanic_df, second)
survive_third = survivability(titanic_df, third)
print "{0:.2f}% of the First Class Passengers survived.".format(survive_first)
print "{0:.2f}% of the Second Class Passengers survived.".format(survive_second)
print "{0:.2f}% of the Third Class Passengers survived.".format(survive_third)

62.96% of the First Class Passengers survived.
47.28% of the Second Class Passengers survived.
24.24% of the Third Class Passengers survived.

ax = sns.countplot(x='Pclass', hue='Survived', data=titanic_df)
ax.set_xticklabels(['First', 'Second', 'Third'])

sns.plt.show()
print "You have a {0:.2f}% chance of surviving the tragedy if you were a First Class Passenger," \
.format(survive_first)
print "than if you were in 3rd Class, who only had a {0:.2f}% chance of surviving." \
.format(survive_third)

You have a 62.96% chance of surviving the tragedy if you were a First Class Passenger,
than if you were in 3rd Class, who only had a 24.24% chance of surviving.

g = sns.barplot(x="womChil", y="Survived", hue="Pclass", data=titanic_df)
sns.plt.show()
print "If we look at the average count of women and children survivors for each class, it seems that, \
in passenger class, women and children had a higher chances of survival if you were in the upper class \
than if you were in the lower class."

If we look at the average count of women and children survivors for each class, it seems that, in passenger class, women and children had a higher chances of survival if you were in the upper class than if you were in the lower class.

# I took a look on the combination of Age grouped according to their Economic Status
# into account and see the probability of Survivability.
fig = sns.boxplot(x='Pclass', y='Age', data=titanic_df, hue='Survived')
plt.show()
print "The Box Plot shows that people on First Class are generally much older than the lower class. \
Though it goes to show that much younger passengers survived the tragedy, the variability amongst \
the classes are greated the higher the class. On average the Younger Survivors belonged on the lower \
class and more older passengers belonged on the upper class."

The Box Plot shows that people on First Class are generally much older than the lower class. Though it goes to show that much younger passengers survived the tragedy, the variability amongst the classes are greated the higher the class. On average the Younger Survivors belonged on the lower class and more older passengers belonged on the upper class.

Conclusions

Looking back on the questions that were asked at the beginning, we can observe certain relations towards survivability of passengers with their Demographics and Socia Economic.

"Women and Children First". Does your age or gender influence your chances of survivability?

With the data provided, it seems that women and children have a higher chances of suriving the tragedy than that of adult males. 67% of the women and children survived the tragedy while only 17% of the adult males survived. Please take note that analysis is limited to the fact that some of the passenger's age were missing in the dataset and that it may skew results.

If you are rich, would you most likely be prioritized?

The answer to this question is most probably. Looking at the data grouped according to the Passenger Class, it would seem that the higher you are in the passenger class, the higher your chances of surviving the tragedy. 63% of the First Class Passengers survived the tragedy. While only 48% of the Second Passengers survived and only 24% of the Third Class Passengers survived. Please take note that this part of the analysis is limited to the fact that there is no direct relationship between socio-economic status and their class. In other words, we cannot be sure that First Class passengers are rich and the rest of the passengers are poor. It would be great to have more details about each passenger such as their occupation or income for example. I looked at the Ticket Fare, but it seems that some passengers had 0 Fare, which could mean many things hence could not be a great basis in this part of the analysis.