Exploratory Data Analysis on Titanic dataset
The sinking of the Titanic is one of the most infamous shipwrecks in history.
On April 15, 1912, during her maiden voyage, the widely considered “unsinkable” RMS Titanic sank after colliding with an iceberg. Unfortunately, there weren’t enough lifeboats for everyone onboard, resulting in the death of 1502 out of 2224 passengers and crew.
While there was some element of luck involved in surviving, it seems some groups of people were more likely to survive than others.
This notebook performs an exploratory data analysis (EDA) of the titanic datasets answers the question: “what sorts of people were more likely to survive?” using passenger data (ie name, age, gender, socio-economic class, etc).
Download dataset from Kaggle via https://www.kaggle.com/c/titanic/data?select=train.csv
Import Python libraries
import pandas as pd
import seaborn as sns
from matplotlib import pyplot as plt
%matplotlib inline
Read the data
titanic=pd.read_csv('train.csv')
Read the information in the data
titanic.info()
Output:
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 891 entries, 0 to 890
Data columns (total 12 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 PassengerId 891 non-null int64
1 Survived 891 non-null int64
2 Pclass 891 non-null int64
3 Name 891 non-null object
4 Sex 891 non-null object
5 Age 714 non-null float64
6 SibSp 891 non-null int64
7 Parch 891 non-null int64
8 Ticket 891 non-null object
9 Fare 891 non-null float64
10 Cabin 204 non-null object
11 Embarked 889 non-null object
dtypes: float64(2), int64(5), object(5)
memory usage: 83.7+ KB
We take a look at the shape of our dataset
titanic.shape
Output:
(891, 12)
The dataset has about 12 columns, which are explained below;
PassengerId: unique IDs for each passenger onboard.
Survived: 1 indicates those who survived the shipwreck while 0 indicate those who failed.
Pclass: Passenger's socio-economic class, i.e 1st, 2nd or 3rd.
Name: Name of each passenger
Sex: Gender of each passemger
Age: Age of each passenger
SibSp: number of siblings and spouses of each passenger.
Parch: number of parents and children of each passenger.
Ticket: Ticket number of each passenger
Fare: Amount paid by each passenger
Cabin: Cabin seat of each passenger
Embarked: location where the passengers embarked from(either S-Southampton, Q-Queenstown or C-Cherbourg)
Now, let's do EDA on this dataset to see the factors that determine survival of the titanic
Pclass
ax = sns.countplot(x="Pclass", hue="Survived", data=titanic)
Output:
ax = sns.barplot(x = "Pclass", y = "Survived", data=titanic)
Higher class passengers(P1) - 65%, survived more than middle class (P2) and lower class passengers (P3) which confirms that Survival is directly related to the class of the passengers.
2. Sex
ax = sns.countplot(x="Sex", hue="Survived", data=titanic)
Output:
ax = sns.barplot(x = "Sex", y = "Survived", data=titanic)
Output:
Female survived more than male which implies sex determine the survival of the titanic ship. Infact, around 75% of the Survivor are female which means Sex plays an important role in survival
3. Fare
ax = sns.barplot(x = "Survived", y = "Fare", data=titanic)
Output:
The higher the fare, the chances of survival of the titanic ship
4. Embarked
ax = sns.barplot(x = "Embarked", y = "Survived", data=titanic)
Those who embarked at Cherbourg (60%) have more chances at survival than others (Southampton - 35%, Queenstown - 40%)
Finally;
x = sns.barplot(y = "Survived", data=titanic)
About 33% survived the Titanic disaster, which is close to the official figures (32%)
In summary;
From the above report, the most important determinant of survival are as follows:
Sex- Females are more likely to survive
Pclass - Those who paid for first class are more likely to survive
Fare- Those who paid higher fare are more likely to survive
Embarked - Those who embarked at 'C' have a higher chance at survival
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