Created by SmirkyGraphs. Source: GitHub.

Predicting Titanic Survivors

Setup Imports

In [1]:
import pandas as pd
from pandas import Series, DataFrame
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline

Getting the Data

In [2]:
titanic_df = pd.read_csv('train.csv')

Getting basic info on the data

In [3]:
# Previewing the Data
titanic_df.head()
Out[3]:
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
In [4]:
# Overall general info
titanic_df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 891 entries, 0 to 890
Data columns (total 12 columns):
PassengerId    891 non-null int64
Survived       891 non-null int64
Pclass         891 non-null int64
Name           891 non-null object
Sex            891 non-null object
Age            714 non-null float64
SibSp          891 non-null int64
Parch          891 non-null int64
Ticket         891 non-null object
Fare           891 non-null float64
Cabin          204 non-null object
Embarked       889 non-null object
dtypes: float64(2), int64(5), object(5)
memory usage: 83.6+ KB
In [5]:
# Viewing the data types of each column
titanic_df.dtypes
Out[5]:
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

There are 891 rows ~100 missing Age and ~700 missing Cabin information

In [6]:
titanic_df.describe()
Out[6]:
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

Exploring the data

Lets try and answer some important questions like

  • Who was on the titanic
  • Who survived the titanic
  • What factors may have lead to their survival

Who was on the titanic

In [7]:
# Graphing by Gender
sns.factorplot('Sex',data=titanic_df,kind="count")
Out[7]:
<seaborn.axisgrid.FacetGrid at 0xc03691d0b8>
In [8]:
# Graphing by class
sns.factorplot('Pclass',data=titanic_df,hue='Sex',kind="count")
Out[8]:
<seaborn.axisgrid.FacetGrid at 0xc0321de978>

Lets find out how many children were onboard

In [9]:
def male_female_child(passenger):
    age,sex = passenger
    if age < 16:
        return 'child'
    else:
        return sex
In [10]:
titanic_df['person'] = titanic_df[['Age','Sex']].apply(male_female_child,axis=1)
In [11]:
sns.factorplot('Pclass',data=titanic_df,hue='person',kind='count')
Out[11]:
<seaborn.axisgrid.FacetGrid at 0xc03691d3c8>

Surprisingly most of the children are located in 3rd class

In [12]:
# Graphing age
titanic_df['Age'].hist(bins=70)
Out[12]:
<matplotlib.axes._subplots.AxesSubplot at 0xc036be9a58>
In [13]:
titanic_df['Age'].mean()
Out[13]:
29.69911764705882
In [14]:
titanic_df['Age'].median()
Out[14]:
28.0
In [15]:
titanic_df['person'].value_counts()
Out[15]:
male      537
female    271
child      83
Name: person, dtype: int64

83 people under the age 16

In [16]:
fig = sns.FacetGrid(titanic_df,hue='Sex',aspect=4)
fig.map(sns.kdeplot,'Age',shade=True)

oldest = titanic_df['Age'].max()

fig.set(xlim=(0,oldest))
fig.add_legend()
Out[16]:
<seaborn.axisgrid.FacetGrid at 0xc037ed1a90>
In [17]:
fig = sns.FacetGrid(titanic_df,hue='person',aspect=4)
fig.map(sns.kdeplot,'Age',shade=True)

oldest = titanic_df['Age'].max()

fig.set(xlim=(0,oldest))
fig.add_legend()
Out[17]:
<seaborn.axisgrid.FacetGrid at 0xc037f90d30>
In [18]:
fig = sns.FacetGrid(titanic_df,hue='Pclass',aspect=4)
fig.map(sns.kdeplot,'Age',shade=True)

oldest = titanic_df['Age'].max()

fig.set(xlim=(0,oldest))
fig.add_legend()
Out[18]:
<seaborn.axisgrid.FacetGrid at 0xc037fe8cc0>

lets check what deck people were on

In [19]:
deck = titanic_df['Cabin'].dropna()
deck.head()
Out[19]:
1      C85
3     C123
6      E46
10      G6
11    C103
Name: Cabin, dtype: object
In [20]:
levels = []

for level in deck:
    levels.append(level[0])

cabin_df = DataFrame(levels)
cabin_df.columns = ['Cabin']
cabin_df = cabin_df[cabin_df.Cabin != 'T']

sns.factorplot('Cabin',data=cabin_df,order='ABCDEFG',kind='count',palette='winter_d')
Out[20]:
<seaborn.axisgrid.FacetGrid at 0xc037fcc1d0>

Where did they depart from?

In [21]:
sns.factorplot('Embarked',data=titanic_df,hue='Pclass',kind='count'
               ,order=['C','Q','S'])
Out[21]:
<seaborn.axisgrid.FacetGrid at 0xc0368a8208>

Lets find out who was alone, and who was with family

for this we will use SibSp (siblings) and Parch (parents)

In [22]:
titanic_df.head()
Out[22]:
PassengerId Survived Pclass Name Sex Age SibSp Parch Ticket Fare Cabin Embarked person
0 1 0 3 Braund, Mr. Owen Harris male 22.0 1 0 A/5 21171 7.2500 NaN S male
1 2 1 1 Cumings, Mrs. John Bradley (Florence Briggs Th... female 38.0 1 0 PC 17599 71.2833 C85 C female
2 3 1 3 Heikkinen, Miss. Laina female 26.0 0 0 STON/O2. 3101282 7.9250 NaN S female
3 4 1 1 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35.0 1 0 113803 53.1000 C123 S female
4 5 0 3 Allen, Mr. William Henry male 35.0 0 0 373450 8.0500 NaN S male
In [23]:
titanic_df['Alone'] = titanic_df.SibSp + titanic_df.Parch
In [24]:
index_1 = titanic_df.Alone > 0
index_2 = titanic_df.Alone == 0

titanic_df.loc[index_1, 'Alone'] = 'With Family'
titanic_df.loc[index_2, 'Alone'] = 'Alone'
In [25]:
titanic_df.head()
Out[25]:
PassengerId Survived Pclass Name Sex Age SibSp Parch Ticket Fare Cabin Embarked person Alone
0 1 0 3 Braund, Mr. Owen Harris male 22.0 1 0 A/5 21171 7.2500 NaN S male With Family
1 2 1 1 Cumings, Mrs. John Bradley (Florence Briggs Th... female 38.0 1 0 PC 17599 71.2833 C85 C female With Family
2 3 1 3 Heikkinen, Miss. Laina female 26.0 0 0 STON/O2. 3101282 7.9250 NaN S female Alone
3 4 1 1 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35.0 1 0 113803 53.1000 C123 S female With Family
4 5 0 3 Allen, Mr. William Henry male 35.0 0 0 373450 8.0500 NaN S male Alone
In [26]:
# Graphing who was alone
sns.factorplot('Alone',data=titanic_df,palette='Blues',kind='count')
Out[26]:
<seaborn.axisgrid.FacetGrid at 0xc0382f8940>

Who Survived the Titanic

In [27]:
titanic_df['Survivor'] = titanic_df.Survived.map({0:'no',1:'yes'})

sns.factorplot('Survivor',data=titanic_df,kind='count')
Out[27]:
<seaborn.axisgrid.FacetGrid at 0xc0381f41d0>

Women and Children first right?
Lets find out

In [28]:
# Graphing by the sex/age
sns.factorplot('Pclass','Survived',hue='person',data=titanic_df)
Out[28]:
<seaborn.axisgrid.FacetGrid at 0xc037f4b0f0>
In [29]:
sns.lmplot('Age','Survived',data=titanic_df)
Out[29]:
<seaborn.axisgrid.FacetGrid at 0xc0383cd668>
In [30]:
sns.lmplot('Age','Survived',hue='Pclass',palette='winter',data=titanic_df)
Out[30]:
<seaborn.axisgrid.FacetGrid at 0xc038541ba8>
In [31]:
generations = [10,20,40,60,80]

sns.lmplot('Age','Survived',hue='Pclass',palette='winter',data=titanic_df,x_bins=generations)
Out[31]:
<seaborn.axisgrid.FacetGrid at 0xc03802a160>
In [32]:
sns.lmplot('Age','Survived',hue='Sex',data=titanic_df,palette='winter_d',x_bins=generations)
Out[32]:
<seaborn.axisgrid.FacetGrid at 0xc039d3fa20>
In [33]:
levels_df = titanic_df.dropna()
levels_df.head()
Out[33]:
PassengerId Survived Pclass Name Sex Age SibSp Parch Ticket Fare Cabin Embarked person Alone Survivor
1 2 1 1 Cumings, Mrs. John Bradley (Florence Briggs Th... female 38.0 1 0 PC 17599 71.2833 C85 C female With Family yes
3 4 1 1 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35.0 1 0 113803 53.1000 C123 S female With Family yes
6 7 0 1 McCarthy, Mr. Timothy J male 54.0 0 0 17463 51.8625 E46 S male Alone no
10 11 1 3 Sandstrom, Miss. Marguerite Rut female 4.0 1 1 PP 9549 16.7000 G6 S child With Family yes
11 12 1 1 Bonnell, Miss. Elizabeth female 58.0 0 0 113783 26.5500 C103 S female Alone yes
In [34]:
char_cabin = titanic_df["Cabin"].astype(str)

new_Cabin = np.array([cabin[0] for cabin in char_cabin])

new_Cabin = pd.Categorical(new_Cabin)

new_Cabin.describe()
Out[34]:
counts freqs
categories
A 15 0.016835
B 47 0.052750
C 59 0.066218
D 33 0.037037
E 32 0.035915
F 13 0.014590
G 4 0.004489
T 1 0.001122
n 687 0.771044
In [35]:
titanic_df['Deck'] = new_Cabin
titanic_df.head()
Out[35]:
PassengerId Survived Pclass Name Sex Age SibSp Parch Ticket Fare Cabin Embarked person Alone Survivor Deck
0 1 0 3 Braund, Mr. Owen Harris male 22.0 1 0 A/5 21171 7.2500 NaN S male With Family no n
1 2 1 1 Cumings, Mrs. John Bradley (Florence Briggs Th... female 38.0 1 0 PC 17599 71.2833 C85 C female With Family yes C
2 3 1 3 Heikkinen, Miss. Laina female 26.0 0 0 STON/O2. 3101282 7.9250 NaN S female Alone yes n
3 4 1 1 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35.0 1 0 113803 53.1000 C123 S female With Family yes C
4 5 0 3 Allen, Mr. William Henry male 35.0 0 0 373450 8.0500 NaN S male Alone no n
In [36]:
# Just getting values with a deck level
deck_df = titanic_df[titanic_df.Deck != 'n']
deck_df = deck_df[deck_df.Deck != 'T']
deck_df.head()
Out[36]:
PassengerId Survived Pclass Name Sex Age SibSp Parch Ticket Fare Cabin Embarked person Alone Survivor Deck
1 2 1 1 Cumings, Mrs. John Bradley (Florence Briggs Th... female 38.0 1 0 PC 17599 71.2833 C85 C female With Family yes C
3 4 1 1 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35.0 1 0 113803 53.1000 C123 S female With Family yes C
6 7 0 1 McCarthy, Mr. Timothy J male 54.0 0 0 17463 51.8625 E46 S male Alone no E
10 11 1 3 Sandstrom, Miss. Marguerite Rut female 4.0 1 1 PP 9549 16.7000 G6 S child With Family yes G
11 12 1 1 Bonnell, Miss. Elizabeth female 58.0 0 0 113783 26.5500 C103 S female Alone yes C
In [37]:
deck_df.describe()
Out[37]:
PassengerId Survived Pclass Age SibSp Parch Fare
count 203.000000 203.000000 203.000000 184.000000 203.000000 203.000000 203.000000
mean 455.970443 0.669951 1.197044 35.779457 0.443350 0.438424 76.341708
std 251.869248 0.471393 0.527080 15.707436 0.629704 0.731133 74.520547
min 2.000000 0.000000 1.000000 0.920000 0.000000 0.000000 0.000000
25% 260.500000 0.000000 1.000000 24.000000 0.000000 0.000000 29.206250
50% 458.000000 1.000000 1.000000 36.000000 0.000000 0.000000 55.441700
75% 686.000000 1.000000 1.000000 48.000000 1.000000 1.000000 89.552100
max 890.000000 1.000000 3.000000 80.000000 3.000000 4.000000 512.329200
In [38]:
sns.lmplot('Age','Survived',hue="Deck", data=deck_df,palette='winter',
           hue_order=['A','B','C','D','E','F','G'], x_bins=generations).set(ylim=[-0.4,1.4])
Out[38]:
<seaborn.axisgrid.FacetGrid at 0xc039e6e5f8>
In [39]:
sns.factorplot('Deck','Survived',data=deck_df, palette='winter', order=['A','B','C','D','E','F','G'])
Out[39]:
<seaborn.axisgrid.FacetGrid at 0xc039fb09e8>
In [40]:
sns.factorplot('Survivor',hue='Deck', data=deck_df, palette='winter',
               hue_order=['A','B','C','D','E','F','G'], kind="count")
Out[40]:
<seaborn.axisgrid.FacetGrid at 0xc039d29b38>
In [41]:
sns.lmplot('Age','Survived',hue='Alone',data=titanic_df,palette='winter',x_bins=generations)
Out[41]:
<seaborn.axisgrid.FacetGrid at 0xc03a6ce240>
In [42]:
sns.factorplot('Alone',hue='Survivor',data=titanic_df,palette='winter',kind="count", hue_order=['yes','no'])
Out[42]:
<seaborn.axisgrid.FacetGrid at 0xc03a6fd5c0>

You were more likely to survive if you went on the titanic with family

Predicting Who Survived

In [53]:
from sklearn.linear_model import LogisticRegression
from sklearn import cross_validation
In [44]:
train_df = pd.read_csv('train.csv')
train_df.describe()
Out[44]:
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

First we have to take care of the nulls and convert our strings to numbers

In [45]:
# Replaicng missing values

# Filling missing values
train_df['Age'] = train_df['Age'].fillna(train_df['Age'].median())
train_df['Fare'] = train_df['Fare'].fillna(train_df['Fare'].median())

# Filling missing with the most common
train_df['Embarked'] = train_df['Embarked'].fillna('S')
In [46]:
# Making Columns

# Gender/Age
train_df['person'] = train_df[['Age','Sex']].apply(male_female_child,axis=1)

train_df['Alone'] = train_df.SibSp + train_df.Parch

# Alone/With Family
index_1 = train_df.Alone > 0
index_2 = train_df.Alone == 0

train_df.loc[index_1, 'Alone'] = 'With Family'
train_df.loc[index_2, 'Alone'] = 'Alone'

# Deck
train_df['Deck'] = new_Cabin
train_df['Deck'] = train_df['Deck'].astype('str')

#############################################################################

# Replacing sex
train_df.loc[train_df['Sex'] == 'male','Sex'] = 0
train_df.loc[train_df['Sex'] == 'female','Sex'] = 1

# Replacing person
train_df.loc[train_df['person'] == 'male','person'] = 0
train_df.loc[train_df['person'] == 'female','person'] = 1
train_df.loc[train_df['person'] == 'child','person'] = 2


# Replacing Location
train_df.loc[train_df['Embarked'] == 'S','Embarked'] = 0
train_df.loc[train_df['Embarked'] == 'C','Embarked'] = 1
train_df.loc[train_df['Embarked'] == 'Q','Embarked'] = 2

# Replace Alone
train_df.loc[train_df['Alone'] == 'With Family','Alone'] = 0
train_df.loc[train_df['Alone'] == 'Alone','Alone'] = 1

# Replace Deck 'A','B','C','D','E','F','G', 'T', 'n'
train_df.loc[train_df['Deck'] == 'A','Deck'] = 0
train_df.loc[train_df['Deck'] == 'B','Deck'] = 1
train_df.loc[train_df['Deck'] == 'C','Deck'] = 2
train_df.loc[train_df['Deck'] == 'D','Deck'] = 3
train_df.loc[train_df['Deck'] == 'E','Deck'] = 4
train_df.loc[train_df['Deck'] == 'F','Deck'] = 5
train_df.loc[train_df['Deck'] == 'G','Deck'] = 6
train_df.loc[train_df['Deck'] == 'T','Deck'] = 7
train_df.loc[train_df['Deck'] == 'n','Deck'] = 7
In [47]:
predictors = ['Pclass', 'Sex', 'Age', 'SibSp', 'Parch', 'Fare', 'Embarked', 'person', 'Alone', 'Deck']

alg = LogisticRegression(random_state=1)
scores = cross_validation.cross_val_score(alg, train_df[predictors], train_df['Survived'], cv=3)

print(scores.mean())

0.806958473625
In [48]:
# Test Data

test_df = pd.read_csv('test.csv')

# Replaicng missing values

# Filling missing values
test_df['Age'] = test_df['Age'].fillna(train_df['Age'].median())
test_df['Fare'] = test_df['Fare'].fillna(train_df['Fare'].median())

# Filling missing with the most common
test_df['Embarked'] = test_df['Embarked'].fillna('S')
In [49]:
test_cabin = test_df["Cabin"].astype(str)

test_Cabin = np.array([cabin[0] for cabin in test_cabin])

test_Cabin = pd.Categorical(test_Cabin)
In [50]:
# Making Columns
test_df['person'] = test_df[['Age','Sex']].apply(male_female_child,axis=1)

test_df['Alone'] = test_df.SibSp + test_df.Parch

index_1 = test_df.Alone > 0
index_2 = test_df.Alone == 0

test_df.loc[index_1, 'Alone'] = 'With Family'
test_df.loc[index_2, 'Alone'] = 'Alone'

test_df['Deck'] = test_Cabin
test_df['Deck'] = test_df['Deck'].astype('str')

# Replacing sex
test_df.loc[test_df['Sex'] == 'male','Sex'] = 0
test_df.loc[test_df['Sex'] == 'female','Sex'] = 1

# Replacing person
test_df.loc[test_df['person'] == 'male','person'] = 0
test_df.loc[test_df['person'] == 'female','person'] = 1
test_df.loc[test_df['person'] == 'child','person'] = 2


# Replacing Location
test_df.loc[test_df['Embarked'] == 'S','Embarked'] = 0
test_df.loc[test_df['Embarked'] == 'C','Embarked'] = 1
test_df.loc[test_df['Embarked'] == 'Q','Embarked'] = 2

# Replace Alone
test_df.loc[test_df['Alone'] == 'With Family','Alone'] = 0
test_df.loc[test_df['Alone'] == 'Alone','Alone'] = 1

# Replace Deck 'A','B','C','D','E','F','G', 'T', 'n'
test_df.loc[test_df['Deck'] == 'A','Deck'] = 0
test_df.loc[test_df['Deck'] == 'B','Deck'] = 1
test_df.loc[test_df['Deck'] == 'C','Deck'] = 2
test_df.loc[test_df['Deck'] == 'D','Deck'] = 3
test_df.loc[test_df['Deck'] == 'E','Deck'] = 4
test_df.loc[test_df['Deck'] == 'F','Deck'] = 5
test_df.loc[test_df['Deck'] == 'G','Deck'] = 6
test_df.loc[test_df['Deck'] == 'T','Deck'] = 7
test_df.loc[test_df['Deck'] == 'n','Deck'] = 7
In [51]:
# Predict Test values

alg.fit(train_df[predictors],train_df['Survived'])

# Make predictions using the test set
predictions = alg.predict(test_df[predictors])

# Create a new dataframe with only the columns Kaggle wants from the dataset

submission = pd.DataFrame({'PassengerId': test_df['PassengerId'], 'Survived': predictions})
In [52]:
submission.to_csv('submission.csv')