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Start here if... You're new to data science and machine learning, or looking for a simple intro to the Kaggle prediction competitions. Competition Description The sinking of the RMS Titanic is one of the most infamous shipwrecks in history. On April 15, 1912, during her maiden voyage, the Titanic sank after colliding with an iceberg, killing 150…
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Titanic Survival Analysis.ipynb

Readme.md

Titanic Passanger Survival Analysis

from IPython.display import Image
Image(url= "https://static1.squarespace.com/static/5006453fe4b09ef2252ba068/5095eabce4b06cb305058603/5095eabce4b02d37bef4c24c/1352002236895/100_anniversary_titanic_sinking_by_esai8mellows-d4xbme8.jpg")

import pandas as pd
import numpy as np
train = pd.read_csv("input/train.csv")
test = pd.read_csv("input/test.csv")
train.isnull().sum()
print("Train Shape:",train.shape)
test.isnull().sum()
print("Test Shape:",test.shape)
Train Shape: (891, 12)
Test Shape: (418, 11)
train.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
test.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 418 entries, 0 to 417
Data columns (total 11 columns):
PassengerId    418 non-null int64
Pclass         418 non-null int64
Name           418 non-null object
Sex            418 non-null object
Age            332 non-null float64
SibSp          418 non-null int64
Parch          418 non-null int64
Ticket         418 non-null object
Fare           417 non-null float64
Cabin          91 non-null object
Embarked       418 non-null object
dtypes: float64(2), int64(4), object(5)
memory usage: 36.0+ KB

Data Dictionary

  • Survived: 0 = No, 1 = Yes
  • pclass: Ticket class 1 = 1st, 2 = 2nd, 3 = 3rd
  • sibsp: # of siblings / spouses aboard the Titanic
  • parch: # of parents / children aboard the Titanic
  • ticket: Ticket number
  • cabin: Cabin number
  • embarked: Port of Embarkation C = Cherbourg, Q = Queenstown, S = Southampton

Total rows and columns

We can see that there are 891 rows and 12 columns in our training dataset.

train.head(10)
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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
5 6 0 3 Moran, Mr. James male NaN 0 0 330877 8.4583 NaN Q
6 7 0 1 McCarthy, Mr. Timothy J male 54.0 0 0 17463 51.8625 E46 S
7 8 0 3 Palsson, Master. Gosta Leonard male 2.0 3 1 349909 21.0750 NaN S
8 9 1 3 Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg) female 27.0 0 2 347742 11.1333 NaN S
9 10 1 2 Nasser, Mrs. Nicholas (Adele Achem) female 14.0 1 0 237736 30.0708 NaN C
train.describe()
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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
test.describe()
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PassengerId Pclass Age SibSp Parch Fare
count 418.000000 418.000000 332.000000 418.000000 418.000000 417.000000
mean 1100.500000 2.265550 30.272590 0.447368 0.392344 35.627188
std 120.810458 0.841838 14.181209 0.896760 0.981429 55.907576
min 892.000000 1.000000 0.170000 0.000000 0.000000 0.000000
25% 996.250000 1.000000 21.000000 0.000000 0.000000 7.895800
50% 1100.500000 3.000000 27.000000 0.000000 0.000000 14.454200
75% 1204.750000 3.000000 39.000000 1.000000 0.000000 31.500000
max 1309.000000 3.000000 76.000000 8.000000 9.000000 512.329200
train.isnull().sum()
PassengerId      0
Survived         0
Pclass           0
Name             0
Sex              0
Age            177
SibSp            0
Parch            0
Ticket           0
Fare             0
Cabin          687
Embarked         2
dtype: int64
test.isnull().sum()
test["Survived"] = ""
test.head()
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PassengerId Pclass Name Sex Age SibSp Parch Ticket Fare Cabin Embarked Survived
0 892 3 Kelly, Mr. James male 34.5 0 0 330911 7.8292 NaN Q
1 893 3 Wilkes, Mrs. James (Ellen Needs) female 47.0 1 0 363272 7.0000 NaN S
2 894 2 Myles, Mr. Thomas Francis male 62.0 0 0 240276 9.6875 NaN Q
3 895 3 Wirz, Mr. Albert male 27.0 0 0 315154 8.6625 NaN S
4 896 3 Hirvonen, Mrs. Alexander (Helga E Lindqvist) female 22.0 1 1 3101298 12.2875 NaN S

Data Visualization using Matplotlib and Seaborn packages.

import matplotlib.pyplot as plt # Plot the graphes
%matplotlib inline
import seaborn as sns
sns.set() # setting seaborn default for plots

Bar Chart for Categorical Features

  • Pclass
  • Sex
  • SibSp ( # of siblings and spouse)
  • Parch ( # of parents and children)
  • Embarked
  • Cabin
def bar_chart(feature):
    survived = train[train['Survived']==1][feature].value_counts()
    dead = train[train['Survived']==0][feature].value_counts()
    df = pd.DataFrame([survived,dead])
    df.index = ['Survived','Dead']
    df.plot(kind='bar',stacked=True, figsize=(10,5))
bar_chart('Sex')
print("Survived :\n",train[train['Survived']==1]['Sex'].value_counts())
print("Dead:\n",train[train['Survived']==0]['Sex'].value_counts())
Survived :
 female    233
male      109
Name: Sex, dtype: int64
Dead:
 male      468
female     81
Name: Sex, dtype: int64

png

The Chart confirms Women more likely survivied than Men.

bar_chart('Pclass')
print("Survived :\n",train[train['Survived']==1]['Pclass'].value_counts())
print("Dead:\n",train[train['Survived']==0]['Pclass'].value_counts())
Survived :
 1    136
3    119
2     87
Name: Pclass, dtype: int64
Dead:
 3    372
2     97
1     80
Name: Pclass, dtype: int64

png

The Chart confirms 1st class more likely survivied than other classes.
The Chart confirms 3rd class more likely dead than other classes

bar_chart('SibSp')
print("Survived :\n",train[train['Survived']==1]['SibSp'].value_counts())
print("Dead:\n",train[train['Survived']==0]['SibSp'].value_counts())
Survived :
 0    210
1    112
2     13
3      4
4      3
Name: SibSp, dtype: int64
Dead:
 0    398
1     97
4     15
2     15
3     12
8      7
5      5
Name: SibSp, dtype: int64

png

The Chart confirms a person aboarded with more than 2 siblings or spouse more likely survived.
The Chart confirms a person aboarded without siblings or spouse more likely dead

bar_chart('Parch')
print("Survived :\n",train[train['Survived']==1]['Parch'].value_counts())
print("Dead:\n",train[train['Survived']==0]['Parch'].value_counts())
Survived :
 0    233
1     65
2     40
3      3
5      1
Name: Parch, dtype: int64
Dead:
 0    445
1     53
2     40
5      4
4      4
3      2
6      1
Name: Parch, dtype: int64

png

The Chart confirms a person aboarded with more than 2 parents or children more likely survived.
The Chart confirms a person aboarded alone more likely dead

bar_chart('Embarked')
print("Survived :\n",train[train['Survived']==1]['Embarked'].value_counts())
print("Dead:\n",train[train['Survived']==0]['Embarked'].value_counts())
Survived :
 S    217
C     93
Q     30
Name: Embarked, dtype: int64
Dead:
 S    427
C     75
Q     47
Name: Embarked, dtype: int64

png

The Chart confirms a person aboarded from C slightly more likely survived.
The Chart confirms a person aboarded from Q more likely dead.
The Chart confirms a person aboarded from S more likely dead.

4. Feature engineering

Feature engineering is the process of using domain knowledge of the data to create features (feature vectors) that make machine learning algorithms work.

feature vector is an n-dimensional vector of numerical features that represent some object. Many algorithms in machine learning require a numerical representation of objects, since such representations facilitate processing and statistical analysis.

train.head()
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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

4.1 how titanic sank?

Image(url= "https://static1.squarespace.com/static/5006453fe4b09ef2252ba068/t/5090b249e4b047ba54dfd258/1351660113175/TItanic-Survival-Infographic.jpg?format=1500w")

train.head(10)
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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
5 6 0 3 Moran, Mr. James male NaN 0 0 330877 8.4583 NaN Q
6 7 0 1 McCarthy, Mr. Timothy J male 54.0 0 0 17463 51.8625 E46 S
7 8 0 3 Palsson, Master. Gosta Leonard male 2.0 3 1 349909 21.0750 NaN S
8 9 1 3 Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg) female 27.0 0 2 347742 11.1333 NaN S
9 10 1 2 Nasser, Mrs. Nicholas (Adele Achem) female 14.0 1 0 237736 30.0708 NaN C
train_test_data = [train,test] # combine dataset

for dataset in train_test_data:
    dataset['Title'] = dataset['Name'].str.extract(' ([A-Za-z]+)\.', expand=False)
train['Title'].value_counts()
Mr          517
Miss        182
Mrs         125
Master       40
Dr            7
Rev           6
Mlle          2
Col           2
Major         2
Lady          1
Sir           1
Mme           1
Ms            1
Don           1
Countess      1
Capt          1
Jonkheer      1
Name: Title, dtype: int64
test['Title'].value_counts()
Mr        240
Miss       78
Mrs        72
Master     21
Rev         2
Col         2
Dr          1
Ms          1
Dona        1
Name: Title, dtype: int64

Title Map

Mr : 0
Miss : 1
Mrs: 2
Others: 3

title_mapping = {"Mr": 0, "Miss": 1, "Mrs": 2, 
                 "Master": 3, "Dr": 3, "Rev": 3, "Col": 3, "Major": 3, "Mlle": 3,"Countess": 3,
                 "Ms": 3, "Lady": 3, "Jonkheer": 3, "Don": 3, "Dona" : 3, "Mme": 3,"Capt": 3,"Sir": 3 }

for dataset in train_test_data:
    dataset['Title'] = dataset["Title"].map(title_mapping)
dataset.head()
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PassengerId Pclass Name Sex Age SibSp Parch Ticket Fare Cabin Embarked Survived Title
0 892 3 Kelly, Mr. James male 34.5 0 0 330911 7.8292 NaN Q 0
1 893 3 Wilkes, Mrs. James (Ellen Needs) female 47.0 1 0 363272 7.0000 NaN S 2
2 894 2 Myles, Mr. Thomas Francis male 62.0 0 0 240276 9.6875 NaN Q 0
3 895 3 Wirz, Mr. Albert male 27.0 0 0 315154 8.6625 NaN S 0
4 896 3 Hirvonen, Mrs. Alexander (Helga E Lindqvist) female 22.0 1 1 3101298 12.2875 NaN S 2
test.head()
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PassengerId Pclass Name Sex Age SibSp Parch Ticket Fare Cabin Embarked Survived Title
0 892 3 Kelly, Mr. James male 34.5 0 0 330911 7.8292 NaN Q 0
1 893 3 Wilkes, Mrs. James (Ellen Needs) female 47.0 1 0 363272 7.0000 NaN S 2
2 894 2 Myles, Mr. Thomas Francis male 62.0 0 0 240276 9.6875 NaN Q 0
3 895 3 Wirz, Mr. Albert male 27.0 0 0 315154 8.6625 NaN S 0
4 896 3 Hirvonen, Mrs. Alexander (Helga E Lindqvist) female 22.0 1 1 3101298 12.2875 NaN S 2
bar_chart('Title')

png

# delete unnecessary feature from dataset
train.drop('Name', axis=1, inplace=True)
test.drop('Name', axis=1, inplace=True)
train.head()
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PassengerId Survived Pclass Sex Age SibSp Parch Ticket Fare Cabin Embarked Title
0 1 0 3 male 22.0 1 0 A/5 21171 7.2500 NaN S 0
1 2 1 1 female 38.0 1 0 PC 17599 71.2833 C85 C 2
2 3 1 3 female 26.0 0 0 STON/O2. 3101282 7.9250 NaN S 1
3 4 1 1 female 35.0 1 0 113803 53.1000 C123 S 2
4 5 0 3 male 35.0 0 0 373450 8.0500 NaN S 0
sex_mapping = {"male": 0, "female": 1}
for dataset in train_test_data:
    dataset['Sex'] = dataset['Sex'].map(sex_mapping)
bar_chart('Sex')

png

test.head()
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PassengerId Pclass Sex Age SibSp Parch Ticket Fare Cabin Embarked Survived Title
0 892 3 0 34.5 0 0 330911 7.8292 NaN Q 0
1 893 3 1 47.0 1 0 363272 7.0000 NaN S 2
2 894 2 0 62.0 0 0 240276 9.6875 NaN Q 0
3 895 3 0 27.0 0 0 315154 8.6625 NaN S 0
4 896 3 1 22.0 1 1 3101298 12.2875 NaN S 2
train["Age"].fillna(train.groupby("Title")["Age"].transform("median"), inplace= True)
test["Age"].fillna(test.groupby('Title')['Age'].transform("median"), inplace= True)
train.head(30)
#train.groupby("Title")["Age"].transform("median")
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PassengerId Survived Pclass Sex Age SibSp Parch Ticket Fare Cabin Embarked Title
0 1 0 3 0 22.0 1 0 A/5 21171 7.2500 NaN S 0
1 2 1 1 1 38.0 1 0 PC 17599 71.2833 C85 C 2
2 3 1 3 1 26.0 0 0 STON/O2. 3101282 7.9250 NaN S 1
3 4 1 1 1 35.0 1 0 113803 53.1000 C123 S 2
4 5 0 3 0 35.0 0 0 373450 8.0500 NaN S 0
5 6 0 3 0 30.0 0 0 330877 8.4583 NaN Q 0
6 7 0 1 0 54.0 0 0 17463 51.8625 E46 S 0
7 8 0 3 0 2.0 3 1 349909 21.0750 NaN S 3
8 9 1 3 1 27.0 0 2 347742 11.1333 NaN S 2
9 10 1 2 1 14.0 1 0 237736 30.0708 NaN C 2
10 11 1 3 1 4.0 1 1 PP 9549 16.7000 G6 S 1
11 12 1 1 1 58.0 0 0 113783 26.5500 C103 S 1
12 13 0 3 0 20.0 0 0 A/5. 2151 8.0500 NaN S 0
13 14 0 3 0 39.0 1 5 347082 31.2750 NaN S 0
14 15 0 3 1 14.0 0 0 350406 7.8542 NaN S 1
15 16 1 2 1 55.0 0 0 248706 16.0000 NaN S 2
16 17 0 3 0 2.0 4 1 382652 29.1250 NaN Q 3
17 18 1 2 0 30.0 0 0 244373 13.0000 NaN S 0
18 19 0 3 1 31.0 1 0 345763 18.0000 NaN S 2
19 20 1 3 1 35.0 0 0 2649 7.2250 NaN C 2
20 21 0 2 0 35.0 0 0 239865 26.0000 NaN S 0
21 22 1 2 0 34.0 0 0 248698 13.0000 D56 S 0
22 23 1 3 1 15.0 0 0 330923 8.0292 NaN Q 1
23 24 1 1 0 28.0 0 0 113788 35.5000 A6 S 0
24 25 0 3 1 8.0 3 1 349909 21.0750 NaN S 1
25 26 1 3 1 38.0 1 5 347077 31.3875 NaN S 2
26 27 0 3 0 30.0 0 0 2631 7.2250 NaN C 0
27 28 0 1 0 19.0 3 2 19950 263.0000 C23 C25 C27 S 0
28 29 1 3 1 21.0 0 0 330959 7.8792 NaN Q 1
29 30 0 3 0 30.0 0 0 349216 7.8958 NaN S 0
facet = sns.FacetGrid(train, hue="Survived",aspect=4)
facet.map(sns.kdeplot,'Age',shade= True)
facet.set(xlim=(0, train['Age'].max()))
facet.add_legend() 
plt.show()

facet = sns.FacetGrid(train, hue="Survived",aspect=4)
facet.map(sns.kdeplot,'Age',shade= True)
facet.set(xlim=(0, train['Age'].max()))
facet.add_legend() 
plt.xlim(10,50)

png

(10, 50)

png

Those who were 20 to 30 years old were more dead and more survived.

train.info()
test.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
Sex            891 non-null int64
Age            891 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
Title          891 non-null int64
dtypes: float64(2), int64(7), object(3)
memory usage: 83.6+ KB
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 418 entries, 0 to 417
Data columns (total 12 columns):
PassengerId    418 non-null int64
Pclass         418 non-null int64
Sex            418 non-null int64
Age            418 non-null float64
SibSp          418 non-null int64
Parch          418 non-null int64
Ticket         418 non-null object
Fare           417 non-null float64
Cabin          91 non-null object
Embarked       418 non-null object
Survived       418 non-null object
Title          418 non-null int64
dtypes: float64(2), int64(6), object(4)
memory usage: 39.3+ KB

Binning

Binning/Converting Numerical Age to Categorical Variable

feature vector map:

  • child: 0
  • young: 1
  • adult: 2
  • mid-age: 3
  • senior: 4
train.head()
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PassengerId Survived Pclass Sex Age SibSp Parch Ticket Fare Cabin Embarked Title
0 1 0 3 0 22.0 1 0 A/5 21171 7.2500 NaN S 0
1 2 1 1 1 38.0 1 0 PC 17599 71.2833 C85 C 2
2 3 1 3 1 26.0 0 0 STON/O2. 3101282 7.9250 NaN S 1
3 4 1 1 1 35.0 1 0 113803 53.1000 C123 S 2
4 5 0 3 0 35.0 0 0 373450 8.0500 NaN S 0
for dataset in train_test_data:
    dataset.loc[ dataset['Age'] <= 16, 'Age'] = 0,
    dataset.loc[(dataset['Age'] > 16) & (dataset['Age'] <= 26), 'Age'] = 1,
    dataset.loc[(dataset['Age'] > 26) & (dataset['Age'] <= 36), 'Age'] = 2,
    dataset.loc[(dataset['Age'] > 36) & (dataset['Age'] <= 62), 'Age'] = 3,
    dataset.loc[ dataset['Age'] > 62, 'Age'] = 4
# for dataset in train_test_data:
#     dataset.loc[]
#train[train['Age'].isin([23])]
train.head()
bar_chart('Age')

png

Pclass1 = train[train['Pclass'] == 1]['Embarked'].value_counts()
Pclass2 = train[train['Pclass'] == 2]['Embarked'].value_counts()
Pclass3 = train[train['Pclass'] == 3]['Embarked'].value_counts()
df = pd.DataFrame([Pclass1,Pclass2,Pclass3])
df.index = ['1st Class','2nd Class','3rd Class']
df.plot(kind = 'bar', stacked =  True, figsize=(10,5))
plt.show()
print("Pclass1:\n",Pclass1)
print("Pclass2:\n",Pclass2)
print("Pclass3:\n",Pclass3)

png

Pclass1:
 S    127
C     85
Q      2
Name: Embarked, dtype: int64
Pclass2:
 S    164
C     17
Q      3
Name: Embarked, dtype: int64
Pclass3:
 S    353
Q     72
C     66
Name: Embarked, dtype: int64

more than 50 % of 1st class are from S embark.
more than 50 % of 2st class are from S embark.
more than 50 % of 3st class are from S embark.

fill out missing embark with S embark

for dataset in train_test_data:
    dataset['Embarked'] =  dataset['Embarked'].fillna('S')
train.head()
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PassengerId Survived Pclass Sex Age SibSp Parch Ticket Fare Cabin Embarked Title
0 1 0 3 0 1.0 1 0 A/5 21171 7.2500 NaN S 0
1 2 1 1 1 3.0 1 0 PC 17599 71.2833 C85 C 2
2 3 1 3 1 1.0 0 0 STON/O2. 3101282 7.9250 NaN S 1
3 4 1 1 1 2.0 1 0 113803 53.1000 C123 S 2
4 5 0 3 0 2.0 0 0 373450 8.0500 NaN S 0
embarked_mapping = {'S':0,'C':1,'Q':2}
for dataset in train_test_data:
    dataset['Embarked'] = dataset['Embarked'].map(embarked_mapping)
# train["Fare"].fillna(train.groupby("Pclass")["Fare"])
# train["Fare"].fillna(train.groupby("Pclass")["Fare"].transform("median"), inplace = True)
# test["Fare"].fillna(test.groupby("Pclass")["Fare"].transform("median"), inplace = True)
# train.head(50)


# fill missing Fare with median fare for each Pclass
train["Fare"].fillna(train.groupby("Pclass")["Fare"].transform("median"), inplace=True)
test["Fare"].fillna(test.groupby("Pclass")["Fare"].transform("median"), inplace=True)
train.head(50)
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PassengerId Survived Pclass Sex Age SibSp Parch Ticket Fare Cabin Embarked Title
0 1 0 3 0 1.0 1 0 A/5 21171 7.2500 NaN 0 0
1 2 1 1 1 3.0 1 0 PC 17599 71.2833 C85 1 2
2 3 1 3 1 1.0 0 0 STON/O2. 3101282 7.9250 NaN 0 1
3 4 1 1 1 2.0 1 0 113803 53.1000 C123 0 2
4 5 0 3 0 2.0 0 0 373450 8.0500 NaN 0 0
5 6 0 3 0 2.0 0 0 330877 8.4583 NaN 2 0
6 7 0 1 0 3.0 0 0 17463 51.8625 E46 0 0
7 8 0 3 0 0.0 3 1 349909 21.0750 NaN 0 3
8 9 1 3 1 2.0 0 2 347742 11.1333 NaN 0 2
9 10 1 2 1 0.0 1 0 237736 30.0708 NaN 1 2
10 11 1 3 1 0.0 1 1 PP 9549 16.7000 G6 0 1
11 12 1 1 1 3.0 0 0 113783 26.5500 C103 0 1
12 13 0 3 0 1.0 0 0 A/5. 2151 8.0500 NaN 0 0
13 14 0 3 0 3.0 1 5 347082 31.2750 NaN 0 0
14 15 0 3 1 0.0 0 0 350406 7.8542 NaN 0 1
15 16 1 2 1 3.0 0 0 248706 16.0000 NaN 0 2
16 17 0 3 0 0.0 4 1 382652 29.1250 NaN 2 3
17 18 1 2 0 2.0 0 0 244373 13.0000 NaN 0 0
18 19 0 3 1 2.0 1 0 345763 18.0000 NaN 0 2
19 20 1 3 1 2.0 0 0 2649 7.2250 NaN 1 2
20 21 0 2 0 2.0 0 0 239865 26.0000 NaN 0 0
21 22 1 2 0 2.0 0 0 248698 13.0000 D56 0 0
22 23 1 3 1 0.0 0 0 330923 8.0292 NaN 2 1
23 24 1 1 0 2.0 0 0 113788 35.5000 A6 0 0
24 25 0 3 1 0.0 3 1 349909 21.0750 NaN 0 1
25 26 1 3 1 3.0 1 5 347077 31.3875 NaN 0 2
26 27 0 3 0 2.0 0 0 2631 7.2250 NaN 1 0
27 28 0 1 0 1.0 3 2 19950 263.0000 C23 C25 C27 0 0
28 29 1 3 1 1.0 0 0 330959 7.8792 NaN 2 1
29 30 0 3 0 2.0 0 0 349216 7.8958 NaN 0 0
30 31 0 1 0 3.0 0 0 PC 17601 27.7208 NaN 1 3
31 32 1 1 1 2.0 1 0 PC 17569 146.5208 B78 1 2
32 33 1 3 1 1.0 0 0 335677 7.7500 NaN 2 1
33 34 0 2 0 4.0 0 0 C.A. 24579 10.5000 NaN 0 0
34 35 0 1 0 2.0 1 0 PC 17604 82.1708 NaN 1 0
35 36 0 1 0 3.0 1 0 113789 52.0000 NaN 0 0
36 37 1 3 0 2.0 0 0 2677 7.2292 NaN 1 0
37 38 0 3 0 1.0 0 0 A./5. 2152 8.0500 NaN 0 0
38 39 0 3 1 1.0 2 0 345764 18.0000 NaN 0 1
39 40 1 3 1 0.0 1 0 2651 11.2417 NaN 1 1
40 41 0 3 1 3.0 1 0 7546 9.4750 NaN 0 2
41 42 0 2 1 2.0 1 0 11668 21.0000 NaN 0 2
42 43 0 3 0 2.0 0 0 349253 7.8958 NaN 1 0
43 44 1 2 1 0.0 1 2 SC/Paris 2123 41.5792 NaN 1 1
44 45 1 3 1 1.0 0 0 330958 7.8792 NaN 2 1
45 46 0 3 0 2.0 0 0 S.C./A.4. 23567 8.0500 NaN 0 0
46 47 0 3 0 2.0 1 0 370371 15.5000 NaN 2 0
47 48 1 3 1 1.0 0 0 14311 7.7500 NaN 2 1
48 49 0 3 0 2.0 2 0 2662 21.6792 NaN 1 0
49 50 0 3 1 1.0 1 0 349237 17.8000 NaN 0 2
facet = sns.FacetGrid(train, hue="Survived",aspect=4 )
facet.map(sns.kdeplot, 'Fare', shade = True)
facet.set(xlim = (0, train['Fare'].max()))
facet.add_legend()
plt.show()

png

facet = sns.FacetGrid(train, hue="Survived",aspect=4)
facet.map(sns.kdeplot,'Fare',shade= True)
facet.set(xlim=(0, train['Fare'].max()))
facet.add_legend()
plt.xlim(0, 20)
(0, 20)

png

for dataset in train_test_data:
    dataset.loc[dataset['Fare'] <= 17, 'Fare'] = 0,
    dataset.loc[(dataset['Fare'] > 17) & (dataset['Fare'] <= 30), 'Fare'] = 1,
    dataset.loc[(dataset['Fare'] > 30) & (dataset['Fare'] <= 100), 'Fare'] = 2,
    dataset.loc[dataset['Fare'] >= 100, 'Fare'] = 3
train.head()
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PassengerId Survived Pclass Sex Age SibSp Parch Ticket Fare Cabin Embarked Title
0 1 0 3 0 1.0 1 0 A/5 21171 0.0 NaN 0 0
1 2 1 1 1 3.0 1 0 PC 17599 2.0 C85 1 2
2 3 1 3 1 1.0 0 0 STON/O2. 3101282 0.0 NaN 0 1
3 4 1 1 1 2.0 1 0 113803 2.0 C123 0 2
4 5 0 3 0 2.0 0 0 373450 0.0 NaN 0 0
train.Cabin.value_counts()
B96 B98            4
G6                 4
C23 C25 C27        4
E101               3
C22 C26            3
D                  3
F2                 3
F33                3
B57 B59 B63 B66    2
E24                2
B20                2
B22                2
D17                2
C92                2
E33                2
E67                2
C52                2
F4                 2
B5                 2
B49                2
C65                2
D36                2
C93                2
C78                2
E25                2
B28                2
D33                2
D20                2
D35                2
B18                2
                  ..
C62 C64            1
B102               1
E46                1
B69                1
E68                1
C50                1
C106               1
D28                1
E50                1
D46                1
B19                1
C47                1
A24                1
C70                1
E36                1
C86                1
A34                1
C111               1
A32                1
D15                1
B101               1
A6                 1
B41                1
B94                1
B50                1
E17                1
C104               1
D56                1
B78                1
C95                1
Name: Cabin, Length: 147, dtype: int64
for dataset in train_test_data:
    dataset['Cabin'] =  dataset['Cabin'].str[:1]
Pclass1 = train[train['Pclass']==1]['Cabin'].value_counts()
Pclass2 = train[train['Pclass']==2]['Cabin'].value_counts()
Pclass3 = train[train['Pclass']==3]['Cabin'].value_counts()
df = pd.DataFrame([Pclass1, Pclass2, Pclass3])
df.index = ['1st class','2nd class', '3rd class']
df.plot(kind='bar',stacked=True, figsize=(10,5))
<matplotlib.axes._subplots.AxesSubplot at 0x2085f6b8748>

png

cabin_mapping = {"A": 0, "B": 0.4, "C": 0.8, "D": 1.2, "E": 1.6, "F": 2, "G": 2.4, "T": 2.8}
for dataset in train_test_data:
    dataset['Cabin'] = dataset['Cabin'].map(cabin_mapping)
# fill missing Fare with median fare for each Pclass
train["Cabin"].fillna(train.groupby("Pclass")["Cabin"].transform("median"), inplace=True)
test["Cabin"].fillna(test.groupby("Pclass")["Cabin"].transform("median"), inplace=True)

family Size

train["FamilySize"] = train["SibSp"] + train["Parch"] + 1
test["FamilySize"] = test["SibSp"] + test["Parch"] + 1
facet = sns.FacetGrid(train, hue="Survived",aspect=4)
facet.map(sns.kdeplot,'FamilySize',shade= True)
facet.set(xlim=(0, train['FamilySize'].max()))
facet.add_legend()
plt.xlim(0)
(0, 11.0)

png

family_mapping = {1: 0, 2: 0.4, 3: 0.8, 4: 1.2, 5: 1.6, 6: 2, 7: 2.4, 8: 2.8, 9: 3.2, 10: 3.6, 11: 4}
for dataset in train_test_data:
    dataset['FamilySize'] = dataset['FamilySize'].map(family_mapping)
train.head()
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PassengerId Survived Pclass Sex Age SibSp Parch Ticket Fare Cabin Embarked Title FamilySize
0 1 0 3 0 1.0 1 0 A/5 21171 0.0 2.0 0 0 0.4
1 2 1 1 1 3.0 1 0 PC 17599 2.0 0.8 1 2 0.4
2 3 1 3 1 1.0 0 0 STON/O2. 3101282 0.0 2.0 0 1 0.0
3 4 1 1 1 2.0 1 0 113803 2.0 0.8 0 2 0.4
4 5 0 3 0 2.0 0 0 373450 0.0 2.0 0 0 0.0
features_drop = ['Ticket','SibSp','Parch']
train = train.drop(features_drop, axis = 1)
test = test.drop(features_drop,axis=1)
train = train.drop(['PassengerId'], axis=1)
train_data = train.drop('Survived', axis = 1)
target = train['Survived']
train_data.shape, target.shape
((891, 8), (891,))
train_data.head(10)
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Pclass Sex Age Fare Cabin Embarked Title FamilySize
0 3 0 1.0 0.0 2.0 0 0 0.4
1 1 1 3.0 2.0 0.8 1 2 0.4
2 3 1 1.0 0.0 2.0 0 1 0.0
3 1 1 2.0 2.0 0.8 0 2 0.4
4 3 0 2.0 0.0 2.0 0 0 0.0
5 3 0 2.0 0.0 2.0 2 0 0.0
6 1 0 3.0 2.0 1.6 0 0 0.0
7 3 0 0.0 1.0 2.0 0 3 1.6
8 3 1 2.0 0.0 2.0 0 2 0.8
9 2 1 0.0 2.0 1.8 1 2 0.4

5. Modelling

# Importing Classifier Modules
from sklearn.neighbors import KNeighborsClassifier
from sklearn.tree import DecisionTreeClassifier,ExtraTreeClassifier
from sklearn.ensemble import RandomForestClassifier,ExtraTreesClassifier,BaggingClassifier,AdaBoostClassifier,GradientBoostingClassifier
from sklearn.naive_bayes import GaussianNB
from sklearn.svm import SVC

import numpy as np
train.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 891 entries, 0 to 890
Data columns (total 9 columns):
Survived      891 non-null int64
Pclass        891 non-null int64
Sex           891 non-null int64
Age           891 non-null float64
Fare          891 non-null float64
Cabin         891 non-null float64
Embarked      891 non-null int64
Title         891 non-null int64
FamilySize    891 non-null float64
dtypes: float64(4), int64(5)
memory usage: 62.7 KB

6.Cross Validation(k-fold)

from sklearn.model_selection import KFold
from sklearn.model_selection import cross_val_score
k_fold = KFold(n_splits=10, shuffle=True, random_state=0)
clf = KNeighborsClassifier(n_neighbors = 13)
scoring = 'accuracy'
score = cross_val_score(clf, train_data, target, cv=k_fold, n_jobs=1, scoring=scoring)
print(score)
[0.82222222 0.76404494 0.80898876 0.83146067 0.87640449 0.82022472
 0.85393258 0.79775281 0.84269663 0.84269663]
#learning_rates = [0.05, 0.1, 0.25, 0.5, 0.75, 1]
clf = [KNeighborsClassifier(n_neighbors = 13),DecisionTreeClassifier(),
       RandomForestClassifier(n_estimators=13),GaussianNB(),SVC(),ExtraTreeClassifier(),
      GradientBoostingClassifier(n_estimators=10, learning_rate=1,max_features=3, max_depth =3, random_state = 10),AdaBoostClassifier(),ExtraTreesClassifier()]
def model_fit():
    scoring = 'accuracy'
    for i in range(len(clf)):
        score = cross_val_score(clf[i], train_data, target, cv=k_fold, n_jobs=1, scoring=scoring)
        print("Score of Model",i,":",round(np.mean(score)*100,2))
#     round(np.mean(score)*100,2)
#     print("Score of :\n",score)
model_fit()
Score of Model 0 : 82.6
Score of Model 1 : 79.8
Score of Model 2 : 80.92
Score of Model 3 : 78.78
Score of Model 4 : 83.5
Score of Model 5 : 80.02
Score of Model 6 : 81.25
Score of Model 7 : 81.03
Score of Model 8 : 80.7
clf1 = SVC()
clf1.fit(train_data, target)
test
test_data = test.drop(['Survived','PassengerId'], axis=1)
prediction = clf1.predict(test_data)
# test_data
test_data['Survived'] = prediction
submission = pd.DataFrame(test['PassengerId'],test_data['Survived'])
submission.to_csv("Submission.csv")
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