Ecosyste.ms: Awesome

An open API service indexing awesome lists of open source software.

Awesome Lists | Featured Topics | Projects

https://github.com/ashishpatel26/titanic-machine-learning-from-disaster

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 1502 out of 2224 passengers and crew. This sensational tragedy shocked the international community and led to better safety regulations for ships. One of the reasons that the shipwreck led to such loss of life was that there were not enough lifeboats for the passengers and crew. Although there was some element of luck involved in surviving the sinking, some groups of people were more likely to survive than others, such as women, children, and the upper-class. In this challenge, we ask you to complete the analysis of what sorts of people were likely to survive. In particular, we ask you to apply the tools of machine learning to predict which passengers survived the tragedy. Practice Skills Binary classification Python and R basics
https://github.com/ashishpatel26/titanic-machine-learning-from-disaster

titanic titanic-data titanic-data-analytics titanic-dataset titanic-disaster titanic-kaggle titanic-passenger-data titanic-problem titanic-survival titanic-survival-exploration titanic-survival-prediction titanickaggle

Last synced: 2 months ago
JSON representation

Host: GitHub
URL: https://github.com/ashishpatel26/titanic-machine-learning-from-disaster
Owner: ashishpatel26
Created: 2018-03-23T12:35:52.000Z (almost 7 years ago)
Default Branch: master
Last Pushed: 2023-01-21T07:47:48.000Z (almost 2 years ago)
Last Synced: 2023-10-20T17:46:49.910Z (about 1 year ago)
Topics: titanic, titanic-data, titanic-data-analytics, titanic-dataset, titanic-disaster, titanic-kaggle, titanic-passenger-data, titanic-problem, titanic-survival, titanic-survival-exploration, titanic-survival-prediction, titanickaggle
Language: Jupyter Notebook
Homepage: https://www.kaggle.com/ashishpatel26/titanic-passenger-survival-analysis
Size: 224 KB
Stars: 45
Watchers: 3
Forks: 69
Open Issues: 1
Metadata Files:
- Readme: Readme.md

Awesome Lists containing this project

README

Titanic Passanger Survival Analysis

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

```python
import pandas as pd
import numpy as np
```

```python
train = pd.read_csv("input/train.csv")
test = pd.read_csv("input/test.csv")
```

```python
train.isnull().sum()
print("Train Shape:",train.shape)
test.isnull().sum()
print("Test Shape:",test.shape)
```

Train Shape: (891, 12)
Test Shape: (418, 11)

```python
train.info()
```

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

```python
test.info()
```

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.

```python
train.head(10)
```

.dataframe tbody tr th:only-of-type {
vertical-align: middle;
}

.dataframe tbody tr th {
vertical-align: top;
}

.dataframe thead th {
text-align: right;
}

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

```python
train.describe()
```

.dataframe tbody tr th:only-of-type {
vertical-align: middle;
}

.dataframe tbody tr th {
vertical-align: top;
}

.dataframe thead th {
text-align: right;
}

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

```python
test.describe()
```

.dataframe tbody tr th:only-of-type {
vertical-align: middle;
}

.dataframe tbody tr th {
vertical-align: top;
}

.dataframe thead th {
text-align: right;
}

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

```python
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

```python
test.isnull().sum()
test["Survived"] = ""
test.head()
```

.dataframe tbody tr th:only-of-type {
vertical-align: middle;
}

.dataframe tbody tr th {
vertical-align: top;
}

.dataframe thead th {
text-align: right;
}

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.

```python
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

```python
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))
```

```python
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](output_17_1.png)

The Chart confirms **Women more likely survivied than Men**.

```python
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](output_19_1.png)

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

```python
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](output_21_1.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

```python
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](output_23_1.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**

```python
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](output_25_1.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.

```python
train.head()
```

.dataframe tbody tr th:only-of-type {
vertical-align: middle;
}

.dataframe tbody tr th {
vertical-align: top;
}

.dataframe thead th {
text-align: right;
}

#### 4.1 how titanic sank?

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

```python
train.head(10)
```

.dataframe tbody tr th:only-of-type {
vertical-align: middle;
}

.dataframe tbody tr th {
vertical-align: top;
}

.dataframe thead th {
text-align: right;
}

```python
train_test_data = [train,test] # combine dataset

for dataset in train_test_data:
dataset['Title'] = dataset['Name'].str.extract(' ([A-Za-z]+)\.', expand=False)
```

```python
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

```python
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

```python
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)
```

```python
dataset.head()
```

.dataframe tbody tr th:only-of-type {
vertical-align: middle;
}

.dataframe tbody tr th {
vertical-align: top;
}

.dataframe thead th {
text-align: right;
}

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

```python
test.head()
```

.dataframe tbody tr th:only-of-type {
vertical-align: middle;
}

.dataframe tbody tr th {
vertical-align: top;
}

.dataframe thead th {
text-align: right;
}

```python
bar_chart('Title')
```

![png](output_40_0.png)

```python
# delete unnecessary feature from dataset
train.drop('Name', axis=1, inplace=True)
test.drop('Name', axis=1, inplace=True)
```

```python
train.head()
```

.dataframe tbody tr th:only-of-type {
vertical-align: middle;
}

.dataframe tbody tr th {
vertical-align: top;
}

.dataframe thead th {
text-align: right;
}

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

```python
sex_mapping = {"male": 0, "female": 1}
for dataset in train_test_data:
dataset['Sex'] = dataset['Sex'].map(sex_mapping)
```

```python
bar_chart('Sex')
```

![png](output_44_0.png)

```python
test.head()
```

.dataframe tbody tr th:only-of-type {
vertical-align: middle;
}

.dataframe tbody tr th {
vertical-align: top;
}

.dataframe thead th {
text-align: right;
}

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

```python
train["Age"].fillna(train.groupby("Title")["Age"].transform("median"), inplace= True)
test["Age"].fillna(test.groupby('Title')['Age'].transform("median"), inplace= True)
```

```python
train.head(30)
#train.groupby("Title")["Age"].transform("median")
```

.dataframe tbody tr th:only-of-type {
vertical-align: middle;
}

.dataframe tbody tr th {
vertical-align: top;
}

.dataframe thead th {
text-align: right;
}

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

```python
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](output_48_0.png)

(10, 50)

![png](output_48_2.png)

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

```python
train.info()
test.info()
```

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

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

```python
train.head()
```

.dataframe tbody tr th:only-of-type {
vertical-align: middle;
}

.dataframe tbody tr th {
vertical-align: top;
}

.dataframe thead th {
text-align: right;
}

```python
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])]
```

```python
train.head()
bar_chart('Age')
```

![png](output_54_0.png)

```python
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](output_55_0.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**

```python
for dataset in train_test_data:
dataset['Embarked'] = dataset['Embarked'].fillna('S')
```

```python
train.head()
```

.dataframe tbody tr th:only-of-type {
vertical-align: middle;
}

.dataframe tbody tr th {
vertical-align: top;
}

.dataframe thead th {
text-align: right;
}

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

```python
embarked_mapping = {'S':0,'C':1,'Q':2}
for dataset in train_test_data:
dataset['Embarked'] = dataset['Embarked'].map(embarked_mapping)
```

```python
# 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)

```

.dataframe tbody tr th:only-of-type {
vertical-align: middle;
}

.dataframe tbody tr th {
vertical-align: top;
}

.dataframe thead th {
text-align: right;
}

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

```python
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](output_61_0.png)

```python
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](output_62_1.png)

```python
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
```

```python
train.head()
```

.dataframe tbody tr th:only-of-type {
vertical-align: middle;
}

.dataframe tbody tr th {
vertical-align: top;
}

.dataframe thead th {
text-align: right;
}

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

```python
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

```python
for dataset in train_test_data:
dataset['Cabin'] = dataset['Cabin'].str[:1]
```

```python
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))
```

![png](output_67_1.png)

```python
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)
```

```python
# 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**

```python
train["FamilySize"] = train["SibSp"] + train["Parch"] + 1
test["FamilySize"] = test["SibSp"] + test["Parch"] + 1
```

```python
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](output_72_1.png)

```python
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)
```

```python
train.head()
```

.dataframe tbody tr th:only-of-type {
vertical-align: middle;
}

.dataframe tbody tr th {
vertical-align: top;
}

.dataframe thead th {
text-align: right;
}

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

```python
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)
```

```python
train_data = train.drop('Survived', axis = 1)
target = train['Survived']
train_data.shape, target.shape
```

((891, 8), (891,))

```python
train_data.head(10)
```

.dataframe tbody tr th:only-of-type {
vertical-align: middle;
}

.dataframe tbody tr th {
vertical-align: top;
}

.dataframe thead th {
text-align: right;
}

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

```python
# 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
```

```python
train.info()
```

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)

```python
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)
```

```python
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]

```python
#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

```python
clf1 = SVC()
clf1.fit(train_data, target)
test
test_data = test.drop(['Survived','PassengerId'], axis=1)
prediction = clf1.predict(test_data)
# test_data

```

```python
test_data['Survived'] = prediction
submission = pd.DataFrame(test['PassengerId'],test_data['Survived'])
submission.to_csv("Submission.csv")
```