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 nptrain = 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
- 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 |
import matplotlib.pyplot as plt # Plot the graphes
%matplotlib inline
import seaborn as sns
sns.set() # setting seaborn default for plots- 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
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
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
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
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
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.
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 |
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
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')
# 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')
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)
(10, 50)
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')
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)
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()
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)
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'] = 3train.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>
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"] + 1facet = 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)
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 |
# 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 nptrain.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
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_datatest_data['Survived'] = prediction
submission = pd.DataFrame(test['PassengerId'],test_data['Survived'])
submission.to_csv("Submission.csv")