ML(0809_day4) - 실습_Predict survival on the Titanic - using scikit-learn

타이타닉 데이터셋 도전¶

• 승객의 나이, 성별, 승객 등급, 승선 위치 같은 속성을 기반으로 하여 승객의 생존 여부를 예측하는 것이 목표

• 캐글의 타이타닉 챌린지에서 train.csv와 test.csv를 다운로드
• 두 파일을 각각 datasets 디렉토리에 titanic_train.csv titanic_test.csv로 저장

1. 데이터 적재¶

In [1]:

import pandas as pd
train_data = pd.read_csv("datasets/titanic_train.csv")
test_data = pd.read_csv("datasets/titanic_test.csv")

2. 데이터 탐색¶

train_data 살펴보기¶

In [2]:

train_data.head()

Out[2]:

	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

• Survived: 타깃. 0은 생존하지 못한 것이고 1은 생존을 의미
• Pclass: 승객 등급. 1, 2, 3등석.
• Name, Sex, Age: 이름 그대로의 의미
• SibSp: 함께 탑승한 형제, 배우자의 수
• Parch: 함께 탑승한 자녀, 부모의 수
• Ticket: 티켓 아이디
• Fare: 티켓 요금 (파운드)
• Cabin: 객실 번호
• Embarked: 승객이 탑승한 곳. C(Cherbourg), Q(Queenstown), S(Southampton)

누락 데이터 살펴보기¶

In [3]:

train_data.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 891 entries, 0 to 890
Data columns (total 12 columns):
 #   Column       Non-Null Count  Dtype  
---  ------       --------------  -----  
 0   PassengerId  891 non-null    int64  
 1   Survived     891 non-null    int64  
 2   Pclass       891 non-null    int64  
 3   Name         891 non-null    object 
 4   Sex          891 non-null    object 
 5   Age          714 non-null    float64
 6   SibSp        891 non-null    int64  
 7   Parch        891 non-null    int64  
 8   Ticket       891 non-null    object 
 9   Fare         891 non-null    float64
 10  Cabin        204 non-null    object 
 11  Embarked     889 non-null    object 
dtypes: float64(2), int64(5), object(5)
memory usage: 83.7+ KB

• Age, Cabin, Embarked 속성의 일부가 null
• 특히 Cabin은 77%가 null. 일단 Cabin은 무시하고 나머지를 활용
• Age는 177개(19%)가 null이므로 이를 어떻게 처리할지 결정해야 함 - null을 중간 나이로 바꾸기 고려
• Name과 Ticket 속성은 숫자로 변환하는 것이 조금 까다로와서 지금은 무시

통계치 살펴보기¶

In [4]:

train_data.describe()

Out[4]:

	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

• 38%만 Survived
• 평균 Fare는 32.20 파운드
• 평균 Age는 30보다 적음

Survived(머신러닝에서 타깃)가 0과 1로 이루어졌는지 확인¶

In [5]:

train_data['Survived'].value_counts()

Out[5]:

0    549
1    342
Name: Survived, dtype: int64

범주형(카테고리) 특성들을 확인¶

In [6]:

train_data['Pclass'].value_counts()

Out[6]:

3    491
1    216
2    184
Name: Pclass, dtype: int64

In [7]:

train_data['Sex'].value_counts()

Out[7]:

male      577
female    314
Name: Sex, dtype: int64

In [8]:

train_data['Embarked'].value_counts()

Out[8]:

S    644
C    168
Q     77
Name: Embarked, dtype: int64

Embarked 특성은 승객이 탑승한 곳 : C=Cherbourg, Q=Queenstown, S=Southampton.

3. 전처리 파이프라인¶

• 특성과 레이블 분리

In [9]:

from sklearn.impute import SimpleImputer
from sklearn.preprocessing import OneHotEncoder
from sklearn.pipeline import Pipeline

In [10]:

data = train_data.drop('Survived', axis=1)
label = train_data['Survived'].copy()

In [11]:

num_attribs = ['Age', 'SibSp', 'Parch', 'Fare']
cat_attribs = ['Pclass', 'Sex', 'Embarked']

• 나만의 파이프라인

In [12]:

from sklearn.base import BaseEstimator, TransformerMixin
import numpy as np

col_names = "SibSp", "Parch"
# 열 인덱스
SibSp_ix, Parch_ix = [num_attribs.index(c) for c in col_names]

class CombinedAttributesAdder(BaseEstimator, TransformerMixin): 
    def fit(self, X):
        return self
    
    def transform(self, X):
        RelativesOnboard = X[:, SibSp_ix] + X[:, Parch_ix] + 1
        return np.c_[X, RelativesOnboard]

• 범주형 파이프라인 구성

In [ ]:

# 1. 누락값을 most_frequent로 대체
# 2. OneHot Encoding

In [13]:

cat_pipeline = Pipeline([
    ('imputer', SimpleImputer(strategy='most_frequent')),
    ('cat', OneHotEncoder(sparse=False))
])

In [14]:

tmp = cat_pipeline.fit_transform(data[cat_attribs])

In [15]:

tmp.shape

Out[15]:

(891, 8)

• 수치형 파이프라인 구성

In [ ]:

# 1. 누락값을 median로 대체

In [16]:

num_pipeline = Pipeline([
    ('imputer', SimpleImputer(strategy='median')),
    ('attribs_adder', CombinedAttributesAdder())
])

In [17]:

tmp2 = num_pipeline.fit_transform(data[num_attribs])

In [18]:

tmp2.shape

Out[18]:

(891, 5)

• 범주형 파이프라인 + 수치형 파이프라인

In [19]:

from sklearn.compose import ColumnTransformer

In [20]:

full_pipeline = ColumnTransformer([
    ('cat', cat_pipeline, cat_attribs),
    ('num', num_pipeline, num_attribs)
])

In [21]:

data_prepared = full_pipeline.fit_transform(data)

In [22]:

data_prepared.shape

Out[22]:

(891, 13)

In [23]:

data.head(10)

Out[23]:

	PassengerId	Pclass	Name	Sex	Age	SibSp	Parch	Ticket	Fare	Cabin	Embarked
0	1	3	Braund, Mr. Owen Harris	male	22.0	1	0	A/5 21171	7.2500	NaN	S
1	2	1	Cumings, Mrs. John Bradley (Florence Briggs Th...	female	38.0	1	0	PC 17599	71.2833	C85	C
2	3	3	Heikkinen, Miss. Laina	female	26.0	0	0	STON/O2. 3101282	7.9250	NaN	S
3	4	1	Futrelle, Mrs. Jacques Heath (Lily May Peel)	female	35.0	1	0	113803	53.1000	C123	S
4	5	3	Allen, Mr. William Henry	male	35.0	0	0	373450	8.0500	NaN	S
5	6	3	Moran, Mr. James	male	NaN	0	0	330877	8.4583	NaN	Q
6	7	1	McCarthy, Mr. Timothy J	male	54.0	0	0	17463	51.8625	E46	S
7	8	3	Palsson, Master. Gosta Leonard	male	2.0	3	1	349909	21.0750	NaN	S
8	9	3	Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)	female	27.0	0	2	347742	11.1333	NaN	S
9	10	2	Nasser, Mrs. Nicholas (Adele Achem)	female	14.0	1	0	237736	30.0708	NaN	C

In [96]:

# 참고
# columns = []
# cat_encoder = full_pipeline.named_transformers_["cat"]["cat"]

# for i in range(len(cat_encoder.categories_)):
#     columns.extend(cat_encoder.categories_[i])
# columns

• 방법 1 : one-hot encoding의 categories_ 속성 활용하기

In [24]:

cat_encoder = full_pipeline.named_transformers_["cat"]["cat"]
columns = list(cat_encoder.get_feature_names(cat_attribs))
columns

Out[24]:

['Pclass_1',
 'Pclass_2',
 'Pclass_3',
 'Sex_female',
 'Sex_male',
 'Embarked_C',
 'Embarked_Q',
 'Embarked_S']

In [98]:

pd.DataFrame(data_prepared, columns = columns+num_attribs+['RelativesOnboard'])

Out[98]:

	Pclass_1	Pclass_2	Pclass_3	Sex_female	Sex_male	Embarked_C	Embarked_Q	Embarked_S	Age	SibSp	Parch	Fare	RelativesOnboard
0	0.0	0.0	1.0	0.0	1.0	0.0	0.0	1.0	22.0	1.0	0.0	7.2500	2.0
1	1.0	0.0	0.0	1.0	0.0	1.0	0.0	0.0	38.0	1.0	0.0	71.2833	2.0
2	0.0	0.0	1.0	1.0	0.0	0.0	0.0	1.0	26.0	0.0	0.0	7.9250	1.0
3	1.0	0.0	0.0	1.0	0.0	0.0	0.0	1.0	35.0	1.0	0.0	53.1000	2.0
4	0.0	0.0	1.0	0.0	1.0	0.0	0.0	1.0	35.0	0.0	0.0	8.0500	1.0
...	...	...	...	...	...	...	...	...	...	...	...	...	...
886	0.0	1.0	0.0	0.0	1.0	0.0	0.0	1.0	27.0	0.0	0.0	13.0000	1.0
887	1.0	0.0	0.0	1.0	0.0	0.0	0.0	1.0	19.0	0.0	0.0	30.0000	1.0
888	0.0	0.0	1.0	1.0	0.0	0.0	0.0	1.0	28.0	1.0	2.0	23.4500	4.0
889	1.0	0.0	0.0	0.0	1.0	1.0	0.0	0.0	26.0	0.0	0.0	30.0000	1.0
890	0.0	0.0	1.0	0.0	1.0	0.0	1.0	0.0	32.0	0.0	0.0	7.7500	1.0

891 rows × 13 columns

• 방법 2 : 컬럼명 직접 지정하기

In [35]:

columns = ['Pclass_1', 'Pclass_2', 'Pclass_3', 'female', 'male', 'Embarked_C', 'Embarked_Q', 'Embarked_S'] + num_attribs + ['RelativesOnboard']
pd.DataFrame(data_prepared, columns=columns)

Out[35]:

	Pclass_1	Pclass_2	Pclass_3	female	male	Embarked_C	Embarked_Q	Embarked_S	Age	SibSp	Parch	Fare	RelativesOnboard
0	0.0	0.0	1.0	0.0	1.0	0.0	0.0	1.0	22.0	1.0	0.0	7.2500	2.0
1	1.0	0.0	0.0	1.0	0.0	1.0	0.0	0.0	38.0	1.0	0.0	71.2833	2.0
2	0.0	0.0	1.0	1.0	0.0	0.0	0.0	1.0	26.0	0.0	0.0	7.9250	1.0
3	1.0	0.0	0.0	1.0	0.0	0.0	0.0	1.0	35.0	1.0	0.0	53.1000	2.0
4	0.0	0.0	1.0	0.0	1.0	0.0	0.0	1.0	35.0	0.0	0.0	8.0500	1.0
...	...	...	...	...	...	...	...	...	...	...	...	...	...
886	0.0	1.0	0.0	0.0	1.0	0.0	0.0	1.0	27.0	0.0	0.0	13.0000	1.0
887	1.0	0.0	0.0	1.0	0.0	0.0	0.0	1.0	19.0	0.0	0.0	30.0000	1.0
888	0.0	0.0	1.0	1.0	0.0	0.0	0.0	1.0	28.0	1.0	2.0	23.4500	4.0
889	1.0	0.0	0.0	0.0	1.0	1.0	0.0	0.0	26.0	0.0	0.0	30.0000	1.0
890	0.0	0.0	1.0	0.0	1.0	0.0	1.0	0.0	32.0	0.0	0.0	7.7500	1.0

891 rows × 13 columns