실습. Feature Engineering with 타이타닉 데이터
August 11, 2020
csv 첨부파일
Feature Engineering
데이터를 분석한 내용과 도메인 지식을 바탕으로 알맞은 Feature vector(컴퓨터가 알 수 있는 숫자)로 바꿔 주는 작업
이 작업이 필요한 이유는 machine learning 을 통해 학습을 시켜서 알고리듬을 만들기 위해서 필요하다.
모델링
Cross validation, K-fold
데이터 셋을 나워서 일부분은 테스트 데이터 일부분은 학습용 데이터로 사용해서 모델의 정확도를 검증하는 방법
점수를 높일 수 있는 스킬
점수를 높이기 위해서는 기본적으로 여러 가정을 하고 테스트를 반복하는 과정이 필요
데어터를 조합 ex 가족 + 가식 수를 더한 데이터 이용
단순히 중간 값을 null값을 채우는 것이 아니라 머신러닝을 이용
Featureing 값 수정
데이터 심화 분석
중요한 데이터는 사이거리를 더 멀리한다 (KNN) 거리가 멀수록 연관성이 없다
# 데이터 분석에 필요한 라이브러리들을 import
import pandas as pd
import numpy as np
import matplotlib as mpl
import matplotlib.pyplot as plt
import seaborn as sns
from scipy import stats
from matplotlib import rc
import missingno as msno
# os x(mac os) 한글 font
rc ( 'font' , family = 'AppleGothic' )
# 노트북 안에 그래프를 그리기 위해 셋팅
% matplotlib inline
# 그래프를 격자 스타일로 (숫자 범위가 눈에 잘 띄도록 ggplot 스타일 사용.)
plt . style . use ( "ggplot" )
# 그래프에서 마이너스 폰트 깨지는 문제 해결을 위해
mpl . rcParams [ "axes.unicode_minus" ] = False
# data load
train = pd . read_csv ( "train.csv" )
test = pd . read_csv ( "test.csv" )
train . head ( 3 )
PassengerId
Survived
Pclass
Name
Sex
Age
SibSp
Parch
Ticket
Fare
Cabin
Embarked
0
1
0
3
Braund, Mr. Owen Harris
male
22.0
1
0
A/5 21171
7.2500
NaN
S
1
2
1
1
Cumings, Mrs. John Bradley (Florence Briggs Th...
female
38.0
1
0
PC 17599
71.2833
C85
C
2
3
1
3
Heikkinen, Miss. Laina
female
26.0
0
0
STON/O2. 3101282
7.9250
NaN
S
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
PassengerId
Pclass
Name
Sex
Age
SibSp
Parch
Ticket
Fare
Cabin
Embarked
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
<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
# 2번쨰 column name
train_test_data = [ train , test ]
print ( type ( train_test_data ))
for dataset in train_test_data :
dataset [ 'Title' ] = dataset [ 'Name' ]. str . extract ( '([A-Za-z]+)\.' , expand = False )
# expand가 True이면 데이터 프레임
# expand가 False이면 시리즈 데이터
train [ 'Title' ]. value_counts ()
Mr 517
Miss 182
Mrs 125
Master 40
Dr 7
Rev 6
Col 2
Major 2
Mlle 2
Ms 1
Sir 1
Mme 1
Don 1
Capt 1
Countess 1
Jonkheer 1
Lady 1
Name: Title, dtype: int64
test [ 'Title' ]. value_counts ()
Mr 240
Miss 78
Mrs 72
Master 21
Col 2
Rev 2
Dona 1
Ms 1
Dr 1
Name: Title, dtype: int64
# Name을 사용해서 유의미한 정보를 얻었기 때문에
test . drop ( 'Name' , axis = 1 , inplace = True )
train . drop ( 'Name' , axis = 1 , inplace = True )
train . head ( 1 )
PassengerId
Survived
Pclass
Sex
Age
SibSp
Parch
Ticket
Fare
Cabin
Embarked
Title
0
1
0
3
male
22.0
1
0
A/5 21171
7.25
NaN
S
Mr
rarelist = []
for a in set ( train [ 'Title' ]):
if list ( train [ 'Title' ]). count ( a ) < 10 :
rarelist . append ( a )
for dataset in train_test_data :
dataset [ 'Title' ] = dataset [ 'Title' ]. replace ( 'Mlle' , 'Miss' )
dataset [ 'Title' ] = dataset [ 'Title' ]. replace ( 'Ms' , 'Miss' )
dataset [ 'Title' ] = dataset [ 'Title' ]. replace ( 'Mmm' , 'Mrs' )
dataset [ 'Title' ] = dataset [ 'Title' ]. replace ( rarelist , 'Rare' )
train [[ 'Title' , 'Survived' ]]. groupby ([ 'Title' ], as_index = False ). mean ()
Title
Survived
0
Master
0.575000
1
Miss
0.702703
2
Mr
0.156673
3
Mrs
0.792000
4
Rare
0.375000
title_mapping = { "Master" : 1 , "Miss" : 2 , "Mr" : 3 , "Mrs" : 4 , "Rare" : 5 }
for dataset in train_test_data :
dataset [ 'Title' ] = dataset [ 'Title' ]. map ( title_mapping )
dataset [ 'Title' ] = dataset [ 'Title' ]. fillna ( 0 )
# title_mapping = {"Mr":0, "Miss":1, "Mrs":2,
# "Master":0, "Dr":3, "Rev":3, "Col": 3, 'Ms': 2, 'Mlle': 3, "Major": 3, 'Lady': 2, 'Capt': 3,
# 'Sir': 0, 'Dona': 3, 'Mme':3, 'Jonkheer': 1, 'Countess': 3 ,'Don':3}
# for dataset in train_test_data:
# dataset['Title'] = dataset['Title'].map(title_mapping)
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
3
1
2
1
1
female
38.0
1
0
PC 17599
71.2833
C85
C
4
PassengerId
Pclass
Sex
Age
SibSp
Parch
Ticket
Fare
Cabin
Embarked
Title
0
892
3
male
34.5
0
0
330911
7.8292
NaN
Q
3.0
1
893
3
female
47.0
1
0
363272
7.0000
NaN
S
4.0
# 4.3 sex
sex_mapping = { 'male' : 0 , 'female' : 1 }
for dataset in train_test_data :
dataset [ 'Sex' ] = dataset [ 'Sex' ]. map ( sex_mapping )
PassengerId
Survived
Pclass
Sex
Age
SibSp
Parch
Ticket
Fare
Cabin
Embarked
Title
0
1
0
3
0
22.0
1
0
A/5 21171
7.25
NaN
S
3
# 4.4 age
train . isnull (). sum ()
PassengerId 0
Survived 0
Pclass 0
Sex 0
Age 177
SibSp 0
Parch 0
Ticket 0
Fare 0
Cabin 687
Embarked 2
Title 0
dtype: int64
train [ 'Age' ]. fillna ( train . groupby ( 'Title' )[ 'Age' ]. transform ( 'median' ), inplace = True )
test [ 'Age' ]. fillna ( test . groupby ( 'Title' )[ 'Age' ]. transform ( 'median' ), inplace = True )
PassengerId 0
Survived 0
Pclass 0
Sex 0
Age 0
SibSp 0
Parch 0
Ticket 0
Fare 0
Cabin 687
Embarked 2
Title 0
dtype: int64
0 22.0
1 38.0
2 26.0
3 35.0
4 35.0
5 30.0
6 54.0
7 2.0
8 27.0
9 14.0
Name: Age, dtype: float64
# Age를 구간별로 나누기
train [ 'AgeBand' ] = pd . cut ( train [ 'Age' ], 5 )
train [[ 'AgeBand' , 'Survived' ]]. groupby ( 'AgeBand' , as_index = False ). mean (). sort_values ( by = 'AgeBand' , ascending = True )
AgeBand
Survived
0
(0.34, 16.336]
0.548077
1
(16.336, 32.252]
0.327345
2
(32.252, 48.168]
0.436893
3
(48.168, 64.084]
0.434783
4
(64.084, 80.0]
0.090909
for dataset in train_test_data :
dataset . loc [ dataset [ 'Age' ] <= 16 , 'Age' ] = 0 ,
dataset . loc [( dataset [ 'Age' ] > 16 ) & ( dataset [ 'Age' ] <= 32 ), 'Age' ] = 1 ,
dataset . loc [( dataset [ 'Age' ] > 32 ) & ( dataset [ 'Age' ] <= 48 ), 'Age' ] = 2 ,
dataset . loc [( dataset [ 'Age' ] > 48 ) & ( dataset [ 'Age' ] <= 64 ), 'Age' ] = 3 ,
dataset . loc [ dataset [ 'Age' ] > 64 , 'Age' ] = 4
PassengerId
Survived
Pclass
Sex
Age
SibSp
Parch
Ticket
Fare
Cabin
Embarked
Title
AgeBand
0
1
0
3
0
1.0
1
0
A/5 21171
7.2500
NaN
S
3
(16.336, 32.252]
1
2
1
1
1
2.0
1
0
PC 17599
71.2833
C85
C
4
(32.252, 48.168]
2
3
1
3
1
1.0
0
0
STON/O2. 3101282
7.9250
NaN
S
2
(16.336, 32.252]
# 4.5 embark
train . isnull (). sum ()
PassengerId 0
Survived 0
Pclass 0
Sex 0
Age 0
SibSp 0
Parch 0
Ticket 0
Fare 0
Cabin 687
Embarked 2
Title 0
AgeBand 0
dtype: int64
plt . rc ( 'font' , family = 'NanumGothic' )
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 ))
<matplotlib.axes._subplots.AxesSubplot at 0x1e73680ba88>
# 탄위치를 가장 많이 탄 위치인 S로 해준다
for dataset in train_test_data :
dataset [ 'Embarked' ] = dataset [ 'Embarked' ]. fillna ( 'S' )
PassengerId 0
Pclass 0
Sex 0
Age 0
SibSp 0
Parch 0
Ticket 0
Fare 1
Cabin 327
Embarked 0
Title 0
dtype: int64
embarked_mapping = {
"S" : 0 ,
"Q" : 1 ,
"C" : 2
}
for dataset in train_test_data :
dataset [ 'Embarked' ] = dataset [ 'Embarked' ]. map ( embarked_mapping )
PassengerId
Survived
Pclass
Sex
Age
SibSp
Parch
Ticket
Fare
Cabin
Embarked
Title
AgeBand
0
1
0
3
0
1.0
1
0
A/5 21171
7.2500
NaN
0
3
(16.336, 32.252]
1
2
1
1
1
2.0
1
0
PC 17599
71.2833
C85
2
4
(32.252, 48.168]
PassengerId
Pclass
Sex
Age
SibSp
Parch
Ticket
Fare
Cabin
Embarked
Title
0
892
3
0
2.0
0
0
330911
7.8292
NaN
1
3.0
1
893
3
1
2.0
1
0
363272
7.0000
NaN
0
4.0
# 4.6 Fare 입장료의 null값을 같은 클래스의 중간값으로
test [ 'Fare' ]. fillna ( test . groupby ( 'Pclass' )[ 'Fare' ]. transform ( "median" ), inplace = True )
PassengerId 0
Pclass 0
Sex 0
Age 0
SibSp 0
Parch 0
Ticket 0
Fare 0
Cabin 327
Embarked 0
Title 0
dtype: int64
# 가격의 구간별로 데이터를 나누어 보자
train [ 'FareBand' ] = pd . cut ( train [ 'Fare' ], 5 )
train [[ 'FareBand' , 'Survived' ]]. groupby ( 'FareBand' , as_index = False ). mean (). sort_values ( by = 'FareBand' , ascending = True )
FareBand
Survived
0
(-0.512, 102.466]
0.361575
1
(102.466, 204.932]
0.757576
2
(204.932, 307.398]
0.647059
3
(307.398, 409.863]
NaN
4
(409.863, 512.329]
1.000000
for dataset in train_test_data :
dataset . loc [ dataset [ 'Fare' ] <= 102 , 'Fare' ] = 0 ,
dataset . loc [( dataset [ 'Fare' ] > 102 ) & ( dataset [ 'Fare' ] <= 204 ), 'Fare' ] = 1 ,
dataset . loc [( dataset [ 'Fare' ] > 204 ) & ( dataset [ 'Fare' ] <= 307 ), 'Fare' ] = 2 ,
dataset . loc [ dataset [ 'Fare' ] > 307 , 'Fare' ] = 4
PassengerId
Survived
Pclass
Sex
Age
SibSp
Parch
Ticket
Fare
Cabin
Embarked
Title
AgeBand
FareBand
0
1
0
3
0
1.0
1
0
A/5 21171
0.0
NaN
0
3
(16.336, 32.252]
(-0.512, 102.466]
1
2
1
1
1
2.0
1
0
PC 17599
0.0
C85
2
4
(32.252, 48.168]
(-0.512, 102.466]
2
3
1
3
1
1.0
0
0
STON/O2. 3101282
0.0
NaN
0
2
(16.336, 32.252]
(-0.512, 102.466]
# 확인 하기위해 만들어준 칼럼을 제거한다
train . drop ( 'AgeBand' , axis = 1 , inplace = True )
train . drop ( 'FareBand' , axis = 1 , inplace = True )
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
3
# 데이터가 많이 없기 때문에 버려준다
train . drop ( 'Cabin' , axis = 1 , inplace = True )
test . drop ( 'Cabin' , axis = 1 , inplace = True )
PassengerId
Survived
Pclass
Sex
Age
SibSp
Parch
Ticket
Fare
Embarked
Title
0
1
0
3
0
1.0
1
0
A/5 21171
0.0
0
3
# ticket 번호로 생존여부를 확인할 수 없으므로 삭제
train . drop ( 'Ticket' , axis = 1 , inplace = True )
test . drop ( 'Ticket' , axis = 1 , inplace = True )
PassengerId
Survived
Pclass
Sex
Age
SibSp
Parch
Fare
Embarked
Title
0
1
0
3
0
1.0
1
0
0.0
0
3
PassengerId
Pclass
Sex
Age
SibSp
Parch
Fare
Embarked
Title
0
892
3
0
2.0
0
0
0.0
1
3.0
train [ 'FamilySize' ] = train [ 'SibSp' ] + train [ 'Parch' ] + 1
test [ 'FamilySize' ] = test [ 'SibSp' ] + test [ 'Parch' ] + 1
train [[ 'FamilySize' , 'Survived' ]]. groupby ( 'FamilySize' , as_index = False ). mean (). sort_values ( by = 'Survived' , ascending = False )
FamilySize
Survived
3
4
0.724138
2
3
0.578431
1
2
0.552795
6
7
0.333333
0
1
0.303538
4
5
0.200000
5
6
0.136364
7
8
0.000000
8
11
0.000000
train [ "FamilySize" ]. value_counts
for dataset in train_test_data :
dataset [ 'isAlone' ] = 0
dataset . loc [ dataset [ 'FamilySize' ] == 1 , 'isAlone' ] = 1
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 )
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 )
PassengerId
Survived
Pclass
Sex
Age
SibSp
Parch
Fare
Embarked
Title
FamilySize
isAlone
0
1
0
3
0
1.0
1
0
0.0
0
3
0.4
0
PassengerId
Pclass
Sex
Age
SibSp
Parch
Fare
Embarked
Title
FamilySize
isAlone
0
892
3
0
2.0
0
0
0.0
1
3.0
0.0
1
dropped_data = [ 'Survived' , 'PassengerId' ]
train_data = train . drop ( dropped_data , axis = 1 )
Modeling
# Importing Classifier Modules
from sklearn.neighbors import KNeighborsClassifier
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.naive_bayes import GaussianNB
from sklearn.svm import SVC
cross_validataion(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 )
train_data . isnull (). sum ()
Pclass 0
Sex 0
Age 0
SibSp 0
Parch 0
Fare 0
Embarked 0
Title 0
FamilySize 0
isAlone 0
dtype: int64
KNN
target = train [ 'Survived' ]
clf = KNeighborsClassifier ( n_neighbors = 11 )
# 주변의 11개의 데이터를 확인한다
scoring = 'accuracy'
score = cross_val_score ( clf , train_data , target , cv = k_fold , n_jobs = 1 , scoring = scoring )
print ( score )
print ( round ( np . mean ( score * 100 ), 2 ))
[0.76666667 0.78651685 0.80898876 0.79775281 0.83146067 0.79775281
0.78651685 0.78651685 0.79775281 0.79775281]
79.58
decision tree
clf = DecisionTreeClassifier ()
scoring = 'accuracy'
score = cross_val_score ( clf , train_data , target , cv = k_fold , n_jobs = 1 , scoring = scoring )
print ( score )
print ( round ( np . mean ( score * 100 ), 2 ))
[0.84444444 0.82022472 0.82022472 0.82022472 0.83146067 0.82022472
0.80898876 0.83146067 0.7752809 0.79775281]
81.7
Naive Bayes
clf = GaussianNB ()
scoring = 'accuracy'
score = cross_val_score ( clf , train_data , target , cv = k_fold , n_jobs = 1 , scoring = scoring )
print ( score )
print ( round ( np . mean ( score * 100 ), 2 ))
[0.81111111 0.76404494 0.76404494 0.80898876 0.79775281 0.80898876
0.82022472 0.84269663 0.83146067 0.80898876]
80.58
svm
clf = SVC ()
scoring = 'accuracy'
score = cross_val_score ( clf , train_data , target , cv = k_fold , n_jobs = 1 , scoring = scoring )
print ( score )
print ( round ( np . mean ( score * 100 ), 2 ))
[0.84444444 0.80898876 0.82022472 0.82022472 0.85393258 0.82022472
0.82022472 0.86516854 0.83146067 0.86516854]
83.5
randomforest
clf = RandomForestClassifier ()
scoring = 'accuracy'
score = cross_val_score ( clf , train_data , target , cv = k_fold , n_jobs = 1 , scoring = scoring )
print ( score )
print ( round ( np . mean ( score * 100 ), 2 ))
[0.81111111 0.82022472 0.7752809 0.78651685 0.83146067 0.79775281
0.83146067 0.84269663 0.7752809 0.82022472]
80.92
testing
clf = SVC ()
clf . fit ( train_data , target )
test_data = test . drop ( 'PassengerId' , axis = 1 ). copy ()
prediction = clf . predict ( test_data )
submission = pd . DataFrame ({
"PassengerId" : test [ "PassengerId" ],
"Survived" : prediction
})
submission . to_csv ( 'submission.csv' , index = False )
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