해당 자료는 전북대학교 최규빈 교수님 2023학년도 2학기 빅데이터분석특강 자료임

03wk-013: 타이타닉, 로지스틱

최규빈
2023-09-21

1. 강의영상

https://youtu.be/playlist?list=PLQqh36zP38-wi9Mkfc849jTCMENvydI8B&si=hotGT-ErLB8dukhs

2. Import

import numpy as np
import pandas as pd 
import sklearn.linear_model

3. Data 불러오기

df_train = pd.read_csv('~/Desktop/titanic/train.csv')
df_test = pd.read_csv('~/Desktop/titanic/test.csv')

df_train

	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
...	...	...	...	...	...	...	...	...	...	...	...	...
886	887	0	2	Montvila, Rev. Juozas	male	27.0	0	0	211536	13.0000	NaN	S
887	888	1	1	Graham, Miss. Margaret Edith	female	19.0	0	0	112053	30.0000	B42	S
888	889	0	3	Johnston, Miss. Catherine Helen "Carrie"	female	NaN	1	2	W./C. 6607	23.4500	NaN	S
889	890	1	1	Behr, Mr. Karl Howell	male	26.0	0	0	111369	30.0000	C148	C
890	891	0	3	Dooley, Mr. Patrick	male	32.0	0	0	370376	7.7500	NaN	Q

891 rows × 12 columns

set(df_train) - set(df_test)

{'Survived'}

train에는 있지만 test에는 없는 것 Survived -> y로 놓자

4. 분석 – 실패

A. 데이터 정리

X = pd.get_dummies(df_train.drop(['PassengerId','Survived'],axis=1))
y = df_train[['Survived']]

B. Predictor 생성

predictr = sklearn.linear_model.LogisticRegression()
predictr

LogisticRegression()

In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.

C. 학습 (fit, learn)

predictr.fit(X,y)

ValueError: Input X contains NaN.
LogisticRegression does not accept missing values encoded as NaN natively. For supervised learning, you might want to consider sklearn.ensemble.HistGradientBoostingClassifier and Regressor which accept missing values encoded as NaNs natively. Alternatively, it is possible to preprocess the data, for instance by using an imputer transformer in a pipeline or drop samples with missing values. See https://scikit-learn.org/stable/modules/impute.html You can find a list of all estimators that handle NaN values at the following page: https://scikit-learn.org/stable/modules/impute.html#estimators-that-handle-nan-values

X가 NaN값이 있어서 오류남

5. 원인분석

df_train.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

- 문제1: Cabin열은 너무 많은 결측치를 가지고 있다. (추후에 빼자.)

- 문제2: Name 혹은 Ticket과 같은 변수는 one-hot 인코딩 하기 어색하다. (object 변수)

len(set(df_train['Name']))

len(set(df_train['Ticket']))

- 문제3: df_train의 Age와 Embarked에 약간 포함된 결측치가 마음에 걸린다.. \(\to\) 빼자!

- 문제4: df_test의 Fare에 포함된 결측값도 걸린다. \(\to\) 빼자!

df_test.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 418 entries, 0 to 417
Data columns (total 11 columns):
 #   Column       Non-Null Count  Dtype  
---  ------       --------------  -----  
 0   PassengerId  418 non-null    int64  
 1   Pclass       418 non-null    int64  
 2   Name         418 non-null    object 
 3   Sex          418 non-null    object 
 4   Age          332 non-null    float64
 5   SibSp        418 non-null    int64  
 6   Parch        418 non-null    int64  
 7   Ticket       418 non-null    object 
 8   Fare         417 non-null    float64
 9   Cabin        91 non-null     object 
 10  Embarked     418 non-null    object 
dtypes: float64(2), int64(4), object(5)
memory usage: 36.0+ KB

Fare는 417인데 애매하구먼? autogluon에서는 어떻게 했었나 한번 살펴보자

6. 분석 – 성공

A. 데이터정리

X = pd.get_dummies(df_train[["Pclass", "Sex", "SibSp", "Parch"]])
y = df_train[["Survived"]]

	Pclass	SibSp	Parch	Sex_female	Sex_male
0	3	1	0	0	1
1	1	1	0	1	0
2	3	0	0	1	0
3	1	1	0	1	0
4	3	0	0	0	1
...	...	...	...	...	...
886	2	0	0	0	1
887	1	0	0	1	0
888	3	1	2	1	0
889	1	0	0	0	1
890	3	0	0	0	1

891 rows × 5 columns

B. Predictor 생성

predictr = sklearn.linear_model.LogisticRegression()

C. 학습

predictr.fit(X, y)

/home/coco/anaconda3/envs/py38/lib/python3.8/site-packages/sklearn/utils/validation.py:1143: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
  y = column_or_1d(y, warn=True)

LogisticRegression()

D. 예측

#predictr.predict(X)
df_train.assign(Survived_hat=predictr.predict(X)).loc[:,['Survived','Survived_hat']]

	Survived	Survived_hat
0	0	0
1	1	1
2	1	1
3	1	1
4	0	0
...	...	...
886	0	0
887	1	1
888	0	1
889	1	0
890	0	0

891 rows × 2 columns

E. 평가

predictr.score(X,y)

0.8002244668911336

7. 제출 (HW)

df_test

	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
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
...	...	...	...	...	...	...	...	...	...	...	...
413	1305	3	Spector, Mr. Woolf	male	NaN	0	0	A.5. 3236	8.0500	NaN	S
414	1306	1	Oliva y Ocana, Dona. Fermina	female	39.0	0	0	PC 17758	108.9000	C105	C
415	1307	3	Saether, Mr. Simon Sivertsen	male	38.5	0	0	SOTON/O.Q. 3101262	7.2500	NaN	S
416	1308	3	Ware, Mr. Frederick	male	NaN	0	0	359309	8.0500	NaN	S
417	1309	3	Peter, Master. Michael J	male	NaN	1	1	2668	22.3583	NaN	C

418 rows × 11 columns

X = pd.get_dummies(df_test[["Pclass", "Sex", "SibSp", "Parch"]])

	Pclass	SibSp	Parch	Sex_female	Sex_male
0	3	0	0	0	1
1	3	1	0	1	0
2	2	0	0	0	1
3	3	0	0	0	1
4	3	1	1	1	0
...	...	...	...	...	...
413	3	0	0	0	1
414	1	0	0	1	0
415	3	0	0	0	1
416	3	0	0	0	1
417	3	1	1	0	1

418 rows × 5 columns

df_test.assign(Survived=predictr.predict(X)).loc[:,['PassengerId','Survived']]\
.to_csv("03wk(get_dummies).csv",index=False)

- kaggle 제출 -> 0.77511