캐글 코리아 4차 대회

• 성인 인구조사 소득 예측 대회

학습 내용

• 대회를 통해 데이터 처리 및 분석을 이해한다.

목차

01. 라이브러리 임포트 및 데이터 준비
02. 데이터 전처리
03. Baseline 모델 만들기

01. 라이브러리 임포트 및 데이터 준비

목차로 이동하기

import seaborn as sns
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import warnings

warnings.filterwarnings('ignore')

train = pd.read_csv('data/4th_kaggle/train.csv')
test = pd.read_csv('data/4th_kaggle/test.csv')
sub = pd.read_csv('data/4th_kaggle/sample_submission.csv')

데이터 탐색

• 컬럼명 : [].columns
• 행열 : [].shape
• 정보 : [].info()
• 수치 데이터 요약정보 : [].describe()
• 결측치 : [].isnull().sum()

데이터 정보

age : 나이
workclass : 고용 형태
fnlwgt : 사람 대표성을 나타내는 가중치 (final weight의 약자)
education : 교육 수준 (최종 학력)
education_num : 교육 수준 수치
marital_status: 결혼 상태
occupation : 업종
relationship : 가족 관계
race : 인종
sex : 성별
capital_gain : 양도 소득
capital_loss : 양도 손실
hours_per_week : 주당 근무 시간
native_country : 국적
income : 연소득 (예측해야 하는 값, target variable) - 50K - $50,000

train.columns

Index(['id', 'age', 'workclass', 'fnlwgt', 'education', 'education_num',
       'marital_status', 'occupation', 'relationship', 'race', 'sex',
       'capital_gain', 'capital_loss', 'hours_per_week', 'native_country',
       'income'],
      dtype='object')

test.columns

Index(['id', 'age', 'workclass', 'fnlwgt', 'education', 'education_num',
       'marital_status', 'occupation', 'relationship', 'race', 'sex',
       'capital_gain', 'capital_loss', 'hours_per_week', 'native_country'],
      dtype='object')

sub.columns

Index(['id', 'prediction'], dtype='object')

print("학습용 데이터 : ", train.shape)
print("테스트용 데이터 : ", test.shape)

학습용 데이터 :  (26049, 16)
테스트용 데이터 :  (6512, 15)

train.isnull().sum()

id                0
age               0
workclass         0
fnlwgt            0
education         0
education_num     0
marital_status    0
occupation        0
relationship      0
race              0
sex               0
capital_gain      0
capital_loss      0
hours_per_week    0
native_country    0
income            0
dtype: int64

test.isnull().sum()

id                0
age               0
workclass         0
fnlwgt            0
education         0
education_num     0
marital_status    0
occupation        0
relationship      0
race              0
sex               0
capital_gain      0
capital_loss      0
hours_per_week    0
native_country    0
dtype: int64

train.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 26049 entries, 0 to 26048
Data columns (total 16 columns):
 #   Column          Non-Null Count  Dtype 
---  ------          --------------  ----- 
 0   id              26049 non-null  int64 
 1   age             26049 non-null  int64 
 2   workclass       26049 non-null  object
 3   fnlwgt          26049 non-null  int64 
 4   education       26049 non-null  object
 5   education_num   26049 non-null  int64 
 6   marital_status  26049 non-null  object
 7   occupation      26049 non-null  object
 8   relationship    26049 non-null  object
 9   race            26049 non-null  object
 10  sex             26049 non-null  object
 11  capital_gain    26049 non-null  int64 
 12  capital_loss    26049 non-null  int64 
 13  hours_per_week  26049 non-null  int64 
 14  native_country  26049 non-null  object
 15  income          26049 non-null  object
dtypes: int64(7), object(9)
memory usage: 3.2+ MB

test.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 6512 entries, 0 to 6511
Data columns (total 15 columns):
 #   Column          Non-Null Count  Dtype 
---  ------          --------------  ----- 
 0   id              6512 non-null   int64 
 1   age             6512 non-null   int64 
 2   workclass       6512 non-null   object
 3   fnlwgt          6512 non-null   int64 
 4   education       6512 non-null   object
 5   education_num   6512 non-null   int64 
 6   marital_status  6512 non-null   object
 7   occupation      6512 non-null   object
 8   relationship    6512 non-null   object
 9   race            6512 non-null   object
 10  sex             6512 non-null   object
 11  capital_gain    6512 non-null   int64 
 12  capital_loss    6512 non-null   int64 
 13  hours_per_week  6512 non-null   int64 
 14  native_country  6512 non-null   object
dtypes: int64(7), object(8)
memory usage: 763.2+ KB

train.income.unique()

array(['>50K', '<=50K'], dtype=object)

sns.countplot(x="income", data=train)

<AxesSubplot:xlabel='income', ylabel='count'>

02. 데이터 전처리

목차로 이동하기

train.loc[ train['income']=='>50K' , 'target'] = 1
train.loc[ train['income']=='<=50K' , 'target'] = 0
train['target'] = train.target.astype("int")

train.head()

	id	age	workclass	fnlwgt	education	education_num	marital_status	occupation	relationship	race	sex	capital_gain	capital_loss	hours_per_week	native_country	income	target
0	0	40	Private	168538	HS-grad	9	Married-civ-spouse	Sales	Husband	White	Male	0	0	60	United-States	>50K	1
1	1	17	Private	101626	9th	5	Never-married	Machine-op-inspct	Own-child	White	Male	0	0	20	United-States	<=50K	0
2	2	18	Private	353358	Some-college	10	Never-married	Other-service	Own-child	White	Male	0	0	16	United-States	<=50K	0
3	3	21	Private	151158	Some-college	10	Never-married	Prof-specialty	Own-child	White	Female	0	0	25	United-States	<=50K	0
4	4	24	Private	122234	Some-college	10	Never-married	Adm-clerical	Not-in-family	Black	Female	0	0	20	?	<=50K	0

test.head()

	id	age	workclass	fnlwgt	education	education_num	marital_status	occupation	relationship	race	sex	capital_gain	capital_loss	hours_per_week	native_country
0	0	28	Private	67661	Some-college	10	Never-married	Adm-clerical	Other-relative	White	Female	0	0	40	United-States
1	1	40	Self-emp-inc	37869	HS-grad	9	Married-civ-spouse	Exec-managerial	Husband	White	Male	0	0	50	United-States
2	2	20	Private	109952	Some-college	10	Never-married	Handlers-cleaners	Own-child	White	Male	0	0	25	United-States
3	3	40	Private	114537	Assoc-voc	11	Married-civ-spouse	Exec-managerial	Husband	White	Male	0	0	50	United-States
4	4	37	Private	51264	Doctorate	16	Married-civ-spouse	Prof-specialty	Husband	White	Male	0	0	99	France

train.columns

Index(['id', 'age', 'workclass', 'fnlwgt', 'education', 'education_num',
       'marital_status', 'occupation', 'relationship', 'race', 'sex',
       'capital_gain', 'capital_loss', 'hours_per_week', 'native_country',
       'income', 'target'],
      dtype='object')

sel = ['id', 'age', 'fnlwgt', 'education_num', 'capital_gain', 'capital_loss', 'hours_per_week']

X = train[sel]
y = train['target']

test_X = test[sel]

from sklearn.model_selection import train_test_split

X_train, X_test, y_train, y_test = train_test_split(X,y,
                                                   stratify=train.target,
                                                   random_state=42)

print(X_train.shape, X_test.shape, y_train.shape, y_test.shape)

(19536, 7) (6513, 7) (19536,) (6513,)

03. Baseline 모델 만들기

목차로 이동하기

로지스틱 모델

from sklearn.linear_model import LogisticRegression

model = LogisticRegression()
model.fit(X_train, y_train)
pred = model.predict(test_X)

sub.columns

Index(['id', 'prediction'], dtype='object')

print( sub.shape )
print( pred.shape )

(6512, 2)
(6512,)

sub['prediction'] = pred
sub.to_csv("firstSub4th.csv", index=False)

0.78545