피처엔지니어링¶
- 스케일
- 인코딩
- 데이터 : data_atype.zip (수업노트에서 다운로드)
In [2]:
# 데이터 불러오기
import pandas as pd
X_train = pd.read_csv(r'C:\Users\Master\Desktop\데이터자격증\빅분기_실기\퇴근후딴짓\data_atype\X_train.csv')
y_train = pd.read_csv(r'C:\Users\Master\Desktop\데이터자격증\빅분기_실기\퇴근후딴짓\data_atype\y_train.csv')
X_test = pd.read_csv(r'C:\Users\Master\Desktop\데이터자격증\빅분기_실기\퇴근후딴짓\data_atype\X_test.csv')
In [3]:
# 데이터 샘플 확인
X_train.head()
Out[3]:
| id | age | workclass | fnlwgt | education | education.num | marital.status | occupation | relationship | race | sex | capital.gain | capital.loss | hours.per.week | native.country | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 3331 | 34.0 | State-gov | 177331 | Some-college | 10 | Married-civ-spouse | Prof-specialty | Husband | Black | Male | 4386 | 0 | 40.0 | United-States |
| 1 | 19749 | 58.0 | Private | 290661 | HS-grad | 9 | Married-civ-spouse | Craft-repair | Husband | White | Male | 0 | 0 | 40.0 | United-States |
| 2 | 1157 | 48.0 | Private | 125933 | Some-college | 10 | Widowed | Exec-managerial | Unmarried | Black | Female | 0 | 1669 | 38.0 | United-States |
| 3 | 693 | 58.0 | Private | 100313 | Some-college | 10 | Married-civ-spouse | Protective-serv | Husband | White | Male | 0 | 1902 | 40.0 | United-States |
| 4 | 12522 | 41.0 | Private | 195661 | Some-college | 10 | Married-civ-spouse | Transport-moving | Husband | White | Male | 0 | 0 | 54.0 | United-States |
데이터 전처리 (이전시간 작업)¶
In [4]:
# X_train데이터
X_train['workclass'] = X_train['workclass'].fillna(X_train['workclass'].mode()[0])
X_train['native.country'] = X_train['native.country'].fillna(X_train['native.country'].mode()[0])
X_train['occupation'] = X_train['occupation'].fillna("X")
X_train['age'] = X_train['age'].fillna(int(X_train['age'].mean()))
X_train['hours.per.week'] = X_train['hours.per.week'].fillna(X_train['hours.per.week'].median())
# X_test데이터
X_test['workclass'] = X_test['workclass'].fillna(X_test['workclass'].mode()[0])
X_test['native.country'] = X_test['native.country'].fillna(X_test['native.country'].mode()[0])
X_test['occupation'] = X_test['occupation'].fillna("X")
X_test['age'] = X_test['age'].fillna(int(X_train['age'].mean()))
X_test['hours.per.week'] = X_test['hours.per.week'].fillna(X_train['hours.per.week'].median())
In [5]:
# 결측치 확인
X_train.isnull().sum()
Out[5]:
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
수치형 데이터와 범주형 데이터 분리¶
In [6]:
# 데이터 타입 확인
X_train.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 29304 entries, 0 to 29303 Data columns (total 15 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 id 29304 non-null int64 1 age 29304 non-null float64 2 workclass 29304 non-null object 3 fnlwgt 29304 non-null int64 4 education 29304 non-null object 5 education.num 29304 non-null int64 6 marital.status 29304 non-null object 7 occupation 29304 non-null object 8 relationship 29304 non-null object 9 race 29304 non-null object 10 sex 29304 non-null object 11 capital.gain 29304 non-null int64 12 capital.loss 29304 non-null int64 13 hours.per.week 29304 non-null float64 14 native.country 29304 non-null object dtypes: float64(2), int64(5), object(8) memory usage: 3.4+ MB
In [12]:
# 수치형 컬럼과 범주형 컬럼 데이터 나누기
n_train = X_train.select_dtypes(exclude='object').copy()
n_test = X_test.select_dtypes(exclude='object').copy()
c_train = X_train.select_dtypes(include='object').copy()
c_test = X_test.select_dtypes(include='object').copy()
# 데이터를 매번 새롭게 불러오기 위해 함수로 제작 함
def get_nc_data():
X_train = pd.read_csv(r'C:\Users\Master\Desktop\데이터자격증\빅분기_실기\퇴근후딴짓\data_atype\X_train.csv')
y_train = pd.read_csv(r'C:\Users\Master\Desktop\데이터자격증\빅분기_실기\퇴근후딴짓\data_atype\y_train.csv')
X_test = pd.read_csv(r'C:\Users\Master\Desktop\데이터자격증\빅분기_실기\퇴근후딴짓\data_atype\X_test.csv')
n_train = X_train.select_dtypes(exclude='object').copy()
n_test = X_test.select_dtypes(exclude='object').copy()
c_train = X_train.select_dtypes(include='object').copy()
c_test = X_test.select_dtypes(include='object').copy()
return n_train, n_test, c_train, c_test
# n_train, n_test, c_train, c_test = get_nc_data() # 데이터 새로 불러오기
In [8]:
# 데이처 확인(수치형 데이터)
n_train.head(2)
Out[8]:
| id | age | fnlwgt | education.num | capital.gain | capital.loss | hours.per.week | |
|---|---|---|---|---|---|---|---|
| 0 | 3331 | 34.0 | 177331 | 10 | 4386 | 0 | 40.0 |
| 1 | 19749 | 58.0 | 290661 | 9 | 0 | 0 | 40.0 |
In [9]:
# 데이처 확인(범주형 데이터)
c_train.head(2)
Out[9]:
| workclass | education | marital.status | occupation | relationship | race | sex | native.country | |
|---|---|---|---|---|---|---|---|---|
| 0 | State-gov | Some-college | Married-civ-spouse | Prof-specialty | Husband | Black | Male | United-States |
| 1 | Private | HS-grad | Married-civ-spouse | Craft-repair | Husband | White | Male | United-States |
스케일링¶
- 트리기반의 모델은 입력의 스케일을 크게 신경쓰지 않아도 됨
- 선형회귀나 로지스틱 회귀 등과 같은 모델은 입력의 스케일링에 영향을 받음
In [10]:
# 스케일링 작업할 컬럼명(id 제외)
cols = ['age', 'fnlwgt', 'education.num', 'capital.gain', 'capital.loss', 'hours.per.week']
In [11]:
# 민-맥스 스케일링 MinMaxScaler (모든 값이 0과 1사이)
# n_train, n_test, c_train, c_test = get_nc_data() # 데이터 새로 불러오기
from sklearn.preprocessing import MinMaxScaler
scaler = MinMaxScaler()
display(n_train.head(2))
n_train[cols] = scaler.fit_transform(n_train[cols])
# transform : 변환, fit : 학습
n_test[cols] = scaler.transform(n_test[cols])
# test데이터는 학습은 하지않고 train데이터에서 학습한 내용을 바탕으로 변환만 함
display(n_train.head(2))
# 시험에서는 display함수 사용 못함(주피터 고유 함수)
| id | age | fnlwgt | education.num | capital.gain | capital.loss | hours.per.week | |
|---|---|---|---|---|---|---|---|
| 0 | 3331 | 34.0 | 177331 | 10 | 4386 | 0 | 40.0 |
| 1 | 19749 | 58.0 | 290661 | 9 | 0 | 0 | 40.0 |
| id | age | fnlwgt | education.num | capital.gain | capital.loss | hours.per.week | |
|---|---|---|---|---|---|---|---|
| 0 | 3331 | 0.5625 | 0.112092 | 0.600000 | 0.04386 | 0.0 | 0.397959 |
| 1 | 19749 | 0.7500 | 0.189060 | 0.533333 | 0.00000 | 0.0 | 0.397959 |
In [14]:
# 표준화 StandardScaler (Z-score 정규화, 평균이 0 표준편차가 1인 표준 정규분포로 변경)
n_train, n_test, c_train, c_test = get_nc_data() # 데이터 새로 불러오기
from sklearn.preprocessing import StandardScaler
scaler = StandardScaler()
display(n_train.head(2))
n_train[cols] = scaler.fit_transform(n_train[cols])
n_test[cols] = scaler.transform(n_test[cols])
display(n_train.head(2))
| id | age | fnlwgt | education.num | capital.gain | capital.loss | hours.per.week | |
|---|---|---|---|---|---|---|---|
| 0 | 3331 | 34.0 | 177331 | 10 | 4386 | 0 | 40.0 |
| 1 | 19749 | 58.0 | 290661 | 9 | 0 | 0 | 40.0 |
| id | age | fnlwgt | education.num | capital.gain | capital.loss | hours.per.week | |
|---|---|---|---|---|---|---|---|
| 0 | 3331 | -0.334094 | -0.117678 | -0.031447 | 0.440284 | -0.216045 | -0.035235 |
| 1 | 19749 | 1.426912 | 0.956304 | -0.420434 | -0.146290 | -0.216045 | -0.035235 |
In [15]:
# 로버스트 스케일링 : 중앙값과 사분위 값 활용, 이상치 영향 최소화 장점
n_train, n_test, c_train, c_test = get_nc_data() # 데이터 새로 불러오기
from sklearn.preprocessing import RobustScaler
scaler = RobustScaler()
display(n_train.head(2))
n_train[cols] = scaler.fit_transform(n_train[cols])
n_test[cols] = scaler.transform(n_test[cols])
display(n_train.head(2))
| id | age | fnlwgt | education.num | capital.gain | capital.loss | hours.per.week | |
|---|---|---|---|---|---|---|---|
| 0 | 3331 | 34.0 | 177331 | 10 | 4386 | 0 | 40.0 |
| 1 | 19749 | 58.0 | 290661 | 9 | 0 | 0 | 40.0 |
| id | age | fnlwgt | education.num | capital.gain | capital.loss | hours.per.week | |
|---|---|---|---|---|---|---|---|
| 0 | 3331 | -0.15 | -0.008765 | 0.000000 | 4386.0 | 0.0 | 0.0 |
| 1 | 19749 | 1.05 | 0.941358 | -0.333333 | 0.0 | 0.0 | 0.0 |
In [17]:
# 로그 변환 예시
X_train['fnlwgt'].hist()
Out[17]:
<Axes: >
In [18]:
# 로그 변환 전후 확인
import numpy as np
print(X_train['fnlwgt'][:3])
np.log1p(X_train['fnlwgt'])[:3]
0 177331 1 290661 2 125933 Name: fnlwgt, dtype: int64
Out[18]:
0 12.085779 1 12.579916 2 11.743513 Name: fnlwgt, dtype: float64
In [19]:
# 로그 변환 후 시각화
np.log1p(X_train['fnlwgt']).hist()
Out[19]:
<Axes: >
In [20]:
# np.exp
np.exp(np.log1p(X_train['fnlwgt']))
Out[20]:
0 177332.0
1 290662.0
2 125934.0
3 100314.0
4 195662.0
...
29299 47169.0
29300 231794.0
29301 201436.0
29302 137723.0
29303 406979.0
Name: fnlwgt, Length: 29304, dtype: float64
인코딩¶
- 라벨(label) 인코딩
- 원핫(one-hot) 인코딩
In [21]:
# 범주형 데이터 확인(인코딩 전)
c_train.head()
Out[21]:
| workclass | education | marital.status | occupation | relationship | race | sex | native.country | |
|---|---|---|---|---|---|---|---|---|
| 0 | State-gov | Some-college | Married-civ-spouse | Prof-specialty | Husband | Black | Male | United-States |
| 1 | Private | HS-grad | Married-civ-spouse | Craft-repair | Husband | White | Male | United-States |
| 2 | Private | Some-college | Widowed | Exec-managerial | Unmarried | Black | Female | United-States |
| 3 | Private | Some-college | Married-civ-spouse | Protective-serv | Husband | White | Male | United-States |
| 4 | Private | Some-college | Married-civ-spouse | Transport-moving | Husband | White | Male | United-States |
In [26]:
# object 컬럼명
cols = list(c_train.columns)
cols
# cols = list(X_train.columns[X_tran.dtype == object])
Out[26]:
['workclass', 'education', 'marital.status', 'occupation', 'relationship', 'race', 'sex', 'native.country']
In [28]:
# 라벨 인코딩
n_train, n_test, c_train, c_test = get_nc_data() # 데이터 새로 불러오기
from sklearn.preprocessing import LabelEncoder
le = LabelEncoder()
for col in cols:
le = LabelEncoder()
c_train[col] = le.fit_transform(c_train[col])
c_test[col] = le.transform(c_test[col])
In [30]:
# 범주형 데이터 확인(인코딩 후)
c_train.head()
Out[30]:
| workclass | education | marital.status | occupation | relationship | race | sex | native.country | |
|---|---|---|---|---|---|---|---|---|
| 0 | 6 | 15 | 2 | 9 | 0 | 2 | 1 | 38 |
| 1 | 3 | 11 | 2 | 2 | 0 | 4 | 1 | 38 |
| 2 | 3 | 15 | 6 | 3 | 4 | 2 | 0 | 38 |
| 3 | 3 | 15 | 2 | 10 | 0 | 4 | 1 | 38 |
| 4 | 3 | 15 | 2 | 13 | 0 | 4 | 1 | 38 |
In [32]:
# 원핫 인코딩 - 판다스에서 지원해줌
n_train, n_test, c_train, c_test = get_nc_data() # 데이터 새로 불러오기
display(c_train.head())
c_train = pd.get_dummies(c_train[cols])
c_test = pd.get_dummies(c_test[cols])
display(c_train.head())
| workclass | education | marital.status | occupation | relationship | race | sex | native.country | |
|---|---|---|---|---|---|---|---|---|
| 0 | State-gov | Some-college | Married-civ-spouse | Prof-specialty | Husband | Black | Male | United-States |
| 1 | Private | HS-grad | Married-civ-spouse | Craft-repair | Husband | White | Male | United-States |
| 2 | Private | Some-college | Widowed | Exec-managerial | Unmarried | Black | Female | United-States |
| 3 | Private | Some-college | Married-civ-spouse | Protective-serv | Husband | White | Male | United-States |
| 4 | Private | Some-college | Married-civ-spouse | Transport-moving | Husband | White | Male | United-States |
| workclass_Federal-gov | workclass_Local-gov | workclass_Never-worked | workclass_Private | workclass_Self-emp-inc | workclass_Self-emp-not-inc | workclass_State-gov | workclass_Without-pay | education_10th | education_11th | ... | native.country_Portugal | native.country_Puerto-Rico | native.country_Scotland | native.country_South | native.country_Taiwan | native.country_Thailand | native.country_Trinadad&Tobago | native.country_United-States | native.country_Vietnam | native.country_Yugoslavia | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 |
| 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 |
| 2 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 |
| 3 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 |
| 4 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 |
5 rows × 99 columns
데이터 합치기¶
In [33]:
# 분리한 데이터 다시 합침
# n_train, n_test, c_train, c_test = get_nc_data() # 데이터 새로 불러오기
X_train = pd.concat([n_train, c_train], axis = 1)
# 기본값은 axis=0으로 아래로 합치게 되어있음
# 옆으로 합쳐야되므로 axis=1
X_test = pd.concat([n_test, c_test], axis=1)
print(X_train.shape, X_test.shape)
X_train.head()
# 원핫인코딩 후 컬럼수를 비교해보니 train데이터와 test데이터의 컬럼수가 다르다
# train데이터와 test데이터를먼저 합친 후 원핫인코딩을 진행한 후 다시 분리하는 작업필요
# 원핫인코딩이 아니더라도 train데이터에서 없었던 데이터가 test데이터에서 있을경우 라벨인코딩 에러
# 라벨인코딩을 하려면 train데이터와 test데이터를 합친 후 인코딩 해줘야함(이때 위아래로 합쳐야함)
(29304, 106) (3257, 102)
Out[33]:
| id | age | fnlwgt | education.num | capital.gain | capital.loss | hours.per.week | workclass_Federal-gov | workclass_Local-gov | workclass_Never-worked | ... | native.country_Portugal | native.country_Puerto-Rico | native.country_Scotland | native.country_South | native.country_Taiwan | native.country_Thailand | native.country_Trinadad&Tobago | native.country_United-States | native.country_Vietnam | native.country_Yugoslavia | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 3331 | 34.0 | 177331 | 10 | 4386 | 0 | 40.0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 |
| 1 | 19749 | 58.0 | 290661 | 9 | 0 | 0 | 40.0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 |
| 2 | 1157 | 48.0 | 125933 | 10 | 0 | 1669 | 38.0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 |
| 3 | 693 | 58.0 | 100313 | 10 | 0 | 1902 | 40.0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 |
| 4 | 12522 | 41.0 | 195661 | 10 | 0 | 0 | 54.0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 |
5 rows × 106 columns
In [40]:
# 데이터 새로 불러오기
import pandas as pd
X_train = pd.read_csv(r'C:\Users\Master\Desktop\데이터자격증\빅분기_실기\퇴근후딴짓\data_atype\X_train.csv')
y_train = pd.read_csv(r'C:\Users\Master\Desktop\데이터자격증\빅분기_실기\퇴근후딴짓\data_atype\y_train.csv')
X_test = pd.read_csv(r'C:\Users\Master\Desktop\데이터자격증\빅분기_실기\퇴근후딴짓\data_atype\X_test.csv')
In [41]:
# train, test 합쳐서 인코딩 후 분리하기
cols = list(X_train.columns[X_train.dtypes == object])
print(X_train.shape, X_test.shape)
all_df = pd.concat([X_train, X_test])
# 기본값이 위아래
all_df = pd.get_dummies(all_df[cols])
# 분리
line = int(X_train.shape[0])
X_train = all_df.iloc[:line, :].copy()
X_train
X_test = all_df.iloc[line:, :].copy()
X_test
print(X_train.shape, X_test.shape)
# 원핫인코딩 후에도 train데이터와 test데이터의 컬럼수가 같아짐
(29304, 15) (3257, 15) (29304, 99) (3257, 99)
In [39]:
X_train.head()
Out[39]:
| workclass_Federal-gov | workclass_Local-gov | workclass_Never-worked | workclass_Private | workclass_Self-emp-inc | workclass_Self-emp-not-inc | workclass_State-gov | workclass_Without-pay | education_10th | education_11th | ... | native.country_Portugal | native.country_Puerto-Rico | native.country_Scotland | native.country_South | native.country_Taiwan | native.country_Thailand | native.country_Trinadad&Tobago | native.country_United-States | native.country_Vietnam | native.country_Yugoslavia | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 |
| 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 |
| 2 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 |
| 3 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 |
| 4 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 |
5 rows × 99 columns
정리¶
In [51]:
# 데이터 분리
# 수치형 컬럼과 범주형 컬럼 데이터 나누기
n_train = X_train.select_dtypes(exclude='object').copy()
n_test = X_test.select_dtypes(exclude='object').copy()
c_train = X_train.select_dtypes(include='object').copy()
c_test = X_test.select_dtypes(include='object').copy()
# 데이터를 매번 새롭게 불러오기 위해 함수로 제작 함
def get_nc_data():
X_train = pd.read_csv(r'C:\Users\Master\Desktop\데이터자격증\빅분기_실기\퇴근후딴짓\data_atype\X_train.csv')
y_train = pd.read_csv(r'C:\Users\Master\Desktop\데이터자격증\빅분기_실기\퇴근후딴짓\data_atype\y_train.csv')
X_test = pd.read_csv(r'C:\Users\Master\Desktop\데이터자격증\빅분기_실기\퇴근후딴짓\data_atype\X_test.csv')
n_train = X_train.select_dtypes(exclude='object').copy()
n_test = X_test.select_dtypes(exclude='object').copy()
c_train = X_train.select_dtypes(include='object').copy()
c_test = X_test.select_dtypes(include='object').copy()
return n_train, n_test, c_train, c_test
n_train, n_test, c_train, c_test = get_nc_data() # 데이터 새로 불러오기
In [52]:
# 수치형 - 민맥스 스케일링
cols = ['age', 'fnlwgt', 'education.num', 'capital.gain', 'capital.loss', 'hours.per.week']
from sklearn.preprocessing import MinMaxScaler
scaler = MinMaxScaler()
n_train[cols] = scaler.fit_transform(n_train[cols])
n_test[cols] = scaler.transform(n_test[cols])
In [54]:
# 라벨 인코딩
cols = ['workclass', 'education', 'marital.status', 'occupation', 'relationship', 'race', 'sex', 'native.country']
from sklearn.preprocessing import LabelEncoder
le = LabelEncoder()
for col in cols:
le = LabelEncoder()
c_train[col] = le.fit_transform(c_train[col])
c_test[col] = le.transform(c_test[col])
In [55]:
# 분리한 데이터 다시 합침
X_train = pd.concat([n_train, c_train], axis = 1)
X_test = pd.concat([n_test, c_test], axis = 1)
print(X_train.shape, X_test.shape)
X_train.head()
(29304, 15) (3257, 15)
Out[55]:
| id | age | fnlwgt | education.num | capital.gain | capital.loss | hours.per.week | workclass | education | marital.status | occupation | relationship | race | sex | native.country | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 3331 | 0.562500 | 0.112092 | 0.600000 | 0.04386 | 0.000000 | 0.397959 | 6 | 15 | 2 | 9 | 0 | 2 | 1 | 38 |
| 1 | 19749 | 0.750000 | 0.189060 | 0.533333 | 0.00000 | 0.000000 | 0.397959 | 3 | 11 | 2 | 2 | 0 | 4 | 1 | 38 |
| 2 | 1157 | 0.671875 | 0.077184 | 0.600000 | 0.00000 | 0.383150 | 0.377551 | 3 | 15 | 6 | 3 | 4 | 2 | 0 | 38 |
| 3 | 693 | 0.750000 | 0.059785 | 0.600000 | 0.00000 | 0.436639 | 0.397959 | 3 | 15 | 2 | 10 | 0 | 4 | 1 | 38 |
| 4 | 12522 | 0.617188 | 0.124541 | 0.600000 | 0.00000 | 0.000000 | 0.540816 | 3 | 15 | 2 | 13 | 0 | 4 | 1 | 38 |
In [57]:
# 데이터 확인
X_test.head()
Out[57]:
| id | age | fnlwgt | education.num | capital.gain | capital.loss | hours.per.week | workclass | education | marital.status | occupation | relationship | race | sex | native.country | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 11574 | 0.601562 | 0.069118 | 0.800000 | 0.0 | 0.0 | 0.397959 | 6 | 9 | 4 | 3 | 1 | 4 | 0 | 38 |
| 1 | 15847 | 0.593750 | 0.164239 | 0.600000 | 0.0 | 0.0 | 0.397959 | 3 | 15 | 3 | 9 | 3 | 2 | 0 | 38 |
| 2 | 17655 | 0.640625 | 0.029278 | 0.533333 | 0.0 | 0.0 | NaN | 6 | 11 | 4 | 2 | 1 | 4 | 1 | 38 |
| 3 | 19790 | 0.664062 | 0.010697 | 0.800000 | 0.0 | 0.0 | 0.500000 | 3 | 9 | 2 | 3 | 0 | 4 | 1 | 38 |
| 4 | 31812 | 0.781250 | 0.118394 | 0.533333 | 0.0 | 0.0 | 0.397959 | 8 | 11 | 4 | 14 | 1 | 4 | 1 | 38 |
In [ ]:
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