라벨 인코딩에서 오류가 납니다..

마지막 강의 영상 <정리>부분에서

데이터 불러오기

X_train = pd.read_csv("https://raw.githubusercontent.com/lovedlim/inf/main/p2/data_atype/X_train.csv")

y_train = pd.read_csv("https://raw.githubusercontent.com/lovedlim/inf/main/p2/data_atype/y_train.csv")

X_test = pd.read_csv("https://raw.githubusercontent.com/lovedlim/inf/main/p2/data_atype/X_test.csv")

데이터 분리

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()

수치형 민맥스 스케일

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])

라벨인코딩

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])

이 부분에서 이러한 에러가 납니다..

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
~\anaconda3\lib\site-packages\sklearn\preprocessing\_label.py in _encode(values, uniques, encode, check_unknown)
    112         try:
--> 113             res = _encode_python(values, uniques, encode)
    114         except TypeError:

~\anaconda3\lib\site-packages\sklearn\preprocessing\_label.py in _encode_python(values, uniques, encode)
     60     if uniques is None:
---> 61         uniques = sorted(set(values))
     62         uniques = np.array(uniques, dtype=values.dtype)

TypeError: '<' not supported between instances of 'str' and 'float'

During handling of the above exception, another exception occurred:

TypeError                                 Traceback (most recent call last)
<ipython-input-95-295cc9604042> in <module>
      7 for col in cols:
      8     le = LabelEncoder()
----> 9     c_train[col] = le.fit_transform(c_train[col])
     10     c_test[col] = le.transform(c_test[col])

~\anaconda3\lib\site-packages\sklearn\preprocessing\_label.py in fit_transform(self, y)
    254         """
    255         y = column_or_1d(y, warn=True)
--> 256         self.classes_, y = _encode(y, encode=True)
    257         return y
    258 

~\anaconda3\lib\site-packages\sklearn\preprocessing\_label.py in _encode(values, uniques, encode, check_unknown)
    115             types = sorted(t.__qualname__
    116                            for t in set(type(v) for v in values))
--> 117             raise TypeError("Encoders require their input to be uniformly "
    118                             f"strings or numbers. Got {types}")
    119         return res

TypeError: Encoders require their input to be uniformly strings or numbers. Got ['float', 'str']