Erro no codigo de normalização

[Edson Gomes Braz]

from sklearn.preprocessing import MinMaxScaler
scaler = MinMaxScaler()
dataset_normalizado = scaler.fit_transform(dataset)

TypeError Traceback (most recent call last)

<ipython-input-67-1b22fcc9588d> in <cell line: 3>()
1 from sklearn.preprocessing import MinMaxScaler
2 scaler = MinMaxScaler()
—-> 3 dataset_normalizado = scaler.fit_transform(dataset)

6 frames
/usr/local/lib/python3.10/dist-packages/sklearn/utils/validation.py in _get_feature_names(X)
1901 # mixed type of string and non-string is not supported
1902 if len(types) > 1 and “str” in types:
-> 1903 raise TypeError(
1904 “Feature names are only supported if all input features have string names, ”
1905 f”but your input has {types} as feature name / column name types. ”

TypeError: Feature names are only supported if all input features have string names, but your input has [‘int’, ‘str’] as feature name / column name types. If you want feature names to be stored and validated, you must convert them all to strings, by using X.columns = X.columns.astype(str) for example. Otherwise you can remove feature / column names from your input data, or convert them all to a non-string data type.
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[Denny Ceccon]

Olá Edson,

Pode seguir a recomendação que a própria mensagem de erro retorna:

X.columns = X.columns.astype(str)

(no seu caso, X é dataset)

[Autor]

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