I got a problem in the to fit LogisticRegression in the titanic file

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I have imported the titanic.csv file and clean the file , when i wanna fit it to the Logistic Regression it cause the error and it wisent solving

Can Anyone help to solve this problem??

*Survived   Age SibSp   Parch   Fare    male    Q   S   2   3
0   0   22.0    1   0   7.2500  1   0   1   0   1
1   1   38.0    1   0   71.2833 0   0   0   0   0
2   1   26.0    0   0   7.9250  0   0   1   0   1
3   1   35.0    1   0   53.1000 0   0   1   0   0
4   0   35.0    0   0   8.0500  1   0   1   0   1*

i cleaned the data like this and perform this code in it


X= titanic_data.drop("Survived", axis = 1)
y= titanic_data["Survived"]
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.3, random_state=1)

from sklearn.linear_model import LogisticRegression

logmodels = LogisticRegression(random_state=1)
logmodels.fit(X_train, y_train)

while compiling the last line i got the error like this

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-45-05d5668a869d> in <cell line: 2>()
      
```
1 logmodels = LogisticRegression(random_state=1)
----> 2 logmodels.fit(X_train, y_train)
```

3 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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