runtime error

Exit code: 1. Reason: Traceback (most recent call last): File "/home/user/app/app.py", line 78, in <module> model.fit(x_train, y_train.values.ravel()) File "/usr/local/lib/python3.10/site-packages/sklearn/base.py", line 1473, in wrapper return fit_method(estimator, *args, **kwargs) File "/usr/local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py", line 1223, in fit X, y = self._validate_data( File "/usr/local/lib/python3.10/site-packages/sklearn/base.py", line 650, in _validate_data X, y = check_X_y(X, y, **check_params) File "/usr/local/lib/python3.10/site-packages/sklearn/utils/validation.py", line 1301, in check_X_y X = check_array( File "/usr/local/lib/python3.10/site-packages/sklearn/utils/validation.py", line 1064, in check_array _assert_all_finite( File "/usr/local/lib/python3.10/site-packages/sklearn/utils/validation.py", line 123, in _assert_all_finite _assert_all_finite_element_wise( File "/usr/local/lib/python3.10/site-packages/sklearn/utils/validation.py", line 172, in _assert_all_finite_element_wise raise ValueError(msg_err) ValueError: Input X contains NaN. LogisticRegression does not accept missing values encoded as NaN natively. For supervised learning, you might want to consider sklearn.ensemble.HistGradientBoostingClassifier and Regressor which accept missing values encoded as NaNs natively. Alternatively, it is possible to preprocess the data, for instance by using an imputer transformer in a pipeline or drop samples with missing values. See https://scikit-learn.org/stable/modules/impute.html You can find a list of all estimators that handle NaN values at the following page: https://scikit-learn.org/stable/modules/impute.html#estimators-that-handle-nan-values

Container logs: