Error in if-then-else python code

[Led_Zeppelin]

When I run the program with the following final code, I get the error shown.

Error:
ValueError                                Traceback (most recent call last)
Input In [34], in <cell line: 2>()
      1 model = ExtraTreesClassifier()
----> 2 model.fit(X, y)
      3 print(model.feature_importances_)

File ~\miniconda3\envs\pump-failure-pred\lib\site-packages\sklearn\ensemble\_forest.py:331, in BaseForest.fit(self, X, y, sample_weight)
    329 if issparse(y):
    330     raise ValueError("sparse multilabel-indicator for y is not supported.")
--> 331 X, y = self._validate_data(
    332     X, y, multi_output=True, accept_sparse="csc", dtype=DTYPE
    333 )
    334 if sample_weight is not None:
    335     sample_weight = _check_sample_weight(sample_weight, X)

File ~\miniconda3\envs\pump-failure-pred\lib\site-packages\sklearn\base.py:596, in BaseEstimator._validate_data(self, X, y, reset, validate_separately, **check_params)
    594         y = check_array(y, input_name="y", **check_y_params)
    595     else:
--> 596         X, y = check_X_y(X, y, **check_params)
    597     out = X, y
    599 if not no_val_X and check_params.get("ensure_2d", True):

File ~\miniconda3\envs\pump-failure-pred\lib\site-packages\sklearn\utils\validation.py:1090, in check_X_y(X, y, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, multi_output, ensure_min_samples, ensure_min_features, y_numeric, estimator)
   1070     raise ValueError(
   1071         f"{estimator_name} requires y to be passed, but the target y is None"
   1072     )
   1074 X = check_array(
   1075     X,
   1076     accept_sparse=accept_sparse,
   (...)
   1087     input_name="X",
   1088 )
-> 1090 y = _check_y(y, multi_output=multi_output, y_numeric=y_numeric, estimator=estimator)
   1092 check_consistent_length(X, y)
   1094 return X, y

File ~\miniconda3\envs\pump-failure-pred\lib\site-packages\sklearn\utils\validation.py:1100, in _check_y(y, multi_output, y_numeric, estimator)
   1098 """Isolated part of check_X_y dedicated to y validation"""
   1099 if multi_output:
-> 1100     y = check_array(
   1101         y,
   1102         accept_sparse="csr",
   1103         force_all_finite=True,
   1104         ensure_2d=False,
   1105         dtype=None,
   1106         input_name="y",
   1107         estimator=estimator,
   1108     )
   1109 else:
   1110     estimator_name = _check_estimator_name(estimator)

File ~\miniconda3\envs\pump-failure-pred\lib\site-packages\sklearn\utils\validation.py:899, in check_array(array, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, estimator, input_name)
    893         raise ValueError(
    894             "Found array with dim %d. %s expected <= 2."
    895             % (array.ndim, estimator_name)
    896         )
    898     if force_all_finite:
--> 899         _assert_all_finite(
    900             array,
    901             input_name=input_name,
    902             estimator_name=estimator_name,
    903             allow_nan=force_all_finite == "allow-nan",
    904         )
    906 if ensure_min_samples > 0:
    907     n_samples = _num_samples(array)

File ~\miniconda3\envs\pump-failure-pred\lib\site-packages\sklearn\utils\validation.py:146, in _assert_all_finite(X, allow_nan, msg_dtype, estimator_name, input_name)
    124         if (
    125             not allow_nan
    126             and estimator_name
   (...)
    130             # Improve the error message on how to handle missing values in
    131             # scikit-learn.
    132             msg_err += (
    133                 f"\n{estimator_name} does not accept missing values"
    134                 " encoded as NaN natively. For supervised learning, you might want"
   (...)
    144                 "#estimators-that-handle-nan-values"
    145             )
--> 146         raise ValueError(msg_err)
    148 # for object dtype data, we only check for NaNs (GH-13254)
    149 elif X.dtype == np.dtype("object") and not allow_nan:

ValueError: Input y contains NaN.

I have changed machine_ status to numeric values using the following subroutine.

df2["machine_status"] = df2["machine_status"].map(
    lambda x: 0
    if x == "NORMAL"
    else 1
    if x == "BROKEN"
    else 2
    if x == "BROKEN"
    else np.NaN

Now machine status is either normal, recovering or broken. But I combine Recovering with Broken. ...

I think I know the answer to this so please be patient and I will check if my idea works.