I have an error while fitting the model
reg.fit(inputs_train, loan_data_targets_train)
ValueError                                Traceback (most recent call last)
<ipython-input-15-966f89d3c717> in <module>
----> 1 reg.fit(inputs_train, loan_data_targets_train)
      2 # Estimates the coefficients of the object from the 'LogisticRegression' class
      3 # with inputs (independent variables) contained in the first dataframe
      4 # and targets (dependent variables) contained in the second dataframe.

~\anaconda3\lib\site-packages\sklearn\linear_model\_logistic.py in fit(self, X, y, sample_weight)
   1340             _dtype = [np.float64, np.float32]
   1341 
-> 1342         X, y = self._validate_data(X, y, accept_sparse='csr', dtype=_dtype,
   1343                                    order="C",
   1344                                    accept_large_sparse=solver != 'liblinear')

~\anaconda3\lib\site-packages\sklearn\base.py in _validate_data(self, X, y, reset, validate_separately, **check_params)
    430                 y = check_array(y, **check_y_params)
    431             else:
--> 432                 X, y = check_X_y(X, y, **check_params)
    433             out = X, y
    434 

~\anaconda3\lib\site-packages\sklearn\utils\validation.py in inner_f(*args, **kwargs)
     71                           FutureWarning)
     72         kwargs.update({k: arg for k, arg in zip(sig.parameters, args)})
---> 73         return f(**kwargs)
     74     return inner_f
     75 

~\anaconda3\lib\site-packages\sklearn\utils\validation.py 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)
    794         raise ValueError("y cannot be None")
    795 
--> 796     X = check_array(X, accept_sparse=accept_sparse,
    797                     accept_large_sparse=accept_large_sparse,
    798                     dtype=dtype, order=order, copy=copy,

~\anaconda3\lib\site-packages\sklearn\utils\validation.py in inner_f(*args, **kwargs)
     71                           FutureWarning)
     72         kwargs.update({k: arg for k, arg in zip(sig.parameters, args)})
---> 73         return f(**kwargs)
     74     return inner_f
     75 

~\anaconda3\lib\site-packages\sklearn\utils\validation.py 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)
    643 
    644         if force_all_finite:
--> 645             _assert_all_finite(array,
    646                                allow_nan=force_all_finite == 'allow-nan')
    647 

~\anaconda3\lib\site-packages\sklearn\utils\validation.py in _assert_all_finite(X, allow_nan, msg_dtype)
     95                 not allow_nan and not np.isfinite(X).all()):
     96             type_err = 'infinity' if allow_nan else 'NaN, infinity'
---> 97             raise ValueError(
     98                     msg_err.format
     99                     (type_err,

ValueError: Input contains NaN, infinity or a value too large for dtype('float64').
PD model estimation - PD model estimation

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