When estimating the PD Model with Logistic Regression I am getting a Value error on the lecture code: "

ValueError                                Traceback (most recent call last)
<ipython-input-32-966f89d3c717> 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.

~/opt/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/_logistic.py in fit(self, X, y, sample_weight)
   1525 
   1526         X, y = check_X_y(X, y, accept_sparse='csr', dtype=_dtype, order="C",
-> 1527                          accept_large_sparse=solver != 'liblinear')
   1528         check_classification_targets(y)
   1529         self.classes_ = np.unique(y)

~/opt/anaconda3/lib/python3.7/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, warn_on_dtype, estimator)
    753                     ensure_min_features=ensure_min_features,
    754                     warn_on_dtype=warn_on_dtype,
--> 755                     estimator=estimator)
    756     if multi_output:
    757         y = check_array(y, 'csr', force_all_finite=True, ensure_2d=False,

~/opt/anaconda3/lib/python3.7/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, warn_on_dtype, estimator)
    576         if force_all_finite:
    577             _assert_all_finite(array,
--> 578                                allow_nan=force_all_finite == 'allow-nan')
    579 
    580     if ensure_min_samples > 0:

~/opt/anaconda3/lib/python3.7/site-packages/sklearn/utils/validation.py in _assert_all_finite(X, allow_nan, msg_dtype)
     58                     msg_err.format
     59                     (type_err,
---> 60                      msg_dtype if msg_dtype is not None else X.dtype)
     61             )
     62     # for object dtype data, we only check for NaNs (GH-13254)

ValueError: Input contains NaN, infinity or a value too large for dtype('float64')"
It works fine in the video lecture, but the same code won't run in jupyter. Can you help me please?

Credit Risk Modeling PD Model Estimation

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