Traceback (most recent call last): File "/tmp/005714092/main.py", line 13, in <module> data['Height'] = imputer.fit_transform(data['Height'].values.reshape(-1,1)) File "/usr/local/lib/python3.9/site-packages/sklearn/utils/_set_output.py", line 157, in wrapped data_to_wrap = f(self, X, *args, **kwargs) File "/usr/local/lib/python3.9/site-packages/sklearn/base.py", line 916, in fit_transform return self.fit(X, **fit_params).transform(X) File "/usr/local/lib/python3.9/site-packages/sklearn/base.py", line 1145, in wrapper estimator._validate_params() File "/usr/local/lib/python3.9/site-packages/sklearn/base.py", line 638, in _validate_params validate_parameter_constraints( File "/usr/local/lib/python3.9/site-packages/sklearn/utils/_param_validation.py", line 95, in validate_parameter_constraints raise InvalidParameterError(sklearn.utils._param_validation.InvalidParameterError: The 'metric' parameter of KNNImputer must be a str among {'nan_euclidean'} or a callable. Got 'nan_enclidean' instead.

ValueError Traceback (most recent call last)Cell In[176], line 6 4 # Standardize features 5 scaler = StandardScaler()----> 6 X_train = scaler.fit_transform(X_train) 7 X_test = scaler.transform(X_test) 9 # Train Random Forest RegressorFile ~\anaconda3\lib\site-packages\sklearn\utils\_set_output.py:313, in _wrap_method_output.<locals>.wrapped(self, X, *args, **kwargs) 311 @wraps(f) 312 def wrapped(self, X, *args, **kwargs):--> 313 data_to_wrap = f(self, X, *args, **kwargs) 314 if isinstance(data_to_wrap, tuple): 315 # only wrap the first output for cross decomposition 316 return_tuple = ( 317 _wrap_data_with_container(method, data_to_wrap[0], X, self), 318 *data_to_wrap[1:], 319 )File ~\anaconda3\lib\site-packages\sklearn\base.py:1098, in TransformerMixin.fit_transform(self, X, y, **fit_params) 1083 warnings.warn( 1084 ( 1085 f"This object ({self.__class__.__name__}) has a `transform`" (...) 1093 UserWarning, 1094 ) 1096 if y is None: 1097 # fit method of arity 1 (unsupervised transformation)-> 1098 return self.fit(X, **fit_params).transform(X) 1099 else: 1100 # fit method of arity 2 (supervised transformation) 1101 return self.fit(X, y, **fit_params).transform(X)File ~\anaconda3\lib\site-packages\sklearn\preprocessing\_data.py:878, in StandardScaler.fit(self, X, y, sample_weight) 876 # Reset internal state before fitting 877 self._reset()--> 878 return self.partial_fit(X, y, sample_weight)File ~\anaconda3\lib\site-packages\sklearn\base.py:1473, in _fit_context.<locals>.decorator.<locals>.wrapper(estimator, *args, **kwargs) 1466 estimator._validate_params() 1468 with config_context( 1469 skip_parameter_validation=( 1470 prefer_skip_nested_validation or global_skip_validation 1471 ) 1472 ):-> 1473 return fit_method(estimator, *args, **kwargs)File ~\anaconda3\lib\site-packages\sklearn\preprocessing\_data.py:914, in StandardScaler.partial_fit(self, X, y, sample_weight) 882 """Online computation of mean and std on X for later scaling. 883 884 All of X is processed as a single batch. This is intended for cases (...)

Traceback (most recent call last): File "main.py", line 8, in <module> bmi = height / (weight * weight)TypeError: can't multiply sequence by non-int of type 'str'

AttributeError Traceback (most recent call last)Cell In[54], line 2 1 # Resample the datasets to a common resolution----> 2 resample_factor = slope.resolutions[0] / aspect.resolutions[0] # Adjust the factor as needed 3 aspect = aspect.read(1, out_shape=(aspect.count, int(aspect.height * resample_factor), int(aspect.width * resample_factor)), resampling=Resampling.bilinear) 4 lulc = lulc.read(1, out_shape=(lulc.count, int(lulc.height * resample_factor), int(lulc.width * resample_factor)), resampling=Resampling.bilinear)AttributeError: 'DatasetReader' object has no attribute 'resolutions'

Print out the mean of np_height_in

Create a numpy array from height_in. Name this new array np_height_in