Traceback (most recent call last): File "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/jupyter_cache/executors/utils.py", line 51, in single_nb_execution executenb( File "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/nbclient/client.py", line 1204, in execute return NotebookClient(nb=nb, resources=resources, km=km, **kwargs).execute() File "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/nbclient/util.py", line 84, in wrapped return just_run(coro(*args, **kwargs)) File "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/nbclient/util.py", line 62, in just_run return loop.run_until_complete(coro) File "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/asyncio/base_events.py", line 647, in run_until_complete return future.result() File "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/nbclient/client.py", line 663, in async_execute await self.async_execute_cell( File "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/nbclient/client.py", line 965, in async_execute_cell await self._check_raise_for_error(cell, cell_index, exec_reply) File "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/nbclient/client.py", line 862, in _check_raise_for_error raise CellExecutionError.from_cell_and_msg(cell, exec_reply_content) nbclient.exceptions.CellExecutionError: An error occurred while executing the following cell: ------------------ from sklearn.datasets import load_boston boston_dataset = load_boston() # boston_dataset is a dictionary # let's check what it contains boston_dataset.keys() ------------------ --------------------------------------------------------------------------- ImportError Traceback (most recent call last) Cell In[16], line 1 ----> 1 from sklearn.datasets import load_boston  3 boston_dataset = load_boston()  5 # boston_dataset is a dictionary  6 # let's check what it contains File ~/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/datasets/__init__.py:157, in __getattr__(name)  108 if name == "load_boston":  109 msg = textwrap.dedent("""  110  `load_boston` has been removed from scikit-learn since version 1.2.  111  (...)  155    156  """) --> 157 raise ImportError(msg)  158 try:  159 return globals()[name] ImportError: `load_boston` has been removed from scikit-learn since version 1.2. The Boston housing prices dataset has an ethical problem: as investigated in [1], the authors of this dataset engineered a non-invertible variable "B" assuming that racial self-segregation had a positive impact on house prices [2]. Furthermore the goal of the research that led to the creation of this dataset was to study the impact of air quality but it did not give adequate demonstration of the validity of this assumption. The scikit-learn maintainers therefore strongly discourage the use of this dataset unless the purpose of the code is to study and educate about ethical issues in data science and machine learning. In this special case, you can fetch the dataset from the original source:: import pandas as pd import numpy as np data_url = "http://lib.stat.cmu.edu/datasets/boston" raw_df = pd.read_csv(data_url, sep="\s+", skiprows=22, header=None) data = np.hstack([raw_df.values[::2, :], raw_df.values[1::2, :2]]) target = raw_df.values[1::2, 2] Alternative datasets include the California housing dataset and the Ames housing dataset. You can load the datasets as follows:: from sklearn.datasets import fetch_california_housing housing = fetch_california_housing() for the California housing dataset and:: from sklearn.datasets import fetch_openml housing = fetch_openml(name="house_prices", as_frame=True) for the Ames housing dataset. [1] M Carlisle. "Racist data destruction?" [2] Harrison Jr, David, and Daniel L. Rubinfeld. "Hedonic housing prices and the demand for clean air." Journal of environmental economics and management 5.1 (1978): 81-102. ImportError: `load_boston` has been removed from scikit-learn since version 1.2. The Boston housing prices dataset has an ethical problem: as investigated in [1], the authors of this dataset engineered a non-invertible variable "B" assuming that racial self-segregation had a positive impact on house prices [2]. Furthermore the goal of the research that led to the creation of this dataset was to study the impact of air quality but it did not give adequate demonstration of the validity of this assumption. The scikit-learn maintainers therefore strongly discourage the use of this dataset unless the purpose of the code is to study and educate about ethical issues in data science and machine learning. In this special case, you can fetch the dataset from the original source:: import pandas as pd import numpy as np data_url = "http://lib.stat.cmu.edu/datasets/boston" raw_df = pd.read_csv(data_url, sep="\s+", skiprows=22, header=None) data = np.hstack([raw_df.values[::2, :], raw_df.values[1::2, :2]]) target = raw_df.values[1::2, 2] Alternative datasets include the California housing dataset and the Ames housing dataset. You can load the datasets as follows:: from sklearn.datasets import fetch_california_housing housing = fetch_california_housing() for the California housing dataset and:: from sklearn.datasets import fetch_openml housing = fetch_openml(name="house_prices", as_frame=True) for the Ames housing dataset. [1] M Carlisle. "Racist data destruction?" [2] Harrison Jr, David, and Daniel L. Rubinfeld. "Hedonic housing prices and the demand for clean air." Journal of environmental economics and management 5.1 (1978): 81-102.