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line 5709 of 2-linear-algebra-ii.ipynb
"* Non-zero singular values of $A$ = square roots of eigenvectors eigenvalues of $AA^T$ = square roots of eigenvectors eigenvalues of $A^TA$\n"

In 6-statistics.ipynb
the result of Dream Island Adélie question isn't correct. At In[133] f and m contains all the Adélie penguins, not just the once on Dream Island. The correct answer is t=-2.43, p=0.0179.

(In[137]: tp.pvalue is nicer than tp[1] )

Discussed in #7

Code "from sklearn.datasets import load_boston" in 6-statistics.ipynb in Ordinary Least Squares Exercises Section is throwing Error in CoLab because load_boston has been removed from scikit-learn since version 1.2

File : https://github.com/jonkrohn/ML-foundations/blob/master/notebooks/6-statistics.ipynb

Section : Ordinary Least Squares Exercises

Code :
from sklearn.datasets import load_boston

Error :
ImportError Traceback (most recent call last)
in <cell line: 1>()
----> 1 from sklearn.datasets import load_boston

/usr/local/lib/python3.10/dist-packages/sklearn/datasets/init.py in getattr(name)
154 """
155 )
--> 156 raise ImportError(msg)
157 try:
158 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?"
https://medium.com/@docintangible/racist-data-destruction-113e3eff54a8

[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.
https://www.researchgate.net/publication/4974606_Hedonic_housing_prices_and_the_demand_for_clean_air

NOTE: If your import is failing due to a missing package, you can
manually install dependencies using either !pip or !apt.

To view examples of installing some common dependencies, click the
"Open Examples" button below.

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