I have two main objectives for the Jupyter notebook in this folder:
1_ Practice maching learning, Python visualizations and Python coding.
2_ Use data science, specifically Machine Learning (ML) with Python's skikit-learn package, to ascertain whether a small semi-opaque glass container found on the island of Nikumaroro in 2010, and possibly belonging to Amelia Earhart, and a jar of similar vintage in the same size and shape (but not color), found on eBay, can be identified as a container by an ML model fitted to a 1987 glass database.
The notebook, ML_20th_Century.ipynb, can be viewed in NBViewer here: https://nbviewer.org/github/joecerniglia/Glass_ML2/blob/main/ML_20th_Century.ipynb
I support the sentiments expressed by The Turing Way: https://the-turing-way.netlify.app/welcome.html#
Many links to related material and source citations can be found in the notebook itself.
Acknowledgements
I owe what I have learned to date in machine learning to Dr. Jason Brownlee:
https://machinelearningmastery.com/machine-learning-with-python/
My ambition is to add to my knowledge in April 2022 when I take the Machine Learning course at eCornell as part of the Python 360 certificate program, in which I am now enrolled: https://ecornell.cornell.edu/certificates/data-science/python-for-data-science/.
Update, December 2022:
The paper has now been published by the Turing Data Institute here:
https://alan-turing-institute.github.io/TuringDataStories-fastpages/amelia%20earhart/fred%20noonan/aviation%20mystery/classification/machine%20learning%20in%20social%20sciences/smote/covariate%20drift/random%20forest/2022/10/14/Glass-ML-20th-Century.html