Learn to handle missing data in Python using the Titanic dataset and Pandas! Discover effective imputation and cleaning techniques. Ideal for data scientists and analysts. #Python #DataScience #Pandas #MissingData #TitanicDataset #DataImputation
https://teguhteja.id/handling-missing-data-in-the-titanic-dataset/
Learn to handle missing data in Python using Pandas with the Titanic dataset! Discover effective data cleaning and imputation techniques. Perfect for data scientists and analysts. #Python #DataScience #Pandas #MissingData #DataCleaning #TitanicDataset
Discover how to handle missing data in Python using the Titanic dataset! Learn essential data wrangling techniques with Pandas, including imputation and deletion. Perfect for data scientists and analysts. #Python #DataScience #Pandas #MissingData #Titanic
https://teguhteja.id/how-to-handle-missing-data-in-python-using-pandas-and-titanic-dataset/
#statstab #105 The Importance of Missing Data
Thoughts: "The best solution to handle missing data is to have none" R.A. Fisher. A nice quick overview of various types and some solutions.
And now we return from our irregularly scheduled scholarly rabbit-hole to actually put words on paper instead of chasing casually dropped details mentioned by a previous scholarly source only in passing without footnotes or citations of any sort whatsoever
I need to fake some #data. It's for a good cause, I swear! My students (intro #statistics & #researchmethods have "class demonstration" surveys posted but few participants. I'd like them to have a better analysis experience.
My preferred platform is #Rstats
can #mice do a forced single-imputation with, say, 80% #MissingData?
Should I just #bootstrap (or something similar)? If so, any suggestions for which package/procedure?
A couple dozen variables, NOIR.
New post! naniar version 1.1.0 "Prince Caspian"
https://www.njtierney.com/post/2024/03/04/naniar-version-1-1-0-prince-caspian/
Another #R code bit:
"Profile-likelihood based confidence intervals in Sample Selection models"