Missingno Python Library | Visualising Missing Values in Data Prior to Machine Learning
Missing data is probably one of the most common issues when working with real datasets. Data can be missing for a multitude of reasons, including sensor failure, data vintage, improper data management, and even human error. Missing data can occur as single values, multiple values within one feature, or entire features may be missing.
It is important that missing data is identified and handled appropriately prior to further data analysis or machine learning. Many machine learning algorithms can’t handle missing data and require entire rows, where a single missing value is present, to be deleted or replaced (imputed) with a new value.
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The notebook for this video can be found on my GitHub repository at: https://github.com/andymcdgeo/Andys_Y...
There is a written version of this video available at: https://towardsdatascience.com/using-...
Libraries used in this video:
pandas: https://pandas.pydata.org
missingno: https://github.com/ResidentMario/miss...
Data Used in this video:
Bormann, Peter, Aursand, Peder, Dilib, Fahad, Manral, Surrender, & Dischington, Peter. (2020). FORCE 2020 Well well log and lithofacies dataset for machine learning competition [Data set]. Zenodo. http://doi.org/10.5281/zenodo.4351156
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