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Good afternoon. I need to do several functions in Python:

#If number of nulls is acceptable (5-40%) --> suggest inferences of missings (MICE? Probabilistic PCA?)

#If number of nulls is very low (0.1-5%) --> suggest drop rows?

#If number of nulls is very high (> 40%) --> drop the column)

#If number of values == number of rows --> suggest drop feature (ID-like)

#If they have stdev == 0 --> suggest drop feature (constant)

#If ratio of number of values is near to number of rows --> add warning

#If number of values is very low --> propose change it to categorical

#If feature is truly numeric, then check skewness and save the results

#Future suggestion of log / Box-Cox / Yeo-Johnson tranformations to make it closer to a normal distribution

#If implemented with sklearn, save the transform to be able to de-normalize for model interpretability

#Add missing flag whenever a missing imputation of any form is applied

#Entropy-based discretization for numeric features

#Variance Inflation Factor (VIF) for detecting multicollinearity (also for categorical data)

Evner: Machine Learning (ML), Python, Software Arkitektur

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Projekt ID: #19267381

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