Handling missing values in Python Explained with example Fillna dropna sklearn KNN Model Imputation

Handling missing values in Python Explained with example. Replace Missing Data in python. Fillna , dropna , sklearn impute , KNN , Model Imputation
Missing Value Analysis. Visualization of Missing values. Missing value EDA. missingno:: • Missing Value Analysis...
Github link for Working missing data file: github.com/par...
#MissingValueHandling #MissingValueImptation #MissingValueInPython
#Fillna #Dropna #SklearnImpute #KNN

[@ajaykushwaha-je6mw]

After krish naik, yours is second best video on imputing missing value. Kindly create couple of more video on Feature engineering and Feature Selection.

[@sriraj8392]

use full insights ...good

[@mihirthakkar6902]

Thanks @Paramita. Nicely Explained. Keep up the good work

[@nikpallsingh7679]

Beautiful work here, so so helpful for me. Thank you so much!! Appreciate it

[@out_aloud]

I have a disputed question. As the knn imputer works on the principles same as knn algo, it does share the pros and cons of knn algo, right. So wont it be better to simply scale the data first ? Also, in case I am separating out the train and test data in order to avoid data leakage, should I split the data and then scale, impute ? Or should I impute and then split,scale it ? In case I split first...which is the most common preference which stats should I use for the user input. And lastly how should I handle the label encoded columns if any ? Nobody is discussing on this when it is one of the most imp problems a person would likely face. Can you please make a video on this ?

[@noahrubin375]

This was really insightful. You got a new subscriber

[@paramita2674]

Thank you..

[@ahpratama]

nice guide, easy to understand. thank you

[@rishisingh6111]

Thanks a ton for sharing this! A minor doubt: If two variables are linearly correlated then would it not be better to omit variable/feature with higher missing values? Other feature is not adding anything to the model and would add to number of features in the model (overfitting.)

[@pouriaforouzesh5349]

👍

[@sujaysahu4829]

Thanks.

[@bobbygajbhiye3139]

Thanks

[@ajaykushwaha-je6mw]

Kindly creating missing value imputation on categorical features.

[@pankajkhatri630]

nice

[@debjyotiroy842]

can you plz share the code file?

[@ajitkumar-ne6df]

miss ap hindi me nhi bna sakhti ho video