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Hands-on Handling missing value using Mean Median mode with Python | Data Cleaning Tutorial 8
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During the Machine Learning Data Cleaning process, you will often need to figure out whether you have missing values in the data set, and if so, how to deal with it. In this video, I have demonstrated to handling the missing value using statistical way mean, median and mode. In this video I only cover the hands-on explanation using python :-
1. We impute the missing data for a quantitative attribute by the mean or median and for qualitative attribute by mode.
2. Generalized Imputation: In this case, we calculate the mean or median for all non missing values of that variable then replace missing value with mean or median.
3. Similar case Imputation: In this case, we calculate mean individually of non missing values then replace the missing value based on other variable.
1. We impute the missing data for a quantitative attribute by the mean or median and for qualitative attribute by mode.
2. Generalized Imputation: In this case, we calculate the mean or median for all non missing values of that variable then replace missing value with mean or median.
3. Similar case Imputation: In this case, we calculate mean individually of non missing values then replace the missing value based on other variable.
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