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what are different types of missing data | types of missing values | Machine Learning
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What are different types of missing Data . in this video we will try to understand what is missing data, types of missing values , different types of Missing Data and how to handle missing data machine learning. and we will try to understand those practically in jupyter notebook, and it will be extremely easy to understand.
First understand what is missing data:-
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In statistics, missing data, or missing values, occur when no data value is stored for the variable in an observation. Missing data are a common occurrence and can have a significant effect on the conclusions that can be drawn from the data.
Types of Missing Data:
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Missing at Random (MAR): Missing at random means that the propensity for a data point to be missing is not related to the missing data, but it is related to some of the observed data
Missing Completely at Random (MCAR): The fact that a certain value is missing has nothing to do with its hypothetical value and with the values of other variables.
Missing not at Random (MNAR): Two possible reasons are that the missing value depends on the hypothetical value (e.g. People with high salaries generally do not want to reveal their incomes in surveys)
Related Tags:
missing data in data mining
missing data rule of thumb
types of missing data
25 missing data
how to handle missing data machine learning
Thanks
First understand what is missing data:-
--------------------------------------------------------------------
In statistics, missing data, or missing values, occur when no data value is stored for the variable in an observation. Missing data are a common occurrence and can have a significant effect on the conclusions that can be drawn from the data.
Types of Missing Data:
--------------------------------------------
Missing at Random (MAR): Missing at random means that the propensity for a data point to be missing is not related to the missing data, but it is related to some of the observed data
Missing Completely at Random (MCAR): The fact that a certain value is missing has nothing to do with its hypothetical value and with the values of other variables.
Missing not at Random (MNAR): Two possible reasons are that the missing value depends on the hypothetical value (e.g. People with high salaries generally do not want to reveal their incomes in surveys)
Related Tags:
missing data in data mining
missing data rule of thumb
types of missing data
25 missing data
how to handle missing data machine learning
Thanks
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