All about Missing Values and their Types
Missing Data can occur when no information is provided for one or more items or for a whole unit. Missing Data is a very big problem in real-life scenarios. Missing Data can also refer to as NA(Not Available) values in pandas. In DataFrame sometimes many datasets simply arrive with missing data, either because it exists and was not collected or it never existed. For Example, Suppose different users being surveyed may choose not to share their income, some users may choose not to share the address in this way many datasets went missing.
Types of Missing Values
Missing Completely at Random (MCAR):- As the name suggests missing completely at random means that there’s no relationship between whether a data point is missing and any values in the data set, missing or observed. The missing data is just a random subset of the data.
Missing Data Not At Random(MNAR):- Missing not at random means that there is a distinct relationship between the propensity of a value to be missing and its values. So in our depression survey, respondents with higher depression values fail to fill the survey because of their level of depression
Missing At Random(MAR):- Missing at random means that the propensity of missing values has a systematic relationship with the observed data but not the missing data. This means that the observation which is missing has nothing to do with the missing values but it has a correlation with the observed variables. For example, if you are taking a survey of mental disorders in men and women, it might be less likely than men will report their depression or vice-versa but it has nothing to do with their level of depression.
Missing at random means that the tendency for a data point to be missing is not related to the missing data itself, but is related to some of the observed data in the dataset.
The takeaway here for MAR is that the values of the missing data can somehow be predicted from some of the other variables in the dataset.
After reading this article you can follow up on this.
How to handle Missing Values in your DataSet.
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