In this case, the data values are missing because the respondents failed to fill in the survey due to their level of depression. MNAR occurs when the missing values on a variable are related to the variable with the missing values itself. However, the missing data is not related to the level of depression itself. In this case, the missing data is related to the gender of the respondents. For example, the data values are missing because males are less likely to respond to a depression survey. MAR occurs when the probability of the missing data on a variable is related to some other measured variable but unrelated to the variable with missing values itself. For example, MCAR would occur when data is missing because the responses to a research survey about depression are lost in the mail. When our dataset is missing values completely at random, the probability of missing data is unrelated to any other variable and unrelated to the variable with missing values itself. MCAR occurs when the missing on the variable is completely unsystematic. What are Missing Values?Ī missing value can be defined as the data value that is not captured nor stored for a variable in the observation of interest. To demonstrate this method, we will use the famous Titanic dataset in this guide. In this article, we will talk about what missing values are, how to identify them, and how to replace them by using the K-Nearest Neighbors imputation method. Missing values exist in almost all datasets and it is essential to handle them properly in order to construct reliable machine learning models with optimal statistical power. How to handle missing data in your dataset with Scikit-Learn’s KNN Imputer
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