Missing Values
In data science, missing values refer to the absence of a value in a variable's observation. Missing values can occur due to various reasons such as data entry errors, data corruption, or incomplete data. Handling missing values is an important step in data preprocessing as it can affect the accuracy and reliability of the analysis. There are several methods to handle missing values such as deletion, imputation, or prediction. Deletion involves removing the observations with missing values, which can lead to loss of information. Imputation involves replacing the missing values with estimated values based on statistical methods such as mean, median, or mode. Prediction involves using machine learning algorithms to predict the missing values based on the available data. The choice of method depends on the amount and pattern of missing values, the type of data, and the research question.
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