Imputations

In data science, imputation is the process of replacing missing data with substituted values. This is done to improve the accuracy and completeness of the data set. Imputation can be done using various techniques such as mean imputation, mode imputation, regression imputation, and k-nearest neighbor imputation. Mean imputation involves replacing missing values with the mean of the available data. Mode imputation involves replacing missing values with the mode of the available data. Regression imputation involves using a regression model to predict the missing values. K-nearest neighbor imputation involves using the values of the k-nearest neighbors to predict the missing values. Imputation is an important step in data preprocessing as it can affect the results of data analysis and modeling.
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