Data Imputation
Data imputation is the process of filling in missing or incomplete data with estimated values. This technique is commonly used in data analysis and machine learning when dealing with datasets that have missing values. The goal of data imputation is to create a complete dataset that can be used for further analysis or modeling. There are various methods for data imputation, including mean imputation, regression imputation, and k-nearest neighbor imputation. Mean imputation involves replacing missing values with the mean of the available data. Regression imputation involves using a regression model to estimate missing values based on other variables in the dataset. K-nearest neighbor imputation involves finding the k-nearest data points to the missing value and using their values to estimate the missing value.
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