Skill

Data Imputation

Data Science, Analytics and AI/ML

Data imputation is the statistical technique of filling in missing or incomplete data values with substituted estimates, such as the mean, median, mode, or model-predicted values. Data scientists and statisticians use imputation to preserve dataset size and avoid the bias or errors that missing values can introduce into analysis or machine learning models. Common methods include mean/median substitution, k-nearest neighbors, regression imputation, and multiple imputation.

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• Statistical Analysis
• R
• Machine Learning
• Data Cleansing