Imputation Strategies for Enhancing Data Preprocessing in Machine Learning

Data preprocessing is considered a significant step in machine learning and data analysis. During this process, raw data are transformed into a normalized form so that they can be effectively used by machine learning algorithms to produce reliable and accurate results. The quality and completeness of input data show a vital role in effectiveness the performance of Machine Learning (ML) models. However, real-world datasets frequently suffer from missing values due to factors such as human error, equipment malfunction, or incomplete data collection processes. Such missing values can reduce prediction accuracy, introduce bias and decrease model accuracy. This study explores various imputation techniques for effectively handling missing values during data preprocessing. Traditional imputation methods, such as mean, median, and mode, are compared with more advanced techniques, including Multivariate Imputation by Chained Equations (MICE) and K-Nearest Neighbors (KNN). Their performance is evaluated using error metrics such as Mean Squared Error (MSE), Mean Absolute Error (MAE), and Root Mean Squared Error (RMSE). The experimental results represent that MICE achieved lowest error among the evaluated imputation methods, with MSE, MAE, and RMSE values of 0.082572, 0.421231, 0.649023 respectively.