Data Pre-processing Solution Using Statistical and Data Mining Techniques

The research explores comprehensive strategies for handling missing data, encompassing statistical imputation methods and the K-Nearest Neighbors (KNN) algorithm. Various statistical techniques, such as mean, median, mode imputation, and machine learning-based approaches, are evaluated, emphasizing their impact on data quality. Additionally, the study delves into outlier detection using K-means clustering, contributing insights into the identification and handling of anomalies. The proposed utility integrates user-friendly interfaces, facilitating effective data preprocessing and analysis. The KNN algorithm, with Euclidean and NaN Euclidean distance metrics, is employed for missing value imputation, enhancing dataset completeness. The investigation concludes with considerations on the significance of Root Mean Squared Error in evaluating model performance. Overall, the research addresses critical aspects of data preprocessing, offering practical insights for researchers and practitioners.