Robust Missing Data Handling using Intelligent Machine Learning Imputation Technique for Heterogeneous Dataset
Keywords:
Heterogenous Datasets, Missing Data, Natural Language Processing, ImputationAbstract
In data analysis, the presence of missing values is a common challenge, especially in heterogeneous datasets that encompass a wide range of data types, including numerical, categorical, and unstructured data. Addressing missing data is crucial as it directly impacts the quality and reliability of subsequent analyses and modeling. This necessitates the development of robust imputation methods capable of handling diverse data types effectively. In light of the aforementioned requirement, this study presents a novel and pioneering methodology for forecasting and completing the imputed data variables throughout the dataset that contains multiple variables. The approach under consideration integrates Natural-Language-Processing (NLP) encoders, feature-extractors motivated by machine-learning, and sequential-regression imputation methods. To ascertain the practicality of the suggested approach, this study meticulously evaluates the way it performs using a well-established medical dataset of heart-disease sourced from the repository of UCI. The findings presented in this paper provide compelling evidence of the method's superiority over existing missing data imputation techniques, notably in terms of accuracy. This demonstration of practical viability and effectiveness addresses a significant concern in the field of data preprocessing and analysis, reaffirming the importance of robust imputation methods for enhancing the quality of data-driven decision-making processes.
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