Multidimensional Perspective to Data Preprocessing for Model Cognition Verity

Abstract

Reliable data analysis depends on effective data preparation, especially since AI-driven business intelligence depends on unbiased and error-free data for decision-making. However, developing a reliable dataset is a difficult task that requires expertise. Due to the costly damage a negligible error in data can cause to a system, a good understanding of the processes of quality data transformation is necessary. Data varies in properties, which determines how it is generated, the errors in it, and the transformations it needs to undergo before it is fed into a model. Also, most data used for analytics is sourced from public stores without means to verify its quality or what further steps need to be taken in preprocessing it for optimal performance. This chapter provides a detailed description of practical and scientific procedures to generate and develop quality data for different models and scenarios. Also, it highlights the tools and techniques to clean and prepare data for optimal performance and prevent unreliable data analytics outcomes.