Experimental Analysis of a Multimodal biometric System using Preprocessing and Feature Extraction Techniques and Their Impact on Analytical Results

Data preprocessing describes a collection of methods for improving the quality of the raw data, such as missing value imputation and outlier elimination. We delve into the significance of pre-processing in improving data quality, addressing issues related to noise, missing values, outliers, and other anomalies. The paper also discusses the impact of pre-processing on different types of data, including numerical, categorical, and textual data. This research paper presents a detailed case study where we applied various preprocessing techniques to a real-world dataset. The study investigates the effects of these techniques on data quality and the subsequent impact on analytical outcomes. By showcasing our own pre-processing results, the research aims to provide practical insights and recommendations for researchers and practitioners engaged in data analysis.