Impact Of Data Visualization In Data Analysis To Improve The Efficiency Of Machine Learning Models

Abstract

An essential component of machine learning is data visualization, which helps analysts comprehend and interpret patterns, connections, and trends in data. Data visualization is a crucial aspect of machine learning that enables analysts to understand and make sense of data patterns, relationships, and trends. Through data visualization, insights and patterns in data can be easily interpreted. This research paper explores the significant impact of data visualization on the efficiency of machine learning (ML) models during the data analysis phase. Data visualization serves as a powerful tool for data scientists and ML practitioners by offering intuitive insights into complex datasets, facilitating a deeper understanding of the underlying data characteristics, and guiding the decision-making process in model development. The visual techniques enhance various aspects of the data analysis phase, including exploratory data analysis (EDA), feature selection and engineering, anomaly detection, and assumption validation, ultimately leading to the development of more accurate and efficient machine learning models.