Exploratory Data Analysis in Data Science: Techniques, Challenges, and Future Directions

Exploratory Data Analysis (EDA) is a vital aspect of the machine learning process. EDA allows machine learning practitioners to make sense of data before actually applying machine learning models. Exploratory data analysis is a way to summarize data, find patterns, identify unusual data points, and verify hypotheses. EDA fills the gap between data and knowledge, resulting in better feature selection, improved machine learning model performance, and error avoidance. This paper discusses the importance of EDA, its history, methods, tools, and applications.