Exploratory Data Analysis Techniques - A Comprehensive Review: Reviewing various exploratory data analysis techniques and their applications in uncovering insights from raw data
Keywords:
Exploratory Data Analysis, EDA TechniquesAbstract
Exploratory Data Analysis (EDA) plays a crucial role in understanding the underlying patterns, trends, and relationships within datasets. This paper provides a comprehensive review of various EDA techniques and their applications across different domains. We begin by defining EDA and its significance in data analysis. Next, we discuss the key principles of EDA, including data visualization, summary statistics, and data preprocessing. We then delve into specific EDA techniques such as univariate analysis, bivariate analysis, and multivariate analysis, highlighting their methodologies and applications. Additionally, we explore advanced EDA techniques such as clustering, outlier detection, and dimensionality reduction, emphasizing their role in extracting meaningful insights from complex datasets. Furthermore, we discuss the challenges and future directions of EDA, including the integration of machine learning and AI technologies. Overall, this paper serves as a comprehensive guide to EDA techniques, providing researchers and practitioners with valuable insights into analyzing and interpreting data effectively.
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