Exploratory Data Analysis (EDA)
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During the data analysis workflow, Exploratory Data Analysis (EDA) constitutes a crucial preliminary step. EDA refers to the initial investigation of collected datasets to uncover their inherent characteristics, parameters, and potential anomalies. The EDA process typically involves data visualization techniques for improved data comprehension, statistical computations to measure central tendencies and distributions, and preliminary model applications for data exploration. By implementing EDA through programming approaches—such as using Python's Pandas for statistical summaries, Matplotlib/Seaborn for visualization plots, and Scikit-learn for preliminary modeling—analysts gain deeper data insights. This foundational understanding enables more informed selection of subsequent analytical methods, ultimately leading to more accurate and reliable results.
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