<![CDATA[The growth of e-commerce generates large and complex transaction data volumes requiring in-depth analysis for business decision-making support. This research applies Exploratory Data Analysis (EDA) approach to the Online Retail UCI dataset (541,909 transactions, 2010-2011) using Python on Google Colab for data cleaning, yielding 392,692 valid records. Analysis focuses on time trends of transactions and revenue, top 7 products by quantity and revenue, and sales distribution across regions through interactive Looker Studio dashboard. Findings reveal seasonal transaction patterns, differences between high-volume vs high-revenue products, and United Kingdom dominance (80% revenue). The interactive dashboard with time and country filters enables flexible data exploration for promotion strategy, inventory management, and market expansion decisions. This research proves data visualization effectiveness as a retail online business decision support system.]]>
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