Tools: Python, Pandas, Matplotlib, Seaborn
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Performed comprehensive EDA on a retail transaction dataset with over 11,000 records to uncover sales trends, customer behavior, and key product insights.
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Cleaned and preprocessed data: handled missing values, removed irrelevant columns, and standardized categorical values.
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Conducted univariate and multivariate analysis to explore:
- Gender and age group trends in purchasing behavior.
- Sales distribution across states and zones.
- Product category-wise revenue contribution.
- Correlation between age, order count, and purchase amount.
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Built visualizations including histograms, bar charts, count plots, and heatmaps for data-driven storytelling.
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Identified high-revenue states and top-performing product categories to support marketing strategy decisions.