This repository contains a comprehensive Exploratory Data Analysis (EDA) of a coffee shop dataset. The project translates raw sales data into actionable business recommendations regarding pricing, product trends, and seasonal growth.
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Top Performers: Latte and Americano with Milk are the primary revenue drivers.
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Action: A slight price increase for these "favorites" could significantly boost total revenue with minimal customer churn.
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Seasonal Trends: Significant "Summer Slump" and "Holiday Decline" (Dec/Jan) were identified.
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Action: Implement "Summer Refresh" specials and "Holiday Bundles" to stabilize revenue during these periods.
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Growth: March 2025 showed a positive Year-over-Year (YoY) growth compared to March 2024, even with incomplete data for the current month.
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Peak Hours: Sales peak in the morning and afternoon, with a significant drop in black coffee demand during evening hours.
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Sales Rhythms: Heatmaps showing peak hours and weekday patterns.
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Revenue Trends: Monthly growth and "Morning vs. Afternoon vs. Night" comparisons.
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Python (Pandas, NumPy)
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Data Visualization: Seaborn, Matplotlib
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coffee-sales-eda.ipynb: The main analysis notebook with detailed code and commentary.
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Coffee_Sales_Analysis.xlsx: Interactive Excel Dashboard including Pivot Tables and Charts.
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Coffee_sales.csv: The raw dataset used for both python and excel analysis.