The dataset that I have chosen to analyze contains detailed records of transactions from a fictional coffee shop chain, capturing information about products sold, quantities, prices, and store locations. With over 149,000 unique records, the dataset provides a comprehensive view of consumer purchasing patterns, allowing for analysis of sales trends over time, popular product categories, and performance across different stores. This dataset is ideal for exploring revenue generation, identifying peak sales periods, and deriving actionable insights to support business decisions and operational improvements.
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transaction_id: Unique sequential ID representing an individual transaction
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transaction_date: Date of the transaction (MM/DD/YY)
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transaction_time: Timestamp of the transaction (HH:MM:SS)
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transaction_qty: Quantity of items sold
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store_id: Unique ID of the coffee shop where the transaction took place
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store_location: Location of the coffee shop where the transaction took place
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product_id: Unique ID of the product sold
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unit_price: Retail price of the product sold
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product_category: Description of the product category
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product_type: Description of the product type
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product_detail: Description of the product detail
Before analysis, the dataset was prepared in Excel to ensure accuracy and usability. The data was first inspected for missing or inconsistent values, and all columns were formatted appropriately with dates as date values, times as time values, and numeric fields as numbers. A new revenue column was created by multiplying transaction_qty by unit_price to calculate total sales per transaction. Additional columns, such as day of week and hour of day, were derived from the transaction date and time to facilitate trend analysis. These preparation steps ensured the dataset was clean, structured, and ready for analysis.