🥅 𝗣𝗿𝗼𝗷𝗲𝗰𝘁 𝗢𝗯𝗷𝗲𝗰𝘁𝗶𝘃𝗲: -
1️⃣ Analyzing a comprehensive pizza sales dataset that Includes 4 csv files providing information about the Pizza.
2️⃣ Executing SQL queries to answer critical business questions, focusing on uncovering patterns in orders, understanding customer preferences, evaluating revenue trends, and assessing product performance.
3️⃣ Converting unprocessed data into actionable insights by answering 13 Business Questions.
➡ 𝗜𝗺𝗽𝗼𝗿𝘁𝗶𝗻𝗴 𝗰𝘀𝘃 𝗙𝗶𝗹𝗲𝘀: -
Import Dataset csv in Workbench using import wizard. order and order_details are too big and data type are not correct So, created table first and then added data in these 2 tables.
➡ Dataset Information:-
The dataset includes four tables:
Orders 2. Order_Detail 3. Pizzas 4. Pizza_Types
➡ 𝗞𝗲𝘆 𝗜𝗻𝘀𝗶𝗴𝗵𝘁𝘀: -
▶ Large pizzas were the most ordered size around 18,526 units, indicating a preference for larger portions among customers.
▶ The classic Deluxe Pizza is the most ordered (Quantity sold) pizza type followed by others.
▶ Classic Category has highest Total quantity of pizza ordered in comparison to Supreme, Veggie, and Chicken.
▶ The busiest hours for orders were in the evening (5–7 PM) and afternoon (12–1 PM), reflecting mealtime peaks for the Billu’s Pizza Hub.
▶ In terms of revenue, the Chicken category dominated, with its top three pizza types contributing significantly. Contribute around 23.96% in total revenue.
▶ Total ordered placed are around 21,350 and total revenue generated is around $8,17,860.
▶ Average pizza ordered per day is 138.
This project showcases my ability to use SQL to solve real-world business problems and extract meaningful insights from raw Data.
➡ Business Impact:-
The analysis underscores the significance of data driven decision-making in understanding sales patterns and optimizing inventory management, particularly in the dynamic food industry.
➡ 𝗖𝗵𝗲𝗰𝗸 𝗼𝘂𝘁 𝘁𝗵𝗲 𝗚𝗶𝘁𝗛𝘂𝗯 𝗟𝗶𝗻𝗸 𝗵𝗲𝗿𝗲: https://github.com/Kanuboss12/SQL-Pizza-Sales-Analysis-
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