Performed SQL-based analysis on a fictional pizza sales dataset to uncover customer trends, top-selling items, peak sales windows, and revenue metrics.
- Source: Provided by WsCube Tech
- Contains transaction details: orders, pizzas, order dates, quantities, prices
- JOIN operations
- GROUP BY and aggregation
- Date filtering and formatting
- Subqueries and nested logic
- Business insights using HAVING and ORDER BY
Basic: -Retrieve the total number of orders placed. -Calculate the total revenue generated from pizza sales. -Identify the highest-priced pizza. -Identify the most common pizza size ordered. -List the top 5 most ordered pizza types along with their quantities.
Intermediate: -Join the necessary tables to find the total quantity of each pizza category ordered. -Determine the distribution of orders by hour of the day. -Join relevant tables to find the category-wise distribution of pizzas. -Group the orders by date and calculate the average number of pizzas ordered per day. -Determine the top 3 most ordered pizza types based on revenue.
Advanced: -Calculate the percentage contribution of each pizza type to total revenue. -Analyze the cumulative revenue generated over time. -Determine the top 3 most ordered pizza types based on revenue for each pizza category.
- Most popular pizzas by revenue and quantity
- Peak ordering days and months
- Daily revenue breakdown with growth trends
- 'Sql_queries': Full query set upto 14 sql file
tables: csv tables like orders, order_details,pizzas,pizza_type