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1- Dashboard :

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2- YouTube Demo : Link

3- Data Description :

  • pizza_id : Unique key identifier that ties the pizza ordered to its details, like size and price
  • order_id : Unique identifier for each order placed by a table
  • quantity : Quantity ordered for each pizza of the same type and size
  • order_date : Date the order was placed (entered into the system prior to cooking & serving)
  • order_time : Time the order was placed (entered into the system prior to cooking & serving)
  • unit_price : Price of the pizza in USD
  • total_price : unit_price * quantity
  • pizza_size : Size of the pizza (Small, Medium, Large, X Large, or XX Large)
  • pizza_category : Category of the pizza (Classic, Supreme, Veggie, or Chicken)
  • pizza_ingredients : ingredients used in the pizza as shown in the menu (they all include Mozzarella Cheese, even if not specified; and they all include Tomato Sauce, unless another sauce is specified)
  • pizza_name : Name of the pizza as shown in the menu

4- In Mind Questions :

  • General Questions related to the existence of :

    • missing values?
    • wrong datatypes for columns?
    • complete duplicates in the data?
    • outliers in numerical columns?

  • Business Questions :

    • What is Total Revenue?
    • What is Average Order Value?
    • What is Total Pizzas Sold?
    • What is Total Orders?
    • Which Daily Trend for Total Orders?
    • There is Monthly Trend for Orders?
    • There is Sales by Category more than other Category?
    • Which Sales by Pizza Size?
    • What is Total Quantity by Pizza Category?
    • What is Top 5 Pizzas by Revenue?
    • What is Top 5 Pizzas by Quantity?
    • What is Hour has greater total orders?
    • What is name of pizza that has lowest price?
    • What is name of pizza that has higher price?

5- Conclusion :

  • Breakfast Orders, Limited orders during early hours suggest breakfast is less popular for pizza consumption, with customers favoring alternative morning food choices.
  • Lunch Rush, The 12 PM - 2 PM timeframe sees the highest customer influx due to lunch breaks, highlighting the shop's convenience for quick meals.
  • Sunday are the least busy orders.
  • Seasonal Sales, Spring sales peak, while autumn sees lower numbers, influenced by factors like weather and cultural preferences.
  • Large Size Preference, Customers opt for large pizzas, often ordering in groups for sharing among family, friends, or colleagues.

6- Summary :

In the beginning, I created the database on a SQL server called Pizza Sales, create table named Pizza Sales and add dataset with format CSV to it .

Additionally, I created two files :

  • Pizza Sales.sql, SQL Query related to questions to Ensure Data Integrity.

  • Cleaning.sql , Sql Query to Cleaning and Preprocessing such as add new columns and Amendments.

In the end, i create dynamic dashboard conected directly with SQL server using Direct mode Power Bi to answer business questions.