1- Dashboard :

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2- YouTube Demo : Link

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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

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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?

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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.

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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.