Analyzing International Restaurant Orders Dataset
Restaurant Orders Analysis: Demand Patterns & Popular Items. Refining Strategies
@kaggle.agungpambudi_analyzing_restaurant_orders_international_dataset
Restaurant Orders Analysis: Demand Patterns & Popular Items. Refining Strategies
@kaggle.agungpambudi_analyzing_restaurant_orders_international_dataset
The dataset comprises a quarterly compilation of orders from a hypothetical restaurant specializing in diverse international cuisines. Each entry includes precise timestamps, dates, items requested, alongside specific details encompassing the category, nomenclature, and corresponding price of the ordered items.
| Field | Description | |
|---|---|---|
| order_details | order_details_id | Unique ID of an item in an order |
| order_details | order_id | ID of an order |
| order_details | order_date | Date an order was put in (MM/DD/YY) |
| order_details | order_time | Time an order was put in (HH:MM:SS AM/PM) |
| order_details | item_id | Matches the menu_item_id in the menu_items table |
| menu_items | menu_item_id | Unique ID of a menu item |
| menu_items | item_name | Name of a menu item |
| menu_items | category | Category or type of cuisine of the menu item |
| menu_items | price | Price of the menu item (US Dollars $) |
Reference :
Maven Analytics. (n.d.). Maven Analytics | Data analytics online training for Excel, Power BI, SQL, Tableau, Python and more. [online] Available at: https://mavenanalytics.io [Accessed 6 Dec. 2023].
CREATE TABLE comprehensive_restaurant_orders_popular_items_high_val_1f5a5da9 (
"order_details_id" BIGINT,
"order_id" BIGINT,
"order_date" TIMESTAMP,
"order_time" VARCHAR,
"item_id" BIGINT,
"item_name" VARCHAR,
"category" VARCHAR,
"price" DOUBLE
);CREATE TABLE metadata (
"table" VARCHAR,
"field" VARCHAR,
"description" VARCHAR
);Anyone who has the link will be able to view this.