https://github.com/sarathchandranpm/restaurant_order_analysis

This Project aims to analyze the order details data of a restaurant.
https://github.com/sarathchandranpm/restaurant_order_analysis

Last synced: about 2 months ago
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This Project aims to analyze the order details data of a restaurant.

• Host: GitHub
• URL: https://github.com/sarathchandranpm/restaurant_order_analysis
• Owner: SarathchandranPM
• Created: 2024-11-30T17:18:16.000Z (3 months ago)
• Default Branch: main
• Last Pushed: 2024-11-30T17:19:29.000Z (3 months ago)
• Last Synced: 2024-11-30T18:27:36.978Z (3 months ago)
• Size: 2.93 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
## Restaurant Order Analysis ##

### 1. analyse_customer_behaviour.sql ###

   

This file focuses on analyzing customer ordering patterns and menu item popularity:

1) Joins order details with menu items to create a comprehensive view of orders

2) Identifies the most frequently ordered items

3) Provides a breakdown of order counts by item and category

4) Determines the single most and least ordered items in the restaurant

#### Key Queries: ####

* Item order frequency ranking

* Identification of top and bottom-selling menu items

### 2. exploring_items_table.sql ###

This file provides an in-depth exploration of the restaurant's menu items:

1) Counts the total number of items in the menu

2) Identifies the most and least expensive items

3) Generates category-wise item statistics (count, price range, average price)

4) Ranks menu items by price within each category

#### Key Queries: ####

* Menu item pricing analysis

* Category-level menu item insights

* Price-based item ranking

### 3. exploring_order_table.sql ###

This file analyzes the order details and ordering patterns:

1) Determines the total number of items ordered

2) Identifies the date range of orders

3) Counts the total number of unique orders

4) Analyzes daily order volumes

5) Explores order sizes (number of items per order)

6) Identifies large orders (orders with 10 or more items)

#### Key Queries: ####

* Order volume tracking

* Order size distribution

* Identification of large orders

### Project Insights ###

The analysis provides valuable insights into:

- Menu item performance

- Pricing strategies

- Customer ordering behavior

- Order characteristics

- Popular and less popular menu categories and items

This project demonstrates a comprehensive approach to restaurant data analysis using SQL, offering a multi-dimensional view of the restaurant's operations and customer preferences.