Restaurant Order Analysis

Project Description: Restaurant Data Analysis

Objective:

The aim of this project is to analyze restaurant data to gain insights into menu pricing, order trends, and customer behavior. This analysis is structured into three main objectives:

1. Explore the Items Table:
   • Goals:
     • Determine the number of rows in the items table.
     • Identify the least and most expensive items.
     • Categorize item prices within each category.
   • Approach:
     • Utilize SQL queries to count rows, find minimum and maximum prices, and group items by category to assess price distribution.
2. Explore the Orders Table:
   • Goals:
     • Find the date range of orders.
     • Calculate the number of items within each order.
     • Identify orders with the highest number of items.
   • Approach:
     • Use SQL queries to determine the range of dates, count items per order, and rank orders by item count to highlight the largest orders.
3. Analyze Customer Behavior:
   • Goals:
     • Combine the items and orders tables to find the least and most ordered categories.
     • Investigate the details of the highest spend orders.
   • Approach:
     • Join the items and orders tables to perform category-wise analysis of order frequency and identify orders with the highest total spend.

Methodology:

1. Data Exploration:
   • Use SQL to explore and understand the structure and contents of the items and orders tables.
   • Perform descriptive statistics to summarize key information about prices and order patterns.
2. Data Analysis:
   • Conduct detailed analysis by writing SQL queries to answer specific questions related to each objective.
   • Aggregate and visualize data where applicable to uncover trends and patterns.
3. Customer Insights:
   • Combine insights from both tables to understand customer preferences and spending behavior.
   • Identify key categories driving sales and analyze high-value orders for deeper insights.

Expected Outcomes:

• Comprehensive understanding of item pricing and categorization.
• Detailed insights into order patterns and trends.
• Identification of key customer behavior metrics, such as most and least ordered categories and high-value orders.
• Actionable insights for optimizing menu offerings and improving customer satisfaction.