🍕 Pizza Sales Dashboard

📊 Project Overview

This Pizza Sales Dashboard is a comprehensive data analytics project designed to track, analyze, and visualize pizza sales data. The project leverages SQL for data extraction and transformation, while Microsoft Excel is used for creating dynamic dashboards.

🚀 Key Features

• KPIs: Total Revenue, Average Order Value, Total Orders, Total Pizzas Sold, Average Pizzas Per Order
• Trend Analysis: Daily and Hourly Sales Trends
• Sales Distribution: Percentage of Sales by Pizza Category and Size
• Performance Insights: Top 5 Best-Selling Pizzas & Bottom 5 Best-Selling Pizzas

🛠️ Tech Stack

• SQL (MySQL): For data manipulation, aggregation, and querying
• Excel: For data visualization, dashboards, and trend analysis

📊 Dashboard Preview

🎥 Project Walkthrough GIF

📈 SQL Queries Highlights

A. KPI’s

1. Total Revenue:

SELECT SUM(total_price) AS Total_Revenue FROM pizza_sales;

2. Average Order Value:

SELECT (SUM(total_price) / COUNT(DISTINCT order_id)) AS Avg_order_Value FROM pizza_sales;

3. Total Pizzas Sold:

SELECT SUM(quantity) AS Total_pizza_sold FROM pizza_sales;

4. Total Orders:

SELECT COUNT(DISTINCT order_id) AS Total_Orders FROM pizza_sales;

5. Average Pizzas Per Order:

SELECT CAST(CAST(SUM(quantity) AS DECIMAL(10,2)) / 
CAST(COUNT(DISTINCT order_id) AS DECIMAL(10,2)) AS DECIMAL(10,2)) AS Avg_Pizzas_per_order
FROM pizza_sales;

B. Trend Analysis

• Daily Trend for Total Orders:

SELECT DAYNAME(order_date) AS order_day, COUNT(DISTINCT order_id) AS total_orders
FROM pizzasales
WHERE order_date IS NOT NULL
GROUP BY DAYOFWEEK(order_date), DAYNAME(order_date)
ORDER BY DAYOFWEEK(order_date);

• Hourly Trend for Orders:

SELECT HOUR(order_time) AS order_hours, COUNT(DISTINCT order_id) AS total_orders
FROM pizzasales
GROUP BY HOUR(order_time);

C. Sales Distribution

• % of Sales by Pizza Category:

SELECT pizza_category, SUM(total_price) AS Total_Sales, 
SUM(total_price) * 100 / (SELECT SUM(total_price) FROM pizzasales WHERE MONTH(STR_TO_DATE(order_date, '%d-%m-%Y')) = 1) AS PCT
FROM pizzasales
WHERE MONTH(STR_TO_DATE(order_date, '%d-%m-%Y')) = 1
GROUP BY pizza_category;

• % of Sales by Pizza Size:

SELECT pizza_size, CAST(SUM(total_price) AS DECIMAL(10,2)) AS Total_Sales, 
CAST(SUM(total_price) * 100 / (SELECT SUM(total_price) FROM pizzasales WHERE QUARTER(STR_TO_DATE(order_date, '%d-%m-%Y')) = 1) AS DECIMAL(10,2)) AS PCT
FROM pizzasales
WHERE QUARTER(STR_TO_DATE(order_date, '%d-%m-%Y')) = 1
GROUP BY pizza_size
ORDER BY PCT DESC;

D. Sales Performance

• Top 5 Best Sellers:

SELECT pizza_name, SUM(quantity) AS Total_Pizzas_Sold
FROM pizzasales
GROUP BY pizza_name
ORDER BY Total_Pizzas_Sold DESC
LIMIT 5;

• Bottom 5 Best Sellers:

SELECT pizza_name, SUM(quantity) AS Total_Pizzas_Sold
FROM pizzasales
GROUP BY pizza_name
ORDER BY Total_Pizzas_Sold ASC
LIMIT 5;

📦 Folder Structure

├── SQL_Queries.docx          # Contains all SQL queries
├── Dashboard.xlsx            # Excel dashboard with visualizations
├── dashboard.png             # Dashboard image (add this image)
├── video.gif                 # Project walkthrough video
└── README.md                 # Project documentation

🔗 Check Out the Project

GitHub Repository

🔍 Keywords for Visibility

#DataAnalytics #SQL #Excel #MySQL #Dashboard #PizzaSales #BusinessInsights #DataVisualization #PortfolioProject #ExcelDashboard #SQLQueries #DataAnalysis

🤝 Connect with Me

If you found this project insightful, feel free to connect with me on LinkedIn to discuss more about data analytics and visualization projects.

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#DataAnalytics #SQL #Excel #Dashboard #PizzaSales #BusinessInsights