🍕 Pizza Sales Analysis — Python Data Analytics Project
A complete data analysis project using Python, Pandas, Matplotlib, Seaborn & Plotly to extract business insights from real pizza sales data. This project analyzes revenue, orders, ingredients, pizza categories, seasonal trends, and identifies best/least-selling pizzas.
🚀 Project Overview
This project performs end-to-end data analysis on pizza sales to help businesses understand:
Total revenue, orders & pizzas sold
Daily, hourly, monthly sales trends
Best & worst performing pizzas
Sales contribution by category & size
Average Order Value & customer behavior
Ingredient demand analysis
All analysis is done using a Jupyter Notebook.
📂 Dataset Information
File: pizza_sales.csv
Includes:
Order details
Pizza categories & sizes
Quantities
Revenue
Ingredients
Timestamp data
📊 Key KPIs
Total Revenue
Total Pizzas Sold
Total Orders
Average Order Value (AOV)
Average Pizzas per Order
📈 Visualizations Included ✔ Daily Trends
Orders
Revenue
Quantity
✔ Hourly Trends
Orders by hour
Quantity by hour
✔ Monthly Trends
Orders trend line
✔ Category & Size Analysis
Pie chart: % revenue by category
Heatmap: revenue % by size & category
Bar chart: pizzas sold by category
✔ Top 5 & Bottom 5
By quantity
By total orders
By revenue
✔ Ingredient Frequency Analysis 🛠️ Technologies Used
Python
Pandas
NumPy
Matplotlib
Seaborn
Plotly
Jupyter Notebook
📁 Repository Structure
pizza-sales-analysis/
│
├── pizza_sales.ipynb
├── pizza_sales.py
├── pizza_sales.csv
├── README.md
└── Business Requirements Document.docx
Clone the repo:
git clone https://github.com/YOUR-USERNAME/pizza-sales-analysis.git
Install dependencies:
pip install pandas matplotlib seaborn plotly
Open notebook:
jupyter notebook pizza_sales.ipynb
📝 Insights Summary
Large-sized pizzas generate the highest revenue.
Classic category sells the most overall.
Evening hours show peak order volume.
Several pizzas consistently underperform and may require menu redesign.
Ingredient frequency points to popular toppings such as cheese and tomato.