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https://github.com/mainak-97/pizza-sales-analysis-project

Pizza Sales Analysis Project: This project optimizes a pizza restaurant's operations by analyzing demand patterns, revenue, and efficiency, providing insights to enhance profitability, streamline production, and improve customer satisfaction.
https://github.com/mainak-97/pizza-sales-analysis-project

business-analytics business-intelligence dashboards data-analysis operations-optimization peak-hours power-bi restaurant-analysis revenue-analysis

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Pizza Sales Analysis Project: This project optimizes a pizza restaurant's operations by analyzing demand patterns, revenue, and efficiency, providing insights to enhance profitability, streamline production, and improve customer satisfaction.

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README 

        

# **🍕 Pizza Sales Analysis Project**



![Logo](https://i.imgur.com/svUYZ8k.jpeg)

## Project Overview 📃

This project focuses on using data analytics to enhance the operational efficiency, customer satisfaction, and revenue performance of a pizza restaurant. By analyzing customer transaction data, the project aims to reveal insights into customer demand, revenue trends, and operational bottlenecks, enabling the restaurant to make data-driven decisions for optimizing business outcomes.

## Business Objectives 🎯

The primary objective is to leverage historical transaction data to:



- Identify peak business periods to optimize staffing and inventory levels.

- Analyze customer preferences for different pizza types and sizes to improve menu offerings.

- Evaluate revenue trends across daily, weekly, and monthly periods to boost average order values.

- Assess operational efficiencies in pizza production to minimize delays and enhance service quality.

## Dataset Source 📀

The dataset (Excel file) includes:



- Order-Level Data: Details each transaction with unique identifiers, date, time, and total price.

- Pizza-Level Data: Provides specific pizza details, including size, type, ingredients, quantity, and unit price.

## Key Business Questions 🔎

The analysis addresses the following questions:



1. Peak Period Identification:

- What are the busiest days and times?

- Are there seasonal or holiday-specific demand trends?

- Pizza Demand Analysis:



2. Which pizza types and sizes are most popular?

- Are there seasonal trends in pizza preferences?



3. Revenue Performance:

- What is the average daily, weekly, and monthly revenue?

- How does revenue fluctuate over time, and what factors influence these changes?



4. Operational Efficiency:

- How many pizzas are produced during peak times?

- Are there inefficiencies or bottlenecks in production?

- Can staffing levels be optimized?

## Methodology 🔅

- Data Cleaning and Preprocessing: Addressing data quality issues and preparing data for analysis.

- Exploratory Data Analysis (EDA): Identifying trends, seasonal patterns, and key metrics.

- Visualization: Using Power BI to create interactive dashboards for in-depth insights.

- Recommendations: Developing actionable insights for peak period optimization, menu engineering, revenue enhancement, and operational efficiency.

## Tools and Technologies 🛠

- Excel for exploratory data analysis.

- Power BI for data visualization and dashboard creation.

## Project Outcomes 💡

The analysis aims to deliver the following:

- Peak Period Optimization: Insights on peak hours to optimize staffing and inventory.

- Menu Engineering: Identification of popular pizzas to adjust menu offerings.

- Revenue Enhancement: Strategies for increasing order value during slow periods.

- Operational Efficiency: Recommendations to improve production and reduce wait times.

## Dashboard 💻

The interactive Power BI dashboard visualizes data insights, enabling stakeholders to monitor business performance and make data-driven decisions. 

Key sections include:

- Peak Hours & Days Analysis

- Pizza Popularity by Type and Size

- Revenue Trends

- Production Efficiency Metrics

## Dashboard Screenshot ✨

![1st](https://i.imgur.com/c36Va6C.jpeg)

![2nd](https://i.imgur.com/cmAkCi6.jpeg)

## Getting Started 📍

- Prerequisites: Install Power BI Desktop.

- Dataset: Load the restaurant’s data into Power BI.

- Running the Analysis:

    - Load data into Power BI.

    - Review and customize visualizations based on specific business needs.

- Exploring the Dashboard: Use Power BI to interact with the dashboard and generate custom reports.



## Author 🎓



- **Mainak Mukherjee**



- **Email:** [email protected]



- **Linkedin:** www.linkedin.com/in/mainakmukherjee08