Pizza Place Sales Dataset EDA

This repository contains code and analysis for exploring a fictitious pizza place's sales dataset, provided by Maven Analytics. The dataset includes information such as the date and time of each order, details on the pizzas served (type, size, quantity, price, ingredients), and more.

Dataset Information

The dataset contains a year's worth of sales data from the pizza place. Key attributes include:

• Date and time of each order
• Type, size, quantity, price, and ingredients of pizzas sold

Analysis Goals

The recommended analysis focuses on several key questions:

1. Customer Traffic and Peak Hours:

   • How many customers do we have each day?
   • Are there any peak hours or times when sales are particularly high?

2. Order Composition and Bestsellers:

   • How many pizzas are typically in an order?
   • Do we have any bestsellers in terms of pizza types, sizes, or combinations?

3. Revenue and Seasonality:

   • How much revenue did we generate this year?
   • Can we identify any seasonal trends or patterns in the sales data?

4. Menu Optimization and Promotions:

   • Are there any pizzas that should be removed from the menu due to low sales?
   • Are there any promotions or marketing strategies we could leverage based on the analysis?

Repository Structure

The repository is structured as follows:

• data_dictionary.csv: Excel file containing the data dictionary with descriptions of each column in the dataset.
• data_import.ipynb: Jupyter Notebook for data importing, merging, and cleaning. This notebook prepares the data for analysis.
• Pizza Sales Report.twbx: Tableau Workbook for exploratory data analysis (EDA) of the pizza sales dataset. The workbook contains visualizations and insights derived from the data.
• pizza_sales: Folder containing the raw data files.
• pizza_salesPandas.ipynb: Jupyter Notebook for EDA using Pandas. This notebook performs in-depth analysis using Python's Pandas library, exploring trends, patterns, and insights in the data.
• pizza_sales_data.csv: Cleaned and processed data file ready for analysis.

Usage

1. Clone or download the repository to your local machine.
2. Open and run data_import.ipynb to import, merge, and clean the data.
3. Open Pizza Sales Report.twbx using Tableau to explore visualizations and insights.
4. Explore the pizza_salesPandas.ipynb notebook for detailed analysis using Pandas.

Additional Notes

• Ensure you have Tableau installed to view and interact with the Tableau workbook (Pizza Sales Report.twbx).
• The data dictionary (data_dictionary.csv) provides descriptions of each column in the dataset, aiding in understanding the data's structure and attributes.

Enjoy exploring the pizza place sales dataset and uncovering valuable insights!