Contents

This pizza sales dataset make up 12 relevant features: - order_id: Unique identifier for each order placed by a table - order_details_id: Unique identifier for each pizza placed within each order (pizzas of the same type and size are kept in the same row, and the quantity increases) - pizza_id: Unique key identifier that ties the pizza ordered to its details, like size and price - quantity: Quantity ordered for each pizza of the same type and size - order_date: Date the order was placed (entered into the system prior to cooking & serving) - order_time: Time the order was placed (entered into the system prior to cooking & serving) - unit_price: Price of the pizza in USD - total_price: unit_price * quantity - pizza_size: Size of the pizza (Small, Medium, Large, X Large, or XX Large) - pizza_type: Unique key identifier that ties the pizza ordered to its details, like size and price - pizza_ingredients: ingredients used in the pizza as shown in the menu (they all include Mozzarella Cheese, even if not specified; and they all include Tomato Sauce, unless another sauce is specified) - pizza_name: Name of the pizza as shown in the menu

🍕The Pizza Challenge

For the Maven Pizza Challenge, you’ll be playing the role of a BI Consultant hired by Plato's Pizza, a Greek-inspired pizza place in New Jersey. You've been hired to help the restaurant use data to improve operations, and just received the following note:

Welcome aboard, we're glad you're here to help!

Things are going OK here at Plato's, but there's room for improvement. We've been collecting transactional data for the past year, but really haven't been able to put it to good use. Hoping you can analyze the data and put together a report to help us find opportunities to drive more sales and work more efficiently.

Here are some questions that we'd like to be able to answer:

1. What days and times do we tend to be busiest?
2. How many pizzas are we making during peak periods?
3. What are our best and worst-selling pizzas?
4. What's our average order value?
5. How well are we utilizing our seating capacity? (we have 15 tables and 60 seats)

That's all I can think of for now, but if you have any other ideas I'd love to hear them – you're the expert!

Thanks in advance,

Mario Maven (Manager, Plato's Pizza)

Colllection Methodology

The public dataset is completely available on the Maven Analytics website platform where it stores and consolidates all available datasets for analysis in the Data Playground. The specific individual datasets at hand can be obtained at this link below: https://www.mavenanalytics.io/blog/maven-pizza-challenge

📌I set up the data model to include all the related instances in one single table so obtaining data for analysis is made easier.

My Inspiration

Complete details were also provided about the challenge in the link if you are interested. The purpose of uploading here is to conduct exploratory data analysis about the dataset beforehand with the use of Pandas and data visualization libraries in order to have a comprehensive review of the data and translate my findings and insights in the form of a single page visualization.

Description:

This dataset contains two related tables: sales and ingredients. It is designed for projects focusing on sales forecasting, ingredient optimization, and supply chain management for a pizza delivery business. By leveraging historical data, users can build predictive models to minimize ingredient waste, prevent stockouts, and optimize purchasing strategies.

Dataset Details:

1. Sales Table:
- Shape: 48,620 rows × 16 columns - Columns: - pizza_id: Unique identifier for a pizza. - order_id: Unique identifier for a sales order. - pizza_name_id: Identifier linking the pizza to its recipe. - quantity: Number of pizzas sold. - unit_price: Price per unit of pizza. - total_price: Total price for the order. - pizza_size, pizza_category: Size and category of the pizza. - pizza_ingredients: Ingredients used in the pizza. - Year, Month, Day, Hour, Minute, Second: Timestamp details of the order.

2. Ingredients Table: - Shape: 518 rows × 4 columns - Columns: - pizza_name_id: Identifier linking the pizza to its recipe. - pizza_name: Name of the pizza. - pizza_ingredients: List of ingredients in the pizza. - Items_Qty_In_Grams: Quantity of each ingredient in grams.

Use Cases:

• Sales forecasting to predict future demand.
• Ingredient optimization for reducing waste and preventing shortages.
• Supply chain analysis and inventory planning.

Potential Applications:

• Time series analysis and forecasting.
• Machine learning models for predictive analytics.
• Data visualization for understanding sales and ingredient trends.

Analysis of ‘🍕 Pizza restaurants and Pizzas on their Menus’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/yamqwe/pizza-restaurants-and-pizzas-on-their-menuse on 13 February 2022.

--- Dataset description provided by original source is as follows ---

About this dataset

About this Data

This is a list of over 3,500 pizzas from multiple restaurants provided by Datafiniti's Business Database. The dataset includes the category, name, address, city, state, menu information, price range, and more for each pizza restaurant.

Note that this is a sample of a large dataset. The full dataset is available through Datafiniti.

What You Can Do with this Data

You can use this data to discover how much you can expect to pay for pizza across the country. E.g.:

• What are the least and most expensive cities for pizza?
• What is the number of restaurants serving pizza per capita (100,000 residents) across the U.S.?
• What is the median price of a large plain pizza across the U.S.?
• Which cities have the most restaurants serving pizza per capita (100,000 residents)?

Data Schema

A full schema for the data is available in our support documentation.

About Datafiniti

Datafiniti provides instant access to web data. We compile data from thousands of websites to create standardized databases of business, product, and property information. Learn more.

Interested in the Full Dataset?

Get this data and more by creating a free Datafiniti account or requesting a demo.

This dataset was created by Datafiniti and contains around 10000 samples along with Longitude, Price Range Max, technical information and other features such as: - Date Updated - Categories - and more.

How to use this dataset

• Analyze Date Added in relation to Province
• Study the influence of Price Range Min on Address
• More datasets

Acknowledgements

If you use this dataset in your research, please credit Datafiniti

Start A New Notebook!

--- Original source retains full ownership of the source dataset ---

This data includes the name and location of food service establishments and the violations that were found at the time of their last inspection. This dataset excludes inspections conducted in New York City (see: https://nycopendata.socrata.com/), Suffolk County (http://apps.suffolkcountyny.gov/health/Restaurant/intro.html) and Erie County. Inspections are a “snapshot” in time and are not always reflective of the day-to-day operations and overall condition of an establishment. Occasionally, remediation may not appear until the following month due to the timing of the updates. Some counties provide this information on their own websites and information found there may be updated more frequently. This dataset is refreshed on a monthly basis.

Last inspection data is the most recently submitted and available data.

For more information, check out http://www.health.ny.gov/regulations/nycrr/title_10/part_14/subpart_14-1.htm, or go to the "About" tab.

Analysis of ‘USA Key Economic Indicators’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/calven22/usa-key-macroeconomic-indicators on 28 January 2022.

--- Dataset description provided by original source is as follows ---

Context

Domino’s Pizza, like many other restaurant chains, is getting pinched by higher food costs. The company’s chief executive, Richard Allison, anticipates “unprecedented increases” in the company’s food costs, which could jump by 8-10%. He said that is three to four times what the pizza chain would normally expect in a year.

This leads to the paramount issue of inflation which affects every aspects of the economy, from consumer spending, business investment and employment rates to government programs, tax policies, and interest rates. The recent release of consumer inflation data showed prices rose at the fastest pace since 1982. Inflation forecasting is key in the conduct of monetary policy and can be used in many other ways such as preserving asset values. This dataset is a consolidated macroeconomic official statistics from 1981 to 2021, containing data available in month and quarterly format.

Content

The Core Consumer Price Index (ccpi) measures the changes in the price of goods and services, excluding food and energy due to their volatility. It measures price change from the perspective of the consumer. It is a often used to measure changes in purchasing trends and inflation.

Do note there are some null values in the dataset.

Acknowledgements

All data belongs to the U.S. Bureau of Economic Analysis official release, and are retrieved from FRED, Federal Reserve Bank of St. Louis.

Inspiration

What are some noticeable patterns or seasonality of the economy? What are the current trends of the economy? Which indicators has an effect on Core CPI or vice-versa based on predictive power or influence?

Quarterly data and monthly data can be merged with forward-fill or interpolation methods.

What is the forecast of Core CPI in 2022?

--- Original source retains full ownership of the source dataset ---

The average daily sales of Pizza Hut operated by Devyani International Limited across India in the fiscal year 2024 was around 37 thousand Indian rupees. This was a decrease in comparison to the previous financial year.