Name: Restaurant Sales Data
Creator: Kaggle
License: http://opendatacommons.org/licenses/dbcl/1.0/

About this Dataset

Restaurant Sales Data

📹 Project Video available on YouTube - https://youtu.be/dQwnyCEZ-UU

It is a sales data of a restaurant company operating in multiple cities in the world. It contains information about individual sales transactions, customer demographics, and product details. The data is structured in a tabular format, with each row representing a single record and each column representing a specific attribute. This dataset can be commonly used for business intelligence, sales forecasting, and customer behaviour analysis.

Using this dataset, we answered multiple questions with Python in our Project.

Q.1) Most Preferred Payment Method ?

Q.2) Most Selling Product - By Quantity & By Revenue ?

Q.3) Which city had maximum revenue , or , Which Manager earned maximum revenue ?

Q.4) Date wise revenue.

Q.5) Average Revenue.

Q.6) Average Revenue of November & December month.

Q.7) Standard Deviation of Revenue and Quantity ?

Q.8) Variance of Revenue and Quantity ?

Q.9) Is revenue increasing or decreasing over time?

Q.10) Average 'Quantity Sold' & 'Average Revenue' for each product ?

These are the main Features/Columns available in the dataset :

Order ID: A unique identifier for each sales order. This can be used to track individual transactions.
Order Date: The date when the order was placed. This column is essential for time-series analysis, such as identifying sales trends over time or seasonality.
Product: The name or type of the product sold. This column is crucial for analyzing sales performance by product category.
Price : The unit price of the product. This, along with 'Quantity Ordered', is used to calculate the total price of each order.
Quantity : The number of units of the product sold in a single order. This is a key metric for calculating revenue and understanding sales volume.
Purchase Type : The order was made online or in-store or drive-thru.
Payment Method : How the payment for the order was done.
Manager : Name of the manager of the store.
City : The location of the store. This can be used for geographical analysis of sales, such as identifying top-performing regions or optimizing logistics.

Tables

N 9 Sales Data Analysis

@kaggle.rohitgrewal_restaurant_sales_data.n_9_sales_data_analysis

9.36 kB
254 rows
9 columns

CREATE TABLE n_9_sales_data_analysis (
  "order_id" BIGINT,
  "date" VARCHAR,
  "product" VARCHAR,
  "price" DOUBLE,
  "quantity" DOUBLE,
  "purchase_type" VARCHAR,
  "payment_method" VARCHAR,
  "manager" VARCHAR,
  "city" VARCHAR
);