Dataset

This dataset was created by Shubham Jagtap

Contents

Analysis of ‘Superstore Sales Dataset’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/rohitsahoo/sales-forecasting on 28 January 2022.

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

Context

Retail dataset of a global superstore for 4 years. Perform EDA and Predict the sales of the next 7 days from the last date of the Training dataset!

Content

Time series analysis deals with time series based data to extract patterns for predictions and other characteristics of the data. It uses a model for forecasting future values in a small time frame based on previous observations. It is widely used for non-stationary data, such as economic data, weather data, stock prices, and retail sales forecasting.

Dataset

The dataset is easy to understand and is self-explanatory

Inspiration

Perform EDA and Predict the sales of the next 7 days from the last date of the Training dataset!

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

Creating a Power BI Dashboard for Sales Data Analysis Hey everyone! Are you looking for a dataset to apply Time Series Forecasting? You can check out this dataset I have posted!

Superstore Sales Dataset

This dataset consists of sales data of a store from 2014 to 2019!

Thank you!

Dataset

This dataset was created by Chirag Rathi

Contents

Dataset

This dataset was created by Rohan Sharma

Contents

🛒 Superstore Sales Analysis 📊 A Deep Dive into Sales, Customers, and Delivery Performance

🔍 Overview This notebook analyzes a fictional Superstore dataset to uncover insights about:

Monthly sales trends

Top-performing customers and products

Delivery times and delays

Regional performance

🧰 Tools Used Python 🐍

Pandas for data manipulation

Matplotlib & Seaborn for visualization

Plotly for interactive charts

📈 Key Findings 🔼 Sales peak during November and December

🧍‍♂️ A few customers generate a large portion of revenue

🕒 Average delivery time is 3–5 days, with some outliers

In December 2023, United States warehouse clubs and superstores sales were estimated to reach **** billion U.S. dollars, up from **** billion attained the same month a year before. Costco and Sam's Club are two of the biggest warehouse club retailers in the United States.

Yonghui Superstore reported CNY17.48B in Sales Revenues for its fiscal quarter ending in March of 2025. Data for Yonghui Superstore | 601933 - Sales Revenues including historical, tables and charts were last updated by Trading Economics this last July in 2025.

Graph and download economic data for Retail Sales: Warehouse Clubs and Superstores (DISCONTINUED) (MRTSSM45291USS) from Jan 1992 to Feb 2025 about warehouse, retail trade, sales, retail, and USA.

Context

Retail dataset of a global superstore for 4 years.

Content

What's inside is more than just rows and columns. Make it easy for others to get started by describing how you acquired the data and what time period it represents, too.

Acknowledgements

We wouldn't be here without the help of others. If you owe any attributions or thanks, include them here along with any citations of past research.

Inspiration

Your data will be in front of the world's largest data science community. What questions do you want to see answered?

According to estimates, Real Canadian Superstore's online store, realcanadiansuperstore.ca generated an estimated ***** million U.S. dollars in e-commerce net sales in Canada in 2023. This marks a significant increase from the company's pre-pandemic online sales, which were estimated at approximately *** million U.S. dollars in 2019. For more information, please visit ecommerceDB.com.

Dataset

This dataset was created by Swarup Shekhar

Contents

The graph presents data on the nominal sales revenue for department stores and superstores in Colombia in 2015 and 2016. According to data, in 2016 sales revenue for these store amounted to a total of **** trillion Colombian pesos.

Japan Large Scale Retail Stores: CS: Supermarket data was reported at 740.896 JPY bn in Sep 2018. This records a decrease from the previous number of 776.375 JPY bn for Jun 2018. Japan Large Scale Retail Stores: CS: Supermarket data is updated quarterly, averaging 803.377 JPY bn from Jun 1982 (Median) to Sep 2018, with 146 observations. The data reached an all-time high of 1,069.078 JPY bn in Dec 2000 and a record low of 506.202 JPY bn in Sep 1985. Japan Large Scale Retail Stores: CS: Supermarket data remains active status in CEIC and is reported by Ministry of Economy, Trade and Industry. The data is categorized under Global Database’s Japan – Table JP.H005: Large Scale Retail Stores: Sales and Commodity Stock Value.

Nothing ever becomes real till it is experienced.

-John Keats

While we don't know the context in which John Keats mentioned this, we are sure about its implication in data science. While you would have enjoyed and gained exposure to real world problems in this challenge, here is another opportunity to get your hand dirty with this practice problem.

Problem Statement :

The data scientists at BigMart have collected 2013 sales data for 1559 products across 10 stores in different cities. Also, certain attributes of each product and store have been defined. The aim is to build a predictive model and find out the sales of each product at a particular store.

Using this model, BigMart will try to understand the properties of products and stores which play a key role in increasing sales.

Please note that the data may have missing values as some stores might not report all the data due to technical glitches. Hence, it will be required to treat them accordingly.

Data :

We have 14204 samples in data set.

Variable Description

Item Identifier: A code provided for the item of sale

Item Weight: Weight of item

Item Fat Content: A categorical column of how much fat is present in the item: ‘Low Fat’, ‘Regular’, ‘low fat’, ‘LF’, ‘reg’

Item Visibility: Numeric value for how visible the item is

Item Type: What category does the item belong to: ‘Dairy’, ‘Soft Drinks’, ‘Meat’, ‘Fruits and Vegetables’, ‘Household’, ‘Baking Goods’, ‘Snack Foods’, ‘Frozen Foods’, ‘Breakfast’, ’Health and Hygiene’, ‘Hard Drinks’, ‘Canned’, ‘Breads’, ‘Starchy Foods’, ‘Others’, ‘Seafood’.

Item MRP: The MRP price of item

Outlet Identifier: Which outlet was the item sold. This will be categorical column

Outlet Establishment Year: Which year was the outlet established

Outlet Size: A categorical column to explain size of outlet: ‘Medium’, ‘High’, ‘Small’.

Outlet Location Type: A categorical column to describe the location of the outlet: ‘Tier 1’, ‘Tier 2’, ‘Tier 3’

Outlet Type: Categorical column for type of outlet: ‘Supermarket Type1’, ‘Supermarket Type2’, ‘Supermarket Type3’, ‘Grocery Store’

Item Outlet Sales: The number of sales for an item.

Evaluation Metric:

We will use the Root Mean Square Error value to judge your response

The coronavirus (COVID-19) pandemic resulted in several fluctuations in DIY (Do-It-Yourself) stores in Italy in 2020. In March of that year, DIY superstore sales decreased over ** percent in comparison to the same month a year earlier. Meanwhile, in May, the industry recorded a rise of over ** percent. As of *********, DIY store sales in the country increased **** percent compared to *********.

Yonghui Superstore reported CNY3.72B in Gross Profit on Sales for its fiscal quarter ending in March of 2025. Data for Yonghui Superstore | 601933 - Gross Profit On Sales including historical, tables and charts were last updated by Trading Economics this last August in 2025.

This statistic shows supermarket sales in the United States in 2020, by department. From January 1 to July 12, 2020 edibles grocery sales in supermarkets totaled ***** billion U.S. dollars.

Context

With growing demands and cut-throat competitions in the market, a Superstore Giant is seeking your knowledge in understanding what works best for them. They would like to understand which products, regions, categories and customer segments they should target or avoid.

You can even take this a step further and try and build a Regression model to predict Sales or Profit.

Go crazy with the dataset, but also make sure to provide some business insights to improve.

Metadata

Row ID => Unique ID for each row. Order ID => Unique Order ID for each Customer. Order Date => Order Date of the product. Ship Date => Shipping Date of the Product. Ship Mode=> Shipping Mode specified by the Customer. Customer ID => Unique ID to identify each Customer. Customer Name => Name of the Customer. Segment => The segment where the Customer belongs. Country => Country of residence of the Customer. City => City of residence of of the Customer. State => State of residence of the Customer. Postal Code => Postal Code of every Customer. Region => Region where the Customer belong. Product ID => Unique ID of the Product. Category => Category of the product ordered. Sub-Category => Sub-Category of the product ordered. Product Name => Name of the Product Sales => Sales of the Product. Quantity => Quantity of the Product. Discount => Discount provided. Profit => Profit/Loss incurred.

Acknowledgements

I do not own this data. I merely found it from the Tableau website. All credits to the original authors/creators. For educational purposes only.

Chile Supermarket Sales: Metropolitan Santiago data was reported at 372,564.577 CLP mn in Apr 2019. This records a decrease from the previous number of 410,215.729 CLP mn for Mar 2019. Chile Supermarket Sales: Metropolitan Santiago data is updated monthly, averaging 344,268.949 CLP mn from Jan 2014 (Median) to Apr 2019, with 64 observations. The data reached an all-time high of 472,334.618 CLP mn in Dec 2018 and a record low of 244,037.946 CLP mn in Feb 2014. Chile Supermarket Sales: Metropolitan Santiago data remains active status in CEIC and is reported by National Institute of Statistics. The data is categorized under Global Database’s Chile – Table CL.H011: Supermarket Sales.