Retail Sales in the United States increased 0.60 percent in June of 2025 over the previous month. This dataset provides - U.S. December Retail Sales Increased More Than Forecast - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Retail Sales in the United States increased 3.90 percent in June of 2025 over the same month in the previous year. This dataset provides - United States Retail Sales YoY - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Retail Sales Control Group in the United States increased to 0.50 percent in June from 0.20 percent in May of 2025. This dataset includes a chart with historical data for the United States Retail Sales Control Group MoM.

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

Introduction

Retail Statistics: Retailing refers to selling goods and services directly to consumers. Elaborating, retailers can directly purchase products and services from manufacturers or through wholesalers and then sell them to consumers, retaining their profits in the process.

The word retail comes from the Old French verb retaillier, meaning "to shape by cutting", while in 1433, as a noun, the meaning changed to “a sale in small quantity†. The retail industry is undergoing significant evolution with the advancement of technology, shifting consumer habits, and broader market trends in sectors such as fashion, electronics, groceries, and lifestyle products.

This article examines the current state of global retail market analyses, its challenges, innovations, and the evolving expectations of today’s customers.

Vietnam Retail Sales: BP: Hanoi: Goods: Means of Transport data was reported at 12,030.000 VND bn in 2023. This records an increase from the previous number of 11,366.000 VND bn for 2022. Vietnam Retail Sales: BP: Hanoi: Goods: Means of Transport data is updated yearly, averaging 11,649.000 VND bn from Dec 2018 (Median) to 2023, with 6 observations. The data reached an all-time high of 12,089.000 VND bn in 2020 and a record low of 9,946.000 VND bn in 2021. Vietnam Retail Sales: BP: Hanoi: Goods: Means of Transport data remains active status in CEIC and is reported by Hanoi Statistics Office. The data is categorized under Global Database’s Vietnam – Table VN.H010: Retail Sales: Hanoi: Annual.

This statistic depicts the influences contributing to apparel sales in the United States from 2004 to 2010 and provides a forecast from 2011 to 2016, by type. In 2008, ***** percent of apparel sales were online influenced.Apparel IndustryDespite the current global economic downturn, the global apparel industry continues to grow at a healthy rate and this, coupled with the absence of switching costs for consumers and great product differentiation, means that rivalry within the industry is no more than moderate. The apparel industry is of great importance to the economy in terms of trade, employment, investment and revenue all over the world. This particular industry has short product life cycles, vast product differentiation and is characterized by great pace of demand change coupled with rather long and inflexible supply processes.Even well-established brands have to work hard to maintain their share of the market. Consumers are demanding more versatile wear with wider functionality, which means retailers continue producing new styles of apparel for men and women.Apparel remains largely a discretionary purchase compared to other consumer goods, making it more prone to economic shocks. The global apparel market has been shaped by three contrasting regional movements - robust growth in emerging markets, fragile recovery in the United States, and a sharp slowdown in Western Europe.

This dataset contains every wholesale purchase of liquor in the State of Iowa by retailers for sale to individuals since January 1, 2012. The State of Iowa controls the wholesale distribution of liquor intended for retail sale, which means this dataset offers a complete view of retail liquor sales in the entire state. The dataset contains every wholesale order of liquor by all grocery stores, liquor stores, convenience stores, etc., with details about the store and location, the exact liquor brand and size, and the number of bottles ordered. In addition to being an excellent dataset for analyzing liquor sales, this is a large and clean public dataset of retail sales data. It can be used to explore problems like stockout prediction, retail demand forecasting, and other retail supply chain problems. The data dictionary is available from the State of Iowa's Alcoholic Beverages Division , within the Iowa Department of Commerce . There are some minor discrepancies in the data, discussed in the web view of the data . This public dataset is hosted in Google BigQuery and is included in BigQuery's 1TB/mo of free tier processing. This means that each user receives 1TB of free BigQuery processing every month, which can be used to run queries on this public dataset. Watch this short video to learn how to get started quickly using BigQuery to access public datasets. What is BigQuery.

The situation of the retail sector in Germany is largely determined by the income of private households, the level of private consumer spending and the development of consumer prices and the economy as a whole. Over the past five years, retail sales have increased at an average annual rate of 1%, meaning that they are likely to total 741.4 billion euros in the current year. This also corresponds to a slight increase of 0.4% compared to 2023. The increase in sector turnover since 2019 is mainly due to the strong growth in online and mail order sales at the beginning of the past five-year period, which at the same time posed major challenges for brick-and-mortar retail. High rents and energy prices have placed an additional burden on bricks-and-mortar retailers in recent years and reduced the profit margins of smaller players in the sector. Brick-and-mortar retailers are increasingly being forced to improve their traditional sales concept and expand it with digital channels in order to avoid being left behind in the competition with online-only retailers.Although IBISWorld expects an increase in average net household income and consumer spending in the current year, the resulting growth in the retail sector is likely to be dampened by continued high consumer prices. Online consumer spending is also likely to increase this year, which should have a positive impact on retailers with their own online platforms as well as pure mail order and online retailers. In the next five years, falling inflation, the expected normalisation of energy and material prices and the continued rise in net household incomes should ensure growth in retail sales. Online retail is also likely to accelerate its expansion and make a significant contribution to sales growth. Turnover in the sector is expected to increase by an average of 0.9% per year and reach a value of 774.6 billion euros in 2029.

Context and Methodology

Research Domain:
The dataset is part of a project focused on retail sales forecasting. Specifically, it is designed to predict daily sales for Rossmann, a chain of over 3,000 drug stores operating across seven European countries. The project falls under the broader domain of time series analysis and machine learning applications for business optimization. The goal is to apply machine learning techniques to forecast future sales based on historical data, which includes factors like promotions, competition, holidays, and seasonal trends.

Purpose:
The primary purpose of this dataset is to help Rossmann store managers predict daily sales for up to six weeks in advance. By making accurate sales predictions, Rossmann can improve inventory management, staffing decisions, and promotional strategies. This dataset serves as a training set for machine learning models aimed at reducing forecasting errors and supporting decision-making processes across the company’s large network of stores.

How the Dataset Was Created:
The dataset was compiled from several sources, including historical sales data from Rossmann stores, promotional calendars, holiday schedules, and external factors such as competition. The data is split into multiple features, such as the store's location, promotion details, whether the store was open or closed, and weather information. The dataset is publicly available on platforms like Kaggle and was initially created for the Kaggle Rossmann Store Sales competition. The data is made accessible via an API for further analysis and modeling, and it is structured to help machine learning models predict future sales based on various input variables.

Technical Details

Dataset Structure:

The dataset consists of three main files, each with its specific role:

1. Train:
   This file contains the historical sales data, which is used to train machine learning models. It includes daily sales information for each store, as well as various features that could influence the sales (e.g., promotions, holidays, store type, etc.).

   https://handle.test.datacite.org/10.82556/yb6j-jw41
   PID: b1c59499-9c6e-42c2-af8f-840181e809db

2. Test2:
   The test dataset mirrors the structure of train.csv but does not include the actual sales values (i.e., the target variable). This file is used for making predictions using the trained machine learning models. It is used to evaluate the accuracy of predictions when the true sales data is unknown.

   https://handle.test.datacite.org/10.82556/jerg-4b84
   PID: 7cbb845c-21dd-4b60-b990-afa8754a0dd9

3. Store:
   This file provides metadata about each store, including information such as the store’s location, type, and assortment level. This data is essential for understanding the context in which the sales data is gathered.

   https://handle.test.datacite.org/10.82556/nqeg-gy34
   PID: 9627ec46-4ee6-4969-b14a-bda555fe34db

Data Fields Description:

• Id: A unique identifier for each (Store, Date) combination within the test set.

• Store: A unique identifier for each store.

• Sales: The daily turnover (target variable) for each store on a specific day (this is what you are predicting).

• Customers: The number of customers visiting the store on a given day.

• Open: An indicator of whether the store was open (1 = open, 0 = closed).

• StateHoliday: Indicates if the day is a state holiday, with values like:

  • 'a' = public holiday,

  • 'b' = Easter holiday,

  • 'c' = Christmas,

  • '0' = no holiday.

• SchoolHoliday: Indicates whether the store is affected by school closures (1 = yes, 0 = no).

• StoreType: Differentiates between four types of stores: 'a', 'b', 'c', 'd'.

• Assortment: Describes the level of product assortment in the store:

  • 'a' = basic,

  • 'b' = extra,

  • 'c' = extended.

• CompetitionDistance: Distance (in meters) to the nearest competitor store.

• CompetitionOpenSince[Month/Year]: The month and year when the nearest competitor store opened.

• Promo: Indicates whether the store is running a promotion on a particular day (1 = yes, 0 = no).

• Promo2: Indicates whether the store is participating in Promo2, a continuing promotion for some stores (1 = participating, 0 = not participating).

• Promo2Since[Year/Week]: The year and calendar week when the store started participating in Promo2.

• PromoInterval: Describes the months when Promo2 is active, e.g., "Feb,May,Aug,Nov" means the promotion starts in February, May, August, and November.

Software Requirements

To work with this dataset, you will need to have specific software installed, including:

• DBRepo Authorization: This is required to access the datasets via the DBRepo API. You may need to authenticate with an API key or login credentials to retrieve the datasets.

• Python Libraries: Key libraries for working with the dataset include:

  • pandas for data manipulation,

  • numpy for numerical operations,

  • matplotlib and seaborn for data visualization,

  • scikit-learn for machine learning algorithms.

Additional Resources

Several additional resources are available for working with the dataset:

1. Presentation:
   A presentation summarizing the exploratory data analysis (EDA), feature engineering process, and key insights from the analysis is provided. This presentation also includes visualizations that help in understanding the dataset’s trends and relationships.

2. Jupyter Notebook:
   A Jupyter notebook, titled Retail_Sales_Prediction_Capstone_Project.ipynb, is provided, which details the entire machine learning pipeline, from data loading and cleaning to model training and evaluation.

3. Model Evaluation Results:
   The project includes a detailed evaluation of various machine learning models, including their performance metrics like training and testing scores, Mean Absolute Percentage Error (MAPE), and Root Mean Squared Error (RMSE). This allows for a comparison of model effectiveness in forecasting sales.

4. Trained Models (.pkl files):
   The models trained during the project are saved as .pkl files. These files contain the trained machine learning models (e.g., Random Forest, Linear Regression, etc.) that can be loaded and used to make predictions without retraining the models from scratch.

5. sample_submission.csv:
   This file is a sample submission file that demonstrates the format of predictions expected when using the trained model. The sample_submission.csv contains predictions made on the test dataset using the trained Random Forest model. It provides an example of how the output should be structured for submission.

These resources provide a comprehensive guide to implementing and analyzing the sales forecasting model, helping you understand the data, methods, and results in greater detail.

In the financial year 2021, retail sales value of home appliances and consumer electronics through online channels in Hong Kong amounted to around 7.2 billion Hong Kong dollars. As estimated, the overall home appliances and consumer electronics retail market had started to recover from the pandemic gloom, in the mean time, share of online retail sales would further expand.

Context

Supplement Sales Prediction

• Your Client WOMart is a leading nutrition and supplement retail chain that offers a comprehensive range of products for all your wellness and fitness needs.

• WOMart follows a multi-channel distribution strategy with 350+ retail stores spread across 100+ cities.

• Effective forecasting for store sales gives essential insight into upcoming cash flow, meaning WOMart can more accurately plan the cashflow at the store level.

• Sales data for 18 months from 365 stores of WOMart is available along with information on Store Type, Location Type for each store, Region Code for every store, Discount provided by the store on every day, Number of Orders everyday etc.

• Your task is to predict the store sales for each store in the test set for the next two months.

Content

Train Data |Variable |Definition | |-------------------------------|-------------------------------| |ID |Unique Identifier for a row | |Store_id |Unique id for each Store| |Store_Type |Type of the Store| |Location_Type |Type of the location where Store is located| |Region_Code |Code of the Region where Store is located| |Date |Information about the Date| |Holiday |If there is holiday on the given Date, 1 : Yes, 0 : No| |Discount |If discount is offered by store on the given Date, Yes/ No| |#Orders |Number of Orders received by the Store on the given Day| |Sales |Total Sale for the Store on the given Day|

Test Data |Variable |Definition | |-----------------------------|-------------------------| |ID |Unique Identifier for a row | |Store_id |Unique id for each Store | |Store_Type |Type of the Store | |Location_Type |Type of the location where Store is located | |Region_Code |Code of the Region where Store is located | |Date |Information about the Date | |Holiday |If there is holiday on the given Date, 1 : Yes, 0 : No | |Discount |If discount is offered by store on the given Date, Yes/ No |

Sample_Submission |Variable |Definition | |------------------------|----------------| |ID |Unique Identifier for a row | |Sales |Total Sale for the Store on the given Day |

Evaluation

• The evaluation metric for this competition is MSLE * 1000 across all entries in the test set.

Public and Private Split

• Test data is further divided into Public (First 20 Days) and Private (Last 41 Days). You will make the prediction for two months (61 days).
• Your initial responses will be checked and scored on the Public data.
• The final rankings would be based on your private score which will be published once the competition is over.

The sales column that we submit would be compared to the actual answer similar to the following. Instead of 8 items it is 22266 items(the function is avable in sklearn).

Sample Input :

actual = [27.5, 55.9, 25.8, 17.7, 27.6, 55.9, 25.7, 17.8] predicted = 24.0, 49.1, 21.0, 16.2, 23.3, 47.0, 12.1, 15.2*1000

Sample Output:

82.9949678377161

Public and Private Split

• Test data is further divided into Public (First 20 Days) and Private (Last 41 Days). You will make the prediction for two months (61 days).

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?

Graph and download economic data for Retail Sales: GAFO (General Merchandise Normally Sold in Department Stores) (MRTSSM4400CUSS) from Jan 1992 to Apr 2025 about merchandise, retail trade, sales, retail, and USA.

Dataset Description :

This is the historical data that covers sales from 2010-02-05 to 2012-11-01, in the file Walmart_Store_sales. Within this file you will find the following fields:

Store - the store number

Date - the week of sales

Weekly_Sales - sales for the given store

Holiday_Flag - whether the week is a special holiday week 1 – Holiday week 0 – Non-holiday week

Temperature - Temperature on the day of sale

Fuel_Price - Cost of fuel in the region

CPI – Prevailing consumer price index

Unemployment - Prevailing unemployment rate

Holiday Events Super Bowl: 12-Feb-10, 11-Feb-11, 10-Feb-12, 8-Feb-13 Labour Day: 10-Sep-10, 9-Sep-11, 7-Sep-12, 6-Sep-13 Thanksgiving: 26-Nov-10, 25-Nov-11, 23-Nov-12, 29-Nov-13 Christmas: 31-Dec-10, 30-Dec-11, 28-Dec-12, 27-Dec-13

Analysis Tasks

Basic Statistics tasks

1) Which store has maximum sales

2) Which store has maximum standard deviation i.e., the sales vary a lot. Also, find out the coefficient of mean to standard deviation

3) Which store/s has good quarterly growth rate in Q3’2012

4) Some holidays have a negative impact on sales. Find out holidays which have higher sales than the mean sales in non-holiday season for all stores together

5) Provide a monthly and semester view of sales in units and give insights

Statistical Model

For Store 1 – Build prediction models to forecast demand

Linear Regression – Utilize variables like date and restructure dates as 1 for 5 Feb 2010 (starting from the earliest date in order). Hypothesize if CPI, unemployment, and fuel price have any impact on sales.

Change dates into days by creating new variable.

Select the model which gives best accuracy.

Historical sales data for 45 Walmart stores located in different regions are available. There are certain events and holidays which impact sales on each day. The business is facing a challenge due to unforeseen demands and runs out of stock some times, due to inappropriate machine learning algorithm. Walmart would like to predict the sales and demand accurately. An ideal ML algorithm will predict demand accurately and ingest factors like economic conditions including CPI, Unemployment Index, etc. The objective is to determine the factors affecting the sales and to analyze the impact of markdowns around holidays on the sales.

Holiday Events Super Bowl: 12-Feb-10, 11-Feb-11, 10-Feb-12, 8-Feb-13 Labour Day: 10-Sep-10, 9-Sep-11, 7-Sep-12, 6-Sep-13 Thanksgiving: 26-Nov-10, 25-Nov-11, 23-Nov-12, 29-Nov-13 Christmas: 31-Dec-10, 30-Dec-11, 28-Dec-12, 27-Dec-13

Analysis Tasks

Basic Statistics tasks 1) Which store has maximum sales

2) Which store has maximum standard deviation i.e., the sales vary a lot. Also, find out the coefficient of mean to standard deviation

3) Which store/s has good quarterly growth rate in Q3’2012

4) Some holidays have a negative impact on sales. Find out holidays which have higher sales than the mean sales in non-holiday season for all stores together

5) Provide a monthly and semester view of sales in units and give insights

Statistical Model For Store 1 – Build prediction models to forecast demand (Linear Regression – Utilize variables like date and restructure dates as 1 for 5 Feb 2010 (starting from the earliest date in order). Hypothesize if CPI, unemployment, and fuel price have any impact on sales.) Change dates into days by creating new variable. Select the model which gives best accuracy.

Analysis of ‘Walmart Dataset (Retail)’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/rutuspatel/walmart-dataset-retail on 28 January 2022.

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

Dataset Description :

This is the historical data that covers sales from 2010-02-05 to 2012-11-01, in the file Walmart_Store_sales. Within this file you will find the following fields:

Store - the store number

Date - the week of sales

Weekly_Sales - sales for the given store

Holiday_Flag - whether the week is a special holiday week 1 – Holiday week 0 – Non-holiday week

Temperature - Temperature on the day of sale

Fuel_Price - Cost of fuel in the region

CPI – Prevailing consumer price index

Unemployment - Prevailing unemployment rate

Holiday Events Super Bowl: 12-Feb-10, 11-Feb-11, 10-Feb-12, 8-Feb-13 Labour Day: 10-Sep-10, 9-Sep-11, 7-Sep-12, 6-Sep-13 Thanksgiving: 26-Nov-10, 25-Nov-11, 23-Nov-12, 29-Nov-13 Christmas: 31-Dec-10, 30-Dec-11, 28-Dec-12, 27-Dec-13

Analysis Tasks

Basic Statistics tasks

1) Which store has maximum sales

2) Which store has maximum standard deviation i.e., the sales vary a lot. Also, find out the coefficient of mean to standard deviation

3) Which store/s has good quarterly growth rate in Q3’2012

4) Some holidays have a negative impact on sales. Find out holidays which have higher sales than the mean sales in non-holiday season for all stores together

5) Provide a monthly and semester view of sales in units and give insights

Statistical Model

For Store 1 – Build prediction models to forecast demand

Linear Regression – Utilize variables like date and restructure dates as 1 for 5 Feb 2010 (starting from the earliest date in order). Hypothesize if CPI, unemployment, and fuel price have any impact on sales.

Change dates into days by creating new variable.

Select the model which gives best accuracy.

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

Context

Online retail is a transnational data set which contains all the transactions occurring between 01/12/2010 and 09/12/2011 for a UK-based and registered non-store online retail. The company mainly sells unique all-occasion gifts. Many customers of the company are wholesalers.

Business Goal

We will be using the online retail trasnational dataset to build a RFM clustering and choose the best set of customers which the company should target.

The beverage retail sector consists of many regionally operating beverage markets or beverage market chains and retailers specialising in specific product groups. Due to the negative trend in demand for alcoholic and sugary drinks, industry sales fell by an average of 1.5% per year in the period from 2019 to 2024. In addition to the declining consumption of alcoholic beverages, this development was also due to consumers' lower willingness to spend as a result of rising energy costs and high inflation. High consumer prices are also having a negative impact on beverage sales in the current year. IBISWorld therefore expects sales to fall by 2.6% to 7.5 billion euros in 2024.The per capita consumption of alcoholic and non-alcoholic beverages has a decisive influence on the demand for beverages and therefore also on the retail trade. Alcohol consumption has been trending downwards in recent years and is likely to continue to fall in the coming years. The consumption of sugary soft drinks is also falling, which is due to the increasing proportion of consumers who are reducing their sugar consumption. However, the rising average temperature in the coming years due to climate change may have a positive impact on demand for beverages.IBISWorld expects industry sales to grow by an average of 0.4% per year between 2024 and 2029, meaning that industry sales are likely to reach €7.6 billion in 2029. As a result of increasing environmental awareness in the coming years, the continuing high demand for glass containers together with the high consumer spending on food and non-alcoholic beverages, which is expected to continue in the future, should have a positive effect on industry sales. At the same time, the declining consumption of alcohol and sugary drinks continues to pose a risk for the industry. As the market is saturated, the number of companies, businesses and employees is expected to decline slightly in the coming years.

The proportion of the population that regularly reads books has been declining for several years. The driving force behind the declining enthusiasm for reading is digitalisation with its consequences for culture and consumer behaviour. This is also making itself felt in the book retail sector and has contributed to an average decline in sales of 0.2% per year in the period from 2019 to 2024. For the current year, sales are expected to fall by 3.4% to 3.3 billion euros.Due to strong competition from online retailers such as Amazon and the growing range of entertainment media such as streaming services, the long-term trend in industry sales is declining. However, the leading industry players formed an alliance in 2013 and launched Tolino, a very successful rival product to Amazon's Kindle e-book reader, meaning that the German e-book market is not dominated by Amazon alone. As a result, industry players have been able to offset their decline in sales in bricks-and-mortar retail during the pandemic with increased e-book sales. Due to the increasing use of digital media, the demand for printed books, which are still the main source of sales for booksellers, continues to decline, leading to falling sales for book retailers. This trend is exacerbated by the fact that customer footfall in city centres is tending to decline.In the period from 2024 to 2029, an average decline in turnover of 1.6% per year is expected. This means that industry revenue is likely to reach 3 billion euros in 2029. The trends of increased internet use and the growing range of entertainment media are likely to continue in the coming years and contribute to a further decline in demand for books. This in turn is likely to lead to a decline in the number of bookshops.

Global Miscellaneous Store Retailers market size is expected to reach $1054.24 billion by 2029 at 5.5%, e-commerce surge miscellaneous store retailers ride the wave of online shopping popularity