Grocery Sales Prediction

This dataset provides a rich resource for researchers and practitioners interested in retail sales prediction and analysis. It contains information about various grocery products, the outlets where they are sold, and their historical sales data.

Product Characteristics:

Item_Identifier: Unique identifier for each product. Item_Weight: Weight of the product item. Item_Fat_Content: Categorical variable indicating the fat content of the product (e.g., low fat, regular). Item_Visibility: Numerical attribute reflecting the visibility of the product in the store (likely a promotional measure). Item_Type: Category of the product (e.g., Snacks, Beverages, Bakery). Item_MRP: Maximum Retail Price of the product. Outlet Information:

Outlet_Identifier: Unique identifier for each outlet (store). Outlet_Establishment_Year: Year the outlet was established. Outlet_Size: Categorical variable indicating the size of the outlet (e.g., Small, Medium, Large). (Note: This data may have missing values) Outlet_Location_Type: Categorical variable indicating the type of location the outlet is in (e.g., Tier 1 City, Tier 2 City, Upstate). Outlet_Type: Categorical variable indicating the type of outlet (e.g., Supermarket, Grocery Store, Convenience Store). Sales Data:

Item_Outlet_Sales: The historical sales data for each product-outlet combination. Profit: The profit margin earned on each product sold. Potential Uses

This dataset can be used for various retail sales analysis and prediction tasks, including:

Demand forecasting: Build models to predict future sales of individual products or product categories at specific outlets. Promotion optimization: Analyze the effectiveness of different promotional strategies (reflected by Item_Visibility) on sales. Assortment planning: Optimize product selection and placement within stores based on sales history and outlet characteristics. Outlet performance analysis: Compare the performance of different outlets based on sales figures and profit margins. Customer segmentation: Identify customer segments with distinct purchasing behavior based on product types and outlet locations. By analyzing these rich data points, retailers can gain valuable insights to improve their sales strategies, optimize inventory management, and maximize profits.

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.

About Data

The sales in the mega marts are critical to make them sustainable. As a matter of fact, the rise of several marts has created buyers of different categories who are critical about the quality of product at the right price.

Here, the data science & machine learning community has been challenged to build an ML model and predict the sales of each product from each outlet. The participants also need to use the model to analyse the properties of the product in the stores and find ways to increase sales.

Data Attributes

Item_ID: Item Identification Number Item_W: Item Weight Item_Type: Item Item_MRP: MRP of the Product Outlet_ID: Outlet ID Outlet_Year: Outlet Establishment year Outlet_Size: Size of the outlet Outlet_Type: Type of the outlet Sales: Total sales from the outlet

This dataset is a merged dataset created from the data provided in the competition "Store Sales - Time Series Forecasting". The other datasets that were provided there apart from train and test (for example holidays_events, oil, stores, etc.) could not be used in the final prediction. According to my understanding, through the EDA of the merged dataset, we will be able to get a clearer picture of the other factors that might also affect the final prediction of grocery sales. Therefore, I created this merged dataset and posted it here for the further scope of analysis.

##### Data Description Data Field Information (This is a copy of the description as provided in the actual dataset)

Train.csv - id: store id - date: date of the sale - store_nbr: identifies the store at which the products are sold. -**family**: identifies the type of product sold. - sales: gives the total sales for a product family at a particular store at a given date. Fractional values are possible since products can be sold in fractional units (1.5 kg of cheese, for instance, as opposed to 1 bag of chips). - onpromotion: gives the total number of items in a product family that were being promoted at a store on a given date. - Store metadata, including ****city, state, type, and cluster.**** - cluster is a grouping of similar stores. - Holidays and Events, with metadata NOTE: Pay special attention to the transferred column. A holiday that is transferred officially falls on that calendar day but was moved to another date by the government. A transferred day is more like a normal day than a holiday. To find the day that it was celebrated, look for the corresponding row where the type is Transfer. For example, the holiday Independencia de Guayaquil was transferred from 2012-10-09 to 2012-10-12, which means it was celebrated on 2012-10-12. Days that are type Bridge are extra days that are added to a holiday (e.g., to extend the break across a long weekend). These are frequently made up by the type Work Day which is a day not normally scheduled for work (e.g., Saturday) that is meant to pay back the Bridge. Additional holidays are days added to a regular calendar holiday, for example, as typically happens around Christmas (making Christmas Eve a holiday). - dcoilwtico: Daily oil price. Includes values during both the train and test data timeframes. (Ecuador is an oil-dependent country and its economic health is highly vulnerable to shocks in oil prices.)

**Note: ***There is a transaction column in the training dataset which displays the sales transactions on that particular date. * Test.csv - The test data, having the same features like the training data. You will predict the target sales for the dates in this file. - The dates in the test data are for the 15 days after the last date in the training data. **Note: ***There is a no transaction column in the test dataset as was there in the training dataset. Therefore, while building the model, you might exclude this column and may use it only for EDA.*

submission.csv - A sample submission file in the correct format.

Rodrigo2204/store-sales-forecast dataset hosted on Hugging Face and contributed by the HF Datasets community

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

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

Context

This dataset contains lot of historical sales data. It was extracted from a Brazilian top retailer and has many SKUs and many stores. The data was transformed to protect the identity of the retailer.

Content

[TBD]

Acknowledgements

This data would not be available without the full collaboration from our customers who understand that sharing their core and strategical information has more advantages than possible hazards. They also support our continuos development of innovative ML systems across their value chain.

Inspiration

Every retail business in the world faces a fundamental question: how much inventory should I carry? In one hand to mush inventory means working capital costs, operational costs and a complex operation. On the other hand lack of inventory leads to lost sales, unhappy customers and a damaged brand.

Current inventory management models have many solutions to place the correct order, but they are all based in a single unknown factor: the demand for the next periods.

This is why short-term forecasting is so important in retail and consumer goods industry.

We encourage you to seek for the best demand forecasting model for the next 2-3 weeks. This valuable insight can help many supply chain practitioners to correctly manage their inventory levels.

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

Forecast: Estimated Retail Sales in the US 2023 - 2027 Discover more data with ReportLinker!

This statistic shows the retail sales value in Saudi Arabia in 2018, with estimates from 2019 to 2025. In 2018, the retail sales value amounted to ***** billion U.S. dollars. It was estimated that the retail sales value would grow until 2025, reaching around ***** billion U.S. dollars.

Dataset

This dataset was created by micgonzalez

Contents

Based on a forecast, retail sales revenues in Germany will amount to over *** billion euros in 2025. Figures are expected to increase annually. This timeline shows the retail sales revenue development in Germany from 2011 to 2025.

Forecast: Estimated Food and Beverage Stores Sales in the US 2024 - 2028 Discover more data with ReportLinker!

According to estimates, the hyper store generated the highest retail sales in Mexico in 2021. The estimated value of retail sales in hyper stores was **** billion U.S. dollars in that year. While traditional channels, such as hyper stores and discounters, were the main channels both in 2016 and 2021, projections showed that e-commerce will grow the fastest, and the retail sales garnered by e-commerce channels will ultimately reach ** billion U.S. dollars in Mexico.

In 2020, global retail sales fell by 2.9 percent as a result of the COVID-19 pandemic, bouncing back in 2021 with a growth of 9.7 percent Global retail sales were projected to amount to around 27.3 trillion U.S. dollars by 2022, up from approximately 23.7 trillion U.S. dollars in 2020.

American retailers worldwide
As a result of globalization and various trade agreements between markets and countries, many retailers are capable of doing business on a global scale. Many of the world’s leading retailers are American companies. Walmart and Amazon are examples of such American retailers. The success of U.S. retailers can also be seen through their performance in online retail.

Retail in the U.S.
The domestic retail market in the United States is a lucrative market, in which many companies compete. Walmart, a retail chain offering low prices and a wide selection of products, is the leading retailer in the United States. Amazon, The Kroger Co., Costco, and Target are a selection of other leading U.S. retailers.

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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.

We can separate this process into four levels: Product level, Store level, Customer level, and Macro level.

Store Level Hypotheses:

1. City type: Stores located in urban or Tier 1 cities should have higher sales because of the higher income levels of people there.
2. Population Density: Stores located in densely populated areas should have higher sales because of more demand.
3. Store Capacity: Stores which are very big in size should have higher sales as they act like one-stop-shops and people would prefer getting everything from one place
4. Competitors: Stores having similar establishments nearby should have less sales because of more competition.
5. Marketing: Stores which have a good marketing division should have higher sales as it will be able to attract customers through the right offers and advertising.
6. Location: Stores located within popular marketplaces should have higher sales because of better access to customers.
7. Customer Behavior: Stores keeping the right set of products to meet the local needs of customers will have higher sales.
8. Ambiance: Stores which are well-maintained and managed by polite and humble people are expected to have higher footfall and thus higher sales.

Product Level Hypotheses:

1. Brand: Branded products should have higher sales because of higher trust in the customer.
2. Packaging: Products with good packaging can attract customers and sell more.
3. Utility: Daily use products should have a higher tendency to sell as compared to the specific use products.
4. Display Area: Products which are given bigger shelves in the store are likely to catch attention first and sell more.
5. Visibility in Store: The location of product in a store will impact sales. Ones which are right at entrance will catch the eye of customer first rather than the ones in back.
6. Advertising: Better advertising of products in the store will should higher sales in most cases.
7. Promotional Offers: Products accompanied with attractive offers and discounts will sell more.

This statistic shows the forecast retail sales of dessert mix in the United States from 2018 to 2022. By 2022, retail sales of dessert mix in the United States were forecast to reach *** billion U.S. dollars.

By 2027, the e-commerce retailer Amazon is forecast to be the leading retailer worldwide, just barely outdoing the Alibaba Group in terms of sales. Specifically, projections for 2027 show that the total chain retail sales of Amazon are going to reach a value of more than **** trillion U.S. dollars. Walmart would rank fifth, generating an estimated *** billion U.S. dollars in chain retail sales that year.

In-store or brick-and-mortar retail sales in the United States were forecast to increase by *** percent in 2022. Total retail sales in the United States amounted to **** trillion U.S. dollars in 2021, up from the previous year.

Retail Sales in Serbia increased 0.50 percent in June of 2025 over the previous month. This dataset provides - Serbia Retail Sales MoM- actual values, historical data, forecast, chart, statistics, economic calendar and news.

Forecast: General Merchandise Stores E-commerce Sales in the US 2024 - 2028 Discover more data with ReportLinker!