Dataset from Singapore Department of Statistics. For more information, visit https://data.gov.sg/datasets/d_187e354026aabe4798383bf6230940f2/view

Gasoline retail prices weekly average by region dataset provides the weekly average retail gasoline prices for New York State and sixteen New York metropolitan regions in U.S. dollars per gallon. Data is a weekly average from January 2017 through current. Average daily retail gasoline prices are collected from the American Automobile Association (AAA) Daily Fuel Gauge Report. The AAA Daily Fuel Gauge Report prices are averaged to produce a weekly average retail price for New York State and each metropolitan region. The New York State metropolitan regions in the dataset are Albany (Albany-Schenectady-Troy), Batavia, Binghamton, Buffalo (Buffalo-Niagara Falls), Dutchess (Dutchess-Putnam), Elmira, Glens Falls, Ithaca, Kingston, Nassau (Nassau-Suffolk), New York City, Rochester, Syracuse, Utica (Utica-Rome), Watertown (Watertown-Fort Drum), and White Plains. The New York State Energy Research and Development Authority (NYSERDA) offers objective information and analysis, innovative programs, technical expertise, and support to help New Yorkers increase energy efficiency, save money, use renewable energy, and reduce reliance on fossil fuels. To learn more about NYSERDA’s programs, visit https://nyserda.ny.gov or follow us on X, Facebook, YouTube, or Instagram.

Monthly average retail prices for selected products, for Canada and provinces. Prices are presented for the current month and the previous four months. Prices are based on transaction data from Canadian retailers, and are presented in Canadian current dollars.

Grocery Store Chain Sales Dataset

Dataset Overview

This comprehensive synthetic dataset contains 1,980 records from a multi-location grocery store chain, capturing detailed transaction data across various store locations, product categories, customer interactions, and promotional activities. The dataset spans approximately 2 years of sales data (2023-2025) and provides insights into customer purchasing behavior, store performance, product popularity, and promotional effectiveness.

Dataset Characteristics

• Total Records: 1,980 transactions
• Time Period: August 2023 - August 2025
• Store Locations: 9 different grocery store branches
• Product Categories: 11 distinct aisles
• Data Quality: Includes realistic missing values and data inconsistencies for cleaning practice

Column Descriptions

Store Locations

The dataset includes transactions from the following store locations: - FreshMart Downtown - GreenGrocer Plaza - SuperSave Central - FamilyFood Express - QuickStop Market - MegaMart Westside - Corner Grocery - City Fresh Store - ValuePlus Market

Product Categories (Aisles)

Data Quality Notes

The dataset includes intentional data quality issues typical of real-world data: - Missing Values: Some store names, promotions, and loyalty tiers are missing - Data Inconsistencies: Mixed formatting in quantity field ("2" vs "2.0") - Negative Values: Some final amounts are negative due to high discounts - Type Variations: Customer IDs stored as strings despite being numeric

Key Business Metrics

• Average Transaction Value: Varies significantly across stores and products
• Discount Penetration: Multiple promotional strategies reflected in discount patterns
• Customer Loyalty: Points system ranging from 0-500 points per transaction
• Product Diversity: 18 core products across 11 major categories
• Geographic Coverage: 9 store locations providing regional insights

Potential Use Cases

This dataset is ideal for: - Customer Segmentation Analysis - Sales Performance Evaluation - Inventory Management Optimization - Promotional Effectiveness Studies - Store Comparison Analysis - Product Popularity Tracking - Customer Lifetime Value Calculation - Demand Forecasting Models - Data Cleaning and Preprocessing Practice

Technical Specifications

• File Format: CSV, Excel (XLSX), JSON
• Encoding: UTF-8
• Missing Value Representation: NULL/empty values
• Date Format: YYYY-MM-DD
• Currency: USD (implied)
• Decimal Precision: 2 decimal places for monetary values

Context : Humans in the Loop is excited to publish an open access dataset meant for detecting packaged products on supermarket shelves. Product and price tag detection is a key challenge in retail AI applications and we are happy to release a fully annotated sample dataset for such purposes.

Content : The dataset contains 45 copyright-free images of shelves with products from around the world. The dataset is open for both academic and commercial usage for advancing product detection.

The dataset consists of 11,743 bounding boxes or an average of 260 boxes per image. The images were annotated by the team of refugee of conflict-affected talent managed by our partner NGO Techfugees in Lebanon.

This supermarket shelves dataset dataset is dedicated to the public domain by Humans in the Loop under CC0 1.0 license

Introduction

The collected data sets come from the multi-branch store computer system. The data shows: stocking, sales, sales statistics, characteristics of products sold from January 2018 - December 2018.

About the store:

Store was open in 2009 and is located in Poland. The shop area is 120m2. We offer general food-and basic chemistry, hygienic articles. We have fresh bread from 4 different bakers,sweets, local vegetables, dairy, basic meat(ham,sausages), newspaper, home chemistry etc. Interior is basic.

Location: Shop is located in city that population is around 28 000 people. Shop is placed in mid of house estate( block of flats), near is sports field. The store is open every day: Monday-Saturday from 06:00 to 22:00, Sunday from 10:00 to 20:00. The store has 4 employees. Work in the store takes place on 3 shifts. First: 06:00- 12:00, second: 10:00-16: 00/18:00 and third: 16: 00-22: 00.

Competition

1. The nearest competition: There is another grocery store nearby (30 m). The second store is smaller - also a delicatessen, but half smaller. They offer similar products for daily use- bread, dairy, some meat and general foods. I'm not sure about alcohol and how wide their offer is. However in our store the offer is richer(bread is delivered from 4 different bakers). To know exactly what are the differences I need check details.

2. Grocery stores in the town:

3. 1 hypermarket

4. 8 supermarkets

5. 25 groceries stores

6. Shopping trends in Poland Connected to our location: People tend to do general food shopping in supermarkets. If they need daily fresh things, something is missing or they need some special product (not valid at supermarket) they do shopping at groceries like ours. Still in Poland people prefer to go to shop in the neighborhood to do: quicker shopping/talk to people/or just throw out rubbish and do shop at once. To do bigger shopping they go by car to supermarket e.g. after work or on weekend.

Online shopping: E-commerce are 1% of the sales of the FMCG goods market in Poland. It is starting to be popular in bigger cities like Warsaw, Krakow etc. Not popular in our city.

Health trend: -Three-quarters of Polish consumers agree with the statement that "you are what you eat". Therefore, we pay more attention to what we eat and do not save on food products

Convenience trend: According to the expert, the habits of buyers will not change so quickly, and the fact is that Poles like to shop flat - Polish shoppers visit 4 shops a month on average. Also the vast majority of them tend to make smaller purchases, which confirms the most popular shopping mission - replenishing stocks. However, the shopping experience is pleasant in the third place among buyers' motivation and selection of the store. 8 out of 10 buyers prefer to shop in a well-organized store with a nice atmosphere. This is one of the reasons for the development of the convenience channel. He also responds very well to other needs of Polish consumers, because Poles definitely have less and less time, so shopping must be fast and convenient. In this situation, the price is not the most important - 30% of Polish buyers declare that anything that saves their time is worth the higher price.

Costumer

Our costumer is located in the neighborhood leave in house estate (block of flats). During events of the sport field our opening hours are adjusted to get more costumers from event. Moreover, during trade free Sundays we have costumers from City. Some of the costumer work abroad and come to our shop when they are at home and have special order- e.g. cigarettes packages.

Demographic of city and wellness of inhabitants

Average age of people is 40 years old. Gender split is equal between men and women. Majority of population are marriages 60% and city has positive natural increase. Unemployment rate is low and similar to country rate- around 7%. Average monthly gross salary is around 3800 PLN gross .This is between minimum and average salary in Poland. (Minimum wage in Poland is :2250 PLN gross and average wage is : 4272 PLN gross.) Occupation split of people is : 40 % industry and construction, 30% agricultural sector, 11%service sector and other. Companies in the city are micro and small ( only few big companies). City is not touristic. In general situation in city is good-budget revenues are growing year to year. Additionally, polish government gives social funds for every second children starts from 2017 and now in 2019 it is going to be extended to every children, without limits. This should boost economy.

In general- Costumer in the city has good shopping condition.

The main problems faced by the owners are:

• • Overhaul of the owners - the store employs 4 employees, but the owners' great involvement in the current operation of the store means that they are unable to assess the situation and take actions to...

Abstract copyright UK Data Service and data collection copyright owner. This project addressed the implication of the growth in concentration in food retailing in the UK – resulting from the consolidation and small store decline over the long term - with reference to its impact on consumer choice. The reference point of the study was the UK Competition Commission (2000) conclusion that the degree to which consumers will have adequate choice will depend on local circumstances. The project addressed this specific issue by exploring changing retail provision between 1980 and 2002 in an ‘average’ situation (Portsmouth), where extensive, large scale quantitative surveys of shopping behaviour were combined with qualitative studies to provide a richer understanding of different households' use and experiences of local retail provision. The baseline for the core of the study was the replication of a survey conducted in Portsmouth by Hallsworth and reported in: Hallsworth, A.G. (1988) The human impact of hypermarkets and superstores, Aldershot: Avebury. In addition, the approach and conceptual framework of the research were informed by two particular prior publications from members of the research team: Clarke, I (2000) 'Retail power, competition and local consumer choice in the UK grocery sector' European Journal of Marketing, 34(8), pp.975-1002; Miller, D. et al (1998) Shopping, place and identity, London, Routledge. Main Topics: This data collection comprises the qualitative and quantitative data from the project 'Retail Competition and Consumer Choice'. The data were gathered between 2002 and 2004 in the Portsmouth area. The quantitative data comprises two files which include the variables and coding from two surveys: the first survey was carried out at supermarkets' sites and the second survey took the form of a postal questionnaire distributed by hand to residents' homes in the study area (Portsmouth). The qualitative data collection comprises: eight transcriptions of accompanied shopping trips; eight transcriptions of kitchen visits to 10 participants in eight households located in Paulsgrove and Purbrook, two contrasting neighbourhoods in the Portsmouth area. For both sets of transcriptions, the original observation and interview guides are included. Guides for the accompanied shopping trips were customised to each participant. Each guide includes also a summary of the first accompanied shopping trip and the diary that participants completed during 10 days. The guide for the kitchen visit was the same for all participants. Standard Measures: Likert Scales were applied in the quantitative phase. Simple random sample Purposive selection/case studies Face-to-face interview Postal survey Observation Semi-structured interview transcripts; Interview summaries; Observation field notes (included with transcripts).

The Average Residential Retail Kerosene Prices dataset provides New York residents and businesses with objective information on average residential retail kerosene pricing in New York State and by region beginning September 4, 2000. Pricing data is obtained via surveys conducted by New York State Energy Research and Development Authority (NYSERDA) staff on a weekly basis during heating season (September to March) and bi-weekly during the rest of the year. All prices are listed in dollars per gallon.

The Average Home Heating Oil Prices dataset, Average Residential Retail Kerosene Prices dataset, and Average Residential Retail Propane Prices dataset are collectively referred to as the Heating Fuel Prices dataset.

For current and historical residential retail price data, regional comparisons, and fuel type comparisons, please visit the Kerosene Prices Dashboard: https://www.nyserda.ny.gov/researchers-and-policymakers/energy-prices/kerosene/average-kerosene-prices

The New York State Energy Research and Development Authority (NYSERDA) offers objective information and analysis, innovative programs, technical expertise, and support to help New Yorkers increase energy efficiency, save money, use renewable energy, and reduce reliance on fossil fuels. To learn more about NYSERDA’s programs, visit https://nyserda.ny.gov or follow us on X, Facebook, YouTube, or Instagram.

This dataset contains data on annual average retail prices of food in Lithuania in 1919-1939. Dataset "Annual Average Retail Prices of Food in Lithuania, 1913-1939" was published implementing project "Historical Sociology of Modern Restorations: a Cross-Time Comparative Study of Post-Communist Transformation in the Baltic States" from 2018 to 2022. Project leader is prof. Zenonas Norkus. Project is funded by the European Social Fund according to the activity "Improvement of researchers' qualification by implementing world-class R&D projects' of Measure No. 09.3.3-LMT-K-712".

1) Data Introduction • The Clickstream Data for Online Shopping is an e-commerce analysis dataset that summarizes user clickstream, product information, country, price, and other session-specific behavior data from April to August 2008 at an online shopping mall specializing in maternity clothing.

2) Data Utilization (1) Clickstream Data for Online Shopping has characteristics that: • Each row contains 14 key variables: year, month, day, click order, country (by access IP), session ID, main category, product code, color, photo location, model photo type, price, category average price, page number, etc. • Data is configured to enable analysis of various consumer behaviors such as click flows for each session, product attributes, and country-specific access patterns. (2) Clickstream Data for Online Shopping can be used to: • Online Shopping Mall User Behavior Analysis: Using clickstream, session, and product information, you can analyze purchase conversion routes, popular products, and behavioral patterns by country and category. • Improve marketing strategies and UI/UX: analyze the relationship between product photo location, color, price, etc. and click behavior and apply to establish effective marketing strategies and improvement of shopping mall UI/UX.

This dataset provides information related to average retail prices of some essential commodities of Himachal Pradesh. This information is avilable at State level from 2020 to June 2022. The datasource is Directorate of Economic & Statistics, H.P.

This dataset contains data on annual average retail prices of non-food goods in Tallinn in 1919-1939. Dataset "Annual Average Retail Prices of Non-Food Goods in Tallinn (Estonia), 1913-1939" was published implementing project "Historical Sociology of Modern Restorations: a Cross-Time Comparative Study of Post-Communist Transformation in the Baltic States" from 2018 to 2022. Project leader is prof. Zenonas Norkus. Project is funded by the European Social Fund according to the activity "Improvement of researchers' qualification by implementing world-class R&D projects' of Measure No. 09.3.3-LMT-K-712".

Analysis of ‘E-Shop Clothing Dataset’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/adityawisnugrahas/eshop-clothing-dataset on 11 August 2021.

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

Data description “e-shop clothing 2008”

Variables:

1. YEAR (2008)

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1. MONTH -> from April (4) to August (8)

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1. DAY -> day number of the month

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1. ORDER -> sequence of clicks during one session

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1. COUNTRY -> variable indicating the country of origin of the IP address with the following categories:

1-Australia 2-Austria 3-Belgium 4-British Virgin Islands 5-Cayman Islands 6-Christmas Island 7-Croatia 8-Cyprus 9-Czech Republic 10-Denmark 11-Estonia 12-unidentified 13-Faroe Islands 14-Finland 15-France 16-Germany 17-Greece 18-Hungary 19-Iceland 20-India 21-Ireland 22-Italy 23-Latvia 24-Lithuania 25-Luxembourg 26-Mexico 27-Netherlands 28-Norway 29-Poland 30-Portugal 31-Romania 32-Russia 33-San Marino 34-Slovakia 35-Slovenia 36-Spain 37-Sweden 38-Switzerland 39-Ukraine 40-United Arab Emirates 41-United Kingdom 42-USA 43-biz (.biz) 44-com (.com) 45-int (.int) 46-net (.net) 47-org (*.org)

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1. SESSION ID -> variable indicating session id (short record)

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1. PAGE 1 (MAIN CATEGORY) -> concerns the main product category: 1-trousers 2-skirts 3-blouses 4-sale

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1. PAGE 2 (CLOTHING MODEL) -> contains information about the code for each product (217 products)

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1. COLOUR -> colour of product

1-beige 2-black 3-blue 4-brown 5-burgundy 6-gray 7-green 8-navy blue 9-of many colors 10-olive 11-pink 12-red 13-violet 14-white

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1. LOCATION -> photo location on the page, the screen has been divided into six parts:

1-top left 2-top in the middle 3-top right 4-bottom left 5-bottom in the middle 6-bottom right

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1. MODEL PHOTOGRAPHY -> variable with two categories:

1-en face 2-profile

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1. PRICE -> price in US dollars

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1. PRICE 2 -> variable informing whether the price of a particular product is higher than the average price for the entire product category

1-yes 2-no

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1. PAGE -> page number within the e-store website (from 1 to 5)

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I want to know how to solve this data regarding any problem (clustering, regression, classification, EDA)

Source: https://archive.ics.uci.edu/ml/datasets/clickstream+data+for+online+shopping

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

The Average Residential Retail Propane Prices dataset provides New York residents and businesses with objective information on average residential retail propane pricing in New York State and by region beginning September 8, 1997. Pricing data is obtained via surveys conducted by New York State Energy Research and Development Authority (NYSERDA) staff on a weekly basis during heating season (September to March) and bi-weekly during the rest of the year. All prices are listed in dollars per gallon.

The Average Home Heating Oil Prices dataset, Average Residential Retail Kerosene Prices dataset, and Average Residential Retail Propane Prices dataset are collectively referred to as the Heating Fuel Prices dataset.

For current and historical residential retail price data, regional comparisons, and fuel type comparisons, please visit the Propane Prices Dashboard: https://www.nyserda.ny.gov/researchers-and-policymakers/energy-prices/propane/average-propane-prices

The New York State Energy Research and Development Authority (NYSERDA) offers objective information and analysis, innovative programs, technical expertise, and support to help New Yorkers increase energy efficiency, save money, use renewable energy, and reduce reliance on fossil fuels. To learn more about NYSERDA’s programs, visit nyserda.ny.gov or follow us on X, Facebook, YouTube, or Instagram.

Our dataset provides detailed and precise insights into the business, commercial, and industrial aspects of any given area in the USA (Including Point of Interest (POI) Data and Foot Traffic. The dataset is divided into 150x150 sqm areas (geohash 7) and has over 50 variables. - Use it for different applications: Our combined dataset, which includes POI and foot traffic data, can be employed for various purposes. Different data teams use it to guide retailers and FMCG brands in site selection, fuel marketing intelligence, analyze trade areas, and assess company risk. Our dataset has also proven to be useful for real estate investment.- Get reliable data: Our datasets have been processed, enriched, and tested so your data team can use them more quickly and accurately.- Ideal for trainning ML models. The high quality of our geographic information layers results from more than seven years of work dedicated to the deep understanding and modeling of geospatial Big Data. Among the features that distinguished this dataset is the use of anonymized and user-compliant mobile device GPS location, enriched with other alternative and public data.- Easy to use: Our dataset is user-friendly and can be easily integrated to your current models. Also, we can deliver your data in different formats, like .csv, according to your analysis requirements. - Get personalized guidance: In addition to providing reliable datasets, we advise your analysts on their correct implementation.Our data scientists can guide your internal team on the optimal algorithms and models to get the most out of the information we provide (without compromising the security of your internal data).Answer questions like: - What places does my target user visit in a particular area? Which are the best areas to place a new POS?- What is the average yearly income of users in a particular area?- What is the influx of visits that my competition receives?- What is the volume of traffic surrounding my current POS?This dataset is useful for getting insights from industries like:- Retail & FMCG- Banking, Finance, and Investment- Car Dealerships- Real Estate- Convenience Stores- Pharma and medical laboratories- Restaurant chains and franchises- Clothing chains and franchisesOur dataset includes more than 50 variables, such as:- Number of pedestrians seen in the area.- Number of vehicles seen in the area.- Average speed of movement of the vehicles seen in the area.- Point of Interest (POIs) (in number and type) seen in the area (supermarkets, pharmacies, recreational locations, restaurants, offices, hotels, parking lots, wholesalers, financial services, pet services, shopping malls, among others). - Average yearly income range (anonymized and aggregated) of the devices seen in the area.Notes to better understand this dataset:- POI confidence means the average confidence of POIs in the area. In this case, POIs are any kind of location, such as a restaurant, a hotel, or a library. - Category confidences, for example"food_drinks_tobacco_retail_confidence" indicates how confident we are in the existence of food/drink/tobacco retail locations in the area. - We added predictions for The Home Depot and Lowe's Home Improvement stores in the dataset sample. These predictions were the result of a machine-learning model that was trained with the data. Knowing where the current stores are, we can find the most similar areas for new stores to open.How efficient is a Geohash?Geohash is a faster, cost-effective geofencing option that reduces input data load and provides actionable information. Its benefits include faster querying, reduced cost, minimal configuration, and ease of use.Geohash ranges from 1 to 12 characters. The dataset can be split into variable-size geohashes, with the default being geohash7 (150m x 150m).

Diesel retail prices weekly average by region dataset provides the weekly average retail diesel prices for New York State and eight New York metropolitan regions in U.S. dollars per gallon. Data is a weekly average from January 2017 through current. Average daily retail diesel prices are collected from the American Automobile Association (AAA) Daily Fuel Gauge Report. The AAA Daily Fuel Gauge Report prices are averaged to produce a weekly average retail price for New York State and each metropolitan region. The New York State metropolitan regions in the dataset are Albany (Albany-Schenectady-Troy), Batavia, Binghamton, Buffalo (Buffalo-Niagara Falls), Dutchess (Dutchess-Putnam), Elmira, Glens Falls, Ithaca, Kingston, Nassau (Nassau-Suffolk), New York City, Rochester, Syracuse, Utica (Utica-Rome), Watertown (Watertown-Fort Drum), and White Plains. The New York State Energy Research and Development Authority (NYSERDA) offers objective information and analysis, innovative programs, technical expertise, and support to help New Yorkers increase energy efficiency, save money, use renewable energy, and reduce reliance on fossil fuels. To learn more about NYSERDA’s programs, visit https://nyserda.ny.gov or follow us on X, Facebook, YouTube, or Instagram.

Cost of food in the United States increased 3 percent in June of 2025 over the same month in the previous year. This dataset provides the latest reported value for - United States Food Inflation - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

The Consumer Price Index (CPI) for food is a component of the all-items CPI. The CPI measures the average change over time in the prices paid by urban consumers for a representative market basket of consumer goods and services. While the all-items CPI measures the price changes for all consumer goods and services, including food, the CPI for food measures the changes in the retail prices of food items only.This record was taken from the USDA Enterprise Data Inventory that feeds into the https://data.gov catalog. Data for this record includes the following resources: Web page with links to Excel files For complete information, please visit https://data.gov.

This comprehensive IKEA USA products dataset contains detailed information about thousands of authentic IKEA furniture items, home decor, and household products available in the United States market. The dataset provides complete product specifications, pricing, availability, and detailed descriptions for ecommerce analysis, price comparison, and furniture retail research.

Key Features:

• Complete IKEA USA product catalog with real pricing data
• Detailed product descriptions and specifications
• Product URLs, article numbers, and availability status
• Furniture categories including office chairs, storage solutions, outdoor furniture
• Home decor items like candle holders, planters, and textiles
• Kitchen cabinets, wardrobes, and organizational systems
• Material specifications and sustainability information
• Product dimensions, weights, and packaging details

Get Free Sample: Download your free sample dataset now to explore the data quality and structure before purchasing the complete IKEA USA products database. The free sample includes representative product entries with all key fields populated.

Applications: Perfect for furniture market analysis, home improvement research, interior design planning, competitive pricing analysis, and retail intelligence. This dataset enables businesses to understand IKEA pricing strategies, product positioning, and market trends in the home furnishing industry.

Product Categories Included: Office furniture, bedroom furniture, storage solutions, outdoor dining sets, kitchen systems, home organization products, decorative accessories, plant containers, and sustainable furniture options. All products include comprehensive details for business intelligence and market research applications.

The Residential Retail Propane dataset provides information on propane prices and sales for residential use. It offers insights into energy costs, consumption trends, and fuel availability in the residential sector.