This dataset was created to simulate a market basket dataset, providing insights into customer purchasing behavior and store operations. The dataset facilitates market basket analysis, customer segmentation, and other retail analytics tasks. Here's more information about the context and inspiration behind this dataset:

Context: Retail businesses, from supermarkets to convenience stores, are constantly seeking ways to better understand their customers and improve their operations. Market basket analysis, a technique used in retail analytics, explores customer purchase patterns to uncover associations between products, identify trends, and optimize pricing and promotions. Customer segmentation allows businesses to tailor their offerings to specific groups, enhancing the customer experience.

Inspiration: The inspiration for this dataset comes from the need for accessible and customizable market basket datasets. While real-world retail data is sensitive and often restricted, synthetic datasets offer a safe and versatile alternative. Researchers, data scientists, and analysts can use this dataset to develop and test algorithms, models, and analytical tools.

Dataset Information: The columns provide information about the transactions, customers, products, and purchasing behavior, making the dataset suitable for various analyses, including market basket analysis and customer segmentation. Here's a brief explanation of each column in the Dataset:

Transaction_ID: A unique identifier for each transaction, represented as a 10-digit number. This column is used to uniquely identify each purchase. Date: The date and time when the transaction occurred. It records the timestamp of each purchase. Customer_Name: The name of the customer who made the purchase. It provides information about the customer's identity. Product: A list of products purchased in the transaction. It includes the names of the products bought. Total_Items: The total number of items purchased in the transaction. It represents the quantity of products bought. Total_Cost: The total cost of the purchase, in currency. It represents the financial value of the transaction. Payment_Method: The method used for payment in the transaction, such as credit card, debit card, cash, or mobile payment. City: The city where the purchase took place. It indicates the location of the transaction. Store_Type: The type of store where the purchase was made, such as a supermarket, convenience store, department store, etc. Discount_Applied: A binary indicator (True/False) representing whether a discount was applied to the transaction. Customer_Category: A category representing the customer's background or age group. Season: The season in which the purchase occurred, such as spring, summer, fall, or winter. Promotion: The type of promotion applied to the transaction, such as "None," "BOGO (Buy One Get One)," or "Discount on Selected Items." Use Cases: Market Basket Analysis: Discover associations between products and uncover buying patterns. Customer Segmentation: Group customers based on purchasing behavior. Pricing Optimization: Optimize pricing strategies and identify opportunities for discounts and promotions. Retail Analytics: Analyze store performance and customer trends. Note: This dataset is entirely synthetic and was generated using the Python Faker library, which means it doesn't contain real customer data. It's designed for educational and research purposes.

Original Data Source: Retail Transactions Dataset

BASKET🏀: A Large-Scale Video Dataset for Fine-Grained Skill Estimation

Yulu Pan, Ce Zhang, Gedas Bertasius UNC Chapel Hill Accepted by CVPR 2025 Paper Project Page Data

  🚀 BASKET Highlights

🔥 Massive Scale: BASKET features 4,477 hours of video showcasing 32,232 basketball players from across the globe! 🔥 Extensive Diversity: Spanning 21 basketball leagues, both professional and amateur, featuring over 7,000 female players and detailed skill level annotations across 20… See the full description on the dataset page: https://huggingface.co/datasets/yulupan/BASKET.

Market Basket Measure (MBM) thresholds for the reference family by MBM region and base year. Total thresholds as well as thresholds for the food, clothing, transportation, shelter and other expenses components are presented, in current and constant dollars, annual.

Polychlorinated biphenyls (PCBs) are a class of industrial chemicals that were used in a wide variety of applications including transformer oils, paints, and lubricants. PCBs are lipophilic compounds and accumulate in the tissues of biological organisms and bioconcentrate through the food chain. These compounds are thermally stable, persist in the environment and are subject to long-range transport. Canadian regulations related to PCBs came into force in 2008, which limit the release of these chemicals to the environment. PCBs can still be detected at very low levels in the Canadian food supply, particularly higher fat foods of animal origin. This dataset includes the following years of surveillance results: 1992-1996, 1998, 2000-2003, 2005-2007, 2009, 2013, and 2015. Learn about the Canadian Total Diet Study (https://www.canada.ca/en/health-canada/services/food-nutrition/food-nutrition-surveillance/canadian-total-diet-study.html) Search through Health Canada's food contaminant data on CANLINE (https://open.canada.ca/data/en/dataset/01c12f93-d14c-4005-b671-e40030a3aa2c)

Abstract

The main purpose of the Household Income Expenditure Survey (HIES) 2016 was to offer high quality and nationwide representative household data that provided information on incomes and expenditure in order to update the Consumer Price Index (CPI), improve National Accounts statistics, provide agricultural data and measure poverty as well as other socio-economic indicators. These statistics were urgently required for evidence-based policy making and monitoring of implementation results supported by the Poverty Reduction Strategy (I & II), the AfT and the Liberia National Vision 2030. The survey was implemented by the Liberia Institute of Statistics and Geo-Information Services (LISGIS) over a 12-month period, starting from January 2016 and was completed in January 2017. LISGIS completed a total of 8,350 interviews, thus providing sufficient observations to make the data statistically significant at the county level. The data captured the effects of seasonality, making it the first of its kind in Liberia. Support for the survey was offered by the Government of Liberia, the World Bank, the European Union, the Swedish International Development Corporation Agency, the United States Agency for International Development and the African Development Bank. The objectives of the 2016 HIES were:

1. Update the Consumer Price Index (CPI): To obtain a new set of weights for the basket of goods and services that upgrade the Monrovia Consumer Price Index (MCPI) and the National Consumer Price Index (NCPI) and to revise the CPI basket of goods and services in Liberia to reflect the current consumption pattern of residence.
2. Improve National Accounts Statistics: To get information on annual household expenditure patterns in order to update the household component of the National Accounts.
3. Measure Poverty: To prepare robust poverty indices that enable the understanding of poverty dynamics across the country and of the factors influencing them.
4. Improve Agricultural Statistics: To obtain nationally representative and policy relevant agricultural statistics in order to undertake in-depth analysis of agricultural households.
5. Capture Socio-economic Impact of Ebola Virus Disease (EVD): To obtain a post-EVD dataset which allows for an in-depth analysis of the socioeconomic impact of EVD on households.
6. Benchmark Agenda for Transformation Indicators: To provide an update on selected socioeconomic indicators used to benchmark the government’s policies embedded within the Agenda for Transformation.
7. Develop Statistical Capacity: Emphasize capacity building and development of sustainable statistical systems through every stage of the project to produce accurate and timely information about Liberia.

Geographic coverage

National

Analysis unit

• Households
• Individuals

Kind of data

Sample survey data [ssd]

Sampling procedure

The original sample design for the HIES exploited two-phased clustered sampling methods, encompassing a nationally representative sample of households in every quarter and was obtained using the 2008 National Housing and Population Census sampling frame. The procedures used for each sampling stage are as follows:
i. First stage
Selection of sample EAs. The sample EAs for the 2016 HIES were selected within each stratum systematically with Probability Proportional to Size from the ordered list of EAs in the sampling frame. They are selected separately for each county by urban/rural stratum. The measure of size for each EA was based on the number of households from the sampling frame of EAs based on the 2008 Liberia Census. Within each stratum the EAs were ordered geographically by district, clan and EA codes. This provided implicit geographic stratification of the sampling frame.

ii. Second stage
Selection of sample households within a sample EA. A random systematic sample of 10 households were selected from the listing for each sample EA. Using this type of table, the supervisor only has to look up the total number of households listed, and a specific systematic sample of households is identified in the corresponding row of the table.

Mode of data collection

Face-to-face [f2f]

Research instrument

There were three questionnaires administered for this survey: 1. Household and Individual Questionnaire 2. Market Price Questionnaire 3. Agricultural Recall Questionnaire

Cleaning operations

The data entry clerk for each team, using data entry software called CSPro, entered data for each household in the field. For each household, an error report was generated on-site, which identified key problems with the data collected (outliers, incorrect entries, inconsistencies with skip patterns, basic filters for age and gender specific questions etc.). The Supervisor along with the Data Entry Clerk and the Enumerator that collected the data reviewed these errors. Callbacks were made to households if necessary to verify information and rectify the errors while in that EA.

Once the data were collected in each EA, they were sent to LISGIS headquarters for further processing along with EA reports for each area visited. The HIES Technical committee converted the data into STATA and ran several consistency checks to manage overall data quality and prepared reports to identify key problems with the data set and called the field teams to update them about the same. Monthly reports were prepared by summarizing observations from data received from the field alongside statistics on data collection status to share with the field teams and LISGIS Management.

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