Analysis of ‘Shopping Cart Database’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/ruchi798/shopping-cart-database on 28 January 2022.

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

Context

This dataset contains synthetic data generated by me for one of my courses at Carnegie Mellon University.

Inspiration

Several deductions and analyses can be drawn from this data, including: - Which products were sold the most in the last month? - How have sales and revenue changed over the past few quarters? - Understanding Customer demographics and their preferences

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

The mission of the VA Clinical Assessment Reporting and Tracking (CART) Program for cardiac catheterization laboratories (CART-CL) is to support a national VA reporting system, data repository, and quality improvement program for procedures performed in VA cardiac catheterization laboratories. CART-CL is intended to improve clinical care/communication, support local and national quality improvement, monitor patient safety, capture workload of cardiac catheterization lab procedures, and inform VA system evaluation to maximize operational efficiency and patient outcomes. CART-CL is a collaborative effort between the VA Patient Care Services, Office of Information and Analytics, Ischemic Heart Disease Quality Enhancement Research Initiative (IHD-QUERI), and Office of Information and Technology (OI&T).

Neighbor Capital is a social enterprise that runs a produce cart program that employs difficult to employ people who manage and operate a cart on a public sidewalk or private plaza across the city. The City of Chicago legalized this business model in 2012 with the requirement that half be set up in low-food access areas. Each cart carries a full line of fresh locally grown produce on an 8 feet by 6 foot stand generally between early morning and early evening hours.

Comprehensive dataset of 0 Golf cart dealers in Hungary as of July, 2025. Includes verified contact information (email, phone), geocoded addresses, customer ratings, reviews, business categories, and operational details. Perfect for market research, lead generation, competitive analysis, and business intelligence. Download a complimentary sample to evaluate data quality and completeness.

Comprehensive dataset of 2 Golf cart dealers in Norway as of June, 2025. Includes verified contact information (email, phone), geocoded addresses, customer ratings, reviews, business categories, and operational details. Perfect for market research, lead generation, competitive analysis, and business intelligence. Download a complimentary sample to evaluate data quality and completeness.

52 Global import shipment records of Cart with prices, volume & current Buyer's suppliers relationships based on actual Global export trade database.

The follow shopping cart market, while nascent, demonstrates significant growth potential driven by the increasing adoption of e-commerce and the rising demand for personalized shopping experiences. The market, estimated at $500 million in 2025, is projected to experience a robust Compound Annual Growth Rate (CAGR) of 15% from 2025 to 2033, reaching approximately $1.8 billion by the end of the forecast period. Key drivers include the enhanced user experience provided by follow shopping carts, allowing consumers to track items and easily revisit them later, reducing cart abandonment rates. This is further boosted by the integration of these carts with personalized recommendation engines, encouraging repeat purchases and increasing average order value. Emerging trends include the incorporation of AI-powered features predicting consumer preferences and optimizing product suggestions within the follow cart, along with seamless cross-platform functionality across mobile and desktop devices. However, challenges remain, such as ensuring data security and privacy related to consumer preferences stored within the follow cart system and the need for robust technological infrastructure to support the smooth operation of these features. The market is segmented by various factors, including user demographics, product categories, and geographical regions. Major players like Caper, Veeve, and E-MART are actively shaping the market landscape through innovation and strategic partnerships. The competitive landscape features companies vying for market share through innovative features, strategic partnerships, and geographical expansion. Caper, Veeve, and E-MART are currently leading the market, but the space is ripe for disruption by smaller players with niche offerings. Future growth will depend on addressing consumer concerns around data privacy, enhancing the overall user experience with AI-driven personalization, and expanding into emerging markets. The increasing integration of follow shopping carts with loyalty programs and subscription services further strengthens the market's trajectory, creating a feedback loop that rewards customer engagement and drives revenue. Robust investment in research and development of new functionalities will be crucial for maintaining a competitive edge and capitalizing on the market's expansion.

Comprehensive dataset of 5,210 Golf cart dealers in United States as of July, 2025. Includes verified contact information (email, phone), geocoded addresses, customer ratings, reviews, business categories, and operational details. Perfect for market research, lead generation, competitive analysis, and business intelligence. Download a complimentary sample to evaluate data quality and completeness.

Russia Avg Producer Price: OKPD2: Vehicles & Parts: Tramway Cart data was reported at 10,593,220.000 RUB in Dec 2018. This stayed constant from the previous number of 10,593,220.000 RUB for Oct 2018. Russia Avg Producer Price: OKPD2: Vehicles & Parts: Tramway Cart data is updated monthly, averaging 10,593,220.000 RUB from Jan 2017 (Median) to Dec 2018, with 11 observations. The data reached an all-time high of 18,149,152.000 RUB in Oct 2017 and a record low of 10,593,220.000 RUB in Dec 2018. Russia Avg Producer Price: OKPD2: Vehicles & Parts: Tramway Cart data remains active status in CEIC and is reported by Federal State Statistics Service. The data is categorized under Russia Premium Database’s Prices – Table RU.PB022: Average Producer Price: Vehicles and Parts.

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Shopping Cart Market Analysis – Forecast Period 2022-2026

The consumer shopping cart market analysis report provides a comprehensive analysis of the market with information such as the potential to grow by USD 86.4 million during 2022-2026, and the market’s growth momentum will accelerate at a CAGR of 2.05%.

With a detailed analysis of the vendors, this report helps established and new market players to have a keen understanding of their competitors and plan their strategies accordingly.

Based on our research output, there has been a positive impact on the shopping cart market growth during and post COVID-19 era. The expanding retail space is notably driving the consumer shopping cart market growth, although factors such as fluctuations in raw material prices may impede the market growth. Our research analysts have studied the historical data and deduced the key market drivers and the COVID-19 pandemic impact on the consumer shopping cart industry.

One of the key factors driving growth in the consumer shopping cart market is the expanding retail space. Globally, the increasing number of retail stores, particularly supermarkets and hypermarkets, is supporting the growth of the global consumer shopping cart market.

Key Data Covered in this Consumer Shopping Cart Market Report:

CAGR of the market during the forecast period 2022-2026
Detailed information on factors that will drive consumer shopping cart market growth during the next five years
Precise estimation of the consumer shopping cart market size and its contribution to the parent market
Accurate predictions on upcoming trends and changes in consumer behavior
The growth of the consumer shopping cart industry across Europe, North America, APAC, Middle East and Africa, and South America
A thorough analysis of the market's competitive landscape and detailed information on vendors
Comprehensive details of factors that will challenge the growth of consumer shopping cart market vendors

This consumer shopping cart analysis report provides an accurate prediction of the contribution of all the segments to the growth of the consumer shopping cart market size and actionable market insights on post COVID-19 impact on each segment.

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The consumer shopping cart market size was valued at $ 84.91 mn in 2021-2025. The market is expected to witness robust growth at a CAGR of 2 % during the forecast period 2021-2025. The market sizing has been built and validated using multiple demand-side and supply-side approaches for a detailed understanding of the consumer shopping cart market.

The size of the global consumer shopping cart market has been derived by triangulating data from multiple sources and approaches. While arriving at the market size, we have considered data points, such as the size of the parent market and the revenues of key market participants, such as Advance Carts Inc., Americana Companies, Changshu Yirunda Business Equipment Factory, National Cart Co., R.W. Rogers Co., Sambo Corp., Shanghai Rongxin PetProduct Co. Ltd., The Peggs Co. Inc., Versacart Systems Inc., and Wanzl Metallwarenfabrik GmbH.

Overview

The SUMR-D CART2 turbine data are recorded by the CART2 wind turbine's supervisory control and data acquisition (SCADA) system for the Advanced Research Projects Agency–Energy (ARPA-E) SUMR-D project located at the National Renewable Energy Laboratory (NREL) Flatirons Campus. For the project, the CART2 wind turbine was outfitted with a highly flexible rotor specifically designed and constructed for the project. More details about the project can be found here: https://sumrwind.com/. The data include power, loads, and meteorological information from the turbine during startup, operation, and shutdown, and when it was parked and idle.

Data Details

Additional files are attached:
sumr_d_5-Min_Database.mat - a database file in MATLAB format of this dataset, which can be used to search for desired data files; sumr_d_5-Min_Database.xlsx - a database file in Microsoft Excel format of this dataset, which can be used to search for desired data files; loadcartU.m - this script loads in a CART data file and puts it in your workspace as a Matlab matrix (you can call this script from your own Matlab scripts to do your own analysis); charts.mat - this is a dependency file needed for the other scripts (it allows you to make custom preselections for cartPlotU.m); cartLoadHdrU.m - this script loads in the header file information for the data file (the header is embedded in each data file at the beginning); cartPlotU.m - this is a graphic user interface (GUI) that allows you to interactively look at different channels (to use it, run the script in Matlab, and load in the data file(s) of interest; from there, you can select different channels and plot things against each other; note that this script has issues with later versions of MATLAB; the preferred version to use is R2011b).

Data Quality

Wind turbine blade loading data were calibrated using blade gravity calibrations prior to data collection and throughout the data collection period. Blade loading was also checked for data quality following data collection as strain gauge measurements drifted throughout the data collection. These drifts in the strain gauge measurements were removed in post processing.

Cart abandonment rates have been climbing steadily since 2014, after reaching an all-time high in 2013. In 2023, the share of online shopping carts that is being abandoned reached 70 percent for the first time since 2013. This is an increase of more than 10 percentage points compared to the start of the time period considered here. Mobiles vs. desktops When global consumers shop online, they spend considerably more when doing so on desktop computers. In December 2023, the average value of e-commerce purchases made through desktops was approximately 159 U.S. dollars. Purchases completed on mobiles and tablets were of comparable values, ranging between 100 and 105 U.S. dollars. Even though consumers spent more when conducting their shopping on computers, they were more inclined to add products to their shopping carts when using mobile devices. Ultimately, mobile devices provide a convenient and more accessible way to shop, but desktop computers remain the preferred choice for more expensive purchases. Where do consumers shop online? Across the globe, digital marketplaces are shoppers’ number-one online shopping destination. As of April 2024, some 29 percent of consumers voted marketplaces as their favorite e-commerce channel, followed by physical stores and retailer sites. Looking at which retailers’ global shoppers prefer to shop at, amazon.com emerged as the world's most popular online marketplace, based on share of visits. The U.S. portal accounted for around one-fifth of the global online marketplace's traffic in December 2023. Amazon's German and Japanese portal sites ranked third and fifth among the leading online marketplaces, further demonstrating Amazon's dominance over the market.

Comprehensive dataset of 52 Golf cart dealers in Indonesia as of July, 2025. Includes verified contact information (email, phone), geocoded addresses, customer ratings, reviews, business categories, and operational details. Perfect for market research, lead generation, competitive analysis, and business intelligence. Download a complimentary sample to evaluate data quality and completeness.

An easy-to-customize, low-cost, low disturbance, motorized, and adjustable proximal sensing cart for field-based high-throughput phenotyping is described. General dimensions, motor specifications, and a remote operation application are given. The cart, named "Professor", supports mounting multiple proximal sensors and cameras for characterizing plant traits grown under field conditions. Professor easily adapts to multiple sensor configurations supporting detection of multiple target traits and has two axes of adjustable clearance by design. Professor is useful as a field-based phenotyping platform and offers a framework for customized development and application. Resources in this dataset:Resource Title: S. File 1. File Name: S. File 1.xlsxResource Description: Components needed for the assembly of Professor. Hyperlinks to websites for purchase also provided.Resource Title: S. File 2. File Name: S. File 2.PDFResource Description: Specifications for Swivel wheel assembly. File provides two views of the swivel wheel mount including dimensions and part order assembly.Resource Title: S. File 3. File Name: S. File 3.PDFResource Description: Specifications for wheel support arm assembly. File provides two views of the wheel support including dimensions and part order assembly.Resource Title: S. File 4. File Name: S. File 4.pdfResource Description: Specifications for Front wheel assembly. File provides two views of the front wheel mount including dimensions and part order assembly.Resource Title: S. File 5. File Name: S. File 5.PDFResource Description: Specifications for Motor assembly. File includes two views of the motor mount including dimensions and part order assembly.Resource Title: S. File 6. File Name: S. File 6.PDFResource Description: Specifications for Custom sprocket. File contains dimensions only.Resource Title: S. File 7. File Name: S. File 7.xlsxResource Description: This file contains the collected GPS and timestamp information used to generate Table 1 in the paper. The file was generated by clipping GPS data to plot polygons in Quantum GIS software to show accuracy of placement and consistency of speed of the motorized cart in the field. Table 1. The drive time for each 12.1 m plot for three replicates (Pass) as recorded by a Hemishpere GPS receiver and Campbell Scientific data logger. The average time per plot is calculated below. The difference in time for each plot from the average is listed in the Difference column. Time is listed as minute:second.milisecondResource Title: S. Video 1. File Name: S. Video 1.mp4Resource Description: A short video showing Professor navigate loose soil with small dirt clods in the field. Note the cart is being operated in a reverse configuration. The camera is mounted on the frame and directed towards the swivel wheels to show displacement.Resource Title: S. Video 2. File Name: S. Video 2.mp4Resource Description: A short video showing Professor navigate very loose soil up a small incline. Note the cart does slip and bounce when traction is lost. The video camera is mounted to the operator’s hat.Resource Title: S. Video 3. File Name: S. Video 3.mp4Resource Description: A short video showing Professor navigate bumpy terrain with medium dirt clods. The video camera is mounted to the operator’s hat.Resource Title: S. Video 4. File Name: S. Video 4.mp4Resource Description: A short video showing Professor navigate rough terrain with large dirt clods. Note the cart does require minor assistance in this video. The video camera is mounted to the operator’s hat.

This dataset provides information about the number of properties, residents, and average property values for Cart Drive cross streets in Saint Simons Island, GA.

The shopping-cart extension for CKAN provides users with the ability to manage and organize datasets in a CKAN instance in a manner analogous to an e-commerce shopping cart. While the current documentation lacks extensive details, the extension likely facilitates the selection and grouping of datasets for specific purposes, potentially integrating with other functionalities such as data download or request processing. By introducing a shopping cart concept, this extension aims to increase user convenience and facilitate the management of selected datasets. Key Features: Dataset Selection and Grouping: This extension most likely permits users to select multiple datasets from the CKAN catalog and add them to a virtual "shopping cart." Cart Management: Users can probably review the contents of their cart, remove datasets, and manage selected datasets for later actions. Integration with CKAN's User Interface: The extension seamlessly integrates into the current CKAN UI, making dataset selections and cart access easy for users. Use Cases (Assumed Based on Functionality): Data Export: Users might add datasets to the cart and then export them as a bundle. Request for Additional Information: Users could add datasets to the shopping cart and then submit a request for a customized form related to those datasets. Data Filtering/Selection from UI: Users can search datasets in their shopping and filter the selected datasets. Technical Integration: The extension is installed as a standard CKAN plugin. The ckan.plugins setting within the CKAN configuration file (/etc/ckan/default/ckan.ini by default) must include shopping-cart. After installation, restart CKAN to enable the plugin. The readme does not fully elaborate on the integration within core CKAN, but we can assume there is extensions of the CKAN APIs. Benefits & Impact: Implementing the shopping-cart extension streamlines dataset management within CKAN. The cart could be beneficial for users who need data that should be organized and downloaded, therefore potentially increasing user engagement and adoption of the CKAN platform.

This dataset provides information about the number of properties, residents, and average property values for Cart Road cross streets in Pamplico, SC.

Instacart reported 0 in Debt for its fiscal quarter ending in December of 2024. Data for Instacart | CART - Debt including historical, tables and charts were last updated by Trading Economics this last July in 2025.

Comprehensive dataset of 1 Golf cart dealers in Israel as of July, 2025. Includes verified contact information (email, phone), geocoded addresses, customer ratings, reviews, business categories, and operational details. Perfect for market research, lead generation, competitive analysis, and business intelligence. Download a complimentary sample to evaluate data quality and completeness.