Note:- Only publicly available data can be worked upon

In today's ever-evolving Ecommerce landscape, success hinges on the ability to harness the power of data. APISCRAPY is your strategic ally, dedicated to providing a comprehensive solution for extracting critical Ecommerce data, including Ecommerce market data, Ecommerce product data, and Ecommerce datasets. With the Ecommerce arena being more competitive than ever, having a data-driven approach is no longer a luxury but a necessity.

APISCRAPY's forte lies in its ability to unearth valuable Ecommerce market data. We recognize that understanding the market dynamics, trends, and fluctuations is essential for making informed decisions.

APISCRAPY's AI-driven ecommerce data scraping service presents several advantages for individuals and businesses seeking comprehensive insights into the ecommerce market. Here are key benefits associated with their advanced data extraction technology:

1. Ecommerce Product Data: APISCRAPY's AI-driven approach ensures the extraction of detailed Ecommerce Product Data, including product specifications, images, and pricing information. This comprehensive data is valuable for market analysis and strategic decision-making.

2. Data Customization: APISCRAPY enables users to customize the data extraction process, ensuring that the extracted ecommerce data aligns precisely with their informational needs. This customization option adds versatility to the service.

3. Efficient Data Extraction: APISCRAPY's technology streamlines the data extraction process, saving users time and effort. The efficiency of the extraction workflow ensures that users can obtain relevant ecommerce data swiftly and consistently.

4. Realtime Insights: Businesses can gain real-time insights into the dynamic Ecommerce Market by accessing rapidly extracted data. This real-time information is crucial for staying ahead of market trends and making timely adjustments to business strategies.

5. Scalability: The technology behind APISCRAPY allows scalable extraction of ecommerce data from various sources, accommodating evolving data needs and handling increased volumes effortlessly.

Beyond the broader market, a deeper dive into specific products can provide invaluable insights. APISCRAPY excels in collecting Ecommerce product data, enabling businesses to analyze product performance, pricing strategies, and customer reviews.

To navigate the complexities of the Ecommerce world, you need access to robust datasets. APISCRAPY's commitment to providing comprehensive Ecommerce datasets ensures businesses have the raw materials required for effective decision-making.

Our primary focus is on Amazon data, offering businesses a wealth of information to optimize their Amazon presence. By doing so, we empower our clients to refine their strategies, enhance their products, and make data-backed decisions.

[Tags: Ecommerce data, Ecommerce Data Sample, Ecommerce Product Data, Ecommerce Datasets, Ecommerce market data, Ecommerce Market Datasets, Ecommerce Sales data, Ecommerce Data API, Amazon Ecommerce API, Ecommerce scraper, Ecommerce Web Scraping, Ecommerce Data Extraction, Ecommerce Crawler, Ecommerce data scraping, Amazon Data, Ecommerce web data]

Dataset Card for "amazon-product-data-filter"

  Dataset Summary

The Amazon Product Dataset contains product listing data from the Amazon US website. It can be used for various NLP and classification tasks, such as text generation, product type classification, attribute extraction, image recognition and more. NOTICE: This is a sample of the full Amazon Product Dataset, which contains 1K examples. Follow the link to gain access to the full dataset.

  Languages… See the full description on the dataset page: https://huggingface.co/datasets/iarbel/amazon-product-data-sample.

Company Datasets for valuable business insights!

Discover new business prospects, identify investment opportunities, track competitor performance, and streamline your sales efforts with comprehensive Company Datasets.

These datasets are sourced from top industry providers, ensuring you have access to high-quality information:

• Owler: Gain valuable business insights and competitive intelligence. -AngelList: Receive fresh startup data transformed into actionable insights. -CrunchBase: Access clean, parsed, and ready-to-use business data from private and public companies. -Craft.co: Make data-informed business decisions with Craft.co's company datasets. -Product Hunt: Harness the Product Hunt dataset, a leader in curating the best new products.

We provide fresh and ready-to-use company data, eliminating the need for complex scraping and parsing. Our data includes crucial details such as:

• Company name;
• Size;
• Founding date;
• Location;
• Industry;
• Revenue;
• Employee count;
• Competitors.

You can choose your preferred data delivery method, including various storage options, delivery frequency, and input/output formats.

Receive datasets in CSV, JSON, and other formats, with storage options like AWS S3 and Google Cloud Storage. Opt for one-time, monthly, quarterly, or bi-annual data delivery.

With Oxylabs Datasets, you can count on:

• Fresh and accurate data collected and parsed by our expert web scraping team.
• Time and resource savings, allowing you to focus on data analysis and achieving your business goals.
• A customized approach tailored to your specific business needs.
• Legal compliance in line with GDPR and CCPA standards, thanks to our membership in the Ethical Web Data Collection Initiative.

Pricing Options:

Standard Datasets: choose from various ready-to-use datasets with standardized data schemas, priced from $1,000/month.

Custom Datasets: Tailor datasets from any public web domain to your unique business needs. Contact our sales team for custom pricing.

Experience a seamless journey with Oxylabs:

• Understanding your data needs: We work closely to understand your business nature and daily operations, defining your unique data requirements.
• Developing a customized solution: Our experts create a custom framework to extract public data using our in-house web scraping infrastructure.
• Delivering data sample: We provide a sample for your feedback on data quality and the entire delivery process.
• Continuous data delivery: We continuously collect public data and deliver custom datasets per the agreed frequency.

Unlock the power of data with Oxylabs' Company Datasets and supercharge your business insights today!

The Product Catalog Data provides a comprehensive overview of products across various categories. This dataset includes detailed product titles, descriptions, barcodes, category-specific attributes, weight, measurements, and imagery. It's tailored for marketplaces, eCommerce sites, and data analysts who require in-depth product information to enhance user experience, SEO, and product categorization.

Popular Attributes:

✔ Detailed product information

✔ High-quality imagery

✔ Extensive attribute coverage

✔ Ideal for UX and SEO optimization

✔ Comprehensive product categorization

Key Information:

Rich dataset with 30+ attributes per product

Pricing: Flexible subscription models

Update Frequency: Daily updates

Coverage: Global and specific markets

Historical Data: 12 Months +

OpenWeb Ninja's Product Data API provides Product Data, Product Reviews Data, Product Offers, sourced in real-time from Google Shopping - the largest product listings aggregate on the web, listing products from all publicly available e-commerce sites (Amazon, eBay, Walmart + many others).

The API covers more than 35 billion Product Data Listings, including Product Reviews and Product Offers across the web. The API provides over 40 product data points including prices, rating and reviews insights, product details and specs, typical price ranges, and more.

OpenWeb Ninja's Product Data common use cases: - Price Optimization & Price Comparison - Market Research & Competitive Analysis - Product Research & Trend Analysis - Customer Reviews Analysis

OpenWeb Ninja's Product Data Stats & Capabilities: - 35B+ Product Listings - 40+ data points per job listing - Global aggregate - Search by keyword or GTIN/EAN

First of all, Amazon product datasets are indispensable for reverse engineering your rivals. For example, you can collect a list of keywords you already rank for or want to, and go through DataForSEO Amazon Products Database to find other sellers appearing as the top results for these terms.

Next, you can narrow down the scope of your contenders to those performing the best. To do so, you can filter out sellers who won the “Amazon’s Choice” and those whose products got listed multiple times on the first page.

Once you’ve compiled the final list of your challengers, Amazon Products Database will help you to quickly examine product titles, descriptions, prices, images, and other details that will let you grasp the main contributors to your competitors’ success. Once you’ve figured that out, you can start optimizing your product listings and pricing strategies to increase conversions.

However, the number of use cases for Amazon product data isn’t limited to competitor analysis. It can be applied to monitoring product rankings, running price comparisons, and more.

These datasets contain peer-to-peer trades from various recommendation platforms.

Metadata includes

• peer-to-peer trades

• have and want lists

• image data (tradesy)

1.Introduction

Sales data collection is a crucial aspect of any manufacturing industry as it provides valuable insights about the performance of products, customer behaviour, and market trends. By gathering and analysing this data, manufacturers can make informed decisions about product development, pricing, and marketing strategies in Internet of Things (IoT) business environments like the dairy supply chain.

One of the most important benefits of the sales data collection process is that it allows manufacturers to identify their most successful products and target their efforts towards those areas. For example, if a manufacturer could notice that a particular product is selling well in a certain region, this information could be utilised to develop new products, optimise the supply chain or improve existing ones to meet the changing needs of customers.

This dataset includes information about 7 of MEVGAL’s products [1]. According to the above information the data published will help researchers to understand the dynamics of the dairy market and its consumption patterns, which is creating the fertile ground for synergies between academia and industry and eventually help the industry in making informed decisions regarding product development, pricing and market strategies in the IoT playground. The use of this dataset could also aim to understand the impact of various external factors on the dairy market such as the economic, environmental, and technological factors. It could help in understanding the current state of the dairy industry and identifying potential opportunities for growth and development.

2. Citation

Please cite the following papers when using this dataset:

1. I. Siniosoglou, K. Xouveroudis, V. Argyriou, T. Lagkas, S. K. Goudos, K. E. Psannis and P. Sarigiannidis, "Evaluating the Effect of Volatile Federated Timeseries on Modern DNNs: Attention over Long/Short Memory," in the 12th International Conference on Circuits and Systems Technologies (MOCAST 2023), April 2023, Accepted

3. Dataset Modalities

The dataset includes data regarding the daily sales of a series of dairy product codes offered by MEVGAL. In particular, the dataset includes information gathered by the logistics division and agencies within the industrial infrastructures overseeing the production of each product code. The products included in this dataset represent the daily sales and logistics of a variety of yogurt-based stock. Each of the different files include the logistics for that product on a daily basis for three years, from 2020 to 2022.

3.1 Data Collection

The process of building this dataset involves several steps to ensure that the data is accurate, comprehensive and relevant.

The first step is to determine the specific data that is needed to support the business objectives of the industry, i.e., in this publication’s case the daily sales data.

Once the data requirements have been identified, the next step is to implement an effective sales data collection method. In MEVGAL’s case this is conducted through direct communication and reports generated each day by representatives & selling points.

It is also important for MEVGAL to ensure that the data collection process conducted is in an ethical and compliant manner, adhering to data privacy laws and regulation. The industry also has a data management plan in place to ensure that the data is securely stored and protected from unauthorised access.

The published dataset is consisted of 13 features providing information about the date and the number of products that have been sold. Finally, the dataset was anonymised in consideration to the privacy requirement of the data owner (MEVGAL).

File	Period	Number of Samples (days)
product 1 2020.xlsx	01/01/2020–31/12/2020	363
product 1 2021.xlsx	01/01/2021–31/12/2021	364
product 1 2022.xlsx	01/01/2022–31/12/2022	365
product 2 2020.xlsx	01/01/2020–31/12/2020	363
product 2 2021.xlsx	01/01/2021–31/12/2021	364
product 2 2022.xlsx	01/01/2022–31/12/2022	365
product 3 2020.xlsx	01/01/2020–31/12/2020	363
product 3 2021.xlsx	01/01/2021–31/12/2021	364
product 3 2022.xlsx	01/01/2022–31/12/2022	365
product 4 2020.xlsx	01/01/2020–31/12/2020	363
product 4 2021.xlsx	01/01/2021–31/12/2021	364
product 4 2022.xlsx	01/01/2022–31/12/2022	364
product 5 2020.xlsx	01/01/2020–31/12/2020	363
product 5 2021.xlsx	01/01/2021–31/12/2021	364
product 5 2022.xlsx	01/01/2022–31/12/2022	365
product 6 2020.xlsx	01/01/2020–31/12/2020	362
product 6 2021.xlsx	01/01/2021–31/12/2021	364
product 6 2022.xlsx	01/01/2022–31/12/2022	365
product 7 2020.xlsx	01/01/2020–31/12/2020	362
product 7 2021.xlsx	01/01/2021–31/12/2021	364
product 7 2022.xlsx	01/01/2022–31/12/2022	365

3.2 Dataset Overview

The following table enumerates and explains the features included across all of the included files.

Feature	Description	Unit
Day	day of the month	-
Month	Month	-
Year	Year	-
daily_unit_sales	Daily sales - the amount of products, measured in units, that during that specific day were sold	units
previous_year_daily_unit_sales	Previous Year’s sales - the amount of products, measured in units, that during that specific day were sold the previous year	units
percentage_difference_daily_unit_sales	The percentage difference between the two above values	%
daily_unit_sales_kg	The amount of products, measured in kilograms, that during that specific day were sold	kg
previous_year_daily_unit_sales_kg	Previous Year’s sales - the amount of products, measured in kilograms, that during that specific day were sold, the previous year	kg
percentage_difference_daily_unit_sales_kg	The percentage difference between the two above values	kg
daily_unit_returns_kg	The percentage of the products that were shipped to selling points and were returned	%
previous_year_daily_unit_returns_kg	The percentage of the products that were shipped to

Envestnet®| Yodlee®'s Retail Sales Data (Aggregate/Row) Panels consist of de-identified, near-real time (T+1) USA credit/debit/ACH transaction level data – offering a wide view of the consumer activity ecosystem. The underlying data is sourced from end users leveraging the aggregation portion of the Envestnet®| Yodlee®'s financial technology platform.

Envestnet | Yodlee Consumer Panels (Aggregate/Row) include data relating to millions of transactions, including ticket size and merchant location. The dataset includes de-identified credit/debit card and bank transactions (such as a payroll deposit, account transfer, or mortgage payment). Our coverage offers insights into areas such as consumer, TMT, energy, REITs, internet, utilities, ecommerce, MBS, CMBS, equities, credit, commodities, FX, and corporate activity. We apply rigorous data science practices to deliver key KPIs daily that are focused, relevant, and ready to put into production.

We offer free trials. Our team is available to provide support for loading, validation, sample scripts, or other services you may need to generate insights from our data.

Investors, corporate researchers, and corporates can use our data to answer some key business questions such as: - How much are consumers spending with specific merchants/brands and how is that changing over time? - Is the share of consumer spend at a specific merchant increasing or decreasing? - How are consumers reacting to new products or services launched by merchants? - For loyal customers, how is the share of spend changing over time? - What is the company’s market share in a region for similar customers? - Is the company’s loyal user base increasing or decreasing? - Is the lifetime customer value increasing or decreasing?

Additional Use Cases: - Use spending data to analyze sales/revenue broadly (sector-wide) or granular (company-specific). Historically, our tracked consumer spend has correlated above 85% with company-reported data from thousands of firms. Users can sort and filter by many metrics and KPIs, such as sales and transaction growth rates and online or offline transactions, as well as view customer behavior within a geographic market at a state or city level. - Reveal cohort consumer behavior to decipher long-term behavioral consumer spending shifts. Measure market share, wallet share, loyalty, consumer lifetime value, retention, demographics, and more.) - Study the effects of inflation rates via such metrics as increased total spend, ticket size, and number of transactions. - Seek out alpha-generating signals or manage your business strategically with essential, aggregated transaction and spending data analytics.

Use Cases Categories (Our data provides an innumerable amount of use cases, and we look forward to working with new ones): 1. Market Research: Company Analysis, Company Valuation, Competitive Intelligence, Competitor Analysis, Competitor Analytics, Competitor Insights, Customer Data Enrichment, Customer Data Insights, Customer Data Intelligence, Demand Forecasting, Ecommerce Intelligence, Employee Pay Strategy, Employment Analytics, Job Income Analysis, Job Market Pricing, Marketing, Marketing Data Enrichment, Marketing Intelligence, Marketing Strategy, Payment History Analytics, Price Analysis, Pricing Analytics, Retail, Retail Analytics, Retail Intelligence, Retail POS Data Analysis, and Salary Benchmarking

1. Investment Research: Financial Services, Hedge Funds, Investing, Mergers & Acquisitions (M&A), Stock Picking, Venture Capital (VC)

2. Consumer Analysis: Consumer Data Enrichment, Consumer Intelligence

3. Market Data: AnalyticsB2C Data Enrichment, Bank Data Enrichment, Behavioral Analytics, Benchmarking, Customer Insights, Customer Intelligence, Data Enhancement, Data Enrichment, Data Intelligence, Data Modeling, Ecommerce Analysis, Ecommerce Data Enrichment, Economic Analysis, Financial Data Enrichment, Financial Intelligence, Local Economic Forecasting, Location-based Analytics, Market Analysis, Market Analytics, Market Intelligence, Market Potential Analysis, Market Research, Market Share Analysis, Sales, Sales Data Enrichment, Sales Enablement, Sales Insights, Sales Intelligence, Spending Analytics, Stock Market Predictions, and Trend Analysis

The Space-based Imaging Spectroscopy and Thermal pathfindER (SISTER) activity originated in support of the NASA Earth System Observatory's Surface Biology and Geology (SBG) mission to develop prototype workflows with community algorithms and generate prototype data products envisioned for SBG. SISTER focused on developing a data system that is open, portable, scalable, standards-compliant, and reproducible. This collection contains EXPERIMENTAL workflows and sample data products, including (a) the Common Workflow Language (CWL) process file and a Jupyter Notebook that run the entire SISTER workflow capable of generating experimental sample data products spanning terrestrial ecosystems, inland and coastal aquatic ecosystems, and snow, (b) the archived algorithm steps (as OGC Application Packages) used to generate products at each step of the workflow, (c) a small number of experimental sample data products produced by the workflow which are based on the Airborne Visible/Infrared Imaging Spectrometer-Classic (AVIRIS or AVIRIS-CL) instrument, and (d) instructions for reproducing the sample products included in this dataset. DISCLAIMER: This collection contains experimental workflows, experimental community algorithms, and experimental sample data products to demonstrate the capabilities of an end-to-end processing system. The experimental sample data products provided have not been fully validated and are not intended for scientific use. The community algorithms provided are placeholders which can be replaced by any user's algorithms for their own science and application interests. These algorithms should not in any capacity be considered the algorithms that will be implemented in the upcoming Surface Biology and Geology mission.

OpenWeb Ninja's Amazon Data API provides fast, reliable, and real-time access to Amazon Data and on all 22 Amazon domains / countries.

The OpenWeb Ninja's Amazon Data API covers over 300 million Product Listings Data (products, books, media, wine, and services) and provides over 40 data points per product.

OpenWeb Ninja's Amazon Data common use cases: - Price Optimization & Price Comparison - Market Research - Competitive Analysis - Product Research & Trend Analysis - Customer Reviews Analysis

OpenWeb Ninja's Amazon Data Stats & Capabilities: - 40+ data points per job listing - 300M+ Product Listings - 22 Amazon countries/domains supported - Real-time Amazon product data, including offers, and deals (Today's Deals) - Detailed product reviews and ratings data - Amazon Best Sellers across multiple Amazon categories - Conversion from Amazon ASIN to GTIN/EAN/ISBN

This dataset contain ventilation ages calculated using the transit time distribution (TTD) method (e.g., Waugh et al., 2004) on the GLODAPv2 data synthesis product (Olsen et al., 2016). Ventilation age is defined as the time elapsed since a water parcel was last in contact with the atmosphere. Our calculated ages are estimated from measured concentrations of the transient tracers sulphur hexafluoride (SF6), and the chlorofluorocarbons (CFCs) CFC-11 and CFC-12. For these TTD calculations we have assumed full (100%) saturation of the transient tracers when subducted, which will generate a bias toward older ages in especially dense water formation regions since it is known that the saturation there is frequently lower than 100%. We assume that the solution to the Greens function is an Inverse Gaussian (IG) function. Furthermore, we have assumed a balance between advection and mixing, i.e., unity ratio between the width and the mean age of the TTDs. This assumption is typically adopted in the global ocean (e.g., Waugh et al., 2006), although there is regional variability (e.g., Stöven and Tanhua, 2014; Rajasakaren et al., 2019). Thus, some care should be taken when utilising the calculated ages in certain regions. The main reason for the published dataset is to give a user-friendly product that can be applied in ocean studies where ventilation ages are of interest, both to give an appreciation of typical ages and gradients in the ocean, and to be adopted in studies calculating biogeochemical rates. A recent example of the latter is the updated calcium carbonate dissolution study by Sulpis et al. (2021), which used these data. All included data are listed and specified in the dataset description below, and most of them are identical to the values found in GLODAPv2 (Key et al., 2015; Olsen et al., 2016). The novel addition in this dataset are the ventilation ages. The files contain both the TTD-based mean ages that are calculated as described above, and, calculated tracer ages, which assumes no mixing and are simply derived by matching the observed tracer concentration to the atmospheric history. For the atmospheric history we used (Walker et al. (2000) and Bullister (2015)), updated to 2016 by extrapolating with the same atmospheric evolution rate as the year before. The dataset consists of files covering four regions, following the GLODAPv2 data synthesis product: the Arctic Mediterranean (ARC), The Atlantic Ocean (ATL), the Indian Ocean (IND), and the Pacific Ocean (PAC). The data are provided both in comma separated (.csv) format and in Matlab® format (.mat).

Context

The Google Merchandise Store sells Google branded merchandise. The data is typical of what you would see for an ecommerce website.

Content

The sample dataset contains Google Analytics 360 data from the Google Merchandise Store, a real ecommerce store. The Google Merchandise Store sells Google branded merchandise. The data is typical of what you would see for an ecommerce website. It includes the following kinds of information:

Traffic source data: information about where website visitors originate. This includes data about organic traffic, paid search traffic, display traffic, etc. Content data: information about the behavior of users on the site. This includes the URLs of pages that visitors look at, how they interact with content, etc. Transactional data: information about the transactions that occur on the Google Merchandise Store website.

Fork this kernel to get started.

Acknowledgements

Data from: https://bigquery.cloud.google.com/table/bigquery-public-data:google_analytics_sample.ga_sessions_20170801

Banner Photo by Edho Pratama from Unsplash.

Inspiration

What is the total number of transactions generated per device browser in July 2017?

The real bounce rate is defined as the percentage of visits with a single pageview. What was the real bounce rate per traffic source?

What was the average number of product pageviews for users who made a purchase in July 2017?

What was the average number of product pageviews for users who did not make a purchase in July 2017?

What was the average total transactions per user that made a purchase in July 2017?

What is the average amount of money spent per session in July 2017?

What is the sequence of pages viewed?

Sample purchasing data containing information on suppliers, the products they provide, and the projects those products are used for. Data created or adapted from publicly available sources.

This dataset contains images (scenes) containing fashion products, which are labeled with bounding boxes and links to the corresponding products.

Metadata includes

• product IDs

• bounding boxes

Basic Statistics:

• Scenes: 47,739

• Products: 38,111

• Scene-Product Pairs: 93,274

The SWOT Level 2 Water Mask Raster Image Data Product from the Surface Water Ocean Topography (SWOT) mission provides global surface water elevation and inundation extent derived from high rate (HR) measurements from the Ka-band Radar Interferometer (KaRIn) on SWOT. SWOT launched on December 16, 2022 from Vandenberg Air Force Base in California into a 1-day repeat orbit for the "calibration" or "fast-sampling" phase of the mission, which completed in early July 2023. After the calibration phase, SWOT entered a 21-day repeat orbit in August 2023 to start the "science" phase of the mission, which is expected to continue through 2025. Water surface elevation, area, water fraction, backscatter, geophysical information are provided in geographically fixed scenes at resolutions of 100 m and 250 m in Universal Transverse Mercator (UTM) projection. Available in netCDF-4 file format. On-demand processing available to users for different resolutions, sampling grids, scene sizes, and file formats. This dataset is the parent collection to the following sub-collections: https://podaac.jpl.nasa.gov/dataset/SWOT_L2_HR_Raster_100m_2.0 https://podaac.jpl.nasa.gov/dataset/SWOT_L2_HR_Raster_250m_2.0

The Shopee Products Dataset is a comprehensive resource that empowers businesses, researchers, and analysts to gain a holistic view of the Shopee e-commerce ecosystem. Whether your goal is to conduct market analysis, optimize pricing strategies, understand customer behavior, or evaluate competitors, this dataset offers the essential information you need to make informed decisions and succeed in the dynamic world of Shopee. At its core, this dataset provides key attributes such as product ID, title, ratings, reviews, pricing details, and seller information, among others. These fundamental data elements offer insights into product performance, customer sentiment, and seller credibility.

Data set represents the average bill of materials for 25 common categories of consumer electronics products collected via product disassembly and physical material identification and measurement. These data records are compiled in two excel workbooks containing BOM data collected and organized at different levels of aggregation. First, the “Disassembly Detail” workbook provides resolved material and component data at the level of each major assembly and sub-assembly. Each worksheet represents a single product category, and most categories contain detailed data for multiple product samples.Second, the “Product Bill of Materials” workbook provides total mass and mass percent of each separable material and component for all products studied and a mean, maximum, and minimum mass (g) and mass percent (%) for each product category calculated using the lab data points. The workbooks also includes literature values, and evaluated using three parameters: traceability, level of detail and category consistency. This qualitative analysis of data from published literature is indicated next to each data point. Third, the "Uncertainty Analysis" workbook provides information on mass of the product prior to disassembly, post disassembly, and manufacturer reported mass, when available for the same make, model, and year of the product studied in the lab. Percentage difference between mass of the product prior and post disassembly, and mass of the product post disassembly and reported mass by manufacturer is provided. This workbook also includes side by side comparison of our lab BOM data for iPod and Amazon kindle to the high quality literature data.

This product contains LCMAP CONUS Intensification Reference plot location data in a .shp format as well as annual land cover, land use, and change process variables for each reference data plot in a separate .csv table. The same information available in the.csv file is also provided in a .xlsx format. The LCMAP Reference Data Product was utilized for evaluation and validation of the Land Change Monitoring, Assessment, and Projection (LCMAP) land cover and land cover change products. The LCMAP Reference Data Product includes the collection of an independent dataset of 25,000 randomly-distributed 30-meter by 30-meter plots across the conterminous United States (CONUS). The LCMAP Reference Data Products collected variables related to primary and secondary land use, primary and secondary land cover(s), change processes, and other ancillary variables annually across CONUS from 1984–2018. This dataset includes 2,000 additional samples collected annually for 1984-2019 via manual image interpretation to increase the sample size of rare change classes at the CONUS level. This increased sample size will improve standard errors of the user's accuracy estimates for rare classes and improve estimates of area amounts of land cover change. These 2,000 samples were selected using a stratified random sampling process with 4 strata based off the LCMAP Collection 1.0 Science Products. These intensification samples were selected to assist in reducing the standard error focused on change classes.

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.