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

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.

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 +

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

Metadata includes

• peer-to-peer trades

• have and want lists

• image data (tradesy)

Envestnet®| Yodlee®'s Sales Transaction 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

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.

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.

Level-1A Data Products are the result of a non-destructive processing applied to the Level-0 data at NASA/JPL. The sensor calibration factors are applied in order to convert the binary encoded measurements to engineering units. Where necessary, time tag integer second ambiguity is resolved and data are time tagged to the respective satellite receiver clock time. Editing and quality control flags are added, and the data is reformatted for further processing. The Level-1A data are reversible to Level-0, except for the bad data packets. This level also includes the ancillary data products needed for processing to the next data level. The Level-1B Data Products are the result of a possibly destructive, or irreversible, processing applied to both the Level-1A and Level-0 data at NASA/JPL. The data are correctly time-tagged, and data sample rate is reduced from the higher rates of the previous levels. Collectively, the processing from Level-0 to Level-1B is called the Level-1 Processing. This level also includes the ancillary data products generated during this processing, and the additional data needed for further processing. The Level-2 data products include the static and time-variable (monthly) gravity field and related data products derived from the application of Level-2 processing at GFZ, UTCSR and JPL to the previous level data products. This level also includes the ancillary data products such as GFZ's Level-1B short-term atmosphere and ocean de-aliasing product (AOD1B) generated during this processing. GRACE-A and GRACE-B Level-1B Data Product • Satellite clock solution [GA-OG-1B-CLKDAT, GB-OG-1B-CLKDAT, GRACE CLKDAT]: Offset of the satellite receiver clock relative to GPS time, obtained by linear fit to raw on-board clock offset estimates. • GPS flight data [GA-OG-1B-GPSDAT, GB-OG-1B-GPSDAT, GRACE GPSDAT]: Preprocessed and calibrated GPS code and phase tracking data edited and decimated from instrument high-rate (10 s (code) or 1 s (phase)) to low-rate (10 s) samples for science use (1 file per day, level-1 format) • Accelerometer Housekeeping data [GA-OG-1B-ACCHKP, GB-OG-1B-ACCHKP, GRACE ACCHKP]: Accelerometer proof-mass bias voltages, capacitive sensor outputs, instrument control unit (ICU) and sensor unit (SU) temperatures, reference voltages, primary and secondary power supply voltages (1 file per day, level-1 format). • Accelerometer data [GA-OG-1B-ACCDAT, GB-OG-1B-ACCDAT, GRACE ACCDAT]: Preprocessed and calibrated Level-1B accelerometer data edited and decimated from instrument high-rate (0.1 s) to low-rate (1s) samples for science use (1 file per day, level-1 format). • Intermediate clock solution [GA-OG-1B-INTCLK, GB-OG-1B-INTCLK, GRACE INTCLK]: derived with GIPSY POD software (300 s sample rate) (1 file per day, GIPSY format) • Instrument processing unit (IPU) Housekeeping data [GA-OG-1B-IPUHKP, GB-OG-1B-IPUHKP, GRACE IPUHKP]: edited and decimated from high-rate (TBD s) to low-rate (TBD s) samples for science use (1 file per day, level-1 format) • Spacecraft Mass Housekeeping data [GA-OG-1B-MASDAT, GB-OG-1B-MASDAT, GRACE MASDAT]: Level 1B Data as a function of time • GPS navigation solution data [GA-OG-1B-NAVSOL, GB-OG-1B-NAVSOL, GRACE NAVSOL]: edited and decimated from instrument high-rate (60 s) to low-rate (30 s) samples for science use (1 file per day, level-1 format) • OBDH time mapping to GPS time Housekeeping data [GA-OG-1B-OBDHTM, GB-OG-1B-OBDHTM, GRACE OBDHTM]: On-board data handling (OBDH) time mapping data (OBDH time to receiver time • Star camera data [GA-OG-1B-SCAATT, GB-OG-1B-SCAATT, GRACE SCAATT]: Preprocessed and calibrated star camera quaternion data edited and decimated from instrument high-rate (1 s) to low-rate (5 s) samples for science use (1 file per day, level-1 format) • Thruster activation Housekeeping data [GA-OG-1B-THRDAT, GB-OG-1B-THRDAT, GRACE THRDAT]: GN2 thruster data used for attitude (10 mN) and orbit (40 mN) control • GN2 tank temperature and pressure Housekeeping data [GA-OG-1B-TNKDAT, GB-OG-1B-TNKDAT, GRACE TNKDAT]: GN2 tank temperature and pressure data • Oscillator frequency data [GA-OG-1B-USODAT, GB-OG-1B-USODAT, GRACE USODAT]: derived from POD productGRACE-A and GRACE-B Combined Level-1B Data Product • Preprocessed and calibrated k-band ranging data [GA-OG-1B-KBRDAT, GB-OG-1B-KBRDAT, GRACE KBRDAT]: range, range-rate and range-acceleration data edited and decimated from instrument high-rate (0.1 s) to low-rate (5 s) samples for science use (1 file per day, level-1 format) • Atmosphere and Ocean De-aliasing Product [GA-OG-1B-ATMOCN, GB-OG-1B-ATMOCN, GRACE ATMOCN]: GRACE Atmosphere and Ocean De-aliasing Product GRACE Level-2 Data Product • GAC [GA-OG-_2-GAC, GB-OG-_2-GAC, GRACE GAC]: Combination of non-tidal atmosphere and ocean spherical harmonic coefficients provided as average over certain time span (same as corresponding GSM product) based on level-1 AOD1B product (1file per time span, level-2 format) • GCM [GA-OG-_2-GCM, GB-OG-_2-GCM, GRACE GCM]: Spherical harmonic coefficients and standard deviations of the long-term static gravity field estimated by combination of GRACE satellite instrument data and other information for a dedicated time span (multiple years) and spatial resolution (1 file per time span, level-2 format) • GAB [GA-OG-_2-GAB, GB-OG-_2-GAB, GRACE GAB]: Non-tidal ocean spherical harmonic coefficients provided as average over certain time span (same as corresponding GSM product) based on level-1 AOD1B product (1file per time span, level-2 format) • GAD [GA-OG-_2-GAD, GB-OG-_2-GAD, GRACE GAD]: bottom pressure product - combination of surface pressure and ocean (over the oceans, and zero over land). Spherical harmonic coefficients provided as average over certain time span (same as corresponding GSM product) based on level-1 AOD1B product (1file per time span, level-2 format) • GSM [GA-OG-_2-GSM, GB-OG-_2-GSM, GRACE GSM]: Spherical harmonic coefficients and standard deviations of the static gravity field estimated from GRACE satellite instrument data only for a dedicated time span (e.g. weekly, monthly, multiple years) and spatial resolution (1 file per time span, level-2 format).

This Appendix to the 2021 Virginia Coastal Resilience Master Plan Technical Study provides a simple consolidated list of study analytical and geospatial products. High-level descriptions of the approaches and products are available in the Coastal Resilience Master Plan document and the Coastal Resilience Web Explorer. The Web Explorer allows users to interact with many of the data products. Details on the data sources and methodologies for the products listed herein are located in the Coastal Resilience Master Plan Technical Appendices.

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 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.

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

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).

Counts and relative abundances of archaeal, bacterial, and fungal taxa observed in surface water microbial communities in lakes, rivers and streams 2014-2018. Beginning with RELEASE-2025, the algorithm was revised and this product was replaced by Surface Water microbe community taxonomy (DP1.20141.002).

Market Basket Analysis

Market basket analysis with Apriori algorithm

The retailer wants to target customers with suggestions on itemset that a customer is most likely to purchase .I was given dataset contains data of a retailer; the transaction data provides data around all the transactions that have happened over a period of time. Retailer will use result to grove in his industry and provide for customer suggestions on itemset, we be able increase customer engagement and improve customer experience and identify customer behavior. I will solve this problem with use Association Rules type of unsupervised learning technique that checks for the dependency of one data item on another data item.

Introduction

Association Rule is most used when you are planning to build association in different objects in a set. It works when you are planning to find frequent patterns in a transaction database. It can tell you what items do customers frequently buy together and it allows retailer to identify relationships between the items.

An Example of Association Rules

Assume there are 100 customers, 10 of them bought Computer Mouth, 9 bought Mat for Mouse and 8 bought both of them. - bought Computer Mouth => bought Mat for Mouse - support = P(Mouth & Mat) = 8/100 = 0.08 - confidence = support/P(Mat for Mouse) = 0.08/0.09 = 0.89 - lift = confidence/P(Computer Mouth) = 0.89/0.10 = 8.9 This just simple example. In practice, a rule needs the support of several hundred transactions, before it can be considered statistically significant, and datasets often contain thousands or millions of transactions.

Strategy

• Data Import
• Data Understanding and Exploration
• Transformation of the data – so that is ready to be consumed by the association rules algorithm
• Running association rules
• Exploring the rules generated
• Filtering the generated rules
• Visualization of Rule

Dataset Description

• File name: Assignment-1_Data
• List name: retaildata
• File format: . xlsx
• Number of Row: 522065

• Number of Attributes: 7

  • BillNo: 6-digit number assigned to each transaction. Nominal.
  • Itemname: Product name. Nominal.
  • Quantity: The quantities of each product per transaction. Numeric.
  • Date: The day and time when each transaction was generated. Numeric.
  • Price: Product price. Numeric.
  • CustomerID: 5-digit number assigned to each customer. Nominal.
  • Country: Name of the country where each customer resides. Nominal.

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Libraries in R

First, we need to load required libraries. Shortly I describe all libraries.

• arules - Provides the infrastructure for representing, manipulating and analyzing transaction data and patterns (frequent itemsets and association rules).
• arulesViz - Extends package 'arules' with various visualization. techniques for association rules and item-sets. The package also includes several interactive visualizations for rule exploration.
• tidyverse - The tidyverse is an opinionated collection of R packages designed for data science.
• readxl - Read Excel Files in R.
• plyr - Tools for Splitting, Applying and Combining Data.
• ggplot2 - A system for 'declaratively' creating graphics, based on "The Grammar of Graphics". You provide the data, tell 'ggplot2' how to map variables to aesthetics, what graphical primitives to use, and it takes care of the details.
• knitr - Dynamic Report generation in R.
• magrittr- Provides a mechanism for chaining commands with a new forward-pipe operator, %>%. This operator will forward a value, or the result of an expression, into the next function call/expression. There is flexible support for the type of right-hand side expressions.
• dplyr - A fast, consistent tool for working with data frame like objects, both in memory and out of memory.
• tidyverse - This package is designed to make it easy to install and load multiple 'tidyverse' packages in a single step.

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Data Pre-processing

Next, we need to upload Assignment-1_Data. xlsx to R to read the dataset.Now we can see our data in R.

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After we will clear our data frame, will remove missing values.

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To apply Association Rule mining, we need to convert dataframe into transaction data to make all items that are bought together in one invoice will be in ...

Xtract.io's location data for home and electronics retailers delivers a comprehensive view of the retail sector. Retail analysts, industry researchers, and business developers can utilize this dataset to understand market distribution, identify potential opportunities, and develop strategic insights into home and electronics retail landscapes.

How Do We Create Polygons? -All our polygons are manually crafted using advanced GIS tools like QGIS, ArcGIS, and similar applications. This involves leveraging aerial imagery and street-level views to ensure precision. -Beyond visual data, our expert GIS data engineers integrate venue layout/elevation plans sourced from official company websites to construct detailed indoor polygons. This meticulous process ensures higher accuracy and consistency. -We verify our polygons through multiple quality checks, focusing on accuracy, relevance, and completeness.

What's More? -Custom Polygon Creation: Our team can build polygons for any location or category based on your specific requirements. Whether it’s a new retail chain, transportation hub, or niche point of interest, we’ve got you covered. -Enhanced Customization: In addition to polygons, we capture critical details such as entry and exit points, parking areas, and adjacent pathways, adding greater context to your geospatial data. -Flexible Data Delivery Formats: We provide datasets in industry-standard formats like WKT, GeoJSON, Shapefile, and GDB, making them compatible with various systems and tools. -Regular Data Updates: Stay ahead with our customizable refresh schedules, ensuring your polygon data is always up-to-date for evolving business needs.

Unlock the Power of POI and Geospatial Data With our robust polygon datasets and point-of-interest data, you can: -Perform detailed market analyses to identify growth opportunities. -Pinpoint the ideal location for your next store or business expansion. -Decode consumer behavior patterns using geospatial insights. -Execute targeted, location-driven marketing campaigns for better ROI. -Gain an edge over competitors by leveraging geofencing and spatial intelligence.

Why Choose LocationsXYZ? LocationsXYZ is trusted by leading brands to unlock actionable business insights with our spatial data solutions. Join our growing network of successful clients who have scaled their operations with precise polygon and POI data. Request your free sample today and explore how we can help accelerate your business growth.

Collection of zooplankton from water column samples in lakes

These four labeled data sets are targeted at ordinal quantification. The goal of quantification is not to predict the label of each individual instance, but the distribution of labels in unlabeled sets of data.

With the scripts provided, you can extract CSV files from the UCI machine learning repository and from OpenML. The ordinal class labels stem from a binning of a continuous regression label.

We complement this data set with the indices of data items that appear in each sample of our evaluation. Hence, you can precisely replicate our samples by drawing the specified data items. The indices stem from two evaluation protocols that are well suited for ordinal quantification. To this end, each row in the files app_val_indices.csv, app_tst_indices.csv, app-oq_val_indices.csv, and app-oq_tst_indices.csv represents one sample.

Our first protocol is the artificial prevalence protocol (APP), where all possible distributions of labels are drawn with an equal probability. The second protocol, APP-OQ, is a variant thereof, where only the smoothest 20% of all APP samples are considered. This variant is targeted at ordinal quantification tasks, where classes are ordered and a similarity of neighboring classes can be assumed.

Usage

You can extract four CSV files through the provided script extract-oq.jl, which is conveniently wrapped in a Makefile. The Project.toml and Manifest.toml specify the Julia package dependencies, similar to a requirements file in Python.

Preliminaries: You have to have a working Julia installation. We have used Julia v1.6.5 in our experiments.

Data Extraction: In your terminal, you can call either

make

(recommended), or

julia --project="." --eval "using Pkg; Pkg.instantiate()"
julia --project="." extract-oq.jl

Outcome: The first row in each CSV file is the header. The first column, named "class_label", is the ordinal class.

Further Reading

Implementation of our experiments: https://github.com/mirkobunse/regularized-oq

This dataset contains a data product including calculated CO2 system parameters based on the Salish cruise data product (https://www.ncei.noaa.gov/access/ocean-carbon-acidification-data-system/oceans/SalishCruise_DataPackage.html). We used R seacarb function carb to calculate the most commonly used derived carbonate system parameters, including pH, partial pressures and fugacities of carbon dioxide at in situ temperatures and pressures (pCO2insitu, and fCO2insitu, respectively), and aragonite and calcite saturation states (OmegaAragonite and OmegaCalcite, respectively) (Gattuso et al., 2023). Input parameters from the Salish cruise compiled dataset (Alin et al. 2021) comprised dissolved inorganic carbon (DIC_UMOL_KG), total alkalinity (TA_UMOL_KG), phosphate (PHOSPHATE_UMOL_KG), and silicate (SILICATE_UMOL_KG) content values from bottle samples analyzed in the laboratory, along with CTD measurements of temperature (CTDTMP_DEG_C_ITS90), salinity (CTDSAL_PSS78), and pressure (CTDPRS_DBAR). Within seacarb, we used the TEOS-10 thermodynamic seawater equations (IOC, SCOR, and IAPSO, 2010). We adopted the total scale for pH (pHT), the Uppstrom (1974) formulation for deriving total boron concentration from salinity, the seacarb default option for Kf (Perez and Fraga, 1987 for temperatures above 9 °C; Dickson and Goyet, 1994 for those below), and the Dickson (Dickson, 1990) option for Ks (following results of Orr et al., 2015). All input content data were first divided by 106 to convert from µmol kg^–1 to mol kg^–1, and pressure (dbar) was divided by 10 to convert to bar, to conform with the default units of seacarb. For equilibrium constants (K1 and K2), we provide calculated values using both the Lueker et al. (2000) and the Waters et al. (2014) dissociation constants. The Lueker constants (for salinity ranges of 19–43 and temperature ranges of 2–35°C) facilitate comparison with publications arising from West Coast Ocean Acidification (WCOA) cruise datasets (https://www.ncei.noaa.gov/access/ocean-carbon-acidification-data-system/oceans/Coastal/WCOA.html), whereas the Waters constants (for salinity ranges of 1–50 and temperature ranges of 0–50°C) allow users working with more brackish salinities to compare their results directly to those in the Salish cruise data product. All references above are included in the seacarb documentation. Alin et al. (2023b) describe the magnitude of differences in calculated values for the Salish cruise data product using the two different sets of dissociation constants. The data product is available on the Index page accessed by clicking the Database Files link above: The file “SalishCruises_2008to2018_MeasCalcParams_NCEIdataProduct_09262023.csv†contains 3971 complete records of DIC, TA, T, S, O2, and nutrient measurements with the highest quality QC flags and includes calculated values for the carbonate system parameters described above. This effort was conducted in support of the estuarine and coastal monitoring and research objectives of the Washington Ocean Acidification Center (WOAC), the Northwest Association of Networked Ocean Observing Systems, the U.S. National Oceanic and Atmospheric Administration's Pacific Marine Environmental Laboratory, and the U.S. National Oceanic and Atmospheric Administration's Ocean Acidification Program and conforms to climate-quality monitoring guidelines of the Global Ocean Acidification Observing Network (goa-on.org). For any questions about appropriate use or limitations of the data set, please contact Drs. Simone Alin and Jan Newton at email addresses above.