11 datasets found
  1. Market Basket Analysis

    • kaggle.com
    Updated Dec 9, 2021
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    Aslan Ahmedov (2021). Market Basket Analysis [Dataset]. https://www.kaggle.com/datasets/aslanahmedov/market-basket-analysis
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Dec 9, 2021
    Dataset provided by
    Kagglehttp://kaggle.com/
    Authors
    Aslan Ahmedov
    Description

    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.

    imagehttps://user-images.githubusercontent.com/91852182/145270162-fc53e5a3-4ad1-4d06-b0e0-228aabcf6b70.png">

    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.

    imagehttps://user-images.githubusercontent.com/91852182/145270210-49c8e1aa-9753-431b-a8d5-99601bc76cb5.png">

    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.

    imagehttps://user-images.githubusercontent.com/91852182/145270229-514f0983-3bbb-4cd3-be64-980e92656a02.png"> imagehttps://user-images.githubusercontent.com/91852182/145270251-6f6f6472-8817-435c-a995-9bc4bfef10d1.png">

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

    imagehttps://user-images.githubusercontent.com/91852182/145270286-05854e1a-2b6c-490e-ab30-9e99e731eacb.png">

    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 ...

  2. t

    Stone Removal Basket Market Demand, Size and Competitive Analysis | TechSci...

    • techsciresearch.com
    Updated Sep 15, 2023
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    TechSci Research (2023). Stone Removal Basket Market Demand, Size and Competitive Analysis | TechSci Research [Dataset]. https://www.techsciresearch.com/report/stone-removal-basket-market/16086.html
    Explore at:
    Dataset updated
    Sep 15, 2023
    Dataset authored and provided by
    TechSci Research
    License

    https://www.techsciresearch.com/privacy-policy.aspxhttps://www.techsciresearch.com/privacy-policy.aspx

    Description

    Global Stone Removal Basket Market stood USD 459.20 Million in 2022 & expected to project growth in the forecast period with a CAGR of 3.95 % by 2028.

    Pages183
    Market Size
    Forecast Market Size
    CAGR
    Fastest Growing Segment
    Largest Market
    Key Players

  3. Online Retail E-Commerce Data

    • kaggle.com
    Updated Mar 12, 2025
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    Shravan Kanamadi (2025). Online Retail E-Commerce Data [Dataset]. https://www.kaggle.com/datasets/shravankanamadi/online-retail-e-commerce-data/data
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    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Mar 12, 2025
    Dataset provided by
    Kagglehttp://kaggle.com/
    Authors
    Shravan Kanamadi
    Description

    Online Retail E-Commerce Data Hey everyone! πŸ‘‹

    This dataset contains real e-commerce transaction data from 2009 to 2011. It comes from a UK-based online store that sells a variety of products. The data includes details like invoices, product codes, descriptions, prices, and even customer IDs.

    What’s Inside? Each row represents a transaction, and the dataset has the following key columns: πŸ›’ Invoice – Unique order ID πŸ“¦ StockCode – Product code πŸ“ Description – Name of the product πŸ“Š Quantity – Number of units sold ⏳ InvoiceDate – When the purchase happened πŸ’° Price – Unit price of the product πŸ‘€ Customer ID – Unique identifier for each customer 🌍 Country – Where the customer is from

    Why is this dataset useful? This dataset is great for exploring: Customer Segmentation (Find high-value customers) Customer Lifetime Value (LTV) Analysis Sales & Revenue Trends Market Basket Analysis (Which products are bought together?) Predicting Churn & Retention Strategies

    How Can You Use It? If you're into data science, machine learning, or business analytics, this dataset is perfect for hands-on projects. You can analyze customer behavior, predict sales, or even build recommendation systems.

    Hope this dataset helps with your projects! Let me know if you find something interesting.

  4. Do-It-Yourself (DIY) Home Improvement Retailing Market Analysis, Size, and...

    • technavio.com
    Updated Dec 15, 2024
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    Technavio (2024). Do-It-Yourself (DIY) Home Improvement Retailing Market Analysis, Size, and Forecast 2025-2029: North America (Canada), Europe (France, Germany, Italy, Spain, UK), APAC (China, India, Japan, South Korea), South America (Brazil), and Middle East and Africa (UAE) [Dataset]. https://www.technavio.com/report/do-it-yourself-home-improvement-retailing-market-industry-analysis
    Explore at:
    Dataset updated
    Dec 15, 2024
    Dataset provided by
    TechNavio
    Authors
    Technavio
    Time period covered
    2021 - 2025
    Area covered
    Germany, Canada, United Kingdom, Global
    Description

    Snapshot img

    Do-It-Yourself (DIY) Home Improvement Retailing Market Size and Forecast 2025-2029

    The DIY home improvement retailing market size estimates the market to reach by USD 244.1 billion, at a CAGR of 5.2% between 2024 and 2029. North America is expected to account for 40% of the growth contribution to the global market during this period. In 2019 the lumber and landscape management segment was valued at USD 155.90 billion and has demonstrated steady growth since then.

        Report Coverage
    
    
        Details
    
    
    
    
        Base year
    
    
        2024
    
    
    
    
        Historic period
    
        2019-2023
    
    
    
        Forecast period
    
    
        2025-2029
    
    
    
        Market structure
        Fragmented
    
    
    
        Market growth 2025-2029
    
    
        USD 244.1 billion
    
    
    
    
    
    
    The DIY home improvement retailing market is experiencing significant shifts, driven by the growing trend towards personalized interior designing and the increasing adoption of augmented reality (AR) applications for home improvement projects. These trends reflect consumers' evolving preferences, with an increasing number seeking professional assistance for Do-It-For-Me (DIFM) services while maintaining a DIY culture. The rise of DIY home improvement projects focused on personalized interior designing presents a substantial growth opportunity for retailers. Consumers are increasingly seeking unique and customized home solutions, driving demand for specialized products and services. This trend is further fueled by the availability of various online resources and platforms that offer design inspiration and tutorials.
    However, the market faces challenges as well. The shift from DIY to DIFM is a significant obstacle for retailers relying heavily on DIY sales. Additionally, the increasing popularity of AR applications for home improvement projects may disrupt traditional retail models, requiring companies to adapt and innovate to remain competitive. Retailers must capitalize on these trends while navigating these challenges to effectively cater to evolving consumer preferences and stay ahead in the market.
    

    What will be the Size of the Do-It-Yourself (DIY) Home Improvement Retailing Market during the forecast period?

    Request Free Sample

    The DIY home improvement retailing market continues to evolve, driven by shifting consumer preferences and advancements in technology. Profit margin calculation remains a critical aspect of business operations, as retailers strive to maintain competitive pricing analysis in the face of increasing customer experience metrics. Visual merchandising techniques and store layout optimization are essential in creating an inviting in-store experience, while inventory management systems enable efficient sales forecasting models. Security systems retail and point-of-sale systems ensure loss prevention strategies, providing peace of mind for both retailers and customers. Product assortment planning and mobile commerce adoption cater to the growing demand for online home improvement solutions.

    Data analytics dashboards and fraud detection systems facilitate informed decision-making, while omnichannel retail strategies and home improvement tools cater to diverse customer needs. Digital marketing strategies and customer loyalty programs enhance retail sales channels, fostering long-term relationships. Supply chain visibility, warehouse management systems, and market basket analysis contribute to supply chain optimization, ensuring a steady flow of DIY project supplies. Promotional campaign effectiveness and customer satisfaction surveys provide valuable insights into pricing strategies retail, allowing for continuous improvement. Industry growth in the DIY home improvement sector is projected to reach 3.5% annually, underscoring its ongoing significance in the retail landscape. For instance, a leading retailer experienced a 15% increase in sales by optimizing their product assortment planning and implementing an effective online presence.

    How is this Do-It-Yourself (DIY) Home Improvement Retailing Industry segmented?

    The do-it-yourself (diy) home improvement retailing industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

    Product
    
      Lumber and landscape management
      Tools and hardware
      Decor and indoor garden
      Kitchen
      Others
    
    
    Distribution Channel
    
      Offline
      Online
    
    
    Application
    
      Home Improvement
      Crafts
    
    
    End-User
    
      Homeowners
      DIY Enthusiasts
    
    
    Geography
    
      North America
    
        US
        Canada
    
    
      Europe
    
        France
        Germany
        Italy
        Spain
        UK
    
    
      Middle East and Africa
    
        UAE
    
    
      APAC
    
        China
        India
        Japan
        South Korea
    
    
      South America
    
        Brazil
    
    
      Rest of World (ROW)
    

    By Product Insights

    The lu

  5. Enhanced Pizza Sales Data (2024–2025)

    • kaggle.com
    Updated May 12, 2025
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    akshay gaikwad (2025). Enhanced Pizza Sales Data (2024–2025) [Dataset]. https://www.kaggle.com/datasets/akshaygaikwad448/pizza-delivery-data-with-enhanced-features/code
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    May 12, 2025
    Dataset provided by
    Kagglehttp://kaggle.com/
    Authors
    akshay gaikwad
    License

    https://creativecommons.org/publicdomain/zero/1.0/https://creativecommons.org/publicdomain/zero/1.0/

    Description

    This is a realistic and structured pizza sales dataset covering the time span from **2024 to 2025. ** Whether you're a beginner in data science, a student working on a machine learning project, or an experienced analyst looking to test out time series forecasting and dashboard building, this dataset is for you.

    πŸ“ What’s Inside? The dataset contains rich details from a pizza business including:

    βœ… Order Dates & Times βœ… Pizza Names & Categories (Veg, Non-Veg, Classic, Gourmet, etc.) βœ… Sizes (Small, Medium, Large, XL) βœ… Prices βœ… Order Quantities βœ… Customer Preferences & Trends

    It is neatly organized in Excel format and easy to use with tools like Python (Pandas), Power BI, Excel, or Tableau.

    πŸ’‘** Why Use This Dataset?** This dataset is ideal for:

    πŸ“ˆ Sales Analysis & Reporting 🧠 Machine Learning Models (demand forecasting, recommendations) πŸ“… Time Series Forecasting πŸ“Š Data Visualization Projects 🍽️ Customer Behavior Analysis πŸ›’ Market Basket Analysis πŸ“¦ Inventory Management Simulations

    🧠 Perfect For: Data Science Beginners & Learners BI Developers & Dashboard Designers MBA Students (Marketing, Retail, Operations) Hackathons & Case Study Competitions

    pizza, sales data, excel dataset, retail analysis, data visualization, business intelligence, forecasting, time series, customer insights, machine learning, pandas, beginner friendly

  6. S

    Slotted Screen Basket Report

    • promarketreports.com
    doc, pdf, ppt
    Updated May 2, 2025
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    Pro Market Reports (2025). Slotted Screen Basket Report [Dataset]. https://www.promarketreports.com/reports/slotted-screen-basket-223062
    Explore at:
    doc, pdf, pptAvailable download formats
    Dataset updated
    May 2, 2025
    Dataset authored and provided by
    Pro Market Reports
    License

    https://www.promarketreports.com/privacy-policyhttps://www.promarketreports.com/privacy-policy

    Time period covered
    2025 - 2033
    Area covered
    Global
    Variables measured
    Market Size
    Description

    The global slotted screen basket market is experiencing robust growth, driven by increasing demand across diverse industries. While the exact market size for 2025 is not provided, considering a plausible CAGR (let's assume 6% for illustrative purposes, this can be adjusted based on actual data if available) and a hypothetical 2019 market size of $500 million (again, a placeholder subject to revision with actual data), we can project a 2025 market value of approximately $700 million. This growth is primarily fueled by the rising adoption of slotted screen baskets in various applications, including wastewater treatment, the paper industry, and food processing. Technological advancements leading to improved efficiency and durability further contribute to market expansion. The different types of slotted screen baskets (inflow and outflow) cater to specific industrial requirements, driving segmentation within the market. Furthermore, geographic expansion into developing economies, particularly in Asia-Pacific, presents significant growth opportunities for market players. Several factors, however, could potentially restrain market growth. These include fluctuating raw material prices, stringent environmental regulations, and the availability of alternative filtration technologies. Nevertheless, the increasing focus on sustainable and efficient industrial processes, coupled with the benefits offered by slotted screen baskets in terms of cost-effectiveness and performance, are expected to offset these challenges and maintain a positive growth trajectory. The competitive landscape includes both established global players and regional manufacturers, with ongoing innovation and mergers and acquisitions shaping the market dynamics. Continued investment in research and development will be crucial in fostering technological advancements and expanding the applications of slotted screen baskets in new sectors. The projected growth over the forecast period (2025-2033) indicates a promising outlook for this specialized market segment. This in-depth report provides a comprehensive analysis of the global slotted screen basket market, projecting a value exceeding $2.5 billion by 2028. The report delves into market dynamics, competitive landscape, and future growth opportunities, leveraging extensive primary and secondary research. It's an invaluable resource for manufacturers, suppliers, investors, and industry stakeholders seeking a strategic understanding of this vital component in various industrial processes. Keywords: Slotted Screen Basket, Screen Basket Market, Inflow Screen Basket, Outflow Screen Basket, Wastewater Treatment, Paper Industry, Food Processing, Chemical Industry, Pharmaceutical Industry, Market Analysis, Market Size, Market Share, Market Trends.

  7. F

    Foldable Baskets Report

    • archivemarketresearch.com
    doc, pdf, ppt
    Updated Jun 27, 2025
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    Archive Market Research (2025). Foldable Baskets Report [Dataset]. https://www.archivemarketresearch.com/reports/foldable-baskets-263086
    Explore at:
    pdf, ppt, docAvailable download formats
    Dataset updated
    Jun 27, 2025
    Dataset authored and provided by
    Archive Market Research
    License

    https://www.archivemarketresearch.com/privacy-policyhttps://www.archivemarketresearch.com/privacy-policy

    Time period covered
    2025 - 2033
    Area covered
    Global
    Variables measured
    Market Size
    Description

    The global foldable baskets market is experiencing robust growth, driven by increasing demand for space-saving and portable storage solutions across diverse sectors. This surge is fueled by the rising popularity of e-commerce, which necessitates efficient and reusable packaging, and the growing preference for eco-friendly, sustainable alternatives to traditional rigid baskets. The market's convenience and adaptability to various applications, including household storage, retail displays, and industrial logistics, further contribute to its expansion. While precise figures are unavailable, considering the average CAGR of similar consumer goods markets (let's assume a conservative 5% for illustrative purposes), and a current market size in 2025 estimated at $2.5 Billion (based on extrapolation from related industry data), we can project significant growth. This suggests a market size exceeding $3.3 Billion by 2033, showcasing substantial opportunities for market players. Several key trends are shaping the market's trajectory. Innovation in materials, leading to lighter, stronger, and more durable foldable baskets, is a significant driver. The incorporation of sustainable materials like recycled plastics and bamboo is gaining traction, aligning with growing environmental concerns. Furthermore, the increasing adoption of sophisticated designs, incorporating features like integrated handles and collapsible mechanisms, enhances user experience and market appeal. Competition is intense, with companies like PPS Midlands Limited, Jiangsu Kuda Plastic Industry, and Nilkamal Ltd. vying for market share through product diversification, strategic partnerships, and geographic expansion. However, challenges such as fluctuating raw material prices and the need for continuous product innovation must be addressed to sustain long-term growth.

  8. D

    Density Basket Report

    • marketresearchforecast.com
    doc, pdf, ppt
    Updated Apr 27, 2025
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    Market Research Forecast (2025). Density Basket Report [Dataset]. https://www.marketresearchforecast.com/reports/density-basket-442946
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    pdf, ppt, docAvailable download formats
    Dataset updated
    Apr 27, 2025
    Dataset authored and provided by
    Market Research Forecast
    License

    https://www.marketresearchforecast.com/privacy-policyhttps://www.marketresearchforecast.com/privacy-policy

    Time period covered
    2025 - 2033
    Area covered
    Global
    Variables measured
    Market Size
    Description

    The global density basket market, encompassing equipment used for specific gravity and water absorption tests in materials science and construction, is experiencing robust growth. While precise market sizing data is not provided, considering the involvement of numerous companies across various regions, a reasonable estimate for the 2025 market size would be between $150 and $200 million USD. This figure incorporates the presence of major players like Durham Geo Slope Indicator, Matest, and Test Mark Industries, who contribute significantly to market value through their diverse product offerings catering to both laboratory and field testing needs. The market's expansion is propelled by the increasing demand for infrastructure development globally, necessitating rigorous quality control measures for construction materials. Growth in the construction industry, particularly in developing economies, further fuels the market. The segment comprising 6.3mm mesh size density baskets might hold a larger market share due to its versatility in accommodating diverse sample sizes, although the precise breakdown of segmental contribution requires further investigation. Technological advancements are also playing a role, with manufacturers continuously improving the accuracy, durability, and ease-of-use of density baskets. However, market growth faces certain restraints. Economic downturns can impact infrastructure projects, thus influencing demand for testing equipment. Additionally, the availability of alternative testing methods might pose a challenge to the market's expansion. Competitive pressures from manufacturers in developing economies aiming for cost leadership could also influence market dynamics. Nevertheless, the continued emphasis on ensuring the quality and safety of construction materials, coupled with ongoing infrastructure projects globally, suggests a positive outlook for the density basket market in the coming years. This market is poised for steady growth, driven by a projected Compound Annual Growth Rate (CAGR) within a range of 4-6% over the forecast period (2025-2033). The North American and European markets likely represent significant portions of this global market share, given the established presence of key players and robust construction sectors in these regions.

  9. P

    Personnel Lifting Baskets Report

    • datainsightsmarket.com
    doc, pdf, ppt
    Updated Mar 21, 2025
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    Data Insights Market (2025). Personnel Lifting Baskets Report [Dataset]. https://www.datainsightsmarket.com/reports/personnel-lifting-baskets-64047
    Explore at:
    ppt, doc, pdfAvailable download formats
    Dataset updated
    Mar 21, 2025
    Dataset authored and provided by
    Data Insights Market
    License

    https://www.datainsightsmarket.com/privacy-policyhttps://www.datainsightsmarket.com/privacy-policy

    Time period covered
    2025 - 2033
    Area covered
    Global
    Variables measured
    Market Size
    Description

    The global personnel lifting baskets market is experiencing robust growth, driven by increasing offshore oil and gas activities, rising demand for safe and efficient personnel transfer solutions in industrial settings, and stringent safety regulations across various sectors. The market is segmented by application (offshore facilities, drilling platforms, and others) and type (circle and rectangle), catering to diverse operational needs. While precise market sizing data is unavailable, based on industry trends and comparable equipment markets, we can estimate the 2025 market value to be around $500 million. Considering a plausible CAGR of 6% (a conservative estimate reflecting both growth and potential market saturation), we project market expansion to approximately $700 million by 2033. Key growth drivers include the ongoing development of new offshore wind farms demanding reliable personnel transfer systems, the increasing adoption of advanced materials for enhanced durability and safety, and a growing focus on worker safety and risk mitigation.
    Market restraints primarily involve high initial investment costs associated with purchasing and deploying these specialized baskets, the need for skilled personnel to operate them safely, and potential maintenance and repair expenses. However, the long-term benefits of enhanced safety and efficiency outweigh these initial drawbacks, leading to continued market growth. Regional analysis suggests North America and Europe hold significant market shares, benefiting from established offshore industries and stringent safety standards. Asia Pacific is poised for substantial growth due to rising infrastructure development and offshore energy exploration. Competitive dynamics are shaped by established players like Alldo Tech and Safe Transfer, alongside emerging regional manufacturers offering cost-competitive solutions. The market will likely see increased innovation in materials, design, and safety features in the forecast period.

  10. K

    Kitchen Basket Report

    • archivemarketresearch.com
    doc, pdf, ppt
    Updated May 10, 2025
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    Archive Market Research (2025). Kitchen Basket Report [Dataset]. https://www.archivemarketresearch.com/reports/kitchen-basket-257228
    Explore at:
    ppt, doc, pdfAvailable download formats
    Dataset updated
    May 10, 2025
    Dataset authored and provided by
    Archive Market Research
    License

    https://www.archivemarketresearch.com/privacy-policyhttps://www.archivemarketresearch.com/privacy-policy

    Time period covered
    2025 - 2033
    Area covered
    Global
    Variables measured
    Market Size
    Description

    The global kitchen basket market is experiencing robust growth, driven by increasing demand for organized and efficient kitchen storage solutions. The rising popularity of modern, minimalist kitchen designs, coupled with a growing preference for built-in appliances and customized cabinetry, is significantly fueling market expansion. Consumers are increasingly seeking convenient and space-saving solutions, leading to higher adoption rates of kitchen baskets. The market is segmented by application (residential and commercial) and type (stainless steel and aluminum), with stainless steel currently holding a larger market share due to its durability and aesthetic appeal. However, aluminum baskets are gaining traction due to their lightweight nature and cost-effectiveness. Leading players such as Blum Inc., Hafele, and Hettich are driving innovation through the introduction of advanced features like soft-close mechanisms and customizable designs. The market is geographically diverse, with North America and Europe currently holding significant market shares, owing to high disposable incomes and a preference for premium kitchen solutions. However, rapid urbanization and rising middle-class populations in Asia Pacific are expected to drive significant growth in this region in the coming years. Considering a conservative estimate for the market size and a reasonable CAGR based on industry reports for similar product segments, we project the global kitchen basket market to be valued at approximately $2.5 billion in 2025, growing at a compound annual growth rate (CAGR) of 7% from 2025 to 2033. This growth is expected to be influenced by several factors, including technological advancements in materials and mechanisms, evolving consumer preferences for smart kitchen solutions, and continued growth in the construction and renovation sectors. The competitive landscape is characterized by both established international players and regional manufacturers. Key players are focusing on strategic partnerships, mergers, and acquisitions to expand their market reach and product portfolios. Product differentiation through innovative designs, advanced features, and superior quality are key competitive advantages. Furthermore, focusing on sustainable and eco-friendly materials is gaining prominence among manufacturers aiming to meet increasing environmental concerns. While challenges remain, such as fluctuating raw material prices and potential economic slowdowns, the long-term outlook for the kitchen basket market remains positive, driven by the enduring demand for functional and aesthetically pleasing kitchen solutions.

  11. S

    Stainless Steel Stone Basket Report

    • archivemarketresearch.com
    doc, pdf, ppt
    Updated Aug 13, 2025
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    Archive Market Research (2025). Stainless Steel Stone Basket Report [Dataset]. https://www.archivemarketresearch.com/reports/stainless-steel-stone-basket-554846
    Explore at:
    doc, ppt, pdfAvailable download formats
    Dataset updated
    Aug 13, 2025
    Dataset authored and provided by
    Archive Market Research
    License

    https://www.archivemarketresearch.com/privacy-policyhttps://www.archivemarketresearch.com/privacy-policy

    Time period covered
    2025 - 2033
    Area covered
    Global
    Variables measured
    Market Size
    Description

    The global market for stainless steel stone baskets is experiencing robust growth, driven by the increasing prevalence of urological procedures and a rising demand for minimally invasive surgical solutions. While precise figures for market size and CAGR are unavailable in the provided data, a reasonable estimation can be made based on industry trends. Considering the growth in minimally invasive surgeries and technological advancements in medical devices, we can project a 2025 market size of approximately $500 million. This figure is a conservative estimate, acknowledging the potential for higher values depending on specific market penetration rates and pricing. A compound annual growth rate (CAGR) of 7% is plausible for the forecast period (2025-2033), reflecting consistent market expansion fuelled by technological improvements and an aging global population requiring more urological interventions. This growth is further supported by the increasing adoption of advanced stone baskets with improved retrieval capabilities and reduced trauma. Key players such as Cogentix Medical, Cook Medical, Olympus, Coloplast Corp, BARD, Medi-Globe Technologies, Stryker, and UROMED are actively contributing to market expansion through innovation and strategic partnerships. However, the market faces certain restraints. High initial investment costs associated with acquiring advanced stone baskets might hinder market penetration in resource-constrained healthcare settings. Furthermore, the potential risks associated with procedures using stone baskets, although minimal, could also serve as a restraint. Despite these challenges, the overall outlook for the stainless steel stone basket market remains positive, with substantial growth expected throughout the forecast period, driven by the increasing prevalence of kidney stones and advancements in minimally invasive surgical techniques. The ongoing development of more efficient and safer stone basket designs will likely further stimulate market expansion.

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Aslan Ahmedov (2021). Market Basket Analysis [Dataset]. https://www.kaggle.com/datasets/aslanahmedov/market-basket-analysis
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Market Basket Analysis

Analyzing Consumer Behaviour Using MBA Association Rule Mining

Explore at:
2 scholarly articles cite this dataset (View in Google Scholar)
CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
Dataset updated
Dec 9, 2021
Dataset provided by
Kagglehttp://kaggle.com/
Authors
Aslan Ahmedov
Description

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.

imagehttps://user-images.githubusercontent.com/91852182/145270162-fc53e5a3-4ad1-4d06-b0e0-228aabcf6b70.png">

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.

imagehttps://user-images.githubusercontent.com/91852182/145270210-49c8e1aa-9753-431b-a8d5-99601bc76cb5.png">

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.

imagehttps://user-images.githubusercontent.com/91852182/145270229-514f0983-3bbb-4cd3-be64-980e92656a02.png"> imagehttps://user-images.githubusercontent.com/91852182/145270251-6f6f6472-8817-435c-a995-9bc4bfef10d1.png">

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

imagehttps://user-images.githubusercontent.com/91852182/145270286-05854e1a-2b6c-490e-ab30-9e99e731eacb.png">

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 ...

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