This fictional sales dataset was created using a R code for the purpose of visualizing trends in customer demographics, product performance, and sales over time. A link to my Github repository containing all the codes used in generating the data frame and all the preceding processes can be found here

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.

https://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.

https://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.

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

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

https://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 ...

This repository was created for my Master's thesis in Computational Intelligence and Internet of Things at the University of Córdoba, Spain. The purpose of this repository is to store the datasets found that were used in some of the studies that served as research material for this Master's thesis. Also, the datasets used in the experimental part of this work are included.

Below are the datasets specified, along with the details of their references, authors, and download sources.

----------- STS-Gold Dataset ----------------

The dataset consists of 2026 tweets. The file consists of 3 columns: id, polarity, and tweet. The three columns denote the unique id, polarity index of the text and the tweet text respectively.

Reference: Saif, H., Fernandez, M., He, Y., & Alani, H. (2013). Evaluation datasets for Twitter sentiment analysis: a survey and a new dataset, the STS-Gold.

File name: sts_gold_tweet.csv

----------- Amazon Sales Dataset ----------------

This dataset is having the data of 1K+ Amazon Product's Ratings and Reviews as per their details listed on the official website of Amazon. The data was scraped in the month of January 2023 from the Official Website of Amazon.

Owner: Karkavelraja J., Postgraduate student at Puducherry Technological University (Puducherry, Puducherry, India)

Features:

• product_id - Product ID
• product_name - Name of the Product
• category - Category of the Product
• discounted_price - Discounted Price of the Product
• actual_price - Actual Price of the Product
• discount_percentage - Percentage of Discount for the Product
• rating - Rating of the Product
• rating_count - Number of people who voted for the Amazon rating
• about_product - Description about the Product
• user_id - ID of the user who wrote review for the Product
• user_name - Name of the user who wrote review for the Product
• review_id - ID of the user review
• review_title - Short review
• review_content - Long review
• img_link - Image Link of the Product
• product_link - Official Website Link of the Product

License: CC BY-NC-SA 4.0

File name: amazon.csv

----------- Rotten Tomatoes Reviews Dataset ----------------

This rating inference dataset is a sentiment classification dataset, containing 5,331 positive and 5,331 negative processed sentences from Rotten Tomatoes movie reviews. On average, these reviews consist of 21 words. The first 5331 rows contains only negative samples and the last 5331 rows contain only positive samples, thus the data should be shuffled before usage.

This data is collected from https://www.cs.cornell.edu/people/pabo/movie-review-data/ as a txt file and converted into a csv file. The file consists of 2 columns: reviews and labels (1 for fresh (good) and 0 for rotten (bad)).

Reference: Bo Pang and Lillian Lee. Seeing stars: Exploiting class relationships for sentiment categorization with respect to rating scales. In Proceedings of the 43rd Annual Meeting of the Association for Computational Linguistics (ACL'05), pages 115–124, Ann Arbor, Michigan, June 2005. Association for Computational Linguistics

File name: data_rt.csv

----------- Preprocessed Dataset Sentiment Analysis ----------------

Preprocessed amazon product review data of Gen3EcoDot (Alexa) scrapped entirely from amazon.in
Stemmed and lemmatized using nltk.
Sentiment labels are generated using TextBlob polarity scores.

The file consists of 4 columns: index, review (stemmed and lemmatized review using nltk), polarity (score) and division (categorical label generated using polarity score).

DOI: 10.34740/kaggle/dsv/3877817

Citation: @misc{pradeesh arumadi_2022, title={Preprocessed Dataset Sentiment Analysis}, url={https://www.kaggle.com/dsv/3877817}, DOI={10.34740/KAGGLE/DSV/3877817}, publisher={Kaggle}, author={Pradeesh Arumadi}, year={2022} }

This dataset was used in the experimental phase of my research.

File name: EcoPreprocessed.csv

----------- Amazon Earphones Reviews ----------------

This dataset consists of a 9930 Amazon reviews, star ratings, for 10 latest (as of mid-2019) bluetooth earphone devices for learning how to train Machine for sentiment analysis.

This dataset was employed in the experimental phase of my research. To align it with the objectives of my study, certain reviews were excluded from the original dataset, and an additional column was incorporated into this dataset.

The file consists of 5 columns: ReviewTitle, ReviewBody, ReviewStar, Product and division (manually added - categorical label generated using ReviewStar score)

License: U.S. Government Works

Source: www.amazon.in

File name (original): AllProductReviews.csv (contains 14337 reviews)

File name (edited - used for my research) : AllProductReviews2.csv (contains 9930 reviews)

----------- Amazon Musical Instruments Reviews ----------------

This dataset contains 7137 comments/reviews of different musical instruments coming from Amazon.

This dataset was employed in the experimental phase of my research. To align it with the objectives of my study, certain reviews were excluded from the original dataset, and an additional column was incorporated into this dataset.

The file consists of 10 columns: reviewerID, asin (ID of the product), reviewerName, helpful (helpfulness rating of the review), reviewText, overall (rating of the product), summary (summary of the review), unixReviewTime (time of the review - unix time), reviewTime (time of the review (raw) and division (manually added - categorical label generated using overall score).

Source: http://jmcauley.ucsd.edu/data/amazon/

File name (original): Musical_instruments_reviews.csv (contains 10261 reviews)

File name (edited - used for my research) : Musical_instruments_reviews2.csv (contains 7137 reviews)

A DataSet of Supply Chains used by the company DataCo Global was used for the analysis. Dataset of Supply Chain , which allows the use of Machine Learning Algorithms and R Software. Areas of important registered activities : Provisioning , Production , Sales , Commercial Distribution.It also allows the correlation of Structured Data with Unstructured Data for knowledge generation.

Types of Products : Clothing , Sports , and Electronic Supplies

Additionally it is attached in another file called DescriptionDataCoSupplyChain.csv, the description of each of the variables of the DataCoSupplyChainDatasetc.csv.

Discover the latest insights from Market Research Intellect's R-410A Refrigerant Sales Market Report, valued at USD 5.1 billion in 2024, with significant growth projected to USD 8.3 billion by 2033 at a CAGR of 6.5% (2026-2033).

this graph was created in R and Canva :

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F16731800%2F1a47e2e6e4836b86b065441359d5c9f0%2Fgraph1.gif?generation=1742159161939732&alt=media" alt=""> https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F16731800%2F87de025c5703cb69483764c4fc9c58ab%2Fgraph2.gif?generation=1742159169346925&alt=media" alt=""> https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F16731800%2Fddf5001438c97c8c030333261685849b%2Fgraph3.png?generation=1742159174793142&alt=media" alt="">

The dataset offers a comprehensive view of grocery inventory, covering 990 products across multiple categories such as Grains & Pulses, Beverages, Fruits & Vegetables, and more. It includes crucial details about each product, such as its unique identifier (Product_ID), name, category, and supplier information, including Supplier_ID and Supplier_Name. This dataset is particularly valuable for businesses aiming to optimize inventory management, sales tracking, and supply chain efficiency.

Key inventory-related fields include Stock_Quantity, which indicates the current stock level, and Reorder_Level, which determines when a product should be reordered. The Reorder_Quantity specifies how much stock to order when inventory falls below the reorder threshold. Additionally, Unit_Price provides insight into pricing, helping businesses analyze cost trends and profitability.

To manage product flow, the dataset includes dates such as Date_Received, which tracks when the product was added to the warehouse, and Last_Order_Date, marking the most recent procurement. For perishable goods, the Expiration_Date column is critical, allowing businesses to minimize waste by monitoring shelf life. The Warehouse_Location specifies where each product is stored, facilitating efficient inventory handling.

Sales and performance metrics are also included. The Sales_Volume column records the total number of units sold, providing insights into consumer demand. Inventory_Turnover_Rate helps businesses assess how quickly a product sells and is replenished, ensuring better stock management. The dataset also tracks the Status of each product, indicating whether it is Active, Discontinued, or Backordered.

The dataset serves multiple purposes in inventory management, sales performance evaluation, supplier analysis, and product lifecycle tracking. Businesses can leverage this data to refine reorder strategies, ensuring optimal stock levels and avoiding stockouts or excessive inventory. Sales analysis can help identify high-demand products and slow-moving items, enabling better decision-making in pricing and promotions. Evaluating suppliers based on their performance, pricing, and delivery efficiency helps streamline procurement and improve overall supply chain operations.

Furthermore, the dataset can support predictive analytics by employing machine learning techniques to estimate reorder quantities, forecast demand, and optimize stock replenishment. Inventory turnover insights can aid in maintaining a balanced supply, preventing unnecessary overstocking or shortages. By tracking trends in sales, businesses can refine their marketing and distribution strategies, ensuring sustained profitability.

This dataset is designed for educational and demonstration purposes, offering fictional data under the Creative Commons Attribution 4.0 International License. Users are free to analyze, modify, and apply the data while providing proper attribution. Additionally, certain products are marked as discontinued or backordered, reflecting real-world inventory dynamics. Businesses dealing with perishable goods should closely monitor expiration and last order dates to avoid losses due to spoilage.

Overall, this dataset provides a versatile resource for those interested in inventory management, sales analysis, and supply chain optimization. By leveraging the structured data, businesses can make data-driven decisions to enhance operational efficiency and maximize profitability.

The Online Baby Product Sales industry has defied Australia's turbulent retail landscape over the past five years, expanding rapidly over the period. The industry owes its success to technological advances and increasing internet connectivity, which have fuelled demand for – and acceptance of – online shopping in Australia. Strong marketing campaigns, competitive pricing and the development of multichannel retailing options have also enhanced the appeal of online baby product retailers. Competitive pricing and convenient shopping platforms have caused sales volumes to soar. The industry has bolstered its consumer appeal by using clever marketing and low prices – particularly for essential items like nappies and baby food – to win over an increasingly value-conscious customer base. Online retailers have also benefited from offering consumers a convenient way to shop, through mobile shopping apps that allow customers to purchase baby products anytime and from many locations. The growing prevalence of remote and hybrid work arrangements has fuelled this trend. However, competition has intensified, squeezing retailers' profit margins as they fight to offer customers the most attractive deals and discounts. Revenue is expected to increase at an annualised 5.7% over the five years through 2024-25, to $1.6 billion. This includes an expected dip of 0.4% in 2024-25, as cost of living pressures force consumers to reduce spending on pricey baby products.Greater acceptance of online shopping platforms will fuel demand over the next few years. In the face of escalating competition, online baby product retailers' revenue growth will lean on premium and niche products. As prominent players intensify price-based competition through robust online presence, smaller retailers will pivot towards niche offerings like branded baby clothing and eco-friendly products. Online retailers are also poised to focus on membership programmes to foster customer loyalty and expand market share. An uptick in birth rates, improving consumer sentiment and household disposable income growth will underpin industry expansion. As the economic recovery continues, the consumption of high-end baby products is forecast to swell, supporting profit margins. Overall, revenue is forecast to rise at an annualised 2.9% over the five years through 2029-30, to $1.7 billion.

Subscribers can find out export and import data of 23 countries by HS code or product’s name. This demo is helpful for market analysis.

Energy time series data structure.

Sales and market dynamics play a pivotal role in determining how businesses reach their customers and ultimately drive revenue. In today's highly competitive landscape, understanding the intricate relationship between sales strategies and market positioning is essential for any organization aiming to thrive. Sales r

Analysis of ‘HVAC Market Share by Efficiency and Capacity: Beginning 2017’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/b80e5928-b8a6-49bb-98c5-f90883ec2284 on 27 January 2022.

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

HVAC Market Share by Efficiency and Capacity: Beginning 2017 dataset is based on heating, ventilation, and air conditioning (HVAC) sales data reported to D+R International by Heating, Air-conditioning & Refrigeration Distributors International (HARDI) members participating in the Unitary HVAC Market Report. Participation in the report is voluntary for distributors. The dataset covers New York State and the Northeast (includes Maine, New Hampshire, Vermont, Massachusetts, Connecticut, and Rhode Island). Blank cells represent data that are not currently available.

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

Dive into Market Research Intellect's R-124 Refrigerant Sales Market Report, valued at USD 1.2 billion in 2024, and forecast to reach USD 1.8 billion by 2033, growing at a CAGR of 5.0% from 2026 to 2033.

Australia's online toy sales market has been on a rollercoaster ride in recent years, mirroring the ups and downs of the broader retail sector. As discretionary purchases, demand for toys and games tends to fluctuate based on income levels and consumer confidence, both of which have faced significant volatility. The onset of the COVID-19 pandemic acted as a turning point, with stay-at-home orders and government stimulus payments driving a surge in online shopping for entertainment products like Lego, puzzles and outdoor toys. However, this boom was short-lived. Rising interest rates and inflation soon sparked a sharp cost-of-living crisis, eating into household budgets and driving down consumer sentiment, ultimately dampening online toy sales. Industry's revenue has grown at an annualised rate of 1.0% over the past five years to reach $513.4 million in 2025-26, with a 2.6% climb in revenue in the current year. Online toy sales have endured peaks and troughs over the past five years. The pandemic years witnessed a dramatic jump in online spending as shoppers flocked to digital platforms out of necessity and convenience. Key retailers invested heavily in enhancing their online offerings, improving website navigation, expanding mobile apps, rolling out click-and-collect and improving delivery services. Continued investment, the entry of new players and heightened demand for niche toys, like STEM and eco-friendly products, expanded the market. However, trading conditions have been challenging in the post-pandemic environment, with rising costs, volatile consumer sentiment and intense competition eroding profitability. Looking ahead, easing inflation, projected interest rate reductions and ongoing wage growth are set to boost discretionary income and consumer sentiment, encouraging renewed consumer spending on toys. Online toy retailers will likely focus on omnichannel strategies, exclusive products and immersive digital experiences to stand out in an increasingly crowded marketplace. Advances in technology will reshape toy preferences, with a strong appetite predicted for smart, STEM and educational toys. Intense competition may persist, but digital innovation, market differentiation and improving import affordability will support both business expansion and profitability. Overall, industry revenue is forecast to expand at an annualised rate of 3.5% over the five years through 2031-32 to reach $610.3 million.

ABSTRACT Aiming to analyzing the commercialization of pesticides in Brazil, its regions and states, an ecological time series study was developed from 2000 to 2014, based on data on sales of pesticides from the Brazilian Institute of the Environment and Renewable Natural Resources and the National Union of Plant Protection Products Industry. The commercialization was calculated as the quotient of the quantity of active ingredients, in kilograms, and the planted area of the main crops, in hectares, annually in the states and regions. The Excel® and R programs were used for data analysis. For trend analysis, linear regression was used with a 5% significance level. There was a trend towards an increase in sales in all regions of the country in the period (p

The global CD-R drive market, while facing a decline in recent years due to the rise of digital media and cloud storage, maintains a niche presence driven by specific applications. While precise market sizing data is unavailable, a reasonable estimation, based on industry trends and the presence of numerous established players like Sony, Yamaha, and Pioneer, suggests a 2025 market size of approximately $150 million. Considering the continued, albeit slow, demand for archival storage, specialized audio applications, and certain industrial uses, we project a compound annual growth rate (CAGR) of -3% from 2025 to 2033. This modest negative growth reflects the ongoing shift towards digital formats, yet acknowledges the persistence of CD-R technology in specific sectors. The market's future hinges on the continued demand in niche applications. Factors influencing this market include the increasing availability of affordable digital storage solutions, technological advancements in data storage, and the preference for streaming services. Despite the overall decline, several factors support the continued, albeit limited, market viability. These include the need for reliable, readily available, and cost-effective data backup solutions in certain industrial settings, the continuing use of CD-R technology for archiving purposes where data integrity and long-term accessibility are paramount, and specialized audio applications which value the inherent quality and simplicity of CD-R technology. Key players in the market are leveraging strategic collaborations and product innovations to tap into these niche markets and maintain a competitive edge. The market segmentation involves various drive types (internal vs. external), storage capacities, and target user segments. While the overall market is shrinking, understanding the specific needs of these niche segments is vital for continued market success.

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

R. PLC's (RELX) Outlook: Analysts Bullish on Data and Analytics Firm.

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

Global Research And Development (R&D) Analytics Market demand is anticipated to be valued at US$ 2,025.0 Million in 2022, forecast a CAGR of 12.1% to be valued at US$ 6,366.6 Million from 2022 to 2032. Growth is attributed to the evolving need in end-use industries. From 2016 to 2021 a CAGR of 9.1% was registered for the Research And Development Analytics Market.

Data Points	Key Statistics
Growth Rate (2016 to 2021)	9.1 % CAGR
Projected Growth Rate (2022 to 2032)	12.1% CAGR
Expected Market Value (2022)	US$ 2,025.0 Million
Anticipated Forecast Value (2032)	US$ 6,366.6 Million

Report Scope

Report Attribute	Details
Growth Rate	CAGR of 12.1 % from 2022 to 2032
Expected Market Value (2022)	US$ 2025.0 Million
Anticipated Forecast Value (2032)	US$ 6366.6 Million
Base Year for Estimation	2021
Historical Data	2016 to 2021
Forecast Period	2022 to 2032
Quantitative Units	Revenue in USD Billion, Volume in Kilotons, and CAGR from 2022 to 2032
Report Coverage	Revenue Forecast, Volume Forecast, Company Ranking, Competitive Landscape, Growth Factors, Trends, and Pricing Analysis
Segments Covered	End-Use Enterprise Size Region
Regions Covered	North America Latin America Europe Asia Pacific Middle East and Africa
Key Countries Profiled	USA Canada Mexico Brazil Germany Italy France United Kingdom Spain China Japan South Korea Singapore Thailand Indonesia Australia New Zealand GCC Countries South Africa Israel
Key Companies Profiled	Teradata Oracle Corporation IBM Corporation SAS Institute Inc. Tableau Software Inc. Microsoft Corporation Sisense Inc. SAP SE TARGET
Customization	Available Upon Request

Check out Market Research Intellect's R-407C Refrigerant Sales Market Report, valued at USD 2.5 billion in 2024, with a projected growth to USD 3.8 billion by 2033 at a CAGR of 5.5% (2026-2033).

product market has been steadily increasing over recent years, and forecasts suggest a substantial growth trajectory in the upcoming period.

ATTRIBUTES	DETAILS
STUDY PERIOD	2018-2031
BASE YEAR	2023
FORECAST PERIOD	2024-2031
HISTORICAL PERIOD	2018-2022
UNIT	VALUE (USD MILLION)
KEY COMPANIES PROFILED	JTEKT, Bosch, NSK, Nexteer, ZF, Mobis, Showa, Thyssenkrupp, Mando
SEGMENTS COVERED	By Product Type - C-EPS, P-EPS, R-EPS By Application - Passenger Vehicle, Commercial Vehicle By Sales Channels - Direct Channel, Distribution Channel By Geography - North America, Europe, Asia-Pacific, South America, Middle East and Africa

product market has been steadily increasing over recent years, and forecasts suggest a substantial growth trajectory in the upcoming period.

ATTRIBUTES	DETAILS
STUDY PERIOD	2018-2031
BASE YEAR	2023
FORECAST PERIOD	2024-2031
HISTORICAL PERIOD	2018-2022
UNIT	VALUE (USD MILLION)
KEY COMPANIES PROFILED	Honeywell, Durex Industries, Cleveland Electric Laboratories, Tanaka, CCPI, Yamari, Omega, JUMO, Watlow, Chongqing Dazhi
SEGMENTS COVERED	By Product Type - R Type, S Type, B Type By Application - Steel, Glass, Semiconductor, Pharmaceutical, Power, Others By Sales Channels - Direct Channel, Distribution Channel By Geography - North America, Europe, Asia-Pacific, South America, Middle East and Africa