Dataset

This dataset was created by Ayush Sarraf0731

Contents

Dataset

This dataset was created by 64 Aashish Chaudhary

Released under Other (specified in description)

Contents

Enbor Eda

## Overview

Enbor Eda is a dataset for object detection tasks - it contains License Plate MgLz Licenseplate annotations for 3,120 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Eda_all

## Overview

Eda_all is a dataset for instance segmentation tasks - it contains All annotations for 1,314 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

This dataset contains 55,000 entries of synthetic customer transactions, generated using Python's Faker library. The goal behind creating this dataset was to provide a resource for learners like myself to explore, analyze, and apply various data analysis techniques in a context that closely mimics real-world data.

About the Dataset: - CID (Customer ID): A unique identifier for each customer. - TID (Transaction ID): A unique identifier for each transaction. - Gender: The gender of the customer, categorized as Male or Female. - Age Group: Age group of the customer, divided into several ranges. - Purchase Date: The timestamp of when the transaction took place. - Product Category: The category of the product purchased, such as Electronics, Apparel, etc. - Discount Availed: Indicates whether the customer availed any discount (Yes/No). - Discount Name: Name of the discount applied (e.g., FESTIVE50). - Discount Amount (INR): The amount of discount availed by the customer. - Gross Amount: The total amount before applying any discount. - Net Amount: The final amount after applying the discount. - Purchase Method: The payment method used (e.g., Credit Card, Debit Card, etc.). - Location: The city where the purchase took place.

Use Cases: 1. Exploratory Data Analysis (EDA): This dataset is ideal for conducting EDA, allowing users to practice techniques such as summary statistics, visualizations, and identifying patterns within the data. 2. Data Preprocessing and Cleaning: Learners can work on handling missing data, encoding categorical variables, and normalizing numerical values to prepare the dataset for analysis. 3. Data Visualization: Use tools like Python’s Matplotlib, Seaborn, or Power BI to visualize purchasing trends, customer demographics, or the impact of discounts on purchase amounts. 4. Machine Learning Applications: After applying feature engineering, this dataset is suitable for supervised learning models, such as predicting whether a customer will avail a discount or forecasting purchase amounts based on the input features.

This dataset provides an excellent sandbox for honing skills in data analysis, machine learning, and visualization in a structured but flexible manner.

This is not a real dataset. This dataset was generated using Python's Faker library for the sole purpose of learning

Enbor Eda Article 2 V4

## Overview

Enbor Eda Article 2 V4 is a dataset for object detection tasks - it contains Plate AQOx Plate XoZD License Plate MgLz Licenseplate E90T 00Hp annotations for 2,125 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Assignment 1: EDA - US Company Bankruptcy Prediction

Student Name: Reef Zehavi Date: November 10, 2025

  📹 Project Presentation Video

[(https://www.loom.com/share/6920e493e8654ef3bb4f67a10eb9b03d)]

  1. Overview and Project Goal

The goal of this project is to perform Exploratory Data Analysis (EDA) on a fundamental dataset of American companies. The analysis focuses on understanding the financial characteristics that differentiate between companies that survived… See the full description on the dataset page: https://huggingface.co/datasets/reefzehavi/EDA-US-Bankruptcy-Prediction.

Exploratory Data Analysis (EDA) on the Online Shoppers Purchasing Intention Dataset

Author: Shira Bash

  Project Overview

This project performs Exploratory Data Analysis (EDA) on the Online Shoppers Purchasing Intention dataset.The goal is to understand which behavioral patterns influence the likelihood that a website visitor completes a purchase(Revenue = True). The analysis includes:

Data exploration & validation
Visualizations (histograms, scatter plots, box… See the full description on the dataset page: https://huggingface.co/datasets/shiraBASH/online-shoppers-eda.

Dataset

This dataset was created by Mitesh Padiya

Contents

According to Cognitive Market Research, the global EDA tools market size will be USD 12524.8 million in 2025. It will expand at a compound annual growth rate (CAGR) of 8.80% from 2025 to 2033.

North America held the major market share for more than 40% of the global revenue with a market size of USD 4634.18 million in 2025 and will grow at a compound annual growth rate (CAGR) of 6.6% from 2025 to 2033.
Europe accounted for a market share of over 30% of the global revenue with a market size of USD 3632.19 million.
APAC held a market share of around 23% of the global revenue with a market size of USD 3005.95 million in 2025 and will grow at a compound annual growth rate (CAGR) of 10.8% from 2025 to 2033.
South America has a market share of more than 5% of the global revenue with a market size of USD 475.94 million in 2025 and will grow at a compound annual growth rate (CAGR) of 7.8% from 2025 to 2033.
The Middle East had a market share of around 2% of the global revenue and was estimated at a market size of USD 500.99 million in 2025 and will grow at a compound annual growth rate (CAGR) of 8.1% from 2025 to 2033.
Africa had a market share of around 1% of the global revenue and was estimated at a market size of USD 275.55 million in 2025 and will grow at a compound annual growth rate (CAGR) of 8.5% from 2025 to 2033.
Verification tools category is the fastest growing segment of the EDA tools industry

Market Dynamics of EDA Tools Market

Key Drivers for EDA Tools Market

Strong Focus on Research and Development in Semiconductor Fabrication Drives Market Growth

The EDA tools market is experiencing significant growth due to the strong focus on research and development in semiconductor fabrication. As semiconductor manufacturers strive to create smaller, faster, and more energy-efficient chips, the complexity of chip design continues to increase. This drives the need for advanced EDA tools that can support intricate design processes, simulation, verification, and testing. Continuous R&D efforts are enabling innovation in fabrication technologies, which in turn require more sophisticated design automation solutions. Additionally, the push for next-generation applications in AI, IoT, and 5G technologies further reinforces the demand for efficient and accurate design tools. Overall, intensive R&D in fabrication fuels sustained growth in the global EDA tools market. For instance, in December 2023, Siemens EDA launched its collaborative venture initiative, Cre8Ventures, and began accepting proposals from European IC startups as part of its support for the European Chips Act. Adopted in 2023, the act aimed to strengthen the EU semiconductor industry and increase its global market share by 2030.

https://newsroom.sw.siemens.com/en-US/cre8ventures-eu-chips-act/

Growing Need for Integrated Circuit (IC) Designs in Consumer Electronics Fuels Market Growth

The growing need for integrated circuit (IC) designs in consumer electronics significantly fuels the growth of the EDA tools market. As consumer devices become more compact, powerful, and multifunctional, the demand for efficient, high-performance ICs continues to rise. From smartphones and tablets to wearable devices and smart home products, manufacturers require advanced design automation tools to meet tight production schedules and performance expectations. EDA tools help streamline complex design processes, enhance simulation accuracy, and reduce time-to-market. Moreover, the increasing consumer appetite for connected and intelligent devices further amplifies the need for innovative IC solutions. This ongoing demand positions EDA tools as essential in the evolving consumer electronics landscape.

Restraint Factor for the EDA Tools Market

High Cost Of EDA Tools Limits Adoption Among Small And Medium-Sized Enterprises

In the EDA tools market, the high cost of software licenses and maintenance remains a major barrier to adoption, particularly among small and medium-sized enterprises (SMEs). These tools often require significant upfront investment, making it difficult for smaller companies with limited budgets to access advanced design technologies. Additionally, ongoing costs for updates, training, and technical support further add to the financial burden. As a result, many SMEs struggle to compete with larger organizations that can afford comprehensive EDA tool suites. This cost-related constraint limits innovation and ...

The data described in this repository has five items:DataSpecsThis excel file has six worksheets with the following information: demographic data, biofiles available, Immersive Tendencies Questionnaire responses, immersive questionnaire responses, items of questionnaires, and EEG electrode positions in Theta/Phi coordinates.LoudspeakerInformationPDF file explaining the alignment and positions of loudspeakers for stereo, PCMA-3D, and ESMA-3D audio playback. RawDataFolder with individual subfolders of participants labeled with assigned ID. Each folder has EEG, EDA, and BVP files in GDF format for three conditions: 1) resting state (Bl), 2) concert hall (Music), and 3) urban park (Park) soundscapes. The assigned audio group (Stereo or 3D) is specified in file names. Sample rates are: EEG = 500 Hz, BVP = 64 Hz, and EDA = 4 Hz. The assigned audio group is specified in file names. For example, file “01_Stereo_BVP_Bl” corresponds to BVP data in the resting state of the participant 01 assigned to the Stereo group.LatencyAdjustmentFolder with individual subfolders of participants labeled with assigned ID in SET/FDT format. The only difference is that "condition 8" onset was adjusted according to the latency caused by the distance between the audio system and participants (2 m). Condition 8 indicates the moment a soundscape (Music or Park) was played.AudioFilesThis folder contains two subfolders:Music: 2-minute long WAV audio files of concert hall recordings prepared to be heard on PCMA-3D and Stereo (Downmix files) loudspeaker array at 48k Hz of sample rate and 24-bit depthPark: 2-minute long WAV audio files of urban park recordings prepared to be heard on ESMA-3D and Stereo (Downmix files) loudspeaker array at 48k Hz of sample rate and 24-bit depthStereo downmix files include the word “_Downmix_”.Note: In the worksheet Items of DataSpecs, the codes that the questionnaires provide are included. Just one item of the Immersive Tendencies Questionnaire and the items of the Self-assessment manikin test do not have codes in their original publications.

🚗 Car MSRP Analysis — Exploratory Data Analysis (EDA)

  📘 Overview

This project performs an in-depth Exploratory Data Analysis (EDA) on a dataset of cars and their characteristics in order to understand which features most strongly influence the MSRP (Manufacturer Suggested Retail Price). The work includes:

Data loading and cleaning
Handling missing values
Basic feature engineering
Outlier handling for visualization
Exploratory Data Analysis and visualizations… See the full description on the dataset page: https://huggingface.co/datasets/netzer97/car_msrp_eda_netzer_v2.

This is a cleaned version of a Netflix movies dataset originally used for exploratory data analysis (EDA). The dataset contains information such as:

• Title
• Release Year
• Rating
• Genre
• Votes
• Description
• Stars

Missing values have been handled using appropriate methods (mean, median, unknown), and new features like rating_level and popular have been added for deeper analysis.

The dataset is ready for: - EDA - Data visualization - Machine learning tasks - Dashboard building

Used in the accompanying notebook

The global Electronic Design Automation (EDA) market is poised for substantial growth, expanding from approximately $10.89 billion in 2021 to an estimated $34.11 billion by 2033, demonstrating a robust CAGR of 9.983%. This expansion is fueled by the escalating complexity of semiconductor designs and the relentless proliferation of advanced technologies such as Artificial Intelligence (AI), the Internet of Things (IoT), and 5G connectivity. North America currently leads in market value, but emerging regions like Africa and South America are showcasing the highest growth rates, signaling a geographic shift in opportunities. The industry's trajectory is increasingly shaped by the adoption of cloud-based platforms and AI-driven design methodologies, which are becoming critical for managing intricate design challenges and accelerating time-to-market for next-generation electronic products. Key strategic insights from our comprehensive analysis reveal:

  High-Growth Frontiers: While North America and Europe are established leaders, the most rapid growth is occurring in emerging markets. Africa and South America exhibit the highest CAGRs (10.618% and 10.321% respectively), presenting untapped opportunities for market expansion and early-mover advantage.
  Dominance of the United States: The United States single-handedly constitutes the largest national market, projected to hold 18.75% of the global market share by 2025. Its leadership in semiconductor R&D and a strong base of fabless companies make it a critical focus area for any EDA vendor.
  Technology-Driven Demand: The market's strong overall growth is intrinsically linked to the increasing complexity of System-on-Chip (SoC) designs, 3D-ICs, and the integration of AI/ML functionalities into hardware. This necessitates continuous innovation in EDA tools, particularly in verification, simulation, and physical design.

Global Market Overview & Dynamics of EDA Market Analysis
The global EDA market is on a significant upward trajectory, driven by the insatiable demand for smaller, more powerful, and energy-efficient electronic devices. The market's value is set to nearly triple between 2021 and 2033, underscoring the critical role of EDA tools in the entire semiconductor value chain. This growth is propelled by technological advancements in automotive, consumer electronics, and telecommunications, which require increasingly sophisticated integrated circuits (ICs). As design complexities surge, the reliance on advanced EDA solutions for design, verification, and testing becomes more pronounced, ensuring sustained market expansion.
Global EDA Market Drivers

  Increasing Complexity of ICs: The continuous push for smaller process nodes (e.g., 5nm, 3nm) and the rise of complex architectures like FinFET and Gate-All-Around (GAA) demand highly advanced EDA tools for design and verification.
  Proliferation of AI, IoT, and 5G: The rapid integration of these technologies across various industries creates a massive demand for specialized chips, which in turn fuels the need for sophisticated EDA software to design them.
  Growth in Automotive Electronics: The shift towards autonomous driving, advanced driver-assistance systems (ADAS), and in-vehicle infotainment systems is driving the demand for powerful automotive-grade semiconductors, directly boosting the EDA market.

Global EDA Market Trends

  Adoption of Cloud-Based EDA: Companies are increasingly moving towards cloud-based EDA platforms to gain access to scalable computing resources, reduce infrastructure costs, and enhance collaboration among geographically dispersed design teams.
  Integration of AI and Machine Learning: AI/ML algorithms are being integrated into EDA tools to automate and optimize various stages of the chip design process, from placement and routing to verification, leading to faster design cycles and improved performance.
  Rise of System-Level Design: There is a growing emphasis on system-level design and analysis, where hardware and software components are co-designed and co-verified, requiring integrated EDA solutions that span the entire system.

Global EDA Market Restraints

  High Cost of Software and Licensing: The high cost associated with acquiring and maintaining licenses for leading-edge EDA tools can be a significant barrier, particularly for startups and smaller design houses.

...

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Electronic Design Automation Market Size 2024-2028

The electronic design automation market size is projected to increase by USD 8.69 billion at a CAGR of 10.26% between 2023 and 2028. Market expansion hinges on multiple factors, prominently the escalating importance of EDA in the electronic design sphere. As technological advancements drive complexity in electronic design, EDA tools play an increasingly crucial role in streamlining and optimizing the design process. Moreover, the growing relevance of EDA extends beyond traditional applications, encompassing emerging domains such as system-level design and hardware-software co-design. This expanding significance underscores the indispensable role of EDA solutions in enabling innovation and accelerating time-to-market for electronic products. Additionally, the proliferation of IoT managed services and IoT devices, the surge in demand for high-performance computing, and the advent of artificial intelligence further underscore the growing relevance of EDA across diverse industries. These factors collectively contribute to the robust growth trajectory of the EDA market, propelling it towards continued expansion and innovation.

What will be the Size of the Electronic Design Automation Market During the Forecast Period?

For more highlights about this market report, Download Free Sample

Electronic Design Automation Market Segmentation

The market research report provides comprehensive data (region wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2024-2028, as well as historical data from 2018 - 2022 for the following segments.

Product Type Outlook 

  Semiconductor IP
  CAE
  IC physical design and verification
  PCB and multi-chip module
  Services


Deployment Outlook

  On-premises
  Cloud-based


Region Outlook

  North America

    The U.S.
    Canada


  Europe

    The U.K.
    Germany
    France
    Rest of Europe


  APAC

    China
    India


  Middle East & Africa

    Saudi Arabia
    South Africa
    Rest of the Middle East & Africa


  South America 

    Chile
    Brazil
    Argentina

Electronic design automation (EDA) is at the forefront of innovation, especially in the cloud based solutions era. As the IoT industry and AI industry expand, demand for miniaturized chips/ICs surges, emphasizing the need for precision and accuracy in designing circuits. EDA tools provide robust support for hardware development, leveraging advanced computer aided design techniques. With a focus on efficiency and reliability, these solutions empower engineers to navigate the complexities of modern hardware design with ease.

This market research report extensively covers market segmentation by product type (semiconductor IP, CAE, IC physical design and verification, PCB and multi-chip module, and services), deployment (on-premises and cloud-based), and geography (APAC, North America, Europe, South America, and Middle East and Africa). It also includes an in-depth analysis of drivers, trends, and challenges. Furthermore, the market forecasting report includes historic market data from 2018 to 2022.

By Product Type

The market share by the semiconductor IP segment will be significant during the forecast period. The semiconductor IP market segment held the largest share of the market in 2022. New products have emerged due to the increasing complexity of semiconductor designs and production techniques as well as their integration with advanced technologies. This is increasing the number of IPs being registered in the semiconductor industry.

Get a glance at the market contribution of various segments. Download the PDF Sample

The semiconductor IP segment showed a gradual increase in the market share of USD 3.26 billion in 2017. The semiconductor industry has been recording increased demand for many devices, including sensors, chips, radio frequency (RF) components, and memory devices. The increasing use of semiconductors in a range of sectors, e.g. automotive, energy, medical care, and engineering, has led to this demand. Such factors will increase segment growth during the forecast period.

By Deployment

The on-premises deployment segment refers to the traditional approach of deploying software or applications on local servers or computing infrastructure that is owned and managed by an organization. On-premises infrastructure gives the organization complete control over the resources, services, and data. The performance of the On-premises systems provides certain advantages, such as latency. On-premises provide provision to store data locally, allowing greater control, which will avoid the cases of sensitive data leaving the company. All these advantages make the on-premises software highly preferable to the chip designers. EDA workflows include front-end design, backend workloads, as well as performance stimulation and verification and include t

Women's Clothing E-Commerce Reviews – EDA Project

This project explores customer reviews from a women's clothing e-commerce store. The goal was to clean, analyze, and visualize the data to uncover key insights about customer satisfaction and behavior.

  Dataset Overview

Source: Public dataset of women’s clothing reviews Size: 23,486 reviews and 11 features Target variable: Recommended IND

  Data Cleaning

Removed index column Dropped duplicates Handled missing… See the full description on the dataset page: https://huggingface.co/datasets/Oriminkowski/womens-clothing-reviews-eda.

Eda Marie Gonzales Export Import Data. Follow the Eximpedia platform for HS code, importer-exporter records, and customs shipment details.

View details of Eda imports shipment data in September with price, HS codes, major Indian ports, countries, importers, buyers in India, quantity and more.

Rolex Chrono24 - EDA Project

Exploratory Data Analysis of Rolex watch listings scraped from Chrono24.This project includes data cleaning, outlier handling, descriptive statistics, visualizations, research questions, and insights.

  📌 Overview

This project performs a full Exploratory Data Analysis (EDA) on a scraped Rolex dataset.It includes:

Data cleaning
Outlier detection
Descriptive statistics
Visualizations
Research questions and insights
Conclusions

The… See the full description on the dataset page: https://huggingface.co/datasets/JosephSafran/Assignment1RolexChrono24.

Concurrent Eda Export Import Data. Follow the Eximpedia platform for HS code, importer-exporter records, and customs shipment details.