In terms of deals over the past five years, artificial intelligence and big data was the largest VC-funded startup industry in 2022, accounting for close to ** percent of the global deals. Meanwhile, fintech accounted for ** percent of the deals, with life sciences and health care behind with ** percent. Blue economy and digital media were the smallest industries with only *** percent each. However, the blue economy saw its funding deals almost ******** over the past five years.

A compiled dataset of key US startup and small-business statistics for 2024–2025, including business formation activity, survival rates, venture funding trends, founder demographics, and unicorn benchmarks. This dataset is curated by HIGH5 for research and educational use.

This dataset was collected by scraping different top startups ranking lists. It can be used to analyze startups as a whole as well as different industries and other factors that affect how successful a startup will be.

We will create a customized startups dataset tailored to your specific requirements. Data points may include startup foundation dates, locations, industry sectors, funding rounds, investor profiles, financial health, market positions, technological assets, employee counts, and other relevant metrics.

Utilize our startups datasets for a variety of applications to boost strategic planning and innovation tracking. Analyzing these datasets can help organizations grasp market trends and growth opportunities within the startup ecosystem, allowing for more precise strategy adjustments and operations. You can choose to access the complete dataset or a customized subset based on your business needs.

Popular use cases include: enhancing competitive analysis, identifying emerging market trends, and finding high-potential investment opportunities.

The global landscape of unicorn companies, privately held startups valued at one billion U.S. dollars or more, has seen significant shifts in recent years. North America has consistently led in producing new unicorns, with a peak of 510 in 2021. However, the Asia Pacific region has also been a strong contender, particularly with the rise of Chinese tech giants.

A comprehensive dataset of small business statistics for 2025, including startup trends, business growth rates, employment contributions, failure rates, financing patterns, industry performance, economic impact, and challenges faced by small business owners.

Startup failure rate data and survival statistics. Why startups fail, at what stage, and how to beat the odds.

Overview

This dataset provides a comprehensive view of global startups across multiple industries, tracking their early-stage revenue growth, founding details, geographic distribution, and final outcomes. It includes a mix of well-known real startups (e.g., SpaceX, Stripe, Airbnb, Zomato) and synthetic but realistic startup profiles designed to mirror real-world startup behavior.

The data is structured to support data analysis, machine learning, business intelligence, and educational use cases, with a strong focus on early-stage startup performance.

Dataset Contents

• 500+ startup records
• Global coverage across 30+ countries
• Multiple industries including AI/ML, FinTech, HealthTech, SaaS, BioTech, and more
• 3-year revenue tracking per startup
• Outcome labels suitable for supervised ML tasks

Example Use Cases

• Startup success vs failure prediction
• Revenue growth trend analysis
• Country-wise and sector-wise ecosystem studies
• Machine learning classification and regression tasks
• Time-series and forecasting experiments
• Business intelligence dashboards
• Academic and classroom data science projects

Industries Covered

• AI / Machine Learning
• FinTech
• HealthTech
• BioTech
• EdTech
• SaaS
• E-commerce
• Logistics
• AgriTech
• CleanTech
• Cybersecurity
• PropTech

This dataset consists of state and industry wise number of startups that gained DPIIT's recognition since inception in 2016.

Note: According to DPIIT‚ As per the Manual for Procurement of Consultancy and other services, an entity should not have completed ten years from the date of its incorporation/registration and its turnover for any of the financial years since incorporation/registration should not have exceeded one hundred crore rupees to get recognized as startup by the department. This recognition is necessary to avail the benefits under various schemes and seek assistance of the government.

Statistics on Startup Companies in Hong Kong | DATA.GOV.HK

As of 2023, unicorns active in the industries of fintech and software-as-a-service (SaaS), and AI accounted for approximately 33 percent of all unicorn companies worldwide.

This DataSet to track the latest trends, we’ve compiled small business and startup statistics to better understand what makes a startup tick. If you’re looking to build a startup or just interested in diving into the numbers, check out these informative statistics on success, failure, funding and more before getting started.

Objective The objective of the project is to predict whether a startup which is currently operating turn into a success or a failure. The success of a company is defined as the event that gives the company's founders a large sum of money through the process of M&A (Merger and Acquisition) or an IPO (Initial Public Offering). A company would be considered as failed if it had to be shutdown.

This problem will be solved through a Supervised Machine Learning approach by training a model based on the history of startups which were either acquired or closed. The trained model will then be used to make predictions on startups which are currently operating to determine their success/failure.

Do an EDA and try to predict which startups and in which field achieve great success! https://www.googleapis.com/download/storage/v1/b/kaggle-forum-message-attachments/o/inbox%2F9770082%2Fd1cc4e53157d2f3f0a9f661b6f2cd28f%2FGroup%202215.jpg?generation=1674420531095211&alt=media" alt="">

You will have to answer the following questions: - How Many New Businesses Fail ? - How Many New Businesses Secsees ? - Reasons for Failing - How to Avoid Failing And many other questions...

In 2025, according to data provided by StartupBlink the top country for startups in Central and Eastern Europe was Estonia, with a total score of *****, followed by Lithuania.

Startup funding data for 2026: round sizes, valuations, investor trends, and fundraising benchmarks by stage.

Open source data for HackerNoon's Startup of The Year Votes: over 623,000 total votes were cast for 30,000+ startups from 4,000+ cities worldwide.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F22852651%2F8174d967d8fe8453b61428258f04855a%2FCover.png?generation=1729164787837661&alt=media" alt="">

As with all years, HackerNoon releases the unique social data gathered from Startups of The Year on GitHub and Hugging Face for valuable insights. In the 2023 version, 30,000+ startups from 4,000+ cities participated to be crowned the best in their city.

Data from the contest found that “.com” predominantly remained the most popular domain pick for startups, highlighting its wide recognition and influence from the early days of the internet. It also found that Sydney, London, Singapore, and San Francisco were among the top trending startup cities in the world based on the HackerNoon community's global interest in those locations.

In 2024, HackerNoon will have even more data to work with and the editorial team will be on the lookout for interesting trends in the world of startups.

Stories About the Learnings from this data:

• About That Open Source Startup Data...
• The Top 100 Trending Startup Cities
• Dotcom Remains the Top Domain For Startups, But Usage Declines
• Trending industries that ran Startups '23

More Stories About Startup of The Year Winners:

• Stand Up for the Champion Startups of The Year!
• Startups of The Year 2023 Winners: North America
• Startups of The Year 2023 Winners: Europe
• Startups of The Year 2023: Winners of South America
• Startups of The Year 2023: Africa Winners
• Startups of The Year 2023: Asia Winners
• Startups of The Year 2023: Oceania Winners
• Startups of The Year 2023: Remote/Misc Winners
• Meet Debt Hunter, Winner of the Startups of The Year in Hong Kong, China
• Meet No Trust Verify: Winner of the Startups of The Year in Neuchâtel
• Startups of The Year 2023: Drope.me - An Influencer Marketing Platform for the Gaming Industry
• Vote for Sniper.xyz in HackerNoon's Startups of The Year Award
• Generative AI & Biotech's Future - Interview with Startups of The Year Nominee, NExtNet Inc.
• HarperDB Wins 1st Place Award for Hackernoon’s 2023 Startup of The Year
• With 600,000+ Tech Community Votes, HackerNoon Announces the Winners of Startups of The Year 2023
• Seamly Clinches HackerNoon’s Kentucky Startup of The Year Award!
• Wallet Factory: The Silver Prize Winner of Startups of The Year 2023
• WP Event Manager Becomes the First Runner-up in the Startup of The Year 2023 Award
• [Finch Named Startup of The Year in San Francisco by HackerNoon](https://www.tryfinc...

Central and Eastern European startups recorded the highest combined enterprise value in 2021 at *** billion euros. In 2022, startup companies in the CEE were valued at *** billion euros.

The share of venture capital (VC) investment allocated in e-commerce startups has never been so low as in 2023. Data showing venture capital investment over the decade 2013 to 2023 show that ************************** startups accounted for just ** percent of total VC investment worldwide. In 2023, **** startups had the biggest share of the pie, with ** percent of investment.

📊 Dataset Features This dataset includes 5,000 startups from 10 countries and contains 15 key features: Startup Name: Name of the startup Founded Year: Year the startup was founded Country: Country where the startup is based Industry: Industry category (Tech, FinTech, AI, etc.) Funding Stage: Stage of investment (Seed, Series A, etc.) Total Funding ($M): Total funding received (in million $) Number of Employees: Number of employees in the startup Annual Revenue ($M): Annual revenue in million dollars Valuation ($B): Startup's valuation in billion dollars Success Score: Score from 1 to 10 based on growth Acquired?: Whether the startup was acquired (Yes/No) IPO?: Did the startup go public? (Yes/No) Customer Base (Millions): Number of active customers Tech Stack: Technologies used by the startup Social Media Followers: Total followers on social platforms Analysis Ideas 📈 What Can You Do with This Dataset? Here are some exciting analyses you can perform:

Predict Startup Success: Train a machine learning model to predict the success score. Industry Trends: Analyze which industries get the most funding. **Valuation vs. Funding: **Explore the correlation between funding and valuation. Acquisition Analysis: Investigate the factors that contribute to startups being acquired.

Discover how data startups now command a 75% valuation premium in VC funding rounds - analyzing 10+ years of trends in startup valuations and market data.

Startups Funding Rounds

A look at startup funding patterns over time

About this dataset

This dataset contains information on startup funding patterns. This data can be used to understand how startups are funded and what types of startups are most likely to receive funding. This data can also be used to understand the trends in startup funding over time.

Columns:SNo,**SNo,Date,**Date,StartupName,**StartupName,IndustryVertical,**IndustryVertical,SubVertical,**SubVertical

How to use the dataset

This dataset can be used to understand how startups are funded and what types of startups are most likely to receive funding. This data can also be used to understand the trends in startup funding over time.

This dataset contains information on startup funding patterns. The data includes the following columns:

SNo: Serial number. (Numeric) Date: Date of funding. (Date) StartupName: Name of the startup. (String) IndustryVertical: Industry vertical of the startup. (String) SubVertical: Sub-vertical of the startup. (String) CityLocation: City of the startup. (String) … … … … … .. Product .. Technology .. Hardware.. Online Accessories.. Data Processing & Management.. eLearning .. Technology.. Cloud Computing, Enterprise Software & SaaS.. Web Hosting and Development .. Open Source Software Development Tools, Services & Platforms.. Electric Vehicle Infrastructure../ big data analytics, IOT etc… . Thus it’s difficult to really profile an ideal investor for a given type of product/ technology/ industry sectorStartups that have raised Series A or later rounds from established venture firms such as Accel Partners, Sequoia Capital, Tiger Global Management etc., tend to have a better chance of success than those that have raisedseed or angel rounds from inexperienced investors.(Statistically speaking, these firms have a better track record in picking winners)

Research Ideas

• Predicting the amount of funding a startup will receive based on factors such as industry vertical, location, and type of investment.
• Understanding which types of startups are most likely to receive funding from venture capitalists.
• Identifying trends in startup funding over time

Columns

File: abc.csv

File: sf.csv | Column name | Description | |:---------------------|:--------------------------------------------| | SNo | Serial number. (Numeric) | | Date | Date of funding. (Date) | | StartupName | Name of the startup. (String) | | IndustryVertical | Industry of the startup. (String) | | SubVertical | Sub-industry of the startup. (String) | | CityLocation | City where the startup is located. (String) | | InvestorsName | Name of the investors. (String) | | InvestmentType | Type of investment. (String) | | AmountInUSD | Amount of investment in USD. (Numeric) | | Remarks | Any additional remarks. (String) |