— by Shloke M, Vibha S, Divyansh T
Exploratory data analysis is an essential step toward gaining insights from data. In this project, we will perform data cleaning and EDA on unicorn companies. The term unicorn refers to a privately held startup company with a value of over $1 billion. The report provides a comprehensive analysis of the data collected on unicorn companies, including their geographical distribution, industry focus, and funding history.
The source of this dataset is kaggle. The dataset has 13 columns and 1035 rows.
Dataset URL: https://www.kaggle.com/datasets/deepcontractor/unicorn-companies-dataset
Columns: Company, Valuation, Date Joined, City, Industry, Select Investors, Founded year, Total Raised, Financial Stage, Investors Count, Deal Terms, Portfolio Exits
This involved a few steps:
- Dropping rows that have “None” values in Founded Year, Total Raised, Investors Count, and Select Investors.
- As for the Columns, Financial Stage and Portfolio Exits most of the data is missing, therefore dropping them will be appropriate.
- Splitting Date Joined into Date, Month, and Year will be helpful to find the years taken to become a unicorn.
- Adding a new column “Years Taken to become Unicorn”
- Fixing data types and spell errors
Industry Wise Analysis:
Fintech, Internet software & services, and E-commerce are the top 3 industries under which many companies have become successful. Artificial Intelligence is also on par with other industries.
Companies in the Auto & Transportation industry take the least number of years on average to become unicorns whereas companies in the Internet software and Edtech sector take considerably more time.
Companies in the Fintech industry have raised the highest amount of money, followed by E-commerce and Internet Software and Services.
City Wise Analysis:
This is a follow-up to the previous graph. San Francisco, a city in the United States, has the highest number of unicorns and total valuation, followed by Beijing and closely by New York, cities of China, and the United States respectively. Many successful companies are also likely to be found in other cities like London, Bengaluru, and Shanghai.
San Francisco, Beijing, and New York are the top 3 cities with respect to the total money raised by all the companies present in the respective cities. Cities like Bengaluru and Berlin also have a quite good share in the total money raised on average.
Quite easy to interpret, the above plot shows us the cities ordered with respect to the time taken to become unicorns.
Country Wise Analysis:
From the above graph, we can imply that the United States has the highest number of companies that become unicorns and emerge successful with a whopping total valuation of nearly 1800 billion followed by China and India; the United Kingdom performing equally well.
Companies in Germany take more years to become unicorns while in countries like Japan, and the US provides a better environment for startups as their startups are becoming successful in a shorter duration.
Companies in the US have raised the highest amount followed by China, India, UK, Germany, and France.
Investors and Time Wise Analysis:
The bar graph indicates that Accel is the top investor in the world and has invested in 55 companies that became unicorns, followed by Tiger Global Management and Andreessen Horowitz.
In 2021 many companies become successful, followed by the years 2020, 2019, and 2018; each year having an equal proportion of companies that became Unicorns.
This plot shows us the Total Raised in terms of the Valuation of the top Unicorns in the world, there’s a correlation of 0.62 between both. The size of the dot is proportional to the number of years that were taken by the company to become a Unicorn.
From the graph we can see that most of the companies founded in the year 2015 became Unicorns in the future.
This plot depicts how many years are taken by companies from each industry in a country to become successful unicorns.
The above scatter plot shows the number of years required by companies to become unicorns with respect to country and city.