A Data Analysis of Startups Valued at $1 Billion or More

In the fast-paced world of business and innovation, startups have become powerful catalysts for economic growth and change. These budding businesses, often born from groundbreaking ideas and driven by an entrepreneurial spirit, are known for their agility, innovation, and potential for remarkable success.

Recently, there has been a significant increase in the number of startups reaching unicorn status — achieving a valuation of $1 billion or more. This trend highlights a growing interest in investing in revolutionary technologies and new business models. Once known for its inherent risks, the startup landscape has transformed into a thriving environment where ventures can quickly rise to unicorn status, attracting attention from investors and industry experts.

This article thoroughly analyzes startups that have reached the coveted $1 billion valuation milestone. Through this exploration, we aim to understand the factors contributing to their success and the broader implications for the startup ecosystem.

Let’s dive in and analyze the factors through Python’s libraries like Pandas, Numpy, and Matplotlib.

Prerequisite

Import the necessary libraries

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import missingno as msno

I got the data that I used for my analysis from Kaggle.

[embed]Startups Valued at $1 Billion or More A Comprehensive Dataset of Successful Startups around the Worldwww.kaggle.com

Load the dataset

df = pd.read_csv('dataset.csv', encoding=('ISO-8859-1'))
df.head() # To understand the dataset

Analysis

Let’s get an understanding of the data we have.

We can see that city names for some of these startups are missing.

industry_counts = df['Industry'].value_counts()
sns.set(font_scale=1.5, rc={'figure.figsize':(6,6)})

sns.barplot(x=industry_counts.values, y=industry_counts.index,orient='h')

plt.xlabel=('Industry')
plt.ylabel=('Count')
plt.title="Number of Observations by Industry"

plt.show()

Fintech is the leading industry.

country_counts = df['Country'].value_counts()
sns.set(font_scale=1.5, rc={'figure.figsize':(10,18)})

sns.barplot(x=country_counts.values, y=country_counts.index,orient='h')

plt.xlabel=('Country')
plt.ylabel=('Count')
plt.title="Number of Observations by Industry"

plt.show()

The US is the leading country.

def industrys_and_country(state):
    data_ind=df.loc[df.Country.isin([state])]
    data_ind.Industry.value_counts().plot(kind='bar',title=state)

col=['United States', 'India', 'Israel', 'France', 'Colombia', 'Norway', 'Japan', 'Sweden', 'Germany', 'Spain', 'Italy', 'Canada', 'United Kingdom', 'Switzerland', 'Chile', 'Ireland', 'Liechtenstein', 'Thailand', 'Mexico']
fig, axs = plt.subplots(len(col), 1, figsize=(15, 6 * len(col)))
for i, ax in enumerate(axs):
    df_ind = df.loc[df.Country.isin([col[i]])]
    sns.countplot(y=df_ind["Industry"], data=df_ind, ax=ax, order=df_ind["Industry"].value_counts().index)
    ax.set_title(col[i])
plt.tight_layout()
plt.show()

Leading industries in each country

The United States

India and Israel

France, Colombia, and Norway

Japan, Sweden, and Germany

Spain, Italy, and Canada

United Kingdom, Switzerland, and Chile

Ireland, Liechtenstein, and Thailand

Mexico

df['Year Joined'].value_counts().plot(kind='barh')

Startups have been on the rise in recent years.

Let’s now focus more on India,

Fintech is in the lead in India as well.

ind_sorted_df = df_ind.sort_values("Last Valuation (Billion $)", ascending=False)

f, ax = plt.subplots(figsize=(8, 18))

sns.barplot(x="Last Valuation (Billion $)", y="Company", data=ind_sorted_df,
            label="Last Valuation (Billion $)")

ax.tick_params(axis='both', which='major', labelsize=14)

ax.set_title("Valuation of Companies by Industry", fontsize=16)

sns.despine(left=True, bottom=True)
plt.show()

BYJU’s has the highest valuation.

ind=['Fintech','E-commerce & direct-to-consumer','Internet software & services',
    'Edtech','Supply chain, logistics, & delivery','Health','Auto & transportation',
    'Data management & analytics','Travel','Mobile & telecommunications']
for i in  ind:
    ind_fin_df=df.loc[df.Country.isin(['India'])]
    fin_df=ind_fin_df.loc[df['Industry'] == i]

    fin_df['City'].value_counts().plot(kind='barh',title=i)
    plt.show()

Conclusion

• Fintech and Software are leading industries all over the world.
• E-commerce and direct-to-customers is the leading industry in India.
• BYJU’s has the highest valuation in India.
• Most startups in India are started in Bengaluru.