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https://github.com/garcane/unicorn-companies-analysis

Tracking unicorn startups (valued at $1B+) provides valuable insights for investors and analysts to identify high-growth industries and emerging trends.
https://github.com/garcane/unicorn-companies-analysis

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Tracking unicorn startups (valued at $1B+) provides valuable insights for investors and analysts to identify high-growth industries and emerging trends.

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README 

          
# Indian Unicorn Startup Dataset



## Overview

Tracking unicorn startups (valued at $1B+) provides valuable insights for investors and analysts to identify high-growth industries and emerging trends. This repository contains the **Indian Unicorn Startup Dataset**, a comprehensive dataset of Indian unicorn companies, including details such as company name, valuation, founding year, industry, and unicorn entry year. The dataset is designed for analysis using a PostgreSQL-compatible database, enabling complex SQL queries to uncover patterns and trends in the unicorn ecosystem.



## Dataset Description

The **Indian Unicorn Startup Dataset** (`Unicorn Startups.csv`) contains information about Indian startups that have achieved unicorn status (valuation of $1 billion or more). The dataset includes the following columns:



- **STARTUP NAME**: The name of the startup (e.g., `Perfios`, `Krutrim`).

- **INDUSTRY**: The industry or sector the startup operates in (e.g., `SaaS (Fintech)`, `Edtech`).

- **FOUNDING YEAR**: The year the startup was founded (e.g., `2008`, `2023`).

- **UNICORN ENTRY YEAR**: The year the startup achieved unicorn status (e.g., `2024`, `2022`).

- **PROFIT/LOSS FY22**: The profit or loss for the fiscal year 2022, in millions or billions (e.g., `$0.94 Million`, `-$47.1 Million`, `NA`).

- **CURRENT VALUATION**: The current valuation of the startup, in billions or millions (e.g., `$1 Billion`, `$0.568 Billion`, `NA`).

- **ACQUISITIONS**: The number of acquisitions made by the startup (e.g., `3`, `0`, `NA`).

- **STATUS**: The current status of the startup (e.g., `Private`, `Acquired`, `Listed`, `IPO-Bound`).



### Data Notes

- The `PROFIT/LOSS FY22`, `CURRENT VALUATION`, and `ACQUISITIONS` columns may contain "NA" for missing data.

- Monetary values in `PROFIT/LOSS FY22` and `CURRENT VALUATION` are stored as text (e.g., `$1 Billion`, `-$47.1 Million`) and may require parsing for numerical analysis.

- The dataset focuses on Indian startups, but the `readme.md` assumes country information is included (though not present in the provided CSV). For analysis, you may assume all startups are from India or extend the dataset to include a `COUNTRY` column.



## SQL Project Idea

This dataset is ideal for building a PostgreSQL-compatible database to perform advanced data analysis using complex SQL queries. The following project objectives can help uncover trends in the Indian unicorn startup ecosystem:



### Analysis Questions

1. **How many unicorns were born each year across industries?**

   - Use `GROUP BY` on `UNICORN ENTRY YEAR` and `INDUSTRY` to count the number of unicorns per year and industry.

2. **Which three industries produced the most unicorns in the past decade?**

   - Filter for `UNICORN ENTRY YEAR` within the last 10 years (e.g., 2015–2024), group by `INDUSTRY`, and use `COUNT` to identify the top three industries.

3. **What is the average valuation by industry and year?**

   - Parse `CURRENT VALUATION` to numeric values (e.g., convert `$1 Billion` to `1000` million) and use `AVG()` with `GROUP BY` on `INDUSTRY` and `UNICORN ENTRY YEAR`.

4. **What’s the distribution of unicorns by country and industry?**

   - Assuming a `COUNTRY` column (or all startups are Indian), use `GROUP BY` on `COUNTRY` and `INDUSTRY` to show the distribution of unicorns.

5. **Which year saw the most unicorns created globally?**

   - Group by `UNICORN ENTRY YEAR` and use `COUNT` to find the year with the highest number of unicorns. (Note: The dataset is India-focused, so this may reflect Indian unicorns unless extended.)



### SQL Table Creation

To prepare the dataset for analysis, create a PostgreSQL table with the following schema:



```sql

CREATE TABLE unicorn_startups (

    startup_name VARCHAR(100),

    industry VARCHAR(100),

    founding_year INTEGER,

    unicorn_entry_year INTEGER,

    profit_loss_fy22 VARCHAR(50),

    current_valuation VARCHAR(50),

    acquisitions VARCHAR(10),

    status VARCHAR(50)

);

```



### Importing the Dataset

1. Save the `Unicorn Startups.csv` file to your local machine.

2. In pgAdmin:

   - Right-click the `unicorn_startups` table, select "Import/Export Data".

   - Choose "Import", select the CSV file, and set:

     - Format: CSV

     - Header: Enabled

     - Delimiter: Comma (`,`)

     - Quote: Double Quote (`"`)

     - Encoding: UTF8

   - Click "OK" to import.

3. Alternatively, use the `\copy` command in `psql`:

   ```sql

   \copy unicorn_startups FROM '/path/to/Unicorn Startups.csv' DELIMITER ',' CSV HEADER;

   ```



### Example SQL Queries

Here are sample queries to address the analysis questions:



1. **Unicorns by Year and Industry**:

   ```sql

   SELECT unicorn_entry_year, industry, COUNT(*) as unicorn_count

   FROM unicorn_startups

   GROUP BY unicorn_entry_year, industry

   ORDER BY unicorn_entry_year, unicorn_count DESC;

   ```



2. **Top Three Industries (Last Decade)**:

   ```sql

   SELECT industry, COUNT(*) as unicorn_count

   FROM unicorn_startups

   WHERE unicorn_entry_year >= EXTRACT(YEAR FROM CURRENT_DATE) - 10

   GROUP BY industry

   ORDER BY unicorn_count DESC

   LIMIT 3;

   ```



3. **Average Valuation by Industry and Year** (assuming valuation parsing):

   ```sql

   SELECT industry, unicorn_entry_year,

          AVG(CAST(REGEXP_REPLACE(current_valuation, '[^0-9.]', '') AS NUMERIC)) as avg_valuation_millions

   FROM unicorn_startups

   WHERE current_valuation != 'NA'

   GROUP BY industry, unicorn_entry_year

   ORDER BY unicorn_entry_year, industry;

   ```



4. **Distribution by Country and Industry** (assuming all are Indian):

   ```sql

   SELECT 'India' as country, industry, COUNT(*) as unicorn_count

   FROM unicorn_startups

   GROUP BY industry

   ORDER BY unicorn_count DESC;

   ```



5. **Year with Most Unicorns**:

   ```sql

   SELECT unicorn_entry_year, COUNT(*) as unicorn_count

   FROM unicorn_startups

   GROUP BY unicorn_entry_year

   ORDER BY unicorn_count DESC

   LIMIT 1;

   ```



## Getting Started

1. **Set Up PostgreSQL**:

   - Install PostgreSQL and pgAdmin on your system.

   - Create a database (e.g., `unicorn_db`) in pgAdmin.



2. **Create the Table**:

   - Copy the `CREATE TABLE` SQL code above into pgAdmin’s Query Tool and execute it.



3. **Import the Data**:

   - Follow the import steps outlined above to load `Unicorn Startups.csv` into the `unicorn_startups` table.



4. **Run Queries**:

   - Use the example queries or write your own to analyze the data.



## Potential Extensions

- **Add Country Data**: Extend the dataset to include a `COUNTRY` column for global analysis.

- **Parse Monetary Values**: Create views or new columns to convert `CURRENT VALUATION` and `PROFIT/LOSS FY22` into numeric values for easier aggregation.

- **Time-Based Analysis**: Use date functions to analyze trends over time, such as the average time from founding to unicorn status (`unicorn_entry_year - founding_year`).

- **Visualization**: Export query results to tools like Tableau or Python (e.g., Matplotlib) for visualizations.



## License

This dataset and repository are provided for educational and analytical purposes. Ensure compliance with any data usage restrictions if the dataset is sourced from a third party.



## Contact

For questions or contributions, please open an issue in this repository.