{"id":31559612,"url":"https://github.com/garcane/unicorn-companies-analysis","last_synced_at":"2026-01-24T07:06:21.412Z","repository":{"id":292738722,"uuid":"848819797","full_name":"garcane/Unicorn-Companies-Analysis","owner":"garcane","description":"Tracking unicorn startups (valued at $1B+) provides valuable insights for investors and analysts to identify high-growth industries and emerging trends.","archived":false,"fork":false,"pushed_at":"2025-05-22T17:44:13.000Z","size":464,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-08-05T23:31:11.088Z","etag":null,"topics":["data-analysis","exploratory-data-analysis","financial-analysis","investor","postgresql","sql"],"latest_commit_sha":null,"homepage":"","language":"SQL","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/garcane.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null,"zenodo":null}},"created_at":"2024-08-28T13:17:33.000Z","updated_at":"2025-05-22T17:44:16.000Z","dependencies_parsed_at":"2025-08-05T23:31:16.225Z","dependency_job_id":"129a4e3d-188b-4ebf-aee6-c06e47400827","html_url":"https://github.com/garcane/Unicorn-Companies-Analysis","commit_stats":null,"previous_names":["gappeah/unicorn-companies-analysis","garcane/unicorn-companies-analysis"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/garcane/Unicorn-Companies-Analysis","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/garcane%2FUnicorn-Companies-Analysis","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/garcane%2FUnicorn-Companies-Analysis/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/garcane%2FUnicorn-Companies-Analysis/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/garcane%2FUnicorn-Companies-Analysis/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/garcane","download_url":"https://codeload.github.com/garcane/Unicorn-Companies-Analysis/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/garcane%2FUnicorn-Companies-Analysis/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":278399690,"owners_count":25980332,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","status":"online","status_checked_at":"2025-10-04T02:00:05.491Z","response_time":63,"last_error":null,"robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":true,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["data-analysis","exploratory-data-analysis","financial-analysis","investor","postgresql","sql"],"created_at":"2025-10-05T01:54:55.665Z","updated_at":"2025-10-05T01:55:00.555Z","avatar_url":"https://github.com/garcane.png","language":"SQL","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Indian Unicorn Startup Dataset\n\n## Overview\nTracking 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.\n\n## Dataset Description\nThe **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:\n\n- **STARTUP NAME**: The name of the startup (e.g., `Perfios`, `Krutrim`).\n- **INDUSTRY**: The industry or sector the startup operates in (e.g., `SaaS (Fintech)`, `Edtech`).\n- **FOUNDING YEAR**: The year the startup was founded (e.g., `2008`, `2023`).\n- **UNICORN ENTRY YEAR**: The year the startup achieved unicorn status (e.g., `2024`, `2022`).\n- **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`).\n- **CURRENT VALUATION**: The current valuation of the startup, in billions or millions (e.g., `$1 Billion`, `$0.568 Billion`, `NA`).\n- **ACQUISITIONS**: The number of acquisitions made by the startup (e.g., `3`, `0`, `NA`).\n- **STATUS**: The current status of the startup (e.g., `Private`, `Acquired`, `Listed`, `IPO-Bound`).\n\n### Data Notes\n- The `PROFIT/LOSS FY22`, `CURRENT VALUATION`, and `ACQUISITIONS` columns may contain \"NA\" for missing data.\n- 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.\n- 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.\n\n## SQL Project Idea\nThis 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:\n\n### Analysis Questions\n1. **How many unicorns were born each year across industries?**\n   - Use `GROUP BY` on `UNICORN ENTRY YEAR` and `INDUSTRY` to count the number of unicorns per year and industry.\n2. **Which three industries produced the most unicorns in the past decade?**\n   - 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.\n3. **What is the average valuation by industry and year?**\n   - 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`.\n4. **What’s the distribution of unicorns by country and industry?**\n   - Assuming a `COUNTRY` column (or all startups are Indian), use `GROUP BY` on `COUNTRY` and `INDUSTRY` to show the distribution of unicorns.\n5. **Which year saw the most unicorns created globally?**\n   - 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.)\n\n### SQL Table Creation\nTo prepare the dataset for analysis, create a PostgreSQL table with the following schema:\n\n```sql\nCREATE TABLE unicorn_startups (\n    startup_name VARCHAR(100),\n    industry VARCHAR(100),\n    founding_year INTEGER,\n    unicorn_entry_year INTEGER,\n    profit_loss_fy22 VARCHAR(50),\n    current_valuation VARCHAR(50),\n    acquisitions VARCHAR(10),\n    status VARCHAR(50)\n);\n```\n\n### Importing the Dataset\n1. Save the `Unicorn Startups.csv` file to your local machine.\n2. In pgAdmin:\n   - Right-click the `unicorn_startups` table, select \"Import/Export Data\".\n   - Choose \"Import\", select the CSV file, and set:\n     - Format: CSV\n     - Header: Enabled\n     - Delimiter: Comma (`,`)\n     - Quote: Double Quote (`\"`)\n     - Encoding: UTF8\n   - Click \"OK\" to import.\n3. Alternatively, use the `\\copy` command in `psql`:\n   ```sql\n   \\copy unicorn_startups FROM '/path/to/Unicorn Startups.csv' DELIMITER ',' CSV HEADER;\n   ```\n\n### Example SQL Queries\nHere are sample queries to address the analysis questions:\n\n1. **Unicorns by Year and Industry**:\n   ```sql\n   SELECT unicorn_entry_year, industry, COUNT(*) as unicorn_count\n   FROM unicorn_startups\n   GROUP BY unicorn_entry_year, industry\n   ORDER BY unicorn_entry_year, unicorn_count DESC;\n   ```\n\n2. **Top Three Industries (Last Decade)**:\n   ```sql\n   SELECT industry, COUNT(*) as unicorn_count\n   FROM unicorn_startups\n   WHERE unicorn_entry_year \u003e= EXTRACT(YEAR FROM CURRENT_DATE) - 10\n   GROUP BY industry\n   ORDER BY unicorn_count DESC\n   LIMIT 3;\n   ```\n\n3. **Average Valuation by Industry and Year** (assuming valuation parsing):\n   ```sql\n   SELECT industry, unicorn_entry_year,\n          AVG(CAST(REGEXP_REPLACE(current_valuation, '[^0-9.]', '') AS NUMERIC)) as avg_valuation_millions\n   FROM unicorn_startups\n   WHERE current_valuation != 'NA'\n   GROUP BY industry, unicorn_entry_year\n   ORDER BY unicorn_entry_year, industry;\n   ```\n\n4. **Distribution by Country and Industry** (assuming all are Indian):\n   ```sql\n   SELECT 'India' as country, industry, COUNT(*) as unicorn_count\n   FROM unicorn_startups\n   GROUP BY industry\n   ORDER BY unicorn_count DESC;\n   ```\n\n5. **Year with Most Unicorns**:\n   ```sql\n   SELECT unicorn_entry_year, COUNT(*) as unicorn_count\n   FROM unicorn_startups\n   GROUP BY unicorn_entry_year\n   ORDER BY unicorn_count DESC\n   LIMIT 1;\n   ```\n\n## Getting Started\n1. **Set Up PostgreSQL**:\n   - Install PostgreSQL and pgAdmin on your system.\n   - Create a database (e.g., `unicorn_db`) in pgAdmin.\n\n2. **Create the Table**:\n   - Copy the `CREATE TABLE` SQL code above into pgAdmin’s Query Tool and execute it.\n\n3. **Import the Data**:\n   - Follow the import steps outlined above to load `Unicorn Startups.csv` into the `unicorn_startups` table.\n\n4. **Run Queries**:\n   - Use the example queries or write your own to analyze the data.\n\n## Potential Extensions\n- **Add Country Data**: Extend the dataset to include a `COUNTRY` column for global analysis.\n- **Parse Monetary Values**: Create views or new columns to convert `CURRENT VALUATION` and `PROFIT/LOSS FY22` into numeric values for easier aggregation.\n- **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`).\n- **Visualization**: Export query results to tools like Tableau or Python (e.g., Matplotlib) for visualizations.\n\n## License\nThis 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.\n\n## Contact\nFor questions or contributions, please open an issue in this repository.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fgarcane%2Funicorn-companies-analysis","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fgarcane%2Funicorn-companies-analysis","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fgarcane%2Funicorn-companies-analysis/lists"}