Unicorn Startup Ecosystem Analysis

Project Overview

This project simulates an End-to-End ETL (Extract, Transform, Load) process and data analysis workflow focusing on the global unicorn startup landscape.

Due to the limited availability of comprehensive open-source unicorn databases, this project utilizes Python to programmatically generate a highly realistic relational dataset. The generated data is then loaded into an in-memory SQLite database, allowing for robust data extraction and aggregation using SQL, followed by data visualization to uncover actionable business insights.

Key Skills Demonstrated

• Data Engineering (ETL): Designed a data generation pipeline using Faker and NumPy vectorization to create thousands of data points efficiently.
• Database Management: Modeled relational data (Companies, Industries, Funding, Dates) and integrated it with an SQLite RDBMS.
• SQL Querying: Extracted and aggregated data using complex SQL JOIN, GROUP BY, and mathematical operations.
• Data Visualization: Translated raw data into clean, business-ready charts using Seaborn and Matplotlib.

Tech Stack

• Language: Python 3.10
• Database: SQLite (In-Memory)
• Libraries: Pandas, NumPy, Faker, Matplotlib, Seaborn

Business Questions Answered

Through SQL queries and visual analysis, this notebook answers three core business questions:

1. Industry Dominance: Which industries produce the highest number of unicorn startups?
2. Top Valuations: Which 5 companies have the highest valuations, and where are they geographically located?
3. Historical Trends: How has the number of newly crowned unicorns grown over the years?

Project Structure

• unicorn_analysis.ipynb: The main Jupyter Notebook containing the ETL pipeline, SQL queries, and visualizations.
• environment.yml: Conda environment configuration file to reproduce the exact dependencies used in this project.
• .gitignore: Configured to ignore auto-generated dummy CSV files and local cache.

How to Run This Project

To replicate the environment and run the notebook locally, follow these steps:

1. Clone this repository:

   git clone [https://github.com/subki72/unicorn-startup-analysis.git](https://github.com/subki72/unicorn-startup-analysis.git)
   cd unicorn-startup-analysis
   

2. Create the conda environment from the provided configuration file:
```bash
conda env create -f environment.yml

3. Activate the environment:

conda activate unicorn_env

4. Launch Jupyter Notebook:

jupyter notebook