Indian Startup Ecosystem Analysis | Startup Growth Analysis

Billion-Dollar Dreams: India's Startup Saga — A data-driven analysis of India's startup landscape: funding, valuation, geography, and sector trends. Built with Python, Pandas, and Seaborn.

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Table of Contents

• About This Project
• What You'll Find
• Dataset
• Visualizations
• Technologies Used
• Key Insights
• Getting Started
• Author & Contact

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About This Project

This repository contains the Indian startup ecosystem analysis project: datasets, Jupyter notebooks, and visualizations for understanding startup growth, funding dynamics, market valuation, and geographical distribution of Indian startups. The analysis explores trends, sector dominance, and the link between funding and success in India's startup ecosystem.

India has emerged as a major global startup hub. This project offers a clear, data-backed view of that landscape—useful for researchers, students, and anyone interested in Indian startups, startup data analysis, or Python data science workflows.

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What You'll Find

Item	Location	Description
Notebook	Project.ipynb	Full analysis: data prep, EDA, and visualizations
Data	datasets/	Indian startups dataset (CSV & Excel)
Charts	visualizations/	Maps, growth curves, sector breakdowns, and more
Docs	Root	Code Documentation PDF, Project Report PDF

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Dataset

The analysis uses an Indian startups dataset in the datasets/ folder:

• Files: Project Data.csv, Project Data.xlsx
• Contents: Startup name, state, city, start year, founder(s), industry, number of employees, funding (USD), funding rounds, number of investors, market valuation (USD)

Use the datasets/ folder for raw data; the notebook shows how it is loaded and cleaned.

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Visualizations

The visualizations/ folder includes charts and maps from the analysis, such as:

• Geography: Startup distribution by state and city (e.g., top 5 cities, North vs South, state-wise)
• Valuation & growth: Highest market valuation, growth of startups, valuation by industry
• Funding: Most/least funding, funding per employee, rounds vs funding
• Sectors: Most common industries (national and state-level, e.g., Karnataka, Haryana), top valuation industries
• Other: Founders vs valuation, average employees, word cloud

Open the folder or the notebook to view and reuse these visuals.

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Technologies Used

• Python — Core language for analysis and scripting
• Pandas — Data loading, cleaning, and manipulation
• NumPy — Numerical operations
• Matplotlib & Seaborn — Data visualization
• Jupyter Notebook — Interactive analysis and reporting

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Key Insights

The analysis highlights:

• Geographical concentration — Major startup hubs and regional patterns (North vs South, state-wise)
• Valuation trends — How startup valuations evolve; billion-dollar companies and industries
• Sector performance — Leading industries and their share of funding/valuation
• Funding dynamics — Relationship between funding rounds, investor count, and success metrics

For a narrative summary and discussion, see the project report PDF and the notebook’s markdown sections.

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Getting Started

1. Clone the repository

   git clone https://github.com/KuchikiRenji/Startup-Growth-Analysis.git
   cd Startup-Growth-Analysis

2. Install dependencies (Python 3, pandas, numpy, matplotlib, seaborn, openpyxl for Excel)

   pip install pandas numpy matplotlib seaborn openpyxl jupyter

3. Run the analysis

   • Open Project.ipynb in Jupyter and run all cells, or
   • Ensure Project Data.xlsx is in the path used by the notebook (e.g. same directory or datasets/ as in the notebook).

4. Explore — Check datasets/ and visualizations/ for data and outputs.

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Author & Contact

KuchikiRenji

Channel	Link / ID
GitHub	github.com/KuchikiRenji
Email	KuchikiRenji@outlook.com
Discord	kuchiki_renji

For questions, collaboration, or feedback about this project, reach out via the channels above.

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Indian Startup Ecosystem Analysis · Startup Growth Analysis · Data Science · Python · Open Source