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Indian Startup Data Analysis

This project focuses on analyzing Indian startup data to gain insights into the startup ecosystem in India. The analysis is performed using Python and various data analysis libraries such as Pandas, matplotlib, and Seaborn.

Table of Contents

  1. Introduction
  2. Data Description
  3. How to Run
  4. Dependencies
  5. Analysis
  6. Results
  7. Conclusion
  8. Contributing
  9. License

Introduction

The Indian startup ecosystem has witnessed significant growth and attracted global attention in recent years. This project aims to explore and analyze the available data on Indian startups to uncover trends, patterns, and key characteristics of the ecosystem. The goal of this analysis is to understand the startup ecosystem in India by examining data on startup funding, sectors, and other key factors.

Data Description

The dataset used in this analysis is sourced from Kaggle. It contains information about Indian startups, including details such as startup name, industry, location, funding amount, funding round, and investors.

How to Run

To run this notebook:

  1. Clone this repository. 
    git clone https://github.com/Mayur-vora/Indian-StartUp-Data-Analysis.git
  2. Ensure you have the necessary dependencies installed (see below).
  3. Navigate to the project directory: 
    cd Indian-StartUp-Data-Analysis
  4. Open the Jupyter Notebook file indian-startup-data-analysis.ipynb in Jupyter Notebook or any compatible environment.
  5. Execute the cells in the notebook sequentially.

Dependencies

List of Python libraries and dependencies required to run the notebook:

  • pandas
  • numpy
  • matplotlib
  • SkLearn
  • seaborn

You can install the required libraries using pip:

pip install pandas matplotlib seaborn

Analysis

The analysis covers various aspects of the Indian startup ecosystem, including:

  • Distribution of startups across different locations
  • Top industries attracting startups
  • Funding trends over the years
  • Most active investors
  • Relationship between funding amount and startup location
  • Top-funded startups

The code in the notebook provides step-by-step instructions and explanations for each analysis task.

Results

The analysis yields several interesting findings about the Indian startup ecosystem. Some key insights include:

  • Bangalore emerges as the hub for startups, followed by Mumbai and New Delhi.
  • E-commerce, consumer internet, and technology are among the top industries attracting startups.
  • Funding for Indian startups has shown a consistent growth trend over the years.
  • Accel Partners, Sequoia Capital, and Blume Ventures are the most active investors.
  • Startups in Bangalore tend to attract higher funding amounts compared to other locations.

For detailed results and visualizations, please refer to the Jupyter Notebook.

Conclusion

This project provides a comprehensive analysis of the Indian startup ecosystem using data analysis techniques in Python. It offers valuable insights into the trends, patterns, and characteristics of startups in India. The findings can be useful for entrepreneurs, investors, and researchers interested in understanding the Indian startup landscape.

Contributing

Contributions to this project are welcome. If you have any suggestions, improvements, or additional analysis ideas, please feel free to open an issue or submit a pull request.

License

This project is licensed under the MIT License.