https://github.com/shrinidhi857/simpledataanalysisonstartups
The Indian startup ecosystem has experienced remarkable growth over the past decade, becoming a hotbed of innovation and entrepreneurship. In this data analysis we are segregating fields ,finding new insights.
https://github.com/shrinidhi857/simpledataanalysisonstartups
data-analysis data-science data-visualization indian-startups
Last synced: 9 months ago
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The Indian startup ecosystem has experienced remarkable growth over the past decade, becoming a hotbed of innovation and entrepreneurship. In this data analysis we are segregating fields ,finding new insights.
- Host: GitHub
- URL: https://github.com/shrinidhi857/simpledataanalysisonstartups
- Owner: Shrinidhi857
- Created: 2024-06-21T14:12:51.000Z (about 2 years ago)
- Default Branch: main
- Last Pushed: 2025-01-31T12:42:21.000Z (over 1 year ago)
- Last Synced: 2025-04-02T04:19:04.353Z (about 1 year ago)
- Topics: data-analysis, data-science, data-visualization, indian-startups
- Language: Jupyter Notebook
- Homepage:
- Size: 1.2 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Data Analysis on Indian Startups
## 📌 Overview
This project focuses on analyzing Indian startups using a dataset containing various attributes such as funding amount, industry sector, location, and funding sources. The analysis aims to uncover trends and insights about the startup ecosystem in India.
## 🚀 Features
- ✅ Data Cleaning and Preprocessing
- 📊 Exploratory Data Analysis (EDA)
- 📈 Visualization of funding trends
- 🏭 Industry-wise startup analysis
- 📍 Location-based startup distribution
- 🔍 Insights and conclusions
## 📦 Requirements
To run this project, you need the following dependencies:
```sh
pip install pandas numpy matplotlib seaborn
```
Run the cells sequentially to process the data and generate insights. Analyze the visualizations to understand trends in Indian startups.
## 📂 Dataset
The dataset contains information on:
- 🏢 Name of the startup
- 🏭 Industry sector
- 💰 Funding amount
- 🤝 Investor details
- 📍 City and state location
- 🔄 Funding rounds
## 📊 Results
Key findings include:
- 📌 Most funded sectors and their trends.
- 🗺️ Geographical distribution of startups.
- 🤝 Common investors and funding patterns.
## 🚀 Future Improvements
- 📈 Expanding the dataset with recent startup data.
- 🧠 Applying machine learning for predictive analytics.
- 🔄 Integrating real-time funding updates.
## 📜 License
This project is open-source and available under the MIT License.
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