https://github.com/girish119628/startup_funding_analysis

Indian startups to identify key trends, patterns, and insights that can help in understanding the startup ecosystem in India.
https://github.com/girish119628/startup_funding_analysis

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Indian startups to identify key trends, patterns, and insights that can help in understanding the startup ecosystem in India.

• Host: GitHub
• URL: https://github.com/girish119628/startup_funding_analysis
• Owner: girish119628
• Created: 2025-04-09T20:34:32.000Z (2 months ago)
• Default Branch: main
• Last Pushed: 2025-04-09T20:39:21.000Z (2 months ago)
• Last Synced: 2025-04-09T21:37:25.030Z (2 months ago)
• Language: Jupyter Notebook
• Size: 674 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# **Startup_Funding_Analysis**

This project presents an end-to-end Data Analytics pipeline for analyzing startup funding trends in India using Python, MySQL, and Power BI. It involves data cleaning, transformation, storage in a relational database, and visual storytelling via a dynamic dashboard.

# Project Structure

startup_funding.csv: Raw dataset containing startup funding data

startup_funding_cln.csv: Cleaned and preprocessed dataset

startup_funding_analysis.ipynb / .py: Python script for data cleaning, transformation, and MySQL integration

startup_funding_dashboard.pbix: Power BI dashboard showcasing insights

README.md: Project documentation

# Objective

To analyze startup funding data by:

Identifying investment trends

Highlighting major investors, investment types, and industries

Understanding city-wise funding patterns

# 🛠️ Tools & Technologies Used

  * Task	Technology

  * Data Cleaning	Python (Pandas, NumPy)

  * Data Visualization	Power BI

  * Database Integration	MySQL, PyMySQL

  * Dashboard	Power BI Desktop

  * Dashboard Preview

# 📌 Key Features

  * Total Funding: $55 Billion

  * Total Startups Analyzed: 3016

  * Top Funded Cities: Bangalore, Mumbai, New Delhi

  * Top Industries: Consumer Internet, E-commerce, Technology

  * Popular Investment Types: Private Equity, Seed Funding

  * Investment Trend Analysis (2015–2020)

🔄 Data Pipeline Overview

  1. Data Cleaning & Preprocessing (Python):

    * Renamed inconsistent column headers

    * Removed uninformative Remarks column

    * Handled null values with forward fill, mean imputation, and random sampling for text fields

    * Standardized date and amount formats

  2. Data Export

    * Cleaned data was exported to startup_funding_cln.csv

  3. MySQL Integration

    * Used PyMySQL to:

      * Establish connection to local MySQL server

      * Create and insert cleaned data into startup_funding_cln table in the codeit database

      * Verified records using SQL queries

  4. Dashboarding in Power BI

    * Connected Power BI directly to MySQL (live connection, no file uploads)

    * Built interactive visuals:

      * Bar charts (Top cities, industries)

      * Line charts (Year-wise trends)

      * Pie charts (Investment types)

      * Maps (City-wise investor locations)

# ✅ How to Run the Project

  * Clone this repo or download the files

  * Ensure MySQL is installed and running locally

  * Update MySQL credentials in the script if needed

Run the Python script:

bash

Copy

Edit

python startup_funding_analysis.py

Open Power BI → Connect to MySQL → Load startup_funding_cln table → Refresh visuals

📌 Sample Query Used in MySQL

sql

Copy

Edit

SELECT city_location, SUM(amount_usd) as total_investment

FROM startup_funding_cln

GROUP BY city_location

ORDER BY total_investment DESC;

📈 Business Insights

Bangalore tops in both number of investors and total funding

Private Equity is the most common investment type (44.8%)

Consumer Internet is the most funded industry

2018 saw the highest spike in funding among all years

💡 Conclusion

This project demonstrates the complete workflow of a data analytics project — from raw data ingestion to real-time insights — and showcases how to transform unstructured data into meaningful business intelligence.