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Startup Fundraising Analysis (Crunchbase Dataset)

Project: 2
Difficulty: Beginner
Tools: SQLite, Juypter Notebook, Python, Focus: Data Cleaning, Analysis, Using SQLite through a Python import, using pandas import.

Project Overview

This project explores startup investment data from the Crunchbase dataset using a combined pandas + SQLite workflow. The objective was to transform raw, unstructured data into a clean, queryable format and derive insights into fundraising trends, sector performance, and funding patterns.

The project follows a full data pipeline:

  • Data ingestion from CSV
  • Data cleaning and transformation
  • Storage in a relational database (SQLite)
  • Analytical querying using SQL
  • Interpretation of business insights

Tools & Technologies

  • Python
  • Pandas
  • SQLite
  • Jupyter Notebook

Dataset

  • Source: Crunchbase Startup Investments dataset

  • Records:

    • Raw dataset: ~54,294 rows
    • Cleaned dataset: 40,907 rows

Data Processing Workflow

1. Data Exploration

  • Inspected dataset structure, data types, and missing values

  • Identified key issues:

    • Funding amounts stored as text
    • Date fields in string format
    • Inconsistent column naming
    • Missing and placeholder values
  1. Data Loading (SQLite Integration)

  • Loaded CSV into SQLite using chunk-based processing

  • Prevented duplication by using:

    • replace for the first chunk
    • append for subsequent chunks

  • Created a structured relational table for querying

  1. Data Cleaning & Transformation

Key transformations included:

  • Renaming inconsistent columns (e.g. _funding_total_usd_ → funding_total_usd)
  • Converting funding values to numeric format
  • Handling invalid placeholders (e.g. '-', empty values)
  • Converting date columns to datetime
  • Standardising founded_year to integer
  • Removing records with missing or invalid funding values

Result:

  • ~25% of records removed due to poor data quality

Key Analysis & Insights

  1. Funding Trends Over Time
  • Startup activity increased significantly from the early 2000s
  • Peak funding observed around 2010–2012
  • Average funding per company decreased over time, indicating broader distribution of capital
  1. Most Successful Startup Verticals
  • Biotechnology received the highest total funding
  • Mobile had the highest average funding per company
  • Software had many companies but lower average funding, indicating a competitive market
  1. Funding Round Distribution
  • Majority of startups received only 1–2 funding rounds
  • Sharp drop-off in later rounds
  • Indicates high attrition beyond early-stage funding
  1. Funding Type Analysis
  • Venture capital dominated total funding
  • Private equity and debt financing contributed significantly at later stages
  • Early-stage funding (seed, angel) occurred frequently but with lower capital amounts
  1. Investment Concentration
  • Funding is highly concentrated among a small number of companies
  • Top firms received disproportionately large amounts of capital
  • Indicates unequal distribution of investment across the startup ecosystem

Challenges & Limitations

  1. Data Quality Issues
  • Missing and inconsistent values required significant cleaning
  • Funding column stored as text with symbols and placeholders
  • Category fields were not fully normalised
  1. Database Duplication Issue

  • Initial data loading caused duplicate records due to repeated append operations

  • Resolved by:

    • Rebuilding the database from scratch
    • Implementing controlled chunk loading (replace + append logic)
  1. Lack of Investor-Level Data

  • Dataset does not include investor names

  • Prevented direct analysis of investor activity

  • Adapted approach by analysing:

    • Funding types
    • Company-level investment patterns
  1. Outliers in Funding Data
  • Extremely large funding values skewed averages
  • Required careful interpretation of results

Key Skills Demonstrated

  • Data cleaning and preprocessing
  • SQL querying and aggregation
  • Database management (SQLite)
  • Handling large datasets with chunking
  • Data validation and debugging
  • Translating data into business insights

Conclusion

This project demonstrates an end-to-end data analysis workflow, from raw data ingestion to actionable insights. It highlights real-world challenges such as data quality issues, duplication errors, and dataset limitations, while showcasing the ability to adapt analysis techniques accordingly.

Future Improvements

  • Normalise category fields for more accurate sector analysis
  • Incorporate additional datasets with investor-level detail
  • Apply visualisation tools (e.g. Power BI, Tableau) for dashboard reporting