Where to Find Clean YC & Crunchbase Data for ML Models: Five Free Datasets and How to Use Them Legally

Introduction

Building machine learning models to predict startup success requires high-quality, legally compliant datasets. For venture capitalists and researchers analyzing Y Combinator (YC) companies, finding clean, comprehensive data can be challenging. The good news is that several free datasets exist, but navigating licensing requirements and data quality issues requires careful consideration.

Rebel Fund has invested in nearly 200 top Y Combinator startups, collectively valued in the tens of billions of dollars and growing. (LinkedIn) The fund has built the world's most comprehensive dataset of YC startups outside of YC itself, now encompassing millions of data points across every YC company and founder in history. (Medium - Rebel Theorem 3.0) This expertise in data-driven investment analysis makes understanding the landscape of available datasets crucial for anyone building similar ML models.

This comprehensive guide explores five free datasets for YC and startup data, explains licensing pitfalls to avoid, and provides practical Python examples for combining multiple data sources. We'll also cover how to augment these datasets with Crunchbase's Enterprise API for enhanced accuracy and completeness.

The Current State of AI Training Data and Legal Considerations

Before diving into specific datasets, it's essential to understand the legal landscape surrounding AI training data. Recent developments highlight significant concerns about data usage rights and copyright infringement.

Artificial Intelligence companies are scraping the Internet for training data, including text, photos, video, music, and more, often disregarding intellectual property rights and copyrights. (JSK Fellows) Many lawsuits have been filed by news publishers, the entertainment industry, authors, photographers, and other creatives against tech companies for infringing their copyrights under the guise of fair use. (JSK Fellows)

The stakes are enormous. AI has been a controversial topic in content for decades, especially in the context of fair use, with financial implications worth trillions of dollars. (Edge Blog) Content is used in large quantities to train AI models, which can also be used to create content, raising questions about fair use and copyright infringement. (Edge Blog)

For startup data specifically, these legal considerations mean that researchers must be extremely careful about data sources and usage rights. The datasets we'll explore have been selected based on their legal accessibility and clear licensing terms.

Five Free Datasets for YC and Startup Analysis

1. Medium-Scraped YC Dataset

One of the most comprehensive publicly available YC datasets comes from Medium articles and blog posts that have aggregated YC company information over the years. This dataset typically includes:

Licensing Considerations: While this data is publicly available, the method of collection matters. Scraping Medium content may violate terms of service, so look for datasets that were compiled through manual research or API access rather than automated scraping.

Data Quality: Medium-sourced data can be inconsistent due to varying article quality and update frequency. Cross-reference with other sources for accuracy.

2. Opendatabay's Accelerator CSV

Opendatabay provides a structured CSV file containing information about various startup accelerators, including Y Combinator companies. This dataset offers:

Licensing: Opendatabay typically provides data under Creative Commons licenses, making it suitable for research and commercial use with proper attribution.

Strengths: Clean, structured format that's immediately usable for ML training. Regular updates and quality control processes.

3. GitHub Repository: YC Company Database

Several GitHub repositories maintain crowd-sourced databases of YC companies. These repositories often include:

Licensing: Most GitHub datasets use MIT or Apache licenses, allowing broad usage rights. Always check the specific repository's LICENSE file.

Community Benefits: Active maintenance by the developer community means faster updates and error corrections.

4. GitHub Repository: Startup Ecosystem Data

Broader startup ecosystem repositories often include YC companies alongside other accelerator graduates. These datasets provide:

Integration Value: Combining YC-specific data with broader startup ecosystem information can improve ML model performance by providing more diverse training examples.

5. Academic Research Datasets

Universities and research institutions occasionally release startup datasets for academic purposes. These sources offer:

Access: Often available through academic databases or direct contact with researchers. May require institutional affiliation or research proposal submission.

Augmenting with Crunchbase Enterprise API

While free datasets provide a solid foundation, Crunchbase's Enterprise API offers the most comprehensive and up-to-date startup information available. Crunchbase Data allows developers to incorporate the latest industry trends, investment insights, and rich company data into their applications. (Crunchbase Data)

As of July 2024, Crunchbase has updated its API and CSV offerings, including changes to the base URL and potential changes to some endpoints. (Crunchbase Data) The CSV export's columns and structure have undergone major changes, requiring developers to update their integration code.

Key Crunchbase API Features

Crunchbase is a comprehensive public resource for financial information of various public and private companies and investments. (Medium - Crunchbase Scraping) Crunchbase contains thousands of company profiles, which include investment data, funding information, leadership positions, mergers, news and industry trends. (Medium - Crunchbase Scraping)

The /organizations endpoint in Crunchbase returns a paginated list of OrganizationSummary items for every Organization. (Crunchbase Organizations) Developers can search the /organizations endpoint using three mutually-exclusive freetext search options: query, name, and domain_name. (Crunchbase Organizations)

API Integration Strategy

Python Examples for Data Integration

Here's how to combine multiple datasets and integrate with Crunchbase API data:

Basic Data Loading and Cleaning

import pandas as pd
import numpy as np
from datetime import datetime
import requests
import time

# Load multiple datasets
yc_medium_data = pd.read_csv('yc_medium_dataset.csv')
opendatabay_data = pd.read_csv('opendatabay_accelerators.csv')
github_data = pd.read_json('github_yc_companies.json')

# Standardize company names for joining
def clean_company_name(name):
    if pd.isna(name):
        return name
    return name.strip().lower().replace(',', '').replace('.', '')

yc_medium_data['clean_name'] = yc_medium_data['company_name'].apply(clean_company_name)
opendatabay_data['clean_name'] = opendatabay_data['company_name'].apply(clean_company_name)
github_data['clean_name'] = github_data['company_name'].apply(clean_company_name)

Joining Company Tables with Founder Metadata

# Create comprehensive company dataset
company_base = yc_medium_data[['clean_name', 'company_name', 'yc_batch', 'industry']].copy()

# Add funding information from Opendatabay
funding_info = opendatabay_data[['clean_name', 'total_funding', 'last_funding_date']]
company_enhanced = company_base.merge(funding_info, on='clean_name', how='left')

# Add founder information from GitHub dataset
founder_info = github_data[['clean_name', 'founder_count', 'founder_backgrounds', 'technical_founders']]
final_dataset = company_enhanced.merge(founder_info, on='clean_name', how='left')

# Handle missing values
final_dataset['founder_count'] = final_dataset['founder_count'].fillna(2)  # Average assumption
final_dataset['technical_founders'] = final_dataset['technical_founders'].fillna(0)

print(f"Final dataset shape: {final_dataset.shape}")
print(f"Companies with complete founder data: {final_dataset['founder_backgrounds'].notna().sum()}")

Crunchbase API Integration

class CrunchbaseEnhancer:
    def __init__(self, api_key):
        self.api_key = api_key
        self.base_url = "https://api.crunchbase.com/api/v4"
        self.headers = {
            "X-cb-user-key": api_key,
            "Content-Type": "application/json"
        }
    
    def get_company_details(self, company_name, max_retries=3):
        """Fetch detailed company information from Crunchbase"""
        endpoint = f"{self.base_url}/entities/organizations"
        params = {
            "field_ids": "identifier,name,short_description,categories,funding_total,num_employees_enum",
            "query": company_name,
            "limit": 1
        }
        
        for attempt in range(max_retries):
            try:
                response = requests.get(endpoint, headers=self.headers, params=params)
                if response.status_code == 200:
                    data = response.json()
                    if data.get('entities'):
                        return data['entities'][0]['properties']
                elif response.status_code == 429:  # Rate limit
                    time.sleep(2 ** attempt)  # Exponential backoff
                    continue
                else:
                    print(f"API error for {company_name}: {response.status_code}")
                    return None
            except Exception as e:
                print(f"Error fetching {company_name}: {str(e)}")
                time.sleep(1)
        
        return None
    
    def enhance_dataset(self, df, company_name_col='company_name'):
        """Enhance dataset with Crunchbase data"""
        enhanced_data = []
        
        for idx, row in df.iterrows():
            company_name = row[company_name_col]
            cb_data = self.get_company_details(company_name)
            
            enhanced_row = row.to_dict()
            if cb_data:
                enhanced_row.update({
                    'cb_funding_total': cb_data.get('funding_total', {}).get('value'),
                    'cb_employee_count': cb_data.get('num_employees_enum'),
                    'cb_description': cb_data.get('short_description'),
                    'cb_categories': [cat.get('value') for cat in cb_data.get('categories', [])]
                })
            
            enhanced_data.append(enhanced_row)
            
            # Rate limiting
            time.sleep(0.1)
            
            if idx % 10 == 0:
                print(f"Processed {idx + 1}/{len(df)} companies")
        
        return pd.DataFrame(enhanced_data)

# Usage example (requires valid Crunchbase API key)
# enhancer = CrunchbaseEnhancer("your_api_key_here")
# enhanced_dataset = enhancer.enhance_dataset(final_dataset.head(50))  # Test with first 50

Feature Engineering for ML Models

def create_ml_features(df):
    """Create features suitable for ML model training"""
    features_df = df.copy()
    
    # Encode categorical variables
    features_df['industry_encoded'] = pd.Categorical(features_df['industry']).codes
    features_df['yc_batch_year'] = features_df['yc_batch'].str.extract(r'(\d{4})').astype(float)
    
    # Create founder-related features
    features_df['has_technical_founder'] = (features_df['technical_founders'] > 0).astype(int)
    features_df['founder_diversity'] = features_df['founder_count'].apply(
        lambda x: 1 if x > 2 else 0 if pd.isna(x) else 0
    )
    
    # Funding features
    features_df['has_funding'] = features_df['total_funding'].notna().astype(int)
    features_df['funding_log'] = np.log1p(features_df['total_funding'].fillna(0))
    
    # Time-based features
    current_year = datetime.now().year
    features_df['company_age'] = current_year - features_df['yc_batch_year']
    
    return features_df

# Apply feature engineering
ml_ready_data = create_ml_features(final_dataset)

# Select features for model training
feature_columns = [
    'industry_encoded', 'yc_batch_year', 'founder_count', 
    'has_technical_founder', 'founder_diversity', 'has_funding', 
    'funding_log', 'company_age'
]

X = ml_ready_data[feature_columns].fillna(0)
print(f"Feature matrix shape: {X.shape}")
print(f"Features: {feature_columns}")

Data Quality and Validation Strategies

Rebel Fund has developed a robust data infrastructure to train its Rebel Theorem machine learning algorithms, which are used to identify high-potential YC startups. (Medium - Rebel Theorem 3.0) This level of sophistication requires careful attention to data quality and validation.

Common Data Quality Issues

Validation Techniques

def validate_dataset_quality(df):
    """Comprehensive data quality assessment"""
    quality_report = {}
    
    # Completeness analysis
    quality_report['completeness'] = {
        col: (df[col].notna().sum() / len(df)) * 100 
        for col in df.columns
    }
    
    # Duplicate detection
    quality_report['duplicates'] = {
        'total_duplicates': df.duplicated().sum(),
        'duplicate_companies': df.duplicated(subset=['clean_name']).sum()
    }
    
    # Value consistency
    quality_report['consistency'] = {
        'funding_negative': (df['total_funding'] < 0).sum() if 'total_funding' in df.columns else 0,
        'future_batch_years': (df['yc_batch_year'] > datetime.now().year).sum() if 'yc_batch_year' in df.columns else 0
    }
    
    return quality_report

# Run quality assessment
quality_metrics = validate_dataset_quality(final_dataset)
print("Data Quality Report:")
for category, metrics in quality_metrics.items():
    print(f"\n{category.upper()}:")
    for metric, value in metrics.items():
        print(f"  {metric}: {value}")

Legal Compliance and Best Practices

Given the recent legal developments around AI training data, following best practices is crucial for any ML project using startup datasets.

Privacy Considerations

DataComp CommonPool, one of the largest open-source data sets used to train image generation models, contains millions of images of passports, credit cards, birth certificates, and other documents with personally identifiable information (PII). (LinkedIn - AI Training Data) While startup datasets typically don't contain such sensitive information, researchers should still be cautious about including personal details of founders or employees.

Recommended Compliance Framework

Legal Risk Mitigation

def create_compliance_report(datasets_info):
    """Generate compliance documentation for dataset usage"""
    report = {
        'generation_date': datetime.now().isoformat(),
        'datasets_used': [],
        'licensing_summary': {},
        'attribution_requirements': []
    }
    
    for dataset in datasets_info:
        report['datasets_used'].append({
            'name': dataset['name'],
            'source': dataset['source'],
            'license': dataset['license'],
            'access_date': dataset['access_date'],
            'record_count': dataset['record_count']
        })
        
        if dataset['license'] not in report['licensing_summary']:
            report['licensing_summary'][dataset['license']] = 0
        report['licensing_summary'][dataset['license']] += dataset['record_count']
        
        if dataset.get('attribution_required'):
            report['attribution_requirements'].append(dataset['attribution_text'])
    
    return report

# Example usage
datasets_info = [
    {
        'name': 'YC Medium Dataset',
        'source': 'Medium articles compilation',
        'license': 'Public Domain',
        'access_date': '2024-08-04',
        'record_count': 1500,
        'attribution_required': False
    },
    {
        'name': 'Opendatabay Accelerators',
        'source': 'Opendatabay.com',
        'license': 'CC BY 4.0',
        'access_date': '2024-08-04',
        'record_count': 800,
        'attribution_required': True,
        'attribution_text': 'Data provided by Opendatabay under CC BY 4.0 license'
    }
]

compliance_report = create_compliance_report(datasets_info)
print("Compliance Report Generated")
print(f"Total datasets: {len(compliance_report['datasets_used'])}")
print(f"License types: {list(compliance_report['licensing_summary'].keys())}")

Advanced ML Applications and Industry Trends

The venture capital industry is increasingly embracing data-driven approaches. Only 1% of VC funds currently have internal data-driven initiatives, according to a report by Earlybird Venture Capital. (LinkedIn - VC AI Usage) This presents a significant opportunity for funds that can effectively leverage ML models.

AI has the potential to perform almost every job in venture capital, potentially reducing the need for large teams. (LinkedIn - VC AI Usage) AI is being used for sourcing and screening startups, reducing the need for large teams to maintain a high-quality deal flow. (LinkedIn - VC AI Usage)

Rebel Fund's Advanced Approach

Rebel Fund has released Rebel Theorem 4.0, an advanced machine-learning (ML) algorithm for predicting Y Combinator startup success. (Medium - Rebel Theorem 4.0) Rebel is one of the largest investors in the Y Combinator startup ecosystem, with 250+ YC portfolio companies valued collectively in the tens of billions of dollars. (Medium - Rebel Theorem 4.0)

The algorithm categorizes startups into 'Success', 'Zombie', and other performance categories, demonstrating the practical application of ML models trained on comprehensive YC datasets. This real-world success story illustrates the potential value of the datasets.

Frequently Asked Questions

What are the best free sources for Y Combinator startup data?

The most comprehensive free YC data sources include Crunchbase's public API, Kaggle datasets, GitHub repositories with YC company lists, and academic research datasets. While companies like Rebel Fund have built proprietary datasets with millions of data points across every YC company in history, public alternatives offer substantial value for ML model development when properly cleaned and validated.

Is it legal to scrape Crunchbase data for machine learning models?

Scraping Crunchbase data requires careful attention to their Terms of Service and API usage policies. The safest approach is using Crunchbase's official API or CSV exports, which allow developers to incorporate company data legally. Recent legal precedents show that unauthorized scraping for AI training can violate copyright laws, making compliance with platform terms essential.

How do venture capital firms use AI and data for startup investment decisions?

Only 1% of VC funds currently have internal data-driven initiatives, but AI adoption is growing rapidly. Firms like Rebel Fund use machine learning algorithms trained on comprehensive datasets to identify high-potential YC startups, having invested in nearly 200 YC companies valued in tens of billions. AI helps with sourcing, screening, and predicting startup success patterns.

What data quality issues should I expect when working with startup datasets?

Common issues include missing funding information, outdated company status, inconsistent naming conventions, and duplicate entries across different data sources. Crunchbase has undergone major changes to its CSV structure as of July 2024, requiring data pipeline updates. Always validate data freshness and implement robust cleaning procedures before training ML models.

What are the key legal considerations when using startup data for AI training?

Major concerns include copyright infringement, fair use limitations, and personally identifiable information (PII) protection. Recent cases show that large-scale data scraping for AI training often doesn't qualify as fair use. Ensure you have proper licensing agreements, remove PII from datasets, and comply with data protection regulations like GDPR when applicable.

How can I integrate multiple startup datasets for better ML model performance?

Start by standardizing company identifiers across datasets, then merge on common fields like company name, domain, or Crunchbase UUID. Implement data validation checks to handle conflicts between sources, prioritize more recent or authoritative data sources, and create feature engineering pipelines that can handle missing values gracefully across different dataset schemas.

Sources