Los Angeles California emails and phone Scrape 100K LA Emails & Phones
Budget: $30 – $250 USD
I need a Python-driven solution that can gather a minimum of 100,000 unique, verified email addresses and phone numbers for the Los Angeles Camarket. The primary source should be established business directories; Yellow Pages is fine as a fallback, but any reputable directory that reliably lists local contacts is acceptable.
My focus is on college-aged Eventbrite users—students attending or living near universities—so the data has to be filtered by
• age group (typical college range),
• campus or university location within Los Angeles or the wider California area, and
• event type that indicates active student participation.
A live, up-to-date list is critical; bounced emails or disconnected numbers defeat the purpose. To keep things manageable, please provide the data in a single CSV (or compatible) file that includes at least name, email, phone, and source URL for each record.
Acceptance criteria
• 100,000+ records that meet the filters above
• Less than 5 % bounce rate on a random sample test
• No obvious duplicates (max 1 duplicate per 5,000 records)
• Script or method documented well enough for me to rerun if needed
If you already have tooling or databases in place, great—just make sure the final dataset is fresh and passes the validation checks.
My focus is on college-aged Eventbrite users—students attending or living near universities—so the data has to be filtered by
• age group (typical college range),
• campus or university location within Los Angeles or the wider California area, and
• event type that indicates active student participation.
A live, up-to-date list is critical; bounced emails or disconnected numbers defeat the purpose. To keep things manageable, please provide the data in a single CSV (or compatible) file that includes at least name, email, phone, and source URL for each record.
Acceptance criteria
• 100,000+ records that meet the filters above
• Less than 5 % bounce rate on a random sample test
• No obvious duplicates (max 1 duplicate per 5,000 records)
• Script or method documented well enough for me to rerun if needed
If you already have tooling or databases in place, great—just make sure the final dataset is fresh and passes the validation checks.