You will get Scrape any website into a validated, structured dataset
Rising Talent
Project details
## As I am new to Upwork, these are introductory prices.
Send me a website and the fields you need, and get back a clean, structured dataset — with every field checked against the page it came from.
A scraper that runs once is easy. A scraper you can trust is not, because when a page's layout varies, a naive scraper silently records an empty value or grabs the wrong element. Here, every field is validated against its own source. A field the page's layout omits is recorded as absent, never defaulted to zero. A value that can't be read is flagged for your review, not guessed. And a record read off the wrong part of the page is caught, because each value must appear on its own listing.
I also don't scrape what I'm not entitled to. I read the site's robots.txt before the first request, a disallowed path is never fetched, and I don't sign in to pages, bypass paywalls, or collect personal data. Pages that can't be requested are reported to you, not quietly skipped.
You receive the dataset in your format (CSV, JSON, or straight into your database), a quality report showing every field with its source, and any flagged records and refused pages listed for your decision.
Send me a website and the fields you need, and get back a clean, structured dataset — with every field checked against the page it came from.
A scraper that runs once is easy. A scraper you can trust is not, because when a page's layout varies, a naive scraper silently records an empty value or grabs the wrong element. Here, every field is validated against its own source. A field the page's layout omits is recorded as absent, never defaulted to zero. A value that can't be read is flagged for your review, not guessed. And a record read off the wrong part of the page is caught, because each value must appear on its own listing.
I also don't scrape what I'm not entitled to. I read the site's robots.txt before the first request, a disallowed path is never fetched, and I don't sign in to pages, bypass paywalls, or collect personal data. Pages that can't be requested are reported to you, not quietly skipped.
You receive the dataset in your format (CSV, JSON, or straight into your database), a quality report showing every field with its source, and any flagged records and refused pages listed for your decision.
Data Tool
PythonWhat's included
| Service Tiers |
Starter
$5
|
Standard
$10
|
Advanced
$15
|
|---|---|---|---|
| Delivery Time | 1 day | 1 day | 2 days |
Number of Pages Mined/Scraped | 5000 | 40000 | 500000 |
Number of Sources Mined/Scraped | 1 | 3 | 8 |
Number of Revisions | 1 | 2 | 5 |
Optional add-ons
You can add these on the next page.
Anti-bot handling (proxy rotation)
(+ 1 Day)
+$5
Deliver into your database
(+ 1 Day)
+$5
Ongoing monitoring & maintenance
+$15Frequently asked questions
About Bhavin
Data science, Automation, ETL and Machine learning
Surat, India - 1:40 pm local time
For the past year I worked as a quantitative researcher at a global trading
firm, where I built an autonomous pipeline that runs hypothesis → validated
result → report in a single unattended, multi-day run. That taught me the part
most AI automation skips: an agent that fails silently is worse than no agent.
So I engineer the guardrails — gating, retries, audit trails, human sign-off on
anything irreversible — as carefully as the capabilities.
WHAT I BUILD
Data engineering & ETL — ingestion from APIs, databases, files and scraped
sources; schema design, incremental loads, deduplication and entity resolution.
Python · DuckDB · Postgres · Parquet · pandas. Pipelines that are idempotent,
monitored, and safe to re-run.
AI agents & LLM systems — agentic workflows over your own APIs and tools;
extraction from documents and unstructured text with schema enforcement and
citation-grounded output; RAG; batch processing at scale; and fixes for
demo-grade agents that break in production. Claude Code · MCP · Agent SDK · REST.
Machine learning — feature engineering, model selection, time-aware and
walk-forward validation, calibration, honest error analysis.
scikit-learn · LightGBM · statsmodels.
Analytics & reporting — exploratory analysis, hypothesis testing, dashboards,
and a write-up that says what the data actually supports.
SCALE
I run an independent research program on reconstructed L3 order-book data —
2.3B+ messages — so large, messy, high-volume datasets are normal for me rather
than exceptional.
HOW I WORK
Discipline you can inspect: a registry of dead ends, drafts I retract when the
data was wrong, replication failures reported next to successes. An audit trail
you can trust, not just a result.
Tell me what you're trying to automate or figure out, and I'll tell you honestly
whether the approach is right — including when it isn't.
Steps for completing your project
After purchasing the project, send requirements so Bhavin can start the project.
Delivery time starts when Bhavin receives requirements from you.
Bhavin works on your project following the steps below.
Revisions may occur after the delivery date.
Check permission before fetching
I read the site's robots.txt first. A disallowed path is never requested, and I don't sign in to pages or work around paywalls.
Fetch and parse at the site's pace
Pages are retrieved respectfully, honouring crawl delays, and each field is read from the exact listing it came from.