Web Scraping & Data Extraction Specialist: Healthcare Directory (Excel / Google Sheets)

@serazummunirz

Hi There, Hope you are well. I have extensive experience in web scraping and data entry, with proficiency in tools like Python and Excel. I ensure 100% accuracy in data extraction and organization, with a keen eye for detail. I am capable of delivering a clean and structured Excel/Google Sheets file meeting all required data fields and specifications. Thanks in advance for the opportunity to working with you. Best Regards, Serazum

@vano101

Hello! I can handle this directory extraction as a structured, automated data-mining job rather than relying on slow manual entry. I have experience building Python-based scrapers and data-processing scripts for websites, extracting structured records from listings, normalizing fields, removing duplicates, validating URLs, and producing clean Excel/CSV outputs. I can work with multiple directory sites and adapt the scraper to each site's structure, including pagination, categories, locations, profiles, contact details, and available images. I would first process a sample batch so you can verify the accuracy and format before scaling to the full dataset. For 5,000–10,000 records, the turnaround depends mainly on the number and structure of the target directories, but automation can make this considerably faster and more consistent than manual extraction. My preferred approach is Python (requests/BeautifulSoup/Scrapy/Selenium where required) combined with pandas/openpyxl for validation, deduplication and Excel generation.

@mattafareed4848

Hello I can efficiently extract, clean, deduplicate, and organize healthcare business data into Excel/Google Sheets, including contact details, categories, source URLs, and images. I have experience with web scraping and data mining using automated and manual methods, with strong attention to accuracy and formatting. Regards Muhammad

@Badhan685

Hi, I have read the job description and now ready to start. I will provide 100% quality. Please send me a message for discussion. Let's discuss the project. Thanks

@Mokshith30

Hi, I can efficiently extract and organize healthcare directory data using **Python (BeautifulSoup/Selenium)** with automated scraping, validation, deduplication, and Excel/Google Sheets formatting. I can capture all required fields including location, entity details, contact information, timings, website URLs, source URLs, and image URLs/downloads. I’ll also standardize phone numbers/addresses, remove duplicates, and validate the collected data for accuracy. I can provide a **sample batch first** for approval and then scale to 5,000–10,000+ records with a clean, structured workbook. I’m ready to start immediately and can share relevant samples of my previous data extraction work.

@LordAskar

Extracting structured records across hospitals, clinics, pharmacies, and diagnostic centers while keeping image URLs verified and phone formats standardized requires a dedicated automated pipeline. My preferred approach is an automated Python scraper using BeautifulSoup or Selenium, paired with Pandas for deduplication, Title Case formatting, and HTTP verification on image links. In previous projects, such as scraping contact and directory listings into spreadsheets using Python and BeautifulSoup, I delivered clean, validated Excel outputs. For a batch of 5,000 to 10,000 records, turnaround time is 2 to 3 days. We can work across pre-agreed milestones, beginning with an initial test batch to verify the schema. Could you share the target directory links, and do you need images downloaded into Google Drive or are verified direct URLs sufficient? Ahmed

@pvsysgroupinc

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@andrey13771

Greetings! I am a proficient python developer. I've lots of experiece in scraping various data and I always make sure to go over it and check for duplicates or inconsistencies. I generally prefer writing my own scripts for data extraction but it does depend on the source. Turnaround once again depends on the data source but because I scrape automatically most of the active time is spent upfront writing and setting up scripts. I can give you a better estimate when I can take a look at the data. If you'd like, I can show you some of my related work. Message me and we can discuss your project! Sincerely, Andrii

@albertc1996

Hi, I can build a reliable Python-based extraction pipeline for your 2–3 healthcare directories and deliver clean Excel/Google Sheets data across hospitals, clinics, doctors, pharmacies, and diagnostic centers. I’ll extract and normalize location, entity details, contact information, working hours, emergency-service information, source URLs, and available image URLs. Using BeautifulSoup/Scrapy or Selenium/Playwright where necessary, I’ll handle pagination and dynamic content efficiently. I’ll apply deduplication, phone/address normalization, missing-field checks, and image URL validation before delivery. Records can also be separated into State/City or Category tabs as requested. For an initial sample, I can process around 5,000 records for validation before scaling to the full dataset. I’ll use automation for consistency and manual checks for quality-critical fields.

@mdabu38

I can efficiently extract and clean all required fields—including images and verified contact details—from your 2–3 target directories using Python scrapers. I have successfully scraped similar healthcare listings before and can easily deliver a 5,000–10,000 record sample batch in 24–48 hours; please share the directory URLs to get started!

@FreeLancers360

As a full stack developer with a special emphasis on web applications, custom software development and AI, I believe I bring a unique perspective to this web scraping project. In terms of sector-relevant experience, my previous projects have included data-driven healthcare solutions. This granular familiarity with the world of clinics, doctors, pharmacies etc. lends itself to my understanding of the requirements for your job - it's precisely the specialization I possess for successful data mining and management. In terms of tools for the job, Python has more than adequately equipped me for these requirements. My proficiency in libraries such as BeautifulSoup, Scrapy and Selenium has given me not only an intricate understanding of web scraping but also an ability to tailor data extraction tools to specific needs. I am skilled at leveraging Google sheets and advanced Excel functions for better management post-scraping including deduplication and filtered organization by agreed categories. Given my background in building scalable backend systems and SaaS products tailored to business requirements assure you that my focus is already aligned with meeting the goals of this task - that is delivering clean, well-structured deliverables. Outside of technical prowess, I am also reliable and detail-oriented; two characteristics crucial to successful completion of such projects. The

@Sohailzk

Hi, I can help extract and organize the healthcare business data from the specified directories into a clean Excel/Google Sheets file. I have experience with web scraping, data extraction, Excel/Google Sheets, data cleaning, deduplication, and structured data collection. I can use Python/Selenium/BeautifulSoup where automation is suitable, with manual verification for data quality. I can provide a sample batch of 5,000–10,000 records and ensure consistent formatting, duplicate removal, source URLs, contact details, timings, and image URLs where available. I’m ready to start with a small sample so you can check the quality before proceeding with the full dataset.

@samyak8840

Hi, I am Samyak. I can create n8n automation for scraping of websites and also desktop based application and can also create AI based scraper. I build the similar tool for my canada client. I can deliver it in 2 days. I will show you a poc in a single day and then finalize. I having 5 years experience as Python Developer in IT Industry and u can check my profile also. Please ping me on chatbox to goahead. Thanks

@Jafferi12

The hard part of a directory like this is duplicates, not scraping. The same hospital appears on 2-3 sites with slightly different names ("Apollo Hospitals" vs "Apollo Hospital, Jubilee Hills"), different phone formats and partial addresses, so exact matching leaves a lot of repeats. I'd standardise phones to one format, normalise addresses and pincodes, then match records on phone + pincode + fuzzy name, so each entity appears once with the best fields merged from every source and its source URLs kept. A Python scraper per directory (respecting each site's terms and rate limits), Title Case and validated fields, image URLs checked live, tabs by State/City or Category as you prefer, and images downloaded into a Drive folder named by Record ID if you want them. Which 2-3 directories, and which states/cities first?

@petropankovdev

Hi, I'd do this with a Python scraper (Requests/BeautifulSoup, Playwright only where a directory renders listings with JS), not by hand. Manual copy at this volume is where duplicates and broken fields come from. How I'd run it: 1. You send the 2-3 directory URLs and the states/cities to cover. I check each site's structure and pagination and confirm which of your columns each one actually publishes (email and hours are often missing on these sites, so I'll tell you upfront rather than leave silent blanks). 2. I scrape one city first and send it as a sample sheet, so you can check columns, casing and phone format before the full run. 3. Full run: Hospitals & Clinics, Doctors, Pharmacies, Diagnostic/Path labs, each row with a Record ID and the source listing URL. 4. Cleanup: dedup across all sources (name + address + phone, not just exact name), phones standardised to one format, Title Case names and addresses, pincode checked against city. 5. Every image URL is requested and only working ones are kept. If you want the images downloaded, they go into a folder per Record ID. Output: .xlsx plus a Google Sheets copy, one tab per state or category as you prefer, plus a short note on what each site did and didn't have. Turnaround: the sample city within a day of getting the URLs, and a 5,000-10,000 record batch in about 3-4 days depending on how the sites paginate. Background: I recently built a contact database for another client by scraping several public directory sources, with deduplication and validation before handover. This account has three completed projects, all rated 5 out of 5, delivered on time and on budget. Which directory sites are they? Once I see them I can confirm field coverage before you award. Petro Pankov, BotCraft Group

@SmartDevLabs

Healthcare deduplication must distinguish branches sharing phone numbers. Send the directory URLs; I'll return finished sample rows before you pay. Python/Scrapy, Selenium where needed, plus manual validation: - Hospitals/Clinics; Doctors/Specialists; Pharmacies; Diagnostics/Pathology - State/City/Area/Address/Pincode; Name/Category/Specialization - Phones/Email/Website; Hours/Emergency; Source - Validated image URLs; optional ID-named Drive images - Deduplication; standardized phones/addresses; Title Case - Excel/Sheets; State/City tabs or Category filters Directories -> Extract -> Validate -> Workbook Delivery: 5 days for 5,000-10,000 records. Are those records spread across all 2-3 directories? I am ready to start immediately with a competitive fixed-price offer; please fund the milestone once you are satisfied with the sample.

@NEHABHAT92

With over 9 years of experience in web development and mobile app development, I am extremely proficient in Python. I have successfully utilized tools such as BeautifulSoup, Scrapy, and Selenium for web scraping projects in the past, making me an ideal candidate for your healthcare directory project. Additionally, my strong attention to detail aligns perfectly with your data quality and hygiene requirements - ensuring accurate information, standardized formats, and active image URLs. Moreover, I have advanced skills in Excel and Google Sheets that includes data formatting, validation, and deduplication. Therefore, organization and maintaining a structured workbook will be second nature to me. My main aim is to provide you with a clean Microsoft Excel or Google Sheets file as you requested. To demonstrate my capabilities further and to give you an estimate on delivery time for a sample batch of records (e.g., 5000–10,000), I would need further details from you regarding the extent and complexity of the extraction task. Being an efficient worker, I can assure you that my ability to quickly grasp new tasks combined with my skill set will lead to a timely completion of your project whilst maintaining high accuracy.

@teerthofficial

Hi, I can extract and organize the healthcare directory data into a clean, duplicate-free Excel/Google Sheets database. • Extract hospitals, clinics, doctors, pharmacies and diagnostic centers • Capture all requested location, entity, contact and operating fields • Extract specialization/category information accurately • Collect and validate official website and source URLs • Capture direct image URLs or organize downloaded images by Record ID • Standardize phone numbers, casing and addresses • Remove duplicates and check missing/corrupted fields • Validate image URLs where available • Organize data by State/City or Category • Deliver clean Excel/Google Sheets with consistent formatting • Provide Google Drive image folders if downloads are required Preferred approach: Python with Requests/BeautifulSoup/Scrapy/Selenium where permitted, combined with manual verification and Excel/Sheets validation. I can first process a sample batch to confirm the structure and accuracy before the full dataset. I can start immediately.

@chamanrajragu

Directory scraping is mostly a question of what breaks at scale, not whether the first page parses. Three things usually decide it: pagination that silently caps out, fields that exist only on the detail page rather than the results list, and the same entity appearing under several categories or locations. How I would build it: 1. Look at the 2-3 directories first and tell you what is actually extractable per category before writing anything. Some fields you want may not be on the page at all, and you should know that before paying for a scraper. 2. Walk categories by location, follow through to detail pages for the fields that live there, and pull images where available with filenames keyed to the listing. 3. Deduplicate on a stable key rather than on name, since the same hospital often appears with slightly different spellings across categories. 4. Deliver one workbook, a sheet per category, plus a count sheet: listings per city and category, and how many rows were missing each field. That is how you confirm the extraction is complete without opening every row. I will pace requests and respect each site's robots rules so nothing gets blocked mid-run. Python with BeautifulSoup, or Scrapy if the volume justifies it. Rather than send unrelated samples, I would run one category in one city first so you can judge the real output before committing to the rest. Which directories, and how many cities are in scope? Chaman Raj, Purffle, Chennai.