Australian Accommodation Business Data Extraction – 4,100 Records

@imraz2016

Hi There, I have strong experience in large-scale business data extraction, enrichment, deduplication, and Excel delivery. For this project, I would combine Python-based extraction with public tourism directories, business websites, government/open datasets, and other legitimate sources to build the required 4,100 Australian accommodation records. I’ll validate phone numbers and emails from public business sources, standardize addresses/state/postcodes, remove duplicates using business name + address + phone checks, and exclude closed or irrelevant properties where possible. I’m happy to provide a 20–30 record sample first so you can verify the quality and format before I complete the full dataset.

@livegoodlife

Hello there, I can get you all the given business categories with Name, address, phone, website, email. post code, sate. I am experienced in web scraping and building scripts or a Windows desktop application using Python. I am also experienced in large data scraping from a given website, bypassing IP, Captcha, and anti-bot or cloud flair protection. Please message me to discuss this project in detail. Best Regards Enamul

@ZohaibRoy

⭐⭐⭐⭐⭐ Build an Accurate Database of Accommodation Businesses in Australia ❇️ Hi My Friend, I hope you're doing well. I reviewed your project requirements and see you are looking for a data extraction specialist to create a clean database of accommodation businesses. You don't need to look any further; Zohaib is here to help you! My team has completed over 50 projects similar to this. I will use efficient methods to gather data from reliable sources, ensuring accuracy and completeness within your budget. ➡️ Why Me? I can easily build your database of 4,100 accommodation businesses as I have 5 years of experience in data extraction and database management. My skills include web scraping, data validation, and using Python for automation. I also have a strong grip on technologies like APIs and data analysis, ensuring a thorough process for your project. ➡️ Let's have a quick chat to discuss your project in detail. I can show you samples of my previous work, demonstrating how I ensure high-quality data. Looking forward to discussing this with you in chat. ➡️ Skills & Experience: ✅ Data Extraction ✅ Web Scraping ✅ Python Programming ✅ Data Validation ✅ Database Management ✅ API Integration ✅ Data Cleaning ✅ Excel/CSV Formatting ✅ Data Analysis ✅ Automation Techniques ✅ Research Skills ✅ Attention to Detail Waiting for your response! Best Regards, Zohaib

@ShaikhAneesa

Hello, I have thoroughly reviewed your project requirements for extracting Australian accommodation business data, targeting 4,100 unique records across various states and territories. Let's chat and discuss it further. To handle your project, I will start with utilizing Python for data extraction, combining APIs and public tourism directories. I will categorize the businesses based on accommodation type, enrich contact details, standardize location information, remove duplicates, and validate entries to ensure data accuracy. The clear deliverables for this project are a clean and accurate database of 4,100 Australian accommodation businesses in Excel or CSV format. Before signing-off my bid, I would like to ask a question, i.e., how do you prefer to handle updates or changes in the data post-delivery? Best Regards, Aneesa.

@intelliresponse

Hello, I understand the real challenge with 4,100 records across every state isn't volume — it's coverage and accuracy together, ensuring every business is real, currently operating, and correctly categorized, without duplicate or generic entries diluting the dataset. My approach: Sourcing: Combine OpenStreetMap/Overpass, government tourism datasets, and public business directories for broad, legitimate coverage across all states/territories. Extraction: Python-based pipeline (requests, Selenium where needed, Pandas for processing) to pull and structure business details efficiently. Categorization & Enrichment: Classify by type (Motel/Resort/Park), enrich with phone/email/address where publicly available. Standardization: Separate address components cleanly into suburb, state, and postcode columns. Validation: Deduplicate by name/address matching, filter out closed businesses, and cross-check active status where possible. Delivery: Sample of 20–30 records first for your review, then full delivery in Excel/CSV. I bring solid experience in Python-based data extraction and large-scale, multi-source business dataset building. Best regards

@WelcomeClient

Hi There, Ready right now I'm ready to extraction specialist to build a clean and accurate database of accommodation businesses across Australia. I will show you sample for your satisfaction and project accuracy then we will go to start, so please contact me and share more details thanks. Check My Profile: https://www.freelancer.pk/u/WelcomeClient I would like to work on this project and can complete with 100% accuracy within the time frame. https://www.freelancer.pk/projects/excel/business-profit-loss-reporting-excel/reviews https://www.freelancer.pk/projects/data-entry/copy-listings-from-website-another/reviews Thanks, Umer

@sunmoon25

Hello sir, I can give you a comprehensive list of hotel, motel, resorts contact leads in Australia. Would you like to see the samples ? These are quality and verified leads that will convert. Thanks.

@starttechno

I will build a consolidated Australian accommodation database covering the requested 4,100 records: approximately 1,700 motels, 400 resorts, and 2,000 holiday/caravan parks across NSW, VIC, QLD, WA, SA, TAS, NT and ACT. I’ll first provide a 20–30 record sample in the exact Excel/CSV structure for approval. My approach will combine Python, Requests/Selenium where needed, Pandas, OpenStreetMap/Overpass, public tourism directories, government/open datasets, and official business websites. Records will be categorised, enriched, and standardised into business name, type, phone, email, street address, suburb/city, state, and postcode. Phone numbers and emails will be taken from publicly available business or directory pages, with format and source checks applied; unavailable details will remain blank rather than guessed. I’ll remove permanently closed listings where identifiable, separate physical locations into individual rows, normalise Australian state/postcode formats, and deduplicate using business name, address, phone, and location matching. I’ll also run validation checks for missing fields, invalid postcodes, duplicate properties, and state coverage before delivering the final .xlsx or CSV. The target is full representation across all states and territories, with phone and email coverage wherever publicly available. Muhammad Saad

@topcreator100

Hello, Drawing upon my strength in data extraction and enrichment, I am confident I can provide you with a comprehensive, reliable and high-quality data set of Australian accommodation businesses. Understanding the crucial significance of current and accurate records, I will not only extract the required business details but also verify their operational status by integrating multiple sources. Using a combination of Python, APIs, OpenStreetMap/Overpass, government/open datasets, tourism directories and relevant public sources, I will ensure that we achieve maximum coverage across all Australian states and territories as per your requirement. My experience in developing Python extraction/enrichment models has enabled me to leverage these sources effectively to categorize the information precisely. This ensures we exclude any generic locations without identifiable businesses. To bolster the credibility of your database, validation will be prioritized. By cross-referencing multiple sources, I will validate all phone numbers and emails wherever publicly available to guarantee they are up-to-date and functional. Data quality control is paramount for me; therefore, duplication will be eliminated meticulously to deliver precise results. I will also remove any permanently closed businesses from the dataset where possible. My aim is to deliver a clean and user-friendly Excel or CSV dataset that separates addresses, suburbs/cities into ind Thanks!

@tmoi06

Hello Sir, I can build a clean, structured database of approximately 4,100 Australian accommodation businesses covering motels, resorts, and holiday/caravan parks across all eight states and territories. ✅ Why Me? ✔ Extensive experience in web research, data extraction, business database building, and large-scale Excel/CSV projects ✔ Proficient with Python-based data extraction, APIs, public directories, Google Maps/business research, and website research ✔ Experienced in business categorisation, data enrichment, address standardisation, deduplication, and quality validation ✔ Strong attention to detail when separating street address, suburb/city, state, and postcode into the correct fields ✔ Experienced in identifying active businesses and removing duplicates, closed properties, and non-business/location-only records ✔ 500+ projects completed | 5.0-star rating With the target mix of approximately 1,700 motels, 400 resorts, and 2,000 holiday/caravan parks, I'll monitor coverage by state and accommodation type throughout the process rather than allowing the dataset to become concentrated in a few major cities. I'll first provide the requested 5–10 record sample for your review, including the exact final column structure: Account Name | Accommodation Type | Phone | Email | Address | Suburb/City | State | Postal Code Once you approve the sample quality and format, I can proceed with the complete database. Best regards, Ayan

@maxscender

I can help you build this database by working directly within your required pipeline rather than adding extra steps. To hit 4,100 records, I won’t rely on scraping alone—the logical approach is to combine structured public directories and tourism listings with a Python extraction layer to target the exact accommodation types and volume you need. Here’s how I’ll execute this practically: 1. Data Sourcing & Coverage I’ll prioritize structured datasets that already categorize properties, such as council tourism registers, park association lists, and accommodation directories, then use Python to scrape business websites for contact enrichment. This approach ensures all states and territories are represented and lets me logically target volume: ~1,700 motels, 400 resorts, and 2,000 parks by pulling from distinct category-specific sources. 2. Enrichment & Validation Phone and email will be cross-checked across at least two sources—the business’s own website and a third-party directory. This filters out disconnected numbers and generic inquiry inboxes. The pipeline will flag records with missing or mismatched fields for manual verification rather than guessing, keeping your data clean. 3. Deduplication Beyond basic name matching, I’ll deduplicate by physical address and phone number using Python’s fuzzy matching, since the same business can appear under slightly different names across sources. This ensures one row per physical location.

@DeveloperStation

Hi, I've delivered a Freelancer contract building an API-based data extraction pipeline, so this workflow is familiar territory: extract, categorise, enrich, deduplicate, validate. One thing worth deciding up front. Australian accommodation email coverage is thin from open sources like OSM and Overpass, so I'd combine those with government business registers and tourism directories, then scrape each business site for the email where public. Phone numbers validate cleaner. Are you fine with some rows having phone but no email where none is published, rather than us guessing? For dedup I'd match on normalised name plus geocoded address so one physical property equals one row. Python throughout, states balanced so all eight are represented. I'll send a 20 to 30 record sample first, weighted across motels, resorts and parks, so you can check format before we scale. Which states matter most to you? Adil

@seefattechnologi

YES, I will build the 4,100 record database using Python based multi source extraction, enrichment, validation, and deduplication to ensure accurate Australian accommodation coverage. I will combine OpenStreetMap/Overpass, public tourism directories, open datasets, and business websites, then validate contact details, standardise addresses/postcodes, remove duplicates and closed businesses, and provide a 20 to 30 record sample first. I can target the full 4,100 records across all states and territories with transparent coverage reporting. Please ping to get started and get outstanding results. Thanks!!!

@gowthamsarathy

Hello, Here's how exactly I am going to build it: I'll use Python with APIs/public datasets and headless extraction where necessary, combining tourism directories, OpenStreetMap/Overpass, publicly available business sources, and individual business websites for enrichment. I'll classify properties into Motel, Resort, and Holiday/Caravan Park, normalize Australian addresses/state/postcodes, collect publicly available phone numbers and business emails, remove closed/duplicate locations, and validate records before export. I'll first deliver a **20–30 record sample covering multiple states and accommodation types** so you can verify the format and quality before I scale the extraction to the full dataset. The final delivery will include the cleaned Excel/CSV database, separated address fields, accommodation classification, deduplication/validation checks, and a brief methodology explaining the sources and enrichment process. Relevant Scraping Work: https://www.freelancer.in/projects/beautifulsoup/Maritime-Job-Board-Scraping https://www.freelancer.in/projects/beautifulsoup/Python-Meetup-Events-Scraper Estimated Timeline: Sample: 1 Day Full 4,100 records: 5–8 Days, depending on source coverage and enrichment requirements. Expected Coverage: I would target the full 4,100 requirement across all eight states/territories, while clearly flagging records where a requested public contact field genuinely isn't available rather than fabricating data. Kind regards, Gowtham

@shihan033010

I will deliver a structured, accurate dataset of ~4,100 accommodation businesses across Australia, covering motels, resorts, and holiday/caravan parks. Scope of Work Target: 1,700 motels, 400 resorts, 2,000 holiday/caravan parks. Coverage: NSW, VIC, QLD, WA, SA, TAS, NT, ACT. Fields: Business Name, Type, Phone, Email, Address, Suburb/City, State, Postcode. Format: Excel (.xlsx) or CSV, one row per property, no duplicates.

@mayankinnovative

Hello I reviewed your Australian Accommodation Business Data Extraction project and it stood out because extracting 4,100 records accurately requires consistent field mapping, validation, deduplication, and reliable handling of large datasets Problem Large-scale business data extraction can produce incomplete records, duplicates, inconsistent formatting, or missing contact details when sources and extraction rules are not handled systematically Solution I can build or run a reliable data extraction workflow using Python, Selenium, APIs, or other suitable tools to collect the required accommodation business information from permitted public sources I can map fields consistently, normalize names and addresses, validate emails and phone numbers where appropriate, remove duplicates, and deliver the final dataset in CSV or Excel format I can also implement pagination, retries, logging, and quality checks to ensure the full 4,100-record dataset is processed accurately while respecting source terms and rate limits Result You will receive a clean, structured, and validated accommodation business dataset with consistent formatting and duplicate control, ready for your intended business use A few questions Which fields are required for each accommodation record? What source website or websites should be used? Do you need email, phone, address, website, business category, or other fields? Should the final data be delivered in CSV, Excel, or another format? Thanks

@TanvirKhanPaple

Hi there! Project is very clear to me and I can build the accommodation database using Python, Google Maps, tourism directories, public datasets, and business websites. I can verify phone/email details, standardize addresses, remove duplicates, and provide a clean Excel file. Just message me I am ready to start now and i will show you few data sample before start. Thank you.

@GdevDataSceince

Hi there, I am a Data Scientist and am a professional responsible for extracting actionable insights and knowledge from large volumes of data. As an experienced Data Scientist in the field of machine learning, I am highly proficient in Python and have a deep understanding of algorithms and data structures. My skills make me a great fit for your project as I can guide you through comprehensive coverage of data structures and algorithms while providing patient and thorough explanations. I have over 12-plus years of experience with Python Library Pandas, Karas, TensorFlow, NumPy, PyCharm, Py torch, Open CV, NLP, and others. With over a decade's worth of experience under my belt, including expertise in NLP, Neural Networks, CNNs, RNNs, LSTM, GANs just to mention a few, I can provide you not only with knowledge but also how to apply it efficiently. Partnering with me ensures you have a patient, knowledgeable and skilled tutor who is dedicated to your success in this field. My top priority is to provide a high quality of work, https://www.freelancer.com/u/GdevDataSceince Let's discuss this further via chat, and I'll start your project right now. Thanks Gdev

@emilesd617

Hi, I am a Python data extraction developer with 8 years of rich experience in software development, with a background in web scraping, data enrichment, validation, and Excel/CSV automation. I am familiar with Python, Pandas, Selenium, web scraping, public APIs, OpenStreetMap/Overpass, data cleansing, deduplication, email/phone enrichment, and Excel reporting. For your 4,100-record Australian accommodation database, I can combine multiple public sources to extract motels, resorts, and holiday/caravan parks, then enrich each record with phone, email, and structured address data. I can also standardize state/postcode fields, remove duplicates and closed businesses where identifiable, validate the final dataset, and provide a 20–30 record sample first so you can confirm the quality and format before the full run. I'm an individual freelancer and can work on any time zone you want. Please contact me with the best time for you to have a quick chat. Looking forward to discussing more details. Thanks. Emile.