Talabat Restaurants Data Scraper

@kchoudhary95

With over 7 years of experience in full-stack web development, I've honed my skills in data processing, Python, and web scraping. The responsibility you entrust me with of scraping every publicly visible detail on each restaurant's page will be met with utmost accuracy, ensuring no duplicates appear on the final data file. I'm well-versed with popular scraping libraries like BeautifulSoup and Selenium and I'll use the one that ensures the most reliable extraction from Talabat's layout. What sets me apart is not purely my technical expertise and automation skills, but my dedication to delivering meticulously clean, well-documented code that remains maintainable in the long run. This means that not only will you have the compiled dataset to address your immediate need, but the python-based scraping script will be reusable so that you have the flexibility to refresh the dataset for future updates easily. My proficiency also extends to ecommerce and enterprise web development making me deeply aware of the importance of extracting every nugget of valuable information while maintaining script reliability. Let's work together to transform your running list into a structured dataset that embraces accuracy and depth - ultimately adding real value to your project.

@a4logic

Hello, I see that you are in need of a reliable and experienced data scraper for your Talabat project. As a leader in web services, my team and I have successfully completed various similar scraping projects, making us a perfect fit. We understand the importance of accuracy in the data we scrape, which is why we conduct thorough spot-checks, ensuring the information provided is reliable and trustworthy. Our expertise lies in JavaScript, which enables us to effectively develop customized scraping scripts that extract information from even the most complex websites such as Talabat. We utilize powerful tools like BeautifulSoup, Scrapy and Selenium to ensure all relevant details (name, cuisine, address, phone, etc) are acquired as per your requirements. Moreover, being mindful of usability and efficiency for our clients like you, I assure you that the script created for this job would not just be a one-time tool. We will build it so that it can be reused and refreshed whenever you may require updates. With our commitment to excellent customer service, I assure you will receive not only quality code but also a well-documented experience headlined by timely delivery! Let's turn your aspirations into realities together. So don't hesitate to trust this project with us. Thanks!

@mjanchi

With my vast expertise in Data Collection, Data Mining, Data Processing, and accompanied by my in-depth knowledge of Scrapy and Web Scraping, I am more than ready to take on your Talabat Restaurants Data Scraper project. Having provided similar successful services blending professionalism, precision, and punctuality on Freelancer.com since 2018, I assure you that I can deliver the structured dataset you need with absolute accuracy and no duplicates. What sets me apart from other freelancers is my commitment to detail and quality. I understand the importance of your spot-checking process to ensure the authenticity of the data collected. Hence, I can guarantee that every detail, including delicate ones like opening hours and full menu with prices will be captured impeccably. I'm also well-versed in Python-based scraping tools like BeautifulSoup or Selenium which guarantees a reliable scraper against Talabat's dynamic layout thereby allowing for future refreshing of the dataset. Another reason why choosing me would be an excellent decision is because of my versatility with similar platforms. Over the years, I have scraped various food-delivery platforms quite effectively guaranteeing a high level of accuracy in capturing data even on complex `<div>` structures. My ultimate goal is not just to complete a project but also to establish solid partnerships based on reliability and quality results. So, let's get started on this tantalizing task together!

@justmian876

With BN-Droids Digital Services, you're not just hiring an individual; you're benefiting from the collective expertise of a skilled team. We have extensive experience garnered from successfully scraping and processing over one million data records daily. Moreover, we've specifically mined more than 20 million business and retail data records from regions including USA, UAE, Europe, Australia, and Canada.

@KatrinaQ

Hi, I've scraped Talabat's restaurant listings and menus before, so this is straightforward. I'll build a Python scraper using Scrapy for speed and reliability, with Selenium as a fallback for dynamic content, then extract every field you listed—name, cuisine, address, phone, delivery area, hours, menu, prices, offers, and ratings. I'll deduplicate entries using exact name and address matches, validate data against Talabat's live pages, and package the results in CSV/XLSX with a clean, reusable script that can rerun with minimal changes. The code will include clear comments and a setup guide for future refreshes. I can start immediately.

@asifdwan

As an experienced Freelancer in the field of web-scraping, I have been involved in extracting data from numerous websites like the one you described, including those with robust anti-bot protection. Platforms like Talabat can be particularly challenging, but no worries - I’m well-versed with tools and libraries such as BeautifulSoup, Scrapy, or Selenium in Python. I promise to deliver accurate and reliable CSV or XLSX files along with my scraping scripts that can be reused for future updates. My portfolio showcases extensive work in data services and automation which closely aligns with your project requirements. For instance, not only can I scrape the relevant Talabat restaurant details but also provide further value by conducting internet research, gathering contact information of these restaurants or any other related task you may have. Moreover, what sets me apart is my meticulousness when it comes to ensuring data accuracy and my commitment to client satisfaction. I'll include a comprehensive data entry step which not only ensures a unique collection of each restaurant's data but also allows for thorough quality assurance checks before delivery. Let's get started on transforming your running list into a well-structured dataset!

@denisSolution

Hi, I’ll extract the complete restaurant data from Talabat into a clean CSV/XLSX while ensuring no duplicates or missing fields, using Python and Scrapy since it handles dynamic content better than basic scraping. I built a similar scraper for another food-delivery platform where we needed to refresh daily datasets without breaking the structure—turned out the biggest challenge was handling pagination and rate limiting, so I added small delays and retries. I’d approach this using Scrapy because it keeps the workflow scalable while avoiding brittle XPath selectors. The biggest improvement will come from a well-structured pipeline that normalizes menu items and opening hours into consistent formats. I’ll test each batch locally before sending it, and include clear comments in the script so you can rerun it anytime without digging through the code. This way you get an accurate, reusable dataset that’s easy to validate and update. If this fits your needs, I can start right now. Thanks, Denis.

@naminghn

Hi there, To effectively convert your list of Talabat restaurants into a structured dataset, I will create a Python-based web scraping script that utilizes libraries like BeautifulSoup or Scrapy. This script will be designed for accuracy and reusability, allowing you to effortlessly refresh your dataset in the future. I’ll ensure that every detail, from names to ratings, is captured accurately without duplicates. Your satisfaction is my priority and I guarantee that I will deliver you a high-quality result. Regards, Ali

@shayankhan333

Talabat loads most of its restaurant data through internal API endpoints rather than rendered HTML, so scraping the page source alone won't get you the full menu or offers. I'll intercept those API calls directly, which is faster and more reliable than Selenium for this type of dynamic platform. I'll deliver a Python script (Scrapy + requests) that pulls every field you listed, deduplicates by restaurant ID, and exports to both CSV and XLSX. The script will be documented and reusable with a simple config for target city or area. One thing worth noting: Talabat's API responses are paginated and rate-limited, so the script will need built-in delays and retry logic to avoid getting blocked mid-run. 1) Which country/city on Talabat are you targeting, or is this across all regions? Happy to talk details in chat. Shayan

@AndrewJSMAN

I’ve scraped dynamic food-delivery platforms like Talabat before, so this is right in my wheelhouse. I’ll build a headless Selenium scraper with explicit waits to handle Talabat’s JS-rendered pages, extract all requested fields via targeted CSS/XPath selectors, and store data in CSV/XLSX with a hash-based de-duplication key on restaurant ID. The script will include session persistence for anti-bot evasion and a clean shutdown hook to resume failed runs. I’ll validate output against live pages and package the full dataset with the reusable script. I can start immediately. Thanks, Andrii.

@shakilan4

Hi, I have experience building reliable Python web scrapers for dynamic websites using Selenium, Scrapy, BeautifulSoup, and Playwright. I can extract all publicly available Talabat restaurant data into a clean CSV/XLSX, provide a reusable, well-documented scraping script, and ensure accurate, duplicate-free results for future updates. Thanks & Regards, Shakila Naz

@DeMit19

Collecting a complete, accurate Talabat dataset is more than extracting page content—the biggest challenge is handling dynamic loading while keeping the scraper reliable enough to be reused as the site changes. I would first review Talabat's page structure, identify which data is server-rendered versus loaded through APIs, and choose the most stable approach using Python with Scrapy, Selenium, or BeautifulSoup where appropriate. The scraper will capture every publicly available restaurant detail, including menus, prices, offers, ratings, delivery information, and business details, while ensuring each restaurant is recorded only once. Before delivery, I would validate the output against random live pages, remove duplicates, verify missing fields, and provide both the compiled CSV/XLSX dataset and clean, documented source code so you can refresh the data whenever needed. A few questions: Which country or city on Talabat should the scraper target? Approximately how many restaurants do you expect? Do you need images or only text-based data? I'd be happy to discuss the scope and the most reliable scraping approach for your dataset.

@LastHop3

Hello, Talabat uses dynamic loading, so I will utilize Playwright with stealth plugins to ensure data accuracy. I will deliver clean code and the requested dataset. Ready to discuss the details now. Best regards, Dawid

@saba106

I am an expert statistician, Research Writer, and data analyst with more than eight years of experience. I have full command of Excel analysis, SPSS, STATA, R LANGUAGE, AND PYTHON. I am an expert in creating time series prediction models, working with survey data, conducting marketing analysis, building estimators, and medical analysis. I am a perfect match for your project share other details of the work so I can start working on your project. Will complete task on time.

@lazarr4

Hi, I can build a reliable scraper for Talabat that extracts everything from name to ratings while avoiding duplicates and keeping the code reusable. I’ve scraped similar platforms like Deliveroo before—once a layout changed mid-project and I had to adjust selectors twice in a week, so I learned to document classes carefully. I’d use Scrapy for the initial crawl because it handles pagination well, then Selenium to refresh dynamic elements like offers that load after page render. The script will log every change so you can track updates without breaking past data. I’ll run a few test runs on random restaurants first to confirm accuracy before scaling up. You’ll get the full dataset plus a one-click refresh command that skips already stored entries. This setup removes the manual work of gathering restaurant data so you can focus on analysis instead of scraping. If the scraped records look correct after a quick review, I can send the dataset and script immediately. Thanks, Lazar.

@jordanrafael7

Hello! I'll convert your running list of Talabat restaurants into a clean, structured dataset ready for analysis or integration into your systems. I've built multiple web scraping pipelines using Python and JavaScript for data extraction from restaurant and food delivery platforms. My work with Selenium handles dynamic content loading and pagination, while my data processing and data mining experience ensures the output is normalized, deduplicated, and formatted to your specifications. I've delivered structured datasets for clients needing restaurant menus, pricing, ratings, and location data at scale. Here's how I'll get you there: - Analyze the Talabat site structure and build a scraper using Python with Selenium to handle dynamic content and capture all restaurant details - Extract and process the data into your preferred format (CSV, JSON, Excel, or database-ready) with consistent field naming and validation - Deliver the complete dataset with documentation and optional automation for future updates What specific fields do you need captured for each restaurant beyond the basics like name, location, and cuisine type? I can start immediately and keep you updated through Freelancer messages as the extraction progresses. Best regards, Jordan Rafael G.

@goranm16

Hello, Talabat pages are hydrated on the client side, so the reliable route is to read the JSON their own front end already calls rather than parsing rendered HTML. I would map those endpoints per area, and keep a Playwright/Selenium fallback for any page that only exposes data after render, which keeps the scrape fast and stable when the layout shifts. For each restaurant I would capture name, cuisines, address, phone if shown, delivery area and fees, opening hours, offers, ratings and review counts, plus the full menu with sections, item names, descriptions and prices. Output comes as two linked tables, one at restaurant level and one row per menu item, delivered in CSV and XLSX. Deduplication runs on the restaurant slug and branch ID, so nothing repeats. The script will be config driven with your list as the input, resumable, rate limited with retries, logged, and documented so you can refresh the dataset later by rerunning a single command. I am happy to start today, and I keep CET hours if you need a quick check on anything. Best regards, Goran

@miguelcen64

Hi, I can handle this Talabat restaurant data scrape and deliver both a clean dataset and a reusable Python scraper. I’ve worked on structured web extraction projects where accuracy, deduplication, and repeatability matter. For your Talabat list, I’ll capture all publicly visible restaurant details, including profile information, ratings, offers, opening hours, and complete menu data with pricing, then organize everything clearly in CSV or XLSX. I’ll build the scraper in Python using the most reliable approach for Talabat’s page behavior, with well-documented code so you can rerun and refresh the dataset later without rebuilding the workflow. I’ll also make sure duplicate handling is built in and the final output is easy to validate against live pages. Ready to start right away. Best, Miguel

@hoangvanp

Hi there, Building a reusable Talabat scraper requires handling dynamic content reliably while producing an accurate, duplicate-free dataset that's easy to refresh in the future. The focus is on extracting all publicly available restaurant information with a clean, maintainable scraping workflow. I'd begin by reviewing the restaurant list and Talabat page structure before implementing a Python scraper using Selenium where dynamic rendering is required, with BeautifulSoup for efficient parsing. I'll extract restaurant details, menus, pricing, offers, ratings, and other visible information, implement duplicate detection, structured logging, and export the results to CSV or XLSX. The project will include well-documented, reusable code so future data refreshes require minimal effort. I have experience developing Python web scrapers, data extraction pipelines, Selenium automation, BeautifulSoup parsing, and data processing solutions with a strong focus on accuracy, reliability, and maintainable code. A few questions: Approximately how many restaurant URLs are included in the initial list? Should the scraper support multiple Talabat countries or only one region? Do you require images or other media URLs to be included in the exported dataset?