Restaurant Menu Data Scraper

@muneeb6767

I’ll build a scalable scraper (Python + Scrapy/Playwright) to extract unique restaurants with full menus, ensuring high coverage via geo-based crawling, strict dedup (name+address hashing), and clean structured output. Can deliver 5k–20k+ unique restaurants depending on region.

@vishal1145

Your scraper will fail if you're hitting the platform's rate limits without rotating proxies - most delivery sites block IPs after 50-100 requests. I've built similar datasets for 3 food-tech clients and consistently see scrapers break when they don't account for dynamic JavaScript rendering and anti-bot detection. Quick question - are you targeting a single city or nationwide coverage? And does the platform use infinite scroll or pagination for restaurant listings? This changes the extraction strategy completely. Here's how I'll deliver 10K+ unique restaurants with zero duplicates: - PYTHON + SCRAPY: Build a distributed crawler with custom middleware for proxy rotation and request throttling to avoid IP bans while maintaining 200+ requests per minute. - SELENIUM + BEAUTIFULSOUP: Handle JavaScript-rendered content and dynamic menu loading that basic HTTP requests miss - I've seen projects lose 40% of data by skipping this. - DEDUPLICATION LOGIC: Hash restaurant names + addresses to create unique identifiers, then cross-reference against Google Places API to catch variations like "Pizza Hut Downtown" vs "Pizza Hut - Main St". - DATA VALIDATION: Run automated checks for missing prices, broken image URLs, and inactive listings before delivery - saves you hours of manual cleanup. I've scraped 50K+ restaurant records from DoorDash-style platforms without getting blocked. The key is mimicking human behavior with randomized delays and browser fingerprinting. I don't take projects where the target platform isn't clearly defined. Let's discuss which delivery site you're targeting and any existing blockers you've hit before we start extraction.

@sarimshoaib1425

can see from your sample this is Talabat Dubai with all 21 fields including legal name, minimum order, currency and last updated timestamp... I can match that exact structure. I'll deliver unique restaurants deduplicated at restaurant level, full menu data per restaurant, clean CSV or Excel matching your sample format exactly. What's the unique restaurant count you're targeting for this first batch? Let's talk.

@b2btechnology20

As a seasoned data professional with over a decade of experience handling diverse web scraping projects, I am confident in my ability to tackle your Restaurant Menu Data collection needs. My team and I are proficient in Python and have successfully executed similar undertakings previously, guaranteeing a seamless delivery and quality results. Our competency is not limited to just scraping but extends to the post-scraping phase ensuring your dataset is clean, structured, free from duplicates, and most importantly high coverage of unique restaurants. Furthermore, as someone who values long-term partnerships, I see this engagement extending beyond just one project. My team's commitment lies in producing successful outcomes and going above and beyond for our clients - attributes that align seamlessly with your stated desire for reliability and potential for future collaborations. I welcome the opportunity to make your acquaintance closer and discuss how we can create value for your business through efficacious data extraction.

@Shrutibimpl

Hello There, You want a comprehensive and deduplicated restaurant dataset with full menu details extracted from a food delivery platform. 1) Which specific delivery platform and target cities should we prioritize for the initial crawl? 2) Do you need the scrapers to run on a recurring schedule to capture daily price changes and discounts? 3) Should the final dataset include latitudinal and longitudinal coordinates for every restaurant address? I will turn raw platform data into a structured asset that gives you an edge in the competitive food market. You will receive a clean list where every entry is a unique business, ensuring your analysis is based on actual market coverage rather than duplicate noise. By capturing complete menus and live pricing, you get the visibility needed to track competitor trends and consumer preferences with absolute certainty. I will build a high speed scraping engine using Playwright and Python to navigate the platform dynamic interface and bypass rate limits. The system will utilize a headless browser cluster to scrape the restaurant core details and expand every menu category to capture the full catalog of items. I will implement a PostgreSQL database to manage deduplication during the run and export the final results into a structured JSON or CSV format ready for your analytical tools. Best regards, Bharat Joshi

@UzeeTech

With my 7+ years as a Full-Stack Developer specialized in Python and Web Scraping, I can ensure not only high coverage of unique restaurants but also clean and structured data in the desired format. In this era of abundant unstructured information, my expertise lies in obtaining valuable structured data through web scraping, which will be pivotal for this project's success. To avoid duplicates, I would employ a two-step process. Initially, I will use restaurant name, cuisine, and address/area to identify unique restaurants accurately. Subsequently, I'll compare each menu category, item name, description, price, and image URL to further eliminate any possibilities of repetitive entries. While the project description doesn't mention any preference regarding the time frame or approximate number of unique restaurants needed, let me assure you that my extensive experience includes extracting large-scale datasets under time constraints without compromising quality. This stems from my proficiency not just in web scraping but also in handling Big Data through databases like MongoDB and MySQL. In conclusion, your search for a reliable developer ends here! My robust Full-Stack skill set paired with a deep understanding of the task at hand makes me an ideal fit for your data scraping project. I'm confident that if given the opportunity to collaborate on this venture, we can achieve outstanding results from our combined efforts.

@kaustubhbawa123

Hi, I am an IITian , worked at fortune 500 companies. I will make it a reality for you. With 7+ years of experience I will deliver a high-quality solution using modern technologies and industry best practices. Kindly click on the chat button so we can discuss and get started. Will share you my prior projects done and my resume too. I have been doing freelancing since 2019 worked at top MNCs in both USA and India. Lets connect

@Hrolgar

Looking at your sample data, is the platform's restaurant_id exposed in the listing page HTML or only in API responses? That determines whether dedup runs as a simple key lookup or needs a name+address hash as fallback, and I want to confirm before locking in the architecture. Setup: Python with Playwright for listing pages (food delivery platforms render them client-side, so you need a real browser context), a discovery pass that walks city, cuisine, and pincode combinations to maximize coverage, then a per-restaurant fetch of the full menu including categories, items, variants, prices, offers, tags, and image URLs. The same restaurant shows up under multiple pincodes and cuisines on most platforms, so the discovery pass deduplicates at write time against a running restaurant_id index rather than cleaning up at the end. Storage in Postgres during the run, exports to CSV, Excel, and JSON. Milestones: M1: Discovery + schema + dedup logic, INR 7000, 3d. M2: Full menu scraper + Postgres storage, INR 7000, 4d. M3: Request pacing, IP rotation if needed, per-restaurant logs, INR 7000, 4d. M4: CSV + Excel + JSON exports + delivery, INR 7000, 3d. Unique restaurant count estimated at the start of M1 and tracked against actuals daily from M2 onward. Which platform is this targeting? That'll sharpen the pacing config before I start.

@CauaAlves

I can build a large-scale restaurant data scraper that extracts structured menu data from food delivery platforms with rigorous deduplication. Full-Stack developer with 100% satisfaction on Freelancer.com. Strong experience with Python scraping and large-scale data processing. HOW I ENSURE HIGH COVERAGE: Multi-City Scraping: Systematically scrape all cities and areas Rigorous Deduplication: Hash-based matching on name + address + city, fuzzy matching for similar names Data Validation: Verify active restaurants, ensure fields populated, validate menu completeness ESTIMATED DELIVERY: 8,000-10,000 unique restaurants with complete menu data DELIVERABLES: Structured Dataset: CSV/JSON with restaurant fields (name, cuisine, address, city, rating, delivery) and menu data (categories, items, pricing, offers, images, tags) Deduplication Report: Count of unique restaurants, duplicate stats, quality metrics Clean Data: Active restaurants, all fields populated, ready to use TECHNICAL APPROACH: - Python with BeautifulSoup/Scrapy - Anti-scraping (delays, rotating user-agents, rate limiting) - Parallel processing - Database-backed deduplication - Error handling and retry logic EXPERIENCE: - Python (large-scale data: 36k+ records) - Web scraping (BeautifulSoup, Scrapy) - Data deduplication and validation - Structured output (CSV, JSON, Excel) - 100% satisfaction rating Available immediately. Open to long-term collaboration. Ready to start once you share the target platform.

@Abuzar00

As a seasoned full stack developer with over 5 years of proven experience, I bring a unique blend of technical expertise and creative problem-solving skills to data scraping projects. With in-depth knowledge of Python and data scraping, I can ensure high coverage of unique restaurants for your dataset. Going beyond " rows" or "item counts", I focus on understanding the core structure of the site and designing a robust scraping system to extract clean, structured and deduplicated data. My proficiency in database development will come in handy to handle large-scale datasets like the one in your project. I will implement an efficient methodology to avoid duplicates ensuring that each unique restaurant is represented accurately. I also take this opportunity to express my eagerness for long-term collaboration if this project is delivered successfully. Being adept at automation, I will deliver a dataset of unique restaurants, clearly highlighted in a structured format with CSV/Excel/JSON along with a count demonstrating my skills. My philosophy has always been quality over quantity; rest assured, I bring the same diligence and meticulousness while handling data. I'm excited by the potential success that lay ahead for this project and am ready to put all my skills and experience into delivering the best possible result

@PyMLExpert1

I want to put my skills as a seasoned Python and Machine Learning Expert who specializes in architecting and deploying end-to-end AI solutions, at your disposal for this restaurant data scraping project. I having deep expertise in designing complex algorithms which is valuable for scraping large scale datasets that is also clean, structured and deduplicated as requested in the project description. To ensure a high coverage of unique restaurants, I will combine scraping algorithms with machine learning techniques to extract comprehensive restaurant details from the food delivery platform. With each restaurant treated as one unique unit, resulting in an accurate count that represents the true extent of the dataset. In terms of avoiding duplicates, I will use a combination of meticulous data cleaning and advanced statistical techniques to identify and remove any replicated entries to deliver a clean dataset. Additionally, I'm able to handle large amounts of data effectively and efficiently, ensuring we achieve your desired outcome within the specified timeframe. I believe my skill set and commitment to quality aligns perfectly with your needs and would be delighted to discuss further on how we can make this project a success.

@Managershashi

I specialize in extracting high-volume, clean restaurant datasets with full menu details. My unique approach ensures total deduplication and maximum coverage of unique entries. Backed by past successes in large-scale scraping, I guarantee structured data ready for analysis. Available 24/7 to ensure reliability. Let us discuss your specific targets to demonstrate the value I can provide.

@manoval09

Hi, extracting and structuring menu data across multiple restaurants is straightforward once you have a solid parser and normalization layer — the real challenge is handling sites with different HTML structures and JavaScript-rendered content. I'd build this using Python with BeautifulSoup for HTML parsing and Playwright for sites requiring JavaScript rendering. After identifying all target domains, I'll create a modular scraper with site-specific handlers that feed into a unified data validation layer. This approach lets me add new restaurant sites incrementally without rewriting core logic. I'll include data quality checks at every step — deduplication, format validation, schema enforcement. In the first 24 hours, I'll analyze your target restaurant sites, map their HTML structures, and deliver a detailed scraping roadmap with timeline estimates for each domain. What's your target number of restaurants, and are there specific data fields (cuisine type, hours, pricing) that need priority? Best regards, Val

@ahmetaksoyx

Hello, I have reviewed the Talabat platform and completed my preliminary work. I was able to successfully extract restaurant and menu information. The purpose of this initial work was to ensure that the project is feasible and to avoid any surprises during the process. I can deliver all restaurants in the requested country or region without any issues. Each restaurant will be treated as unique (each has its own unique ID in the system) and all core details and menu information can be retrieved accurately. If you wish, I can prepare a sample dataset for you so that you can verify that the restaurants are indeed unique and that all requested information has been captured. While reviewing the data, I noticed that some products are missing from the list due to option-based pricing (for example, at Britbalti Restaurant, “Mon Pasanda” is listed with a price, but “Pasanda” is not). The current data extraction captures all other information correctly. If needed, an additional effort can be made to include these products, or the main dataset can be updated after delivery. I would prefer to discuss the pricing along with the project details.

@Akash7015

Hello, i read your requirement .I have experience in excel and done many projects, i give you best work on your time and budget. Thanks waiting for your response...

@muskaannadaf

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@vertechsolutions

Hi, I can fix your Restaurant Menu Data Scraper I've solved this exact problem many times. Here is what I will do: 1) Build a scraper to collect only active, unique restaurants with full menu hierarchy and all required fields. 2) Deduplicate by restaurant identity and URL/ID so repeated menu rows are counted as one restaurant only. 3) Deliver clean CSV/Excel/JSON with validated counts, structured menu data, and an organized sample set. 10 days free support after delivery Milestone-based payment Reply "YES" and Best regards, syed ribal

@Anika1802

With my broad skill set, I am confident in my ability to successfully generate a high volume of unique restaurant data for your project. My 5+ years of experience in web and mobile app development, SaaS platforms, AI, and business automation has given me expertise in web scraping and Python - essential skills for extracting the needed information from food delivery platforms. To ensure a high coverage of unique restaurants, I propose implementing an iterative scraping process that combines numerous data sources to maximize diversity. This approach will not only benefit the current project but also lay the foundation for long-term collaboration by minimizing duplicate entries and increasing clean, structured data outputs. Additionally, I have carefully studied your sample data and understanding your goal of obtaining accurate and reliable dataset of restaurants is clear to me. I embrace this challenge head-on and always make sure that the projects I handle are done with utmost accuracy and precision. Whether it’s managing backend architecture or optimizing performance, my focus is on delivering valuable work that helps your business grow and thrive. Let's embark on this collaboration to build something useful, impactful, and genuinely beneficial for your business.

@danielledjomi

Bonjour, Votre projet correspond parfaitement à mes compétences en extraction et structuration de données. En tant que data analyst maîtrisant Python, SQL et Excel, je suis en mesure de constituer une base de données complète et propre de restaurants uniques avec leurs menus détaillés. Mon approche concrète : Extraction : utilisation de Python (BeautifulSoup, Scrapy ou Selenium) pour collecter automatiquement les données des restaurants depuis les plateformes ciblées, en respectant les champs obligatoires que vous avez définis. Déduplication : chaque restaurant est identifié par une clé unique combinant son nom, son adresse et sa ville. Aucun doublon ne sera comptabilisé dans la livraison finale. Nettoyage et structuration : les données sont normalisées, vérifiées et organisées proprement avant livraison au format CSV, Excel ou JSON selon votre préférence. Couverture maximale : je cible uniquement les restaurants actifs et disponibles au moment de l'extraction pour garantir la qualité des données. Livrable : un fichier structuré avec le nombre exact de restaurants uniques inclus, chaque entrée contenant toutes les informations demandées du restaurant et de son menu complet. Disponible immédiatement pour démarrer. N'hésitez pas à me partager la plateforme cible pour que j'estime précisément le volume extractible. Cordialement