Are you looking to identify B2B leads to promote your business, product, or service? Outscraper Google Maps Scraper might just be the tool you've been searching for. This powerful software enables you to extract business data directly from Google's extensive database, which spans millions of businesses across countless industries worldwide.

Outscraper Google Maps Scraper is a tool built with advanced technology that lets you scrape a myriad of valuable information about businesses from Google's database. This information includes but is not limited to, business names, addresses, contact information, website URLs, reviews, ratings, and operational hours.

Whether you are a small business trying to make a mark or a large enterprise exploring new territories, the data obtained from the Outscraper Google Maps Scraper can be a treasure trove. This tool provides a cost-effective, efficient, and accurate method to generate leads and gather market insights.

By using Outscraper, you'll gain a significant competitive edge as it allows you to analyze your market and find potential B2B leads with precision. You can use this data to understand your competitors' landscape, discover new markets, or enhance your customer database. The tool offers the flexibility to extract data based on specific parameters like business category or geographic location, helping you to target the most relevant leads for your business.

In a world that's growing increasingly data-driven, utilizing a tool like Outscraper Google Maps Scraper could be instrumental to your business' success. If you're looking to get ahead in your market and find B2B leads in a more efficient and precise manner, Outscraper is worth considering. It streamlines the data collection process, allowing you to focus on what truly matters – using the data to grow your business.

https://outscraper.com/google-maps-scraper/

As a result of the Google Maps scraping, your data file will contain the following details:

Query Name Site Type Subtypes Category Phone Full Address Borough Street City Postal Code State Us State Country Country Code Latitude Longitude Time Zone Plus Code Rating Reviews Reviews Link Reviews Per Scores Photos Count Photo Street View Working Hours Working Hours Old Format Popular Times Business Status About Range Posts Verified Owner ID Owner Title Owner Link Reservation Links Booking Appointment Link Menu Link Order Links Location Link Place ID Google ID Reviews ID

If you want to enrich your datasets with social media accounts and many more details you could combine Google Maps Scraper with Domain Contact Scraper.

Domain Contact Scraper can scrape these details:

Email Facebook Github Instagram Linkedin Phone Twitter Youtube

Explore APISCRAPY, your AI-powered Google Map Data Scraper. Easily extract Business Location Data from Google Maps and other platforms. Seamlessly access and utilize publicly available map data for your business needs. Scrape All Publicly Available Data From Google Maps & Other Platforms.

Outscraper's Location Intelligence Service is a powerful and innovative tool that harnesses the rich data available from Google Maps to provide valuable Point of Interest (POI) data for businesses. This service is an excellent solution for local intelligence needs, using advanced technology to efficiently gather and analyze data from Google Maps, creating precise and relevant POI datasets​.

This Location Intelligence Service is backed by reliable and up-to-date data, thanks to Outscraper's advanced web scraping technology. This ensures that the data extracted from Google Maps is both accurate and fresh, providing a dependable source of data for your business operations and strategic planning​.

A key feature of Outscraper's Location Intelligence Service is its advanced filtering capabilities, enabling you to retrieve only the POI data you require. This means you can target specific categories, locations, and other criteria to get the most relevant and valuable data for your business needs, eliminating the need to sift through irrelevant records​.

With Outscraper, you also get worldwide coverage for your POI data needs. The service's advanced data scraping technology allows you to collect data from any country and city without limitations, making it an invaluable tool for businesses with global operations or those seeking to expand internationally​.

Outscraper provides a vast amount of data, offering the largest number of fields available to compile and enrich your POI data. With more than 40 data fields, you can create comprehensive and detailed datasets that provide deep insights into your areas of interest​.

Outscraper's Location Intelligence Service is designed to be user-friendly, even for those without coding skills. Creating a Google Maps scraping task is quick and simple with the Outscraper App Dashboard, where you select a few parameters like category, location, limits, language, and file extension to scrape data from Google Maps​.

Outscraper also offers API support, providing a fast and easy way to fetch Google Maps results in real-time. This feature is ideal for businesses that need to access location data quickly and efficiently​.

Outscraper's Global Location Data service is an advanced solution for harnessing location-based data from Google Maps. Equipped with features such as worldwide coverage, precise filtering, and a plethora of data fields, Outscraper is your reliable source of fresh and accurate data.

Outscraper's Global Location Data Service leverages the extensive data accessible via Google Maps to deliver critical location data on a global scale. This service offers a robust solution for your global intelligence needs, utilizing cutting-edge technology to collect and analyze data from Google Maps and create accurate and relevant location datasets. The service is supported by a constant stream of reliable and current data, powered by Outscraper's advanced web scraping technology, guaranteeing that the data pulled from Google Maps is both fresh and accurate.

One of the key features of Outscraper's Global Location Data Service is its advanced filtering capabilities, allowing you to extract only the location data you need. This means you can specify particular categories, locations, and other criteria to obtain the most pertinent and valuable data for your business requirements, eliminating the need to sort through irrelevant records.

With Outscraper, you gain worldwide coverage for your location data needs. The service's advanced data scraping technology lets you collect data from any country and city without restrictions, making it an indispensable tool for businesses operating on a global scale or those looking to expand internationally. Outscraper provides a wealth of data, offering an unmatched number of fields to compile and enrich your location data. With over 40 data fields, you can generate comprehensive and detailed datasets that offer deep insights into your areas of interest.

The global reach of this service spans across Africa, Asia, and Europe, covering over 150 countries, including but not limited to Zimbabwe in Africa, Yemen in Asia, and Slovenia in Europe. This broad coverage ensures that no matter where your business operations or interests lie, you will have access to the location data you need.

Experience the Outscraper difference today and elevate your location data analysis to the next level.

Unlock insights into Moroccan banking customer experiences! 🇲🇦

This dataset contains scraped and cleaned Google Maps reviews for banks across all cities in Morocco. Collected as part of a collaborative student/freelancer project, it’s perfect for sentiment analysis, market research, or academic projects.

What’s Inside?

• 2 Versions:
  • Raw Data: As scraped from Google Maps.
  • Cleaned Data: Filtered to exclude non-bank businesses (e.g., cash services, unrelated entries).
• Columns:
  City, Business Name, Address, Phone Number, Website, Google Map ID, Review Text, Timestamp, Stars.
• Cities Sourced from data.gov.ma: Ensured comprehensive coverage of Moroccan regions.

Methodology:

1. City Identification: Used official data from data.gov.ma to target cities with banks.
2. Search Strategy: Queried “bank in [city name]” on Google Maps to compile business links.
3. Scraping: Extracted business details (name, address, etc.) and latest reviews using Python + Playwright (automation) and BeautifulSoup (parsing).
4. Cleaning: Removed duplicates and non-bank entries for accuracy.

Potential Use Cases:

• 📈 Sentiment Analysis: Analyze customer satisfaction trends.
• 🗺️ Geospatial Visualization: Map bank ratings by city/region.
• 🔍 Competitor Analysis: Compare bank reputations.
• 🎓 Academic Projects: Practice NLP, data cleaning, or visualization.

Tech Stack:

• Python 🐍
• Playwright (for browser automation)
• BeautifulSoup (HTML parsing)
• Pandas (data cleaning)

Why This Dataset?

• First-of-its-kind: Focused on Moroccan banks.
• Ready-to-use: Cleaned version requires minimal preprocessing.
• Transparent: Raw data included for reproducibility.

License: CC0: Public Domain (Free to use, modify, and share).

Dataset ini berisi ulasan dari Google Maps yang terkait dengan kampus BUMN. Data dikumpulkan untuk keperluan penelitian dan edukasi, dengan fokus pada pengalaman dan umpan balik pengguna. Dataset ini dapat digunakan untuk analisis data eksploratif, analisis sentimen, dan penelitian lainnya.

Deskripsi Dataset
File: CyberU_reviews.csv
Format: CSV
Jumlah Baris: 78
Jumlah Kolom: 9

The Easiest Way to Collect Data from the Internet Download anything you see on the internet into spreadsheets within a few clicks using our ready-made web crawlers or a few lines of code using our APIs

We have made it as simple as possible to collect data from websites

Easy to Use Crawlers Amazon Product Details and Pricing Scraper Amazon Product Details and Pricing Scraper Get product information, pricing, FBA, best seller rank, and much more from Amazon.

Google Maps Search Results Google Maps Search Results Get details like place name, phone number, address, website, ratings, and open hours from Google Maps or Google Places search results.

Twitter Scraper Twitter Scraper Get tweets, Twitter handle, content, number of replies, number of retweets, and more. All you need to provide is a URL to a profile, hashtag, or an advance search URL from Twitter.

Amazon Product Reviews and Ratings Amazon Product Reviews and Ratings Get customer reviews for any product on Amazon and get details like product name, brand, reviews and ratings, and more from Amazon.

Google Reviews Scraper Google Reviews Scraper Scrape Google reviews and get details like business or location name, address, review, ratings, and more for business and places.

Walmart Product Details & Pricing Walmart Product Details & Pricing Get the product name, pricing, number of ratings, reviews, product images, URL other product-related data from Walmart.

Amazon Search Results Scraper Amazon Search Results Scraper Get product search rank, pricing, availability, best seller rank, and much more from Amazon.

Amazon Best Sellers Amazon Best Sellers Get the bestseller rank, product name, pricing, number of ratings, rating, product images, and more from any Amazon Bestseller List.

Google Search Scraper Google Search Scraper Scrape Google search results and get details like search rank, paid and organic results, knowledge graph, related search results, and more.

Walmart Product Reviews & Ratings Walmart Product Reviews & Ratings Get customer reviews for any product on Walmart.com and get details like product name, brand, reviews, and ratings.

Scrape Emails and Contact Details Scrape Emails and Contact Details Get emails, addresses, contact numbers, social media links from any website.

Walmart Search Results Scraper Walmart Search Results Scraper Get Product details such as pricing, availability, reviews, ratings, and more from Walmart search results and categories.

Glassdoor Job Listings Glassdoor Job Listings Scrape job details such as job title, salary, job description, location, company name, number of reviews, and ratings from Glassdoor.

Indeed Job Listings Indeed Job Listings Scrape job details such as job title, salary, job description, location, company name, number of reviews, and ratings from Indeed.

LinkedIn Jobs Scraper Premium LinkedIn Jobs Scraper Scrape job listings on LinkedIn and extract job details such as job title, job description, location, company name, number of reviews, and more.

Redfin Scraper Premium Redfin Scraper Scrape real estate listings from Redfin. Extract property details such as address, price, mortgage, redfin estimate, broker name and more.

Yelp Business Details Scraper Yelp Business Details Scraper Scrape business details from Yelp such as phone number, address, website, and more from Yelp search and business details page.

Zillow Scraper Premium Zillow Scraper Scrape real estate listings from Zillow. Extract property details such as address, price, Broker, broker name and more.

Amazon product offers and third party sellers Amazon product offers and third party sellers Get product pricing, delivery details, FBA, seller details, and much more from the Amazon offer listing page.

Realtor Scraper Premium Realtor Scraper Scrape real estate listings from Realtor.com. Extract property details such as Address, Price, Area, Broker and more.

Target Product Details & Pricing Target Product Details & Pricing Get product details from search results and category pages such as pricing, availability, rating, reviews, and 20+ data points from Target.

Trulia Scraper Premium Trulia Scraper Scrape real estate listings from Trulia. Extract property details such as Address, Price, Area, Mortgage and more.

Amazon Customer FAQs Amazon Customer FAQs Get FAQs for any product on Amazon and get details like the question, answer, answered user name, and more.

Yellow Pages Scraper Yellow Pages Scraper Get details like business name, phone number, address, website, ratings, and more from Yellow Pages search results.

2,274 roofers in United States identified as having no website via Google Maps data extraction. Data collected and maintained by MapsLeadExtractor.

LinkedIn Data for Talent Acquisition, CRM Enrichment & Company Insights

LinkedIn data is one of the most essential sources of alternative business intelligence — enabling real-time visibility into company growth, hiring behavior, and lead signals. Canaria’s LinkedIn data product delivers structured insights across LinkedIn company data, LinkedIn job postings, and job market trends, verified and enriched with Google Maps metadata.

This LinkedIn data product is specifically designed for use cases including talent acquisition, business development, CRM data enrichment, HR intelligence, and company analysis. It empowers organizations to track high-growth companies, hiring signals, employee trends, and location-level expansions — all sourced from LinkedIn and optimized for integration.

Use Cases: What Problems This LinkedIn Data Solves Our LinkedIn data helps transform fragmented online business profiles into clean, analyzable signals. Whether you need to enrich CRM records, build a talent pipeline, or score leads by job activity, this product enables precision targeting and faster decision-making.

LinkedIn Company Analysis • Identify company size, industry tags, and employee count trends using LinkedIn company data • Monitor company presence through LinkedIn follower growth, location footprints, and job post activity • Benchmark similar companies using standardized LinkedIn data fields and activity metrics • Track changes in business strategy based on real-time LinkedIn job and company page updates

Talent Acquisition & HR Intelligence • Discover which companies are actively hiring through LinkedIn job postings • Analyze job title demand, skill trends, and role seniority using normalized job market LinkedIn data • Track employer branding and recruiting momentum across key locations • Use LinkedIn data to identify companies hiring for specific departments or regions

Risk Detection & Workforce Insights • Spot slowing hiring momentum using LinkedIn job volume trends • Detect early signs of restructuring or regional downsizing • Cross-reference LinkedIn data with Google Maps to validate real-world branch activity • Compare declared headcount with public-facing recruiting behavior

CRM Enrichment & B2B Lead Generation • Enrich company records with verified LinkedIn company data (industry, size, hiring) • Score accounts based on hiring momentum and LinkedIn engagement • Use job postings data to find companies actively hiring for the roles you serve • Identify ideal B2B lead targets based on title trends and LinkedIn hiring signals

Why This LinkedIn Data Product Is Different Matchable Across Systems • Our LinkedIn data is designed to integrate with your job market data, CRM tools, BI platforms, and talent dashboards

Location Intelligence Included • All LinkedIn company profiles include verified HQs and branches, cross-validated with Google Maps metadata

Weekly Updates • Stay current with weekly-updated LinkedIn data streams, covering new companies, job postings, and hiring shifts

Taxonomy-Mapped & Clean • Data is normalized into standard LinkedIn company data fields, ready for matching across systems and teams

Who Benefits from LinkedIn Data • Talent acquisition platforms and recruiting teams • CRM and RevOps teams enriching lead and account records • Strategy and BI teams monitoring workforce and hiring dynamics • Investment teams tracking company growth and hiring behavior • B2B marketers building lead scoring and account targeting models • HR tech tools offering benchmarking and job market insight

Summary Canaria’s LinkedIn Data product delivers enriched, structured, and match-ready business intelligence sourced directly from LinkedIn. By combining LinkedIn company data, LinkedIn job postings, and job market data with location validation via Google Maps, this product enables confident execution across talent acquisition, HR intelligence, CRM enrichment, and company analysis.

About Canaria Inc. Canaria Inc. is a leader in alternative data, specializing in job market intelligence, LinkedIn company data, Glassdoor salary analytics, and Google Maps location insights. We deliver clean, structured, and enriched datasets at scale using proprietary data scraping pipelines and advanced AI/LLM-based modeling, all backed by human validation. Our platform also includes Google Maps data, providing verified business location intelligence — such as addresses, coordinates, hours, categories, and ratings — which is fully matchable with our company datasets for powerful geospatial analysis and location-based enrichment. Our AI-powered pipeline is developed by a seasoned team of machine learning experts from Google, Meta, and Amazon, and by alumni of Stanford, Caltech, and Columbia — combining cutting-edge research with enterprise-grade engineering. This foundation enables us to deliver precise, reliable insights that power corporate intelligence, market research, and lead generation at scale. With insights drawn from ...

This dataset contains anonymised data of accounts and reviews, labelled as fake/real collected through scraping of Google Maps. It is a part of the research described under this link.

Please cite the article describing this dataset as: P. Gryka and A. Janicki, “Detecting Fake Reviews in Google Maps—A Case Study,” Applied Sciences, vol. 13, no. 10, p. 6331, May 2023, doi: 10.3390/app13106331.

1,290 electricians in United States identified as having not mobile-friendly via Google Maps data extraction. Data collected and maintained by MapsLeadExtractor.

This work presents a gridded dataset on real estate and transportation in 192 worldwide urban areas, obtained from the Google Maps API and the web scraping of real estate websites. For each city of the sample, these data have been associated with the corresponding population density and land cover data, extracted from the GHS POP and ESA CCI data respectively, and aggregated on a 1 km resolution grid, allowing for an integrated analysis. This dataset is the first to include spatialized real estate and transportation data in a large sample of cities covering 800 million people in both developed and developing countries. These data can be used as inputs for urban modeling purposes, transport modeling, or between-city comparisons in urban forms and transportation networks, and allow further analyses on e.g. urban sprawl, access to transportation, or equity in housing prices and access to transportation.

This data set covers global extraction and production of coal and metal ores on an individual mine level. It covers
1171 individual mines, reporting mine-level production for 80 different materials in the period 2000-2021. Furthermore, also data on mining coordinates, ownership, mineral reserves, mining waste, transportation of mining products, as well
as mineral processing capacities (smelters and mineral refineries) and production is included. The data was gathered manually from more than 1900 openly available sources, such as annual or sustainability reports of mining companies. All datapoints are linked to their respective sources. After manual screening and entry of the data, automatic cleaning, harmonization and data checking was conducted. Geoinformation was obtained either from coordinates available in company reports, or by retrieving the coordinates via Google Maps API and subsequent manual checking. For mines where no coordinates could be found, other geospatial attributes such as province, region, district or municipality were recorded, and linked to the GADM data set, available at www.gadm.org.

The data set consists of 12 tables. The table “facilities” contains descriptive and spatial information of mines and processing facilities, and is available as a GeoPackage (GPKG) file. All other tables are available in comma-separated values (CSV) format. A schematic depiction of the database is provided as in PNG format in the file database_model.png.

LinkedIn Job Postings Data - Comprehensive Professional Intelligence for HR Strategy & Market Research

LinkedIn Job Postings Data represents the most comprehensive professional intelligence dataset available, delivering structured insights across millions of LinkedIn job postings, LinkedIn job listings, and LinkedIn career opportunities. Canaria's enriched LinkedIn Job Postings Data transforms raw LinkedIn job market information into actionable business intelligence—normalized, deduplicated, and enhanced with AI-powered enrichment for deep workforce analytics, talent acquisition, and market research.

This premium LinkedIn job postings dataset is engineered to help HR professionals, recruiters, analysts, and business strategists answer mission-critical questions: • What LinkedIn job opportunities are available in target companies? • Which skills are trending in LinkedIn job postings across specific industries? • How are companies advertising their LinkedIn career opportunities? • What are the salary expectations across different LinkedIn job listings and regions?

With real-time updates and comprehensive LinkedIn job posting enrichment, our data provides unparalleled visibility into LinkedIn job market trends, hiring patterns, and workforce dynamics.

Use Cases: What This LinkedIn Job Postings Data Solves

Our dataset transforms LinkedIn job advertisements, market information, and career listings into structured, analyzable insights—powering everything from talent acquisition to competitive intelligence and job market research.

Talent Acquisition & LinkedIn Recruiting Intelligence • LinkedIn job market mapping • LinkedIn career opportunity intelligence • LinkedIn job posting competitive analysis • LinkedIn job skills gap identification

HR Strategy & Workforce Analytics • Organizational network analysis • Employee mobility tracking • Compensation benchmarking • Diversity & inclusion analytics • Workforce planning intelligence • Skills evolution monitoring

Market Research & Competitive Intelligence • Company growth analysis • Industry trend identification • Competitive talent mapping • Market entry intelligence • Partnership & business development • Investment due diligence

LinkedIn Job Market Research & Economic Analysis • Regional LinkedIn job analysis • LinkedIn job skills demand forecasting • LinkedIn job economic impact assessment • LinkedIn job education-industry alignment • LinkedIn remote job trend analysis • LinkedIn career development ROI

What Makes This LinkedIn Job Postings Data Unique

AI-Enhanced LinkedIn Job Intelligence • LinkedIn job posting enrichment with advanced NLP • LinkedIn job seniority classification • LinkedIn job industry expertise mapping • LinkedIn job career progression modeling

Comprehensive LinkedIn Job Market Intelligence • Real-time LinkedIn job postings with salary, requirements, and company insights • LinkedIn recruiting activity tracking • LinkedIn job application analytics • LinkedIn job skills demand analysis • LinkedIn compensation intelligence

Company & Organizational Intelligence • Company growth indicators • Cultural & values intelligence • Competitive positioning

LinkedIn Job Data Quality & Normalization • Advanced LinkedIn job deduplication • LinkedIn job skills taxonomy standardization • LinkedIn job geographic normalization • LinkedIn job company matching • LinkedIn job education standardization

Who Uses Canaria's LinkedIn Data

HR & Talent Acquisition Teams • Optimize recruiting pipelines • Benchmark compensation • Identify talent pools • Develop data-driven hiring strategies

Market Research & Intelligence Analysts • Track industry trends • Build competitive intelligence models • Analyze workforce dynamics

HR Technology & Analytics Platforms • Power recruiting tools and analytics solutions • Fuel compensation engines and dashboards

Academic & Economic Researchers • Study labor market dynamics • Analyze career mobility trends • Research professional development

Government & Policy Organizations • Evaluate workforce development programs • Monitor skills gaps • Inform economic initiatives

Summary

Canaria's LinkedIn Job Postings Data delivers the most comprehensive LinkedIn job market intelligence available. It combines job posting insights, recruiting intelligence, and organizational data in one unified dataset. With AI-enhanced enrichment, real-time updates, and enterprise-grade data quality, it supports advanced HR analytics, talent acquisition, job market research, and competitive intelligence.

About Canaria Inc. Canaria Inc. is a leader in alternative data, specializing in job market intelligence, LinkedIn company data, Glassdoor salary analytics, and Google Maps location insights. We deliver clean, structured, and enriched datasets at scale using proprietary data scraping pipelines and advanced AI/LLM-based modeling, all backed by human validation. Our platform also includes Google Maps data, providing verified business location intelligen...

Welcome to the "Bangalore Chain Restaurants: Ratings, Reviews, Categories, and Locations" dataset! This comprehensive collection provides valuable insights into the dynamic restaurant landscape of Bangalore, India. Whether you're a data enthusiast, a foodie seeking the best dining spots, or a researcher investigating restaurant trends, this dataset is a valuable resource.

Dataset Overview:

This dataset contains information on various chain restaurants operating in Bangalore, showcasing a diverse array of food categories and sub-categories. The data includes key metrics such as ratings, review counts, as well as geographical data comprising latitude and longitude coordinates.

Data Collection Process:

Data Source Selection: Google Maps was chosen as the primary data source due to its comprehensive coverage of restaurants and its accessibility for users.

Web Scraping: Automated web scraping tools were employed to systematically navigate through Google Maps and extract data from multiple pages. The scraper was programmed to follow links, access individual restaurant pages, and extract specific details.

Data Extraction: Key data points, including restaurant names, ratings, review counts, food categories, sub-categories, latitude, and longitude, were extracted from each restaurant's page.

Data Cleaning: The extracted data was subjected to a rigorous cleaning process to handle missing values, remove duplicates, and ensure uniformity in formatting. Cleaning also involved standardizing categories and sub-categories for consistency.

Geocoding: Geocoding was performed on the restaurant addresses to derive latitude and longitude coordinates. This step facilitated precise geospatial analysis of the restaurants' locations.

1,316 plumbers in United States identified as having not mobile-friendly via Google Maps data extraction. Data collected and maintained by MapsLeadExtractor.

Pakistan Healthcare Facilities & Accessibility Dataset

This dataset provides a comprehensive directory of hospitals and medical centers across Pakistan, including both government and private healthcare facilities. It contains hospital names, categories, cities, addresses, contact numbers, and website links where available.

The data was collected using publicly available sources (Google Maps) and automated scraping tools, then cleaned and structured using Python for analysis and research purposes.

Purpose and Motivation Access to reliable healthcare information is a major challenge in many parts of Pakistan, especially in rural and underdeveloped regions. Many hospitals lack proper digital presence, websites, or publicly listed contact numbers.

This dataset aims to:

• Map healthcare infrastructure across Pakistan • Help researchers analyze hospital distribution • Identify gaps in digital accessibility • Support public health studies • Enable hospital finder applications • Raise awareness about missing or limited healthcare information

Data Limitations and Observations This is real-world data and may contain:

• Missing phone numbers • Missing websites • Incomplete addresses • Inconsistent naming or categories

Interestingly, these gaps themselves highlight an important issue: A large number of healthcare facilities in Pakistan still lack proper digital documentation, making it harder for patients to access information quickly.

Therefore, this dataset should be viewed both as a directory and as a reflection of healthcare accessibility challenges.

Potential Use Cases • Exploratory Data Analysis (EDA) • Healthcare accessibility research • GIS mapping and visualization • Government vs private hospital comparison • Public health dashboards • Location-based hospital finder apps • Data science and machine learning projects • Kaggle notebooks and academic research

Collection Methodology • Source: Google Maps public listings • Tool: Apify Google Maps Crawlers • Cleaning: Python (Pandas)

Disclaimer Information is gathered from public sources and may not always be fully accurate or up-to-date. Users are encouraged to verify critical details before use.

If you use this dataset in research or projects, attribution is appreciated.

915 chiropractors in United States identified as having no website via Google Maps data extraction. Data collected and maintained by MapsLeadExtractor.

The Entertainment_KSA.csv dataset contains data on various entertainment spots in Saudi Arabia. With over 500 rows of data, this dataset provides information on the name, rating, review count, genre, location, and best comment for each entertainment spot. This dataset can be used to analyze the entertainment industry in Saudi Arabia and understand the types of entertainment spots available in the country.

The way of creating datasets like Entertainment_KSA.csv is by web scraping information from public sources such as Google Maps or Yelp. Web scraping is the process of automatically extracting data from websites using software tools. In this case, a web scraper would be programmed to visit the relevant pages on Google Maps or Yelp and extract information on entertainment spots such as name, rating, review count, genre, location, and best comment.

The scraped data can then be saved in a CSV file, like the Entertainment_KSA.csv dataset. Once the data is collected, it can be cleaned and processed to remove any errors or duplicates and then analyzed to gain insights into the entertainment industry in Saudi Arabia.

As for inspiration, datasets like Entertainment_KSA.csv can be used for a variety of purposes, including market research, trend analysis, and predictive modeling. Researchers and data analysts can use this dataset to explore the types of entertainment spots available in Saudi Arabia, identify popular spots, and understand the factors that influence customer reviews and ratings.

For example, this dataset could be used to predict which new entertainment spots are likely to be successful based on their genre, location, and other factors. It could also be used to identify trends in the entertainment industry in Saudi Arabia, such as the increasing popularity of certain genres or the growth of entertainment spots in specific regions.

2,335 real estate agents in United States identified as having not mobile-friendly via Google Maps data extraction. Data collected and maintained by MapsLeadExtractor.