#Airbnb Data Extraction

[books-bread-and-brigandage]

The only reason AI exists is to try and extract value from every aspect of our lives while also giving capital access to talent without giving talent access to capital. Gone are the artists jobs the authour jobs, the coder jobs. They stole the sum of the internet and got away with it, turning out bottom drawer pap with a prompt in exchange for whole contries worth of electricity. And meanwhile, its firced into every aspect of our lives. the hunger for more data to feed the NNs only matched by the hunger to fund a use case they can actually profit off. Becase make no misake like Uber, like airbnb, like all the other tech bro bids to 'distrupt' industries by taking all the proffit and shoving all the externalities down stream for other people to deal with, these Gneritave NN and LLM are only free to try and supress alternataves, to get people dependant, and then they jack up the price to get that ROI for their centure capitalist backers. And the worst bit is, when the convinance so many once demanded becomes mandatory? They wont even have the ability to express the problem. That capaicity will wilt wih overrelyaince on Slop, and even the idea that some things could be expressed will be routed around.

It wont even be the obvous 'unalived' where people want to say 'kill'. It will be asking some privately owned LLM to write a letter to your representave about the problem of thsi crap being in everything, so it costs more and more just to exist, and getting a cheery athoritave recomendation to seek an LLM therapist (not licenced credted or private) that keeps tou busy till something snaps. Because these things will not let you express anything that might harm their owners proffits by them.

those ads for ai integration on phones are so funny bc it seems like they cant. come up with that many use cases that arent already on a phone? "ask gemini to give you recipes when youre cooking!" "use our AI assistant to find the perfect gift for your girlfriend" yeah or i could just like. google it. you've spent millions on a slightly fancier version of an alexa. good job man.

[sciativesolution]

Hotel Competitor Analysis: A Complete Guide for Revenue Management

Introduction

The hospitality industry is evolving rapidly, and hotel competition is fiercer than ever. Hotels that fail to adapt to market trends, guest expectations, and competitor pricing strategies risk losing market share and revenue. This is where hotel competitor analysis becomes a game-changer. It delivers crucial insights into dynamic pricing in hospitality, occupancy rates, hotel marketing strategies, and guest experience trends.

A comprehensive competitive intelligence strategy empowers hoteliers to improve offerings, optimize revenue, and outperform rivals. In this guide, we’ll explore what hotel competitor analysis is, why hotel revenue management needs it, how to identify your competitors, how AI in hospitality transforms it, and how a pricing intelligence tool like Zettaprice can help.

What is Hotel Competitor Analysis?

Hotel competitor analysis is the process of collecting, monitoring, and analyzing data about hotels that compete in the same market. The goal is to understand their pricing models, revenue strategies, customer reviews, and marketing campaigns to fine-tune your hotel’s positioning.

Key components include:

Identifying direct and indirect hotel competitors

Tracking hotel pricing strategies and discounts

Monitoring hotel online reputation management

Studying distribution channels and OTA strategies

Analyzing service offerings, amenities, and guest feedback

Understanding the competitive landscape helps hotels use revenue optimization software and hotel pricing software to make data-driven decisions.

Why is Competitive Intelligence Crucial in the Hotel Industry?

1. Optimize Hotel Pricing Strategy

Hotel pricing must adapt to changing demand. With competitive pricing analysis, hotels can monitor real-time rates and adjust prices dynamically to stay competitive and profitable.

Example: If a competitor lowers prices due to low occupancy, a hotel can use dynamic hotel pricing software to decide whether to match, undercut, or enhance value instead.

2. Improve Guest Experience Using Competitor Insights

Travelers rely heavily on hotel reviews and social proof. Analyzing competitor feedback on platforms like TripAdvisor or Google Reviews reveals service gaps and improvement opportunities.

Example: If a rival gets poor feedback on slow check-ins, your hotel can highlight faster check-ins as a USP.

3. Boost Hotel Occupancy Rates

Understanding when and why competitors have high occupancy helps shape demand-based pricing, seasonal promotions, and value-added packages.

Example: During local festivals, if a competitor fills rooms quickly, create festival-centric offers and promote via hotel digital marketing.

4. Gain Competitive Edge

Competitive intelligence tools allow hotels to anticipate market changes and guest preferences. They help adopt sustainable travel trends, tailor hospitality pricing strategies, and offer exclusive services to stand out.

How to Identify Your Hotel Competitors?

1. Direct Competitors

These are hotels with similar pricing, amenities, customer base, and location.

2. Indirect Competitors

Alternatives like Airbnb, serviced apartments, or boutique stays that target similar travelers.

3. Aspirational Competitors

Premium or luxury hotels you benchmark against for innovation and service excellence, even if they’re in a different tier.

4. Emerging Competitors

New entrants, renovated properties, or tech-savvy competitors implementing AI hotel technology or automated pricing tools.

How AI Transforms Hotel Competitive Intelligence

1. Real-Time Hotel Price Tracking

AI-powered price intelligence tools like Zettaprice analyze rates across OTAs, metasearch engines, and direct websites. Hotels can instantly react to pricing changes and optimize rates for profitability.

2. Guest Sentiment Analysis

AI interprets thousands of guest reviews, extracting key sentiments around service, cleanliness, amenities, and staff behavior.

Example: If competitor reviews mention poor room cleanliness, you can highlight your hotel’s hygiene standards in marketing.

3. Accurate Demand Forecasting

Using AI, hotels can predict future demand by analyzing booking patterns, event calendars, weather, and economic indicators—resulting in precise rate recommendations and revenue management automation.

4. Marketing Intelligence

AI tracks competitors' digital advertising efforts, SEO keywords, social media campaigns, and content strategy. Hotels can replicate successful tactics or pivot to underserved channels.

5. Automation for Efficiency

AI replaces manual tracking with dashboards, real-time alerts, and hotel business intelligence tools—saving time and increasing decision-making speed.

How Zettaprice Enhances Hotel Competitor Analysis

Zettaprice is an AI-powered hotel pricing optimization tool designed to automate and enhance competitive rate analysis. It enables:

Real-time competitor price tracking

Dynamic rate recommendations

Historical trend analysis

Seamless integration with OTAs and PMS

Actionable insights through automated dashboards

By using Zettaprice’s pricing intelligence, hotels can reduce revenue leakage, adapt faster to market shifts, and improve RevPAR (Revenue per Available Room).

👉 Outsmart the competition — See Zettaprice in action!

Conclusion

Hotel competitor analysis is no longer optional—it’s essential for success in today’s data-driven hospitality landscape. By leveraging AI-driven pricing software, competitive benchmarking, and guest sentiment analysis, hoteliers can make smarter, faster decisions. Tools like Zettaprice give your property the edge to thrive in competitive markets, elevate guest satisfaction, and unlock sustained profitability.                                                                                                         

To know more visit- https://sciative.com/research-articles/hotel-competitor-analysis-a-complete-guide

[webscrapingservicescompany]

How To Scrape Airbnb Listing Data Using Python And Beautiful Soup: A Step-By-Step Guide

The travel industry is a huge business, set to grow exponentially in coming years. It revolves around movement of people from one place to another, encompassing the various amenities and accommodations they need during their travels. This concept shares a strong connection with sectors such as hospitality and the hotel industry.

Here, it becomes prudent to mention Airbnb. Airbnb stands out as a well-known online platform that empowers people to list, explore, and reserve lodging and accommodation choices, typically in private homes, offering an alternative to the conventional hotel and inn experience.

Scraping Airbnb listings data entails the process of retrieving or collecting data from Airbnb property listings. To Scrape Data from Airbnb's website successfully, you need to understand how Airbnb's listing data works. This blog will guide us how to scrape Airbnb listing data.

What Is Airbnb Scraping?

Airbnb serves as a well-known online platform enabling individuals to rent out their homes or apartments to travelers. Utilizing Airbnb offers advantages such as access to extensive property details like prices, availability, and reviews.

Data from Airbnb is like a treasure trove of valuable knowledge, not just numbers and words. It can help you do better than your rivals. If you use the Airbnb scraper tool, you can easily get this useful information.

Effectively scraping Airbnb’s website data requires comprehension of its architecture. Property information, listings, and reviews are stored in a database, with the website using APIs to fetch and display this data. To scrape the details, one must interact with these APIs and retrieve the data in the preferred format.

In essence, Airbnb listing scraping involves extracting or scraping Airbnb listings data. This data encompasses various aspects such as listing prices, locations, amenities, reviews, and ratings, providing a vast pool of data.

What Are the Types of Data Available on Airbnb?

Navigating via Airbnb's online world uncovers a wealth of data. To begin with, property details, like data such as the property type, location, nightly price, and the count of bedrooms and bathrooms. Also, amenities (like Wi-Fi, a pool, or a fully-equipped kitchen) and the times for check-in and check-out. Then, there is data about the hosts and guest reviews and details about property availability.

Here's a simplified table to provide a better overview:

Property Details Data regarding the property, including its category, location, cost, number of rooms, available features, and check-in/check-out schedules.

Host Information Information about the property's owner, encompassing their name, response time, and the number of properties they oversee.

Guest Reviews Ratings and written feedback from previous property guests.

Booking Availability Data on property availability, whether it's available for booking or already booked, and the minimum required stay.

Why Is the Airbnb Data Important?

Extracting data from Airbnb has many advantages for different reasons:

Market Research

Scraping Airbnb listing data helps you gather information about the rental market. You can learn about prices, property features, and how often places get rented. It is useful for understanding the market, finding good investment opportunities, and knowing what customers like.

Getting to Know Your Competitor

By scraping Airbnb listings data, you can discover what other companies in your industry are doing. You'll learn about their offerings, pricing, and customer opinions.

Evaluating Properties

Scraping Airbnb listing data lets you look at properties similar to yours. You can see how often they get booked, what they charge per night, and what guests think of them. It helps you set the prices right, make your property better, and make guests happier.

Smart Decision-Making

With scraped Airbnb listing data, you can make smart choices about buying properties, managing your portfolio, and deciding where to invest. The data can tell you which places are popular, what guests want, and what is trendy in the vacation rental market.

Personalizing and Targeting

By analyzing scraped Airbnb listing data, you can learn what your customers like. You can find out about popular features, the best neighborhoods, or unique things guests want. Next, you can change what you offer to fit what your customers like.

Automating and Saving Time

Instead of typing everything yourself, web scraping lets a computer do it for you automatically and for a lot of data. It saves you time and money and ensures you have scraped Airbnb listing data.

Is It Legal to Scrape Airbnb Data?

Collecting Airbnb listing data that is publicly visible on the internet is okay, as long as you follow the rules and regulations. However, things can get stricter if you are trying to gather data that includes personal info, and Airbnb has copyrights on that.

Most of the time, websites like Airbnb do not let automatic tools gather information unless they give permission. It is one of the rules you follow when you use their service. However, the specific rules can change depending on the country and its policies about automated tools and unauthorized access to systems.

How To Scrape Airbnb Listing Data Using Python and Beautiful Soup?

Websites related to travel, like Airbnb, have a lot of useful information. This guide will show you how to scrape Airbnb listing data using Python and Beautiful Soup. The information you collect can be used for various things, like studying market trends, setting competitive prices, understanding what guests think from their reviews, or even making your recommendation system.

We will use Python as a programming language as it is perfect for prototyping, has an extensive online community, and is a go-to language for many. Also, there are a lot of libraries for basically everything one could need. Two of them will be our main tools today:

Beautiful Soup — Allows easy scraping of data from HTML documents

Selenium — A multi-purpose tool for automating web-browser actions

Getting Ready to Scrape Data

Now, let us think about how users scrape Airbnb listing data. They start by entering the destination, specify dates then click "search." Airbnb shows them lots of places.

This first page is like a search page with many options. But there is only a brief data about each.

After browsing for a while, the person clicks on one of the places. It takes them to a detailed page with lots of information about that specific place.

We want to get all the useful information, so we will deal with both the search page and the detailed page. But we also need to find a way to get info from the listings that are not on the first search page.

Usually, there are 20 results on one search page, and for each place, you can go up to 15 pages deep (after that, Airbnb says no more).

It seems quite straightforward. For our program, we have two main tasks:

looking at a search page, and getting data from a detailed page.

So, let us begin writing some code now!

Getting the listings

Using Python to scrape Airbnb listing data web pages is very easy. Here is the function that extracts the webpage and turns it into something we can work with called Beautiful Soup.

def scrape_page(page_url): """Extracts HTML from a webpage""" answer = requests.get(page_url) content = answer.content soup = BeautifulSoup(content, features='html.parser') return soup

Beautiful Soup helps us move around an HTML page and get its parts. For example, if we want to take the words from a “div” object with a class called "foobar" we can do it like this:

text = soup.find("div", {"class": "foobar"}).get_text()

On Airbnb's listing data search page, what we are looking for are separate listings. To get to them, we need to tell our program which kinds of tags and names to look for. A simple way to do this is to use a tool in Chrome called the developer tool (press F12).

The listing is inside a "div" object with the class name "8s3ctt." Also, we know that each search page has 20 different listings. We can take all of them together using a Beautiful Soup tool called "findAll.

def extract_listing(page_url): """Extracts listings from an Airbnb search page""" page_soup = scrape_page(page_url) listings = page_soup.findAll("div", {"class": "_8s3ctt"}) return listings

Getting Basic Info from Listings

When we check the detailed pages, we can get the main info about the Airbnb listings data, like the name, total price, average rating, and more.

All this info is in different HTML objects as parts of the webpage, with different names. So, we could write multiple single extractions -to get each piece:

name = soup.find('div', {'class':'_hxt6u1e'}).get('aria-label') price = soup.find('span', {'class':'_1p7iugi'}).get_text() ...

However, I chose to overcomplicate right from the beginning of the project by creating a single function that can be used again and again to get various things on the page.

def extract_element_data(soup, params): """Extracts data from a specified HTML element"""

# 1. Find the right tag

if 'class' in params: elements_found = soup.find_all(params['tag'], params['class']) else: elements_found = soup.find_all(params['tag'])

# 2. Extract text from these tags

if 'get' in params: element_texts = [el.get(params['get']) for el in elements_found] else: element_texts = [el.get_text() for el in elements_found]

# 3. Select a particular text or concatenate all of them tag_order = params.get('order', 0) if tag_order == -1: output = '**__**'.join(element_texts) else: output = element_texts[tag_order] return output

Now, we've got everything we need to go through the entire page with all the listings and collect basic details from each one. I'm showing you an example of how to get only two details here, but you can find the complete code in a git repository.

RULES_SEARCH_PAGE = { 'name': {'tag': 'div', 'class': '_hxt6u1e', 'get': 'aria-label'}, 'rooms': {'tag': 'div', 'class': '_kqh46o', 'order': 0}, } listing_soups = extract_listing(page_url) features_list = [] for listing in listing_soups: features_dict = {} for feature in RULES_SEARCH_PAGE: features_dict[feature] = extract_element_data(listing, RULES_SEARCH_PAGE[feature]) features_list.append(features_dict)

Getting All the Pages for One Place

Having more is usually better, especially when it comes to data. Scraping Airbnb listing data lets us see up to 300 listings for one place, and we are going to scrape them all.

There are different ways to go through the pages of search results. It is easiest to see how the web address (URL) changes when we click on the "next page" button and then make our program do the same thing.

All we have to do is add a thing called "items_offset" to our initial URL. It will help us create a list with all the links in one place.

def build_urls(url, listings_per_page=20, pages_per_location=15): """Builds links for all search pages for a given location""" url_list = [] for i in range(pages_per_location): offset = listings_per_page * i url_pagination = url + f'&items_offset={offset}' url_list.append(url_pagination) return url_list

We have completed half of the job now. We can run our program to gather basic details for all the listings in one place. We just need to provide the starting link, and things are about to get even more exciting.

Dynamic Pages

It takes some time for a detailed page to fully load. It takes around 3-4 seconds. Before that, we could only see the base HTML of the webpage without all the listing details we wanted to collect.

Sadly, the "requests" tool doesn't allow us to wait until everything on the page is loaded. But Selenium does. Selenium can work just like a person, waiting for all the cool website things to show up, scrolling, clicking buttons, filling out forms, and more.

Now, we plan to wait for things to appear and then click on them. To get information about the amenities and price, we need to click on certain parts.

To sum it up, here is what we are going to do:

Start up Selenium.

Open a detailed page.

Wait for the buttons to show up.

Click on the buttons.

Wait a little longer for everything to load.

Get the HTML code.

Let us put them into a Python function.

def extract_soup_js(listing_url, waiting_time=[5, 1]): """Extracts HTML from JS pages: open, wait, click, wait, extract""" options = Options() options.add_argument('--headless') options.add_argument('--no-sandbox') driver = webdriver.Chrome(options=options) driver.get(listing_url) time.sleep(waiting_time[0]) try: driver.find_element_by_class_name('_13e0raay').click() except: pass # amenities button not found try: driver.find_element_by_class_name('_gby1jkw').click() except: pass # prices button not found time.sleep(waiting_time[1]) detail_page = driver.page_source driver.quit() return BeautifulSoup(detail_page, features='html.parser')

Now, extracting detailed info from the listings is quite straightforward because we have everything we need. All we have to do is carefully look at the webpage using a tool in Chrome called the developer tool. We write down the names and names of the HTML parts, put all of that into a tool called "extract_element_data.py" and we will have the data we want.

Running Multiple Things at Once

Getting info from all 15 search pages in one location is pretty quick. When we deal with one detailed page, it takes about just 5 to 6 seconds because we have to wait for the page to fully appear. But, the fact is the CPU is only using about 3% to 8% of its power.

So. instead of going to 300 webpages one by one in a big loop, we can split the webpage addresses into groups and go through these groups one by one. To find the best group size, we have to try different options.

from multiprocessing import Pool with Pool(8) as pool: result = pool.map(scrape_detail_page, url_list)

The Outcome

After turning our tools into a neat little program and running it for a location, we obtained our initial dataset.

The challenging aspect of dealing with real-world data is that it's often imperfect. There are columns with no information, many fields need cleaning and adjustments. Some details turned out to be not very useful, as they are either always empty or filled with the same values.

There's room for improving the script in some ways. We could experiment with different parallelization approaches to make it faster. Investigating how long it takes for the web pages to load can help reduce the number of empty columns.

To Sum It Up

We've mastered:

Scraping Airbnb listing data using Python and Beautiful Soup.

Handling dynamic pages using Selenium.

Running the script in parallel using multiprocessing.

Conclusion

Web scraping today offers user-friendly tools, which makes it easy to use. Whether you are a coding pro or a curious beginner, you can start scraping Airbnb listing data with confidence. And remember, it's not just about collecting data – it's also about understanding and using it.

The fundamental rules remain the same, whether you're scraping Airbnb listing data or any other website, start by determining the data you need. Then, select a tool to collect that data from the web. Finally, verify the data it retrieves. Using this info, you can make better decisions for your business and come up with better plans to sell things.

So, be ready to tap into the power of web scraping and elevate your sales game. Remember that there's a wealth of Airbnb data waiting for you to explore. Get started with an Airbnb scraper today, and you'll be amazed at the valuable data you can uncover. In the world of sales, knowledge truly is power.

[thoughtdreamer]

Ways To Extract Airbnb Data

There are four ways to get Airbnb data:

Scraped Dataset

Ready-made scrapers

Web scraping API

Web Scraping Service

Source

[3idatascraping]

Enhance your business strategies with Airbnb Data Scraping, a powerful method to gather valuable insights from rental listings. Use an advanced Airbnb Data Scraper to extract details like property prices, reviews, host data, and availability. Stay ahead of competitors by analyzing trends, optimizing pricing strategies, and making data-driven decisions for your short-term rental business.

[iwebscrapingblogs]

Vacation Rental Website Data Scraping | Scrape Vacation Rental Website Data

In the ever-evolving landscape of the vacation rental market, having access to real-time, accurate, and comprehensive data is crucial for businesses looking to gain a competitive edge. Whether you are a property manager, travel agency, or a startup in the hospitality industry, scraping data from vacation rental websites can provide you with invaluable insights. This blog delves into the concept of vacation rental website data scraping, its importance, and how it can be leveraged to enhance your business operations.

What is Vacation Rental Website Data Scraping?

Vacation rental website data scraping involves the automated extraction of data from vacation rental platforms such as Airbnb, Vrbo, Booking.com, and others. This data can include a wide range of information, such as property listings, pricing, availability, reviews, host details, and more. By using web scraping tools or services, businesses can collect this data on a large scale, allowing them to analyze trends, monitor competition, and make informed decisions.

Why is Data Scraping Important for the Vacation Rental Industry?

Competitive Pricing Analysis: One of the primary reasons businesses scrape vacation rental websites is to monitor pricing strategies used by competitors. By analyzing the pricing data of similar properties in the same location, you can adjust your rates to stay competitive or identify opportunities to increase your prices during peak seasons.

Market Trend Analysis: Data scraping allows you to track market trends over time. By analyzing historical data on bookings, occupancy rates, and customer preferences, you can identify emerging trends and adjust your business strategies accordingly. This insight can be particularly valuable for making decisions about property investments or marketing campaigns.

Inventory Management: For property managers and owners, understanding the supply side of the market is crucial. Scraping data on the number of available listings, their features, and their occupancy rates can help you optimize your inventory. For example, you can identify underperforming properties and take corrective actions such as renovations or targeted marketing.

Customer Sentiment Analysis: Reviews and ratings on vacation rental platforms provide a wealth of information about customer satisfaction. By scraping and analyzing this data, you can identify common pain points or areas where your service excels. This feedback can be used to improve your offerings and enhance the guest experience.

Lead Generation: For travel agencies or vacation rental startups, scraping contact details and other relevant information from vacation rental websites can help generate leads. This data can be used for targeted marketing campaigns, helping you reach potential customers who are already interested in vacation rentals.

Ethical Considerations and Legal Implications

While data scraping offers numerous benefits, it’s important to be aware of the ethical and legal implications. Vacation rental websites often have terms of service that prohibit or restrict scraping activities. Violating these terms can lead to legal consequences, including lawsuits or being banned from the platform. To mitigate risks, it’s advisable to:

Seek Permission: Whenever possible, seek permission from the website owner before scraping data. Some platforms offer APIs that provide access to data in a more controlled and legal manner.

Respect Robots.txt: Many websites use a robots.txt file to communicate which parts of the site can be crawled by web scrapers. Ensure your scraping activities respect these guidelines.

Use Data Responsibly: Avoid using scraped data in ways that could harm the website or its users, such as spamming or creating fake listings. Responsible use of data helps maintain ethical standards and builds trust with your audience.

How to Get Started with Vacation Rental Data Scraping

If you’re new to data scraping, here’s a simple guide to get you started:

Choose a Scraping Tool: There are various scraping tools available, ranging from easy-to-use platforms like Octoparse and ParseHub to more advanced solutions like Scrapy and Beautiful Soup. Choose a tool that matches your technical expertise and requirements.

Identify the Data You Need: Before you start scraping, clearly define the data points you need. This could include property details, pricing, availability, reviews, etc. Having a clear plan will make your scraping efforts more efficient.

Start Small: Begin with a small-scale scrape to test your setup and ensure that you’re collecting the data you need. Once you’re confident, you can scale up your scraping efforts.

Analyze the Data: After collecting the data, use analytical tools like Excel, Google Sheets, or more advanced platforms like Tableau or Power BI to analyze and visualize the data. This will help you derive actionable insights.

Stay Updated: The vacation rental market is dynamic, with prices and availability changing frequently. Regularly updating your scraped data ensures that your insights remain relevant and actionable.

Conclusion

Vacation rental website data scraping is a powerful tool that can provide businesses with a wealth of information to drive growth and innovation. From competitive pricing analysis to customer sentiment insights, the applications are vast. However, it’s essential to approach data scraping ethically and legally to avoid potential pitfalls. By leveraging the right tools and strategies, you can unlock valuable insights that give your business a competitive edge in the ever-evolving vacation rental market.

[iwebdatascrape]

Analyzing U.S. Leading Hotel Chains: Insights from Hotel Data Scraping

Leading hotel chains are renowned for providing top-tier services, luxury, and an abundant experience to their clientele. With a global presence boasting tens of thousands of properties, these chains rake in billions in revenue, solidifying their positions among the world's largest companies.

In the fiercely competitive hospitality industry, susceptible to economic fluctuations, these giants—Wyndham, Marriott, Hilton, IHG, and Hyatt—stand out. Online travel platforms are valuable resources, offering insights into hotel details, room availability, and price fluctuations.

Exploring the landscape of the largest hotel in the United States using hotel chain data scraping unveils the dominance of Wyndham, Marriott, Hilton, IHG, and Hyatt, each contributing significantly to the industry's grandeur.

Number of Hotel Chains in the United States

On scraping hotel chain data, our examination covered 21,607 hotels across five major hotel chains in the United States. Wyndham emerged as the largest hotel chain, boasting 6,007 locations, followed by Marriott with 5,325 and Hilton with 5,010 establishments.

Exploring Wyndham Hotels in the United States

Scrape Wyndham Brand Hotel Count

Scrape Marriott's Presence in the US

Scrape Marriott hotel data to boast a network of 5,325 locations across the United States, with Texas (569), California (530), and Florida (485) leading in the number of establishments.

Marriott's Diverse Portfolio in the US

Extract Store Locations Data to know that under the Marriott Brand, there are 23 hotels, with Fairfield Inn (18.2%), Courtyard (17%), and Residence Inn (14.3%) leading the count. Together, these three brands contribute to 53% of the total locations.

Hilton's Nationwide Presence and Global Impact

Scrape Number of Hotels Under the Hilton Brand

The Hilton Brand encompasses 13 hotels, where Hampton leads with 44.5%, followed by Hilton Garden Inn (11.6%) and Homewood Suites (8.4%). Hampton is the top upper midscale brand, constituting 23.5% of Hilton locations.

Scrape IHG Hotels in the US: An Overview

Scrape InterContinental Hotels Group data to boast 4,567 locations nationwide, with Texas (515), California (270), and Florida (261) leading in numbers.

IHG Brand Overview: A Total of 15 Hotels

IHG brand encompasses 15 hotels, with Holiday Inn Express (55.7%), Holiday Inn (13.9%), and Candlewood Suites (11.7%) leading in numbers.

Scrape Hyatt Hotels in the US

Number of Hotels Under Hyatt Brand

Hyatt, a prominent player in the hospitality industry, boasts a diverse portfolio of 18 brands, collectively featuring 16 hotels. Hyatt Place, Hyatt House, and Hyatt Regency are leading the pack, accounting for 43.1%, 13.1%, and 11.6% of the brand's hotel count, respectively.

Hyatt has established itself in the higher-end market and is renowned for its focus on upscale accommodations. Retail Store Location Data Scraping Emphasizes modern, design-forward, and creative spaces that inspire and rejuvenate, Hyatt's brand lineup spans various luxury properties. Among these are Andaz, Alila, Grand Hyatt, Park Hyatt, Thompson Hotels, and Miraval.

States with the Highest Number of Hotels

Shaping the Future of Hospitality Amidst Pandemic Challenges

The tourism industry, profoundly impacted by the pandemic, grapples with uncertainty, prompting a shift towards a new era of hospitality. In the wake of COVID-19, hospitality is becoming more faceless, emphasizing social distancing, cleanliness, and housekeeping as top priorities within hotels.

Despite the challenges the pandemic poses, there remains a strong desire for new experiences among individuals. Even as social interactions transform, those venturing out still seek the personal attention and special care synonymous with hospitality. Airbnb has disrupted traditional hotel chains, offering more affordable accommodation options.

A growing reliance on data marks the evolving landscape of the travel industry. Online booking platforms empower travelers to compare room tariffs across multiple hotels, prompting hotel chains to adjust prices dynamically based on travelers' interests. Leveraging data extraction from iWeb Data Scraping, hotel chains can stay ahead by obtaining valuable insights from platforms like Booking.com and TripAdvisor, allowing them to adapt to changing market dynamics.

iWeb Data Scraping offers a comprehensive perspective, tracking store closures and openings in various sectors, including supermarkets, discount stores, department stores, and healthcare. It provides access to datasets featuring information such as store openings, closures, parking availability, in-store pickup options, services, subsidiaries, nearest competitor stores, and more. Texas has the highest hotel count, boasting 2,300 locations, followed by California with 1,700 and Florida with 1,400.

[actowiz-123]

Airbnb Data API | Scrape Airbnb Listings

Unlock valuable insights with the Airbnb Data API - Seamlessly scrape Airbnb listings and access comprehensive property data for smarter decisions and enhanced experiences.

[arctechnolabs1]

Airbnb Travel Datasets for Web Scraping

Extract Airbnb travel datasets with web scraping for insights on listings, prices, reviews, and availability. Ideal for data-driven travel analysis.

Read More >> https://www.arctechnolabs.com/airbnb-travel-datasets.php

[myprogrammingsolver]

Data Mining Programming assignment #1

Python data structures and other essentials 1.1 Tuples Create the tuple named apps tuple with the string elements “Google”, “Facebook”, “Amazon”, “Net ix”, “AirBnB”, “Instagram”. Extract the second element by using its index and the last element by using its neg-ative index. Slice the elements from “Facebook” to “Net ix” inclusive on both ends. Try appending the element “Messenger” to the…

[vivekavicky12]

Exploring Varied Programming Languages in Data Science Beyond Python and R.

Data science has become integral to industries, extracting insights and steering data-driven decisions. Programming languages form its core, with Python and R reigning supreme for years. However, a trend of diversification beyond these has emerged. In this blog, we delve into the significance of programming languages in data science, the emerging trend of using diverse languages, and the relevance of enrolling in a Data Science Course in Coimbatore for staying ahead in this dynamic field.

Overview of Python and R in Data Science:

Python and R, cherished for their simplicity and robust libraries, dominate data science. Python's versatility suits analysis, machine learning, and web development, while R excels in statistical computing. Limitations like speed and memory usage, particularly with vast datasets, motivate exploration beyond these languages.

Alternative Programming Languages for Data Science:

To overcome Python and R limitations, data scientists are exploring alternatives. Julia's speed and capacity for large datasets gain traction, Scala integrates with Apache Spark for big data, Java boasts a robust ecosystem for large-scale processing, and SQL remains vital for database operations. These alternatives offer expanded tools and functionalities.

Specialized Languages for Niche Applications:

MATLAB, tailored for engineering and science, excels in matrix operations. SAS, a stalwart in statistical analysis and business intelligence, remains reliable. JavaScript, primarily a web language, gains popularity for dynamic data visualization.

Emerging Languages and Trends:

As data science evolves, so do programming languages. Emerging languages like Go (Golang) for simplicity and concurrency, Rust for performance and safety, and Kotlin for interoperability with Java and flexibility in data-driven applications gain prominence. Enrolling in a Data Science Course Online is crucial for mastering these innovative tools.

Considerations for Language Selection:

Choosing the right language amid the multitude can be overwhelming. Project requirements, community support, learning curves, and accessibility are crucial considerations. Successful implementations, such as Airbnb's use of R, SQL, Python, Scala, and JavaScript, underscore the need for careful language selection and fostering a culture of learning.

In a rapidly evolving data science landscape, embracing diverse languages and continuous learning is pivotal. The strategic choice of programming languages, coupled with education through courses like the "Data Science Course in Coimbatore" or online, empowers data scientists to navigate complexities, unlock insights, and contribute significantly to their fields.

[reviewgatorsusa]

Unlock The Travel Trends And Secrets With Tripadvisor Review Scraping

What do you think travellers are saying about the hottest- travel destinations, new must-try restaurants or their stay experiences? Wonder no more because Tripadvisor has all these answers. From millions of reviews, let us find those hidden gems, insider tips and travel preferences of people around the globe. Tripadvisor Review Scraping is a valuable tool that enables you to keep up with your customers. It lets you share your experiences and helps you learn about new travelling and preferences. Whether you are a travel planner, a hotel business owner, or a travel buff, this blog will allow you to explore everything about Tripadvisor review scraper tools and how to stay ahead of the curve.

What is Tripadvisor Review Scraping?

Tripadvisor Review Scraping is an automated process of extracting data from the Tripadvisor platform. Tripadvisor is the most extensive website for travel, with over 800 million reviews on places like hotels, restaurants and airlines worldwide. Tripadvisor has a lot of information that helps us understand new and ongoing trends, feedback based on location, and how competitors price their services. A TripAdvisor review scraper is a tool that gets many user reviews and feedback. This tool also does tasks like organizing the data, analyzing it, and other complex tasks.

Trends from Tripadvisor Review Scraping

Travelling isn't just about going to a place anymore; it's about making mindful decisions and about the experiences and way of life. Here are some new trends coming up in the world of travel.

Health and Wellness Tourism

The health and wellness travel business is booming since people care more about their health and happiness. Nowadays, travellers want vacation deals that provide a memorable place to stay and involve activities that are good for their body and mind. These activities can be anything from spa treatments, yoga, and cooking lessons to fitness boot camps, stress relief exercises, outdoor adventures, meditation, and various specific therapies.

Travelers are learning how important it is to live healthy and do activities that make them feel good. They like spending money on places to stay and travel spots that help them relax and escape daily stress. Social media has a big part in motivating people to take breaks and enjoy unique experiences. Using information extracted from Tripadvisor review scraping and social media sites, local businesses, small companies, and those who love to travel can find places that provide a unique trip and make them feel better in general.

Eco-Conscious Travelling

More people choose to travel in a way that's good for the environment, known as eco-conscious or sustainable traveling. Travelers care about their environment and how it affects the Earth. Sustainable travel aims to help the environment and the local people and preserve the culture and nature. It also means travelling in ways that harm the environment less, allowing travelers to connect better with the place and take time to explore local areas.

With so many reviews and ratings on TripAdvisor, businesses can get better at what they do and know what their customers like. The travel industry is now helping customers choose where to stay and what to do. Travellers prefer places with Eco-friendly certificates, those that are good for the environment, save water, and help nature. They also like outdoor activities that promote taking care of the environment.

Economic Opportunities

Thanks to websites like Airbnb and Zostel, smaller businesses and people can make more money. They give travellers a unique place to stay while making them feel special and building a solid connection with the locals. Because of new technology, it's now easy to book a place to stay, and this helps small businesses show what they can offer.

This trend is changing how people use hotels and taxis, encouraging them to stay and travel within their local facilities. This saves customers some bucks and shows them how to be smart and thoughtful when travelling. By looking into the reviews on TripAdvisor and social media, small businesses can get to know their customers better, creating a bond that keeps them returning.

Remote Working and Digital Lifestyle

The pandemic has unexpectedly benefitted the travel industry and people who enjoy working and travelling from any place, experiencing new places, and adventuring. With improved technology, people have found many ways to mix work and travel. The desire for places that offer good internet for work and comfortable living conditions is increasing a lot.

People working remotely are keen to stay in a new place longer. This boosts the income from hotels and vacation rentals and helps the local community's economy. Scraping reviews from TripAdvisor lets businesses see how their prices and strategies stack up against their rivals. This doesn't just help them make their services better but also shows them where they need to improve.

Continue reading Unlock The Travel Trends And Secrets With Tripadvisor Review Scraping

[kabira125]

BEST CERTIFICATION DATA SCIENCE COURSE IN DELHI & GURUGRAM.

The School of Core AI Institute has positioned itself as a trailblazer in the realm of data science education. With a commitment to providing top-notch training, the institute empowers students with the skills and knowledge needed to thrive in the dynamic world of data.

The School of Core AI Institute:

Nestled in the heart of Delhi NCR, and Best Data Science Course in Delhi NCR, the School of Core AI Institute has emerged as a premier institution for Data Science education. Renowned for its state-of-the-art infrastructure, experienced faculty, and industry-aligned curriculum, this institute is setting new standards in the field.

What is Data Science in AI?

Data science (8 Months Program) is the process of extracting useful insights from unstructured data. It’s an interdisciplinary approach that merges various fields of computer science, statistics, and scientific processes and methods in order to draw conclusions from raw data points.

Data science is believed to have brought about a fourth industrial revolution, and it’s now at the heart of business decision-making. Companies have realized the enormous value in data processing and analysis. 

Businesses large and small are capitalizing on the value of data science every day. The more data a company has, the better business insights they can generate.

Companies like Airbnb use data science to process and analyze their customer-generated data to predict customer behaviour. This allows the company to address service issues and develop new features, products and services to offer their customers. Even insurance companies and banks now extract contact information using data science methods. 

Data science involves steps and procedures like data extraction, manipulation, visualization, and data maintenance.

A data scientist is expected to have knowledge of many different concepts and technologies, including machine learning algorithms and AI. If you want to work with artificial intelligence in depth, you’ll pursue a role like that of an artificial intelligence engineer.

What will your Road Map study in Data Science?

Introduction to Data Science:

Overview of data science and its applications

Understanding the data science workflow

Mathematics and Statistics Foundations:

Basic and advanced statistics

Linear algebra and calculus relevant to data science

Programming Languages:

Proficiency in a programming language (commonly Python or Java)

Data manipulation and analysis using libraries like Pandas and NumPy

Data Cleaning and Preprocessing:

Handling missing data

Cleaning and transforming data

Feature engineering techniques

Exploratory Data Analysis (EDA):

Visualization techniques (using libraries like Matplotlib and Seaborn)

Descriptive statistics and data summarization

Machine Learning Basics:

Overview of machine learning concepts

Supervised and unsupervised learning

Model evaluation and validation

Machine Learning Algorithms:

Regression and classification algorithms

Clustering algorithms

Ensemble methods

Big Data Technologies:

Introduction to big data concepts

Working with distributed computing frameworks (e.g., Apache Spark)

Deep Learning Fundamentals:

Introduction to neural networks

Deep learning frameworks (e.g., TensorFlow, PyTorch)

Natural Language Processing (NLP) and Text Mining:

Analysing and processing text data

Building NLP models for tasks like sentiment analysis

Model Deployment:

Strategies for deploying machine learning models

Model monitoring and maintenance

After completing 80% of Our Course, you get certification with 10+ live projects in AI industries. Data Science Certification Course in Delhi NCR & Gurugaon.

Features of our Institute: -

Comprehensive Curriculum: The institute offers a meticulously designed curriculum that covers the entire spectrum of Data Science. From fundamental concepts to advanced machine learning algorithms, students are equipped with a robust skill set that is highly relevant in the industry.

Hands-On Learning: Theory is essential, but practical application is paramount. The institute emphasizes hands-on learning through real-world projects, ensuring that students gain practical experience in solving complex data problems.

Industry Collaboration: School of Core AI Institute has established strong ties with leading industry players. This collaboration provides students with exposure to real-world challenges and opportunities, preparing them for seamless integration into the professional landscape.

Expert Faculty: The faculty at the institute comprises seasoned professionals and industry experts who bring a wealth of knowledge and experience to the classroom. Their guidance ensures that students receive the best possible education in the field.

100% Placement Assistance: Recognizing the importance of career development, the institute provides robust placement assistance to its students. Industry connections and career counselling pave the way for a smooth transition from education to employment and 100% guaranteed Placement in 150+ AI or Branding Companies.

Conclusion- The best part of School of core AI is provide the all elements related to required Students and regular basis improvement, and give access practise collaboration in Artificial Intelligence or Branding companies. School of core AI Institute check regular way of students’ capacity of Data Science and others.

Thankyou for reading our content and appreciate our Data science career program.

[coreai-5]

BEST CERTIFICATION DATA SCIENCE COURSE IN DELHI & GURUGRAM.

The School of Core AI Institute has positioned itself as a trailblazer in the realm of data science education. With a commitment to providing top-notch training, the institute empowers students with the skills and knowledge needed to thrive in the dynamic world of data.

The School of Core AI Institute:

Nestled in the heart of Delhi NCR, and Best Data Science Course in Delhi NCR, the School of Core AI Institute has emerged as a premier institution for Data Science education. Renowned for its state-of-the-art infrastructure, experienced faculty, and industry-aligned curriculum, this institute is setting new standards in the field.

What is Data Science in AI?

Data science (8 Months Program) is the process of extracting useful insights from unstructured data. It’s an interdisciplinary approach that merges various fields of computer science, statistics, and scientific processes and methods in order to draw conclusions from raw data points.

Data science is believed to have brought about a fourth industrial revolution, and it’s now at the heart of business decision-making. Companies have realized the enormous value in data processing and analysis. 

Businesses large and small are capitalizing on the value of data science every day. The more data a company has, the better business insights they can generate.

Companies like Airbnb use data science to process and analyze their customer-generated data to predict customer behaviour. This allows the company to address service issues and develop new features, products and services to offer their customers. Even insurance companies and banks now extract contact information using data science methods. 

Data science involves steps and procedures like data extraction, manipulation, visualization, and data maintenance.

A data scientist is expected to have knowledge of many different concepts and technologies, including machine learning algorithms and AI. If you want to work with artificial intelligence in depth, you’ll pursue a role like that of an artificial intelligence engineer.

What will your Road Map study in Data Science?

Introduction to Data Science:

Overview of data science and its applications

Understanding the data science workflow

Mathematics and Statistics Foundations:

Basic and advanced statistics

Linear algebra and calculus relevant to data science

Programming Languages:

Proficiency in a programming language (commonly Python or Java)

Data manipulation and analysis using libraries like Pandas and NumPy

Data Cleaning and Preprocessing:

Handling missing data

Cleaning and transforming data

Feature engineering techniques

Exploratory Data Analysis (EDA):

Visualization techniques (using libraries like Matplotlib and Seaborn)

Descriptive statistics and data summarization

Machine Learning Basics:

Overview of machine learning concepts

Supervised and unsupervised learning

Model evaluation and validation

Machine Learning Algorithms:

Regression and classification algorithms

Clustering algorithms

Ensemble methods

Big Data Technologies:

Introduction to big data concepts

Working with distributed computing frameworks (e.g., Apache Spark)

Deep Learning Fundamentals:

Introduction to neural networks

Deep learning frameworks (e.g., TensorFlow, PyTorch)

Natural Language Processing (NLP) and Text Mining:

Analysing and processing text data

Building NLP models for tasks like sentiment analysis

Model Deployment:

Strategies for deploying machine learning models

Model monitoring and maintenance

After completing 80% of Our Course, you get certification with 10+ live projects in AI industries. Data Science Certification Course in Delhi NCR & Gurugaon.

Features of our Institute: -

Comprehensive Curriculum: The institute offers a meticulously designed curriculum that covers the entire spectrum of Data Science. From fundamental concepts to advanced machine learning algorithms, students are equipped with a robust skill set that is highly relevant in the industry.

Hands-On Learning: Theory is essential, but practical application is paramount. The institute emphasizes hands-on learning through real-world projects, ensuring that students gain practical experience in solving complex data problems.

Industry Collaboration: School of Core AI Institute has established strong ties with leading industry players. This collaboration provides students with exposure to real-world challenges and opportunities, preparing them for seamless integration into the professional landscape.

Expert Faculty: The faculty at the institute comprises seasoned professionals and industry experts who bring a wealth of knowledge and experience to the classroom. Their guidance ensures that students receive the best possible education in the field.

100% Placement Assistance: Recognizing the importance of career development, the institute provides robust placement assistance to its students. Industry connections and career counselling pave the way for a smooth transition from education to employment and 100% guaranteed Placement in 150+ AI or Branding Companies.

Conclusion- The best part of School of core AI is provide the all elements related to required Students and regular basis improvement, and give access practise collaboration in Artificial Intelligence or Branding companies. School of core AI Institute check regular way of students’ capacity of Data Science and others.

Thank you for reading our content and appreciate our Data science course.

[mobileapp14]

Data Scraping for Travel Apps

Mobile App Scraping offers Travel Mobile App Data Scraping Services to extract data from popular Travel Mobile Apps such as Airbnb, TripAdvisor, Tripit, Yelp, Kayak, Expedia, Uber, Booking.com, etc.

know more: https://medium.com/@ridz.2811/data-scraping-for-travel-apps-mastering-the-how-to-process-758e8a3a9ca4

[iwebdatascrape]

Airbnb Hotel Listing Data Scraper | Airbnb Hotel Scraping Tool

Use Airbnb hotel listing data scraper to scrape Airbnb hotel prices data, including accommodation name, type, etc. Our Airbnb scraping tool can extract data across the USA, UK, etc.

Know More: https://www.iwebdatascraping.com/airbnb-scraper.php