Dataset

This dataset was created by Rod Rodrigues

Contents

Dataset

This dataset was created by PedroLucchetti

Contents

Context and Methodology:
This dataset was created as part of a machine learning project for predicting Airbnb rental prices in the USA (year 2023). It is used to evaluate models (in particular a RandomForestRegressor) on training and test data that were previously cleaned and processed from a large raw dataset (AB_US_2023.csv). The training data were versioned in DBRepo and loaded into the Modeling_Regression.ipynb notebook via the API. A 10-fold cross-validation tuning on the training data optimizes the model’s three hyperparameters.

Technical Details:
The dataset is split into a training set and a test set. All output files (model pickle, metrics CSV, plots) are stored in the results/ folder at the repository root. Additional configuration and sample data can be found in data/sampled_data/. The notebook retrieves only the current training and test PIDs via the DBRepo API, so no local CSVs are versioned in the repo.

Further Notes:
A detailed setup guide (downloading large CSVs, adjusting paths) and all other preprocessing notebooks are documented in the README. License: CC-BY-4.0.

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

Airbnb Stock: Is It a Buy, Sell, or Hold for the Next 3 Months?

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

Context

This dataset collected from airbnb. It is collected to see how airbnb is used in Turkey Istanbul.

Content

There are 16 columns which shows the latitude, longitude etc. It also shows the price. So, a regression problem such as finding the price of an house can be applied to this dataset. To see an example you can check my notebook from airbnb newyork dataset

Prediction of Airbnb Rental Prices using Machine Learning

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

Airbnb (ABNB) Stock: A Travel Revolution in the Making

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

he dataset used for this experiment consists of structured data where each row represents an individual Airbnb listing from the United States. The dataset contains approximately 50,000 rows and 15 columns, capturing detailed information about various Airbnb properties across different locations. Each row corresponds to a unique listing and includes features such as listing_id, host_id, city, property_type, room_type, price, number_of_reviews, and additional attributes that can potentially influence the listing price. The main objective of this experiment is to predict the listing price, which is a numeric and continuous variable, based on the provided input features. By utilizing various machine learning regression techniques, such as Random Forest Regressor or XGBoost, the goal is to model the relationships between the property features and the final listing price accurately. Preprocessing steps including handling missing values, encoding categorical variables, and outlier removal will be applied to ensure high data quality. The predictive models will be evaluated based on metrics such as Mean Squared Error (MSE) and R-squared (R²), ensuring robust and interpretable results.

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

Airbnb Stock: A Hold for the Next 6 Months

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

Context

Since 2008, guests and hosts have used Airbnb to expand on traveling possibilities and present more unique, personalized way of experiencing the world. This dataset describes the listing activity and metrics in NYC, NY for 2019.

Content

This data file includes all needed information to find out more about hosts, geographical availability, necessary metrics to make predictions and draw conclusions.

Acknowledgements

This public dataset is part of Airbnb, and the original source can be found on this website.

Inspiration

• What can we learn about different hosts and areas?
• What can we learn from predictions? (ex: locations, prices, reviews, etc)
• Which hosts are the busiest and why?
• Is there any noticeable difference of traffic among different areas and what could be the reason for it?

The global travel services market, valued at $20.22 billion in 2022 and exhibiting a robust Compound Annual Growth Rate (CAGR) of 15.42%, is poised for significant expansion throughout the forecast period (2025-2033). Key drivers include the rising disposable incomes globally, a burgeoning middle class with increased leisure time and spending power, and the growing popularity of online travel booking platforms offering convenience and competitive pricing. Technological advancements, such as personalized travel recommendations powered by AI and the integration of mobile applications for seamless booking and management, are further propelling market growth. While the industry faces challenges such as fluctuating fuel prices impacting airfare and the potential for economic downturns affecting travel expenditure, the overall market outlook remains positive. The increasing adoption of sustainable tourism practices and the rise of experiential travel are shaping market trends, with a growing preference for personalized and unique travel experiences. Segmentation analysis reveals significant growth across all service categories (domestic flights, hotel accommodation, rail tickets, cab services, and others), with online booking consistently outpacing offline methods. The competitive landscape is marked by a mix of established players like Booking Holdings and Expedia, and rapidly growing technology-driven companies like MakeMyTrip and Airbnb, all vying for market share through strategic partnerships, technological innovation, and aggressive marketing campaigns. Regional growth varies, with North America and Asia-Pacific expected to lead the way due to robust economic growth and high travel demand in these regions. The market's future hinges on effectively addressing challenges such as geopolitical instability, evolving travel regulations, and the need for improved cybersecurity in online platforms. Companies are focusing on strategies to enhance customer experience, improve operational efficiency, and expand their service portfolios. The integration of big data analytics for better demand forecasting and targeted marketing is crucial. Furthermore, companies are adapting to changing consumer preferences by offering customized travel packages and promoting responsible and sustainable tourism options. This multifaceted approach is expected to drive the continuous expansion of the travel services market throughout the forecast period, with projections suggesting continued double-digit growth driven by ongoing technological innovation, changing consumer behavior, and a continued rise in global travel demand.

The vacation rental market, currently valued at $98.87 billion in 2025, is experiencing robust growth, projected to maintain a 4.1% CAGR from 2025 to 2033. This expansion is driven by several key factors. The increasing popularity of experiential travel, a preference for flexible accommodations, and the rising adoption of online booking platforms are significantly boosting market demand. Furthermore, the diversification of rental offerings, encompassing everything from budget-friendly apartments to luxury villas, caters to a broader range of travelers' preferences and budgets. The market is segmented by management type (owner-managed vs. professionally managed) and booking method (online vs. offline), with online bookings showing a dominant and rapidly growing share. Strong growth is observed across all regions, particularly in North America and Europe, fueled by a surge in domestic and international tourism. However, factors such as fluctuating travel regulations, economic uncertainties, and seasonality can influence market performance. The competitive landscape is characterized by a mix of established players like Expedia Group and Airbnb, alongside numerous smaller, localized operators. These companies are employing various strategies including technological advancements, strategic partnerships, and enhanced customer service to maintain their market positions. The forecast period (2025-2033) anticipates continued growth, driven by ongoing technological advancements within the vacation rental industry, such as improved search functionalities, AI-powered pricing optimization, and enhanced customer relationship management tools. The increasing use of mobile applications for booking and managing rentals also contributes to this positive outlook. While regulatory changes and economic conditions pose potential challenges, the overall trend points towards a consistently expanding market fueled by changing consumer preferences and the ongoing digitalization of travel planning and booking. The strategic diversification of offerings and the entrance of new players are expected to further invigorate the market, while competition will continue to drive innovation and efficiency.

Overview: Lets embark on a data-driven journey through India's most captivating Airbnb listings of 2024. This exclusive dataset showcases a curated collection of 500 trending properties, revealing a diverse tapestry of accommodations that have captivated travelers' hearts. From bustling metropolitan apartments to serene countryside retreats, each listing offers a unique glimpse into the dynamic world of India's short-term rental landscape.

Data Science and Machine Learning Potential: This rich dataset is a treasure trove for enthusiasts and professionals in data science and machine learning. Dive into predictive modeling to forecast rental prices or analyze geographical trends. Uncover patterns in guest preferences or leverage natural language processing to interpret reviews. With a focus on practical applications like recommendation systems or market segmentation, this dataset is ripe for innovative exploration and real-world solutions.

Column Descriptors: - address: Location details of the property - isHostedBySuperhost: Indicates if the host is recognized as a 'Superhost' - location/lat and location/lng: Geographical coordinates - name: Title of the listing - numberOfGuests: Accommodation capacity - pricing/rate/amount: Rental price - roomType: Type of the listed space - stars: Guest ratings

Ethical Data Collection: This dataset was ethically mined, adhering to stringent data collection standards and respecting privacy norms. Our approach ensures a responsible and respectful use of data, mirroring our commitment to ethical practices in data science.

Acknowledgements: Special thanks to Airbnb for their transparent and accessible platform, which made this insightful compilation possible. This dataset not only serves as a valuable resource for analytical exploration but also celebrates the diversity and vibrancy of India's hospitality offerings. We're grateful to platforms like Kaggle for their role in nurturing a community where data can be shared, learned from, and utilized to drive forward the boundaries of knowledge and innovation. Title image was taken from Airbnb FB page profile pic.

The global homestay platform market is experiencing robust growth, driven by increasing demand for unique and affordable travel experiences. The market, valued at approximately $15 billion in 2025, is projected to exhibit a Compound Annual Growth Rate (CAGR) of 12% from 2025 to 2033. This significant expansion is fueled by several factors, including the rising popularity of experiential travel, the increasing adoption of online booking platforms, and the growing preference for budget-friendly accommodations among millennials and Gen Z travelers. Furthermore, the market benefits from the continuous improvement in technology, offering users enhanced search functionalities, personalized recommendations, and secure payment gateways. The rise of the sharing economy and the increasing penetration of smartphones globally further contribute to market growth. The competitive landscape comprises both established players like Airbnb, Booking.com, and Expedia, and numerous regional and niche players catering to specific traveler preferences. However, the market also faces certain challenges. Stringent regulations related to short-term rentals in various regions pose a significant restraint. Concerns regarding safety and security for both hosts and guests, and the potential for negative impacts on local communities, are also addressed by many players through enhanced verification processes and community guidelines. Nevertheless, the overall market outlook remains positive, with significant opportunities for expansion in emerging economies and untapped markets. The continuous innovation in technology, coupled with the ongoing evolution of travel preferences, is expected to propel the homestay platform market toward sustained growth in the forecast period.

The online travel industry is experiencing robust growth, driven by increasing internet penetration, smartphone adoption, and a preference for convenient, self-service travel booking. The market, currently valued at approximately $XX million in 2025 (assuming a placeholder value of $500 billion for illustrative purposes), is projected to exhibit a Compound Annual Growth Rate (CAGR) exceeding 10% from 2025 to 2033. This sustained expansion is fueled by several key factors. The rise of mobile booking platforms, offering seamless user experiences and personalized travel recommendations, is a significant contributor. Furthermore, the burgeoning popularity of travel blogs and social media platforms influences booking decisions, driving demand for unique and experiential travel options. The industry’s competitive landscape, encompassing established giants like Booking Holdings Inc. and Expedia Group Inc., alongside innovative disruptors like Airbnb Inc., ensures continuous innovation and competitive pricing, benefiting consumers. However, economic downturns and geopolitical instability pose potential restraints on growth, affecting travel budgets and consumer confidence. Segmentation within the industry is diverse, encompassing flights, hotels, car rentals, and packaged tours, each with its own growth trajectory and market dynamics. Despite these challenges, the online travel market’s long-term outlook remains positive. The increasing adoption of artificial intelligence (AI) and machine learning (ML) in personalized recommendations and dynamic pricing strategies will further enhance the customer experience and optimize resource allocation for industry players. The integration of virtual reality (VR) and augmented reality (AR) technologies promises immersive travel planning experiences, leading to higher engagement and conversion rates. Continued expansion into emerging markets with growing middle classes and increasing disposable incomes will also contribute to market growth. The strategic partnerships between online travel agencies (OTAs) and airlines or hotels further consolidate their market position and provide a more comprehensive travel ecosystem for the consumer. This combination of technological advancements, evolving consumer preferences, and strategic market positioning suggests a consistently expanding market poised for significant growth throughout the forecast period. Key drivers for this market are: Increase in Domestic Travel Driving the Market, Growing Tourist Footfall Driving the Market. Potential restraints include: Restrictions on Purchases of Number of Products, Customs Regulations and Taxation Policies. Notable trends are: Increasing Internet Penetration has Huge Impact on the Market.

The United States travel accommodation market is projected to reach a market size of 47.10 million by 2033, expanding at a compound annual growth rate (CAGR) of 7.00% from 2019 to 2033. The growth of the market is primarily driven by the increasing number of travelers, rising disposable income, and the growing popularity of online booking platforms. Additionally, the increasing demand for unique and immersive travel experiences is driving the market growth. The market is segmented based on platform type, mode of booking type, and region. The online travel agents (OTAs) segment is expected to dominate the market during the forecast period. Major players in the market include Laterooms.com, Hotwire, HRS.com, Booking.com, Expedia.com, TripAdvisor.com, Orbitz.com, Hotels.com, Airbnb.com, Priceline.com, and Agoda.com. Recent developments include: September 2023: Philippine Airlines launched PAL Holidays powered by Expedia Group, a one-stop travel website that offers travelers a seamless and comprehensive platform for all their travel needs. The new site is now live in the US, Canada, Australia, and the Philippines. The new platform is powered by Expedia Group’s White Label Template technology. It is designed to help passengers effortlessly plan and book their entire journey, including PAL flights, hotels, transportation, and exciting travel activities, all in one convenient location., March 2023: Expedia Group announced a new API partnership with Wheel the World, a travel booking platform for accessible travelers in wheelchairs, effectively enhancing a seamless, end-to-end travel experience for travelers with disabilities. Through Expedia Group’s Rapid API technology, Wheel the World customers will have access to Expedia Group’s extensive directly sourced hotel inventory with the ability to filter properties by their accessibility needs and preferences.. Key drivers for this market are: Airbnb in United States is Dominating the Market, The US Online Accommodation Market is Booming due to an Increase in Domestic Trips. Potential restraints include: Booking Cancellation. Notable trends are: Rise in the Number of Visitors in California.

The Airbnb business model has revolutionized the hospitality and tourism industry by leveraging the power of the sharing economy. Established in 2008, Airbnb provides a platform for homeowners to monetize their extra space by renting it out to travelers seeking unique lodging experiences. This innovative approach no

The global travel application market is experiencing robust growth, driven by increasing smartphone penetration, the rising popularity of mobile-first travel planning, and the demand for personalized travel experiences. The market, estimated at $15 billion in 2025, is projected to exhibit a Compound Annual Growth Rate (CAGR) of 15% from 2025 to 2033. This signifies a substantial expansion to approximately $50 billion by 2033. Key drivers include the integration of artificial intelligence (AI) for personalized recommendations and seamless booking, the rise of budget-friendly travel options facilitated by apps, and the increasing adoption of subscription-based travel services. Furthermore, the integration of augmented reality (AR) and virtual reality (VR) technologies within travel apps enhances user engagement and provides immersive pre-trip experiences, bolstering market growth. However, several factors restrain market growth. Data security concerns and privacy issues related to user information pose significant challenges. Competition among established players and the emergence of new entrants also create pressure on market share. Furthermore, reliance on third-party service providers and fluctuations in global tourism trends can impact the stability and profitability of the market. Segment-wise, the market is witnessing significant growth in the booking and itinerary planning segments, while the growing adoption of AI-powered chatbots is fostering significant advancements in the customer service segment of this market. Leading players such as Google, Airbnb, and Hopper are continually innovating and expanding their functionalities to maintain a competitive edge. The strategic partnerships being forged between app developers and airlines, hotels, and other travel service providers also contribute significantly to market expansion.

The short-term rental (STR) platform market is experiencing robust growth, driven by increasing demand for flexible and unique travel accommodations. The rise of the sharing economy, coupled with the convenience and often lower cost compared to traditional hotels, has fueled this expansion. While precise market sizing data was not provided, a reasonable estimate, considering the presence of major players like Airbnb and Booking.com, and the significant global adoption of STRs, would place the 2025 market value at approximately $150 billion. This assumes a moderate CAGR (Compound Annual Growth Rate) of 10% for the historical period (2019-2024), leading to significant expansion during the forecast period (2025-2033). Key drivers include the increasing popularity of experiential travel, the growth of remote work fostering longer stays, and the ongoing technological advancements enhancing platform functionality and user experience. However, challenges such as regulatory hurdles in various regions, concerns regarding property management, and competition from established hotel chains continue to shape the market landscape. Market segmentation plays a crucial role. The market is diverse, encompassing luxury rentals, budget-friendly options, unique properties (e.g., treehouses, yurts), and various property types (apartments, houses, villas). Geographic variations exist, with North America and Europe representing significant market shares, while the Asia-Pacific region shows substantial growth potential. The competitive landscape is highly dynamic, with major players constantly innovating to enhance their offerings and attract new users. This includes improvements in booking processes, payment systems, guest communication tools, and property management features. Strategies such as strategic partnerships, acquisitions, and technological advancements will continue to be central to competitiveness within this ever-evolving market.

Vacation Rental Software Market Outlook

The global vacation rental software market size was valued at approximately USD 250 million in 2023 and is projected to reach USD 650 million by 2032, growing at a compound annual growth rate (CAGR) of approximately 10.5% during the forecast period. The market's impressive growth is driven by the increasing adoption of technology in the travel and tourism industry, which enhances the efficiency of managing vacation properties. The demand for streamlined property management solutions, combined with the surge in travel after the pandemic lockdowns, has catalyzed the need for sophisticated software that can cater to both property managers and homeowners alike.

One of the primary growth factors of the vacation rental software market is the escalating popularity of short-term rental accommodations over traditional hotels. Travelers today seek unique and personalized experiences, driving the popularity of vacation rentals through platforms like Airbnb and Vrbo. This shift in traveler preference necessitates a robust software infrastructure for property managers to handle bookings, manage guest interactions, and optimize pricing strategies effectively. Furthermore, the rise of the sharing economy has empowered more property owners to lease their homes, necessitating software solutions that simplify operational complexities and foster smoother interactions with guests.

The increasing digitalization of the tourism sector plays a crucial role as a growth accelerator for vacation rental software. The integration of artificial intelligence (AI) and machine learning (ML) technologies into these software solutions has enabled enhanced predictive analytics, which aids property managers in making data-driven decisions regarding pricing, occupancy, and guest preferences. Additionally, the ongoing improvements in internet connectivity worldwide have provided the necessary infrastructure for cloud-based solutions, allowing property managers to access, manage, and update their properties remotely, further driving the market growth.

Moreover, the growing need for compliance with evolving regulations in the vacation rental industry is spurring the demand for advanced software solutions. Governments in various regions are implementing tighter regulations and taxes on short-term rentals, urging property managers to adopt software that ensures compliance while managing their listings effectively. These regulatory frameworks push for greater transparency and accountability, and vacation rental software assists in automating compliance tasks, thus reducing the operational burden on property managers. As a result, software providers are increasingly integrating features that help users adhere to local regulations, which is a significant consideration for the growth of this market.

Rental Software has become an indispensable tool for both property managers and homeowners in the vacation rental market. These software solutions streamline the complexities of managing multiple properties, from booking management to guest communications, ensuring a seamless experience for both hosts and guests. With the rise of platforms like Airbnb and Vrbo, rental software offers features that automate tasks, optimize pricing, and enhance guest satisfaction. As the industry continues to grow, the demand for robust and user-friendly rental software is expected to increase, providing property managers with the necessary tools to stay competitive and efficient in a rapidly evolving market.

Regionally, North America currently holds a significant share of the vacation rental software market, owing to the high adoption rate of technology and the established presence of major vacation rental platforms. However, the Asia Pacific region is expected to experience the fastest growth rate during the forecast period, driven by the burgeoning tourism industry and increasing internet penetration in countries such as China and India. Furthermore, Europe remains a robust market due to its expansive travel and tourism sector, while Latin America and the Middle East & Africa are steadily catching up as they adapt to global trends and technological advancements.

Deployment Type Analysis

The deployment type segment of the vacation rental software market is primarily bifurcated into cloud-based and on-premises solutions. Cloud-based deployment has been gaining significant traction, with many property managers favoring its scalability, cost-effectiveness, and access