100+ datasets found
  1. Travel Recommendation Dataset

    • kaggle.com
    Updated Jan 23, 2024
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Aman Mehra (2024). Travel Recommendation Dataset [Dataset]. https://www.kaggle.com/datasets/amanmehra23/travel-recommendation-dataset
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Jan 23, 2024
    Dataset provided by
    Kagglehttp://kaggle.com/
    Authors
    Aman Mehra
    License

    MIT Licensehttps://opensource.org/licenses/MIT
    License information was derived automatically

    Description

    Title: India Travel Recommender System Dataset

    Description

    Context
    Travel is a diverse and vibrant industry, and India, with its rich cultural heritage and varied landscapes, offers a myriad of experiences for travelers. The India Travel Recommender System Dataset is designed to facilitate the development of personalized travel recommendation systems. This dataset provides an extensive compilation of travel destinations across India, along with user profiles, reviews, and historical travel data. It's an invaluable resource for anyone looking to create AI-powered travel applications focused on the Indian subcontinent.

    Content
    The dataset is divided into four primary components:

    1. Destinations: Information about various travel destinations in India, including details like type of destination (beach, mountain, historical site, etc.), popularity, and best time to visit.

    2. Users: Profiles of users including their preferences and demographic information. This dataset has been enriched with gender diversity and includes details on the number of adults and children for travel.

    3. Reviews: User-generated reviews and ratings for the different destinations, offering insights into visitor experiences and satisfaction.

    4. User History: Records of users' past travel experiences, including destinations visited and ratings provided.

    Each of these components is presented in a separate CSV file, allowing for easy integration and manipulation in data processing and machine learning workflows.

    Acknowledgements
    This dataset was generated for educational and research purposes and is intended to be used in hackathons, academic projects, and by AI enthusiasts aiming to enhance the travel experience through technology.

    Inspiration
    The dataset is perfect for exploring a variety of questions and tasks, such as: - Building a recommendation engine to suggest travel destinations based on user preferences. - Analyzing travel trends in India. - Understanding the relationship between user demographics and travel preferences. - Sentiment analysis of travel destination reviews. - Forecasting the popularity of travel destinations based on historical data.

    We encourage Kaggle users to explore this dataset to uncover unique insights and develop innovative solutions in the realm of travel technology. Whether you're a data scientist, a student, or a travel tech enthusiast, this dataset offers a wealth of opportunities for exploration and creativity.

    Usage

    This dataset is free to use for non-commercial purposes. For commercial use, please contact the dataset provider. Remember to cite the source when using this dataset in your projects.

    License

    CC0: Public Domain - The dataset is in the public domain and can be used without restrictions.

  2. Multimodal Recommendation System Datasets

    • kaggle.com
    Updated Aug 21, 2023
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Ignacio Avas (2023). Multimodal Recommendation System Datasets [Dataset]. http://doi.org/10.34740/kaggle/dsv/6338676
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Aug 21, 2023
    Dataset provided by
    Kagglehttp://kaggle.com/
    Authors
    Ignacio Avas
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Quick start

    To read any dataset you can use the following code

    >>> import numpy as np
    >>> embed_image = np.load('embed_image.npy')
    >>> embed_image.shape
    (33962, 768)
    >>> embed_text = np.load('embed_text.npy')
    >>> embed_text.shape
    (33962, 768)
    >>> import pandas as pd
    >>> items = pd.read_csv('items.txt')
    >>> m = len(items)
    >>> print(f'{m} items in dataset')
    33962
    >>> users = pd.read_csv('users.txt')
    >>> n = len(users)
    >>> print(f'{n} users in dataset')
    14790
    >>> train = pd.read_csv('train.txt')
    >>> train
         user  item
    0    13444 23557
    1    13444 33739
    ...    ...  ...
    317109 13506 29993
    317110 13506 13931
    >>> from scipy.sparse import csr_matrix
    >>> train_matrix = csr_matrix((np.ones(len(train)), (train.user, train.item)), shape=(n,m))
    

    Folders

    This dataset contains six datasets. Each dataset is duplicated with seven combinations of different Image and Text encoders, so you should see 42 folders.

    Each folder is the name of the dataset and the encoder used for the visual and textual parts. For example: bookcrossing-vit_bert.

    The datasets are: - Clothing, Shoes and Jewelry (Amazon) - Home and Kitchen (Amazon) - Musical Instruments (Amazon) - Movies and TV (Amazon) - Book-Crossing - Movielens 25M

    And the encoders are: - CLIP (Image and Text) (*-clip_clip). This is the main one used in the experiments. - ViT and BERT (*-vit_bert) - CLIP (only visual data) *-clip_none - ViT only *-vit_none - BERT only *-none_bert - CLIP (text only) *-clip_none - No textual or visual information *-none_none

    Files per folder

    For each dataset, we have the following files, considering we have M items and N users, textual embeddings with D (like 1024) dimensions, and Visual with E dimensions (like 768) - embed_image.npy A NumPy array of MxE elements. - embed_text.npy A NumPy array of MXD elements. - items.csv A CSV with the Item ID in the original dataset (like the Amazon ASIN, the Movie ID, etc.) and the item number, an integer from 0 to M-1 - users.csv A CSV with the User ID in the original dataset (like the Amazon Reviewer Id) and the item number, an integer from 0 to N-1 - train.txt, validation.txt and test.txt are CSV files with the portions of the reviews for train validation and test. It has the item the user liked or reviewed positively. Each row has a positive user item.

    We consider a review "positive" if the rating is four or more (or 8 or more for Book-crossing).

    The vector is zeroed out if an Item does not have an image or text.

    Dataset stats

    DatasetUsersItemRatingsDensity
    Clothing & Shoes & Jewelry23318384931789440.020%
    Home & Kitchen5968576451358390.040%
    Movies & TV21974239582161100.041%
    Musical Instruments1442929040939230.022%
    Book-crossing14790339625196130.103%
    Movielens 25M16254159047250000950.260%

    Modifications from the original source

    Only a tiny fraction of the dataset was taken for the Amazon Datasets by considering reviews in a specific date range.

    For the Bookcrossing dataset, only items with images were considered.

    There are various other minor tweaks on how to obtain images and texts. The repo https://github.com/igui/MultimodalRecomAnalysis has the Notebook and scripts to reproduce the dataset extraction from scratch.

  3. u

    Pinterest Fashion Compatibility

    • cseweb.ucsd.edu
    json
    + more versions
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    UCSD CSE Research Project, Pinterest Fashion Compatibility [Dataset]. https://cseweb.ucsd.edu/~jmcauley/datasets.html
    Explore at:
    jsonAvailable download formats
    Dataset authored and provided by
    UCSD CSE Research Project
    Description

    This dataset contains images (scenes) containing fashion products, which are labeled with bounding boxes and links to the corresponding products.

    Metadata includes

    • product IDs

    • bounding boxes

    Basic Statistics:

    • Scenes: 47,739

    • Products: 38,111

    • Scene-Product Pairs: 93,274

  4. u

    Goodreads Book Reviews

    • cseweb.ucsd.edu
    json
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    UCSD CSE Research Project, Goodreads Book Reviews [Dataset]. https://cseweb.ucsd.edu/~jmcauley/datasets.html
    Explore at:
    jsonAvailable download formats
    Dataset authored and provided by
    UCSD CSE Research Project
    Description

    These datasets contain reviews from the Goodreads book review website, and a variety of attributes describing the items. Critically, these datasets have multiple levels of user interaction, raging from adding to a shelf, rating, and reading.

    Metadata includes

    • reviews

    • add-to-shelf, read, review actions

    • book attributes: title, isbn

    • graph of similar books

    Basic Statistics:

    • Items: 1,561,465

    • Users: 808,749

    • Interactions: 225,394,930

  5. Recommendation System Movie

    • kaggle.com
    Updated Jan 9, 2020
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Zeeshan Mulla (2020). Recommendation System Movie [Dataset]. https://www.kaggle.com/zeeshanmulla/recommendation-system-movie/kernels
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Jan 9, 2020
    Dataset provided by
    Kaggle
    Authors
    Zeeshan Mulla
    License

    http://www.gnu.org/licenses/lgpl-3.0.htmlhttp://www.gnu.org/licenses/lgpl-3.0.html

    Description

    Take a look at the Recommender System Movies Kernel for the EDA of the dataset.

  6. u

    Social Recommendation Data

    • cseweb.ucsd.edu
    • berd-platform.de
    json
    + more versions
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    UCSD CSE Research Project, Social Recommendation Data [Dataset]. https://cseweb.ucsd.edu/~jmcauley/datasets.html
    Explore at:
    jsonAvailable download formats
    Dataset authored and provided by
    UCSD CSE Research Project
    Description

    These datasets include ratings as well as social (or trust) relationships between users. Data are from LibraryThing (a book review website) and epinions (general consumer reviews).

    Metadata includes

    • reviews

    • price paid (epinions)

    • helpfulness votes (librarything)

    • flags (librarything)

  7. TMDB Movies List For movie recommender system

    • kaggle.com
    Updated Aug 14, 2022
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    ABDUL BASIT JAWAID (2022). TMDB Movies List For movie recommender system [Dataset]. https://www.kaggle.com/datasets/abjr002/movies-list-for-movie-recommender-system
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Aug 14, 2022
    Dataset provided by
    Kaggle
    Authors
    ABDUL BASIT JAWAID
    License

    https://creativecommons.org/publicdomain/zero/1.0/https://creativecommons.org/publicdomain/zero/1.0/

    Description

    Context

    These files contain metadata for over 20,000 movies listed in the Full TMDB Dataset. The dataset consists of movies released on or before August 2022 as well as some of the upcoming movies till Dec 2028. Data points include title, release dates, languages, genre, popularity, TMDB vote counts, and vote averages.

    Acknowledgements

    The Movie Details have been collected from the TMDB Open API. This product uses the TMDb API but is not endorsed or certified by TMDb. Their API also provides access to data on many additional movies, actors and actresses, crew members, and TV shows. You can try it for yourself here.

    Inspiration

    This dataset is assembled as part of my Project for Recommender Systems. I wanted to perform an extensive EDA on Movie Data to build various types of Recommender Systems.

  8. u

    Steam Video Game and Bundle Data

    • cseweb.ucsd.edu
    json
    + more versions
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    UCSD CSE Research Project, Steam Video Game and Bundle Data [Dataset]. https://cseweb.ucsd.edu/~jmcauley/datasets.html
    Explore at:
    jsonAvailable download formats
    Dataset authored and provided by
    UCSD CSE Research Project
    Description

    These datasets contain reviews from the Steam video game platform, and information about which games were bundled together.

    Metadata includes

    • reviews

    • purchases, plays, recommends (likes)

    • product bundles

    • pricing information

    Basic Statistics:

    • Reviews: 7,793,069

    • Users: 2,567,538

    • Items: 15,474

    • Bundles: 615

  9. A

    ‘Movie Recommender System Dataset’ analyzed by Analyst-2

    • analyst-2.ai
    Updated Nov 15, 2021
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Analyst-2 (analyst-2.ai) / Inspirient GmbH (inspirient.com) (2021). ‘Movie Recommender System Dataset’ analyzed by Analyst-2 [Dataset]. https://analyst-2.ai/analysis/kaggle-movie-recommender-system-dataset-62d8/d67b6ed7/?iid=001-397&v=presentation
    Explore at:
    Dataset updated
    Nov 15, 2021
    Dataset authored and provided by
    Analyst-2 (analyst-2.ai) / Inspirient GmbH (inspirient.com)
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Analysis of ‘Movie Recommender System Dataset’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/gargmanas/movierecommenderdataset on 12 November 2021.

    --- Dataset description provided by original source is as follows ---

    Task

    Build a Movie Recommender System using the dataset available.

    --- Original source retains full ownership of the source dataset ---

  10. TMDB 10000 Movies Dataset

    • kaggle.com
    Updated Dec 4, 2022
    + more versions
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Muqarrish Zaib (2022). TMDB 10000 Movies Dataset [Dataset]. https://www.kaggle.com/datasets/muqarrishzaib/tmdb-10000-movies-dataset
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Dec 4, 2022
    Dataset provided by
    Kagglehttp://kaggle.com/
    Authors
    Muqarrish Zaib
    License

    https://creativecommons.org/publicdomain/zero/1.0/https://creativecommons.org/publicdomain/zero/1.0/

    Description

    Context

    Recommendation systems are used everywhere nowadays. Netflix, Amazon Prime, YouTube, Online shopping sites, etc. Datasets like this are a great way to start working on a Recommendation system. The Dataset was created from the official API provided by TMDB.

    Content

    What's inside is more than just rows and columns. This is the dataset for 10,000 Popular movies based on the TMDB ratings. Ideal database to start off with Recommendation algorithms.

    Columns in the file include :

    • id
    • original_language
    • original_title
    • overview
    • popularity
    • release_date
    • title
    • vote_average
    • vote_count # Inspiration This dataset was assembled as part of a semester Project in Data Science Career Track. I wanted to perform an extensive EDA on Movie Data to narrate the history and the story of Cinema and use this metadata to build various types of Recommender Systems.

    Some of the things you can do with this dataset: Predicting movie revenue and/or movie success based on a certain metric. What movies tend to get higher vote counts and vote averages on TMDB? Building Content-Based and Collaborative Filtering Based Recommendation Engines.

    Acknowledgments

    This dataset was generated from The Movie Database API. This product uses the TMDb API but is not endorsed or certified by TMDb. Their API also provides access to data on many additional movies, actors and actresses, crew members, and TV shows. You can try it for yourself here.

  11. u

    Product Exchange/Bartering Data

    • cseweb.ucsd.edu
    json
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    UCSD CSE Research Project, Product Exchange/Bartering Data [Dataset]. https://cseweb.ucsd.edu/~jmcauley/datasets.html
    Explore at:
    jsonAvailable download formats
    Dataset authored and provided by
    UCSD CSE Research Project
    Description

    These datasets contain peer-to-peer trades from various recommendation platforms.

    Metadata includes

    • peer-to-peer trades

    • have and want lists

    • image data (tradesy)

  12. u

    Marketing Bias data

    • cseweb.ucsd.edu
    json
    + more versions
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    UCSD CSE Research Project, Marketing Bias data [Dataset]. https://cseweb.ucsd.edu/~jmcauley/datasets.html
    Explore at:
    jsonAvailable download formats
    Dataset authored and provided by
    UCSD CSE Research Project
    Description

    These datasets contain attributes about products sold on ModCloth and Amazon which may be sources of bias in recommendations (in particular, attributes about how the products are marketed). Data also includes user/item interactions for recommendation.

    Metadata includes

    • ratings

    • product images

    • user identities

    • item sizes, user genders

  13. u

    Behance Community Art Data

    • cseweb.ucsd.edu
    json
    + more versions
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    UCSD CSE Research Project, Behance Community Art Data [Dataset]. https://cseweb.ucsd.edu/~jmcauley/datasets.html
    Explore at:
    jsonAvailable download formats
    Dataset authored and provided by
    UCSD CSE Research Project
    Description

    Likes and image data from the community art website Behance. This is a small, anonymized, version of a larger proprietary dataset.

    Metadata includes

    • appreciates (likes)

    • timestamps

    • extracted image features

    Basic Statistics:

    • Users: 63,497

    • Items: 178,788

    • Appreciates (likes): 1,000,000

  14. u

    Amazon Question and Answer Data

    • cseweb.ucsd.edu
    json
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    UCSD CSE Research Project, Amazon Question and Answer Data [Dataset]. https://cseweb.ucsd.edu/~jmcauley/datasets.html
    Explore at:
    jsonAvailable download formats
    Dataset authored and provided by
    UCSD CSE Research Project
    Description

    These datasets contain 1.48 million question and answer pairs about products from Amazon.

    Metadata includes

    • question and answer text

    • is the question binary (yes/no), and if so does it have a yes/no answer?

    • timestamps

    • product ID (to reference the review dataset)

    Basic Statistics:

    • Questions: 1.48 million

    • Answers: 4,019,744

    • Labeled yes/no questions: 309,419

    • Number of unique products with questions: 191,185

  15. u

    Google Restaurants dataset

    • cseweb.ucsd.edu
    csv
    + more versions
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    UCSD CSE Research Project, Google Restaurants dataset [Dataset]. https://cseweb.ucsd.edu/~jmcauley/datasets.html
    Explore at:
    csvAvailable download formats
    Dataset authored and provided by
    UCSD CSE Research Project
    Description

    This is a mutli-modal dataset for restaurants from Google Local (Google Maps). Data includes images and reviews posted by users, as well as metadata for each restaurant.

  16. u

    PDMX

    • cseweb.ucsd.edu
    json
    + more versions
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    UCSD CSE Research Project, PDMX [Dataset]. https://cseweb.ucsd.edu/~jmcauley/datasets.html
    Explore at:
    jsonAvailable download formats
    Dataset authored and provided by
    UCSD CSE Research Project
    Description

    We introduce PDMX: a Public Domain MusicXML dataset for symbolic music processing, including over 250k musical scores in MusicXML format. PDMX is the largest publicly available, copyright-free MusicXML dataset in existence. PDMX includes genre, tag, description, and popularity metadata for every file.

  17. u

    Recipe Pairs

    • cseweb.ucsd.edu
    csv
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    UCSD CSE Research Project, Recipe Pairs [Dataset]. https://cseweb.ucsd.edu/~jmcauley/datasets.html
    Explore at:
    csvAvailable download formats
    Dataset authored and provided by
    UCSD CSE Research Project
    Description

    This is a collection recipes paired with variants, e.g. a recipe matched with a vegan version of the same recipe.

  18. Songs Recommendation Dataset

    • kaggle.com
    Updated Jun 12, 2023
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Noor Saeed (2023). Songs Recommendation Dataset [Dataset]. https://www.kaggle.com/datasets/noorsaeed/songs-recommendation-dataset
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Jun 12, 2023
    Dataset provided by
    Kagglehttp://kaggle.com/
    Authors
    Noor Saeed
    Description

    Dataset

    This dataset was created by Noor Saeed

    Contents

  19. CARSKit - Context-aware in-car music

    • kaggle.com
    Updated Nov 18, 2022
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Stefano Giannini (2022). CARSKit - Context-aware in-car music [Dataset]. http://doi.org/10.34740/kaggle/dsv/4537974
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Nov 18, 2022
    Dataset provided by
    Kagglehttp://kaggle.com/
    Authors
    Stefano Giannini
    License

    http://www.gnu.org/licenses/old-licenses/gpl-2.0.en.htmlhttp://www.gnu.org/licenses/old-licenses/gpl-2.0.en.html

    Description

    Recommender system has been demonstrated as one of the most useful tools to assist users' decision makings. Several recommendation algorithms have been developed and implemented by both commercial and open-source recommendation libraries. Context-aware recommender system (CARS) emerged as a novel research direction during the past decade and many contextual recommendation algorithms have been proposed. Unfortunately, no recommendation engines start to embed those algorithms in their kits, due to the special characteristics of the data format and processing methods in the domain of CARS. This paper introduces an open-source Java-based context-aware recommendation engine named as CARSKit which is recognized as the 1st open source recommendation library specifically designed for CARS. It implements the state-of-the-art context-aware recommendation algorithms, and we will showcase the ease with which CARSKit allows recommenders to be configured and evaluated in this demo.

  20. Recommendation System

    • kaggle.com
    Updated Jul 2, 2022
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Samratsingh Dikkhat (2022). Recommendation System [Dataset]. https://www.kaggle.com/datasets/samratsinghdikkhat/recommendation-system/code
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Jul 2, 2022
    Dataset provided by
    Kagglehttp://kaggle.com/
    Authors
    Samratsingh Dikkhat
    Description

    Dataset

    This dataset was created by Samratsingh Dikkhat

    Contents

Share
FacebookFacebook
TwitterTwitter
Email
Click to copy link
Link copied
Close
Cite
Aman Mehra (2024). Travel Recommendation Dataset [Dataset]. https://www.kaggle.com/datasets/amanmehra23/travel-recommendation-dataset
Organization logo

Travel Recommendation Dataset

Explore at:
CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
Dataset updated
Jan 23, 2024
Dataset provided by
Kagglehttp://kaggle.com/
Authors
Aman Mehra
License

MIT Licensehttps://opensource.org/licenses/MIT
License information was derived automatically

Description

Title: India Travel Recommender System Dataset

Description

Context
Travel is a diverse and vibrant industry, and India, with its rich cultural heritage and varied landscapes, offers a myriad of experiences for travelers. The India Travel Recommender System Dataset is designed to facilitate the development of personalized travel recommendation systems. This dataset provides an extensive compilation of travel destinations across India, along with user profiles, reviews, and historical travel data. It's an invaluable resource for anyone looking to create AI-powered travel applications focused on the Indian subcontinent.

Content
The dataset is divided into four primary components:

  1. Destinations: Information about various travel destinations in India, including details like type of destination (beach, mountain, historical site, etc.), popularity, and best time to visit.

  2. Users: Profiles of users including their preferences and demographic information. This dataset has been enriched with gender diversity and includes details on the number of adults and children for travel.

  3. Reviews: User-generated reviews and ratings for the different destinations, offering insights into visitor experiences and satisfaction.

  4. User History: Records of users' past travel experiences, including destinations visited and ratings provided.

Each of these components is presented in a separate CSV file, allowing for easy integration and manipulation in data processing and machine learning workflows.

Acknowledgements
This dataset was generated for educational and research purposes and is intended to be used in hackathons, academic projects, and by AI enthusiasts aiming to enhance the travel experience through technology.

Inspiration
The dataset is perfect for exploring a variety of questions and tasks, such as: - Building a recommendation engine to suggest travel destinations based on user preferences. - Analyzing travel trends in India. - Understanding the relationship between user demographics and travel preferences. - Sentiment analysis of travel destination reviews. - Forecasting the popularity of travel destinations based on historical data.

We encourage Kaggle users to explore this dataset to uncover unique insights and develop innovative solutions in the realm of travel technology. Whether you're a data scientist, a student, or a travel tech enthusiast, this dataset offers a wealth of opportunities for exploration and creativity.

Usage

This dataset is free to use for non-commercial purposes. For commercial use, please contact the dataset provider. Remember to cite the source when using this dataset in your projects.

License

CC0: Public Domain - The dataset is in the public domain and can be used without restrictions.

Search
Clear search
Close search
Google apps
Main menu