This dataset is extracted from the records of cosmetics advertisements filed by various county and city health bureaus. The displayed fields are limited to those open to the system, but the dataset may change due to subsequent revisions. This does not necessarily mean that the products of the subject of sanctions are illegal. Please use caution when referring to it.

Outdoor Advertising

## Overview

Outdoor Advertising is a dataset for object detection tasks - it contains Reklame Outdoor Advertising annotations for 2,224 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

This competition involves advertisement data provided by BuzzCity Pte. Ltd. BuzzCity is a global mobile advertising network that has millions of consumers around the world on mobile phones and devices. In Q1 2012, over 45 billion ad banners were delivered across the BuzzCity network consisting of more than 10,000 publisher sites which reach an average of over 300 million unique users per month. The number of smartphones active on the network has also grown significantly. Smartphones now account for more than 32% phones that are served advertisements across the BuzzCity network. The "raw" data used in this competition has two types: publisher database and click database, both provided in CSV format. The publisher database records the publisher's (aka partner's) profile and comprises several fields:

publisherid - Unique identifier of a publisher. Bankaccount - Bank account associated with a publisher (may be empty) address - Mailing address of a publisher (obfuscated; may be empty) status - Label of a publisher, which can be the following: "OK" - Publishers whom BuzzCity deems as having healthy traffic (or those who slipped their detection mechanisms) "Observation" - Publishers who may have just started their traffic or their traffic statistics deviates from system wide average. BuzzCity does not have any conclusive stand with these publishers yet "Fraud" - Publishers who are deemed as fraudulent with clear proof. Buzzcity suspends their accounts and their earnings will not be paid

On the other hand, the click database records the click traffics and has several fields:

id - Unique identifier of a particular click numericip - Public IP address of a clicker/visitor deviceua - Phone model used by a clicker/visitor publisherid - Unique identifier of a publisher adscampaignid - Unique identifier of a given advertisement campaign usercountry - Country from which the surfer is clicktime - Timestamp of a given click (in YYYY-MM-DD format) publisherchannel - Publisher's channel type, which can be the following: ad - Adult sites co - Community es - Entertainment and lifestyle gd - Glamour and dating in - Information mc - Mobile content pp - Premium portal se - Search, portal, services referredurl - URL where the ad banners were clicked (obfuscated; may be empty). More details about the HTTP Referer protocol can be found in this article. Related Publication: R. J. Oentaryo, E.-P. Lim, M. Finegold, D. Lo, F.-D. Zhu, C. Phua, E.-Y. Cheu, G.-E. Yap, K. Sim, M. N. Nguyen, K. Perera, B. Neupane, M. Faisal, Z.-Y. Aung, W. L. Woon, W. Chen, D. Patel, and D. Berrar. (2014). Detecting click fraud in online advertising: A data mining approach, Journal of Machine Learning Research, 15, 99-140.

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Description:

The advertising dataset captures the sales revenue generated with respect to advertisement costs across multiple channels like radio, tv, and newspapers.

It is required to understand the impact of ad budgets on the overall sales.

Acknowledgement:

The dataset is taken from Kaggle

Objective:

• Understand the Dataset & cleanup (if required).
• Build Regression models to predict the sales w.r.t a single & multiple features.
• Also evaluate the models & compare their respective scores like R2, RMSE, etc.

Point geometry with attributes for outdoor advertising signs in East Baton Rouge Parish, Louisiana.

About

This dataset provides insights into user behavior and online advertising, specifically focusing on predicting whether a user will click on an online advertisement. It contains user demographic information, browsing habits, and details related to the display of the advertisement. This dataset is ideal for building binary classification models to predict user interactions with online ads.

Features

• id: Unique identifier for each user.
• full_name: User's name formatted as "UserX" for anonymity.
• age: Age of the user (ranging from 18 to 64 years).
• gender: The gender of the user (categorized as Male, Female, or Non-Binary).
• device_type: The type of device used by the user when viewing the ad (Mobile, Desktop, Tablet).
• ad_position: The position of the ad on the webpage (Top, Side, Bottom).
• browsing_history: The user's browsing activity prior to seeing the ad (Shopping, News, Entertainment, Education, Social Media).
• time_of_day: The time when the user viewed the ad (Morning, Afternoon, Evening, Night).
• click: The target label indicating whether the user clicked on the ad (1 for a click, 0 for no click).

Goal

The objective of this dataset is to predict whether a user will click on an online ad based on their demographics, browsing behavior, the context of the ad's display, and the time of day. You will need to clean the data, understand it and then apply machine learning models to predict and evaluate data. It is a really challenging request for this kind of data. This data can be used to improve ad targeting strategies, optimize ad placement, and better understand user interaction with online advertisements.

MichaelOswald/headline-copy-advertising dataset hosted on Hugging Face and contributed by the HF Datasets community

14 Datasets used in experiments contain user data of the day of online advertisements from a cross-border e-commerce enterprise from September 1st (9.01) to September 14th (9.14), 2018. Table 3 summarizes the 14 datasets. Each instance of the datasets represents the corresponding online advertisement and is described by 22 attributes.

Per Local Law 83 of 2021, the Mayor's Office of Ethnic and Community Media is required to report annually on each agency's full advertising spend across all media categories, including ethnic and community (ECM), mainstream, out-of-home, social media, etc. This dataset reflects the raw data that MOECM received from City Agencies on their annual advertising spend. For more information, please visit the MOECM website.

hjayne/audio-advertising dataset hosted on Hugging Face and contributed by the HF Datasets community

Between 2025 and 2029, the ad spending is forecast to increase in all segments. The trend observed from 2017 to 2024 remains consistent throughout the entire forecast period. Notably, the Search Advertising Mobile segment achieves the highest value of ****** billion U.S. dollars in 2029. Find other insights concerning similar markets and segments, such as a comparison of ad spending growth in the United States and a comparison of revenue in the Netherlands.The Statista Market Insights cover a broad range of additional markets.

The ad spending in the 'banner advertising' segment of the digital advertising market in the United States was forecast to continuously increase between 2025 and 2029 by a total ***** billion U.S. dollars. After nearly a decade of consecutive growth, the spending is estimated to reach ** billion U.S. dollars in 2025.Find other key market indicators concerning the average ad spending per internet user (ARPU) and revenue growth.The Statista Market Insights cover a broad range of additional markets.

The average ad spending per internet user in the 'Social Media Advertising' segment of the digital advertising market in the United States was forecast to continuously increase between 2023 and 2028 by in total **** U.S. dollars (+***** percent). After the ninth consecutive increasing year, the indicator is estimated to reach ****** U.S. dollars and therefore a new peak in 2028. Notably, the average ad spending per internet user of the 'Social Media Advertising' segment of the digital advertising market was continuously increasing over the past years.Find other key market indicators concerning the revenue and revenue growth.The Statista Market Insights cover a broad range of additional markets.

Online Advertising Market Outlook

The online advertising market has witnessed significant growth over the past decade and is poised to continue on this trajectory, with a global market size valued at approximately USD 378.16 billion in 2023. It is projected to reach a staggering USD 1,081.22 billion by 2032, growing at a robust compound annual growth rate (CAGR) of 12.5% during the forecast period. The proliferation of digital platforms, coupled with the ever-increasing penetration of the internet and mobile devices, has propelled this growth, making online advertising a crucial component of marketing strategies worldwide. The shift from traditional media to digital channels has been a significant driver, as businesses recognize the unparalleled reach and efficiency offered by online advertising.

Several factors contribute to the rapid expansion of the online advertising market. Firstly, the growing adoption of smartphones and the increasing time consumers spend online have created a fertile ground for advertisers to reach their target audiences. The ubiquity of the internet allows for precise targeting and personalized ad experiences, enhancing user engagement and conversion rates. Furthermore, advancements in data analytics and artificial intelligence have revolutionized how advertisers understand consumer behavior, enabling them to deliver highly relevant and timely advertisements. This data-driven approach not only improves the return on investment for advertisers but also enhances the overall user experience by reducing ad fatigue.

Another key growth factor is the evolution of social media platforms as powerful advertising channels. Social media networks like Facebook, Instagram, and TikTok have become integral parts of people's lives, providing advertisers with access to large, engaged audiences. The interactive and visual nature of social media ads makes them particularly effective in capturing users' attention and fostering brand loyalty. Additionally, social media platforms offer sophisticated targeting options based on user demographics, interests, and behaviors, allowing advertisers to reach niche markets with precision. As social media continues to grow in popularity, so too will its significance as a primary avenue for online advertising.

The increasing prevalence of e-commerce and online shopping has also contributed to the growth of the online advertising market. As more consumers turn to online platforms for their shopping needs, businesses are compelled to enhance their online presence and invest in digital advertising to remain competitive. The ability to seamlessly integrate advertising with e-commerce platforms provides a direct path from advertisement to purchase, streamlining the customer journey and increasing conversion rates. Furthermore, the rise of video content and the popularity of streaming services have opened new avenues for advertisers to engage consumers through compelling and immersive ad formats.

Contextual Advertising has emerged as a vital strategy in the digital marketing landscape, allowing brands to place their ads in environments that align with the content being consumed by users. This method enhances the relevance of advertisements by ensuring they appear alongside related content, thereby increasing the likelihood of user engagement. Unlike behavioral targeting, which relies on tracking user behavior, contextual advertising focuses on the context of the content, making it a privacy-friendly option. As privacy regulations tighten, many advertisers are turning to contextual advertising as a way to maintain ad effectiveness while respecting user privacy. This approach not only improves user experience by reducing the intrusiveness of ads but also helps brands connect with audiences in a more meaningful way.

Regionally, the online advertising market presents diverse opportunities and challenges. North America, with its mature digital ecosystem and high internet penetration rates, continues to dominate the market. The region's advanced technological infrastructure and early adoption of digital marketing strategies have positioned it as a leader in online advertising. However, Asia Pacific is experiencing the fastest growth, driven by the rapid digitization of economies and the surge in internet users across countries like China and India. The increasing investments in digital infrastructure and the growing middle class in these regions are expected to further fuel market growth. Europe, Latin America, and the Middle East & Africa also present significant opportunities fo

Corporation Advertisement Dataset

This dataset contains information about well-known companies that are commonly featured as sponsors on YouTube and other digital platforms. The data focuses on brands frequently seen in influencer marketing and online advertising campaigns.

  Dataset Structure

The dataset includes the following fields for each company:

Name: Company/brand name Category: Business sector (VPN/Privacy, Tech/Electronics, Education, Shopping/E-commerce, etc.)… See the full description on the dataset page: https://huggingface.co/datasets/HectorRguez/corp_advertisement_dataset.

Comprehensive dataset of 108 Advertising services in New York, United States as of July, 2025. Includes verified contact information (email, phone), geocoded addresses, customer ratings, reviews, business categories, and operational details. Perfect for market research, lead generation, competitive analysis, and business intelligence. Download a complimentary sample to evaluate data quality and completeness.

This dataset provides information on advertising expenditures reported by Government of Canada (GC) institutions and major campaigns, from fiscal year 2015/2016 to fiscal year 2019/2020. The information is broken down by fiscal year, GC institution and campaign, also including a description of the campaign, production expenditures, media expenditures, as well as the media types and the creative agency used. For more information on the content of this dataset, consult the supporting documentation and data dictionary. Please find below the up to date datasets for advertising expenditures: - Advertising Expenditures by Institution (https://open.canada.ca/data/en/dataset/d2d94471-e34b-482d-a0e1-895a09e2a6d2) - Advertising Media expenditures associated with agency responsible for media planning and buying, the Agency of Record (AOR), for the Government of Canada (https://open.canada.ca/data/en/dataset/f9c132bc-4573-4bfd-bab5-3d242740bfea) For more information on GC advertising activities and expenditures, consult the Annual Reports on Government of Canada Advertising Activities: https://www.tpsgc-pwgsc.gc.ca/pub-adv/annuel-annual-eng.html#reports

In 2024, search was the largest digital advertising format in the United States, with a revenue of nearly *** billion U.S. dollars. Static display ranked second, with ** billion dollars. In total, U.S. digital ad revenue stood at *** billion U.S. dollars that year.

Dataset

This dataset was created by Duy Trương

Contents

This data table looks at tobacco advertisements that are likely to draw a child’s attention (e.g. advertisements below three feet, advertisements near candy). The California Tobacco Retail Surveillance Study (CTRSS), formerly the California Tobacco Advertising Survey (CTAS), is the longest-running tobacco marketing surveillance system in any state in the United States. The study assessed the availability, placement, and promotions of tobacco products in the retail setting. The study’s sample excluded tobacco retailers that require either club membership (e.g. Costco, golf courses) or had minimum-age restrictions (e.g. bars). Unusual retailer categories that were unlikely to display or advertise tobacco products, such as donut shops, were also excluded