As of June 2024, The Times newspaper had 107,000 print subscribers. The number of digital subscribers on the other hand was over five times higher. However, The Times had the lowest average issue print readership among all News UK publications, and under one million.

In 2023, the average weekday print circulation of The New York Times was approximately 279,000 copies, less than half the figure recorded in 2014. In that year, the company ceased publishing its figures based on weekday circulation for print, online, and other digital platforms, and published only its print circulation. The New York Times The New York Times was founded in 1851 and has been a household name in the United States for decades. The newspaper has adapted well to changes in the media industry, and between the final quarters of 2014 and 2020, paid subscribers to The New York Times’ digital only news product increased from 910 thousand to over five million. The New York Times is also one of the world’s leading podcast publishers, with unique streams and downloads of the company’s podcasts reaching tens and sometimes even hundreds of millions per month. Popularity and reliability As one of the most popular news websites in the United States, the NYT has been known to achieve 70 million unique monthly visitors, outperforming the likes of NBC News, The Washington Post, and The Guardian. That said, like many news publications, The New York Times has been the subject of controversy over the years. From accusations of liberal bias to its hiring practices, the newspaper has faced challenges regarding not only its published content but also its employees. In spite of this, just 15 percent of respondents to a survey seriously doubted the credibility of The New York Times, with most finding the publication to be a reliable source.

This data set lists the sex and number of birth registrations for each first name, from 1900 onward. Years are grouped by the date of the birth registration, not by the date of birth. Some birth registrations are not included, such as registrations with a sex other than Male or Female (i.e. indeterminate or not recorded), or where the birth registration date is not recorded. These excluded records are so few their exclusion is unlikely to have any significant impact on the data. Where a name has less than 10 instances in a particular year, the name will not be included in the data for that year. Due to this, total volumes will be less than the total birth registrations in that year. As first and middle names are recorded in our system together, the first name has been split off from the middle names. Due to the size of the data set, this was done with an automated system, generally looking for the first space in the name. This means there may be names not correctly added. Also, certain symbols in names may not carry through to the data correctly. Please let us know using the contact email address if you find any errors in the data.

This dataset is part of the Monash, UEA & UCR time series regression repository. http://tseregression.org/

The goal of this dataset is to predict sentiment score for news title. This dataset contains 83164 time series obtained from the News Popularity in Multiple Social Media Platforms dataset from the UCI repository. This is a large data set of news items and their respective social feedback on multiple platforms: Facebook, Google+ and LinkedIn. The collected data relates to a period of 8 months, between November 2015 and July 2016, accounting for about 100,000 news items on four different topics: economy, microsoft, obama and palestine. This data set is tailored for evaluative comparisons in predictive analytics tasks, although allowing for tasks in other research areas such as topic detection and tracking, sentiment analysis in short text, first story detection or news recommendation. The time series has 3 dimensions.

Please refer to https://archive.ics.uci.edu/ml/datasets/News+Popularity+in+Multiple+Social+Media+Platforms for more details

Citation request
Nuno Moniz and Luis Torgo (2018), Multi-Source Social Feedback of Online News Feeds, CoRR

Sports Illustrated Covers Through Time

Analyzing Popularity of Sports Through Time

By CrowdFlower [source]

About this dataset

Welcome to Sports Illustrated Covers by Sport since 1955. This comprehensive dataset offers a closer look at the sports that have graced the cover of Sports Illustrated Magazine for over 60 years! Explore which sports have gained traction and popularity over the decades, tracking each sport's journey from its first appearance to present day. With 32000 data rows detailing American football, baseball, basketball, bowling, boxing, cycling and road bike racing, fencing, figure skating , golf , horse racing , ice hockey , Indianapolis 500, kayaking mixed martial arts NASCAR other motor sports other team sports skiing snowboarding soccer surfing swimming tennis track & field wrestling - this dataset has something for everyone from armchair athletes to seasoned pros! So what are you waiting for? Dive in deep and unlock the secrets behind these stunning Sports Illustrated covers today!

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Featured Notebooks

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How to use the dataset

Research Ideas

• Analyzing the changing trends of sports coverage in the media by looking at the number of Sports Illustrated covers over time.
• Creating a visualization that plots out popular sports over time to show which have become increasingly important and/or less relevant in terms of media coverage.
• Building a predictive model that can forecast future SI cover athletes and teams based on historic data sets, athletes popularity, and other sports-trends related factors

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

Unknown License - Please check the dataset description for more information.

Columns

File: SI-Cover-by-Sport-DFE.csv | Column name | Description | |:------------------------------------|:--------------------------------------------------------------------------------------------------------| | American football | Number of Sports Illustrated covers featuring American football from 1955-2015. (Integer) | | Baseball | Number of Sports Illustrated covers featuring baseball from 1955-2015. (Integer) | | Basketball | Number of Sports Illustrated covers featuring basketball from 1955-2015. (Integer) | | Bowling | Number of Sports Illustrated covers featuring bowling from 1955-2015. (Integer) | | Boxing | Number of Sports Illustrated covers featuring boxing from 1955-2015. (Integer) | | Cycling and Road Bicycle Racing | Number of Sports Illustrated covers featuring cycling and road bicycle racing from 1955-2015. (Integer) | | Fencing | Number of Sports Illustrated covers featuring fencing from 1955-2015. (Integer) | | Figure Skating | Number of Sports Illustrated covers featuring figure skating from 1955-2015. (Integer) | | Golf | Number of Sports Illustrated covers featuring golf from 1955-2015. (Integer) | | Horse Racing | Number of Sports Illustrated covers featuring horse racing from 1955-2015. (Integer) | | Ice hockey | Number of Sports Illustrated covers featuring ice hockey from 1955-2015. (Integer) | | Indianapolis 500 | Number of Sports Illustrated covers featuring the Indianapolis 500 from 1955-2015. (Integer) | | Kayaking | Number of Sports Illustrated covers featuring kayaking from 1955-2015. (Integer) | | Mixed martial arts | Number of Sports Illustrated covers featuring mixed martial arts from 1955-2015. (Integer) | | NASCAR | Number of Sports Illustrated covers featuring NASCAR from 1955-2015. (Integer) | | Other motor sports | Number of Sports Illustrated covers featuring other motor sports from 1955-2015. (Integer) | | Other team sports | Number of Sports Illustrated covers featuring other team sports from 1955-2015. (Integer) | | **Ski...

Title Global Meme Popularity Dataset: Tracking Meme Trends Across Platforms and Regions

Overview This dataset provides a comprehensive look at the popularity of memes across various social media platforms and geographic regions. It includes metrics such as views, likes, shares, comments, and sentiment analysis, making it a valuable resource for analyzing meme trends, understanding cultural differences in humor, and predicting viral content.

Context Memes have become a universal language on the internet, transcending borders and cultures. However, there is limited publicly available data on how memes gain popularity, which platforms they thrive on, and how they resonate with different audiences. This dataset aims to fill that gap by providing a synthetic yet realistic representation of meme popularity data.

Contents The dataset contains 10,000 entries with the following columns:

Meme ID: A unique identifier for each meme.

Meme Name: The name or description of the meme.

Platform: The social media platform where the meme was posted (e.g., Reddit, Instagram, Twitter, TikTok, 9GAG).

Region: The geographic region where the meme is popular (e.g., North America, Europe, Asia).

Date: The date the meme was posted or became popular.

Views: The number of views the meme received.

Likes: The number of likes the meme received.

Shares: The number of times the meme was shared.

Comments: The number of comments on the meme.

Sentiment Score: The overall sentiment of the meme (Positive, Neutral, Negative).

Potential Use Cases Trend Analysis: Identify which memes are trending on specific platforms or regions.

Viral Prediction: Build models to predict which memes are likely to go viral.

Cultural Insights: Study how different regions respond to the same meme.

Sentiment Analysis: Analyze the emotional impact of memes on audiences.

Platform Comparison: Compare meme performance across different social media platforms.

Acknowledgments This dataset was synthetically generated using Python libraries such as Faker and pandas. While the data is not real, it is designed to mimic real-world meme popularity trends.

License This dataset is licensed under the Creative Commons Attribution-ShareAlike 4.0 International (CC BY-SA 4.0) license. This means:

You are free to share (copy and redistribute the material in any medium or format) and adapt (remix, transform, and build upon the material) for any purpose, even commercially.

You must give appropriate credit, provide a link to the license, and indicate if changes were made.

If you remix, transform, or build upon the material, you must distribute your contributions under the same license as the original.

This dataset contains 1934 videos posted between February 2, 2022 and February 16, 2022. For every video it records the amount of views every 5 minutes.

A September 2023 survey on exercise habits in the United States revealed that around 37 percent of respondents worked out before 9am. Meanwhile, 12 percent of respondents did exercise late in the day, after 8pm.

Count of popularity of adult first names (forenames, given names) in Peru, from an approximately 7% sample of the adult population.

In Peru, many people are registered as supporters of political parties, and their names are published by the Registro de Organizaciones Políticas. The lists include a DNI (national identity number) for each person to avoid duplicates. The 1,572,002 people on these lists (excluding the regional movements) represent around 7% of the adult population of Peru.

The first and middle names have been sorted and counted (there are an average of 1.6 first names for each person).

These 2,538,011 first (and middle) names represent 76,720 different names, most of which are infrequent. The file has been limited to names that occur ten or more times in the sample, which is 7,250 unique names (2,417,750 names, more than 95% of the total).

Each row in the file contains the rank, a percentage of that name in the entire set of 2,538,011 names, a count of the times the name occurs in the sample, and the name.

Count of family names (surnames, last names) in Peru, from an approximately 7% sample of the adult population.

In Peru, many people are registered as supporters of political parties, and their names are published by the Registro de Organizaciones Políticas. The lists include a DNI (national identity number) for each person to avoid duplicates. The 1,572,002 people on these lists (excluding the regional movements) represent around 7% of the adult population of Peru.

Their maternal and paternal family names have been sorted and counted. Nearly all of the names have entries for both paternal and maternal names.

These 3,142,561 family names represent 85,395 different names, most of which are infrequent. The file has been limited to names that occur ten or more times in the sample, which is 12,139 unique names (3,021,655 names, more than 96% of the total).

Each row in the file contains the rank, a percentage of that name in the entire set of 3,142,561 names, a count of the times the name occurs in the sample, and the name.

There are some names (around 800) in this file that contain a space. In most cases, these are names like "GARCIA DE RUIZ", where RUIZ is the name of the woman's husband. There are also cases where the name is like "DE LA CRUZ", which is a complete family name. No attempt has been made to remove the part of names which refer to the husband's name, this could be considered for a later version.

According to data gathered in the second quarter of 2024, the most popular newspapers in the United Kingdom were The Sunday Times, The Guardian, and The Sun, with 37 percent of respondents reporting that they had a positive opinion of these publications. Ranked second was Metro, followed by Daily Mail.

Dataset contains information about ~0.9 million Spotify tracks.
Data sampled using Spotify API

Each object may be uniquely identified by track_id

Description of the data:

track_id: id of the track
streams: number of times the track has been listened to
artist_followers: number of the followers of the track's author
genres: track genres
album_total_tracks: number of tracks in the album the track is a part of
track_artists: name of the track's author
artist_popularity: popularity of the track's author estimated by Spotify
explicit: whether the lyrics contain obscene words
tempo: track's tempo estimated by Spotify
chart: chart the track is in (if any)
album_release_date: the date on which the album the track is a part of was released
energy: track's energy estimated by Spotify
key: track's tonality estimated by Spotify
added_at: moment in time when the track was uploaded
popularity: track's popularity
track_album_album: type of the album the track is a part of
duration_ms: length of the track in milliseconds
available_markets: in what countries the track is available
track_track_number: track's disc number according to Spotify (the number of the track in the album it belongs to)
rank: position in the chart (if the track is a part of a chart)
mode: modality of the track
time_signature: time signature of the track
album_name: name of the album the track is a part of
speechiness: speechiness of the track estimated by Spotify
region: region of the chart (if track is a part of any chart)
danceability: danceability of the track estimated by Spotify
valence: valence of the track estimated by Spotify
acousticness: acousticness of the track estimated by Spotify
liveness: liveness of the track estimated by Spotify
trend: change in track's position within the chart (if track is a part of a chart)
instrumentalness: instrumentalness of the track estimated by Spotify
loudness: loudness of the track estimated by Spotify
name: track title as displayed at Spotify

This dataset is the result of a project creating a detailed description of the trends and cross-national differences in national cultural consumption. It focuses on the consumption of domestic and foreign music, films and literature and its relation with contextual country conditions. Country's globalization level, EU membership, nationalistic climate, national legislation, and supply are used to explain the popularity of domestic cultural products and the differences between countries.The aim of the project is examining the consumption of national cultural goods, and its cross-EU sharing in times of further EU integration and intra and extra-EU migration. Trends within countries, and differences between countries in regional, national, European and American consumed cultural goods are studied from a macro-level perspective. Theories from both economics and sociology are used to explain trends and differences.The data of this project is divided over three datasets. This dataset contains the data concerning domestic and foreign music. For the datasets concerning film and literature see the links under ‘relation’.

This dataset presents the top songs currently trending for over 70 countries.

Top 50 songs for each country is updated daily to provide the most up-to-date information on the popularity of songs in the world.

If you find this dataset helpful, don't forget to leave a upvote ❤️🎧

Interesting Task Ideas:

1. Identify the most popular genres of music in different countries over time.
2. Analyze the change in rankings of songs over time to identify trends and patterns.
3. Investigate whether there is a correlation between song popularity and its danceability or energy level.
4. Explore the relationship between the explicitness of songs and their popularity.
5. Analyze the relationship between song features (such as acousticness or instrumentalness) and their popularity.
6. Predict the future popularity of songs based on historical data and machine learning algorithms.
7. Compare the top songs in different countries to identify cultural music preferences.

NOTES

• Data may be missing / incomplete from 2024-11-27 to 2024-12-09 due to Spotify API changes.

Gain valuable insights into music trends, artist popularity, and streaming analytics with our comprehensive Spotify Dataset. Designed for music analysts, marketers, and businesses, this dataset provides structured and reliable data from Spotify to enhance market research, content strategy, and audience engagement.

Dataset Features

Track Information: Access detailed data on songs, including track name, artist, album, genre, and release date. Streaming Popularity: Extract track popularity scores, listener engagement metrics, and ranking trends. Artist & Album Insights: Analyze artist performance, album releases, and genre trends over time. Related Searches & Recommendations: Track related search terms and suggested content for deeper audience insights. Historical & Real-Time Data: Retrieve historical streaming data or access continuously updated records for real-time trend analysis.

Customizable Subsets for Specific Needs Our Spotify Dataset is fully customizable, allowing you to filter data based on track popularity, artist, genre, release date, or listener engagement. Whether you need broad coverage for industry analysis or focused data for content optimization, we tailor the dataset to your needs.

Popular Use Cases

Market Analysis & Trend Forecasting: Identify emerging music trends, genre popularity, and listener preferences. Artist & Label Performance Tracking: Monitor artist rankings, album success, and audience engagement. Competitive Intelligence: Analyze competitor music strategies, playlist placements, and streaming performance. AI & Machine Learning Applications: Use structured music data to train AI models for recommendation engines, playlist curation, and predictive analytics. Advertising & Sponsorship Insights: Identify high-performing tracks and artists for targeted advertising and sponsorship opportunities.

Whether you're optimizing music marketing, analyzing streaming trends, or enhancing content strategies, our Spotify Dataset provides the structured data you need. Get started today and customize your dataset to fit your business objectives.

Musical Time Keepers Market Outlook

The global musical time keepers market size is projected to grow from USD 1.2 billion in 2023 to USD 2.3 billion by 2032, registering a CAGR of 7.1% during the forecast period. The surge in the global market can be attributed to the rising popularity of music education, increasing number of professional musicians, and technological advancements in musical instruments. Factors such as the integration of digital technology in traditional devices and the growing interest in music as a hobby or profession are driving the growth of this market.

One of the primary growth factors for the musical time keepers market is the increasing interest in music education and training. Educational institutions and music schools are placing more emphasis on rhythm and timing as essential components of musical training. As a result, the demand for advanced and accurate musical time-keeping devices has surged. Furthermore, the proliferation of online music education platforms has made it easier for individuals to access music lessons, further boosting the market for musical time keepers.

Technological advancements in musical instruments have also played a pivotal role in the growth of the musical time keepers market. The integration of digital technology has led to the development of sophisticated and highly accurate digital timekeepers, which are widely preferred by both professional musicians and music educators. These devices offer enhanced features such as programmable tempos, visual indicators, and connectivity with other digital music instruments and software, thereby driving their adoption.

The increasing number of professional musicians and the growing popularity of live music performances also contribute significantly to the market's growth. Professional musicians require precise timing devices to maintain accurate rhythms during performances and practice sessions. The rise in live music events, concerts, and competitions has further fueled the demand for reliable and durable musical time keepers. As the music industry continues to expand, the need for high-quality timing devices is expected to rise, propelling the market's growth.

The metronome, a vital tool for musicians, has evolved significantly over the years. Traditionally, it was a simple mechanical device used to keep a steady tempo, aiding musicians in maintaining rhythm and timing during practice sessions. Today, metronomes come in various forms, including digital versions that offer a range of features such as adjustable tempos, visual cues, and even integration with music apps. This evolution reflects the broader trend in the musical time keepers market towards more sophisticated and user-friendly devices. As the demand for precision and versatility in music practice grows, metronomes continue to be an essential component for both amateur and professional musicians.

Regionally, North America and Europe are expected to dominate the musical time keepers market, owing to the strong presence of established music schools, professional musicians, and advanced music industries. The Asia Pacific region is anticipated to witness the highest growth during the forecast period, driven by the increasing popularity of music education and the rising number of music enthusiasts. The growing disposable income and urbanization in countries like China and India are also contributing to the market's expansion in this region.

Product Type Analysis

The musical time keepers market can be segmented by product type into metronomes, digital time keepers, and mechanical time keepers. Each of these segments caters to different customer preferences and needs, contributing uniquely to the overall market growth. Metronomes, for instance, have been a staple in music practice for decades. Traditional metronomes, which often use a pendulum mechanism, are still widely used for their simplicity and effectiveness in teaching rhythm and timing. The mechanical segment remains popular among classical musicians and traditionalists who prefer the tactile feedback and visual cues of a swinging pendulum.

Digital time keepers have emerged as a significant segment in recent years, owing to their advanced features and versatility. These devices offer precise timing, multiple rhythm settings, and often include visual aids like flashing lights or digital displays. They can be easily integrated with other digital music instruments and software, which makes them a pr

All parameter estimates are significant at the 1% level.Estimates of Poisson models using simulated data.

The popularity of digital alternatives to printed materials has been holding the Printing industry back in recent years. Demand for printed materials has fallen as consumers and businesses have gradually transitioned towards online trading. Demand has also been edging downwards due to the growing popularity of reading online and through other electronic alternatives (e.g. e-readers and smartphones). Revenue is anticipated to tumble at a compound annual rate of 4.1% over the five years through 2024-25 to £9.2 billion, while profit is set to inch up to 5.5% as printing companies get some respite from pressures like inflation and high utility costs. The industry tanked in 2020-21 as the already declining circulation of newspapers and magazines sank to a new low owing to COVID-19 closures. According to ABC data, newspaper circulation of most major publications, like The Sun, The Times and The Guardian, fell by almost 50% during this period, slashing printing demand for these products. This was followed by the cost-of-living crisis – characterised by rampant inflation and climbing interest rates – denting demand in 2022-23 and 2023-24. Despite inflation inching back towards its target level in 2024-25, manufacturing output remains subdued, limiting demand for printed goods. As a result, revenue is forecast to drop by 0.8% in 2024-25. High substitute competition and shrinking demand from key markets like newspaper and magazine printing is also eating into revenue. However, some markets remain healthy, particularly book printing, as both domestic and export demand for British books remains strong despite economic headwinds. Revenue is expected to rise at a modest compound annual rate of 0.5% over the five years through 2029-30 to reach £9.5 billion. The industry is likely to continue to face fierce competition from substitutes for commercially printed material, including online advertising and publication of information. Many retailers, financiers and service providers are likely to move their operations online, limiting demand for printing services. However, printed books are likely to coexist with their electronic counterparts, supporting a steady source of demand for book printers. Commercial printers are likely to introduce different services, like digital printing and personalisation services, to increase revenue and remain profitable, but some of these services won’t be relevant to the industry. Surging niche markets will also offer new opportunities, like high fashion print advertising. These new opportunities will require printers to specialise in high-end materials and leverage partnerships with creative industries to be successful.

Overview

Big Brands spend a significant amount on popularizing a product. Nevertheless, their efforts go in vain while establishing the merchandise in the hyperlocal market. Based on different geographical conditions same attributes can communicate a piece of much different information about the customer. Hence, insights this is a must for any brand owner.

In this competition, we have brought the data gathered from one of the top apparel brands in India. Provided the details concerning category, score, and presence in the store, participants are challenged to predict the popularity level of the merchandise.

The popularity class decides how popular the product is given the attributes which a store owner can control to make it happen.

Dataset Description:

Train.csv - 18208 rows x 12 columns (Includes popularity Column as Target variable) Test.csv - 12140 rows x 11 columns Sample Submission.csv - Please check the Evaluation section for more details on how to generate a valid submission

Attributes:

store_ratio basket_ratio category_1 store_score category_2 store_presence score_1 score_2 score_3 score_4 time popularity - Class of popularity (Target Column)

Skills:

Multi-class Classification Modeling Advance Feature engineering Optimizing Multi-Class log loss score as a metric to generalize well on unseen data

Contest Link --> Link

Prize

Top-3 winners will get MLDS 2021 passes MLDS (Machine Learning Developer's Summit) INDIA’S NO.1 CONFERENCE EXCLUSIVELY FOR MACHINE LEARNING PRACTITIONERS ECOSYSTEM MLDS21 brings together India’s leading Machine Learning innovators and practitioners to share their ideas and experience about machine learning tools, advanced development in this sphere and gives the attendees a first look at new trends & developer products.

How to Generate a valid Submission File

Use y_true as provided as class Labels(y_true) as predicted probabilities per class (y_pred) from the model using the predict_proba() method

You should submit a .csv/.xlsx file with exactly 12140 rows with 5 columns (i.e. 0, 1, 2, 3, 4). Your submission will return an Invalid Score if you have extra columns or rows.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F5602038%2Ffacdb791dcf4105ce5e606087c0cf8cc%2Fxyz.png?generation=1611324853494826&alt=media" alt="">

The file should have exactly 5 columns.

Using pandas, one can do

submission_df.to_csv('my_submission_file.csv', index=False)

Dataset Instructions:

Dataset Name: Podcast Listening Time Prediction

Dataset Description: The dataset contains information about various podcast episodes and their attributes. The goal is to analyze and predict the average listening duration of podcast episodes based on various features.

Columns in the Dataset:

Podcast_Name (Type: string) Description: Names of popular podcasts. Example Values: "Tech Talk", "Health Hour", "Comedy Central"

Episode_Title (Type: string) Description: Titles of the podcast episodes. Example Values: "The Future of AI", "Meditation Tips", "Stand-Up Special"

Episode_Length (Type: float, minutes) Description: Length of the episode in minutes. Example Values: 5.0, 10.0, 30.0, 45.0, 60.0, 90.0

Genre (Type: string) Description: Genre of the podcast episode. Possible Values: "Technology", "Education", "Comedy", "Health", "True Crime", "Business", "Sports", "Lifestyle", "News", "Music"

Host_Popularity (Type: float, scale 0-100) Description: A score indicating the popularity of the host. Example Values: 50.0, 75.0, 90.0

Publication_Day (Type: string) Description: Day of the week the episode was published. Possible Values: "Monday", "Tuesday", "Wednesday", "Thursday", "Friday", "Saturday", "Sunday"

Publication_Time (Type: string) Description: Time of the day the episode was published. Possible Values: "Morning", "Afternoon", "Evening", "Night"

Guest_Popularity (Type: float, scale 0-100) Description: A score indicating the popularity of the guest (if any). Example Values: 20.0, 50.0, 85.0

Number_of_Ads (Type: int) Description: Number of advertisements within the episode. Example Values: 0, 1, 2, 3

Episode_Sentiment (Type: string) Description: Sentiment of the episode's content. Possible Values: "Positive", "Neutral", "Negative"

Listening_Time (Type: float, minutes) Description: The actual average listening duration (target variable). Example Values: 4.5, 8.0, 30.0, 60.0