This timeline presents Nike's North American revenue from 2009 to 2024, by segment. Nike's North American revenue from footwear amounted to roughly 14.5 billion U.S. dollars in the year ended May 31, 2024, which was far greater than that of the apparel and equipment segments combined. That said, only the equipment segment recorded noticeable sales growth in the last year. A broad portfolio Nike offers an extremely broad array of products within the apparel and sports equipment market. As one of the leading companies, Nike tries to stay ahead of the game and create new, unique and innovative products to give their athletes, and their profit margins the edge. This can be seen in the number of patents filed by Nike. These patents cover a wide array of technology areas, primarily design, followed by footwear. Nike shoes And Nike’s investment in footwear is rewarded, as the revenue of Nike’s footwear segment compared to Adidas and Puma is far greater than that of its competitors. Nike has many lines of iconic shoes, from their air Jordan line to the extremely limited-edition Nike Air mags. These were shoes based on the film Back to the Future, which feature self-lacing technology.

In the year ended May 2024, Nike's global revenue amounted to about ***** billion U.S. dollars, which is only a very slight increase compared to the previous financial year. In comparison, between fiscal 2022 and 2023, the company's overall revenue increased by roughly ** percent. Founded in January 1964, Nike has become the world's leading supplier of athletic shoes and apparel and a major manufacturer of sports equipment. As of 2024, the company employed over ****** people worldwide. Nike's sponsorships The company sponsors many high-profile professional athletes, such as Cristiano Ronaldo, Rafael Nadal, Lebron James, Naomi Osaka, and Rory Mcllroy to use their products and promote and advertise their technology and design. On top of that, Nike also manufactures and provides kits (uniforms) for a wide range of sports teams like Barcelona and Chelsea. Popularity of Nike in Europe While Nike is an American enterprise, its products are ubiquitous. A Statista survey conducted in Europe showed the overwhelming majority (approximately ** percent) of online sports and outdoor goods shoppers in the United Kingdom and Germany recognized the Nike brand, at least by name. Furthermore, over ** percent of these UK and German respondents stated they liked the brand. The fiscal year end of the company is May 31st

NIKE revenue for the twelve months ending February 28, 2025 was $47.818B, a 7.3% decline year-over-year. NIKE annual revenue for 2024 was $51.362B, a 0.28% increase from 2023. NIKE annual revenue for 2023 was $51.217B, a 9.65% increase from 2022. NIKE annual revenue for 2022 was $46.71B, a 4.88% increase from 2021.

*** Newly Emerged Rumors in Twitter ***

These 12 datasets are the results of an empirical study on the spreading process of newly emerged rumors in Twitter. Newly emerged rumors are those rumors whose rise and fall happen in a short period of time, in contrast to the long standing rumors. Particularly, we have focused on those newly emerged rumors which have given rise to an anti-rumor spreading simultaneously against them. The story of each rumor is as follow :

1- Dataset_R1 : The National Football League team in Washington D.C. changed its name to Redhawks.

2- Dataset_R2 : A Muslim waitress refused to seat a church group at a restaurant, claiming "religious freedom" allowed her to do so.

3- Dataset_R3 : Facebook CEO Mark Zuckerberg bought a "super-yacht" for $150 million.

4- Dataset_R4 : Actor Denzel Washington said electing President Trump saved the U.S. from becoming an "Orwellian police state."

5- Dataset_R5 : Joy Behar of "The View" sent a crass tweet about a fatal fire in Trump Tower.

6- Dataset_R6 : Harley-Davidson's chief executive officer Matthew Levatich called President Trump "a moron."

7- Dataset_R7 : The animated children's program 'VeggieTales' introduced a cannabis character in August 2018.

8- Dataset_R8 : Michael Jordan resigned from the board at Nike and took his Air Jordan line of apparel with him.

9- Dataset_R9 : In September 2018, the University of Alabama football program ended its uniform contract with Nike, in response to Nike's endorsement deal with Colin Kaepernick.

10- Dataset_R10 : During confirmation hearings for Supreme Court nominee Brett Kavanaugh, congressional Democrats demanded that the nominee undergo DNA testing to prove he is not Adolf Hitler.

11- Dataset_R11 : Singer Michael Bublé's upcoming album will be his last, as he is retiring from making music.Singer Michael Bublé's upcoming album will be his last, as he is retiring from making music.

12- Dataset_R12 : A screenshot from MyLife.com confirms that mail bomb suspect Cesar Sayoc was registered as a Democrat.

The structure of excel files for each dataset is as follow :

- Each row belongs to one captured tweet/retweet related to the rumor, and each column of the dataset presents a specific information about the tweet/retweet. These columns from left to right present the following information about the tweet/retweet :

- User ID (user who has posted the current tweet/retweet)

- The description sentence in the profile of the user who has published the tweet/retweet

- The number of published tweet/retweet by the user at the time of posting the current tweet/retweet

- Date and time of creation of the the account by which the current tweet/retweet has been posted

- Language of the tweet/retweet

- Number of followers

- Number of followings (friends)

- Date and time of posting the current tweet/retweet

- Number of like (favorite) the current tweet had been acquired before crawling it

- Number of times the current tweet had been retweeted before crawling it

- Is there any other tweet inside of the current tweet/retweet (for example this happens when the current tweet is a quote or reply or retweet)

- The source (OS) of device by which the current tweet/retweet was posted

- Tweet/Retweet ID

- Retweet ID (if the post is a retweet then this feature gives the ID of the tweet that is retweeted by the current post)

- Quote ID (if the post is a quote then this feature gives the ID of the tweet that is quoted by the current post)

- Reply ID (if the post is a reply then this feature gives the ID of the tweet that is replied by the current post)

- Frequency of tweet occurrences which means the number of times the current tweet is repeated in the dataset (for example the number of times that a tweet exists in the dataset in the form of retweet posted by others)

- State of the tweet which can be one of the following forms (achieved by an agreement between the annotators) :

r : The tweet/retweet is a rumor post

a : The tweet/retweet is an anti-rumor post

q : The tweet/retweet is a question about the rumor, however neither confirm nor deny it

n : The tweet/retweet is not related to the rumor (even though it contains the queries related to the rumor, but does not refer to the rumor)

Automatic Gender recognition

https://www.techgig.com/hackathon/colombiaonline

Introduction Demographic segmentation has been a key part of the marketing strategy for decades, and as more and more buyers conduct their research and make purchase decisions online, it's become even more pivotal to marketing's overall success. From highly targeted ad campaigns to personalized web pages for returning website visitors, it's now easier than ever to reach a specific persona, group, or individual online, and with so many companies competing for buyers' attention online today, knowing just where to find your prospects and how to best communicate with them is crucial. Under Demographic segmentation, marketing professionals divide the population based on demographic variables, such as age, gender, income, etc. The problem that we are trying to solve is Gender segmentation.

Gender Segmentation - Under gender segmentation, the database is divided into male or female. Both men and women have different interest in terms of shopping for various products such as apparel, cosmetics, perfumes, shoes, etc. and even food habits. The segmentation based on gender is important for lots of industries which have portfolios for both male as well as female. For example, Nike as a sportswear company has a separate portfolio for both male as well as female. As a company, Nike will have to come out with different strategies to market products differently for male and female. The company even has to create a separate segment for both men and women in the showroom itself. This is much evident if you go to an apparel showroom such as Zara, Marks & Spencers, etc.

Times Internet Limited is No 1 Premium Digital Publisher in the world with over 400 million monthly unique visitors, consuming 10K+ unique contents which are published daily generating 80 billion monthly pageviews. Since, most of TIL revenue comes from Digital Advertising where TIL focuses on serving relevant ads to its audiences. Hence, Gender Identification is very crucial for us in order to plan and execute successful marketing campaigns for brands and provide maximum ROI to our Advertisers. Unlike other social media platforms where users submit their personal details like gender, age, etc TIL is a publishing company where users consumes content without sharing their personal details. Hence, we need a solution to identify their gender by understanding how they interact with TIL digital contents. Problem Statement Automatic Gender recognition based on digital content reading pattern through Machine Learning.

Data There will be 2 data sets that would be shared with the participants for understanding the structure of the datasets, training their algorithm(s)/model(s) and to test their model and present their findings/results.

***Sample Data (for training) ***- To understand the structure of the data, so, that they can extract the information from sample data. File will contain fields for users from different gender and the content they had consumed, the participants have to analyse, understand and train their algorithm/model on this data.

***Testing Data ***- This is the system data (with certain modified fields, see Privacy Policy section), where the participants have to run their model and submit their results and presentation over the same. Data Set Download Data Set File Name Description Format Size UserIdToUrl.zip Url visited by user zip Urls_Json_Data.zip Urls details zip UserIdToGender_Train.csv User to gender data for training csv UserId_Test.csv User to gender data for testing csv sample_submission.csv Sample submission csv

Data Dictionary Here's a brief version of what you'll find in the data description file.

Variable Description userid User id gender Gender url Content URL title Title of the Content description Short Description of the Content long_description Long Description of the Content alt_titles Alternate Title of the Content brand Content Brand Name language Language of the Content Submission Model Implementation Findings & Insights of the model results of the above Presentation Evaluation Metric The score is calculated with the following formula:

Score = Number of correct predictions / Total records*100

Under Armour is an American sporting goods manufacturer, based in Baltimore, Maryland. The company has experienced substantial growth over the past decade, with its global net revenues amounting to approximately 5.7 billion U.S. dollars in the financial year ended March 31, 2024. Under Armour had revenues of less than one billion U.S. dollars as recently as 2009. The majority of Under Armour’s sales come from apparel, with the remainder coming from footwear and sporting equipment, and accessories. How does Under Armour compare to other companies within the sportswear and sporting goods industry? The global sportswear industry is a lucrative sector, which is forecast to keep expanding in the coming years. Nike and adidas are two of the world's leading athletic apparel companies and their brand values are on another level. Despite Under Armour being one of the largest sportswear and sporting goods manufacturers in the world, the company is still far behind these two industry giants. Puma, on the other hand, is a much closer competitor to UA and is possibly a better benchmark of where Under Armour currently stands within this industry. In 2022, the German sportswear company generated approximately 8.46 billion euros' worth of net sales.