In the year ended May 31, 2025, Nike's North American revenue amounted to about 19.6 billion U.S. dollars. In the EMEA region (Europe, Middle East, and Africa), the company generated some 12.3 billion U.S. dollars' worth of sales that year. Nike Nike, Inc. is a sportswear and equipment supplier based in the United States that was founded in January 1964. The company's headquarters are located near Beaverton, Oregon. Nike is the world's leading supplier of athletic shoes and apparel and is also a major manufacturer of sports equipment. Sports sponsorships Given Nike's success, size, and popularity, it comes to no surprise that the company is actively involved in the world of sports sponsorships. In fact, Nike has become one of the most reputable athletic sponsors in the world over the years. Nike looks for and uses sponsorship agreements with celebrity athletes, professional sports teams, and college athletic programs for the promotion of their products, technology, and design.

About this Dataset

This dataset offers a comprehensive, up-to-date look at the historical stock performance of Nike, Inc. (NKE), a global leader in athletic footwear, apparel, and equipment.

About the Company

Nike, Inc. is an American multinational corporation headquartered near Beaverton, Oregon. Founded as Blue Ribbon Sports in 1964 by Bill Bowerman and Phil Knight, it officially became Nike, Inc. in 1971. The company is the world's largest supplier of athletic shoes and apparel, known for its iconic "Swoosh" logo and "Just Do It" slogan. As a key player in the sportswear and fashion industries, Nike's stock performance is a significant indicator of economic trends and consumer demand.

Key Features

Daily OHLCV Data: The dataset contains essential Open, High, Low, Close, and Volume metrics for each trading day. Comprehensive History: Includes data from Nike's early trading history to the present, offering a long-term perspective. Regular Updates: The dataset is designed for regular, automated updates to ensure data freshness for time-sensitive projects.

Data Dictionary

Date: The date of the trading session in YYYY-MM-DD format.

ticker: The standard ticker symbol for Nike, Inc. on the NYSE: 'NKE'.

name: The full name of the company: 'Nike, Inc.'.

Open: The stock price in USD at the start of the trading session.

High: The highest price reached during the trading day in USD.

Low: The lowest price recorded during the trading day in USD.

Close: The final stock price at market close in USD.

Volume: The total number of shares traded on that day.

Data Collection

The data for this dataset is collected using the yfinance Python library, which pulls information directly from the Yahoo Finance API.

Potential Use Cases

Financial Analysis: Analyze historical price trends, volatility, and trading volume of Nike stock. Machine Learning: Develop and test models for stock price prediction and time series forecasting. Educational Projects: A perfect real-world dataset for students and data enthusiasts to practice data cleaning, visualization, and modeling.

This dataset is about companies. It has 6 rows and is filtered where the company is Nike. It features 5 columns: city, country, industry, and foundation year.

This timeline presents Nike's North American revenue from 2009 to 2025, by segment. Nike's North American revenue from footwear amounted to roughly 12.7 billion U.S. dollars in the year ended May 31, 2025, 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.

This Power BI dashboard provides an in-depth analysis of Nike's US sales performance for 2020-21. It includes key insights on revenue trends, top-selling products, regional performance, customer segmentation, and seasonal variations. The interactive visualizations help identify growth opportunities and areas for improvement. Ideal for business analysts, marketers, and data enthusiasts looking to explore Nike’s sales data effectively.

In the year ended May 2025, Nike's global revenue amounted to about ***** billion U.S. dollars, which was a decrease 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.

Background

Virtually all NIKE, Inc. materials and finished products are sourced from, or manufactured by independent suppliers that also make or provide products for other global companies. That means that NIKE does not own, operate or employ workers at these locations. NIKE also has license agreements that permit unaffiliated parties to manufacture and sell products using NIKE-owned trademakrs.

The NIKE, Inc. company portfolio includes Nike, Jordan Brand, Hurley and Converse. The data in this dataset is reflected of, and can be filtered by, Nike (which includes the Jordan Brand), Converse and Hurley.

Key Terms

Workers: The total number of workers is calculated based on the total number of employees at each contract factory. This includes line workers (involved in the production of NIKE products) and all other employees (such as managers, administrative staff, HR employees and maintenance staff)

The average age and percentag of female and migrant workers are calculated based on the percentage of total line workers.

Migrants: For the purpose of this dataset, migrant workers refer to workers who have migrated from one country to another (foreign migration), as well as people within a country who have moved from one location to another within its borders (internal migration).

Supplier Group: Where applicable, this site lists the supplier group name for the contract factories. A supplier group includes a parent entity and all of the subsidiary entities the parent company directly or indirectly owns or controls.

Original Data

Acknowlegement

Foto von wu yi auf Unsplash

The dataset contains reviews from customers about some of Nike's selling shoes. It can be used for customer review sentiment analysis, which is the task of automatically identifying the sentiment (positive, negative, or neutral) of a customer review. The dataset also includes additional information about the reviews, such as the rating, review date, location, username, and title.

Here is a detailed description of each column:

• Rating: The rating that the customer gave to the shoe.
• Review Date: The date on which the review was written.
• Location: The location of the customer who wrote the review.
• Username: The username of the customer who wrote the review.
• Review: The full text of the customer review.
• Fit Feedback: The customer's feedback on the fit of the shoe.
• Comfort Feedback: The customer's feedback on the comfort of the shoe.
• Recommend Feedback: Whether or not the customer recommends the shoe to others.
• Title: The title of the customer review.
• IsPromoReview: Whether or not the customer was incentivized to write the review (e.g., with a discount or free product).
• Subtitle: The subtitle of the customer review.
• ColorDescription: The colour of the shoe that the customer reviewed.
• FullPrice: The full price of the shoe that the customer reviewed.
• Discounted: Whether or not the shoe was on sale when the customer purchased it.
• EmployeePrice: The price that Nike employees pay for the shoe.
• CurrentPrice: The current price of the shoe.
• IsLaunch: Whether or not the shoe is a new release.
• Pid: The unique identifier of the shoe.
• Label: The label of the shoe, such as "Men's" or "Women's."

This dataset is a valuable resource for businesses wanting to understand customers' feedback on their products. The data can be used to identify areas where the effects can be improved, as well as to develop marketing and sales strategies that are tailored to the needs of the customers.

Here are some specific examples of how the data can be used:

• Identify the most important factors to customers: By analyzing the customer reviews, businesses can identify the factors that are most important to customers when choosing a shoe. For example, if many customers are writing reviews about the comfort of the shoe, then this suggests that comfort is an essential factor for customers. This information can be used to design and market shoes that are more comfortable.
• Understand customer sentiment: By analyzing the sentiment of the customer reviews, businesses can understand how customers feel about their products. This information can be used to identify areas where the effects can be improved, as well as to develop marketing and sales strategies that are tailored to the needs of the customers.
• Identify trends: By analyzing customer reviews over time, businesses can identify trends in customer preferences. For example, if more and more customers are writing reviews about sustainable shoes, then this suggests that sustainability is becoming an increasingly important factor for customers. This information can be used to develop new products that meet the needs of the customers.

Overall, the Nike shoe data is a valuable resource for businesses that want to understand their customers' feedback on their products. The data can be used to improve products, develop marketing and sales strategies, and identify trends in customer preferences.

Dataset Labels

['converse', 'adidas', 'nike']

  Number of Images

{'train': 576, 'test': 83, 'valid': 166}

  How to Use

Install datasets:

pip install datasets

Load the dataset:

from datasets import load_dataset

ds = load_dataset("keremberke/shoe-classification", name="full") example = ds['train'][0]

  Roboflow Dataset Page

https://universe.roboflow.com/popular-benchmarks/nike-adidas-and-converse-shoes-classification/dataset/4… See the full description on the dataset page: https://huggingface.co/datasets/keremberke/shoe-classification.

Context

When you think of sneakers for a trip, the importance of good footwear cannot be discarded, and the obvious brands that come to mind are Adidas and Nike. Adidas vs Nike is a constant debate as the two giants in the apparel market, with a large market cap and market share, battle it out to come on top. As a newly hired Data Scientist in a market research company, you have been given the task of extracting insights from the data of men's and women's shoes, and grouping products together to identify similarities and differences between the product range of these renowned brands.

Objective To perform an exploratory data analysis and cluster the products based on various factors.

Key Questions Which variables are most important for clustering? How each cluster is different from the others? What are the business recommendations?

Data Description The dataset consists of 3268 products from Nike and Adidas with features of information including their ratings, discount, sales price, listed price, product name, and the number of reviews.

Product Name: Name of the product Product ID: ID of the product Listing Price: Listed price of the product Sale Price: Sale price of the product Discount: Percentage of discount on the product Brand: Brand of the product Rating: Rating of the product Reviews: Number of reviews for the product