Apple is one of the most influential and recognisable brands in the world, responsible for the rise of the smartphone with the iPhone. Valued at over $2 trillion in 2021, it is also the most valuable...

This dataset offers a focused and invaluable window into user perceptions and experiences with applications listed on the Apple App Store. It is a vital resource for app developers, product managers, market analysts, and anyone seeking to understand the direct voice of the customer in the dynamic mobile app ecosystem.

Dataset Specifications:

• Investment: $45.0
• Status: Published and immediately available.
• Category: Ratings and Reviews Data
• Format: Compressed ZIP archive containing JSON files, ensuring easy integration into your analytical tools and platforms.
• Volume: Comprises 10,000 unique app reviews, providing a robust sample for qualitative and quantitative analysis of user feedback.
• Timeliness: Last crawled: (This field is blank in your provided info, which means its recency is currently unknown. If this were a real product, specifying this would be critical for its value proposition.)

Richness of Detail (11 Comprehensive Fields):

Each record in this dataset provides a detailed breakdown of a single App Store review, enabling multi-dimensional analysis:

1. Review Content:

   • review: The full text of the user's written feedback, crucial for Natural Language Processing (NLP) to extract themes, sentiment, and common keywords.
   • title: The title given to the review by the user, often summarizing their main point.
   • isEdited: A boolean flag indicating whether the review has been edited by the user since its initial submission. This can be important for tracking evolving sentiment or understanding user behavior.

2. Reviewer & Rating Information:

   • username: The public username of the reviewer, allowing for analysis of engagement patterns from specific users (though not personally identifiable).
   • rating: The star rating (typically 1-5) given by the user, providing a quantifiable measure of satisfaction.

3. App & Origin Context:

   • app_name: The name of the application being reviewed.
   • app_id: A unique identifier for the application within the App Store, enabling direct linking to app details or other datasets.
   • country: The country of the App Store storefront where the review was left, allowing for geographic segmentation of feedback.

4. Metadata & Timestamps:

   • _id: A unique identifier for the specific review record in the dataset.
   • crawled_at: The timestamp indicating when this particular review record was collected by the data provider (Crawl Feeds).
   • date: The original date the review was posted by the user on the App Store.

Expanded Use Cases & Analytical Applications:

This dataset is a goldmine for understanding what users truly think and feel about mobile applications. Here's how it can be leveraged:

• Product Development & Improvement:

  • Bug Detection & Prioritization: Analyze negative review text to identify recurring technical issues, crashes, or bugs, allowing developers to prioritize fixes based on user impact.
  • Feature Requests & Roadmap Prioritization: Extract feature suggestions from positive and neutral review text to inform future product roadmap decisions and develop features users actively desire.
  • User Experience (UX) Enhancement: Understand pain points related to app design, navigation, and overall usability by analyzing common complaints in the review field.
  • Version Impact Analysis: If integrated with app version data, track changes in rating and sentiment after new app updates to assess the effectiveness of bug fixes or new features.

• Market Research & Competitive Intelligence:

  • Competitor Benchmarking: Analyze reviews of competitor apps (if included or combined with similar datasets) to identify their strengths, weaknesses, and user expectations within a specific app category.
  • Market Gap Identification: Discover unmet user needs or features that users desire but are not adequately provided by existing apps.
  • Niche Opportunities: Identify specific use cases or user segments that are underserved based on recurring feedback.

• Marketing & App Store Optimization (ASO):

  • Sentiment Analysis: Perform sentiment analysis on the review and title fields to gauge overall user satisfaction, pinpoint specific positive and negative aspects, and track sentiment shifts over time.
  • Keyword Optimization: Identify frequently used keywords and phrases in reviews to optimize app store listings, improving discoverability and search ranking.
  • Messaging Refinement: Understand how users describe and use the app in their own words, which can inform marketing copy and advertising campaigns.
  • Reputation Management: Monitor rating trends and identify critical reviews quickly to facilitate timely responses and proactive customer engagement.

• Academic & Data Science Research:

  • Natural Language Processing (NLP): The review and title fields are excellent for training and testing NLP models for sentiment analysis, topic modeling, named entity recognition, and text summarization.
  • User Behavior Analysis: Study patterns in rating distribution, isEdited status, and date to understand user engagement and feedback cycles.
  • Cross-Country Comparisons: Analyze country-specific reviews to understand regional differences in app perception, feature preferences, or cultural nuances in feedback.

This App Store Reviews dataset provides a direct, unfiltered conduit to understanding user needs and ultimately driving better app performance and greater user satisfaction. Its structured format and granular detail make it an indispensable asset for data-driven decision-making in the mobile app industry.

Apple App Store Key StatisticsApps & Games in the Apple App StoreApps in the Apple App StoreGames in the Apple App StoreMost Popular Apple App Store CategoriesPaid vs Free Apps in Apple App...

This comprehensive iOS application reviews dataset contains thousands of authentic user reviews from the Apple App Store in English. The dataset provides valuable insights for app developers, marketers, and researchers studying mobile application performance and user sentiment.

Key Features:

• Real user reviews from popular iOS apps
• Star ratings from 1 to 5 stars
• Review dates and timestamps
• App store URLs and metadata
• User demographics and location data
• App version information
• Review titles and detailed feedback

Applications: Perfect for sentiment analysis, app store optimization, mobile app development research, user experience studies, and competitive analysis. This dataset enables businesses to understand user preferences, identify app improvement opportunities, and develop better mobile applications.

Data Quality: All reviews are genuine user feedback collected from the official Apple App Store, ensuring authenticity and reliability for research and business intelligence purposes. The dataset covers various app categories including fitness, shopping, education, entertainment, and productivity applications.

1) Data introduction • Apple-iphone-se-reviews dataset is a dataset that scrapes data from the Flipkart website using Selenium and BeautifulSoup links.

2) Data utilization (1)Apple-iphone-se-reviews data has characteristics that: • User ratings for Apple iPhone SE on Indian e-commerce website Flipkart are . We aim at NLP text classification through user ratings, review titles, and review text. (2)Apple-iphone-se-reviews data can be used to: • Rating prediction: You can support automated review analysis and summarization by developing machine learning models to predict ratings based on review text. • Product Improvement: Insights gained from reviews can help us identify common issues and areas for improvement in iPhone SE and guide product development and quality improvements.

There has been an increased emphasis on plant-based foods and diets. Although mobile technology has the potential to be a convenient and innovative tool to help consumers adhere to dietary guidelines, little is known about the content and quality of free, popular mobile health (mHealth) plant-based diet apps. The objective of the study was to assess the content and quality of free, popular mHealth apps supporting plant-based diets for Canadians. Free mHealth apps with high user ratings, a high number of user ratings, available on both Apple App and GooglePlay stores, and primarily marketed to help users follow plant-based diet were included. Using pre-defined search terms, Apple App and GooglePlay App stores were searched on December 22, 2020; the top 100 returns for each search term were screened for eligibility. Included apps were downloaded and assessed for quality by three dietitians/nutrition research assistants using the Mobile App Rating Scale (MARS) and the App Quality Evaluation (AQEL) scale. Of the 998 apps screened, 16 apps (mean user ratings±SEM: 4.6±0.1) met the eligibility criteria, comprising 10 recipe managers and meal planners, 2 food scanners, 2 community builders, 1 restaurant identifier, and 1 sustainability assessor. All included apps targeted the general population and focused on changing behaviors using education (15 apps), skills training (9 apps), and/or goal setting (4 apps). Although MARS (scale: 1–5) revealed overall adequate app quality scores (3.8±0.1), domain-specific assessments revealed high functionality (4.0±0.1) and aesthetic (4.0±0.2), but low credibility scores (2.4±0.1). The AQEL (scale: 0–10) revealed overall low score in support of knowledge acquisition (4.5±0.4) and adequate scores in other nutrition-focused domains (6.1–7.6). Despite a variety of free plant-based apps available with different focuses to help Canadians follow plant-based diets, our findings suggest a need for increased credibility and additional resources to complement the low support of knowledge acquisition among currently available plant-based apps. This research received no specific grant from any funding agency.

Fruits-262 dataset: A dataset containing a vast majority of the popular and known fruits

Versions and Updates:

The dataset's final version is the one presented here. No expectations for further development of the dataset at this moment.

If there seem to be any problems with the dataset, its descriptions, its properties or anything at all please contact me and I will help in what ways I can.

15/12/2021 - SYNASC2021 Paper

Based on the Bachelor's Thesis, a paper has been built and published at the SYNASC 2021 conference.

25/05/2021 - Bachelor's Thesis

The thesis, that had at its core building this fruit dataset and training CNN models on it, is finished and has been added. Information about the tasks and phases through which the dataset has been can also be found within the thesis.

25/05/2021 - Resized Dataset and Useful Scripts.

The resized dataset has been uploaded on 5 different dimensions along with some scripts that help in organizing and altering the dataset. The deployment took around 3-4 hours since some errors kept appearing when uploading. Sorry for any inconvenience.

Content

The following fruit types/labels/clades are included: abiu, acai, acerola, ackee, alligator apple, ambarella, apple, apricot, araza, avocado, bael, banana, barbadine, barberry, bayberry, beach plum, bearberry, bell pepper, betel nut, bignay, bilimbi, bitter gourd, black berry, black cherry, black currant, black mullberry, black sapote, blueberry, bolwarra, bottle gourd, brazil nut, bread fruit, buddha s hand, buffaloberry, burdekin plum, burmese grape, caimito, camu camu, canistel, cantaloupe, cape gooseberry, carambola, cardon, cashew, cedar bay cherry, cempedak, ceylon gooseberry, che, chenet, cherimoya, cherry, chico, chokeberry, clementine, cloudberry, cluster fig, cocoa bean, coconut, coffee, common buckthorn, corn kernel, cornelian cherry, crab apple, cranberry, crowberry, cupuacu, custard apple, damson, date, desert fig, desert lime, dewberry, dragonfruit, durian, eggplant, elderberry, elephant apple, emblic, entawak, etrog, feijoa, fibrous satinash, fig, finger lime, galia melon, gandaria, genipap, goji, gooseberry, goumi, grape, grapefruit, greengage, grenadilla, guanabana, guarana, guava, guavaberry, hackberry, hard kiwi, hawthorn, hog plum, honeyberry, honeysuckle, horned melon, illawarra plum, indian almond, indian strawberry, ita palm, jaboticaba, jackfruit, jalapeno, jamaica cherry, jambul, japanese raisin, jasmine, jatoba, jocote, jostaberry, jujube, juniper berry, kaffir lime, kahikatea, kakadu plum, keppel, kiwi, kumquat, kundong, kutjera, lablab, langsat, lapsi, lemon, lemon aspen, leucaena, lillipilli, lime, lingonberry, loganberry, longan, loquat, lucuma, lulo, lychee, mabolo, macadamia, malay apple, mamey apple, mandarine, mango, mangosteen, manila tamarind, marang, mayhaw, maypop, medlar, melinjo, melon pear, midyim, miracle fruit, mock strawberry, monkfruit, monstera deliciosa, morinda, mountain papaya, mountain soursop, mundu, muskmelon, myrtle, nance, nannyberry, naranjilla, native cherry, native gooseberry, nectarine, neem, nungu, nutmeg, oil palm, old world sycomore, olive, orange, oregon grape, otaheite apple, papaya, passion fruit, pawpaw, pea, peanut, pear, pequi, persimmon, pigeon plum, pigface, pili nut, pineapple, pineberry, pitomba, plumcot, podocarpus, pomegranate, pomelo, prikly pear, pulasan, pumpkin, pupunha, purple apple berry, quandong, quince, rambutan, rangpur, raspberry, red mulberry, redcurrant, riberry, ridged gourd, rimu, rose hip, rose myrtle, rose-leaf bramble, saguaro, salak, salal, salmonberry, sandpaper fig, santol, sapodilla, saskatoon, sea buckthorn, sea grape, snowberry, soncoya, strawberry, strawberry guava, sugar apple, surinam cherry, sycamore fig, tamarillo, tangelo, tanjong, taxus baccata, tayberry, texas persimmon, thimbleberry, tomato, toyon, ugli fruit, vanilla, velvet tamarind, watermelon, wax gourd, white aspen, white currant, white mulberry, white sapote, wineberry, wongi, yali pear, yellow plum, yuzu, zigzag vine, zucchini

Dataset properties

Total number of images: 225,640.

Number of classes: 262 fruits.

Number of images per label: Average: 861, Median: 1007, StDev: 276. (Initial target was 1,000 per label)

Image Width: Average: 213, Median: 209, StDev: 19.

Image Height: Average: 262, Median: 255, StDev: 30.

Missing Images from the initial 1,000 target: Average: 580, Median: 567, StDev: 258.

Format: a directory name represents a label and in each directory all the image data under the said label (the images are numbered but there might be missing numbers. The "renumber.py" script, if run, will fix the number gap problem).

Different varieties of the same fruit are generally stored in the same directory (Example: green, yellow and red apple).

The fruit images present in the dataset can contain the fruit in all the stages o...

Context

The subject matter of this dataset contains the stock prices of the 10 popular companies ( Apple, Amazon, Netflix, Microsoft, Google, Facebook, Tesla, Walmart, Uber and Zoom)

Content

Within the dataset one will encounter the following: The date - "Date" The opening price of the stock - "Open" The high price of that day - "High" The low price of that day - "Low" The closed price of that day - "Close" The amount of stocks traded during that day - "Volume" The stock's closing price that has been amended to include any distributions/corporate actions that occurs before next days open - "Adj[usted] Close" Time period - 2015 to 2021 (day level)

Tasks - Exploratory Data Analysis - Tell a visualization story - Compare stock price growth between companies - Stock price prediction - Time series analysis

Fruits-360 dataset: A dataset of images containing fruits, vegetables, nuts and seeds

Version: 2025.06.07.0

Content

The following fruits, vegetables and nuts and are included: Apples (different varieties: Crimson Snow, Golden, Golden-Red, Granny Smith, Pink Lady, Red, Red Delicious), Apricot, Avocado, Avocado ripe, Banana (Yellow, Red, Lady Finger), Beans, Beetroot Red, Blackberry, Blueberry, Cabbage, Caju seed, Cactus fruit, Cantaloupe (2 varieties), Carambula, Carrot, Cauliflower, Cherimoya, Cherry (different varieties, Rainier), Cherry Wax (Yellow, Red, Black), Chestnut, Clementine, Cocos, Corn (with husk), Cucumber (ripened, regular), Dates, Eggplant, Fig, Ginger Root, Goosberry, Granadilla, Grape (Blue, Pink, White (different varieties)), Grapefruit (Pink, White), Guava, Hazelnut, Huckleberry, Kiwi, Kaki, Kohlrabi, Kumsquats, Lemon (normal, Meyer), Lime, Lychee, Mandarine, Mango (Green, Red), Mangostan, Maracuja, Melon Piel de Sapo, Mulberry, Nectarine (Regular, Flat), Nut (Forest, Pecan), Onion (Red, White), Orange, Papaya, Passion fruit, Peach (different varieties), Pepino, Pear (different varieties, Abate, Forelle, Kaiser, Monster, Red, Stone, Williams), Pepper (Red, Green, Orange, Yellow), Physalis (normal, with Husk), Pineapple (normal, Mini), Pistachio, Pitahaya Red, Plum (different varieties), Pomegranate, Pomelo Sweetie, Potato (Red, Sweet, White), Quince, Rambutan, Raspberry, Redcurrant, Salak, Strawberry (normal, Wedge), Tamarillo, Tangelo, Tomato (different varieties, Maroon, Cherry Red, Yellow, not ripened, Heart), Walnut, Watermelon, Zucchini (green and dark).

Branches

The dataset has 5 major branches:

-The 100x100 branch, where all images have 100x100 pixels. See _fruits-360_100x100_ folder.

-The original-size branch, where all images are at their original (captured) size. See _fruits-360_original-size_ folder.

-The meta branch, which contains additional information about the objects in the Fruits-360 dataset. See _fruits-360_dataset_meta_ folder.

-The multi branch, which contains images with multiple fruits, vegetables, nuts and seeds. These images are not labeled. See _fruits-360_multi_ folder.

-The _3_body_problem_ branch where the Training and Test folders contain different (varieties of) the 3 fruits and vegetables (Apples, Cherries and Tomatoes). See _fruits-360_3-body-problem_ folder.

How to cite

Mihai Oltean, Fruits-360 dataset, 2017-

Dataset properties

For the 100x100 branch

Total number of images: 138704.

Training set size: 103993 images.

Test set size: 34711 images.

Number of classes: 206 (fruits, vegetables, nuts and seeds).

Image size: 100x100 pixels.

For the original-size branch

Total number of images: 58363.

Training set size: 29222 images.

Validation set size: 14614 images

Test set size: 14527 images.

Number of classes: 90 (fruits, vegetables, nuts and seeds).

Image size: various (original, captured, size) pixels.

For the 3-body-problem branch

Total number of images: 47033.

Training set size: 34800 images.

Test set size: 12233 images.

Number of classes: 3 (Apples, Cherries, Tomatoes).

Number of varieties: Apples = 29; Cherries = 12; Tomatoes = 19.

Image size: 100x100 pixels.

For the meta branch

Number of classes: 26 (fruits, vegetables, nuts and seeds).

For the multi branch

Number of images: 150.

Filename format:

For the 100x100 branch

image_index_100.jpg (e.g. 31_100.jpg) or

r_image_index_100.jpg (e.g. r_31_100.jpg) or

r?_image_index_100.jpg (e.g. r2_31_100.jpg)

where "r" stands for rotated fruit. "r2" means that the fruit was rotated around the 3rd axis. "100" comes from image size (100x100 pixels).

Different varieties of the same fruit (apple, for instance) are stored as belonging to different classes.

For the original-size branch

r?_image_index.jpg (e.g. r2_31.jpg)

where "r" stands for rotated fruit. "r2" means that the fruit was rotated around the 3rd axis.

The name of the image files in the new version does NOT contain the "_100" suffix anymore. This will help you to make the distinction between the original-size branch and the 100x100 branch.

For the multi branch

The file's name is the concatenation of the names of the fruits inside that picture.

Alternate download

The Fruits-360 dataset can be downloaded from:

Kaggle https://www.kaggle.com/moltean/fruits

GitHub https://github.com/fruits-360

How fruits were filmed

Fruits and vegetables were planted in the shaft of a low-speed motor (3 rpm) and a short movie of 20 seconds was recorded.

A Logitech C920 camera was used for filming the fruits. This is one of the best webcams available.

Behind the fruits, we placed a white sheet of paper as a background.

Here i...

The number of Apple iPhone unit sales dramatically increased between 2007 and 2023. Indeed, in 2007, when the iPhone was first introduced, Apple shipped around *** million smartphones. By 2023, this number reached over *** million units. The newest models and iPhone’s lasting popularity Apple has ventured into its 17th smartphone generation with its Phone ** lineup, which, released in September 2023, includes the **, ** Plus, ** Pro and Pro Max. Powered by the A16 bionic chip and running on iOS **, these models present improved displays, cameras, and functionalities. On the one hand, such features come, however, with hefty price tags, namely, an average of ***** U.S. dollars. On the other hand, they contribute to making Apple among the leading smartphone vendors worldwide, along with Samsung and Xiaomi. In the first quarter of 2024, Samsung shipped over ** million smartphones, while Apple recorded shipments of roughly ** million units. Success of Apple’s other products Apart from the iPhone, which is Apple’s most profitable product, Apple is also the inventor of other heavy-weight players in the consumer electronics market. The Mac computer and the iPad, like the iPhone, are both pioneers in their respective markets and have helped popularize the use of PCs and tablets. The iPad is especially successful, having remained as the largest vendor in the tablet market ever since its debut. The hottest new Apple gadget is undoubtedly the Apple Watch, which is a line of smartwatches that has fitness tracking capabilities and can be integrated via iOS with other Apple products and services. The Apple Watch has also been staying ahead of other smart watch vendors since its initial release and secures around ** percent of the market share as of the latest quarter.

This dataset provides information about the number of properties, residents, and average property values for Apple Tree Common cross streets in Livermore, CA.

This dataset contains 76,535 real user reviews collected from the Google Play Store across seven popular music streaming applications: Spotify, Apple Music, SoundCloud, TIDAL, Deezer, Shazam, and Google Play Music.

Each review includes: * 🌐 The app name * 📝 The review content * ⭐ A star rating from 1 to 5 * 📱 The app version (if available) * 📅 The date the review was written

This dataset is cleaned (empty or invalid entries removed) but intentionally unaltered in tone, preserving user expressions (including slang, emojis, and punctuation). Total entries: 76,535 Language: Primarily English Date range: Varied (depending on app)

Wearable physical activity monitors are growing in popularity and provide the opportunity for large numbers of the public to self-monitor physical activity behaviours. The latest generation of these devices feature multiple sensors, ostensibly similar or even superior to advanced research instruments. However, little is known about the accuracy of their energy expenditure estimates. Here, we assessed their performance against criterion measurements in both controlled laboratory conditions (simulated activities of daily living and structured exercise) and over a 24 hour period in free-living conditions. Thirty men (n=15) and women (n=15) wore three multi-sensor consumer monitors (Microsoft Band, Apple Watch and Fitbit Charge HR), an accelerometry-only device as a comparison (Jawbone UP24) and validated research-grade multi-sensor devices (BodyMedia Core and individually calibrated Actiheart™). During discrete laboratory activities when compared against indirect calorimetry, the Apple Watch performed similarly to criterion measures. The Fitbit Charge HR was less consistent at measurement of discrete activities, but produced similar free-living estimates to the Apple Watch. Both these devices underestimated free-living energy expenditure (-394 kcal/d and -405 kcal/d, respectively; P<0.01). The multi-sensor Microsoft Band and accelerometry-only Jawbone UP24 devices underestimated most laboratory activities and substantially underestimated free-living expenditure (-1128 kcal/d and -998 kcal/d, respectively; P<0.01). None of the consumer devices were deemed equivalent to the reference method for daily energy expenditure. For all devices, there was a tendency for negative bias with greater daily energy expenditure. No consumer monitors performed as well as the research-grade devices although in some (but not all) cases, estimates were close to criterion measurements. Thus, whilst industry-led innovation has improved the accuracy of consumer monitors, these devices are not yet equivalent to the best research-grade devices or indeed equivalent to each other. We propose independent quality standards and/or accuracy ratings for consumer devices are required.

What is the data?

The datasets contain the top songs from the said era or year accordingly (as presented in the name of each dataset). Note that only the KPopHits90s dataset represents an era (1989-2001). Although there is a lack of easily available and reliable sources to show the actual K-Pop hits per year during the 90s, this era was still included as this time period was when the first generation of K-Pop stars appeared. Each of the other datasets represent a specific year after the 90s.

How was it obtained?

A song is considered to be a K-Pop hit during that era or year if it is included in the annual series of K-Pop Hits playlists, which is created officially by Apple Music. Note that for the dataset that represents the 90s, the playlist 90s K-Pop Essentials was used as the reference.

1. These playlists were transferred into Spotify through the Tune My Music site. After transferring, the site also presented all the missing songs from each Spotify playlist when compared to the original Apple Music playlists.
   • Any data besides the names and artists of the hit songs were not directly obtained from Apple Music since these other details of songs in this music service are only available for those enrolled as members of the Apple Developer Program.
2. The presented missing songs from each playlist was manually searched and, if found, added to the respective Spotify playlist.
   • For the songs that were found, there are three types: (1) the song by the original artist, (2) the instrumental of the original song and (3) a cover of the song. When the first type is not found, the two other types are searched and are compared to each other. The one that sounded the most like the original song (from the Apple Music playlist) is chosen as the substitute in the Spotify playlist.
   • Presented is a link containing all the missing data per playlist (when the initial Spotify playlists were compared to the original Apple Music playlists) and the action done to each one.
3. The necessary identification details and specific audio features of each track were obtained through the use of the Spotipy library and Spotify Web API documentation.

Why did you make this?

As someone who has a particular curiosity to the field of data science and a genuine love for the musicality in the K-Pop scene, this data set was created to make something out of the strong interest I have for these separate subjects.

Acknowledgements

I would like to express my sincere gratitude to Apple Music for creating the annual K-Pop playlists, Spotify for making their API very accessible, Spotipy for making it easier to get the desired data from the Spotify Web API, Tune My Music for automating the process of transferring one's library into another service's library and, of course, all those involved in the making of these songs and artists included in these datasets for creating such high quality music and concepts digestible even for the general public.

Analysis of ‘Historical Stock Price of (FAANG + 5) companies’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/suddharshan/historical-stock-price-of-10-popular-companies on 28 January 2022.

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

Context

The subject matter of this dataset contains the stock prices of the 10 popular companies ( Apple, Amazon, Netflix, Microsoft, Google, Facebook, Tesla, Walmart, Uber and Zoom)

Content

Within the dataset one will encounter the following: The date - "Date" The opening price of the stock - "Open" The high price of that day - "High" The low price of that day - "Low" The closed price of that day - "Close" The amount of stocks traded during that day - "Volume" The stock's closing price that has been amended to include any distributions/corporate actions that occurs before next days open - "Adj[usted] Close" Time period - 2015 to 2021 (day level)

Tasks - Exploratory Data Analysis - Tell a visualization story - Compare stock price growth between companies - Stock price prediction - Time series analysis

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

Estimates suggest that Apple Music had 95 million subscribers worldwide in June 2024, up by 2 million from the previous year. Launched in 2015 by U.S. tech giant Apple, Apple Music is the second largest music streaming service worldwide, competing with market leader Spotify. Spotify remains market leader While Apple Music is a popular music streaming platform, accounting for 12.6 percent of subscribers worldwide, the 2008 founded streaming service Spotify remains the market leader with a subscriber share of nearly 32 percent. Financially this meant that the Swedish company generated a global revenue of 3.7 billion euros through its Premium accounts in the fourth quarter of 2024 alone.Music streaming overall increasesOverall, music streaming has experienced significant growth over the last decade. Even if the annual growth rate is gradually declining, it still stood at over 7 percent in 2024, becoming the music industry’s main revenue driver and reaching a revenue of 20 billion U.S. dollars worldwide in 2024.

Description

This dataset presents a comprehensive compilation of the most streamed songs on Spotify in 2024. It provides extensive insights into each track's attributes, popularity, and presence on various music platforms, offering a valuable resource for music analysts, enthusiasts, and industry professionals. The dataset includes information such as track name, artist, release date, ISRC, streaming statistics, and presence on platforms like YouTube, TikTok, and more.

Here is the link for the 2023 data: "https://www.kaggle.com/datasets/nelgiriyewithana/top-spotify-songs-2023">Most Streamed Spotify Songs 2023 🟢

Key Features

• Track Name: Name of the song.
• Album Name: Name of the album the song belongs to.
• Artist: Name of the artist(s) of the song.
• Release Date: Date when the song was released.
• ISRC: International Standard Recording Code for the song.
• All Time Rank: Ranking of the song based on its all-time popularity.
• Track Score: Score assigned to the track based on various factors.
• Spotify Streams: Total number of streams on Spotify.
• Spotify Playlist Count: Number of Spotify playlists the song is included in.
• Spotify Playlist Reach: Reach of the song across Spotify playlists.
• Spotify Popularity: Popularity score of the song on Spotify.
• YouTube Views: Total views of the song's official video on YouTube.
• YouTube Likes: Total likes on the song's official video on YouTube.
• TikTok Posts: Number of TikTok posts featuring the song.
• TikTok Likes: Total likes on TikTok posts featuring the song.
• TikTok Views: Total views on TikTok posts featuring the song.
• YouTube Playlist Reach: Reach of the song across YouTube playlists.
• Apple Music Playlist Count: Number of Apple Music playlists the song is included in.
• AirPlay Spins: Number of times the song has been played on radio stations.
• SiriusXM Spins: Number of times the song has been played on SiriusXM.
• Deezer Playlist Count: Number of Deezer playlists the song is included in.
• Deezer Playlist Reach: Reach of the song across Deezer playlists.
• Amazon Playlist Count: Number of Amazon Music playlists the song is included in.
• Pandora Streams: Total number of streams on Pandora.
• Pandora Track Stations: Number of Pandora stations featuring the song.
• Soundcloud Streams: Total number of streams on Soundcloud.
• Shazam Counts: Total number of times the song has been Shazamed.
• TIDAL Popularity: Popularity score of the song on TIDAL.
• Explicit Track: Indicates whether the song contains explicit content.

Potential Use Cases

• Music Analysis: Analyze trends in audio features to understand popular song characteristics.
• Platform Comparison: Compare song popularity across different music platforms.
• Artist Impact: Study the relationship between artist attributes and song success.
• Temporal Trends: Identify changes in music attributes and preferences over time.
• Cross-Platform Presence: Investigate song performance across various streaming services.

Your support through an upvote would be greatly appreciated if you find this dataset useful! ❤️🙂 Thank you.

About Dataset

Inspiration:

1. SVM Model

2. Logistic Regression Model

Please feel free to share it with others and consider supporting me if you find it helpful ⭐️.

Context

The Apple M1 MacBook is a popular laptop that has gained a lot of attention in recent years due to its impressive performance and energy efficiency. If you are considering purchasing an M1 MacBook, there are several factors that you may want to consider before making your decision.

• One factor to consider is your budget. The M1 MacBook is available in a range of prices, depending on the specific model and configuration you choose. It's important to carefully consider your budget and choose a model that fits your needs and your financial situation.

• Another factor to consider is your computing needs. The M1 MacBook is a powerful machine that is well-suited for a wide range of tasks, including running demanding software, playing games, and handling heavy workloads. However, if you only need a laptop for basic tasks like web browsing and word processing, you may be able to get by with a less powerful and less expensive model.

• You may also want to consider the design and form factor of the M1 MacBook. The M1 MacBook is available in both 13-inch and 16-inch sizes, and you'll need to decide which size is right for you. Additionally, the M1 MacBook is available in both a standard laptop form factor and a more portable "MacBook Air" form factor.

• Finally, you'll want to consider the availability and support options for the M1 MacBook. Apple is known for its strong support network, and the M1 MacBook is no exception. You can find support through Apple's online resources, as well as through authorized Apple service providers.

Content

A quantitative methodology was used to conducted a survey for a period of 2 weeks across all social media channels for primary data collection. Therefore, the objective of this project is to determine which features are you going to use to understand purchasing behaviors. Think about the input dataset and output dataset that your model will have to give.

You might want to have a look at this study for some ideas: Bank Marketing Data Set

General features

• trust_apple - Brand trust : (Yes, No)

• Interest_computers - Level of interest in computers : (1 Not interested - 5 Very interested)

• age_computer - Age of your current computer : (0 means less than one year - 6 years or more )

• user_pc or mac - Type of computer : ( 0 PC , 1 Apple, 2 Hp or Other )

• appleproducts_count - Count of apple products your own : (0 - 10 or more)

• familiarity_m1 - Brand familiarity (Yes, No)

Apple M1 Macs features

• f_batterylife - Importance of (1 Not important - 5 is very import )

• f_price - Cheaper price (1 Not important - 5 is very import )

• f_size - Thinner of computer (1 Not important - 5 is very import )

• f_multitasking - Improved multitasking power (1 Not important - 5 is very import )

• f_noise - Less noisy (1 Not important - 5 is very import )

• f_performance - Improved performance (1 Not important - 5 is very import )

• f_neural - Neural engine (1 Not important - 5 is very import )

• f_synergy - How important is a seamless experience (1 Not important - 5 is very import )

• f_performanceloss - A small loss in performance (1 Not important - 5 is very import )

• m1_consideration - M1 Chip into account in the selection process of buying a new Apple computer (1 Not important - 5 is very import )

• m1_purchase - Would you buy one of the new Apple M1 Macs ( Yes, No)

Demographic Features

• gender

• age_group

• income_group

• status

• domain

📄 Dataset Description

This dataset contains high-quality images of four popular fruits—**apple, guava, mango, and pomegranate**—categorized into healthy and various disease conditions. It is designed to support research and development in plant disease detection, image classification, and agricultural AI applications.

Each fruit type includes multiple classes: - Apple: Blotch, Rot, Scab, and Healthy - Guava: Anthracnose, Fruit Fly, and Healthy - Mango: Alternaria, Anthracnose, Black Mould Rot (Aspergillus), Stem and Rot (Lasiodiplodia), and Healthy - Pomegranate: Alternaria, Anthracnose, Bacterial Blight, Cercospora, and Healthy

🧠 Use Cases

• Fruit disease classification using machine learning / deep learning
• Image-based plant pathology detection
• Educational demos in computer vision for agriculture

📦 Format

• Images organized in folders by fruit and disease class
• Suitable for training CNNs and other image classification models

🔍 Potential Projects

• Build a disease detection app for farmers
• Fine-tune a pre-trained model (like ResNet or MobileNet)
• Create a fruit disease classifier with transfer learning

Description

UTKPAD is a replay attack database prepared for face age verification as part of the paper "Vulnerability of Face Age Verification to Replay Attacks" published in ICASSP 2024 conference. This database is originated from the well-known large dataset, UTKFace of face images with age labels ranging from 1 to 100 plus years old. Replay attack are recorded with three different mobiles phones: Apple iPhone 12, Samsung Galaxy S9 and Huawei Mate 30. With UTKPAD database, we also provide file lists that can be used for training and testing of replay attacks detection and for vulnerability assessment of age verification systems.

The face images in the UTKFace are first enhanced with a face restoration CodeFormer method. Each enhanced image is converted into a video clip with a subsequent concatenation process to have one video clip including all image-to-video converted files. The final video clip is then replayed on Apple iPad Pro in order to record it with three different mobiles phones: Apple iPhone 12, Samsung Galaxy S9 and Huawei Mate 30. And, finally, the recordings are de-concatenated/segmented into sub video clips and each video clip is sampled by taking the middle frame to construct the database of replay attack images.

To avoid breaching the copyright, we release the dataset in the form of deltas that are not actual images. The attack images can be recovered only if the user also obtains and downloads the original UTKFace dataset. When UTKFace is downloaded, however, the attack images of UTKPAD can be easily computed using the script we provide.

Reference

If you're using this dataset, please cite the following publication

@INPROCEEDINGS{Korshunov_ICASSP_2024,
author = {Korshunov, Pavel and George, Anjith and {\"O}zbulak, G{\"o}khan and Marcel, S{\'{e}}bastien},
projects = {Idiap, Biometrics Center},
title = {Vulnerablity of Face Age Verification to Replay Attacks},
booktitle = {ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
year = {2024},
}