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...

Cash-and-Equivalents Time Series for Apple Inc. Apple Inc. designs, manufactures, and markets smartphones, personal computers, tablets, wearables, and accessories worldwide. The company offers iPhone, a line of smartphones; Mac, a line of personal computers; iPad, a line of multi-purpose tablets; and wearables, home, and accessories comprising AirPods, Apple TV, Apple Watch, Beats products, and HomePod. It also provides AppleCare support and cloud services; and operates various platforms, including the App Store that allow customers to discover and download applications and digital content, such as books, music, video, games, and podcasts, as well as advertising services include third-party licensing arrangements and its own advertising platforms. In addition, the company offers various subscription-based services, such as Apple Arcade, a game subscription service; Apple Fitness+, a personalized fitness service; Apple Music, which offers users a curated listening experience with on-demand radio stations; Apple News+, a subscription news and magazine service; Apple TV+, which offers exclusive original content; Apple Card, a co-branded credit card; and Apple Pay, a cashless payment service, as well as licenses its intellectual property. The company serves consumers, and small and mid-sized businesses; and the education, enterprise, and government markets. It distributes third-party applications for its products through the App Store. The company also sells its products through its retail and online stores, and direct sales force; and third-party cellular network carriers, wholesalers, retailers, and resellers. Apple Inc. was founded in 1976 and is headquartered in Cupertino, California.

Apple’s iPhone sales accounted for around ** percent of the company’s overall revenue in the first quarter of fiscal year 2025, the largest share of all Apple products. Over the years, services as well as wearables, home and accessories have made a growing contribution to Apple’s net sales. Apple’s revenue growth amid the pandemic In the first quarter of financial year 2025, Apple’s global revenue reached around *** billion U.S. dollars. The Americas are Apple’s largest regional market and contributed to around ** percent of the firm’s sales in that quarter. Who are Apple’s competitors? Having a broad family of products, Apple competes with different companies in different markets. Samsung is Apple’s largest adversaries in the global smartphone market, where the company had a share of almost ** percent in the second quarter of 2024. Similarly, Apple has a solid position in the PC market without a leading advantage. The situation is reversed in the tablet market and the smartwatch market, where Apple has remained the leader since the early days, staying ahead of Samsung, Huawei, Amazon, etc.

Change-Receivables Time Series for Apple Inc. Apple Inc. designs, manufactures, and markets smartphones, personal computers, tablets, wearables, and accessories worldwide. The company offers iPhone, a line of smartphones; Mac, a line of personal computers; iPad, a line of multi-purpose tablets; and wearables, home, and accessories comprising AirPods, Apple TV, Apple Watch, Beats products, and HomePod. It also provides AppleCare support and cloud services; and operates various platforms, including the App Store that allow customers to discover and download applications and digital content, such as books, music, video, games, and podcasts, as well as advertising services include third-party licensing arrangements and its own advertising platforms. In addition, the company offers various subscription-based services, such as Apple Arcade, a game subscription service; Apple Fitness+, a personalized fitness service; Apple Music, which offers users a curated listening experience with on-demand radio stations; Apple News+, a subscription news and magazine service; Apple TV+, which offers exclusive original content; Apple Card, a co-branded credit card; and Apple Pay, a cashless payment service, as well as licenses its intellectual property. The company serves consumers, and small and mid-sized businesses; and the education, enterprise, and government markets. It distributes third-party applications for its products through the App Store. The company also sells its products through its retail and online stores, and direct sales force; and third-party cellular network carriers, wholesalers, retailers, and resellers. Apple Inc. was founded in 1976 and is headquartered in Cupertino, California.

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.

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 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.

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.

Current-Deferred-Revenue Time Series for Apple Inc. Apple Inc. designs, manufactures, and markets smartphones, personal computers, tablets, wearables, and accessories worldwide. The company offers iPhone, a line of smartphones; Mac, a line of personal computers; iPad, a line of multi-purpose tablets; and wearables, home, and accessories comprising AirPods, Apple TV, Apple Watch, Beats products, and HomePod. It also provides AppleCare support and cloud services; and operates various platforms, including the App Store that allow customers to discover and download applications and digital content, such as books, music, video, games, and podcasts, as well as advertising services include third-party licensing arrangements and its own advertising platforms. In addition, the company offers various subscription-based services, such as Apple Arcade, a game subscription service; Apple Fitness+, a personalized fitness service; Apple Music, which offers users a curated listening experience with on-demand radio stations; Apple News+, a subscription news and magazine service; Apple TV+, which offers exclusive original content; Apple Card, a co-branded credit card; and Apple Pay, a cashless payment service, as well as licenses its intellectual property. The company serves consumers, and small and mid-sized businesses; and the education, enterprise, and government markets. It distributes third-party applications for its products through the App Store. The company also sells its products through its retail and online stores, and direct sales force; and third-party cellular network carriers, wholesalers, retailers, and resellers. Apple Inc. was founded in 1976 and is headquartered in Cupertino, California.

In the first quarter of its 2025 fiscal year, Apple generated around ** billion U.S. dollars in revenue from the sales of iPhones. Apple iPhone revenue The Apple iPhone is one of the biggest success stories in the smartphone industry. Since its introduction to the market in 2007, Apple has sold more than *** billion units worldwide. As of the third quarter of 2024, the Apple iPhone’s market share of new smartphone sales was over ** percent. Much of its accomplishments can be attributed to Apple’s ability to keep the product competitive throughout the years, with new releases and updates. Apple iPhone growth The iPhone has shown to be a crucial product for Apple, considering that the iPhone’s share of the company’s total revenue has consistently grown over the years. In the first quarter of 2009, the iPhone sales were responsible for about ********* of Apple’s revenue. In the third quarter of FY 2024, this figure reached a high of roughly ** percent, equating to less than ** billion U.S. dollars in that quarter. In terms of units sold, Apple went from around **** million units in 2010 to about *** million in 2023, but registered a peak in the fourth quarter of 2020 with more than ** million iPhones sold worldwide.

This is a detailed description of the dataset, a data sheet for the dataset as proposed by Gebru et al.

Motivation for Dataset Creation Why was the dataset created? Embrapa ADD 256 (Apples by Drones Detection Dataset — 256 × 256) was created to provide images and annotation for research on *apple detection in orchards for UAV-based monitoring in apple production.

What (other) tasks could the dataset be used for? Apple detection in low-resolution scenarios, similar to the aerial images employed here.

Who funded the creation of the dataset? The building of the ADD256 dataset was supported by the Embrapa SEG Project 01.14.09.001.05.04, Image-based metrology for Precision Agriculture and Phenotyping, and FAPESP under grant (2017/19282-7).

Dataset Composition What are the instances? Each instance consists of an RGB image and an annotation describing apples locations as circular markers (i.e., presenting center and radius).

How many instances of each type are there? The dataset consists of 1,139 images containing 2,471 apples.

What data does each instance consist of? Each instance contains an 8-bits RGB image. Its corresponding annotation is found in the JSON files: each apple marker is composed by its center (cx, cy) and its radius (in pixels), as seen below:

"gebler-003-06.jpg": [ { "cx": 116, "cy": 117, "r": 10 }, { "cx": 134, "cy": 113, "r": 10 }, { "cx": 221, "cy": 95, "r": 11 }, { "cx": 206, "cy": 61, "r": 11 }, { "cx": 92, "cy": 1, "r": 10 } ],

Dataset.ipynb is a Jupyter Notebook presenting a code example for reading the data as a PyTorch's Dataset (it should be straightforward to adapt the code for other frameworks as Keras/TensorFlow, fastai/PyTorch, Scikit-learn, etc.)

Is everything included or does the data rely on external resources? Everything is included in the dataset.

Are there recommended data splits or evaluation measures? The dataset comes with specified train/test splits. The splits are found in lists stored as JSON files.

| | Number of images | Number of annotated apples | | --- | --- | --- | |Training | 1,025 | 2,204 | |Test | 114 | 267 | |Total | 1,139 | 2,471 |

Dataset recommended split.

Standard measures from the information retrieval and computer vision literature should be employed: precision and recall, F1-score and average precision as seen in COCO and Pascal VOC.

What experiments were initially run on this dataset? The first experiments run on this dataset are described in A methodology for detection and location of fruits in apples orchards from aerial images by Santos & Gebler (2021).

Data Collection Process How was the data collected? The data employed in the development of the methodology came from two plots located at the Embrapa’s Temperate Climate Fruit Growing Experimental Station at Vacaria-RS (28°30’58.2”S, 50°52’52.2”W). Plants of the varieties Fuji and Gala are present in the dataset, in equal proportions. The images were taken during December 13, 2018, by an UAV (DJI Phantom 4 Pro) that flew over the rows of the field at a height of 12 m. The images mix nadir and non-nadir views, allowing a more extensive view of the canopies. A subset from the images was random selected and 256 × 256 pixels patches were extracted.

Who was involved in the data collection process? T. T. Santos and L. Gebler captured the images in field. T. T. Santos performed the annotation.

How was the data associated with each instance acquired? The circular markers were annotated using the VGG Image Annotator (VIA).

WARNING: Find non-ripe apples in low-resolution images of orchards is a challenging task even for humans. ADD256 was annotated by a single annotator. So, users of this dataset should consider it a noisy dataset.

Data Preprocessing What preprocessing/cleaning was done? No preprocessing was applied.

Dataset Distribution How is the dataset distributed? The dataset is available at GitHub.

When will the dataset be released/first distributed? The dataset was released in October 2021.

What license (if any) is it distributed under? The data is released under Creative Commons BY-NC 4.0 (Attribution-NonCommercial 4.0 International license). There is a request to cite the corresponding paper if the dataset is used. For commercial use, contact Embrapa Agricultural Informatics business office.

Are there any fees or access/export restrictions? There are no fees or restrictions. For commercial use, contact Embrapa Agricultural Informatics business office.

Dataset Maintenance Who is supporting/hosting/maintaining the dataset? The dataset is hosted at Embrapa Agricultural Informatics and all comments or requests can be sent to Thiago T. Santos (maintainer).

Will the dataset be updated? There is no scheduled updates.

If others want to extend/augment/build on this dataset, is there a mechanism for them to do so? Contributors should contact the maintainer by e-mail.

No warranty The maintainers and their institutions are exempt from any liability, judicial or extrajudicial, for any losses or damages arising from the use of the data contained in the image database.

1) Data Introduction • This dataset is image data of fire blight and similar diseases of major fruit trees (apples, pears).

2) Data Utilization (1) Fire Blight Disease Data has characteristics that: • The dataset contains images showing the different stages and types of fire blight affecting different fruit species. • Provides the information needed to understand the symptoms, progression, and effects of fire blight on a variety of fruits. (2) Fire Blight Disease Data can be used to: • Develop Disease Recognition and Classification Models: AI models can be trained to recognize and classify different types of fire blight diseases. For example, analyzing images from agricultural monitoring systems can identify specific symptoms, enabling early diagnosis and treatment. • Plant Health Monitoring: Analyzing disease patterns in fruit will allow for early detection and intervention to manage fire blight disease by developing systems to monitor plant health. • Disease Control and Prevention: Analyzing the conditions under which fire blight disease occurs and spreads helps identify effective management practices. • Environmental Impact Studies: Provides data to study how environmental factors affect the occurrence and severity of fire blight disease to help develop preventive measures.

Apple Inc. designs, manufactures, and markets smartphones, personal computers, tablets, wearables, and accessories worldwide. The company offers iPhone, a line of smartphones; Mac, a line of personal computers; iPad, a line of multi-purpose tablets; and wearables, home, and accessories comprising AirPods, Apple TV, Apple Watch, Beats products, and HomePod. It also provides AppleCare support and cloud services; and operates various platforms, including the App Store that allow customers to discover and download applications and digital content, such as books, music, video, games, and podcasts. In addition, the company offers various services, such as Apple Arcade, a game subscription service; Apple Fitness+, a personalized fitness service; Apple Music, which offers users a curated listening experience with on-demand radio stations; Apple News+, a subscription news and magazine service; Apple TV+, which offers exclusive original content; Apple Card, a co-branded credit card; and Apple Pay, a cashless payment service, as well as licenses its intellectual property. The company serves consumers, and small and mid-sized businesses; and the education, enterprise, and government markets. It distributes third-party applications for its products through the App Store. The company also sells its products through its retail and online stores, and direct sales force; and third-party cellular network carriers, wholesalers, retailers, and resellers. Apple Inc. was founded in 1976 and is headquartered in Cupertino, California.

The Apple share market data of 10 years can be used for educational purposes in a variety of ways, such as:

To learn about the stock market and how it works. By studying the historical price movements of Apple stock, you can learn about the different factors that can affect the stock market, such as economic conditions, interest rates, and company earnings. To develop investment strategies. By analyzing the Apple share market data, you can identify patterns and trends that can help you make better investment decisions. For example, you might notice that Apple stock tends to perform well in certain economic conditions or when the company releases new products. To learn about Apple's business. By tracking the company's stock price, you can get a sense of how investors are viewing Apple's financial performance and future prospects. This information can be helpful for making decisions about whether or not to invest in Apple stock. To conduct research on financial topics. The Apple share market data can be used to support research on a variety of financial topics, such as the impact of inflation on stock prices, the relationship between stock prices and interest rates, and the performance of different investment strategies. In addition to these educational purposes, the Apple share market data can also be used for other purposes, such as:

To create trading algorithms. Trading algorithms are computer programs that automatically buy and sell stocks based on certain criteria. The Apple share market data can be used to train trading algorithms to identify profitable trading opportunities. To develop risk management strategies. Risk management strategies are used to protect investors from losses. The Apple share market data can be used to identify risks associated with investing in Apple stock and to develop strategies to mitigate those risks. To make corporate decisions. The Apple share market data can be used by companies to make decisions about their business, such as how much to invest in research and development, how to allocate capital, and when to issue new shares. Overall, the Apple share market data is a valuable resource that can be used for a variety of educational and practical purposes. If you are interested in learning more about the stock market or investing, I encourage you to explore the Apple share market data.

In the first quarter of 2023, Lenovo shipped 22.4 percent of all personal computers worldwide, whilst HP Inc. occupied 21.1 percent of the PC market. Dell ranked third among vendors in terms of PC shipments, accounting for 16.7 percent of the market.

Here are a few use cases for this project:

1. Grocery Store Automation: The model can be employed in automated systems within grocery or fruit stores to identify if a person is picking up apples or lemons. This could serve in real-time inventory management and loss prevention.

2. Health & Wellness Applications: This model could be used in health and diet-based applications to track and count the number of apples or lemons a user consumes, further providing insights about their daily fruit intake.

3. Interactive Educational Games: The model can be leveraged to develop interactive, educational games where children or adults need to identify and count the number of persons, apples, or lemons in an image.

4. Surveillance Systems in Farming: The model could be beneficial in monitoring orchards of apple and lemon trees for signs of harvesting or any unusual activity.

5. New AI Training: The model's output could be used as input to other AI models for complex tasks, for instance, recognizing actions such as a person eating an apple or a lemon.

About this dataset

Context

The dataset tabulates the data for the Apple Valley, CA population pyramid, which represents the Apple Valley population distribution across age and gender, using estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates. It lists the male and female population for each age group, along with the total population for those age groups. Higher numbers at the bottom of the table suggest population growth, whereas higher numbers at the top indicate declining birth rates. Furthermore, the dataset can be utilized to understand the youth dependency ratio, old-age dependency ratio, total dependency ratio, and potential support ratio.

Key observations

• Youth dependency ratio, which is the number of children aged 0-14 per 100 persons aged 15-64, for Apple Valley, CA, is 40.2.
• Old-age dependency ratio, which is the number of persons aged 65 or over per 100 persons aged 15-64, for Apple Valley, CA, is 28.7.
• Total dependency ratio for Apple Valley, CA is 68.9.
• Potential support ratio, which is the number of youth (working age population) per elderly, for Apple Valley, CA is 3.5.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Variables / Data Columns

• Age Group: This column displays the age group for the Apple Valley population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Apple Valley for the selected age group is shown in the following column.
• Population (Female): The female population in the Apple Valley for the selected age group is shown in the following column.
• Total Population: The total population of the Apple Valley for the selected age group is shown in the following column.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Apple Valley Population by Age. You can refer the same here

About this dataset

Context

The dataset tabulates the data for the Pine Apple, AL population pyramid, which represents the Pine Apple population distribution across age and gender, using estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates. It lists the male and female population for each age group, along with the total population for those age groups. Higher numbers at the bottom of the table suggest population growth, whereas higher numbers at the top indicate declining birth rates. Furthermore, the dataset can be utilized to understand the youth dependency ratio, old-age dependency ratio, total dependency ratio, and potential support ratio.

Key observations

• Youth dependency ratio, which is the number of children aged 0-14 per 100 persons aged 15-64, for Pine Apple, AL, is 23.9.
• Old-age dependency ratio, which is the number of persons aged 65 or over per 100 persons aged 15-64, for Pine Apple, AL, is 217.4.
• Total dependency ratio for Pine Apple, AL is 241.3.
• Potential support ratio, which is the number of youth (working age population) per elderly, for Pine Apple, AL is 0.5.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Variables / Data Columns

• Age Group: This column displays the age group for the Pine Apple population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Pine Apple for the selected age group is shown in the following column.
• Population (Female): The female population in the Pine Apple for the selected age group is shown in the following column.
• Total Population: The total population of the Pine Apple for the selected age group is shown in the following column.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Pine Apple Population by Age. You can refer the same here

This dataset consists of four sets of flower images, from three different species: apple, peach, and pear, and accompanying ground truth images. The images were acquired under a range of imaging conditions. These datasets support work in an accompanying paper that demonstrates a flower identification algorithm that is robust to uncontrolled environments and applicable to different flower species. While this data is primarily provided to support that paper, other researchers interested in flower detection may also use the dataset to develop new algorithms. Flower detection is a problem of interest in orchard crops because it is related to management of fruit load. Funding provided through ARS Integrated Orchard Management and Automation for Deciduous Tree Fruit Crops. Resources in this dataset:Resource Title: AppleA images. File Name: AppleA.zipResource Description: 147 images of an apple tree in bloom acquired with a Canon EOS 60D.Resource Title: Training image names from Apple A dataset. File Name: train.txtResource Description: This is a list of filenames used in training; see related paper for details.Resource Title: AppleA labels. File Name: AppleA_Labels.zipResource Description: Binary images for the Apple A set, where white represents flower pixels and black, non-flower pixels. June 25, 2018: 5 files added: 275.png, 316.png, 328.png, 336.png, 369.png.Resource Title: Validation image names from Apple A dataset. File Name: val.txtResource Description: This is a list of filenames used in testing; see related paper for details. June 25, 2018: 5 filenames added. IMG_0275.JPG IMG_0316.JPG IMG_0328.JPG IMG_0336.JPG IMG_0369.JPGResource Title: AppleB images. File Name: AppleB.zipResource Description: 15 images of an apple tree in bloom acquired with a GoPro HERO 5. June 25, 2018: 3 files added. 23.bmp 28.bmp 42.bmpResource Title: AppleB labels. File Name: AppleB_Labels.zipResource Description: Binary images for the Apple B set, where white represents flower pixels and black, non-flower pixels. June 25, 2018: 3 files added. 23.bmp 28.bmp 42.bmpResource Title: Peach. File Name: Peach.zipResource Description: 20 images of an peach tree in bloom acquired with a GoPro HERO 5. June 25, 2018: 4 files added. 14.bmp 34.bmp 40.bmp 41.bmpResource Title: Peach labels. File Name: PeachLabels.zipResource Description: Binary images for the Peach set, where white represents flower pixels and black, non-flower pixels. June 25, 2018: 4 files added. 14.bmp 34.bmp 40.bmp 41.bmpResource Title: Pear. File Name: Pear.zipResource Description: 15 images of a free-standing pear tree in bloom, acquired with a GoPro HERO5. June 25, 2018: 3 files added. 1_25.bmp 1_62.bmp 2_28.bmpResource Title: Pear labels. File Name: PearLabels.zipResource Description: Binary images for the pear set, where white represents flower pixels and black, non-flower pixels. June 25, 2018: 3 files added. 1_25.bmp 1_62.bmp 2_28.bmpResource Title: Apple A Labeled images from training set . File Name: AppleALabels_Train.zipResource Description: Binary images for the Apple A set, where white represents flower pixels and black, non-flower pixels. These images form the training set. Resource added August 20, 2018. User noted that this resource was missing.

App Reviews

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F1842206%2Fd4a6033b6bd31af45d5175d02e697934%2FAPPLEAPPS2.png?generation=1700357122842963&alt=media" alt="">

1. uber-request-a-ride-us- 73787 rows
2. waze-navigation-live-traffic-us- 26260 rows
3. facebook-us- 24200 rows
4. spotify-music-and-podcasts-us- 15580 rows
5. netflix-us- 11760 rows
6. pinterest-us- 10860 rows
7. X-us- 8160 rows
8. tiktok-us- 2542 rows
9. tinder-dating-chat-friends-us- 1060 rows
10. instagram-us- 300 rows

These reviews are from Apple App Store

Usage

This dataset should paint a good picture on what is the public's perception of the apps over the years. Using this dataset, we can do the following

1. Extract sentiments and trends
2. Identify which version of an app had the most positive feedback, the worst.
3. Use topic modelling to identify the pain points of the application.

(AND MANY MORE!)

Note

Images generated using Bing Image Generator