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...

The number of Apple iPhone unit sales dramatically increased between 2007 and 2024. Indeed, in 2007, when the iPhone was first introduced, Apple shipped around **** million smartphones. By 2024, 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 2025, includes the **, ** Plus, ** Pro and Pro Max. Powered by the A19 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 contains over 1 million rows of Apple Retail Sales data. It includes information on products, stores, sales transactions, and warranty claims across various Apple retail locations worldwide.

The dataset is designed to reflect real-world business scenarios — including multiple product categories, regional sales variations, and customer service data — making it suitable for end-to-end data analytics and machine learning projects.

Important Note

This dataset is not based on real Apple Inc. data. It was created using Python and LLM-generated insights to simulate realistic sales patterns and business metrics.

Like most company-related datasets on Kaggle (e.g., Amazon, Tesla, or Samsung), this one is synthetic, as companies do not share their actual sales or confidential data publicly due to privacy and legal restrictions.

Purpose

This dataset is intended for: Practicing data analysis, visualization, and forecasting Building and testing machine learning models Learning ETL and data-cleaning workflows on large datasets

Usage You may freely use, modify, and share this dataset for learning, research, or portfolio projects.

iPhone Reviews from Amazon.com

30,000+ User-Submitted Reviews

By Crawl Feeds [source]

About this dataset

This 30,000+ reviews dataset for Apple iPhone from Amazon.com provides insights and comprehensive opinion data that can be used to understand current customer sentiment towards the product. With helpful_count as one of the columns, this dataset provides an opportunity to find out which reviews are most helpful for customers and highlights the key areas of improvement for other brands in a similar product range. Exceptional review ratings and detailed text reviews give readers an idea about why customers liked or disliked the product, providing valuable market feedback information such as what went wrong (or right). Alongside this, knowledge about where a review was made gives better context on whether comments should be taken lightly or with more pressing importance. An invaluable resource for industry stakeholders and researchers alike, use this dataset to gain a clearer picture of customer satisfaction surrounding Apple's latest release - The iPhone!

More Datasets

For more datasets, click here.

Featured Notebooks

• 🚨 Your notebook can be here! 🚨!

How to use the dataset

This dataset contains over 30,000 reviews for the Apple iPhone from Amazon.com. It includes information such as the product name, helpful count, total comments, URL of the review, review country, date and time of the review, rating of the product given by reviewer, product company name and profile name. You can use this dataset to analyze customer feedback about the Apple iPhone from Amazon.

To get started with this dataset you should first read through each column and understand what it represents. Once you are familiar with each column then you can start exploring the data further by filtering out particular reviews or performing a sentiment analysis on particular reviews using tools such as Python's Natural Language Toolkit (NLTK). You could also look at analyzing trends in customer ratings over time or breaking down customer feedback into gender specific segments to gain more insights about user preferences.

You can also group reviews based on their geographical location and look at regional differences in user opinion towards a particular product feature or implementation style which may indicate alterations in usability/ technicalities that need to be addressed along with other factors such as cultural influence which may have an effect on user opinion towards a certain brand/product feature etc. This info could be used to inform your marketing strategies across different parts of your target market region thus providing more targeted results while creating ad campaigns aimed at driving sales for the aforementioned products/brands-helping improve ROI performance efficiently!

Lastly if you are looking for insights particularly regarding Apple’s competitors-it would be useful for you to analyze comparative feedback between customers regarding similar competitive brands/products allowing potential investments pivoting around stronger performers!

We hope this guide provides some useful insight into how to use this dataset effectively from Amazon mobile phones reviews set! Have fun exploring!!

Research Ideas

• To train a sentiment analysis model to better understand customers’ attitudes towards Apple's iPhone.
• To analyze the review comments and look for correlations between certain words and ratings, in order to gain insights on how customers perceive the phone based on their experiences with it.
• Create a combination of product reviews with video reviews from YouTube in order to provide potential buyers a more comprehensive overview of the features, performance and beauty of the iPhone before purchasing it

Acknowledgements

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

License

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

Columns

File: apple_iphone_11_reviews.csv | Column name | Description | |:--------------------|:-------------------------------------------------------------| | product | The product being reviewed. (String) | | helpful_count | The number of people who found the review helpful. (Integer) | | total_comments | The total number of comments on the review. (Integer) | | url | The URL of the review post. (String) | | review_country | The country from which the review was posted. (Strin...

“The people who are crazy enough to think they can predict the market... are the ones who do.”

Here’s to the crazy ones—the data dreamers, the analysts, the visionaries who believe that a handful of numbers can reveal the DNA of innovation. This dataset is more than a collection of Apple Inc.’s historical stock prices; it’s a chronicle of invention, perseverance, and thinking differently.

What’s Inside

• Time Span: Daily stock price data for Apple Inc. over multiple years
• Features:
  • Date: The day of the record
  • Open: Price at market open
  • High: Highest price of the day
  • Low: Lowest price of the day
  • Close: Price at market close
  • Volume: Number of shares traded
• Format: CSV, clean and ready for analysis

Why This Matters

Apple is not just a company, it’s a movement. Its stock price reflects not only financial performance, but the world’s response to innovation—launches, leadership changes, economic cycles, and the occasional “one more thing.”

Possibilities

• Visualize long-term growth and volatility
• Model trends, moving averages, or momentum
• Forecast future prices with machine learning
• Detect the impact of major product launches or events
• Explore relationships between volume and price action

Inspiration

As you explore this data, don’t just look for patterns—look for stories. See how moments of genius and risk-taking ripple through the numbers. Use this dataset to inspire your own creativity, your own analysis, your own ‘insanely great’ discoveries.

Whether you’re here to build a predictive model, craft beautiful visualizations, or simply marvel at the journey, remember:
The people who are crazy enough to think they can change the world with data… are the ones who do.

These datasets provide a comprehensive overview of Apple's financial performance by product and geographical segments from 2010 to 2024. The data is compiled directly from the company's annual 10-K filings with the U.S. Securities and Exchange Commission (SEC), ensuring a high degree of reliability and accuracy.

Dataset 1: Apple Sales by Product Segment This dataset details Apple's net sales across its major product and service categories. It shows the evolution of Apple's business model from a hardware-centric company to one with a significant and growing services component. The columns represent different business lines that have changed over time:

iPhone, Mac, iPad: Core hardware product categories.

iPod: An early product category whose sales data was eventually reclassified into a broader segment.

Other Products / Wearables, Home & Accessories: This category has evolved. Prior to 2015, this was a more general "Other Products" category. In later years, it was expanded and renamed to include products like the Apple Watch, AirPods, and HomePod, reflecting their growing strategic importance.

Services: This segment includes revenue from the App Store, Apple Music, iCloud, Apple Pay, and other digital services. Its consistent growth highlights Apple's successful diversification strategy.

Dataset 2: Apple Sales by Geographic Segment This dataset breaks down Apple's net sales by major global regions, providing insight into the company's international market penetration and performance. It includes data for:

Americas: Includes North and South America.

Europe: Includes European countries, as well as Africa, India, and the Middle East.

Greater China: Includes mainland China, Hong Kong, and Taiwan.

Japan: A distinct and long-standing key market for Apple.

Rest of Asia Pacific: Includes Australia and various other countries in Asia.

Example Use Cases These datasets can be used for a wide range of analytical purposes, including:

Trend Analysis and Forecasting: Analysts can use this data to identify long-term trends in Apple's sales. For example, by plotting the data, one could easily visualize the rapid growth of the Services segment, or the decline of the iPod as a standalone product. This can help in forecasting future revenue and strategic planning.

Market Share Analysis: By comparing Apple's sales data to the total market size for smartphones, personal computers, or tablets, analysts can estimate Apple's market share over time. This can be done on a global or regional basis to identify areas of strength and weakness.

Strategic Decision Making: Business leaders can use the geographic sales data to evaluate the success of market-specific strategies. For instance, a company could compare the growth rates in Greater China and Europe to determine which region offers more potential for a new product launch.

Comparative Analysis: The data can be used to compare Apple's performance against its competitors like Samsung or Google. By normalizing the data, one could analyze which company has a more diversified revenue stream or a stronger presence in specific international markets. I

Abhishek’s Apple Health CSV Dataset

About: This dataset is a cleaned export of my personal Apple Health data collected through my iPhone and Apple Watch over several years. I parsed the raw HealthKit XML export and transformed it into a ready-to-use CSV so that students, developers, and data scientists can easily practice real-world health data analysis.

What’s included:

Heart Rate: 800,000+ records Steps & Walking Metrics: Step count, distance, walking speed, stride length, gait symmetry Calories: Active & basal energy burned Sleep Analysis: Core, in bed, deep sleep patterns Vitals: Body mass index, weight, height, oxygen saturation, headphone audio exposure Timestamps: Creation, start, and end dates & times in local timezone Device & Source: Source app, device info, source version Columns: type, sourceName, sourceVersion, unit, m_creationDate, m_creationTime, m_creationTime_am_pm, m_creationTimeZone, m_startDate, m_startTime, m_startTime_am_pm, m_startTimeZone, m_endDate, m_endTime, m_endTime_am_pm, m_endTimeZone, value, device

Why I shared this: I believe real, authentic data helps people learn better. This dataset is perfect for:

Time series analysis Data cleaning & transformation practice Exploratory data analysis (EDA) Visualizing health & wellness trends Building forecasting models Personal Note: This is my real data, and I’ve kept my name to give it a personal touch. Sensitive info like GPS or location data has been removed. Please use it respectfully, for non-commercial, educational, and research purposes only.

License: This dataset is shared under CC BY-NC-SA 4.0 — you must give credit, can’t use it commercially, and must share any derivatives under the same license.

In the fourth 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.

Description:

This dataset provides insights into consumer electronics sales, featuring product categories, brands, prices, customer demographics, purchase behavior, and satisfaction metrics. It aims to analyze factors influencing purchase intent and customer satisfaction in the consumer electronics market.

Features:

• ProductID: Unique identifier for each product.
• ProductCategory: Category of the consumer electronics product (e.g., Smartphones, Laptops).
• ProductBrand: Brand of the product (e.g., Apple, Samsung).
• ProductPrice: Price of the product ($).
• CustomerAge: Age of the customer.
• CustomerGender: Gender of the customer (0 - Male, 1 - Female).
• PurchaseFrequency: Average number of purchases per year.
• CustomerSatisfaction: Customer satisfaction rating (1 - 5).
• PurchaseIntent (Target Variable): Intent to purchase.

Conclusion:

This dataset facilitates analysis on consumer behavior and purchase patterns in the consumer electronics sector, aiding insights into market dynamics and customer preferences.

Dataset Usage and Attribution Notice

This dataset, shared by Rabie El Kharoua, is original and has never been shared before. It is made available under the CC BY 4.0 license, allowing anyone to use the dataset in any form as long as proper citation is given to the author. A DOI is provided for proper referencing. Please note that duplication of this work within Kaggle is not permitted.

Exclusive Synthetic Dataset

This dataset is synthetic and was generated for educational purposes, making it ideal for data science and machine learning projects. It is an original dataset, owned by Mr. Rabie El Kharoua, and has not been previously shared. You are free to use it under the license outlined on the data card. The dataset is offered without any guarantees. Details about the data provider will be shared soon.

this graph was created in R and Canva :

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Recent years have witnessed a rapid growth in the use of mobile devices, enabling people to access the Internet in various contexts. More than 77% of Americans now own a smartphone, with an increasing trend in terms of the time people spend on their phones. More recently, with the release of intelligent assistants such as Google Assistant, Apple Siri, and Microsoft Cortana, people are experiencing mobile search through a single voice-based interface. These systems introduce several research challenges. Given that people spend most of their times in apps and, as a consequence, most of their search interactions would be with apps (rather than a browser), one limitation is that users are unable to use a intelligent assistants to search within many apps.

Due to changes in the collection and availability of data on COVID-19, this website will no longer be updated. The webpage will no longer be available as of 11 May 2023. On-going, reliable sources of data for COVID-19 are available via the COVID-19 dashboard and the UKHSA GLA Covid-19 Mobility Report Since March 2020, London has seen many different levels of restrictions - including three separate lockdowns and many other tiers/levels of restrictions, as well as easing of restrictions and even measures to actively encourage people to go to work, their high streets and local restaurants. This reports gathers data from a number of sources, including google, apple, citymapper, purple wifi and opentable to assess the extent to which these levels of restrictions have translated to a reductions in Londoners' movements. The data behind the charts below come from different sources. None of these data represent a direct measure of how well people are adhering to the lockdown rules - nor do they provide an exhaustive data set. Rather, they are measures of different aspects of mobility, which together, offer an overall impression of how people Londoners are moving around the capital. The information is broken down by use of public transport, pedestrian activity, retail and leisure, and homeworking. Public Transport For the transport measures, we have included data from google, Apple, CityMapper and Transport for London. They measure different aspects of public transport usage - depending on the data source. Each of the lines in the chart below represents a percentage of a pre-pandemic baseline. activity Source Latest Baseline Min value in Lockdown 1 Min value in Lockdown 2 Min value in Lockdown 3 Citymapper Citymapper mobility index 2021-09-05 Compares trips planned and trips taken within its app to a baseline of the four weeks from 6 Jan 2020 7.9% 28% 19% Google Google Mobility Report 2022-10-15 Location data shared by users of Android smartphones, compared time and duration of visits to locations to the median values on the same day of the week in the five weeks from 3 Jan 2020 20.4% 40% 27% TfL Bus Transport for London 2022-10-30 Bus journey ‘taps' on the TfL network compared to same day of the week in four weeks starting 13 Jan 2020 - 34% 24% TfL Tube Transport for London 2022-10-30 Tube journey ‘taps' on the TfL network compared to same day of the week in four weeks starting 13 Jan 2020 - 30% 21% Pedestrian activity With the data we currently have it's harder to estimate pedestrian activity and high street busyness. A few indicators can give us information on how people are making trips out of the house: activity Source Latest Baseline Min value in Lockdown 1 Min value in Lockdown 2 Min value in Lockdown 3 Walking Apple Mobility Index 2021-11-09 estimates the frequency of trips made on foot compared to baselie of 13 Jan '20 22% 47% 36% Parks Google Mobility Report 2022-10-15 Frequency of trips to parks. Changes in the weather mean this varies a lot. Compared to baseline of 5 weeks from 3 Jan '20 30% 55% 41% Retail & Rec Google Mobility Report 2022-10-15 Estimates frequency of trips to shops/leisure locations. Compared to baseline of 5 weeks from 3 Jan '20 30% 55% 41% Retail and recreation In this section, we focus on estimated footfall to shops, restaurants, cafes, shopping centres and so on. activity Source Latest Baseline Min value in Lockdown 1 Min value in Lockdown 2 Min value in Lockdown 3 Grocery/pharmacy Google Mobility Report 2022-10-15 Estimates frequency of trips to grovery shops and pharmacies. Compared to baseline of 5 weeks from 3 Jan '20 32% 55.00% 45.000% Retail/rec Google Mobility Report 2022-10-15 Estimates frequency of trips to shops/leisure locations. Compared to baseline of 5 weeks from 3 Jan '20 32% 55.00% 45.000% Restaurants OpenTable State of the Industry 2022-02-19 London restaurant bookings made through OpenTable 0% 0.17% 0.024% Home Working The Google Mobility Report estimates changes in how many people are staying at home and going to places of work compared to normal. It's difficult to translate this into exact percentages of the population, but changes back towards ‘normal' can be seen to start before any lockdown restrictions were lifted. This value gives a seven day rolling (mean) average to avoid it being distorted by weekends and bank holidays. name Source Latest Baseline Min/max value in Lockdown 1 Min/max value in Lockdown 2 Min/max value in Lockdown 3 Residential Google Mobility Report 2022-10-15 Estimates changes in how many people are staying at home for work. Compared to baseline of 5 weeks from 3 Jan '20 131% 119% 125% Workplaces Google Mobility Report 2022-10-15 Estimates changes in how many people are going to places of work. Compared to baseline of 5 weeks from 3 Jan '20 24% 54% 40% Restriction Date end_date Average Citymapper Average homeworking Work from home advised 17 Mar '20 21 Mar '20 57% 118% Schools, pubs closed 21 Mar '20 24 Mar '20 34% 119% UK enters first lockdown 24 Mar '20 10 May '20 10% 130% Some workers encouraged to return to work 10 May '20 01 Jun '20 15% 125% Schools open, small groups outside 01 Jun '20 15 Jun '20 19% 122% Non-essential businesses re-open 15 Jun '20 04 Jul '20 24% 120% Hospitality reopens 04 Jul '20 03 Aug '20 34% 115% Eat out to help out scheme begins 03 Aug '20 08 Sep '20 44% 113% Rule of 6 08 Sep '20 24 Sep '20 53% 111% 10pm Curfew 24 Sep '20 15 Oct '20 51% 112% Tier 2 (High alert) 15 Oct '20 05 Nov '20 49% 113% Second Lockdown 05 Nov '20 02 Dec '20 31% 118% Tier 2 (High alert) 02 Dec '20 19 Dec '20 45% 115% Tier 4 (Stay at home advised) 19 Dec '20 05 Jan '21 22% 124% Third Lockdown 05 Jan '21 08 Mar '21 22% 122% Roadmap 1 08 Mar '21 29 Mar '21 29% 118% Roadmap 2 29 Mar '21 12 Apr '21 36% 117% Roadmap 3 12 Apr '21 17 May '21 51% 113% Roadmap out of lockdown: Step 3 17 May '21 19 Jul '21 65% 109% Roadmap out of lockdown: Step 4 19 Jul '21 07 Nov '22 68% 107%

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 contains Q&A based on what kind of questions do people ask online with their respective answers. This can be used as an LLM project where we finetune an LLM and create a chatbot where one can ask the chatbot any question related to Apple products and get respective answers.

  To use this dataset try running the following code:

dataset = load_dataset("Aashi/All_About_Apple_Devices", data_files={"train": "QandA.csv"})

df_train = dataset['train'].to_pandas()

Information Related To The Dataset:

1️⃣**Date:** This column represents the calender date when the data about the stock is recorded.

2️⃣**Open:** This column represents the first recorded price of the stock for a trading session.

3️⃣**High:** The high price represents the highest traded price of the stock during a given trading session. It reflects the peak value that the stock reached during the day.

4️⃣**Low:** The low price is the lowest traded price of the stock during a specific trading session. It indicates the minimum value that the stock reached during the day.

5️⃣**Close:** The closing price is the last traded price of the stock at the end of a trading session. It reflects the final value at which the stock was traded before the market closes.

6️⃣**Adj Close(Adjusted Close):** The adjusted closing price accounts for corporate actions, such as dividends, stock splits, and new stock offerings, that may affect the stock's price but are not directly related to its performance. The adjusted close is often used to assess the stock's performance over time.

7️⃣**Volume:** Volume represents the total number of shares traded during a specific time period. It gives an indication of the level of market activity and liquidity for that stock. High volume often suggests increased investor interest, while low volume may indicate less active trading.

A financial dataset with columns such as Date, Open, High, Low, Close, Adj Close, and Volume information is crucial for technical analysis, trend identification, and understanding the historical performance of a stock.

Feel free to use this dataset for your analyses, visualisations, and research. Your feedback is appreciated!

Here are a few use cases for this project:

1. Food Quality Assurance: By using the "apple_2" model in industries related to food production and distribution, food quality can be monitored in an automated manner. For instance, supermarkets or fruit vendors could utilize the model to identify rot in apples before they are sold.

2. Agricultural Practices: Farmers could use this model to scan their apple orchards, spotting and isolating any rotten apple trees before the rot has a chance to spread to healthy trees.

3. Waste Management: Garbage sorting facilities could use the "apple_2" model to automatically sort through waste and separate decomposing, organic apple waste from general waste. It can offer a more efficient method in recycling operations or composting initiatives.

4. Humanitarian Efforts: Non-profit organizations or public services could implement the "apple_2" model in initiatives aimed at redistributing perishable goods to people in need, avoiding waste by identifying rotting apples and creating higher quality control.

5. Education: The model could be used as part of a teaching tool for botany or horticultural courses, helping students to visually identify signs of rot in apples and understand the underlying causes and management practices.

This dataset contains the results of running the automatic audio annotation algorithms for pitch, tempo and key used for the evaluation of algorithms developed during the AudioCommons H2020 EU project and which are part of the Audio Commons Audio Extractor tool. It also includes estimation results information for the single-eventness audio descriptor also developed for the same tool.

These estimation results data has been used to generate the following documents:

• Deliverable D4.4: Evaluation report on the first prototype tool for the automatic semantic description of music samples
• Deliverable D4.10: Evaluation report on the second prototype tool for the automatic semantic description of music samples
• Deliverable D4.12: Release of tool for the automatic semantic description of music samples

All these documents are available in the materials section of the AudioCommons website.

All data in this repository is provided in the form of CSV files. Each CSV file corresponds to the analysis results of one musical task and one of the individual datasets used in the aforementioned deliverables. This repository does not include the audio files of each individual dataset, but includes references to the audio files. The following paragraphs describe the structure of the CSV files and give some notes about how to obtain the audio files in case these would be needed.

Structure of the CSV files

All the CSV files in this repository (with the sole exception of SINGLE EVENT - Estimation Results Truth.csv) are named according to the following convention: "DATASET_NAME - ESTIMATION_TASK Estimation Results.csv". Therefore, estimation results for pitch, tempo and tonality music tasks are separated in different files. All these files share the same structure for the first 2 CSV columns:

1. Audio reference: reference to the corresponding audio file. This will either be a string withe the filename, or the Freesound ID (for one dataset based on Freesound content). See below for details about how to obtain those files.
2. Audio reference type: will be one of Filename or Freesound ID, and specifies how the previous column should be interpreted.

The rest of the columns include the estimation results for each one of the algorithms included in the evaluation of each music facet. For each algorithms two columns are reserved, the first one containing the actual estimation and the second one the confidence of this estimation (see CSV file previews below). The format of actual estimations depends on the musical task, check the description of the corresponding ground truth dataset for more information on that. The confidence value is a float number, typically in the range from 0.0 to 1.0. It can happen that one or both columns are empty for a given analysis algorithm and CSV row. This will be the case if the algorithm could not successfully produce an estimation for the audio file row corresponding to the CSV row.

The remaining CSV file, SINGLE EVENT - Estimation Results.csv, has the following 4 columns:

• Freesound ID: sound ID used in Freesound to identify the audio clip.
• ACExtractorV2: single-eventness estimation of the algorithm included in the second version of the Audio Commons Audio Extractor tool (bool).
• ACExtractorV2-opt: single-eventness estimation of the algorithm included in the second version of the Audio Commons Audio Extractor tool with optimized parameters (bool).
• ACExtractorV3: single-eventness estimation of the algorithm included in the third version of the Audio Commons Audio Extractor tool (bool).

How to get the audio data

In this section we provide some notes about how to obtain the audio files corresponding to the estimation results provided here. Note that due to licensing restrictions we are not allowed to re-distribute the audio data corresponding to most of these automatic annotations.

• Apple Loops (APPL): This dataset includes some of the music loops included in Apple's music software such as Logic or GarageBand. Access to these loops requires owning a license for the software. Detailed instructions about how to set up this dataset are provided here.
• Carlos Vaquero Instruments Dataset (CVAQ): This dataset includes single instrument recordings carried out by Carlos Vaqueroas part of this master thesis. Sounds are available as Freesound packs and can be downloaded at this page: https://freesound.org/people/Carlos_Vaquero/packs
• Freesound Loops 4k (FSL4): This dataset set includes a selection of music loops taken from Freesound. Detailed instructions about how to set up this dataset are provided here.
• Giant Steps Key Dataset (GSKY): This dataset includes a selection of previews from Beatport annotated by key. Audio and original annotations available here.
• Good-sounds Dataset (GSND): This dataset contains monophonic recordings of instrument samples. Full description, original annotations and audio are available here.
• University of IOWA Musical Instrument Samples (IOWA): This dataset was created by the Electronic Music Studios of the University of IOWA and contains recordings of instrument samples. The dataset is available upon request by visiting this website.
• Mixcraft Loops (MIXL): This dataset includes some of the music loops included in Acoustica's Mixcraft music software. Access to these loops requires owning a license for the software. Detailed instructions about how to set up this dataset are provided here.
• NSynth Dataset Test and Validation sets (NSYT and NSYV): NSynth is a large-scale and high-quality dataset of annotated musical notes built with synthesized sounds by Google's Magenta team. Full dataset description including original annotations and audio files is available here.
• Philarmonia Orchestra Sound Samples Dataset (PHIL): This includes thousands of free, downloadable sound samples specially recorded by Philharmonia Orchestra players. Audio files are freely downloadable from the philarmonia orchestra website.
• Freesound Single Events Dataset (SINGLE EVENT): This includes a selection of Freesound audio clips representing audio signals containing either a single audio eventor multiple ones. Original audio files can be retrieved by downloading individual audio clips from Freesound using the ID identifier provided in the CSV file. A similar procedure to that described here could be followed.

Context

I wanted to find a better way to provide live traffic updates. We dont all have access to the data from traffic monitoring sensors or whatever gets uploaded from people's smart phones to Apple, Google etc plus I question how accurate the traffic congestion is on Google Maps or other apps. So I figured that since buses are also in the same traffic and many buses stream their GPS location and other data live, that would be an ideal source for traffic data. I investigated the data streams available from many bus companies around the world and found MTA in NYC to be very reliable.

Content

This dataset is from the NYC MTA buses data stream service. In roughly 10 minute increments the bus location, route, bus stop and more is included in each row. The scheduled arrival time from the bus schedule is also included, to give an indication of where the bus should be (how much behind schedule, or on time, or even ahead of schedule).

Acknowledgements

Data is recorded from the MTA SIRI Real Time data feed and the MTA GTFS Schedule data.

Inspiration

I want to see what exploratory & discovery people come up with from this data. Feel free to download this dataset for your own use however I would appreciate as many Kernals included on Kaggle as we can get.

Based on the interest this generates I plan to collect more data for subsequent months down the track.

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

AAPL Apple Inc. Common Stock

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

Exploring Daily Weather Report Usage

Understanding Regional and Socioeconomic Variations

By FiveThirtyEight [source]

About this dataset

This dataset contains survey responses from people about their daily weather report usage and weather check. It consists of columns such as Do You Typically Check a Daily Weather Report?, How do you Typically Check the Weather?, If You Had a Smartwatch (like the Soon to be Released Apple Watch), How Likely or Unlikely Would You Be to Check the Weather on That Device? Age, What is Your Gender?, and US Region. With this data, we can explore usage patterns in checking for daily weather reports across different regions, genders, ages and preferences for smartwatch devices in doing so. This dataset offers an interesting insight into our current attitudes towards checking for the weather with technology - and by understanding these patterns better, we can create more engaging experiences tailored to individuals’ needs

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How to use the dataset

To get started, it is helpful to first examine the columns in the dataset. The columns are Do you typically check a daily weather report?, How do you typically check the weather?, If you had a smartwatch (like the soon to be released Apple Watch), how likely or unlikely would you be to check the weather on that device?, Age, What is your gender?, US Region. Each row contains data for one survey participant, with their answers for each column included in each row.

The data can be used for exploring correlations between factors such as age, gender, region/location, daily weather checking habits/preferences etc.. Some of these variables are numerical (such as age) and others are categorical (such as gender). You can use this data when creating visualizations showing relationships between these factors. You may also want to create summary tables showing average values for different categories of each factor, allowing for easy comparison across groups or over time periods (depending on how much historical data is available).

Research Ideas

• Analyzing trends in the usage of daily weather reports by age, gender and region.
• Exploring consumer preferences for checking the weather via smartwatches and mobile devices in comparison to other methods (e.g., TV or radio).
• Examining correlations between people's likelihood to check their daily weather report and their demographic characteristics (location, age, gender)

Acknowledgements

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

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: weather-check.csv | Column name | Description | |:-------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------| | Do you typically check a daily weather report? | This column indicates whether or not the respondent typically checks a daily weather report. (Categorical) | | How do you typically check the weather? | This column indicates how the respondent typically checks the weather. (Categorical) | | If you had a smartwatch (like the soon to be released Apple Watch), how likely or unlikely would you be to check the weather on that device? | This column indicates how likely or unlikely the respondent would be to check the weather on a smartwatch. (Categorical) | | Age | This column indicates the age of the respondent. (Numerical) ...

This was a exiting case study for the Google Data Analytics Certification 2023. I choose to do the Case Study 2, the goal was as a business analyst for a small health tracker company how can we use the data from Fitbit users to inform a decision for growth when comparing it to one of Bellabeat's products. I included apple watch users since the data did appear limited in the sample size being 33 participants and with the apple watch users the sample size went up to 59 participants.

I have included my notes from data cleaning process and a power point on my findings and recommendation.

Datasets were not my own and belong to Datasets - ‘FitBit Fitness Tracker Data’ by Mobius, 2022, https://www.kaggle.com/datasets/arashnic/fitbit License: CC0: Public Domain, sources: https://zenodo.org/record/53894#.X9oeh3Uzaao - ‘Apple Watch and Fitbit data’ by Alejandro Espinosa, 2022, https://www.kaggle.com/datasets/aleespinosa/apple-watch-and-fitbit-data, License: CC0: Public Domain, sources: https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/ZS2Z2J