In the fourth quarter of 2024, Samsung shipped around ** million smartphones, a decrease from the both the previous quarter and the same quarter of the previous year. Samsung’s sales consistently place the smartphone giant among the top three smartphone vendors in the world, alongside Xiaomi and Apple. Samsung smartphone sales – how many phones does Samsung sell? Global smartphone sales reached over *** billion units during 2024. While the global smartphone market is led by Samsung and Apple, Xiaomi has gained ground following the decline of Huawei. Together, these three companies hold more than ** percent of the global smartphone market share.

Description for each of the variables:

1. Brands: The brands of smartphones included in the dataset.
2. Colors: The colors available for the smartphones.
3. Memory: The storage capacity of the smartphones, typically measured in gigabytes (GB) or megabytes (MB).
4. Storage: The internal storage capacity of the smartphones, often measured in gigabytes (GB) or megabytes (MB).
5. Rating: The user ratings or scores assigned to the smartphones, reflecting user satisfaction or performance.
6. Selling Price: The price at which the smartphones are sold to consumers.
7. Original Price: The original or list price of the smartphones before any discounts or promotions.
8. Mobile: Indicates whether the device is a mobile phone.
9. Discount: The discount applied to the original price to calculate the selling price.
10. Discount percentage: The percentage discount applied to the original price to calculate the selling price.

This is the public release of the Samsung Open Mean Opinion Scores (SOMOS) dataset for the evaluation of neural text-to-speech (TTS) synthesis, which consists of audio files generated with a public domain voice from trained TTS models based on bibliography, and numbers assigned to each audio as quality (naturalness) evaluations by several crowdsourced listeners.DescriptionThe SOMOS dataset contains 20,000 synthetic utterances (wavs), 100 natural utterances and 374,955 naturalness evaluations (human-assigned scores in the range 1-5). The synthetic utterances are single-speaker, generated by training several Tacotron-like acoustic models and an LPCNet vocoder on the LJ Speech voice public dataset. 2,000 text sentences were synthesized, selected from Blizzard Challenge texts of years 2007-2016, the LJ Speech corpus as well as Wikipedia and general domain data from the Internet.Naturalness evaluations were collected via crowdsourcing a listening test on Amazon Mechanical Turk in the US, GB and CA locales. The records of listening test participants (workers) are fully anonymized. Statistics on the reliability of the scores assigned by the workers are also included, generated through processing the scores and validation controls per submission page.

To listen to audio samples of the dataset, please see our Github page.

The dataset release comes with a carefully designed train-validation-test split (70%-15%-15%) with unseen systems, listeners and texts, which can be used for experimentation on MOS prediction.

This version also contains the necessary resources to obtain the transcripts corresponding to all dataset audios.

Terms of use

The dataset may be used for research purposes only, for non-commercial purposes only, and may be distributed with the same terms.

Every time you produce research that has used this dataset, please cite the dataset appropriately.

Cite as:

@inproceedings{maniati22_interspeech, author={Georgia Maniati and Alexandra Vioni and Nikolaos Ellinas and Karolos Nikitaras and Konstantinos Klapsas and June Sig Sung and Gunu Jho and Aimilios Chalamandaris and Pirros Tsiakoulis}, title={{SOMOS: The Samsung Open MOS Dataset for the Evaluation of Neural Text-to-Speech Synthesis}}, year=2022, booktitle={Proc. Interspeech 2022}, pages={2388--2392}, doi={10.21437/Interspeech.2022-10922} }

References of resources & models used

Voice & synthesized texts:K. Ito and L. Johnson, “The LJ Speech Dataset,” https://keithito.com/LJ-Speech-Dataset/, 2017.

Vocoder:J.-M. Valin and J. Skoglund, “LPCNet: Improving neural speech synthesis through linear prediction,” in Proc. ICASSP, 2019.R. Vipperla, S. Park, K. Choo, S. Ishtiaq, K. Min, S. Bhattacharya, A. Mehrotra, A. G. C. P. Ramos, and N. D. Lane, “Bunched lpcnet: Vocoder for low-cost neural text-to-speech systems,” in Proc. Interspeech, 2020.

Acoustic models:N. Ellinas, G. Vamvoukakis, K. Markopoulos, A. Chalamandaris, G. Maniati, P. Kakoulidis, S. Raptis, J. S. Sung, H. Park, and P. Tsiakoulis, “High quality streaming speech synthesis with low, sentence-length-independent latency,” in Proc. Interspeech, 2020.Y. Wang, R. Skerry-Ryan, D. Stanton, Y. Wu, R. J. Weiss, N. Jaitly, Z. Yang, Y. Xiao, Z. Chen, S. Bengio et al., “Tacotron: Towards End-to-End Speech Synthesis,” in Proc. Interspeech, 2017.J. Shen, R. Pang, R. J. Weiss, M. Schuster, N. Jaitly, Z. Yang, Z. Chen, Y. Zhang, Y. Wang, R. Skerrv-Ryan et al., “Natural TTS Synthesis by Conditioning Wavenet on MEL Spectrogram Predictions,” in Proc. ICASSP, 2018.J. Shen, Y. Jia, M. Chrzanowski, Y. Zhang, I. Elias, H. Zen, and Y. Wu, “Non-Attentive Tacotron: Robust and Controllable Neural TTS Synthesis Including Unsupervised Duration Modeling,” arXiv preprint arXiv:2010.04301, 2020.M. Honnibal and M. Johnson, “An Improved Non-monotonic Transition System for Dependency Parsing,” in Proc. EMNLP, 2015.M. Dominguez, P. L. Rohrer, and J. Soler-Company, “PyToBI: A Toolkit for ToBI Labeling Under Python,” in Proc. Interspeech, 2019.Y. Zou, S. Liu, X. Yin, H. Lin, C. Wang, H. Zhang, and Z. Ma, “Fine-grained prosody modeling in neural speech synthesis using ToBI representation,” in Proc. Interspeech, 2021.K. Klapsas, N. Ellinas, J. S. Sung, H. Park, and S. Raptis, “WordLevel Style Control for Expressive, Non-attentive Speech Synthesis,” in Proc. SPECOM, 2021.T. Raitio, R. Rasipuram, and D. Castellani, “Controllable neural text-to-speech synthesis using intuitive prosodic features,” in Proc. Interspeech, 2020.

Synthesized texts from the Blizzard Challenges 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2016:M. Fraser and S. King, "The Blizzard Challenge 2007," in Proc. SSW6, 2007.V. Karaiskos, S. King, R. A. Clark, and C. Mayo, "The Blizzard Challenge 2008," in Proc. Blizzard Challenge Workshop, 2008.A. W. Black, S. King, and K. Tokuda, "The Blizzard Challenge 2009," in Proc. Blizzard Challenge, 2009.S. King and V. Karaiskos, "The Blizzard Challenge 2010," 2010.S. King and V. Karaiskos, "The Blizzard Challenge 2011," 2011.S. King and V. Karaiskos, "The Blizzard Challenge 2012," 2012.S. King and V. Karaiskos, "The Blizzard Challenge 2013," 2013.S. King and V. Karaiskos, "The Blizzard Challenge 2016," 2016.

Contact

Alexandra Vioni - a.vioni@samsung.com

If you have any questions or comments about the dataset, please feel free to write to us.

We are interested in knowing if you find our dataset useful! If you use our dataset, please email us and tell us about your research.

The number of smartphone users in the United States was forecast to continuously increase between 2024 and 2029 by in total 17.4 million users (+5.61 percent). After the fifteenth consecutive increasing year, the smartphone user base is estimated to reach 327.54 million users and therefore a new peak in 2029. Notably, the number of smartphone users of was continuously increasing over the past years.Smartphone users here are limited to internet users of any age using a smartphone. The shown figures have been derived from survey data that has been processed to estimate missing demographics.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).Find more key insights for the number of smartphone users in countries like Mexico and Canada.

This synthetic but realistic dataset contains 90+ customer reviews for 6 smartphone models (from Apple, Samsung, and Google), along with: - Product specifications (Price, Screen Size, Battery, Camera, RAM, Storage, 5G, Water Resistance) - Customer reviews (Star Ratings, Review Text, Verified Purchase Status) - Sales data (Units Sold per Model)

Potential Use Cases: ✅ Feature importance analysis (Which specs drive ratings?) ✅ Sentiment analysis (NLP on reviews) ✅ Pricing strategy optimization ✅ Market research (Comparing Apple vs. Samsung vs. Google)

Smartphone Customer Satisfaction Survey

Objective: Understand how product features influence purchasing decisions and satisfaction.

Section 1: Demographic & Purchase Behavior

1. Which smartphone brand did you purchase?

   • ☐ Apple
   • ☐ Samsung
   • ☐ Google
   • Maps to brand column.

2. Which model did you purchase?

   • Apple: ☐ iPhone 14 | ☐ iPhone 15
   • Samsung: ☐ Galaxy S22 | ☐ Galaxy S23
   • Google: ☐ Pixel 7 | ☐ Pixel 8
   • Maps to model_name column.

3. Where did you purchase the phone?

   • ☐ Online (e.g., Amazon, Brand Website)
   • ☐ Physical Store
   • Justifies verified_purchase (assumed online = verified).

Section 2: Product Feature Ratings

1. How would you rate the following features? (1 = Poor, 5 = Excellent)

   • Battery Life: ⭐⭐⭐⭐⭐
   • Camera Quality: ⭐⭐⭐⭐⭐
   • Screen Size: ⭐⭐⭐⭐⭐
   • Performance (RAM/Processor): ⭐⭐⭐⭐⭐
   • Aggregates into star_rating (average of these).

2. Which feature is MOST important to you?

   • ☐ Battery Life
   • ☐ Camera Quality
   • ☐ Screen Size
   • ☐ Performance
   • ☐ Price
   • Explains review_text keywords (e.g., "battery" mentions).

Section 3: Price & Satisfaction

1. How do you feel about the price of your phone?

   • ☐ Very Affordable
   • ☐ Fairly Priced
   • ☐ Slightly Expensive
   • ☐ Too Expensive
   • Maps to price vs. star_rating correlation.

2. Would you recommend this phone to others?

   • ☐ Definitely Yes
   • ☐ Probably Yes
   • ☐ Neutral
   • ☐ Probably No
   • ☐ Definitely No
   • Linked to star_rating (5 = Definitely Yes).

Column Details (Metadata)

Column Name (Type) Description "Example"**

model_id (Integer) Unique ID for each phone model 1 (iPhone 14)

brand (String) Manufacturer (Apple, Samsung, Google) "Apple"

model_name (String) Name of the phone model "iPhone 15"

price (Integer) Price in USD 999

screen_size (Float) Screen size in inches 6.1

battery (Integer) Battery capacity in mAh 4000

camera_main (String) Main camera resolution (MP) "48MP"

ram (Integer) RAM in GB 8

storage (Integer) Storage in GB 128

has_5g (Boolean) Whether the phone supports 5G TRUE

water_resistant (String) Water resistance rating (IP68 or None) "IP68"

units_sold (Integer) Estimated units sold (for market analysis) 15000

review_id (Integer) Unique ID for each review 1

user_name (String) Randomly generated reviewer name "John"

star_rating (Integer) Rating from 1 (worst) to 5 (best) 5

verified_purchase (Boolean) Whether the reviewer bought the product TRUE

review_date (Date) Date of the review (YYYY-MM-DD) "2023-05-10"

review_text (String) Simulated review text based on features & rating "The 48MP camera is amazing!"

Suggested Analysis Ideas to inspire data analysis: A. Feature Impact on Ratings Regression: star_rating ~ battery + camera_main + price Key drivers: Does battery life affect ratings more than camera quality?

B. Sentiment Analysis (NLP) Use tidytext (R) or NLTK (Python) to extract most-loved/hated features. Example: r library(tidytext) reviews_tidy <- final_data %>% unnest_tokens(word, review_text)
reviews_tidy %>% count(word, sort = TRUE) %>% filter(n > 5)

C. Brand Comparison Apple vs. Samsung vs. Google: Which brand has higher average ratings? Price sensitivity: Do cheaper phones (e.g., Pixel) get better value ratings?

D. Sales vs. Features Correlation: units_sold ~ price + brand Premium segment analysis: Do iPhones sell more despite higher prices?

About Flipkart:

Flipkart is an Indian e-commerce company, headquartered in Bangalore, Karnataka, India. It is the largest e-commerce company in India and was founded by Sachin and Binny Bansal. The company has wide variety of products electronics like laptops, tablets, smartphones, and mobile accessories to in-vogue fashion staples like shoes, clothing and lifestyle accessories; from modern furniture like sofa sets, dining tables, and wardrobes to appliances that make your life easy like washing machines, TVs, ACs, mixer grinder juicers and other time-saving kitchen and small appliances; from home furnishings like cushion covers, mattresses and bedsheets to toys and musical instruments.

Mobile Phones

Mobile phones are one of the most rapidly rising industries, as well as one of the most prominent industries in the technology sector. The rate of increase has been exponential, with the number of mobile phone customers increasing fivefold in the last decade. Globally, the number of smartphones sold to end users climbed from 300 million in 2010 to 1.5 billion by 2020.

Flipkart and Mobile Phones

As previously stated, mobile phones are in high demand and are one of the ideal products for a novice to sell. Flipkart will be the ideal spot for a vendor to market their stuff because its reach.

Content

The dataset contains description of top 5 most popular mobile brand in India. Columns : There are 16 columns each having a title which is self explanatory. Rows : There are 430 rows each having a mobile with at least a distinct feature.

Acknowledgements

The data was retrieved directly from Flipkart website using some web crawling techniques

Assumption

We don’t have direct sales report of how many units of a mobile model was sold. In general, number of people rating a product is directly proportional to number of units sold. So, for the purpose of the solution, we are using number of people rating the product as the equivalent units sold.

Inspiration

The objective is to address a hypothetical business problem for a Flipkart Authorized Seller. According to the hypothesis the individual is looking to sell mobile phones on Flipkart. For this, the individual is looking for the best product, brand, specification and deals that can generate the most revenue with the least amount of investment and budget constraints.

Questions to be answered: 1. Whether he should sell product for a particular brand only or try to focus on model from different brands? 2. Using EDA and Data Visualization find out insights and relation between different features 3. Perform detailed analysis of each brand. 4. Assuming a budget for the problem come to a solution with maximum return.

Samsung saw a slight decrease in sales from the third quarter of 2021 to the ******, selling ***** million units in the fourth quarter of 2021. Samsung ranked second in terms of global smartphone sales to end users during this quarter, falling behind Apple, although Apple follows a strong trend of high sales at the end of every year.

Context Bob has started his own mobile company. He wants to give tough fight to big companies like Apple,Samsung etc.

He does not know how to estimate price of mobiles his company creates. In this competitive mobile phone market you cannot simply assume things. To solve this problem he collects sales data of mobile phones of various companies.

Bob wants to find out some relation between features of a mobile phone(eg:- RAM,Internal Memory etc) and its selling price. But he is not so good at Machine Learning. So he needs your help to solve this problem.

In this problem you do not have to predict actual price but a price range indicating how high the price is

The global smartphone penetration in was forecast to continuously increase between 2024 and 2029 by in total 20.3 percentage points. After the fifteenth consecutive increasing year, the penetration is estimated to reach 74.98 percent and therefore a new peak in 2029. Notably, the smartphone penetration of was continuously increasing over the past years.The penetration rate refers to the share of the total population.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).Find more key insights for the smartphone penetration in countries like North America and the Americas.

This dataset contains the code for the Android application “COTS Control Centre Decision Support Tool” (CCC-DST). The CCC-DST is one part of the “COTS Control Centre Decision Support System” (CCC-DSS) which is a hardware and software solution, comprising 32 Samsung Galaxy Tab Active2 tablets. The tablets run a kiosk operating system and provide three data collection apps, developed for GBRMPA by ThinkSpatial, and three decision support components developed by CSIRO as part of the NESP COTS IPM Research Program.
The CCC-DST application is an implementation of the principles outlined in “An ecologically-based operational strategy for COTS Control” (Fletcher, et al., 2020).


Methods:

The COTS Control Centre Decision Support Tool (CCC-DST) is part of the COTS Control Centre Decision Support System (CCC-DSS). The CCC-DSS is a combined hardware and software solution developed by CSIRO as part of the National Environmental Science Program (NESP) Integrated Pest Management (IPM) Crown-of-thorns starfish (COTS) Research Program to help guide on-water decision making and implement the ecologically-informed management program outlined in the report “An ecologically-based operational strategy for COTS Control: Integrated decision making from the site to the regional scale” (Fletcher, Bonin, & Westcott, 2020).

The COTS Control Centre DSS is built around a fleet of 32 ruggedised Samsung Galaxy Tab Active2 Android tablets, along with a suite of three data collection apps, developed for the Great Barrier Reef Marine Park Authority (GBRMPA) by ThinkSpatial, and three decision support components developed by CSIRO as part of the NESP COTS IPM Research Program. The fleet of tablets are able to be managed remotely, including locating hardware and updating software, using the Samsung Knox Manage Enterprise Mobility Management platform, and run a custom kiosk launcher. Data is shared between the apps that make up the CCC-DSS within a tablet using the Android file system, between tablets on a vessel independent of cellular connectivity with the Android Nearby Communications protocol, and with GBRMPA’s Eye on the Reef Database when cellular networking is available.

In addition to designing and implementing the overarching CCC-DSS system, CSIRO has developed a suite of three software components, consisting of the main Decision Support Tool (CCC-DST), a Data Explorer functionality, which is currently implemented as part of the CCC-DST but may, in future, be separated into a second app, and a utility Data Sync Tool for sharing data between tablets when internet connectivity is not available.

Details of the underlying philosophy and implementation notes of the CCC-DST can be found in the technical report:
Fletcher C. S. and Westcott D. A. (2021) The COTS Control Centre: Supporting ecologically-informed decision making when and where the decisions need to be made. Report to the National Environmental Science Program. Reef and Rainforest Research Centre Limited, Cairns (189pp.).

Architecture:
In general, Android applications are designed to require minimal memory footprint by cycling data out of their sqlite database on the fly as required. This is an efficient method of designing lightweight apps coexisting on mobile hardware will limited memory and processing capacity. It works well for some of the tasks required of the NESP COTS Control Centre apps, but is not intuitively suited to all the functionality required. This is especially the case for situations where data must be compared longitudinally through time or across many Sites or Reefs in order to guide decision making. On the other hand, because the COTS Control Centre is run on dedicated hardware of known specification and containing only the suite of apps necessary to guide on-water actions, we can bias the design of our system towards functionality within the hardware being used, rather than optimising for universal efficiency.

As a result, the COTS Control Centre apps were developed with a hybrid philosophy that aims to target the data loaded into memory to that required to inform a decision, and which structures the data in memory using custom classes that reflect the actual data being analysed. This approach puts an emphasis on both logical database design and well-structured custom types to support the functionality of the apps, each of which are described in further detail in the sections that follow.

The apps were developed in stages, starting with prototypes early in 2016. As a result, they retain some legacy functionality, such as the use of loaders rather than the ViewModel and Room functionality introduced in Android after this time. They also include components, such as a ContentProvider framework, that were expected to be important at one stage in app development, but which are not deeply leveraged in the current configuration. The structure of the apps also reflects the fact that their development will continue to incorporate feedback from on-water operators, as well as scientific advances in biological understanding, field measurements, and management strategies. As a result, in some places a more general programming approach has been favoured over more optimised code in order to maintain or provide flexibility to incorporate new functionality in future.

Note: The database file has been omitted from the source files. The details of the database layout can be found in the technical report (section 2.2):
Fletcher C. S. and Westcott D. A. (2021) The COTS Control Centre: Supporting ecologically-informed decision making when and where the decisions need to be made. Report to the National Environmental Science Program. Reef and Rainforest Research Centre Limited, Cairns (189pp.)


Format:

Java application (Android)


References:

Fletcher C. S. and Westcott D. A. (2021) The COTS Control Centre: Supporting ecologically-informed decision making when and where the decisions need to be made. Report to the National Environmental Science Program. Reef and Rainforest Research Centre Limited, Cairns (189pp.)

Fletcher C. S., Bonin M. C, Westcott D. A.. (2020) An ecologically-based operational strategy for COTS Control: Integrated decision making from the site to the regional scale. Reef and Rainforest Research Centre Limited, Cairns (65pp.).


Data Location:

This dataset is filed in the eAtlas enduring data repository at: data\custodian\2019-2022-NESP-TWQ-5\5.1_COTS-pest-management

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.

In 2022, smartphone vendors sold around 1.39 billion smartphones were sold worldwide, with this number forecast to drop to 1.34 billion in 2023.

Smartphone penetration rate still on the rise

Less than half of the world’s total population owned a smart device in 2016, but the smartphone penetration rate has continued climbing, reaching 78.05 percent in 2020. By 2025, it is forecast that almost 87 percent of all mobile users in the United States will own a smartphone, an increase from the 27 percent of mobile users in 2010.

Smartphone end user sales

In the United States alone, sales of smartphones were projected to be worth around 73 billion U.S. dollars in 2021, an increase from 18 billion dollars in 2010. Global sales of smartphones are expected to increase from 2020 to 2021 in every major region, as the market starts to recover from the initial impact of the coronavirus (COVID-19) pandemic.

The number of smartphone users in Vietnam was forecast to continuously increase between 2024 and 2029 by in total 12.9 million users (+15.05 percent). After the fifteenth consecutive increasing year, the smartphone user base is estimated to reach 98.64 million users and therefore a new peak in 2029. Notably, the number of smartphone users of was continuously increasing over the past years.Smartphone users here are limited to internet users of any age using a smartphone. The shown figures have been derived from survey data that has been processed to estimate missing demographics.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).Find more key insights for the number of smartphone users in countries like Malaysia and Indonesia.