In the third quarter of 2025, Samsung shipped approximately **** million smartphones, marking an increase compared to both the previous quarter and the same period in the prior year. The company’s strong sales performance consistently positions Samsung as the world’s leading smartphone vendor, ahead of 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.

Context

The Samsung India Electronics Employee Reviews dataset is a collection of valuable insights extracted from employee reviews of Samsung India Electronics, a renowned Consumer Electronics & Appliances company. The dataset offers a unique window into the experiences, sentiments and perspectives of individuals who have worked at Samsung India Electronics.

Samsung India Electronics

Sources

The dataset was curated by Web Scraping employee reviews from Ambition Box, a platform where employees share their experiences and opinions about their workplaces. The data includes reviews spanning a wide range of topics including work-life balance, career growth, company culture and more.

Inspiration

This dataset was inspired by the desire to better understand the employee experience at Samsung India Electronics and to provide a resource for anyone interested in gaining insights into the company's work environment. It serves as a valuable resource for HR professionals, job seekers, researchers and anyone looking to explore the world of Samsung India Electronics through the eyes of its employees.

Additionally, The motivation behind curating this dataset is to empower data enthusiasts, NLP researchers, AI developers, and culture analytics enthusiasts to explore the dynamic world of Samsung India Electronics through the eyes of its employees. It serves as an invaluable resource for projects aimed at sentiment analysis, language processing and culture analytics.

We hope that this dataset will not only inform but also inspire discussions and analyses that can benefit both current and future members of the Samsung India Electronics community, as well as the wider public interested in workplace insights and data-driven exploration.

This dataset has been artificially generated to mimic real-world user interactions within a mobile application. It contains 100,000 rows of data, each row of which represents a single event or action performed by a synthetic user. The dataset was designed to capture many of the attributes commonly tracked by app analytics platforms, such as device details, network information, user demographics, session data, and event-level interactions.

Key Features Included

User & Session Metadata

User ID: A unique integer identifier for each synthetic user. Session ID: Randomly generated session identifiers (e.g., S-123456), capturing the concept of user sessions. IP Address: Fake IP addresses generated via Faker to simulate different network origins. Timestamp: Randomized timestamps (within the last 30 days) indicating when each interaction occurred. Session Duration: An approximate measure (in seconds) of how long a user remained active. Device & Technical Details

Device OS & OS Version: Simulated operating systems (Android/iOS) with plausible version numbers. Device Model: Common phone models (e.g., “Samsung Galaxy S22,” “iPhone 14 Pro,” etc.). Screen Resolution: Typical screen resolutions found in smartphones (e.g., “1080x1920”). Network Type: Indicates whether the user was on Wi-Fi, 5G, 4G, or 3G. Location & Locale

Location Country & City: Random global locations generated using Faker. App Language: Represents the user’s app language setting (e.g., “en,” “es,” “fr,” etc.). User Properties

Battery Level: The phone’s battery level as a percentage (0–100). Memory Usage (MB): Approximate memory consumption at the time of the event. Subscription Status: Boolean flag indicating if the user is subscribed to a premium service. User Age: Random integer ranging from teenagers to seniors (13–80). Phone Number: Fake phone numbers generated via Faker. Push Enabled: Boolean flag indicating if the user has push notifications turned on. Event-Level Interactions

Event Type: The action taken by the user (e.g., “click,” “view,” “scroll,” “like,” “share,” etc.). Event Target: The UI element or screen component interacted with (e.g., “home_page_banner,” “search_bar,” “notification_popup”). Event Value: A numeric field indicating additional context for the event (e.g., intensity, count, rating). App Version: Simulated version identifier for the mobile application (e.g., “4.2.8”). Data Quality & “Noise” To better approximate real-world data, 1% of all fields have been intentionally “corrupted” or altered:

Typos and Misspellings: Random single-character edits, e.g., “Andro1d” instead of “Android.” Missing Values: Some cells might be blank (None) to reflect dropped or unrecorded data. Random String Injections: Occasional random alphanumeric strings inserted where they don’t belong. These intentional discrepancies can help data scientists practice data cleaning, outlier detection, and data wrangling techniques.

Usage & Applications

Data Cleaning & Preprocessing: Ideal for practicing how to handle missing values, inconsistent data, and noise in a realistic scenario. Analytics & Visualization: Demonstrate user interaction funnels, session durations, usage by device/OS, etc. Machine Learning & Modeling: Suitable for building classification or clustering models (e.g., user segmentation, event classification). Simulation for Feature Engineering: Experiment with deriving new features (e.g., session frequency, average battery drain, etc.).

Important Notes & Disclaimer

Synthetic Data: All entries (users, device info, IPs, phone numbers, etc.) are artificially generated and do not correspond to real individuals. Privacy & Compliance: Since no real personal data is present, there are no direct privacy concerns. However, always handle synthetic data ethically.

Our organization provides technology websites with content about the current value of used smartphones and the value per dollar of phones in the market to help people buy and sell smartphones. A model that could predict the real value of phones based on several factors would be helpful.

Attributes of the dataset include:

• PhoneID The identification number of a phone in this dataset.
• Brand The smartphone manufacturer (e.g., Apple, Google, Samsung, etc.)
• Model The smartphone model (e.g., iPhone 13, Galaxy S21, etc.)
• Release Year The year the smartphone model was released.
• Screen Size (inches) The model's screen size is in inches.
• RAM (GB) The model's RAM in GB (2, 4, 6, 8, 12, 16)
• Storage (GB) The internal storage of the smartphone in GB (32, 64, 128, 256, 512)
• Battery Capacity (mAh) The battery life is measured in milliampere-hours. The abbreviation mAh stands for milliampere-hour, a unit of electrical charge that measures a battery's capacity. It indicates how much current a battery can supply over time. A higher mAh rating means the battery can store more energy and power a device for longer.
• Camera (MP) is the smartphone's camera's megapixel rating. The higher the number of MP, the better the camera's resolution.
• 5G Support If the smartphone supports 5G connectivity, Yes. Otherwise, No.
• Operating System One of two, Android or iOS.
• Price (USD) is the current market price of the smartphone in US Dollars.

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.

The number of smartphone users in France was forecast to continuously increase between 2024 and 2029 by in total 3.2 million users (+5.96 percent). After the fifteenth consecutive increasing year, the smartphone user base is estimated to reach 56.89 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 Belgium and Luxembourg.

simple dataset is for beginners who have started their journey in data analytics.

dataset from a real shop repair phone technician. this dataset contains some phones problems that have been fixed and some other that not fixed during two months.

• Name : brand name + model , for example : SAMSUNG A50 , Brand : SAMSUNG, Model : A50.
• Type : this device is smartphone , pad or cell 'not smartphone'.
• Problem : 1. LCD = change broken display 2.FRP = Factory reset protection . 'this issue comes from people who factory their phone and forget their google account' 3.lock = passcode 4.off = this device turn off without indicate anything 5.flash = firmware issue 6.touch = phone touch screen 7.charge = charge issue 8.battery = change battery 9.mic = microphone 10. sound = speaker issue
• fixed YES/NO = Boolean indicate if fixed or not

-- feel free to ask any question -

vote if this dataset helpful for you, to improve it

iBeta Level 2 Dataset: Presentation Attacks Liveness Detection & Verifications

The dataset consists of more than 32,300 spoofing attacks of 6 different types specifically curated for a passing iBeta Level 2 and getting a certification. It is compliant with the ISO 30107-3 standard, which sets the highest quality requirements for biometric testing and attack detection solutions.

By geting the iBeta Level 2 certification, biometric technology companies demonstrate their commitment to developing robust and reliable biometric systems that can effectively detect and prevent fraud - Get the data

Attacks in the dataset

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F22059654%2F352f8d1d97a5c1eb03967775f60fdcdb%2FFrame%20126%20(1).png?generation=1725872087101598&alt=media" alt="">

This dataset is designed to evaluate the performance of biometric authentication and identity recognition technology in detecting presentation attacks, it includes different pad tests.

Videos in the dataset: 1. Real Person: real videos of people 2. 2D Mask: printed photos of people's faces cut out along the contour 3. 2D Mask with Eyeholes: printed photos of people with holes for eyes 4. Latex Mask: latex masks on people 5. Wrapped 3D Mask: 3D cardboard mask attached to a mannequin 6. Silicone Mask: silicone masks on people

💵 Buy the Dataset: This is a limited preview of the data. To access the full dataset, please contact us at https://unidata.pro to discuss your requirements and pricing options.

Metadata for the dataset

Devices: Mi10s, Google Pixel 4, Samsung Galaxy A03s, iPhone 11, iPhone SE 2

Resolution: 1920 x 1080 and higher https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F22059654%2F4ee72191bdbc72d18148ebf79a2bd591%2FFrame%20127.png?generation=1725878560196276&alt=media" alt="">

The iBeta Level 2 dataset is an essential tool for the biometrics industry, as it helps to ensure that biometric systems meet the highest standards of anti-spoofing technology. This dataset is used by various biometric companies in various applications and products to test and improve their biometric authentication solutions, face recognition systems and facial liveness detection methods.

🌐 UniData provides high-quality datasets, content moderation, data collection and annotation for your AI/ML projects

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.

According to Cognitive Market Research, The Global NFC enabled Handsets market will grow at a compound annual growth rate (CAGR) of 20.50% from 2023 to 2030.

The demand for NFC enabled handsets is rising due to increasing demand for mobile payments.
Demand for feature phones remains higher in the NFC enabled handsets market.
The mobile payment category held the highest NFC enabled handsets market revenue share in 2023.
North America will continue to lead, whereas the Asia Pacific NFC enabled handsets market will experience the most robust growth until 2030.

Increasing Demand for Mobile Payments to Drive Market Growth

Mobile payments using NFC enabled handsets offer a faster and more convenient alternative to traditional payment methods. Users can complete transactions with a simple tap, reducing the time spent at the checkout. The increasing penetration of smartphones, including NFC enabled handsets, provides a larger user base for mobile payment solutions. As more people own smartphones, the potential for mobile payments grows.

Smartphone shipments from India reached 168 million units in 2021, and it is anticipated that they will reach 190 million units in 2022.

(Source: www.ibef.org/industry/electronics-system-design-manufacturing-esdm)

Mobile wallet applications like Apple Pay, Google Pay, and Samsung Pay have gained traction. These wallets rely on NFC technology and have become increasingly integrated into daily routines. Mobile payments extend beyond physical retail stores. Users can make online and in-app purchases using their NFC enabled handsets, broadening the scope of mobile payment applications.

Growing Adoption of Wearable Technology to Drive Market Growth

Wearable devices, especially smartwatches, are increasingly used for mobile ticketing applications. Users can store electronic tickets for public transportation, events, or flights on their wearables, simplifying ticketing. Some banks and financial institutions offer apps that are compatible with wearable devices. Users can check their account balances, receive transaction alerts, and even make mobile payments using NFC enabled wearables.

Exports of electronic goods increased by 50.52% from US$ 15.66 billion in FY22 to US$ 23.57 billion in FY23, a record high.

(Source: www.ibef.org/industry/electronics-system-design-manufacturing-esdm)

NFC enabled wearables are used for health and fitness applications. Users can tap their devices to collect data from fitness equipment, make payments for health services, or even access their medical records securely.

Market Dynamics Of the NFC enabled Handsets

Lack of Awareness and Education to Hinder Market Growth

The lack of education about the various applications of NFC technology can result in a limited understanding of its potential use cases beyond mobile payments. This can hinder the development of new NFC-based services. The lack of awareness about NFC security features can lead to unfounded concerns and reluctance to use NFC enabled handsets for secure transactions. Some individuals and businesses may perceive NFC technology as too complex or difficult to implement. This can discourage exploration and adoption.

Key Trends of the NFC enabled Handsets

The Rapid Increase in the Use of Contactless Payments and Digital Wallets

With the worldwide growth of mobile payment systems such as Google Pay, Apple Pay, and Samsung Pay, there is a significant demand for NFC-enabled smartphones. Consumers are favoring fast, secure, and touchless transactions, particularly in the aftermath of the pandemic, which has led to a rise in the incorporation of NFC chips in mid-range and entry-level devices across various markets.

Integration with IoT and Smart Ecosystems

NFC-enabled smartphones are being utilized increasingly for purposes beyond payments, including pairing with smart devices, access control, ticketing, and identity verification. As smart home technologies, wearables, and interconnected infrastructure expand, NFC devices act as a central hub, enhancing their importance in everyday digital interactions.

Impact of COVID-19 on the NFC enabled handsets market

COVID-19, both positive and negative, significantly impacted the market for NFC enabled smartphones. The pandemic accelerated the adoption of contactless payment methods due to concerns about the transmission of the virus through ...

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.

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 ubiquity of Smartphone applications that aim to identify organisms, including plants, make them potentially useful for increasing people’s engagement with the natural world. However, how well such applications actually identify plants has not been compressively investigated nor has an easily repeatable scoring system to compare across plant groups been developed. This study investigated the ability of six common Smartphone applications (Google Lens, iNaturalist, Leaf Snap, Plant Net, Plant Snap, Seek) to identify herbaceous plants and developed a repeatable scoring system to assess their success. Thirty-eight species of plant were photographed in their natural habitats using a standard Smartphone (Samsung Galaxy A50) and assessed in each app without image enhancement. All apps showed considerable variation across plant species and were better able to identify flowers than leaves. Plant Net and Leaf Snap outperformed the other apps. Even the higher preforming apps did not have an accuracy above ~88% and lower scoring apps were considerably below this. Smartphone apps present a clear opportunity to encourage people to engage more with plants. Their accuracy can be good, but should not be considered excellent or assumed to be correct, particularly if the species in question may be toxic or otherwise problematic.

Key Mobile Payments StatisticsTop Mobile Payments AppsFinance App Market LandscapeMobile Payments Transaction VolumeMobile Payments UsersMobile Payments Adoption by CountryMobile Payments TPV in...

Face Antispoofing dataset for recognition systems

The dataset consists of 98,000 videos and selfies from 170 countries, providing a foundation for developing robust security systems and facial recognition algorithms.

While the dataset itself doesn't contain spoofing attacks, it's a valuable resource for testing liveness detection system, allowing researchers to simulate attacks and evaluate how effectively their systems can distinguish between real faces and various forms of spoofing.

By utilizing this dataset, researchers can contribute to the development of advanced security solutions, enabling the safe and reliable use of biometric technologies for authentication and verification. - Get the data

Examples of data

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F22059654%2Fe46e401a5449bacce5f934aaea9bb06e%2FFrame%20155.png?generation=1730591437955112&alt=media" alt=""> The dataset offers a high-quality collection of videos and photos, including selfies taken with a range of popular smartphones, like iPhone, Xiaomi, Samsung, and more. The videos showcase individuals turning their heads in various directions, providing a natural range of movements for liveness detection training.

💵 Buy the Dataset: This is a limited preview of the data. To access the full dataset, please contact us at https://unidata.pro to discuss your requirements and pricing options.

Metadata for the dataset

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F22059654%2F8350718e93ee92840995405815739c61%2FFrame%20136%20(1).png?generation=1730591760432249&alt=media" alt=""> Furthermore, the dataset provides detailed metadata for each set, including information like gender, age, ethnicity, video resolution, duration, and frames per second. This rich metadata provides crucial context for analysis and model development.

Researchers can develop more accurate liveness detection algorithms, which is crucial for achieving the iBeta Level 2 certification, a benchmark for robust and reliable biometric systems that prevent fraud.

🌐 UniData provides high-quality datasets, content moderation, data collection and annotation for your AI/ML projects

Snapshot img

The mobile security software market share is expected to increase by USD 2.75 billion from 2020 to 2025, and the market’s growth momentum will accelerate at a CAGR of 9.68%.

This mobile security software market research report provides valuable insights on the post COVID-19 impact on the market, which will help companies evaluate their business approaches. Furthermore, this report extensively covers mobile security software market segmentation by end-user (enterprises and individual users) and geography (North America, APAC, Europe, MEA, and South America). The mobile security software market report also offers information on several market vendors, including AO Kaspersky Lab, Avast Plc, Broadcom Inc., F-Secure Corp., International Business Machines Corp., Ivanti Inc., McAfee Corp., Panda Security SL, Samsung Electronics Co. Ltd., and Trend Micro Inc. among others.

What will the Mobile Security Software Market Size be During the Forecast Period?

Download the Free Report Sample to Unlock the Mobile Security Software Market Size for the Forecast Period and Other Important Statistics

'North America, as countries in this region, such as the US and Canada, are among the most technologically advanced countries and are pioneers in the adoption of technologies'

Mobile Security Software Market: Key Drivers, Trends, and Challenges

The increasing incidence of cyberattacks is notably driving the mobile security software market growth, although factors such as availability of free mobile security software may impede the market growth. Our research analysts have studied the historical data and deduced the key market drivers and the COVID-19 pandemic impact on the mobile security software industry. The holistic analysis of the drivers will help in deducing end goals and refining marketing strategies to gain a competitive edge.

Key Mobile Security Software Market Driver

One of the major factors contributing to the mobile security market growth is the increasing cases of business information thefts and insider fraud. Cyberattacks are becoming advanced and sophisticated, which are targeting people, networks, and devices. In the rapidly changing IT infrastructure, attackers are finding new ways of stealing valuable information and disrupting businesses and individuals by infiltrating into mobile devices and acquiring sensitive information. The increasing dependency on mobile applications for critical purposes such as transactions, purchases, and other related activities is leading to a rise in the number of data theft cases. Furthermore, with the emergence of social networking websites, it has become easy for attackers to extract information from vulnerable users. Such increasing cases of advanced and sophisticated thefts are forcing mobile users to adopt mobile security software.

Key Mobile Security Software Market Trend

Growing penetration of smartphones across the globe is the key trend driving the mobile security market growth. In 2020, the sales of smartphones witnessed strong growth, owing to the rising disposable incomes of consumers and increasing household spending power in emerging economies. In the same year, major smartphone manufacturers, such as Samsung Group, Apple Inc., and Huawei Technologies Co., Ltd., witnessed strong growth in the sales of smartphones. Therefore, an increase in the sales of smartphones around the world has provided high growth opportunities for vendors in the global mobile security software market.

Key Mobile Security Software Market Challenge

The availability of free mobile security software is posing a severe threat to the mobile security software market growth. Such software can be downloaded and run on all platforms and is becoming popular in developing economies such as India and China. Most major vendors, such as Avast Software SRO, Quick Heal Technologies Ltd., Bitdefender, and AO Kaspersky Lab, among others, provide free mobile security software. Vendors use this as a strategy to attract customers and encourage them to upgrade it to premium mobile security software that offers additional features. However, most individual smartphone users prefer using free mobile security software. Hence, the increasing adoption of free mobile security software is reducing the overall revenue generated in the mobile security software market.

This mobile security software market analysis report also provides detailed information on other upcoming trends and challenges that will have a far-reaching effect on the market growth. The actionable insights on the trends and challenges will help companies evaluate and develop growth strategies for 2021-2025.

Market Overview

The global systems software market within the global IT software market. The super parent global IT software market covers companies engaged in developing and producing application and systems software. It also includes companies offering database management software. The

Key Health App StatisticsTop Health AppsHealth & Fitness App Market LandscapeHealth App RevenueHealth Revenue by AppHealth App UsageHealth App Market ShareHealth App DownloadsKeeping track of...

Objective: Clinical voice quality assessments typically use external microphones meeting technical standards for instrumental assessment of voice. As smartphones advance, this study aimed to determine their suitability for voice recordings through a systematic review with meta-analysis.Method: Three database searches were conducted, ranging from their inception to December 2024, as well as a manual search. Cross-sectional studies were included on widely used clinical acoustic voice quality measures of the software Praat (i.e., jitter, shimmer, harmonics-to-noise ratio [HNR], smoothed cepstral peak prominence [CPPS], and acoustic voice quality index [AVQI]).Results: We found 10 eligible research studies with a total of 379 participants who were simultaneously compared between a clinical recording system (CRS) and different smartphones by Apple and Samsung products. All included studies focused on individuals with vocally healthy voices, while four of the studies also included those with voice disorders. In comparison with CRS, iPhones revealed significant differences and large effect sizes in HNR (mean difference of 2.20, 95% CI [0.59, 3.82], p = .008, Cohen’s d = 2.54) and in AVQI (mean difference of −0.53, 95% CI [−1.00, −0.06], p = .027, Cohen’s d = −1.99), but in the direct comparison between Apple and Samsung mobile device recordings, significant differences and large effect sizes were found in jitter (mean difference of −0.17, 95% CI −0.27, −0.08, p < .001, Cohen’s d = −1.18) and CPPS (mean difference of 0.87, 95% CI [0.20, 1.53], p = .011, Cohen’s d = 1.26). Recordings with Samsung products showed only significant differences and a large effect size with CRS in jitter (mean difference of −0.16, 95% CI [−0.29, −0.03], p = .019, Cohen’s d = −0.84).Conclusions: The present meta-analysis indicated some inconsistency in the outcomes of acoustic voice quality parameters between smartphone recordings and CRS. While acoustic measurements are frequently used in clinical voice assessments and smartphones are widely available, it is important to note that for certain parameters, current smartphone recordings may not yet match the precision of CRSs for voice quality analyses.Supplemental Material S1. Study quality appraisal analyzed with NHLBI Quality Assessment Tool for Observational Cohort and Cross‐Sectional Studies.Barsties v. Latoszek, B., Lammertz, C. Z., Awan, S. N., Binkofski, F., Hetjens, S. (2025). The accuracy of smartphone recordings for clinical voice diagnostics in acoustic voice quality assessments: A systematic review and meta‐analysis. American Journal of Speech-Language Pathology, 34(6), 3531–3548. https://doi.org/10.1044/2025_AJSLP‐25‐00140

Trendyol Smartphones Data

This dataset has 19 columns and 939 rows, these data were taken from Trendyol.com with Python-BeautifulSoup.

Click on the link to see the Python script we used to create the dataset and our project with the dataset. Link

The people who created this dataset: Ahmet Furkan DEMIR, Batuhan TURK

Detailed information about the dataset

One of the 19 columns in the dataset is the product titles, one is the product links, and the other is the prices of the products, so the regression column that we will use for the estimation, remaining 16 columns are the features of products, namely phones, by using these columns you can develop machine learning algorithms or perform various operations on the data.

Columns: * Urun Başlığı : Title of products, it is used for verification only. |->No missing data<-| * Link : Ürünlerin Link, it is used for verification only (Unique for each product). |->No missing data<-| * Marka : Phone Manufacturer (Samsung, Apple etc.) |->No missing data<-| * İşletim Sistemi : Phones operating system (Android, IOS) |->No missing data<-| * Dahili Hafıza : Total ROM memory of the phone (X GB) |->No missing data<-| * RAM Kapasitesi : Total RAM capacity of the phone (Y GB) |->No missing data<-| * Görüntü Teknolojisi : Display Technology (LCD, AMOLED etc.) |->Yes missing data<-| * Ekran Çözünürlüğü : Screen resolution (HD+, FHD+ etc.) |->Yes missing data<-| * Kamera Çözünürlüğü : Camera Resolution (Z-X MP etc.) |->Yes missing data<-| * Batarya Kapasitesi Aralığı : Battery Capacity Range (4500-5000 mAh etc.) |->No missing data<-| * Mobil Bağlantı Hızı : Mobile Connection Speed (4G, 4.5G, 5G) |->No missing data<-| * Ekran Boyutu : Screen size (6 inç ve üstü) |->Yes missing data<-| * Parmak İzi Okuyucu : Fingerprint reader (yes, no) |->Yes missing data<-| * Yüz Tanıma : Face recognition (yes, no) |->Yes missing data<-| * Suya/Toza Dayanıklılık : Water/Dust Resistance (yes, no) |->No missing data<-| * Çift Hat : Double Line (yes, no) |->No missing data<-| * Arttırılabilir Hafıza (Hafıza Kartı Desteği) : Expandable Memory (Memory Card Support) (yes, no) |->Yes missing data<-| * Ekran Yenileme Hızı : Screen Refresh Rate (120 Hz etc.) |->Yes missing data<-| * Fiyat : Price of phones (Z TL) |->No missing data<-|

What you can do with the dataset

• With dataset, you can perform a price estimation application using the attributes of the Phones.
• You can visualize data with dataset.
• You can clear or fill in the missing data in the dataset.

The number of smartphone users in Nigeria, Africa’s biggest economy and most populous country, is forecast to grow to more than *** million by 2025. Currently, estimates from different sources put the number of smartphone users in Nigeria at roughly ** and ** million. The exact number of users is hard to pin down - however, the data found shows a strong growth outlook for the Nigerian smartphone market with user numbers to at least triple within the next five to six years. Strong population and GDP growth forecast The population and gross domestic product (GDP) in Nigeria are both forecast to grow steadily at an annual rate of * to * percent until 2022. Nigeria’s population is very young (average age of 18) and expected to grow to more than *** million people by 2020. Concurrently, the GDP is set to reach more than *** billion U.S. dollars by that time. The country’s telecoms industry also stands to profit from Nigeria’s overall growth due to its contribution to the country’s GDP being steady as of 2012 (*** to ** percent of annual GDP). Smartphone penetration set to rise The dynamic growth of Nigeria’s economy and population is set to impact the development of the country’s mobile market as well. There are around *** million mobile subscriptions in Nigeria. But currently, only around ** to ** percent of the population is using a smartphone. The majority of mobile users are still using feature phones which offer basic phone functions like voice calling and text messaging. Smartphone penetration is set to grow though to around ** percent by 2025, presenting strong growth opportunities for feature phone and smartphone manufacturers alike. At present, Samsung is the leading smartphone vendor in Nigeria. Although, Chinese manufacturers like Tecno, Itel (Hong Kong), and Infinix (Hong Kong) are also holding strong positions in the market.