This dataset provides a comprehensive collection of information about all the latest smartphones available in the market as of the current time.

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The dataset was created by web scraping reputable online sources to gather accurate and up-to-date information about various smartphone models, their specifications, features, and pricing.

The global number of smartphone users in was forecast to continuously increase between 2024 and 2029 by in total *** billion users (+***** percent). After the fifteenth consecutive increasing year, the smartphone user base is estimated to reach *** billion 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 *** 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 the Americas and Asia.

Looking to gain insights into the world of mobile phones? Look no further than our comprehensive dataset, which provides detailed specifications and prices for a wide range of smartphones. With data on everything from screen size and camera quality to battery life and processing power, this dataset is a must-have for anyone interested in the mobile phone market. Whether you're a researcher, a tech enthusiast, or just looking to make an informed purchase, our data will give you the information you need to make smart decisions. So why wait? Download our dataset today and start exploring the world of mobile phones like never before! The prices are in PKR. as the dataset is extracted from Pakistan Mobile market website

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.

China is leading the ranking by number of smartphone users, recording ****** million users. Following closely behind is India with ****** million users, while Seychelles is trailing the ranking with **** million users, resulting in a difference of ****** million users to the ranking leader, China. 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).

The number of smartphone users in the United Kingdom was forecast to continuously increase between 2024 and 2029 by in total *** million users (+**** percent). After the ninth consecutive increasing year, the smartphone user base is estimated to reach ***** 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 *** 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 information concerning Denmark and Latvia.

DESCRIPTION: This dataset provides comprehensive information about various smartphone models, facilitating detailed analysis and comparison. It encompasses crucial attributes such as model name, price, rating, similarity index (sim), processor details, RAM capacity, battery specifications, display features, camera specifications, expandable memory card support, and operating system (OS) details.

Columns:

1.Model: The name or designation of the smartphone model.

2.Price: The retail price of the smartphone, typically in a specified currency.

3.Rating: The user or expert rating of the smartphone, providing an indication of its overall quality and user satisfaction.

4.Sim: The number of SIM cards supported by the smartphone.

5.Processor: Details about the processor or chipset used in the smartphone, including brand, model, and specifications.

6.RAM: The amount of random-access memory (RAM) available in the smartphone, measured in gigabytes (GB).

7.Battery: Information regarding the battery capacity and type, providing insight into the device's endurance and usage duration.

8.Display: Specifications related to the smartphone's display, such as size, resolution, technology (e.g., LCD, OLED), and any additional features (e.g., HDR support).

9.Camera: Details about the smartphone's camera setup, including megapixel count, lens specifications, and additional features like image stabilization or AI enhancements.

10.Card: Indicates whether the smartphone supports expandable memory cards, providing users with the option to increase storage capacity.

11.OS: The operating system installed on the smartphone, including the version number if applicable.

This dataset is valuable for various analyses, including:

1.Price-performance comparisons: Assessing the correlation between price and features like processor, RAM, and camera quality.

2.Market trend analysis: Identifying popular models based on ratings and sales data.

3.Feature preference analysis: Examining which features (e.g., battery capacity, camera quality) are most valued by consumers.

4.Brand comparison: Comparing specifications and performance across different smartphone brands.

5.Predictive modeling: Using historical data to predict future trends in smartphone design and consumer preferences.

The dataset is ideal for data scientists, researchers, and analysts interested in the smartphone industry, consumer behavior, and technology trends. It can be utilized for exploratory data analysis, machine learning modeling, and generating insights to inform business decisions within the mobile device market.

Dataset Overview This dataset provides a curated, example-based snapshot of selected Samsung smartphones released (or expected to be released) between 2020 and 2024. It includes various technical specifications such as camera details, processor type, RAM, internal storage, display size, GPU, battery capacity, operating system, and pricing. Note that these values are illustrative and may not reflect actual market data.

What’s Inside?

Phone Name & Release Year: Quickly reference the time frame and model. Camera Specs: Understand the rear camera configurations (e.g., “108+10+10+12 MP”) and compare imaging capabilities across models. Processor & GPU: Gain insights into the performance capabilities by checking the processor and graphics chip. Memory & Storage: Review RAM and internal storage options (e.g., “8 GB RAM” and “128 GB Internal Storage”). Display & Battery: Compare screen sizes (from 6.1 to over 7 inches) and battery capacities (e.g., 5000 mAh) to gauge device longevity and usability. Operating System: Note the Android version at release. Price (USD): Examine relative pricing trends over the years. How to Use This Dataset

Exploratory Data Analysis (EDA): Use Python libraries like Pandas and Matplotlib to explore pricing trends over time, changes in camera configurations, or the evolution of battery capacities.

Example: df.groupby('Release Year')['Price (USD)'].mean().plot(kind='bar') can show how average prices have fluctuated year to year. Feature Comparison & Filtering: Easily filter models based on specs. For instance, query phones with at least 8 GB RAM and a 5000 mAh battery to identify devices suitable for power users.

Example: df[(df['RAM (GB)'] >= 8) & (df['Battery Capacity (mAh)'] >= 5000)] Machine Learning & Predictive Analysis: Although this dataset is example-based and not suitable for precise forecasting, you could still practice predictive modeling. For example, create a simple regression model to predict price based on features like RAM and display size.

Example: Train a regression model (e.g., LinearRegression in scikit-learn) to see if increasing RAM or battery capacity correlates with higher prices. Comparing Release Trends: Investigate how flagship and mid-range specifications have evolved. See if there’s a noticeable shift towards larger displays, bigger batteries, or higher camera megapixels over the years.

Recommended Tools & Libraries

Python & Pandas: For data cleaning, manipulation, and initial analysis. Matplotlib & Seaborn: For creating visualizations to understand trends and distributions. scikit-learn: For modeling and basic predictive tasks, if you choose to use these example values as a training ground. Jupyter Notebooks or Kaggle Kernels: For interactive analysis and iterative exploration. Disclaimer This dataset is a synthetic, illustrative example and may not match real-world specifications, prices, or release timelines. It’s intended for learning, experimentation, and demonstration of various data analysis and machine learning techniques rather than as a factual source.

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...

Context, Sources & Inspiration:

This dataset was created to provide a comprehensive overview of the global smartphone market in 2025. The dataset contains over 1000 mobile phone models from popular brands including Apple, Samsung, Xiaomi, Oppo, Vivo, Google, OnePlus, Realme, and Infinix.

Sources & Methodology:

Data was compiled using publicly available specifications from brand websites, tech blogs, and market reports.

Prices are in USD and represent typical retail pricing for 2025.

Technical specifications such as RAM, storage, camera, battery, display size, processor, and OS were collected from official manufacturer sources and verified against multiple tech review sites.

Ratings are simulated realistic averages (3.5–5.0) to provide analysis opportunities.

Inspiration:

The goal is to provide an easy-to-use dataset for ML projects, price prediction, market trend visualization, and exploratory data analysis (EDA).

Designed for students, analysts, and ML enthusiasts who want a complete and realistic dataset for smartphones.

This competition involves advertisement data provided by BuzzCity Pte. Ltd. BuzzCity is a global mobile advertising network that has millions of consumers around the world on mobile phones and devices. In Q1 2012, over 45 billion ad banners were delivered across the BuzzCity network consisting of more than 10,000 publisher sites which reach an average of over 300 million unique users per month. The number of smartphones active on the network has also grown significantly. Smartphones now account for more than 32% phones that are served advertisements across the BuzzCity network. The "raw" data used in this competition has two types: publisher database and click database, both provided in CSV format. The publisher database records the publisher's (aka partner's) profile and comprises several fields:

publisherid - Unique identifier of a publisher. Bankaccount - Bank account associated with a publisher (may be empty) address - Mailing address of a publisher (obfuscated; may be empty) status - Label of a publisher, which can be the following: "OK" - Publishers whom BuzzCity deems as having healthy traffic (or those who slipped their detection mechanisms) "Observation" - Publishers who may have just started their traffic or their traffic statistics deviates from system wide average. BuzzCity does not have any conclusive stand with these publishers yet "Fraud" - Publishers who are deemed as fraudulent with clear proof. Buzzcity suspends their accounts and their earnings will not be paid

On the other hand, the click database records the click traffics and has several fields:

id - Unique identifier of a particular click numericip - Public IP address of a clicker/visitor deviceua - Phone model used by a clicker/visitor publisherid - Unique identifier of a publisher adscampaignid - Unique identifier of a given advertisement campaign usercountry - Country from which the surfer is clicktime - Timestamp of a given click (in YYYY-MM-DD format) publisherchannel - Publisher's channel type, which can be the following: ad - Adult sites co - Community es - Entertainment and lifestyle gd - Glamour and dating in - Information mc - Mobile content pp - Premium portal se - Search, portal, services referredurl - URL where the ad banners were clicked (obfuscated; may be empty). More details about the HTTP Referer protocol can be found in this article. Related Publication: R. J. Oentaryo, E.-P. Lim, M. Finegold, D. Lo, F.-D. Zhu, C. Phua, E.-Y. Cheu, G.-E. Yap, K. Sim, M. N. Nguyen, K. Perera, B. Neupane, M. Faisal, Z.-Y. Aung, W. L. Woon, W. Chen, D. Patel, and D. Berrar. (2014). Detecting click fraud in online advertising: A data mining approach, Journal of Machine Learning Research, 15, 99-140.

The number of smartphone users in the Philippines was forecast to increase between 2024 and 2029 by in total 5.6 million users (+7.29 percent). This overall increase does not happen continuously, notably not in 2026, 2027, 2028 and 2029. The smartphone user base is estimated to amount to 82.33 million users 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).

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.

OPPO has significantly increased the production and sales of their smartphone lineup over the past five years, shipping almost 29 million units in the third quarter of 2024. The company – launched in 2004 – shipped 7.3 million units in the first quarter of 2015. Despite considerable growth over the past few years, the total smartphone units shipped by OPPO in the first quarter of 2024 was not the highest. OPPO’s growth: Leading five vendors OPPO’s growth has seen their output consistently place the company among the top five smartphone vendors in the world, shipping around 29 million units in the third quarter of 2024. While many of those shipments were domestic shipments in the company’s home country of China, OPPO has gained a footing in international markets, accounting for four percent of the smartphone market in Europe. OPPOrtunities in emerging markets Many of OPPO’s smartphones are available at a lower price-point than the flagship phones of vendors such as Apple, giving the company opportunities in emerging markets. For instance, the company regularly appears among the top vendors in the African smartphone market. A key reason for OPPO’s success in Africa is that 97 percent of all phones sold in the region sell for less than 400 U.S. dollars.

📊 About the Dataset

Technology adoption has been evolving rapidly, shaping industries and consumer behaviors worldwide. This dataset provides insights into global gadget consumption trends from 2015 to 2025, covering smartphones, laptops, gaming consoles, smartwatches, and 5G penetration rates.

🎯 Use Cases

• 📈 Market research & consumer behavior analysis
• 🔮 Forecasting future tech adoption trends
• ♻️ Studying the impact of e-waste generation
• 🌐 Understanding 5G penetration across different countries

📑 Column Descriptions

Context

Sourced directly from the popular price comparison platform Smartprix, this dataset offers a raw and dynamic look into the mobile phone market. It is updated daily, ensuring you have access to the latest device information. The dataset is intentionally left in its "uncleaned" state, presenting a fantastic real-world challenge for data enthusiasts looking to practice their data wrangling, preprocessing, and feature engineering skills.

Content

The data is provided in a single CSV file, mobiles.csv. Each row corresponds to a specific mobile phone model. A key characteristic of this dataset is its hierarchical column structure, which uses a dot notation (e.g., Category.Feature) to represent nested attributes. This format reflects the raw data as it was collected and requires parsing for effective analysis.

The dataset is comprehensive, covering a wide range of mobile phone specifications, including:

• General: Brand, Model, OS, Release Date, Sim Type
• Design: Dimensions, Weight, Colors, Build Material
• Display: Size, Type, Resolution, Refresh Rate, PPI, Screen to Body Ratio
• Camera: Rear and Front Camera specifications (megapixels, features, video recording)
• Battery: Capacity, Fast Charging support, Type
• Connectivity: 5G/4G support, Bluetooth, Wi-Fi, USB, GPS
• Performance: Processor details, RAM, Internal Storage
• Pricing: Current Price

Key characteristics of this dataset:

• Raw and Uncleaned: The data contains missing values, requires data type conversions, and has inconsistencies that need to be handled before analysis.
• Dynamic: Reflecting the fast-paced mobile market, this dataset is updated daily.
• Hierarchical Columns: The dot-separated column names (Display.Size, Camera.Features, etc.) will challenge you to parse and flatten the data into a usable format for machine learning.

Potential Uses & Inspiration

This dataset is a perfect playground for various data science projects:

• Data Cleaning and Preprocessing: Master the art of preparing messy, real-world data for analysis.
• Exploratory Data Analysis (EDA): Discover trends in the mobile phone industry. Which features are becoming standard? How do camera specs correlate with price? What are the most common battery capacities?
• Price Prediction: Develop a model to predict the price of a mobile phone based on its features.
• Feature Engineering: Create new, insightful features from the existing raw data to improve your model's performance.

Dive in and explore the ever-evolving world of mobile technology!

An Approach to Optimize Device Power Performance Towards Energy Efficient Next Generation 5G Networks

A Thermal Aware Approach to Enhance 5G Device Performance and Reliability in mmWave Networks

If you use this dataset and code or any herein modified part of it in any publication, please cite the papers:

A. Thantharate, C. Beard and S. Marupaduga, "An Approach to Optimize Device Power Performance Towards Energy Efficient Next Generation 5G Networks," 2019 IEEE 10th Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON), 2019, pp. 0749-0754, doi: 10.1109/UEMCON47517.2019.8993067.

A. Thantharate, C. Beard and S. Marupaduga, "A Thermal Aware Approach to Enhance 5G Device Performance and Reliability in mmWave Networks," 2020 International Symposium on Networks, Computers and Communications (ISNCC), 2020, pp. 1-5, doi: 10.1109/ISNCC49221.2020.9297313.

Please reach out adtmv7@umkc.edu if you have any additional questions.

An Approach to Optimize Device Power Performance Towards Energy Efficient Next Generation 5G Networks https://ieeexplore.ieee.org/abstract/document/8993067/

In Fifth Generation (5G), wireless cellular networks, smartphone battery efficiency, and optimal utilization of power have become a matter of utmost importance. Battery and power are an area of significant challenges considering smartphones these days are equipped with advanced technological network features and systems. These features require much simultaneous power to make decisions and to transfer information between devices and network to provide best the user experience. Furthermore, to meet the demands of increased data capacity, data rate, and to provide the best quality of service, there is a need to adopt energy-efficient architectures. This paper presents system-level architectural changes on both User Equipment (UE) and Network elements along with a proposal to modify control signaling as part of Radio Resource Control messages using smartphone battery level. Additionally, we presented real-world 5G mmWave field results, showing impacts on device battery life in varying RF conditions and proposed methods to allocate optimal network resources and improve the energy efficiency by modifying radio layer parameters between devices and base stations. Without these proposed architecture level and system-level algorithm changes, realizing optimal and consistent 5G speeds will be near impossible.

**A Thermal Aware Approach to Enhance 5G Device Performance and Reliability in mmWave Networks https://ieeexplore.ieee.org/abstract/document/9297313 ** 5G NR (New Radio) mmWave networks are creating novel avenues of numerous possibilities and improving mobile broadband in terms of capacity, throughput, and performance, driven by the insatiable demand for faster and better user experience. However, one of the critical problem areas for User Equipment (UE) in mmWave networks is the fast depletion of UE battery power, increase in thermal levels caused by limited coverage and lot of overhead signaling due to rapid radio frequency (RF) and environment changes. With the growing inclusion of advanced functionality on mobile devices, power consumption is growing in parallel, which causes devices to increase thermal temperature, causing an impact on overall system performance. This paper presents system-level change proposal on control signaling between UE and network elements along with changes in UE thermal algorithms based on device battery levels and the coverage of the 5G mmWave networks to deliver the best device performance and user experience. Furthermore, we present real-world field results captured on mmWave networks showing impacts on UE performance with respect to thermal generation in different RF conditions. Our proposal will allocate optimal network resources by modifying the system selection on both UE and base stations. Without the proposed model, realizing the benefits of the 5G NR system along with achieving seamless cellular user experience would be near impossible.

5G , NR , LTE , mmWave , Smartphone , Battery , Power Optimization , Energy Efficiency , Network Efficiency, 5G NR , mmWave , Smartphone , Thermal , Device Temperature , User Equipment , Battery , Network Efficiency , Load Balancing , 3GPP , Power Efficiency , Green Energy

After fierce competition among vivo, Samsung, and Xiaomi over the past few years, vivo became the leading smartphone brand in India. Vivo led the market in the last three quarters. However, in the first two quarters of 2024, Xiaomi and Samsung ranked as high as or higher than Vivo. Aside from Samsung, the other four top smartphone brands are Chinese. Smartphone market share in India The number of smartphone users in India, the most populous country in the world, was on the rise. In 2023, the number of smartphone users in the country surpassed one billion for the first time. This figure was forecasted to jump to nearly 1.55 billion by 2040. And, around seven percent of the population in India purchase their phones online. This growth can also be observed in the volume of smartphone shipments in India. The number of smartphone shipments in India increased from four million units in the second quarter of 2012 to 47 million units in the third quarter of 2024. Major players South Korean giant Samsung, a leader in the global smartphone market, had been the top smartphone vendor in India since early 2013, when the company held about 30 percent of the market share, until the end of 2017. But its position has been challenged by the Chinese smartphone manufacturers like vivo, Xiaomi and OPPO. Vivo is a Chinese tech company based in Guangdong. It's one of the top five smartphone manufacturers in the world. And Xiaomi has quickly risen to the top of China's crowded technology market and is now one of the leading consumer electronics manufacturers globally, since its founding in 2010. Xiaomi specializes primarily in smartphones, but is also active in other markets, and it started manufacturing electric vehicles in 2023.

The Arabic Sign Language (ASL) 20-Words Dataset v1 was carefully designed to reflect natural conditions, aiming to capture realistic signing environments and circumstances. Recognizing that nearly everyone has access to a smartphone with a camera as of 2020, the dataset was specifically recorded using mobile phones, aligning with how people commonly record videos in daily life. This approach ensures the dataset is grounded in real-world conditions, enhancing its applicability for practical use cases.

Each video in this dataset was recorded directly on the authors' smartphones, without any form of stabilization—neither hardware nor software. As a result, the videos vary in resolution and were captured across diverse locations, places, and backgrounds. This variability introduces natural noise and conditions, supporting the development of robust deep learning models capable of generalizing across environments.

In total, the dataset comprises 8,467 videos of 20 sign language words, contributed by 72 volunteers aged between 20 and 24. Each volunteer performed each sign a minimum of five times, resulting in approximately 100 videos per participant. This repetition standardizes the data and ensures each sign is adequately represented across different performers. The dataset’s mean video count per sign is 423.35, with a standard deviation of 18.58, highlighting the balance and consistency achieved across the signs.

For reference, Table 2 (in the research article) provides the count of videos for each sign, while Figure 2 (in the research article) offers a visual summary of the statistics for each word in the dataset. Additionally, sample frames from each word are displayed in Figure 3 (in the research article), giving a glimpse of the visual content captured.

For in-depth insights into the methodology and the dataset's creation, see the research paper: Balaha, M.M., El-Kady, S., Balaha, H.M., et al. (2023). "A vision-based deep learning approach for independent-users Arabic sign language interpretation". Multimedia Tools and Applications, 82, 6807–6826. https://doi.org/10.1007/s11042-022-13423-9

Please consider citing the following if you use this dataset:

@misc{balaha_asl_2024_db,
 title={ASL 20-Words Dataset v1},
 url={https://www.kaggle.com/dsv/9783691},
 DOI={10.34740/KAGGLE/DSV/9783691},
 publisher={Kaggle},
 author={Mostafa Magdy Balaha and Sara El-Kady and Hossam Magdy Balaha and Mohamed Salama and Eslam Emad and Muhammed Hassan and Mahmoud M. Saafan},
 year={2024}
}

@article{balaha2023vision,
 title={A vision-based deep learning approach for independent-users Arabic sign language interpretation},
 author={Balaha, Mostafa Magdy and El-Kady, Sara and Balaha, Hossam Magdy and Salama, Mohamed and Emad, Eslam and Hassan, Muhammed and Saafan, Mahmoud M},
 journal={Multimedia Tools and Applications},
 volume={82},
 number={5},
 pages={6807--6826},
 year={2023},
 publisher={Springer}
}

This dataset is available under the CC BY-NC-SA 4.0 license, which allows for sharing and adaptation under conditions of non-commercial use, proper attribution, and distribution under the same license.

For further inquiries or information: https://hossambalaha.github.io/.

Content

This dataset contains pre-installed applications that are installed in Android smartphones. Applications are collected from mobile phone users around the world using crowd-sourcing methods. In total, this dataset contains approximately 15.000 apk files. Researchers that take advantage of dataset should give reference to our work and dataset.

Metadata

Detailed information about pre-installed application in this dataset is located in our website (preappcollector.com). In this website, many information about applications like in which device they exist, apk file hashes and detailed analysis of these applications. We did not provide these information here because of better explanation of dataset which is not possible in here.

Acknowledgements

Abdullah Özbay Kemal Bıçakçı