This Dataset is instrumental if you are working on a machine-learning project where you are working in which you need information about smartphones, and feature phone available in the Indian market. This Dataset is having 5 columns -> model name, price, ratings, reviews, and specifications. Do not confuse it with the duplicated values in the name and the price columns, because in the model name, there are the same phones available with different color options Google pixel 6pro is available in 2-3 color options but the price was the same. So your domain knowledge and how better you do the feature engineering over this dataset is dependent. The price is in the Indian rupee you can convert it according to your use case. Now I Updated the dataset and added a new version of the dataset after some Preprocessing (Updated_Mobile_Dataset.csv) In which the new version does not contain any null values added the company column in the new version and also separated the Rom and Ram columns. The shape of the newly updated data set is (28036, 8) The objective here is to forecast the price of mobile phones. Please upvote if you find the dataset useful.

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.

Smart phone price index (CPPI) by North American Product Classification System (NAPCS). The table includes annual data for the most recent reference period and the last four periods. Data are available from January 2015. The base period for the index is (2015=100).

Percentage of smartphone users by selected smartphone use habits in a typical day.

Related article: Bergroth, C., Järv, O., Tenkanen, H., Manninen, M., Toivonen, T., 2022. A 24-hour population distribution dataset based on mobile phone data from Helsinki Metropolitan Area, Finland. Scientific Data 9, 39.

In this dataset:

We present temporally dynamic population distribution data from the Helsinki Metropolitan Area, Finland, at the level of 250 m by 250 m statistical grid cells. Three hourly population distribution datasets are provided for regular workdays (Mon – Thu), Saturdays and Sundays. The data are based on aggregated mobile phone data collected by the biggest mobile network operator in Finland. Mobile phone data are assigned to statistical grid cells using an advanced dasymetric interpolation method based on ancillary data about land cover, buildings and a time use survey. The data were validated by comparing population register data from Statistics Finland for night-time hours and a daytime workplace registry. The resulting 24-hour population data can be used to reveal the temporal dynamics of the city and examine population variations relevant to for instance spatial accessibility analyses, crisis management and planning.

Please cite this dataset as:

Bergroth, C., Järv, O., Tenkanen, H., Manninen, M., Toivonen, T., 2022. A 24-hour population distribution dataset based on mobile phone data from Helsinki Metropolitan Area, Finland. Scientific Data 9, 39. https://doi.org/10.1038/s41597-021-01113-4

Organization of data

The dataset is packaged into a single Zipfile Helsinki_dynpop_matrix.zip which contains following files:

HMA_Dynamic_population_24H_workdays.csv represents the dynamic population for average workday in the study area.

HMA_Dynamic_population_24H_sat.csv represents the dynamic population for average saturday in the study area.

HMA_Dynamic_population_24H_sun.csv represents the dynamic population for average sunday in the study area.

target_zones_grid250m_EPSG3067.geojson represents the statistical grid in ETRS89/ETRS-TM35FIN projection that can be used to visualize the data on a map using e.g. QGIS.

Column names

YKR_ID : a unique identifier for each statistical grid cell (n=13,231). The identifier is compatible with the statistical YKR grid cell data by Statistics Finland and Finnish Environment Institute.

H0, H1 ... H23 : Each field represents the proportional distribution of the total population in the study area between grid cells during a one-hour period. In total, 24 fields are formatted as “Hx”, where x stands for the hour of the day (values ranging from 0-23). For example, H0 stands for the first hour of the day: 00:00 - 00:59. The sum of all cell values for each field equals to 100 (i.e. 100% of total population for each one-hour period)

In order to visualize the data on a map, the result tables can be joined with the target_zones_grid250m_EPSG3067.geojson data. The data can be joined by using the field YKR_ID as a common key between the datasets.

License Creative Commons Attribution 4.0 International.

Related datasets

Järv, Olle; Tenkanen, Henrikki & Toivonen, Tuuli. (2017). Multi-temporal function-based dasymetric interpolation tool for mobile phone data. Zenodo. https://doi.org/10.5281/zenodo.252612

Tenkanen, Henrikki, & Toivonen, Tuuli. (2019). Helsinki Region Travel Time Matrix [Data set]. Zenodo. http://doi.org/10.5281/zenodo.3247564

The global number of smartphone users in was forecast to continuously increase between 2024 and 2029 by in total 1.8 billion users (+42.62 percent). After the ninth consecutive increasing year, the smartphone user base is estimated to reach 6.1 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 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 Australia & Oceania and Asia.

The average time spent daily on a phone, not counting talking on the phone, has increased in recent years, reaching a total of * hours and ** minutes as of April 2022. This figure was expected to reach around * hours and ** minutes by 2024.

Information Technology Usage and Penetration - Table 720-90006 : Persons aged 10 and over who had a mobile phone (including smartphone and non-smartphone) by sex and age group

Mobile Phone Dataset | Smartphone & Feature Phone

Crowdsourced original images of a wide variety of mobile phones

About Dataset

This dataset is collected by* DataCluster Labs*, India. To download full dataset or to submit a request for your new data collection needs, please drop a mail to: sales@datacluster.ai

This dataset is an extremely challenging set of over 3000+ original Mobile Phone images captured and crowdsourced from over 1000+ urban and rural areas, where each image is manually reviewed and verified by computer vision professionals at ****DC Labs.

Dataset Features 1. Dataset size : 3000+ 2. Captured by : Over 1000+ crowdsource contributors 3. Resolution : 99% images HD and above (1920x1080 and above) 4. Location : Captured with 600+ cities accross India 5. Diversity : Various lighting conditions like day, night, varied distances, view points etc. 6. Device used : Captured using mobile phones in 2020-2021 7. Applications : Mobile Phone detection, cracked screen detection, etc.

Available Annotation formats COCO, YOLO, PASCAL-VOC, Tf-Record

The images in this dataset are exclusively owned by Data Cluster Labs and were not downloaded from the internet. To access a larger portion of the training dataset for research and commercial purposes, a license can be purchased. Contact us at sales@datacluster.ai

Visit www.datacluster.ai to know more.

This dataset will provide the data of mobile phones in amazon(in a single page) alongwith image url. We can use this dataset to develop a recommender system in for a website to practise .

Mobile phone log data used to mine contextual behavioral rules of individual mobile phone users

Percentage of Canadians using a smartphone for personal use and selected habits of use during a typical day.

Comprehensive dataset of 3 Mobile phones in United States as of June, 2025. Includes verified contact information (email, phone), geocoded addresses, customer ratings, reviews, business categories, and operational details. Perfect for market research, lead generation, competitive analysis, and business intelligence. Download a complimentary sample to evaluate data quality and completeness.

the dataset contains phone data. scraped the data from flipkart. useful for regression model and EDA columns: model price rating ram display camera battery processor warranty

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).

Dataset Overview: A collection of features characterizing mobile phones, including battery power, camera specifications, network support, memory, screen dimensions, and other attributes. The 'price_range' column categorizes phones into price ranges, making this dataset suitable for mobile phone classification and price prediction tasks.

We present the dataset for the article "Keep on scrolling? Using intensive longitudinal smartphone sensing data to assess how everyday smartphone usage behaviors are related to well-being". The data were collected as part of the Smartphone Sensing Panel Study and comprise several dataset parts, as we replicated our analysis for two different 14-day measurement periods (A and B). At the macro level, we aggregated different measures of smartphone use (measured by mobile sensing) over 14 days and examined their associations with global survey-based measures of well-being (Flourishing, Satisfaction WIth Life, Positive Activation, Negative Activation, Valence; Dataset A: N = 236, Dataset B: N = 305). At the micro level, we aggregated various measures of smartphone use (measured via mobile sensing) over 60-minute windows before asking participants about their current mood using experience sampling questionnaires (Dataset A: N = 378, n = 5775; Dataset B: N = 534, n = 7287). In our supplementary analysis, we also aggregated the smartphone usage data for 15-minute windows to analyse social and non-social situations. Demographic variables (age, gender, education) that were not used for the data analyses were removed for privacy reasons, but can be provided upon request. The datasets are documented by a comprehensive accompanying codebook. Additional materials (e.g., preprocessing and analysis code) can also be found at https://osf.io/ckwge/ Further details on the variables provided and the associated study procedures can be found in the journal article: große Deters, F., & Schoedel, R. (2024). Keep on scrolling? Using intensive longitudinal smartphone sensing data to assess how everyday smartphone usage behaviors are related to well-being, Computers in Human Behavior, 150, 107977, https://doi.org/10.1016/j.chb.2023.107977

BackgroundThis study aimed to explore the adverse influences of mobile phone usage on pilots’ status, so as to improve flight safety.MethodsA questionnaire was designed, and a cluster random sampling method was adopted. Pilots of Shandong Airlines were investigated on the use of mobile phones. The data was analyzed by frequency statistics, linear regression and other statistical methods.ResultsA total of 340 questionnaires were distributed and 317 were returned, 315 of which were valid. The results showed that 239 pilots (75.87%) used mobile phones as the main means of entertainment in their leisure time. There was a significant negative correlation between age of pilots and playing mobile games (p

The PerfLoc Prize Competition (https://perfloc.nist.gov) was developed by NIST during 2015-2017 and was run during 2017-2018. The Competition was concluded with a single winner on May 16, 2018. However, NIST believes the data collected for the PerfLoc Competition is still of value to the R&D community, because there is still room to develop better signal processing and data fusion algorithms that would fuse various types of smartphone data collected in this project to develop indoor localization apps with higher localization accuracy. For that reason, NIST continues to make the PerfLoc data available to the R&D community.One thing has changed compared to when the PerfLoc Competition was running in 2017-2018. It is no longer possible for app developers to upload the location estimates generated by their apps at the PerfLoc website for performance evaluation purposes and to get statistics of localization accuracy. However, the PerfLoc data is still useful, because there is training data with ground-truth location annotation that would be useful to anyone wishing to develop indoor localization apps and getting an idea of the performance of their apps.“There are a total of 14 files that can be downloaded from this web page (see below). The descriptions for these files can be found at the relevant PerfLoc web pages (https://www.nist.gov/ctl/pscr/perfloc-user-guide and https://www.nist.gov/ctl/pscr/perfloc-data.”

The Mozambique Population-Based Survey (PBS) provides a comprehensive assessment of the current status of agriculture and food security in two provinces, Zambizia and Nampula. These areas were selected based on national estimates that indicate that the incidence of poverty, malnutrition, and stunting among children less than five years of age is disproportionately high. These provinces are adjacent to three of the country's main trade corridors: Nacala (linking Mozambique to Malawi and Zambia), Beira (linking Mozambique to Zimbabwe), and the N1 (key North-South road connecting Nacala and Beira corridors). This spreadsheet describes the use of mobile phones and mobile banking.