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.

We will create a customized phones dataset tailored to your specific requirements. Data points may include brand names, model specifications, pricing information, release dates, market availability, feature sets, and other relevant metrics.

Utilize our phones datasets for a variety of applications to boost strategic planning and market analysis. Analyzing these datasets can help organizations grasp consumer preferences and technological trends within the mobile phone industry, allowing for more precise product development and marketing strategies. You can choose to access the complete dataset or a customized subset based on your business needs.

Popular use cases include: enhancing competitive benchmarking, identifying pricing trends, and optimizing product portfolios.

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 .

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 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

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

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

To download full datasets or to submit a request for your dataset needs, please ping us at sales@datacluster.ai Visit www.datacluster.ai to know more.

Note: All the images are manually captured and verified by a large contributor base on DataCluster platform

Classroom Cell Phone Detection

## Overview

Classroom Cell Phone Detection is a dataset for object detection tasks - it contains Cell Phones annotations for 253 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Phone Call Usage

## Overview

Phone Call Usage is a dataset for object detection tasks - it contains Cell Phone annotations for 3,115 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [MIT license](https://creativecommons.org/licenses/MIT).

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.

Content

This database contains multiple images in different dimensions. Besides, the images were separated and categorized into two types: There is a cellphone (label = 1), there is no cellphone (label = 0). Thus, it is possible to build algorithms for the binary classification of objects or a computational model that allows locating the position of mobile phones in the image, and this will depend on your creativity to work with this dataset.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F3457313%2F45a0ab95281bf9664a55406fbacfa2fe%2Fsave-cellphone.JPG?generation=1568096853341492&alt=media" alt="">

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.

This statistic shows a ranking of the estimated worldwide number of mobile cellular subscriptions per 100 inhabitants in 2020, differentiated by country. Included are only subscriptions that also allow voice communication over the Public Switched Telephone Network (PSTN). Pure data and M2M (machine-to-machine) connections are excluded.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 more than *** countries and regions worldwide. All input data are sourced from international institutions, national statistical offices, and trade associations. All data has been are processed to generate comparable datasets (see supplementary notes under details for more information).

Cell Phone Object Detector Dataset

## Overview

Cell Phone Object Detector Dataset is a dataset for object detection tasks - it contains I Labeled All The Cell Phones annotations for 2,126 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [Public Domain license](https://creativecommons.org/licenses/Public Domain).

The article "A systematic review of the educational use of mobile phones in times of COVID-19" aims to review what research has delved into the educational use of mobile phones during the COVID-19 pandemic. To do this, 38 papers indexed in the Journal Citation Reports database between 2020 and 2021 were analyzed. These works were categorized into the following categories: the mobile phone as part of educational innovation, improvement of results and academic performance, positive attitude towards mobile phone use in education, and risks and/or barriers to mobile phone use. The conclusions show that most teaching innovation experiences focus more on the device than on the student. Beyond its innovative nature, the mobile phone became a tool to allow access and continuity of training during the pandemic, especially in post-compulsory and higher education.

This data set is composed of the table with the references used for the review.

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

Mobile phone use logs from 279 Android phone users for an average duration of four weeks during summer 2016.

Do you know?

Do you know how much time you spend on an app? Do you know the total use time of a day or average use time of an app?

What it consists of?

This data set consists of - how many times a person unlocks his phone. - how much time he spends on every app on every day. - how much time he spends on his phone.

It lists the usage time of apps for each day.

What we can do?

Use the test data to find the Total Minutes that we can use the given app in a day. we can get a clear stats of apps usage. This data set will show you about the persons sleeping behavior as well as what app he spends most of his time. with this we can improve the productivity of the person.

The dataset was collected from the app usage app.

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.

Content

This data set includes 7 csv data files, which have a total of 1.4 million user ratings and reviews for different brands of cell phones.

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

you can use this dataset for classification.

Collumn's Description :

battery_power : Total energy of the battery can be stored(mAh) blue : the device has blutooth or not, yes/no (1/0) clock_speed : the speed of execution of instructions by microprocessor dual_sim : the device has two SIM card at the same time or not fc : the quality of front camera in MegaPixel four_g : the device has 4G network or not int_memory : internal memory in GigaByte m_dep : the device depth in CM mobile_wt : the weight of device n_cores : the number of processor cores pc : the quality of primary camera in MegePixel px_height : the height of pixel resolution px_width : the width of pixel resolution ram : random access memory in MegaByte sc_h : the height of device screen in CM sc_w : the width of device screen in CM talk_time : the maximum talk time that the full charge battery of the device can support three_g : the device has 3G network or not touch_screen : the device has touch screen or not wifi : the device has wifi or not price_range : the categorized price of the device