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.

1) Data Introduction • The Phones price classification dataset is a collection of mobile phone sales data from various companies to estimate the price of a mobile phone.

2) Data Utilization (1) Phones price classification data has characteristics that: • The dataset includes factors related to the performance of the mobile phone such as battery power, speed, dual sim and internal memory. (2) Phones price classification data can be used to: • Market Research: Help you understand competitors' product features and pricing strategies, and develop differentiation strategies. • Customer Preference Analysis: Identify the features of your mobile phone that you value.

Dataset

This dataset was created by Tanuja sreekanth

Contents

Analysis of ‘Mobile Price Classification’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/iabhishekofficial/mobile-price-classification on 28 January 2022.

--- Dataset description provided by original source is as follows ---

Context

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

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

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

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

--- Original source retains full ownership of the source dataset ---

Phone Name

## Overview

Phone Name is a dataset for classification tasks - it contains Ref annotations for 8,485 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).

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

This dataset was created by Metta G

Released under Apache 2.0

Contents

Roughly 16 million mobile devices are available with the majority of the population worldwide and this number going to increase exponentially. Many mobile devices do include sensors and hence can be used for other applications rather than mobile gaming. In the present work

🚀 MASC Dataset: Mobile Application Screen Classification

📌 Overview
MASC (Mobile Application Screen Classification) is a manually curated dataset containing 7,065 mobile UI screens classified into 10 distinct categories. Designed for UI/UX research and ML applications, it enables:
- 📱 Accurate screen type classification
- 🤖 Automated UI testing
- 🎨 Design pattern analysis

Dataset

This dataset was created by Diego López

Released under Apache 2.0

Contents

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).

MOBILE GALLERY

## Overview

MOBILE GALLERY is a dataset for classification tasks - it contains Memes Trees Selfies Screenshots annotations for 1,094 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).

This repository is part of the ITC-NetMingledApp dataset, which includes network traffic data from 36 Android applications, with each capture featuring concurrent traffic from multiple applications and smartphones. This repository contains part #2 of the data related to the Iran-Tehran scenario. Each capture is stored in a compressed file containing the relevant PCAP files of the associated applications. The PCAP files are named according to a convention: {TimeStamp}_{Application Name}{Download-Upload Speed}.pcap Part #1 of Iran-Tehran scenario is in the Tehran Dataset #1 (https://doi.org/10.17632/9frgkybxhn.1) repository.

Mobile Price Dataset

This is a synthetic dataset containing mobile phone hardware features and a target label price_range (0 to 3). It is intended for classification tasks using models like Random Forests or Neural Networks.

  Features

20 numerical features describing mobile phone specs (e.g., battery power, RAM, screen size) 1 target feature: price_range (0: low, 1: medium, 2: high, 3: premium)

  Usage

You can load this dataset via datasets library: from… See the full description on the dataset page: https://huggingface.co/datasets/Ph14/mobile_price_dataset.

Dataset

This dataset was created by Tembalake Beja

Contents

The following dataset has been used for the paper entitled "Design Analytics for Mobile Learning: Scaling up theClassification of Learning Designs based onCognitive and Contextual Elements".

Abstract

This research was triggered by the identified need in literature for large-scale studies about the kind of designs that teachers create for Mobile Learning (m-learning). These studies require analyses of large datasets of learning designs. The common approach followed by researchers when analysing designs has been to manually classify them following high-level pedagogically-guided coding strategies, which demands extensive work. Therefore, the first goal of this paper is to explore the use of Supervised Machine Learning (SML) to automatically classify the textual content of m-learning designs, through pedagogically-relevant classifications, such as the cognitive level demanded by students to carry out specific designed tasks, the phases of inquiry learning represented in the designs, or the role that the situated environment has in them. As not all the SML models are transparent, while often researchers need to understand the behaviour behind them, the second goal of this paper considers the trade-off between models’ performance and interpretability in the context of design analytics for m-learning. To achieve these goals we compiled a dataset of designs deployed through two tools, Avastusrada and Smartzoos. With it, we trained and compared different models and feature extraction techniques. We further optimized andcompared the best-performing and most interpretable algorithms (EstBERT and Logistic Regression) to consider the second goal through an illustrative case. We found that SML can reliably classify designs, with accuracy>0.86and Cohen’s kappa>0.69.

The Mobile Icon | Mobile Screenshot Dataset is a meticulously curated collection of 9,000+ high-quality mobile screenshots, categorized across 13 diverse application types. This dataset is designed to support AI/ML researchers, UI/UX analysts, and developers in advancing mobile interface understanding, image classification, and content recognition.

Each image has been manually reviewed and verified by computer vision professionals at DataCluster Labs, ensuring high-quality and reliable data for research and development purposes.

Categories Included

• Technical Applications
• Wallpapers
• News & Magazines
• Business & Finance
• Agriculture
• Entertainment and many more.

Potential Applications:

• AI & ML model training (image classification, UI/UX analysis, OCR).
• Mobile app usability and accessibility research.
• Content recognition and recommendation systems.

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.

Driver Phone

## Overview

Driver Phone is a dataset for classification tasks - it contains Phone annotations for 234 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).

This dataset contains accelerometer and gyroscope readings of 76 undergraduate students (from different ages, different genders) during writing random sentences for 1 minute period with a mobile phone (iPhone X). We provide two datasets. One is for binary classification (one-vs-all) and the other one contains the whole data. These datasets can be used for writing behavior analysis.

The columns of datasets are: ID,Gender,Age,AccX,AccY,AccZ,GyroX,GyroY,GyroZ

Here are a few use cases for this project:

1. Sports Analytics: The "action classification" model can be beneficial in analyzing players' performances in volleyball or related sports. It could assess the techniques used by players, thus helping coaches to make strategic decisions.

2. Broadcasting & Media Coverage: This model could be utilized by sports broadcasting or media companies to provide more in-depth, real-time analysis of volleyball games. Automated identifications of actions could enhance viewer experiences by enriching commentary and enabling advanced visual effects.

3. Sports Training Apps: Mobile or desktop training apps for aspiring volleyball players can incorporate this computer vision model to provide users with real-time feedback on their action class, helping them improve their skills effectively.

4. Injury Prevention and Rehabilitation: Physiotherapists and fitness trainers can employ this model to monitor athletes' actions during practice or actual games. It could provide insights into anomalies or wrong techniques that may lead to injury, facilitating proactive preventive measures.

5. Automated Refereeing: In sports competitions, especially in amateur leagues where expert referees may not always be available, the model can be deployed to act as an automated referee system that ensures all rules are adhered to by identifying all action categories during the game.

chatgpt wrote.