This dataset contains detailed specifications and official launch prices of various mobile phone models from different companies. It provides insights into smartphone hardware, pricing trends, and brand competitiveness across multiple countries. The dataset includes key features such as RAM, camera specifications, battery capacity, processor details, and screen size.

One important aspect of this dataset is the pricing information. The recorded prices represent the official launch prices of the mobile phones at the time they were first introduced in the market. Prices vary based on the country and the launch period, meaning older models reflect their original launch prices, while newer models include their most recent launch prices. This makes the dataset valuable for studying price trends over time and comparing smartphone affordability across different regions.

Features:

• Company Name: The brand or manufacturer of the mobile phone.
• Model Name: The specific model of the smartphone.
• Mobile Weight: The weight of the mobile phone (in grams).
• RAM: The amount of Random Access Memory (RAM) in the device (in GB).
• Front Camera: The resolution of the front (selfie) camera (in MP).
• Back Camera: The resolution of the primary rear camera (in MP).
• Processor: The chipset or processor used in the device.
• Battery Capacity: The battery size of the smartphone (in mAh).
• Screen Size: The display size of the smartphone (in inches).
• Launched Price: (Pakistan, India, China, USA, Dubai): The official launch price of the mobile in the respective country at the time of its release. Prices vary based on the year the mobile was launched.
• Launched Year: The year the mobile phone was officially launched.

Title: Samsung Mobiles Latest Dataset: Comprehensive Information on Model Names, Ratings, Prices, and More!

Description:

Welcome to the Samsung Mobiles Latest Dataset, a comprehensive collection of Samsung smartphones providing valuable insights into Samsung's cutting-edge range of mobile devices. This dataset is a result of meticulous web scraping from trusted sources, including official Samsung websites and reputable online retailers.

Dataset Overview: The dataset comprises ten key columns that shed light on Samsung mobiles' essential attributes:

• name: Names of various Samsung smartphone models, showcasing the diversity and variety of offerings.
• ratings: User ratings and reviews associated with each model, reflecting customer satisfaction and feedback.
• price: Prices of the Samsung mobiles, helping users understand the affordability and value proposition of each device.
• imgURL: Image URLs corresponding to each model, facilitating visual exploration and comparison.
• storage_ram: Details about the storage capacity and RAM configuration for each device, crucial for storage-intensive applications.
• os_processor: Operating system and processor details, essential for assessing device performance and capabilities.
• camera: Information about the camera specifications, catering to photography enthusiasts and content creators.
• display: Display-related specifications, such as size, resolution, and technology, offering insights into the visual experience provided by each Samsung mobile.
• battery: Battery-related specifications, including capacity and endurance, vital factors for heavy users and on-the-go productivity.

Potential Applications: The Samsung Mobiles Latest Dataset serves as a valuable resource for various applications:

• Product Research and Comparison: Prospective buyers can make well-informed decisions based on their preferences and budget, comparing various Samsung mobile models.

• Price Analysis and Market Trends: Researchers and analysts can study pricing patterns and market trends of Samsung mobiles across different models and regions, providing valuable market insights.

• Machine Learning and Recommender Systems: Data scientists can leverage this dataset to build recommendation engines, suggesting Samsung mobiles based on user preferences, ratings, and other attributes.

Acknowledgment: This dataset acknowledges the efforts of numerous websites and sources that provided the data. As responsible data practitioners, we respect their terms of use and ensure the data is utilized for non-commercial and educational purposes only.

Note: This dataset is static and represents a snapshot of Samsung mobiles available at the time of scraping. As the mobile industry evolves rapidly, newer models and updated information may not be included. Users are encouraged to verify and cross-reference data with official Samsung sources before making any critical decisions.

Feel free to explore, analyze, and share your findings with the Kaggle community and beyond using the Samsung Mobiles Latest Dataset. Happy analyzing!

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.

This dataset simulates sales transactions for mobile phones and laptops, including product specifications, customer details, and sales information. It contains 50,000 rows of randomly generated data to help analyze product sales trends, customer purchasing behavior, and regional distribution of sales.

Dataset Overview

• Dataset Type: Structured tabular data
• Number of Rows: 50,000
• Number of Columns: 16

Purpose of the Dataset
This dataset can be used for:
✅ Sales Analysis – Understanding product demand and pricing trends.
✅ Customer Behavior Analysis– Identifying buying patterns across locations.
✅ Inventory Management – Monitoring inward and dispatched product movements.
✅ Machine Learning & AI – Predicting sales trends, customer preferences, and stock management.

Key Features in the Dataset

1. Product Information

   • Product: Type of product (Mobile Phone / Laptop).
   • Brand: Various brands like Apple, Samsung, Dell, Lenovo, OnePlus, etc.
   • Product Code: Unique identifier for each product.
   • Product Specification: Brief description of the product features.

2. Sales & Pricing Details

   • Price: Cost of the product (randomly generated).
   • Inward Date: Date when the product was received in stock.
   • Dispatch Date: Date when the product was sold/dispatched.
   • Quantity Sold: Number of units sold per transaction.

3. Customer & Location Details

   • Customer Name: Randomly generated customer names.
   • Customer Location: City of the customer.
   • Region: Sales region (North, South, East, West, Central).

4. Technical Specifications -Core Specification (For Laptops): Includes processor models like i3, i5, i7, i9, Ryzen 3-9.
   -Processor Specification (For Mobiles): Includes processors like Snapdragon, Exynos, Apple A-Series, and MediaTek Dimensity.
   -RAM: Randomly assigned memory sizes (4GB to 32GB).
   -ROM: Storage capacity (64GB to 1TB).
   -SSD (For Laptops): Additional storage (256GB to 2TB), "N/A" for mobile phones.

Potential Use Cases: Business Intelligence Dashboards Market Trend Analysis Supply Chain Optimization
Customer Segmentation
Machine Learning Model Training (Sales Prediction, Price Optimization, etc.)

This dataset contains the percentage of population using Cellular Phones by province, 2015-2016. This data, derived from the National Socio-Economic Survey (SUSENAS March) that published through the People’s Welfare Statistic report by BPS. The data is available at province level (Admin 1) and downloadable in MS. Excel (XLS) format: https://www.bps.go.id/dynamictable/2018/05/21/1348/proporsi-individu-yang-menggunakan-telepon-genggam-2015---2016.html

This file contains the complete dataset collected by the three experiments described in the companion paper, in Microsoft Excel (XLSX) format. The workbook contains a data keys sheet explaining any abbreviations, annotations, and labels used throughout the datafile, followed by a sheet for each of the experiments. The file has been verified to open in Microsoft Excel (https://products.office.com/excel) and Libre Office (https://www.libreoffice.org)

The Stata data file "CAP_Demographics_Jumla_Kavre_recoded.dta” and equivalent excel file of the same name comprises data collected by adolescent secondary school students during a "Citizen Science" project in the district of Kavre in the central hills of Nepal during April 2022 and in the district of Jumla in the remote mountains of West Nepal during June 2022. The project was part of a CIFF-funded Children in All Policies 2030 (CAP2030) project. The data were generated by the students using a mobile device data collection form developed using "Open Data Kit (ODK) Collect" electronic data collection platform by Kathmandu Living Labs (KLL) and University College London (UCL) for the purposes of this study. Researchers from KLL and UCL trained the adolescents to record basic socio-demographic information about themselves and their households including caste/ethnicity, religion, education, water sources, assets, household characteristics, and income sources. The form also asked about their access to mobile phones or other devices and internet and their concerns with respect to climate change. The resulting data describe the participants in the citizen science project, but their names and addresses have been removed. The app and the process of gathering the data are described in a paper entitled "Citizen science for climate change resilience: engaging adolescents to study climate hazards, biodiversity and nutrition in rural Nepal" submitted to Wellcome Open Research in Feb 2023. The data contributed to Tables 2 and 3 of this paper.

Abstract

The World Bank in collaboration with the Kenya National Bureau of Statistics and the University of California, Berkeley are conducting the Kenya COVID-19 Rapid Response Phone Survey to track the socioeconomic impacts of the COVID-19 pandemic, the recovery from it as well as other shocks to provide timely data to inform policy. This dataset contains information from eight waves of the COVID-19 RRPS, which is part of a panel survey that targets Kenyan nationals and started in May 2020. The same households were interviewed every two months for five survey rounds, in the first year of data collection and every four months thereafter, with interviews conducted using Computer Assisted Telephone Interviewing (CATI) techniques.

The data set contains information from two samples of Kenyan households. The first sample is a randomly drawn subset of all households that were part of the 2015/16 Kenya Integrated Household Budget Survey (KIHBS) Computer-Assisted Personal Interviewing (CAPI) pilot and provided a phone number. The second was obtained through the Random Digit Dialing method, by which active phone numbers created from the 2020 Numbering Frame produced by the Kenya Communications Authority are randomly selected. The samples cover urban and rural areas and are designed to be representative of the population of Kenya using cell phones. Waves 1-7 of this survey include information on household background, service access, employment, food security, income loss, transfers, health, and COVID-19 knowledge and vaccinations. Wave 8 focused on how households were exposed to shocks, in particular adverse weather shocks and the increase in the price of food and fuel, but also included parts of the previous modules on household background, service access, employment, food security, income loss, and subjective wellbeing.

The data is uploaded in three files. The first is the hh file, which contains household level information. The ‘hhid’, uniquely identifies all household. The second is the adult level file, which contains data at the level of adult household members. Each adult in a household is uniquely identified by the ‘adult_id’. The third file is the child level file, available only for waves 3-7, which contains information for every child in the household. Each child in a household is uniquely identified by the ‘child_id’.

The duration of data collection and sample size for each completed wave was: Wave 1: May 14 to July 7, 2020; 4,061 Kenyan households Wave 2: July 16 to September 18, 2020; 4,492 Kenyan households Wave 3: September 28 to December 2, 2020; 4,979 Kenyan households Wave 4: January 15 to March 25, 2021; 4,892 Kenyan households Wave 5: March 29 to June 13, 2021; 5,854 Kenyan households Wave 6: July 14 to November 3, 2021; 5,765 Kenyan households Wave 7: November 15, 2021, to March 31, 2022; 5,633 Kenyan households Wave 8: May 31 to July 8, 2022: 4,550 Kenyan households

The same questionnaire is also administered to refugees in Kenya, with the data available in the UNHCR microdata library: https://microdata.unhcr.org/index.php/catalog/296/

Geographic coverage

National coverage covering rural and urban areas

Analysis unit

Household, Individual

Sampling procedure

The COVID-19 RRPS with Kenyan households has two samples. The first sample consists of households that were part of the 2015/16 KIHBS CAPI pilot and provided a phone number. The 2015/16 KIHBS CAPI pilot is representative at the national level stratified by county and place of residence (urban and rural areas). At least one valid phone number was obtained for 9,007 households and all of them were included in the COVID-19 RRPS sample. The target respondent was the primary male or female household member from the 2015/16 KIHBS CAPI pilot. The second sample consists of households selected using the Random Digit Dialing method. A list of random mobile phone numbers was created using a random number generator from the 2020 Numbering Frame produced by the Kenya Communications Authority. The initial sampling frame therefore consisted of 92,999,970 randomly ordered phone numbers assigned to three networks: Safaricom, Airtel and Telkom. An introductory text message was sent to 5,000 randomly selected numbers to determine if numbers were in operation. Out of these, 4,075 were found to be active and formed the final sampling frame. There was no stratification and individuals that were called were asked about the households they live in. Until wave 7 sampled households that were not reached in earlier waves were also contacted along with households that were interviewed before. In wave 8 only households that had previously participated in the survey were contacted for interview. The “wave” variable represents in which wave the households were interviewed in.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

The questionnaire was administered in English and is provided as a resource in pdf format. Additionally, questionnaires for each wave are also provided in Excel format coded for SCTO. The same questionnaire is also administered to refugees in Kenya, with the data available in the UNHCR microdata library: https://microdata.unhcr.org/index.php/catalog/296/

Gait recognition is the characterization of unqiue biometric patterns associated with each inidvidual which can be utilized to identify a person without direct contact. A public gain database with relatively large number of subjects can provide a great oppportunity to future studies to build and validate gait authentication models. The goal of this study is to introduce a comprehensive gait database of 93 human subjects who walked between two end points (320 meters) during two different sessions and record their gait data using two smart phones, one was attached to right thigh and another one on left side of waist. This data is collected with intention to be utilized by deep learning-based method which requires enough time points. The meta data including age, gender, smoking, daily exercise time, height, and weight of an individual is recorded. this data set is publicly available.

Except 19 subjects who did not attend for second session, every subject is associated with 4 different log files (each session contains two log files). Every file name has one of the following patterns: · sub0-lw-s1.csv: subject number 0, left waist, session 1 · sub0-rp-s1.csv: subject number 0, right thigh, session 1 · sub0-lw-s2.csv: subject number 0, left waist, session 2 · sub0-rp-s2.csv: subject number 0, right thigh, session 2 Every log file contains 58 features that are internally captured and calculated using SensorLog app. Additionally, an Excel file contain the meta data is provided for each subject.

This dataset represents sales data from a retail shop that specializes in selling mobile phones, laptops, and watches. It covers detailed transaction records, including: 🧾 Product category and name 🏷️ Brand 💰 Selling price 🏪 Branch (if multiple) 📦 Quantity sold 💵 Total revenue per sale The data is ideal for analyzing sales trends, popular products, revenue distribution, and seasonal demand across different product types. Whether you're learning Excel, Power BI, or practicing data cleaning and visualization, this dataset offers a simple and realistic scenario for data exploration.

The Electronics Environmental Benefits Calculator (EEBC) was developed to assist organizations in estimating the environmental benefits of greening their purchase, use and disposal of electronics. The EEBC estimates the environmental and economic benefits of: Purchasing Electronic Product Environmental Assessment Tool (EPEAT)-registered products; Enabling power management features on computers and monitors above default percentages; Extending the life of equipment beyond baseline values; Reusing computers, monitors and cell phones; and Recycling computers, monitors, cell phones and loads of mixed electronic products. The EEBC may be downloaded as a Microsoft Excel spreadsheet. See https://www.federalelectronicschallenge.net/resources/bencalc.htm for more details.