Mobile usage and productivity impact dataset

Description

obile Usage and Productivity Dataset is a survey-based dataset containing 10,000 responses collected to investigate the relationship between mobile phone usage habits and self-reported productivity levels. The dataset includes information on respondents' age group, daily mobile screen-time range, daily phone-check frequency, primary purpose of mobile phone use, perceived phone distraction, notification management habits, and productivity rating. The data was collected through a structured multiple-choice survey, allowing respondents to provide answers quickly without requiring access to personal records or device statistics. The dataset contains both categorical and ordinal/numerical features and can be used for exploratory data analysis, statistical analysis, data visualization, feature encoding, classification, and machine learning applications. The dataset is particularly suitable for studying whether mobile phone usage patterns and related behaviors can be used to predict self-reported productivity levels. It can be used for educational, academic, and research purposes involving data preprocessing, supervised machine learning, and predictive modeling.

Steps to reproduce

Forms. The survey consisted of seven questions covering age group, daily mobile screen-time, daily phone-check frequency, primary purpose of mobile phone use, perceived phone distraction, notification management, and self-reported productivity. All questions were presented as predefined multiple-choice or rating options to ensure consistent responses and simplify data analysis. The survey was distributed online to participants through the survey link. A total of 10,000 responses were collected. No names, phone numbers, or email addresses were intentionally collected as part of the survey. Responses were automatically recorded in a linked Google Sheets spreadsheet. The raw responses were exported as a CSV file and inspected for unnecessary fields and data consistency. For machine learning analysis, ordinal responses were converted into numerical values using predefined mappings, while nominal categorical variables were encoded using appropriate categorical encoding techniques. The resulting dataset was saved as a CSV file for exploratory data analysis and machine learning applications.

Institutions

• Bharati Vidyapeeth Deemed University

  Maharashtra, Pune

Categories

Licence