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https://github.com/aelluminate-campus/mobile-user-behavior-analysis

This project analyzes mobile user behavior based on device data, usage patterns, and demographics. Data includes device models, OS, app usage, screen time, battery, data usage, age, gender, and user behavior classification.
https://github.com/aelluminate-campus/mobile-user-behavior-analysis

data-science data-visualisation exploratory-data-analysis jupyter-notebook python3

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This project analyzes mobile user behavior based on device data, usage patterns, and demographics. Data includes device models, OS, app usage, screen time, battery, data usage, age, gender, and user behavior classification.

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# Mobile User Behavior Analysis



This project aims to analyze mobile user behavior by exploring various factors influencing app usage, device performance, and user demographics. The dataset utilized contains comprehensive information regarding users, including device models, operating systems, app usage time, screen on time, battery drain, data usage, age, gender, and user behavior classification.



## Objectives



The primary objectives of this project are:



- To explore the distribution of key behavioral metrics among users.

- To conduct comparative analysis between different device models and operating systems.

- To analyze user behavior segmentation based on demographics, specifically age and gender.

- To derive insights about the relationships between app usage, battery performance, and demographic features.



## The Data



This dataset provides a comprehensive analysis of mobile device usage patterns and user behavior classification. It contains 700 samples of user data, including metrics such as app usage time, screen-on time, battery drain, and data consumption. Each entry is categorized into one of five user behavior classes, ranging from light to extreme usage, allowing for insightful analysis and modeling.



The dataset contains the following columns:



- `User ID`: Unique identifier for each user.

- `Device Model`: Model of the user's smartphone.

- `Operating System`: The OS of the device (iOS or Android).

- `App Usage Time`: Daily time spent on mobile applications, measured in minutes.

- `Screen On Time`: Average hours per day the screen is active.

- `Battery Drain`: Daily battery consumption in mAh.

- `Number of Apps Installed`: Total apps available on the device.

- `Data Usage`: Daily mobile data consumption in megabytes.

- `Age`: Age of the user.

- `Gender`: Gender of the user (Male or Female).

- `User Behavior Class`: Classification of user behavior based on usage patterns (1 to 5).

  

This dataset is ideal for researchers, data scientists, and analysts interested in understanding mobile user behavior and developing predictive models in the realm of mobile technology and applications.



## Methodology



- **Data Importation and Preprocessing**: Libraries like Pandas, NumPy, Matplotlib, and Seaborn are utilized for data manipulation and visualization. Initial exploration includes reading the dataset, checking for missing values, and dropping unnecessary columns (e.g., User ID).



- **Exploratory Data Analysis (EDA)**:



  - The dataset is inspected to understand its structure and summarize statistics.

  - A distribution analysis is conducted for numerical features to visualize how metrics like app usage time, battery drain, and data usage vary.



- **Data Visualization**:



  - **Distribution Plots**: Individual visualizations are created for the distributions of key metrics.

  - **Correlation Analysis**: A correlation matrix is generated and visualized to identify relationships between numeric variables.

  - **Comparative Analysis**: Bar plots illustrate the differences in metrics across device models and operating systems, providing insights into performance variations.

  - **Segmentation Analysis**: Grouped metrics by age and gender to understand average app usage and battery drain differences among demographics.



## Visualizations



For visualizations, you can refer to this [link](https://learn.aelluminate.com/projects/data-science/projects/mobile-user-behavior-analysis).



## Results



This analysis can reveal significant insights:



- **User Usage Patterns**: Understanding how different demographics interact with devices and apps.

- **Performance Insights**: Identifying which devices perform better in terms of battery life and app efficiency.

- **Data-Driven Decision Making**: Providing stakeholders with actionable insights that can guide product development, marketing strategies, and customer engagement efforts.



## Conclusion



This project provides a comprehensive analysis of mobile user behavior, focusing on key metrics such as app usage time, battery drain, and data consumption. By exploring the dataset and visualizing the relationships between various features, we can derive valuable insights into user behavior patterns, device performance, and demographic segmentation. These insights can be leveraged to optimize app development, enhance user experience, and drive business growth in the mobile technology sector.