Authors:

• Araba Addaquay: GitHub / LinkedIn
• Vijay #2: GitHub / LinkedIn

Mobile Usage Patterns and User Behavior Classification Across Devices

Project Overview:

Our project is to review a mobile usage dataset and explore how mobile phones are being used and the behaviours of their owners.

Features

• Exploratory Data Analysis and Visual Distributions
• Correlation Matrices
• Linear Regression

Installation:

*Download data from Kaggle: https://www.kaggle.com/datasets/valakhorasani/mobile-device-usage-and-user-behavior-dataset?resource=download

*Miniconda and Jupyter notebook launch

*Clone the project locally

*Pandas, Matplotlib, Seaborn, Scikit-learn

Results: Visualizations and statistical inferences for graphs answering the following questions:

*Most Popular Devices Based On User Age & Gender

*Most Popular Operating System Types by User Age & Gender

*How The Rate Of Battery drain is impacted by app usage time, screen time & data usage time

*Is Drain impacted by number of app or gender of the user?

*Does the user age or gener impact screen on time or number of apps installed?

*Recommendations for Apple(iOS) and Google (Androd) to improve appeal of their operating system running on their devices