An interactive data analysis web application built with Streamlit that explores, visualizes, and analyzes mobile screen time patterns, usage habits, and indicators of smartphone dependency or digital fatigue.
Live Dashboard: View the App
- Interactive Visualizations: Deep-dive charts showing usage patterns, peak times, and app categories that dominate screen time.
- Behavioral Analysis: Analytical insights into how mobile habits affect daily life, productivity, or sleep routines.
- User Segmentation: Data-driven categorization of users based on severity metrics or behavioral indicators.
- Clean User Interface: Simple, easy-to-use controls leveraging the power of Streamlit for filtering and toggling metrics.
- Python — Core backend and data manipulation
- Streamlit — Web application framework for interactive dashboards
- Pandas / NumPy — Data processing, cleansing, and aggregation
- Matplotlib / Seaborn / Plotly — Interactive and publication-quality visual plots
├── app.py # Main Streamlit application file
├── requirements.txt # Project dependencies and libraries
├── data/ # Dataset directory (contains raw/processed CSV or Excel files)
│ └── mobile_usage_data.csv
└── notebooks/ # Jupyter Notebooks used for initial EDA (Exploratory Data Analysis)