This project aims to analyze screen time usage on different applications by creating insightful visualizations. The dataset used contains details about how much time is spent on specific applications, how often they are opened, and the number of notifications received. The goal is to provide meaningful insights into screen time habits and the relationship between app notifications and usage.
Screen Time Analysis provides a deep dive into the user's app usage patterns, allowing you to visualize:
- App Usage: How much time is spent on each app.
- App Notifications: The number of notifications received from apps.
- Times Opened: How many times each app is opened in a day.
- Relationship Between Notifications and Usage: A linear relationship that shows how app notifications increase usage.
The dataset used in this project consists of the following columns:
- Date: The date of the app usage record.
- Usage: The total time spent on the app in minutes.
- Notifications: The number of notifications received from the app.
- Times Opened: The number of times the app was opened in a day.
- App: The name of the app.
- Pandas: For data manipulation and analysis.
- Numpy: For numerical operations.
- Plotly Express: For creating interactive visualizations.
- Plotly Graph Objects: For customizable visualizations.
- App Usage Over Time: Visualized using a bar chart showing how usage changes over time for each app.
- Number of Notifications: Displaying notifications per app over time.
- Times Opened: Showing how frequently each app is opened daily.
- Relationship Between Notifications and Usage: A scatter plot with a trend line to show how more notifications lead to increased usage.
This project showcases how screen time can be analyzed for better understanding of user habits. More notifications often lead to more screen time, which is crucial information for app developers and users aiming to manage their digital well-being.