This project analyzes user behavior based on app usage, screen time, data consumption, and demographic information. Using a dataset of daily app usage, screen-on time, data usage, and user age, the analysis explores patterns and correlations to understand trends in mobile device engagement.
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Data Loading: The dataset is loaded and basic information, including the first few rows, dataset structure, and descriptive statistics, is printed.
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Visualizations:
- Distribution of App Usage Time: A histogram is plotted to show the frequency distribution of app usage time.
- Distribution of Screen On Time: A histogram is plotted for daily screen-on time.
- Distribution of Data Usage: The distribution of data usage per day is visualized using a histogram.
- Distribution of Age: A histogram shows the age distribution of users in the dataset.
- Scatter Plot - App Usage Time vs Screen On Time: A scatter plot is generated to visualize the relationship between app usage time and screen-on time.
- Scatter Plot - Data Usage vs Age: A scatter plot is used to examine the correlation between data usage and user age.
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Correlation Analysis: The correlation matrix is calculated and visualized using a heatmap to identify relationships between numeric variables such as app usage time, screen-on time, data usage, and age.
pandas: For data loading and manipulation.matplotlib: For data visualization.seaborn: For enhanced visualizations.
- Install the required Python libraries:
pip install pandas matplotlib seaborn
- Clone this repository and place the dataset
user_behavior_dataset.csvin the root directory. - Run the script to generate visualizations and insights from the data:
python app_usage_analysis.py
This project provides a comprehensive analysis of mobile app usage and user behavior, offering insights into how different variables like age, data usage, and screen time relate to each other.
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