This Python project analyzes web traffic and user engagement data from a single website to uncover behavioral insights and optimize performance across marketing channels and time periods. It demonstrates a full data analysis pipeline — from cleaning and preprocessing to advanced visualization — to extract actionable insights.
- Discover traffic trends and usage patterns
- Identify effective marketing channels
- Analyze user engagement across different dimensions
- Optimize content and campaign timing based on user behavior
- Renamed columns for clarity
- Converted data types (e.g., datetime formats)
- Handled missing values and removed inconsistencies
- Sessions & Users Over Time: Trend analysis of site visits
- User Distribution by Channel: Marketing channel-wise breakdown of total users
- Average Engagement Time by Channel: Highlights the most engaging sources
- Engagement Rate Distribution: Visual spread of engagement rates across channels
- Engaged vs. Non-Engaged Sessions: Quality comparison of visitor types
- Traffic by Hour & Channel (Heatmap): Identifies peak hours for each channel
- Engagement Rate vs. Sessions Over Time: Correlation analysis between traffic and interest
- Python
- pandas, numpy – Data wrangling & manipulation
- matplotlib, seaborn – Data visualization
This analysis provides data-driven insights to:
- Improve marketing strategies
- Optimize user engagement efforts
- Determine best content timing based on real user behavior