- Monday, Dec 15th, 2025
International Journal of Advanced Research in Arts, Science, Engineering & Management (IJARASEM)
(A High Impact Factor, Monthly, Peer Reviewed Journal)
(A High Impact Factor, Monthly, Peer Reviewed Journal)
| TITLE | Analyzing User Behavior through Web Traffic with Python and Data Science |
|---|---|
| ABSTRACT | Imagine a bustling website where every click, scroll, and exit tells a story about the users behind it stories of curiosity, frustration, or delight that can make or break a business. In today's digital landscape, unlocking these stories from raw web traffic logs is more important than ever for improving user experiences, boosting engagement, and predicting what users will do next. However, traditional tools like Google Analytics often stop at surface-level metrics, leaving organizations hungry for deeper, customizable insights . This paper introduces a comprehensive, Python-powered web traffic analysis system that dives into the heart of user behavior. We start by ingesting and cleaning multi-source data, then use exploratory analysis to uncover trends like peak traffic hours or popular pages. From there, we apply advanced clustering techniques, such as K-Means and DBSCAN, to segment users into meaningful groups—like casual browsers versus loyal shoppers—based on session patterns, device preferences, and navigation habits. Predictive modeling takes it further, blending time series methods (ARIMA and Prophet) with machine learning (Random Forest) and deep learning (LSTM) to forecast traffic spikes and predict outcomes like user churn or conversions. We also integrate anomaly detection to spot unusual patterns, such as bot attacks, ensuring website security. Interactive dashboards, built with tools like Plotly, make these insights accessible and actionable for non-technical stakeholders. Experiments on a real-world dataset show our system outperforming baselines: clustering achieves a silhouette score of 0.65, forecasting reduces RMSE by 15%, and classification hits an F1-score of 0.82. The modular, automated architecture scales effortlessly on cloud platforms while prioritizing privacy through anonymization and GDPR compliance. By bridging descriptive analytics with predictive intelligence, this framework empowers businesses to move from reacting to user behavior to anticipating it, fostering growth and innovation in the digital realm. |
| AUTHOR | DR. S. Gnanapriya, Mohammedanfas. J Associate professor, Department of Computer Applications, Nehru College of Management, Coimbatore, Tamil Nadu, India Student of II MCA, Department of Computer Applications, Nehru College of Management, Coimbatore, Tamil Nadu, India |
| VOLUME | 12 |
| ISSUE | 5 |
| 23_Analyzing User Behavior through Web Traffic with Python and Data Science.pdf | |
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