Web-Traffic Predication using Python Incremental Machine Learning for Enhancing Business Infrastructure Management

Abstract

The In today's digital era, every organization prioritizes the end- user experience while accessing websites and important business assets, since it directly reflects the company's online visibility. Most businesses have migrated their infrastructure to cloud platforms such as AWS and Azure, ensuring optimal performance and user experience. These cloud applications offer both horizontal and vertical scaling of infrastructure. This means that they may adjust the amount of resources allocated to the application based on factors such as application response time and the threshold for bottleneck due to abrupt increases in web traffic load. The infrastructure can be automatically scaled up or down as needed. Objective of the research: To enhance the effectiveness of the infrastructure configuration employed or defined by the Business, these papers propose a novel solution to the industry by utilizing a Python-based incremental learning technique. It aids in forecasting the volume of web traffic on an application. This will assist in organizing and managing resources and infrastructure during periods of high web traffic. The Python programming language is a versatile and adaptable tool that may be utilized to generate these models in many business contexts and data sources. This study aids businesses in optimizing customer satisfaction, reducing operational downtime, and proactively allocating resources by accurately predicting web traffic patterns.

This study report emphasizes the superiority of the incremental model compared to the classic machine learning approach when dealing with continually evolving Web traffic data. Traditional machine learning models require retraining on the entire dataset when new data becomes available, which can be computationally expensive and time-consuming, especially for constantly evolving web traffic data. Incremental machine learning enables the ongoing updating of models with new data points, enhancing flexibility and decreasing computing expenses. In addition, we examine previous studies on forecasting web traffic and emphasize the constraints of conventional machine-learning methods within this domain. Next, we analyse the many elements and sub-elements that impact online traffic and examine the possible advantages of utilizing Python tools such as sci-kit-learn and Python for creating incremental learning models. Lastly, we delineate the subsequent actions to be taken for future research in this field.