Enhancing Big Data Analytics and Recommendation Systems With Python

Abstract

The advancement of technology has led to an exponential increase in the volume, velocity, and variety of data generated, necessitating the development of effective methods for analyzing and extracting valuable insights from large datasets. This research focuses on enhancing big data analytics and recommendation systems using Python, specifically employing hierarchical clustering and a filtering approach with the slicing technique. This study proposes a novel approach to leverage Python's capabilities in processing and analyzing big data. Hierarchical clustering algorithms organize and structure data into hierarchical groups, enabling efficient exploration and extraction of relevant information. Additionally, a filtering mechanism is integrated with the slicing technique, allowing for identifying and extracting specific subsets of data based on predefined criteria. Experiments are conducted using real-world datasets in the context of recommendation systems to evaluate the approach's effectiveness.