Utilising Machine Learning Algorithms to Address Computational Challenges in Big Data Analytics

Abstract

The rapid growth of data across industries including finance, healthcare, and e-commerce has led to significant computational hurdles in big data analytics. Challenges include dimensionality reduction, scalability, real-time processing, and the handling of large data volumes. The use of sophisticated machine learning (ML) algorithms is necessary to tackle these complexity, as traditional data processing methods are insufficient. This paper examines the efficacy of supervised, unsupervised, and deep learning algorithms to improve the efficiency, scalability, and accuracy of data processing to overcome these computing problems. Random Forest, XGBoost, K-means clustering, Convolutional Neural Networks (CNNs), and Long Short-Term Memory Networks (LSTMs) are approaches utilized in the analysis of extensive datasets. The findings demonstrate that CNNs surpass alternative models in image-based datasets, achieving an accuracy of 93.8%. XGBoost attains an equilibrium between computing efficiency and accuracy (91.7%) in consumer segmentation and fraud detection. The remarkable scalability of K-means clustering makes it a suitable technique for analyzing customer behavior. The study incorporates distributed platforms like Apache Spark and TensorFlow to address critical difficulties, including high memory consumption, real-time data processing, and model interpretability. The findings align with existing studies and highlight the importance of scalable and resource-efficient machine learning methods. Moreover, the research provides significant insights into the capacity of stream processing frameworks and hybrid methodologies to improve real-time analytics. This study significantly advances the burgeoning domain of big data analytics by offering pragmatic machine learning techniques. This approach guarantees the effective management of large volumes of data by producing actionable insights.