Computational Methods

Research Article

Big Data Analytics Using Machine Learning Techniques for Prediction on Datasets

  • By Jian Li - 23 Jun 2024
  • Computational Methods, Volume: 1, Issue: 1, Pages: 32 - 41
  • Received: April 04, 2024; Accepted: June 07, 2024; Published: June 23, 2024


Data analytics is the systematic application of scientific and statistical methods to raw data, with the aim of converting it into actionable information that can be utilised for acquiring knowledge. One current development in feature abstraction involves the integration of computational approaches and big data analysis. Acquiring information from reliable data sources, processing it efficiently, and creating precise forecasts about the future are necessary for this. The main aim of this work is to identify the machine learning techniques that yield the best precise prediction by employing the proposed model. The MapReduce methodology has been utilised to apply both supervised and unsupervised tactics in many ways. However, the proposed model employs the Apache Spark framework to compare the several current methods. This study focuses on clarifying the attributes of datasets to enable the most precise analysis using machine learning techniques. In order to analyse the data sets, machine learning techniques such as linear regression, decision trees, random forests, and gradient boosting tree algorithms are employed. Based on the research findings, it can be inferred that implementing the Spark framework on Machine Learning methods enhances the model's efficiency by a significant 70% compared to the Mapreduce paradigm.