Analysis and Prediction for IT Sector Growth Using Machine Learning and Stochastic Modeling Approaches

Abstract

Data mining involves extracting valuable insights, patterns, correlations, and trends from large datasets stored in database repositories. Machine learning and data mining approaches are used to analyze and predict various research areas with the help of statistical, mathematical, and computational modeling or techniques. The main objective is to convert raw data into actionable knowledge. In this paper considers the information technology sector data for analysis and prediction using data mining, machine learning, stochastic models, and test statistics. It is used to find future predictions based on four different parameters: open, high, low, and close using familiar machine learning approaches and stochastic model are linear regression, multilayer perceptron, M5P, random forest, random tree, REP tree and proposed stochastic model. Numerical illustrations are provided to prove the proposed results with test statistics or accuracy parameters.