A Study on Machine Learning Techniques for Predictive Data Analysis

Abstract

Predictive data analysis has become an essential instrument in decision-making processes in different fields including finance, health sector, marketing, and production. Machine learning (ML) algorithms allow retrieving insightful patterns of past data to predict further trends and results. The paper will discuss different machine learning algorithms such as supervised and unsupervised techniques to predictive analytics. It analyses their levels of applicability, benefits, constraints, and performance statistics in other contexts. The study reveals the significance of the data preprocessing, feature selection, and model evaluation in the quest to get precise predictions. The findings present a guide to the practitioners and researchers on how to choose appropriate machine learning methods to use in predicting data.