NEXT GEN BUSINESS INTELLIGENCE: LEVARAGING PREDICTIVE ANALYTICS, AI & REAL-TIME DECISION- MAKING

In today’s dynamic business landscape, the ability to harness data effectively has become a cornerstone of competitive advantage. Business Intelligence (BI) is evolving rapidly, integrating cutting-edge technologies like predictive analytics, artificial intelligence (AI), and real-time data processing to deliver actionable insights. Predictive modeling plays a pivotal role in modern data-driven decision-making, offering valuable insights into future trends and behaviors across various data sets. This paper investigates the performance of prevalent machine learning models for predictive analytics, with a particular focus on XGBoost. We demonstrate how hyperparameter tuning can significantly enhance the accuracy prediction of XGBoost comparing its performance against other popular models such as decision trees, random forests, and logistic regression. Through a systematic evaluation, we highlighted the effectiveness of XGBoost in capturing complex patterns within the data, resulting in superior predictive performance. Our findings provide evidence that XGBoost, when appropriately tuned, outperforms traditional models, offering a more robust approach for predictive analytics. This research contributes to the ongoing discourse on the application of machine learning techniques, providing practical insights for researchers and practitioners aiming to improve predictive modeling accuracy.