Enterprise Big Data Framework For Predictive Analytics And Business Optimization

Abstract

Predictive analytics is important in business optimization during the big data era through facilitating decision-making based on data. This paper provides an enterprise big data framework for major business applications using machine learning methods, such as sales forecast, customer churn prediction, fraud detection, and employee performance review. We utilize XGBoost for sales forecasting on time-series, logistic regression to predict churn, and random forest classifiers for fraud detection and employee performance evaluation. The model combines feature engineering, data preprocessing, and model validation based on metrics like Mean Absolute Error (MAE) and Accuracy Score. Our results demonstrate the importance of data-driven information in business performance enhancement. In addition, we address issues like data imbalance and feature selection, suggesting optimization solutions. The suggested framework offers a scalable and efficient solution to enterprise analytics, showing its applicability in real-world.