Predictive Analytics: Applications, Techniques, and Ethics

Focus on applying predictive analytics and machine learning models to solve crucial business challenges. One paper investigates the use of predictive analytics, particularly the Logistic Regression (LR) and eXtreme Gradient Boosting (XGBoost) models, to predict and reduce customer churn within the telecommunications industry, emphasizing the importance of ethical data use and data processing techniques like binning. The second paper details a case study employing the Gradient Boosting model to predict inventory status (understock or overstock) and the corresponding inventory amount for a Fast-Moving Consumer Goods (FMCG) company, utilizing historical sales and demand forecast data for classification and regression tasks. Both sources highlight how advanced analytical methods can offer significant competitive advantages by improving forecasting and decision-making in their respective sectors.