Performance Analysis for Business Insights Prediction Using Machine Learning Algorithms

Predictive analytics is a crucial tool in business decision-making, enabling companies to forecast trends, optimize strategies, and enhance profitability. This study provides a comprehensive performance analysis of three machine learning algorithms-Random Forest (RF), Artificial Neural Networks (ANN), and Gradient Boosting (GB)-for business insights prediction. A structured dataset undergoes preprocessing, including feature engineering and encoding, before model training. Each algorithm is evaluated based on accuracy, precision, recall, Fl-score, and computational efficiency. Results indicate that Gradient Boosting outperforms the others in accuracy (90.2 %), while ANN offers superior generalization. Random Forest provides a balance between interpretability and performance. This comparative analysis aids businesses in selecting appropriate models based on their computational constraints and prediction goals.