Optimizing E-Commerce with Random Forest Spending Predictions

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This presentation encompasses the innovative application of machine learning to predict retail spending, highlighting the shift from intuition-based strategies to data-driven methodologies. Addressing inefficiencies in promotions and existing gaps, the project involves the analysis of 5,000 transaction data, utilizing four model types. Impressively, Random Forest emerges as the top performer with an R² of 0.9899, emphasizing the importance of pricing data. Despite limitations like a small...