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Can Machine Learning Models Truly Revolutionize Retail Sales Forecasting?
- Traditional methods such as linear regression (LR) have been widely used for decades in retail sales forecasting, but these models often fail to address the complexities of modern datasets.
- Recent research by Priyam Ganguly and Isha Mukherjee delves into the transformative potential of machine learning (ML) models in overcoming these challenges, offering a nuanced perspective on their efficacy and exploring new techniques for improving forecasting accuracy.
- The researchers evaluated a suite of advanced ML techniques to uncover the most effective solution for retail sales forecasting.
- The outcome was a landmark R-squared value of 0.945 with an optimized Random Forest model, which far outperformed traditional LR, demonstrating its inability to effectively capture the intricate patterns and seasonal trends inherent in retail data.
- While Random Forest stood out in this study, the other techniques also provided valuable insights. Gradient Boosting excelled in strong relationships between the features and target variables, while SVR excelled in smaller, non-linear patterns.
- The research underscores the superior capabilities of Random Forest in handling complex datasets with a high degree of accuracy.
- Accurate sales forecasting enables retailers to optimize inventory management, reduce waste, and enhance customer satisfaction by ensuring product availability.
- As businesses increasingly turn to data-driven strategies, the adoption of advanced machine learning models could become a defining factor for competitive advantage in the retail sector.
- The researchers emphasize the importance of addressing biases in historical data, which, if left unchecked, could perpetuate inequities in decision-making.
- Transparency, fairness, and ethics must be at the core of the design and deployment of machine learning solutions, particularly when they influence business practices that affect consumers’ lives.
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