Explaining machine learning models in sales predictions

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Machine Learning in Agriculture: A Review (2018, background): “Year by year, ML applies in more and more scientiﬁc ﬁelds including, for example, bioinformatics [2,3], biochemistry [4,5], medicine [6–8], meteorology [9–11], economic sciences [12–14], robotics [15,16], aquaculture [17,18], and food security [19,20], and climatology [21].”
Predictive big data analytics for supply chain demand forecasting: methods, applications, and research opportunities (2020, methodology): “These techniques are computationally intensive to process and require complex machine-pro-grammed algorithms [17].”
A Survey on the Explainability of Supervised Machine Learning (2020, methodology): “IME further uses the Shapley value from coalitional game theory to assign each feature a contribution value (Bohanec et al., 2017).”
Explainable Artificial Intelligence: a Systematic Review (2020, methodology): “[150, 151], were applied to a complex real-world business problem of business-to-business sales forecasting [321, 322].”