Optimizing accuracy of consumer goods recommendations using Decision Trees and Random Forests: A case study of M5 Walmart Sales Forecasting data

Additional Metadata
Keywords	Machine Learning; FMCG (Fast Moving Consumer Goods) Industry; Decision Trees (DT); Random Forests (RF); Predictive Analytics; Recommendation Systems; Model Interpretability; Accuracy-Interpretability Trade-off; Data Analysis; M5 Walmart Sales Forecasting; Consumer Behavior Prediction; Retail Analytics; Forecasting Models; Statistical Modeling; Business Intelligence
Thesis Advisor	Raviv, E
Persistent URL	hdl.handle.net/2105/71128
Series	Business Economics
Organisation	Erasmus School of Economics
Citation APA APA Style APA-ALL Style AAA Style Cell Style Chicago Style Harvard Style IEEE Style MLA Style Nature Style Vancouver Style American-Institute-of-Physics Style Council-of-Science-Editors Style BibTex Format Endnote Format RIS Format CSL Format DOIs only Format	Kok, V.C. de. (2024). Optimizing accuracy of consumer goods recommendations using Decision Trees and Random Forests: A case study of M5 Walmart Sales Forecasting data. In Business Economics.http://hdl.handle.net/2105/71128

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