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 Style AAA Style APA 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, March 26). Optimizing accuracy of consumer goods recommendations using Decision Trees and Random Forests: A case study of M5 Walmart Sales Forecasting data. Business Economics. Retrieved from http://hdl.handle.net/2105/71128

Publication