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1. |
Record Nr. |
UNINA9910776894703321 |
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Autore |
Frosterus, Benj |
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Titolo |
Quatrieme commission : commission pour la nomenclature et la classification des sols : Commission pour l'europe / B. Frosteurs ; D. C. Glinka |
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Pubbl/distr/stampa |
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Descrizione fisica |
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Altri autori (Persone) |
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Disciplina |
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Locazione |
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Collocazione |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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2. |
Record Nr. |
UNINA9910523751603321 |
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Autore |
Cohen Maxime C. |
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Titolo |
Demand Prediction in Retail : A Practical Guide to Leverage Data and Predictive Analytics / / by Maxime C. Cohen, Paul-Emile Gras, Arthur Pentecoste, Renyu Zhang |
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Pubbl/distr/stampa |
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Cham : , : Springer International Publishing : , : Imprint : Springer, , 2022 |
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ISBN |
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Edizione |
[1st ed. 2022.] |
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Descrizione fisica |
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1 online resource (166 pages) |
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Collana |
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Springer Series in Supply Chain Management, , 2365-6409 ; ; 14 |
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Disciplina |
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Soggetti |
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Sales management |
Business logistics |
Production management |
Quantitative research |
Retail trade |
Data mining |
Sales and Distribution |
Supply Chain Management |
Operations Management |
Data Analysis and Big Data |
Trade and Retail |
Data Mining and Knowledge Discovery |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Nota di contenuto |
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1. Introduction -- 2. Data Pre-Processing and Modeling Factors -- 3. Common Demand Prediction Methods -- 4. Tree-Based Methods -- 5. Clustering Techniques -- 6. Evaluation and Visualization -- 7. More Advanced Methods -- 8. Conclusion and Advanced Topics. |
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Sommario/riassunto |
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From data collection to evaluation and visualization of prediction results, this book provides a comprehensive overview of the process of predicting demand for retailers. Each step is illustrated with the relevant code and implementation details to demystify how historical data can be leveraged to predict future demand. The tools and methods |
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presented can be applied to most retail settings, both online and brick-and-mortar, such as fashion, electronics, groceries, and furniture. This book is intended to help students in business analytics and data scientists better master how to leverage data for predicting demand in retail applications. It can also be used as a guide for supply chain practitioners who are interested in predicting demand. It enables readers to understand how to leverage data to predict future demand, how to clean and pre-process the data to make it suitable for predictive analytics, what the common caveats are in terms of implementation and how to assess prediction accuracy. |
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