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 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.
The career of Arthur L-F. Askins is celebreated in a panorama of current scholarship on the Iberian peninsula during the Middle Ages and the Renaissance. This volume is dedicated to Professor Arthur L-F. Askins, whose scholarship on Spanish and Portuguese literatures of the Medieval and Renaissance periods is esteemed by colleagues around the world. Many North American and European scholars have contributed with essays of an exceptionally high scholarly quality, in English, Spanish and Portuguese, to this wide-ranging tribute, dealing with Spanish and Portuguese literary culture from the end of the fourteenth to the late sixteenth century. Some tackle problems concerning manuscripts, texts, and books; other essays are literary, theoretical, and interpretive in nature; topics range from medieval and Renaissance epic and love poetry to spiritual, travel and chivalric literature, as well as balladry and pliegos sueltos. CONTRIBUTORS: Gemma Avenoza, Nieves Baranda, Vicenç Beltran, Alberto Blecua, Pedro M. Cátedra, Manuel da Costa Fontes, Alan Deyermond, Aida Fernanda Dias, Dru Dougherty, Thomas F. Earle, Charles B. Faulhaber, María del Mar Fernández Vega, Helder Godinho, Angel Gómez Moreno, Thomas R. Hart, Ana Hatherly, David Hook, Victor Infantes, Paul Lewis-Smith, Beatriz Mariscal Hay, Aires A. Nascimento, Joao David Pinto-Correia, Dorothy Sherman Severin, Harvey L. Sharrer. Martha E. Schaffer is Associate Professor of Spanish at the University of San Francisco; Antonio CortijoOcaña is Professor of Spanish at the University of California.
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 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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