Inhaltsangabe
The model which we have presented is a Linear Regression Model. In the results above we see that predictions can be done on the basis of the data available and is approximately accurate. An accurate forecast is very important for the demand planning team. The data used in this project and building the model is using the sales-in data for different stores. The important factor to be considered is the stability of the model and removing the game-playing. A community version of a platform is used to build the model. Linear Regression model is developed in pyspark. After the results are generated, dataframe of results is validated and generated and is sent backto the Azure SQL database to be used in Power BI.In the future work, different techniques will be considered and researched. Time-Series and Machine Learning to be built in one platform and check how the minimization of mse produces the forecast. The predictions can be hyper parameterized to give more accurately tuned results. Also, in the PowerBI report more measures and visualizations can be made on basis of individual's thought process.
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Über die Autorin bzw. den Autor
Dr. Punit Gupta ist außerordentlicher Professor in der Abteilung für Computer und Kommunikation an der Manipal Universität Jaipur, Jaipur. Er erhielt 2010 einen B.Tech-Abschluss in Informatik und Ingenieurwesen von Rajiv Gandhi Prouduogiki Vishwavidyalaya, Madhya Pradesh.
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Suchergebnisse für Real-Time Demand Forecasting: Azure, ML, Demand Forecasting
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Real-Time Demand Forecasting
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LAP LAMBERT Academic Publishing Jul 2020, 2020
ISBN 10: 6202674474
ISBN 13: 9786202674478
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Taschenbuch. Zustand: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -The model which we have presented is a Linear Regression Model. In the results above we see that predictions can be done on the basis of the data available and is approximately accurate. An accurate forecast is very important for the demand planning team. The data used in this project and building the model is using the sales-in data for different stores. The important factor to be considered is the stability of the model and removing the game-playing. A community version of a platform is used to build the model. Linear Regression model is developed in pyspark. After the results are generated, dataframe of results is validated and generated and is sent backto the Azure SQL database to be used in Power BI.In the future work, different techniques will be considered and researched. Time-Series and Machine Learning to be built in one platform and check how the minimization of mse produces the forecast. The predictions can be hyper parameterized to give more accurately tuned results. Also, in the PowerBI report more measures and visualizations can be made on basis of individual's thought process. 104 pp. Englisch. Bestandsnummer des Verkäufers 9786202674478
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Real-Time Demand Forecasting
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ISBN 10: 6202674474
ISBN 13: 9786202674478
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Zustand: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Gupta PunitPunit Gupta is an Associate Professor in the Department of Computer and Communiction Engineering, Manipal University Jaipur, Jaipur, Rajisthan, India.Has got M.Tech. Degree in Computer Science and Engineering from Jaypee I. Bestandsnummer des Verkäufers 389068752
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Real-Time Demand Forecasting
Verlag:
LAP LAMBERT Academic Publishing Jul 2020, 2020
ISBN 10: 6202674474
ISBN 13: 9786202674478
Neu
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Taschenbuch. Zustand: Neu. This item is printed on demand - Print on Demand Titel. Neuware -The model which we have presented is a Linear Regression Model. In the results above we see that predictions can be done on the basis of the data available and is approximately accurate. An accurate forecast is very important for the demand planning team. The data used in this project and building the model is using the sales-in data for different stores. The important factor to be considered is the stability of the model and removing the game-playing. A community version of a platform is used to build the model. Linear Regression model is developed in pyspark. After the results are generated, dataframe of results is validated and generated and is sent backto the Azure SQL database to be used in Power BI.In the future work, different techniques will be considered and researched. Time-Series and Machine Learning to be built in one platform and check how the minimization of mse produces the forecast. The predictions can be hyper parameterized to give more accurately tuned results. Also, in the PowerBI report more measures and visualizations can be made on basis of individual's thought process.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 104 pp. Englisch. Bestandsnummer des Verkäufers 9786202674478
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Real-Time Demand Forecasting : Azure, ML, Demand Forecasting
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Taschenbuch. Zustand: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - The model which we have presented is a Linear Regression Model. In the results above we see that predictions can be done on the basis of the data available and is approximately accurate. An accurate forecast is very important for the demand planning team. The data used in this project and building the model is using the sales-in data for different stores. The important factor to be considered is the stability of the model and removing the game-playing. A community version of a platform is used to build the model. Linear Regression model is developed in pyspark. After the results are generated, dataframe of results is validated and generated and is sent backto the Azure SQL database to be used in Power BI.In the future work, different techniques will be considered and researched. Time-Series and Machine Learning to be built in one platform and check how the minimization of mse produces the forecast. The predictions can be hyper parameterized to give more accurately tuned results. Also, in the PowerBI report more measures and visualizations can be made on basis of individual's thought process. Bestandsnummer des Verkäufers 9786202674478
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Real-Time Demand Forecasting | Azure, ML, Demand Forecasting
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LAP LAMBERT Academic Publishing, 2020
ISBN 10: 6202674474
ISBN 13: 9786202674478
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Taschenbuch. Zustand: Neu. Real-Time Demand Forecasting | Azure, ML, Demand Forecasting | Punit Gupta (u. a.) | Taschenbuch | 104 S. | Englisch | 2020 | LAP LAMBERT Academic Publishing | EAN 9786202674478 | Verantwortliche Person für die EU: BoD - Books on Demand, In de Tarpen 42, 22848 Norderstedt, info[at]bod[dot]de | Anbieter: preigu. Bestandsnummer des Verkäufers 118796218
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