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A Comparative Study of Big Mart Sales Prediction

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Computer Vision and Image Processing (CVIP 2019)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1147))

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Abstract

Nowadays shopping malls and Big Marts keep the track of their sales data of each and every individual item for predicting future demand of the customer and update the inventory management as well. These data stores basically contain a large number of customer data and individual item attributes in a data warehouse. Further, anomalies and frequent patterns are detected by mining the data store from the data warehouse. The resultant data can be used for predicting future sales volume with the help of different machine learning techniques for the retailers like Big Mart. In this paper, we propose a predictive model using Xgboost technique for predicting the sales of a company like Big Mart and found that the model produces better performance as compared to existing models. A comparative analysis of the model with others in terms performance metrics is also explained in details.

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Authors and Affiliations

  1. Malaviya National Institute of Technology Jaipur, Jaipur, India

    Gopal Behera & Neeta Nain

Authors
  1. Gopal Behera
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  2. Neeta Nain
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Corresponding author

Correspondence to Gopal Behera .

Editor information

Editors and Affiliations

  1. Malaviya National Institute of Technology, Jaipur, Rajasthan, India

    Neeta Nain

  2. Malaviya National Institute of Technology, Jaipur, Rajasthan, India

    Santosh Kumar Vipparthi

  3. Indian Institute of Technology Roorkee, Roorkee, Uttarakhand, India

    Balasubramanian Raman

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Behera, G., Nain, N. (2020). A Comparative Study of Big Mart Sales Prediction. In: Nain, N., Vipparthi, S., Raman, B. (eds) Computer Vision and Image Processing. CVIP 2019. Communications in Computer and Information Science, vol 1147. Springer, Singapore. https://doi.org/10.1007/978-981-15-4015-8_37

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  • DOI: https://doi.org/10.1007/978-981-15-4015-8_37

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-15-4014-1

  • Online ISBN: 978-981-15-4015-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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