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IoT 24(1):

Research Article

Big Mart Sales Prediction using Machine Learning

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  • @ARTICLE{10.4108/eetiot.6453,
        author={Koh Ya Wen and Minnu Helen Joseph and V. Sivakumar},
        title={Big Mart Sales Prediction using Machine Learning},
        journal={EAI Endorsed Transactions on Internet of Things},
        volume={10},
        number={1},
        publisher={EAI},
        journal_a={IOT},
        year={2024},
        month={12},
        keywords={Big Mart, sales prediction, machine learning, prediction model, regression, linear regression, decision tree, random forest, XGBoost regression, K-nearest neighbours},
        doi={10.4108/eetiot.6453}
    }
    
  • Koh Ya Wen
    Minnu Helen Joseph
    V. Sivakumar
    Year: 2024
    Big Mart Sales Prediction using Machine Learning
    IOT
    EAI
    DOI: 10.4108/eetiot.6453
Koh Ya Wen1,*, Minnu Helen Joseph1, V. Sivakumar1
  • 1: Asia Pacific University of Technology & Innovation
*Contact email: tp056587@mail.apu.emy.my

Abstract

INTRODUCTION: Sales prediction, also known as revenue forecasting or sales forecasting, refers to the process of accurately and timely estimating future revenue for manufacturers, distributors, and retailers, providing them with valuable insights. Sales prediction plays a crucial role in various industries, particularly in sectors such as retail, automotive leasing, real estate transactions, and other conventional businesses. OBJECTIVES: This paper focuses on developing a sales prediction model for Big Mart, a supermarket chain, using machine learning algorithms. The developed model aims to provide Big Mart with accurate sales forecasts, enabling better decision-making, improved profitability, and enhanced customer service. METHODS: The study utilises the CRISP-DM methodology and explores various machine learning algorithms, including Linear Regression, Decision Tree, Random Forest, XGBoost, Stacked Ensemble Model, and K-Nearest Neighbours (KNN). The dataset used for model development is sourced from Kaggle and includes information about products, stores, and sales. Pre-processing techniques are applied to handle missing data and feature engineering. RESULTS: The XGBoost Regression Model Tuned with RandomizedSearchCV outperforms the existing models with an RMSE of 1018.82 and an R² of 0.6181. CONCLUSION: This research contributes to the field of sales forecasting in the retail industry and provides insights for businesses looking to enhance their revenue prediction capabilities.

Keywords
Big Mart, sales prediction, machine learning, prediction model, regression, linear regression, decision tree, random forest, XGBoost regression, K-nearest neighbours
Received
2024-12-05
Accepted
2024-12-05
Published
2024-12-05
Publisher
EAI
http://dx.doi.org/10.4108/eetiot.6453

Copyright © 2023 Y. W. Koh, M. H. Joseph and V. Sivakumar, licensed to EAI. This is an open access article distributed under the terms of the CC BY-NC-SA 4.0, which permits copying, redistributing, remixing, transformation, and building upon the material in any medium so long as the original work is properly cited.

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