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Updated: Dec 1, 2025

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A Computational Model to Predict Consumer Behaviour During COVID-19 Pandemic.

Fatemeh Safara1

  • 1Department of Computer Engineering, Islamic Azad University, Islamshahr Branch, Islamshahr, Iran.

Computational Economics
|November 10, 2020
PubMed
Summary
This summary is machine-generated.

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Machine learning models predict online shopping behavior changes due to COVID-19. Decision tree ensembles with Bagging achieved 95.3% accuracy in forecasting consumer behavior during the pandemic.

Area of Science:

  • Data Science
  • Machine Learning
  • Consumer Behavior Analysis

Background:

  • The knowledge-based economy increasingly relies on online transactions and consumer data.
  • The COVID-19 pandemic significantly altered daily life, particularly impacting shopping habits and accelerating online retail adoption.
  • Predicting electronic consumer behavior is crucial for government, supply chain, and retail management.

Purpose of the Study:

  • To develop and evaluate machine learning models for predicting consumer behavior in online shopping.
  • To analyze the impact of the COVID-19 pandemic on online purchasing patterns.
  • To identify key features influencing online purchase volume during the pandemic.

Main Methods:

  • Utilized machine learning techniques to analyze online shopping data.
Keywords:
BaggingBoostingConsumer behaviorCoronavirus disease (COVID-19)E-commerceMachine learningPrediction model

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  • Examined five individual classifiers and their ensembles using Bagging and Boosting.
  • Performed correlation analysis to identify significant influencing features.

    • Main Results:

    • The decision tree ensemble model with Bagging demonstrated the highest prediction accuracy at 95.3%.
    • Identified key factors influencing online purchase volume during the COVID-19 pandemic through correlation analysis.

    Conclusions:

    • Machine learning, particularly decision tree ensembles with Bagging, is effective for predicting online consumer behavior shifts.
    • Understanding these shifts is vital for businesses and policymakers navigating the post-pandemic retail landscape.