Prediction of consumers refill frequency of LPG: A study using explainable machine learning

Shrawan Kumar Trivedi, Abhijit Deb Roy, Praveen Kumar, Debashish Jena, Avik Sinha*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)

Abstract

Launched in 2016, the PMUY Programme of the Government of India aimed to provide 8 crore LPG connections to women in rural households over four years. After acquiring a new connection, some households appeared uninterested in ordering subsequent subsidized LPG refills, impacting programme's sustainability, and targeting strategy. We propose a prediction model using “Explainable Machine Learning” to anticipate the beneficiaries' refill frequency with a view to improving LPG-refills and social targeting. In this paper, we suggest an enhanced stacked SVM (ISS) model for classification, which is contrasted with state-of-art ML models: Random Forest (RF), SVM-RBF, Naive Bayes (NB), and Decision Tree (C5.0). Some of the performance matrices that are used to evaluate the models include accuracy, sensitivity, specificity, Cohen's Kappa statistics, Receiver Operating Characteristic curve (ROC), and area under the curve (AUC). The proposed approach, which was validated with 10-fold cross validation, produced the best overall accuracies for data splits of 50–50, 66–34, and 80–20. The “Explainable AI (XAI)” model has also been used to describe how models and features interact, and to discuss the importance of features and their contributions to prediction. The recommended XAI will aid in efficient “beneficiary targeting” and “policy interventions”.

Original languageEnglish
Article numbere23466
JournalHeliyon
Volume10
Issue number1
DOIs
Publication statusPublished - 15 Jan 2024
Externally publishedYes

Keywords

  • Artificial intelligence
  • Machine learning
  • Public policy
  • Ujjwala scheme
  • Welfare economics

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