TY - JOUR
T1 - Prediction of consumers refill frequency of LPG
T2 - A study using explainable machine learning
AU - Trivedi, Shrawan Kumar
AU - Roy, Abhijit Deb
AU - Kumar, Praveen
AU - Jena, Debashish
AU - Sinha, Avik
N1 - Publisher Copyright:
© 2023 The Authors
PY - 2024/1/15
Y1 - 2024/1/15
N2 - 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”.
AB - 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”.
KW - Artificial intelligence
KW - Machine learning
KW - Public policy
KW - Ujjwala scheme
KW - Welfare economics
UR - http://www.scopus.com/inward/record.url?scp=85180357416&partnerID=8YFLogxK
U2 - 10.1016/j.heliyon.2023.e23466
DO - 10.1016/j.heliyon.2023.e23466
M3 - Article
AN - SCOPUS:85180357416
SN - 2405-8440
VL - 10
JO - Heliyon
JF - Heliyon
IS - 1
M1 - e23466
ER -