Enhancing the Operational Efficiency of FPOs by Using Predictive Model Building in Machine Learning Through Feature Selection Method
feature selection method; FPO operational efficiency; FPO policy inputs; FPO shortcomings; predictive power
This study employs a machine learningbased feature selection approach to identify key factors that significantly enhance the predictive accuracy of models assessing farmer producer organization (FPO) performance. It focused on an FPO in the Anantapur and Kadapa districts of Andhra Pradesh, India; the research investigated the operational effectiveness of small and marginal farmers who are members by the end of 2022. The study reviewed existing shortcomings and proposed necessary policy amendments to ensure the sustainability of FPOs. The study utilized a survey design method, with the exploratory questionnaire through the field study. A sample of 73 small-scale producer farmers from the region was selected for the analysis. The result indicated that a) the reason for the operational efficiency of FPOs, b) the gradient boosting regressor achieved the highest testing accuracy of 0.78 to predict the best feature for FPO performance, and c) FPO insights for policy guidelines. 2025 Scrivener Publishing LLC.
Sreelakshmi, P.; Basavaraj, Santosh; Josephine, V. L. Helen
Green Innovations in Computational Intelligence: Sustainable Strategies and Emerging Technologies;pp.159-187
Restricted Access; Hardcopy may be available in the library
https://doi.org/10.1002/9781394383658.ch8 https://www.scopus.com/pages/publications/105023235995?origin=resultslist
Sreelakshmi P., School of Business and Management (SBM), Christ University, Bangalore, India; Basavaraj S., School of Business and Management (SBM), Christ University, Bangalore, India; Josephine V.L.H., School of Business and Management (SBM), Christ University, Bangalore, India