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Churn Prediction and Fraud Detection in Dairy Sector Using Machine Learning
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Author(s): Hitarth Deepak Shah (Charotar University of Science and Technology, India), Chintan M. Bhatt (Charotar University of Science and Technology, India), Shubham Mitul Patel (Charotar University of Science and Technology, India), Jayshil Bhavin Khajanchi (Charotar University of Science and Technology, India)and Jaimin Narendrakumar Makwana (Charotar University of Science and Technology, India)
Copyright: 2021
Pages: 16
Source title:
Handbook of Research on Records and Information Management Strategies for Enhanced Knowledge Coordination
Source Author(s)/Editor(s): Collence Takaingenhamo Chisita (Department of Information Science, University of South Africa, South Africa), Rexwhite Tega Enakrire (Department of Information Science, University of South Africa, South Africa), Oluwole Olumide Durodolu (Department of Information Science, University of South Africa, South Africa), Vusi Wonderboy Tsabedze (Department of Information Science, University of South Africa, South Africa)and Joseph M. Ngoaketsi (Department of Information Science, University of South Africa, South Africa)
DOI: 10.4018/978-1-7998-6618-3.ch023
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Abstract
India has globally been the largest milk-producing country in the world for two decades. About 400 million litres of milk is produced every day. It is the responsibility of a dairy sector to look after the farmers by providing them with various services for their livelihood. The growing financial capital of the dairy industry has enticed various fraudulent behaviour. The majority of suspicious activities are seen during the collection at local collection centres, fake farmer entries, tempered quantity and fat entries manually, and adulteration are the profound malpractices exercised by farmers. So, in this research work, the authors present a profound study on the most popular machine learning methods applied to the problems of farmer churn prediction and fraud detection in the dairies. They applied a plethora of machine learning algorithms to get accurate results for churn and fraud detection. XGBoost Classifier was the best for churn prediction with 93% accuracy, while random forest classifier turns out to be effective for fraud detection with 94% accuracy.
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