农业贷款推荐系统——一种机器学习方法

Arsal Imtiaz, S. Nachiket, K. Nishanth, J. Angadi, T. C. Pramod
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引用次数: 1

摘要

在印度这样的国家,农业贷款为农业的整体运作提供了急需的支持结构,因为大多数农田由众多人拥有,这导致了整个农田的所有权分散,从而限制了农业的潜在增长。这就需要一个适当的制度来提高农民端的贷款获取效率和银行端的贷款供给效率。本文利用K近邻算法,提出了一种可行的农业贷款推荐系统。它使农民能够查询与农业贷款相关的统计和图形数据,并获得有关贷款的建议。使用这个系统可以帮助农民更好地了解贷款申请的整个过程,以及根据他们的需要,哪家银行最适合申请贷款。根据所要求的贷款金额,分析了该方案的结果与银行推荐的概率有关。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Agricultural Loan Recommender System - A Machine Learning Approach
Agricultural loans provide a much-needed support structure for the overall functioning of the agricultural industry in a country like India where a majority of farmland is owned by a multitude of people, which leads to scattered ownership of the overall farmland and in turn restricts the potential growth of the agricultural industry. This leads to the need for a proper system to improve the efficiency of loan acquisition on the farmer's end and loan supply on the bank's end. In this paper, a feasible Agricultural Loan Recommender system is presented using K- nearest neighbour algorithm. It enables the farmers to look up statistical and graphical data relevant to agricultural loans and to get recommendations for said loans. Using this system can help farmers be better informed on the overall process of the loan application as well as which bank would be the most suitable to apply for a loan based on their needs. The results of the scheme are analysed with respect to the probability of bank recommendation based on the requested loan amount.
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