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Prediction of Loan Repayability of Farmer using Machine Learning Approach

Author(s):

Waheeda Dhokley , M. H. Saboo Siddik College of Engineering; Samad Ansari, M. H. Saboo Siddik College of Engineering; Nehal Ansari, M. H. Saboo Siddik College of Engineering; Umair Shaikh, M. H. Saboo Siddik College of Engineering

Keywords:

Machine learning, Weather forecasting, Weather Prediction, Farming, Agricultural Sector, Loans, Suicides, Farmer Debt, Algorithms

Abstract

The premise of our paper is with the help of Machine Learning, we aim to develop a comprehensive rating system using the predicted outcome of weather prediction and an individual's credit history. With our system, an individual could find out the likelihood of the ability of fulfilling the loan undertaken, similarly financial institutions can use the system to find out the likelihood of the credit being repaid and the individual's eligibility of the maximum credit. The demand of agriculture sector has increased due to large population in India. It has become essential to consider effective technologies in agriculture. Through our portal, the idea is to develop an algorithm via machine learning would give a rating on a scale (0-10), which would determine the eligibility of the farmer being able to fulfil the loan. Through our portal, we will try to inform farmer and banker what all changes in weather are taken place and climate of all the region. Our portal uses the past forecasted raw data of few year to predict the recent forecast.

Other Details

Paper ID: IJSRDV6I21764
Published in: Volume : 6, Issue : 2
Publication Date: 01/05/2018
Page(s): 2789-2792

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