International Journal For Multidisciplinary Research

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Loan Prediction Using Machine Learning Algorithms

Author(s) Sanika Narayanpethkar, S. Chandana, M. Rishitha, Preethi Jeevan
Country India
Abstract Loans are a major prerequisite in the present-day world. By this, as it were, Banks get a major
portion of the entire benefit. It is advantageous for understudies to oversee their instruction and
living costs, and for individuals to purchase any kind of extravagance like houses, cars, etc. But
when it comes to choosing whether the applicant's profile is pertinent to be allowed a credit
or not. Banks must see many aspects. Giving credit is the most commerce of banks. A
noteworthy parcel of the bank's income comes straightforwardly from benefits from credits.
Indeed, on the off chance that the bank favors the advance after the confirmation and
certification relapses prepare, it cannot be beyond any doubt that the chosen candidate is the
genuine candidate. Doing this handle physically requires unused time. Able to anticipate whether.
a specific candidate is secure, and the whole confirmation handle is automatized with the machine.
fashion. After the presentation of innovation to the world everything began to be automatized.
With the introduction of machine learning, the world has seen a modern side of innovation.
When the diversion of the forecast is played, machine learning gives a few of the most excellent.
models to test the information for the exactness of the result. A few of them are broadly utilized.
Machine learning calculations are Decision trees, support vector machine, logistic regression,
random forest, etc.
Keywords Decision trees, support vector machine, logistic regression, random forest, data pre-processing, training and testing data
Field Engineering
Published In Volume 5, Issue 3, May-June 2023
Published On 2023-05-12
Cite This Loan Prediction Using Machine Learning Algorithms - Sanika Narayanpethkar, S. Chandana, M. Rishitha, Preethi Jeevan - IJFMR Volume 5, Issue 3, May-June 2023. DOI 10.36948/ijfmr.2023.v05i03.2970
DOI https://doi.org/10.36948/ijfmr.2023.v05i03.2970
Short DOI https://doi.org/gr77pt

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