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ISSN Approved Journal No: 2456-3315 | Impact factor: 8.14 | ESTD Year: 2016
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Impact Factor : 8.14
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| Paper Title: | An approach for prediction of loan approval using Supervised Algorithm in Machine learning algorithm. |
| Authors Name: | Kondreddy Umesh Kumar Reddy , K. Tulasi Krishna kumar |
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IJRTI_185466
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| Published Paper Id: | IJRTI2303026 |
| Published In: | Volume 8 Issue 3, March-2023 |
| DOI: | |
| Abstract: | In the banking industry, loans are the primary source of income for banks, as they earn from the interest charged on these loans. The profitability of a bank largely depends on the timely repayment of loans by customers. Therefore, it is crucial for banks to predict loan defaulters in order to minimize their Non-Performing Assets (NPA). Several methods have been proposed to address this problem, but it is essential to compare these methods to determine their effectiveness in predicting loan defaulters accurately. One popular approach for predictive analytics is the logistic regression model. To study loan default, data was collected from Kaggle, and logistic regression models were used to predict the likelihood of loan default. Performance measures such as sensitivity and specificity were computed to compare the models. The results indicated that the model that included personal attributes such as age, purpose, credit history, credit amount, and credit duration, in addition to checking account information (which reflects a customer's wealth), produced marginally better results. This suggests that a customer's other attributes should also be taken into account while calculating the probability of default on loans to accurately predict loan defaulters. By using the logistic regression approach, banks can identify the right customers to grant loans to by evaluating their likelihood of default on loans. This approach highlights the importance of assessing all aspects of a customer's profile, rather than just their wealth, when making credit decisions and predicting loan defaulters. |
| Keywords: | Prediction, component, Overfitting, banking system, credit line, interest, loan defaulters, Non-Performing Assets, predictive analytics, logistic regression model, Kaggle. |
| Cite Article: | "An approach for prediction of loan approval using Supervised Algorithm in Machine learning algorithm.", International Journal of Science & Engineering Development Research (www.ijrti.org), ISSN:2455-2631, Vol.8, Issue 3, page no.154 - 160, March-2023, Available :http://www.ijrti.org/papers/IJRTI2303026.pdf |
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2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator |
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Published Paper ID: IJRTI2303026
Registration ID:185466
Published In: Volume 8 Issue 3, March-2023
DOI (Digital Object Identifier):
Page No: 154 - 160 Country: Visakhapatnam , Andhra Pradesh, India Research Area: Master of Computer Application Publisher : IJ Publication Published Paper URL : https://www.ijrti.org/viewpaperforall?paper=IJRTI2303026 Published Paper PDF: https://www.ijrti.org/papers/IJRTI2303026 |
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ISSN: 2456-3315
Impact Factor: 8.14 and ISSN APPROVED,
Journal Starting Year (ESTD) : 2016
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