• G.Rohini Phaneendra Kumari
• K Venkateswara Rao
• Vasudha Chavali
• Boyapati Triveni
• Pentareddy Divya, Maddukuri pooja naga sai Lakshmi

Machine learning algorithms are revolutionizing forms in all areas counting; real-estate, security, bioinformatics, moreover it is exceptionally valuable in agribusiness industry like gold credits , trim credits conjointly instruction advance is there for students and the budgetary industry. This paper presents six (6) machine learning calculations (Arbitrary Forest, Random woodland classifier, Random woodland regressor, Nave bayes, sum and Calculated Relapse) for anticipating credit qualification. The models were prepared on the authentic dataset 'Loan Qualified Dataset,' accessible on Kaggle and authorized beneath Database Substance Permit (DbCL) v1.0. The dataset was handled and analyzed utilizing Python programming libraries on Kaggles Jupyter Scratch pad cloud environment. Our inquire about result appeared high-performance exactness, with the Arbitrary timberland calculation having the most noteworthy score of 95.55% and Calculated relapse with the least score of 80%. Our Models beated two of the three advance forecast models found within the writing in terms of precision-recall and precision.

KNN, SVM, Sacking and Boosting procedures, Productive ML Calculations, Advanceendorsement forecast.

"BANK LOAN ELIGIBILITY USING: MACHINE LEARNING-DRIVENSOFTWAREENGINEERING", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 11, page no.109-121, November-2024, Available :http://www.jetir.org/papers/JETIRGO06011.pdf

"BANK LOAN ELIGIBILITY USING: MACHINE LEARNING-DRIVENSOFTWAREENGINEERING", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 11, page no. pp109-121, November-2024, Available at : http://www.jetir.org/papers/JETIRGO06011.pdf