• Savita Sangam
• Chaitanya R Dumbre
• Shripad Anil Patil
• Soham S Chile

This study explores the application of support vector machine (SVM) in predicting loan approval outcomes, aiming to revolutionize the efficiency and objectivity of financial decision-making processes. Traditionally, loan approval has relied on manual methods fraught with inefficiencies and biases, prompting the adoption of machine learning techniques for automation and enhanced predictive accuracy.The paper outlines a comprehensive methodology encompassing data collection, pre-processing, exploratory data analysis model training, evaluation, and the development of a predictive system. Emphasis is placed on acquiring a diverse and representative loan data set, followed by rigorous pre-processing to cleanse and transform the data. Exploratory data analysis (eda) uncovers hidden relationships and trends, providing valuable insights for stakeholders.Support vector machine is selected as the primary predictive model due to its capability to delineate complex decision boundaries. The SVM model undergoes training using various kernel functions and hyper parameters, with cross-validation techniques ensuring robustness and generalizability. Evaluation metrics such as accuracy, precision, recall, f1-score, and roc-auc are utilized to assess the SVM model's efficacy on both training and test datasets. Additionally, a predictive system is developed to enable real-time loan approval predictions, further streamlining workflows and expediting loan approval processes.The abstract concludes by underlining the potential of SVM in enhancing financial decision-making and setting the stage for future research in loan approval prediction. It highlights the importance of integrating data science, finance, and machine learning to create a more inclusive and sustainable financial landscape.

Machine Learning, Support Vector Machine, Loan Approval Prediction, Loan Data set, Loan eligibility, Data Collection , Data Pre-processing, Exploratory Data Analysis, Data Visualization, Model Evaluation, Predictive System

"Loan status prediction using machine learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 6, page no.14-20, June-2024, Available :http://www.jetir.org/papers/JETIRGH06003.pdf

"Loan status prediction using machine learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 6, page no. pp14-20, June-2024, Available at : http://www.jetir.org/papers/JETIRGH06003.pdf