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Loan Eligibility Prediction Using Machine Learning

 

 

Pinnamraju T S Priya, B. Bhanu Prakash

HOD,Assistant Professor, 2 MCA Final Semester,

Master of Computer Applications,

Sanketika Vidya Parishad Engineering College, Vishakhapatnam, Andhra Pradesh, India.

 

 

Abstract:

Loan eligibility prediction is a crucial process in the financial sector, helping banks and lending institutions assess the creditworthiness of applicants and reduce the risk of default. This project aims to automate and enhance the loan approval process by building a predictive model that determines whether a loan applicant is eligible for a loan based on their demographic and financial information. Using machine learning algorithms, such as Logistic Regression, Decision Trees, and Random Forest, the model is trained on historical loan data to identify patterns and correlations between applicant features (e.g., income, employment status, credit history, loan amount) and loan approval outcomes. The dataset preprocesses to handle missing values, encode categorical variables, and normalize numerical features. Performance metrics such as accuracy, precision, recall, and F1-score are used to evaluate the model's effectiveness. The results demonstrate that machine learning can significantly improve the efficiency and accuracy of loan eligibility assessments, leading to faster processing times and more consistent decision-making. This work highlights the potential of data-driven solutions in financial services and paves the way for smarter, automated lending systems.

Index Terms Loan Eligibility Prediction, Machine Learning, Classification Algorithm, Data Preprocessing, Feature Engineering, Support Vector Machine (SVM), Logistic Regression, Decision Tree, Model Evaluation, Accuracy Score, Financial Risk Assessment, Predictive Analytics, Python Programming, Loan Approval System, Supervised Learning

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