Loan approval prediction in python. By leveraging popular Python libraries such as NumPy, Pandas, Scikit-learn (sklearn), and Seaborn, this project provides an end-to-end solution for loan status prediction. Model Results indicate that machine learning-based credit risk prediction systems can significantly enhance decision-making processes in financial. Jun 20, 2025 · During my internship at Celebal Technologies, I was tasked with building a loan approval prediction model to help financial institutions make faster, data-driven decisions. A loan approval prediction system using machine learning, featuring a web-based interface with Python (Flask) backend and HTML frontend for classifying loan approval based on applicant data. By analyzing historical data of past loan applications, we can train a machine learning model to recognize patterns and predict the likelihood of approval for new applications. Loan approval prediction means using credit history data of the loan applicants and algorithms to build an intelligent system that can determine loan approvals. It is beneficial for students to manage their education and living expenses, and for individuals to purchase various luxuries, such as houses and cars. Example 1: Loan Approval Prediction using Logistic Regression Sep 9, 2024 · Key Points Covered: Introduction to Loan Approval Prediction: Learn about the importance of predicting loan approvals in the banking industry. The best-performing model, the Stacked Classifier, achieved an accuracy of 93. . pim17 vg1oi bxxee o9p jx vhmdm5 o7o wtceafm ht ym