A decision-safety lab for loan approval: trains a baseline classifier, calibrates probabilities (ECE/Brier), sweeps confidence thresholds to build a coverage, quality frontier and outputs a defensible abstention policy (auto-decide vs review). Includes a Streamlit dashboard for report cards, triage UI, and data quality checks.

This is a Loan Approval Prediction Web App built with FastAPI (Backend) and HTML, CSS, JavaScript (Frontend). It predicts whether a loan application will be approved or not approved based on user input.

Loan Approval using Random Forest Algorithm

FoxTrend uses advanced machine learning to provide insightful stock price forecasts and comprehensive company information. The platform also offers additional features, such as car price prediction, loan approval assessment, and housing price estimation.

A Django-based Credit Approval System that intelligently determines loan eligibility and offers real-time insights based on past loan data and customer profiles using PostgreSQL.

Loan approval classification using Apache Spark and Big Data analytics with Logistic Regression and Random Forest.

Predict loan approval from applicant data using scikit-learn. Includes EDA, training pipeline, and a Streamlit demo app.

Credit Score Modelling: Perform a Weight of Evidence Logistic Regression Modelling (WoELR) to generate credit scorecard for loan approval.

Proof of concept unbiased loan calculator.

Loan Approval Business Process and Web Services parent project

This project focuses on building a machine learning model to predict the approval status of loan applications based on applicant information. It explores data preprocessing, visualization, feature engineering, and classification modeling.

Predicting personal loan approval using machine learning techniques

ai powered loan approval prediction system built using machine learning and streamlit. the project analyzes applicant financial data to predict loan approval probability, generate risk scores, provide model insights, and support data driven credit decision making through an interactive analytics dashboard.

Exploratory Data Analysis (EDA) of Loan Approval dataset using Python — uncovering trends, correlations and approval patterns.

A data-driven analysis of loan approval processes in the banking domain using SQL and Power BI. This project identifies key demographic, financial, and asset-based factors influencing loan approvals and provides actionable insights to optimize risk assessment and decision-making.

CreditWise Loan System is a machine learning–powered loan approval solution designed to help financial institutions make fast, accurate, and unbiased loan decisions. The system analyzes applicant financial, personal, and credit data to predict whether a loan should be Approved or Rejected before final human verification.

This project focuses on predicting loan approval for LoanTap’s personal loans using Logistic Regression. It covers EDA, feature engineering, and model evaluation, including classification metrics, ROC-AUC and precision-recall analysis. The study highlights key factors affecting creditworthiness to guide better lending and minimize default risk.

A web app built with React and Flask to predict loan approval using machine learning. Evaluates user inputs (income, loan amount, CIBIL score) and provides predictions, probability scores, and feature importance.

Intelligent loan approval system using Support Vector Machine (SVM) for automated credit assessment and loan status prediction