loan-approval

TrySelf Decision-Safe Loan Underwriting Lab A governance-first framework for credit decisions that treats uncertainty as a first-class citizen. Instead of shipping a black-box classifier, TrySelf builds a calibrated, abstention-aware approval pipeline you can defend to regulators

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.

Binary Classification Model for Credit Risk Assessment

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.

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.

An end-to-end credit risk prediction web application built with Python and Streamlit, featuring three machine learning models (Random Forest 93.34%, Decision Tree 89.09%, Logistic Regression 83.12%) trained on real-world financial data. Includes interactive risk assessment, feature importance analysis, and automated PDF report generation.

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 using Random Forest Algorithm

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.

ML pipeline predicting bank loan approval (97% accuracy) with a tested, production-ready Random Forest model, CI/CD, and reusable Python modules

Statistical analysis and feature engineering for 614 loan applications, including six hypothesis tests, 12 engineered features, and leakage-safe ML preparation.

Built an end-to-end supervised ML pipeline using KNN, Logistic Regression, and Naive Bayes to predict loan approval. Implemented Binary classification along with EDA, feature engineering & model evaluation (Precision, Recall, F1).

Data cleaning, encoding, scaling, outlier analysis, and feature selection for a 614-record loan approval dataset.

Loan Approval Business Process and Web Services parent project

Proof of concept unbiased loan calculator.

Interpretable loan approval analysis using logistic regression, exploratory data analysis, and Python.

Machine Learning-based Loan Eligibility Prediction System that evaluates applicant data and predicts loan approval eligibility using predictive analytics.