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.

Proof of concept unbiased loan calculator.

Loan Approval Business Process and Web Services parent project

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

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.

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

Predicting personal loan approval using machine learning techniques

A Cryptocurrency based on Cryptonote Technology that can easily run on your WordPress website, and designed to give you financial freedom

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.

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

Loan approval prediction means using credit history data of the loan applicants and algorithms to build an intelligent system that can determine loan approvals.

Predicts loan approval using demographic and financial data. Includes data cleaning, EDA, feature engineering, and ML models (Logistic Regression, Random Forest). Achieved ~79% accuracy. Full notebook, predictions, and insights documented.

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.

Loan Approval Dashboard using SQL, Python, and Power BI

Loan Approval Prediction Web App (Flask + ML)

🏦 Loan Approval Prediction App using Python, Scikit-learn & Streamlit — includes full EDA, feature engineering, model training, and deployment as an interactive web app.

💳 Manage customer loans and credit approvals easily with this Django-based REST API, featuring automated calculations and loan eligibility checks.