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.

Loan Approval using Random Forest Algorithm

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.

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.

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

Predicting personal loan approval using machine learning techniques

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

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

Analyze a loan data approval dataset

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

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.

AI-Powered Mortgage Processing Crew automates mortgage applications using CrewAI agents for document validation, data extraction, and credit assessment. The Streamlit app accelerates loan decisions, generates detailed reports, and reduces manual work for faster, reliable processing.

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.

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.