Machine Learning - End to End Data Science Projects

A Classification Problem which predicts if a loan will get approved or not.

Predicts a chance of loan repayment based on historical data

Predicting Loan State with SVM method, preprocessing and feature selction

This project automates bank credit risk assessment using AI and machine learning models to predict loan defaults. It streamlines the credit process with predictive analytics, model evaluation, explainability (SHAP), and deployment readiness.

Predict if your loan will be accepted or not. This happens by using a labeled data for applicants who applied for a loan before, analyzing these data and using some classification models on it.

A demo pipeline of using Redis as an online feature store with Feast for orchestration and Ray for training and model serving

An example of using Redis + RedisAI for a microservice that predicts consumer loan probabilities using Redis as a feature and model store and RedisAI as an inference server.

This repository contains a machine learning-based predictive model for automating loan eligibility assessments. Using features such as demographic details, loan information, and credit history, the model predicts whether a loan should be approved or denied.

💳 End-to-end ML web app for loan approval prediction | Logistic Regression | 88% Accuracy | F1: 80.9% | Deployed on Streamlit

AI-powered Loan & CRM system with ML predictions, AI Loan Assistant, Flagged Loans, CRM dashboards, and Supabase integration (Heroku deployed).

Loan Prediction using Machine Learning algorithms

Loan eligibility prediction in Lasiandra Finance Inc. (LFI) using SAS studio.

What's up This project was mainly training my self on training ML models 🤖 and also to train on doing EDA 📜 to get the acceptance of the loan.

I’d be walking us through Loan prediction using some selected Machine Learning Algorithms.

Code developed for my AC classes, FEUP - MEIC (2021/22)

A Decision Tree Classifier was implemented to predict personal loan acceptance using a dataset of 5,000 customers. Key features included income, education, mortgage, and credit card usage. The model achieved 97% accuracy, with 92% precision and 76% recall for positive loan predictions, validated using a classification report and confusion matrix.

This repo is for derived from a competition from analytics vidhya for predicting loan using the data given.

贷款计算器 & 还贷模拟器。支持商贷/公积金/组合贷、等额本息/等额本金/自由还款三种方式，可多次利率变更和提前还款并按天精确计息。提供还款模拟（智能分析最优方案）、机会成本分析（提前还款 vs 投资理财）、多方案对比、数据可视化。内置 LPR 利率表，数据全部本地运算及存储，PWA 离线可用。