Predict who possible Defaulters are for the Consumer Loans Product

A predictive system for automating the loan approval process.

This project demonstrates the implementation of a loan approval system that utilizes MongoDB for distributed data storage and management, and PyMongo for database operations. The project aims to automate the assessment of loan eligibility using customer details from online applications.

Used machine learning models to predict spam mails, loan, diabetes etc... also done web scraping, sentiment analysis, EDA. Have fun.

This project is a Streamlit web application that predicts whether a loan will be approved or rejected based on various input features provided by the use

All of these previous analyses were done in SAS. I transitioned them over to Python to practice the language.

Loan Status Prediction using Machine learning, Python

In this project, I analyzed the prosper load data, studied the trends and concluded that monthly income, loan amount and borrower's rate significantly affect the prosper rating and a good predictors of delinquency.

This project about deployment machine learning model Loan Approval Prediction using Streamlit

This repo contain Machine learning Projects

The "Comprehensive Machine Learning Framework in R" is an all-inclusive toolkit for data preprocessing, WOE calculation, and model evaluation, designed for robust machine learning applications and equipped with cross-validation and extensibility features.

This repository contains the codebase and resources for a machine learning-based project aimed at predicting loan eligibility for individuals. The project utilizes various algorithms and data preprocessing techniques to build predictive models that assess the likelihood of an applicant being eligible for a loan based on historical data.

Loan Prediction using machine learning

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.

Data Analysis and prediction on Kaggle dataset: House Loan Data Analysis-Deep Learning

Plataforma web para calcular e organizar empréstimos e investimentos pessoais

Developed a loan approval amount prediction model utilizing Decision Tree Regressor and Random Forest Regressor and Conducted exploratory data analysis (EDA) to identify key features.

Click below to visit the swagger docs of the website

Explaratory data analysis on the loan dataset. ML model to automate the loan eligibility process (real-time) based on customer detail provided while filling out online application forms.

The project entails building a model that predicts if someone who seeks a loan might be a defaulter or a non-defaulter. We have several independent variables like, checking account balance, credit history, purpose, loan amount etc. Ensemble Models such as Bagging, AdaBoosting, GradientBoost, XGBoost, Random Forest etc will be used for the modelling