The open-source lending infra

Prediction of loan defaulter based on more than 5L records using Python, Numpy, Pandas and XGBoost

Applying machine learning to predict loan charge-offs on LendingClub.com

Loan Calculator a small web application encoded in HTML, PHP, JS, and CSS. If you want to earn from BANK NICHE then you can use Loan Calculator script.

openNPL is an open source platform for the management of non-performing loans

Explanatory Data Analysis and ML model building using Apache Spark and PySpark

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.

Plotly showing total loan amount from each state in US, Bubble Map

Academic project for Advances in Data Science and Architecture course

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.

Simple loan payment calculator & stats

Exploratory data analysis of datasets available in Kaggle.(IPL dataset, Zomato dataset, Loan dataset, Telecom customer churn dataset)

This project is on a data set from Prosper, which is America’s first marketplace lending platform, with over $7 billion in funded loans. This data set contains 113,937 loans with 81 variables on each loan, including loan amount, borrower rate (or interest rate), current loan status, borrower income, borrower employment status, borrower credit hi…

Knowledge Aggregators & Generators.

Our team (Versed Chimpanzee) came first among 340 people and 148 registered teams (119 teams did submission) in TUM Analytics Cup 2022 challenge sponsored by Siemens Advanta Consulting and organized by TUM Informatics Decision Sciences & Systems Department.

This repo contains the materials for a workshop that shows how to gains insights and visualize data for a credit score/loan bank transactions use case.

Project idea – The idea behind this ML project is to build a model that will classify how much loan the user can take. It is based on the user’s marital status, education, number of dependents, and employments. We can build a linear model for this project.

Analysis of Loan Data from Prosper

Minimization of risk and maximization of profit on behalf of the bank

Supporting material for the Open Risk Academy course: "Loan Level Templates Using Python"