Data Science in the Banking Industry [Volume 1]
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Updated
Jul 16, 2020 - R
Data Science in the Banking Industry [Volume 1]
Prediction of people's future financial situation based on ML algorithms
Kaggle Give Me Some Credit Challenge
Este repositorio contiene el proceso completo de importación, exploración, limpieza y modelado; tiene como objetivo probar algunas librerías python en particular.
Build a machine learning model that can automatically assess loans with goal to predict client’s repayment abilities and speed up inspection filing without spending more money.
Home Credit is currently using various statistical methods and Machine Learning to make credit score predictions to ensure clients who are able to make payments are not rejected when applying for the loan.
A sufficiently decentralized anonoymous and on-chain KYC oracle
This is where I tried different Machine Learning methods to predict loan defaults
Using a credit score data from Kaggle, determine clients to provide loans and are less likely to default.
This GitHub repository contains the project I developed during my participation in the Virtual Internship Experience organized by Rakamin Academy in collaboration with Home Credit Indonesia.
A Credit Scoring Model is a statistical tool used by lenders to assess the creditworthiness of borrowers based on various factors.
Introduction to FinTech - Machine Learning Project
Exploratory data analysis of Prosper.com loans from 2005 - early 2014
Plugin for auto-calculation (no-code) for Weight of Evidence & Information Value (IV) in Dataiku DSS
Credit scoring is a statistical analysis performed by lenders and financial institutions to determine the creditworthiness of a person or a small owner-operated business. A higher score refers to a lower probability of default. The goal is to create a credit scoring system to automatically predict whether the loan will be repaid or defaulted.
This project employs ML algorithms for risk management to accurately predict credit defaults.
Add a description, image, and links to the credit-scoring topic page so that developers can more easily learn about it.
To associate your repository with the credit-scoring topic, visit your repo's landing page and select "manage topics."