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]
In this data science project, we will predict borrowers chance of defaulting on loans by building a default prediction model.
Prediction of people's future financial situation based on ML algorithms
Algoritmo de machine learning para análise de crédito desenvolvido em um curso na Alura
Kaggle Give Me Some Credit Challenge
Comparison of Machine Learning Methods on a Sample of Bank Customers When Estimating Probability of Default Status
NCTU ECM Deep Learning course final project
Innovative solution for credit scoring leveraging machine learning to predict credit risk using The International Bank for Reconstruction and Development (IBRD) Loans dataset.
Data analysis, preprocessing and custom ML model implementation for a credit score app.
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.
Tujuan utama Credit-Scoring Modeling ini adalah untuk melakukan prediksi terhadap suatu individu akan kemampuan mereka untuk melakukan pembayaran terhadap pinjaman/kredit yang diberikan
PD model using Logistic Regression in Python
This is pyspark based K-means clustering model which categorized Mobile Telecommunication customer based on their credit behaviors
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.
Developed a Credit Scoring Model utilizing Logistic Regression and decile methodology to empower data-driven lending decisions for banks, optimizing profitability and market penetration.
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."