Classification Modeling: Probability of Default

Overview

Credit Risk Modeling in Python

Introduction:

If you've ever applied for a credit card or loan, you know that financial firms process your information before making a decision. This is because giving you a loan can have a serious financial impact on their business. But how do they make a decision? In this porject+, we will wrangle and prepare credit application data. After that, we will apply machine learning and business rules to reduce risk and ensure profitability. we will use two data sets that emulate real credit applications while focusing on business value.

So, what exactly is credit risk?

  • The possibility that someone who has borrowed money will not repay it all
  • Calculated risk di(erence between lending someone money and a government bond
  • When someone fails to repay a loan, it is said to be in default
  • The likelihood that someone will default on a loan is the probability of default (PD)

Expected loss

  • The dollar amount the firm loses as a result of loan default
  • Three primary components:
    • Probability of Default (PD): is the likelihood someone will default on a loan.
    • Exposure at Default (EAD): is the ratio of the exposure against any recovery from the loss.
    • Loss Given Default (LGD): is the ratio of the exposure against any recovery from the loss.
Formula for expected loss:

Expected loss= PD * EAD * LGD

Dataset

For modeling probability of default we generally have two primary types of data available:

  • Application data: which is data that is directly tied to the loan application like loan grade.
  • Behavioral data: which describes the recipient of the loan, such as employment length.

The data we will use for our predictions of probability of default includes a mix. This is important because application data alone is not as good as application and behavioral data together. Included are two columns which emulate data that can be purchased from credit bureaus. Acquiring external data is a common practice in most organizations. These are the columns available in the data set. Some examples are: personal income, the loan amount's percentage of the person's income, and credit history length. Consider the percentage of income. This could affect loan status if the loan amount is more than their income, because they may not be able to afford payments.

Owner
Aktham Momani
Data Scientist ▪️ Machine Learning ▪️ Advanced Analytics ▪️ Customer Experience
Aktham Momani
Repo for our ICML21 paper Unsupervised Learning of Visual 3D Keypoints for Control

Unsupervised Learning of Visual 3D Keypoints for Control [Project Website] [Paper] Boyuan Chen1, Pieter Abbeel1, Deepak Pathak2 1UC Berkeley 2Carnegie

Boyuan Chen 34 Jul 22, 2022
DynaTune: Dynamic Tensor Program Optimization in Deep Neural Network Compilation

DynaTune: Dynamic Tensor Program Optimization in Deep Neural Network Compilation This repository is the implementation of DynaTune paper. This folder

4 Nov 02, 2022
Code implementation from my Medium blog post: [Transformers from Scratch in PyTorch]

transformer-from-scratch Code for my Medium blog post: Transformers from Scratch in PyTorch Note: This Transformer code does not include masked attent

Frank Odom 27 Dec 21, 2022
Reviatalizing Optimization for 3D Human Pose and Shape Estimation: A Sparse Constrained Formulation

Reviatalizing Optimization for 3D Human Pose and Shape Estimation: A Sparse Constrained Formulation This is the implementation of the approach describ

Taosha Fan 47 Nov 15, 2022
PyTorch implementation of "MLP-Mixer: An all-MLP Architecture for Vision" Tolstikhin et al. (2021)

mlp-mixer-pytorch PyTorch implementation of "MLP-Mixer: An all-MLP Architecture for Vision" Tolstikhin et al. (2021) Usage import torch from mlp_mixer

isaac 27 Jul 09, 2022
Imaging, analysis, and simulation software for radio interferometry

ehtim (eht-imaging) Python modules for simulating and manipulating VLBI data and producing images with regularized maximum likelihood methods. This ve

Andrew Chael 5.2k Dec 28, 2022
Dataset for the Research2Clinics @ NeurIPS 2021 Paper: What Do You See in this Patient? Behavioral Testing of Clinical NLP Models

Behavioral Testing of Clinical NLP Models This repository contains code for testing the behavior of clinical prediction models based on patient letter

Betty van Aken 2 Sep 20, 2022
Negative Sample is Negative in Its Own Way: Tailoring Negative Sentences forImage-Text Retrieval

NSGDC Some codes in this repo are copied/modified from opensource implementations made available by UNITER, PyTorch, HuggingFace, OpenNMT, and Nvidia.

Zhihao Fan 2 Nov 07, 2022
Code and results accompanying our paper titled Mixture Proportion Estimation and PU Learning: A Modern Approach at Neurips 2021 (Spotlight)

Mixture Proportion Estimation and PU Learning: A Modern Approach This repository is the official implementation of Mixture Proportion Estimation and P

Approximately Correct Machine Intelligence (ACMI) Lab 23 Dec 28, 2022
Auxiliary data to the CHIIR paper Searching to Learn with Instructional Scaffolding

Searching to Learn with Instructional Scaffolding This is the data and analysis code for the paper "Searching to Learn with Instructional Scaffolding"

Arthur Câmara 2 Mar 02, 2022
a Pytorch easy re-implement of "YOLOX: Exceeding YOLO Series in 2021"

A pytorch easy re-implement of "YOLOX: Exceeding YOLO Series in 2021" 1. Notes This is a pytorch easy re-implement of "YOLOX: Exceeding YOLO Series in

91 Dec 26, 2022
Implementation of Research Paper "Learning to Enhance Low-Light Image via Zero-Reference Deep Curve Estimation"

Zero-DCE and Zero-DCE++(Lite architechture for Mobile and edge Devices) Papers Abstract The paper presents a novel method, Zero-Reference Deep Curve E

Tauhid Khan 15 Dec 10, 2022
An elaborate and exhaustive paper list for Named Entity Recognition (NER)

Named-Entity-Recognition-NER-Papers by Pengfei Liu, Jinlan Fu and other contributors. An elaborate and exhaustive paper list for Named Entity Recognit

Pengfei Liu 388 Dec 18, 2022
An unofficial personal implementation of UM-Adapt, specifically to tackle joint estimation of panoptic segmentation and depth prediction for autonomous driving datasets.

Semisupervised Multitask Learning This repository is an unofficial and slightly modified implementation of UM-Adapt[1] using PyTorch. This code primar

Abhinav Atrishi 11 Nov 25, 2022
The dataset of tweets pulling from Twitters with keyword: Hydroxychloroquine, location: US, Time: 2020

HCQ_Tweet_Dataset: FREE to Download. Keywords: HCQ, hydroxychloroquine, tweet, twitter, COVID-19 This dataset is associated with the paper "Understand

2 Mar 16, 2022
A python3 tool to take a 360 degree survey of the RF spectrum (hamlib + rotctld + RTL-SDR/HackRF)

RF Light House (rflh) A python script to use a rotor and a SDR device (RTL-SDR or HackRF One) to measure the RF level around and get a data set and be

Pavel Milanes (CO7WT) 11 Dec 13, 2022
KDD CUP 2020 Automatic Graph Representation Learning: 1st Place Solution

KDD CUP 2020: AutoGraph Team: aister Members: Jianqiang Huang, Xingyuan Tang, Mingjian Chen, Jin Xu, Bohang Zheng, Yi Qi, Ke Hu, Jun Lei Team Introduc

96 May 30, 2022
Residual Dense Net De-Interlace Filter (RDNDIF)

Residual Dense Net De-Interlace Filter (RDNDIF) Work in progress deep de-interlacer filter. It is based on the architecture proposed by Bernasconi et

Louis 7 Feb 15, 2022
Official implement of Evo-ViT: Slow-Fast Token Evolution for Dynamic Vision Transformer

Evo-ViT: Slow-Fast Token Evolution for Dynamic Vision Transformer This repository contains the PyTorch code for Evo-ViT. This work proposes a slow-fas

YifanXu 53 Dec 05, 2022
CONditionals for Ordinal Regression and classification in PyTorch

CONDOR pytorch implementation for ordinal regression with deep neural networks. Documentation: https://GarrettJenkinson.github.io/condor_pytorch About

7 Jul 25, 2022