Credit risk analysis for credit card applicants
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Updated
Dec 7, 2023 - Jupyter Notebook
Credit risk analysis for credit card applicants
Predicting how much loan will be approved
Application to finance
Predicting the ability of a borrower to pay back the loan through Traditional Machine Learning Models and comparing to Ensembling Methods
The aim is to understand which are the key factors for a certain level of credit risk to occur. In addition, some ML models capable to predict the credit risk level for a company in an year - given past years data - have been built and compared.
In 2019, more than 19 million Americans had at least one unsecured personal loan. Personal lending is growing at an extremely fast rate, and FinTech firms need to go through an organize large amounts of data in order to optimize lending. Python will be used to evaluate several machine learning models to predict credit risk. Algorithms such as Ra…
Credit risk poses a classification problem that’s inherently imbalanced. Using a dataset of historical lending activity from a peer-to-peer lending services company, build a model that can identify the creditworthiness of borrowers.
Reverse engineering of the FICO algorithm
Credit risk analysis using scikit-learn and imbalanced-learn.
CSCI316 Group assignment 1
The project involved developing a credit risk default model on Indian companies using the performance data of several companies to predict whether a company is going to default on upcoming loan payments.
I'll use various techniques to train and evaluate a model based on loan risk. I will use a dataset of historical lending activity from a peer-to-peer lending services company to build a model that can identify the creditworthiness of borrowers.
Credit Risk Analysis - PD Modelling
Our group chose this question to bring attention to the little knowledge that young loan applicants have. Based on our findings in our models we explore: Which age group is the least likely to apply for loans? Which group is most likely to default on loans?
All Main Projects
Machine Learning pipelines are deployed to accomplish the objective of credit risk analysis.
This work is about "Loan Default Prediction" which is one of the most important and critical problems faced by banks and other financial institutions as it has a huge effect on profit
Credit risk analysis determines a borrower's ability to meet debt obligations and the lender's aim when advancing credit. The goal is to identify patterns that indicate if a person is unlikely to repay the loan or labeled as a bad risk through automated machine learning algorithms.
Credit Risk Analysis using Python
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