Inspiration taken from the course:https://udemy.com/course/credit-risk-modeling-in-python Setup Download the dataset from:https://www.kaggle.com/wordsforthewise/lending-club Copy the dataset "accepted_2007_to_2018Q4.csv" to the "data/raw" folder ...
An end-to-endCredit Risk Modellingapp using machine learning, deployed withStreamlit. Predict the likelihood of a borrower defaulting based on financial history, income, loan details, and behavioral metrics. Built as part of a portfolio project to demonstrate data science and model deployment skills....
The problem of accurately classifying credit scores is critical for financial institutions to assess individual creditworthiness and effectively manage credit risk. Traditional methods often face limitations when processing large datasets, resulting in lower accuracy and longer processing time. To address this...
We then repeat similar numerical studies in a one-factor Gaussian copula model. We also numerically benchmark our method to other computational methods. Keywords: portfolio credit risk; intensity-based models; factor models, Value-at-Risk, conditional inde- pendent dependence modelling, saddlepoint-...
Introduction to Predictive Analytics using Python from University of Edinburgh Successfully Evaluating Predictive Modelling from University of Edinburgh★★☆☆☆(1) Statistical Predictive Modelling and Applications from University of Edinburgh Predictive Analytics using Machine Learning from University of Edinburgh...
Credit Analyst Job In Portland, OR We are seeking a a candidate with excellent customer service skills to oversee their own portfolio of customer accounts. Conductingcreditrisk reviews to determine if accounts should be given a line ofcreditCollections and disputes Skills required: Excellent Customer ...
While it is standard fare in credit risk modelling, it is under-utilized in other settings though its formulation makes it generic enough for use in other domains too. The WOE method primarily aims to bin variables into buckets that deliver the most information to a potential classification ...
The underlying Python library is split into a number of major modules: Utils - These are utility functions used to assist you with modelling a security. These include dates (Date), calendars, schedule generation, some finance-related mathematics functions and some helper functions. Market - These...
The weight-of-evidence (WOE) method of evaluating strength of predictors is an understated one in the field of analytics. While it is standard fare in credit risk modelling, it is under-utilized in other settings though its formulation makes it generic enough for use in other domains too. ...
The underlying Python library is split into a number of major modules: Utils - These are utility functions used to assist you with modelling a security. These include dates (Date), calendars, schedule generation, some finance-related mathematics functions and some helper functions. ...