karagul Goto Github PK

cqf-project

cqf-projects

cqf_course

files from the CQF Course

cqf_final_project

Repository for my CQF Final Project

cqf_repo

cqf projects and exercises

cqffa

R package for CQF final project

cqfproject

CDS Kth to Default Basket Pricer

cqfproject-1

Main.Py does calibration of LMM to Caps and Swaptions

cqfproject-2

cqfprojectcvasection

CVA for Interest Rate Swap

credit-analysis-enhancement---geospatial

Enhancing Credit analysis using Geospatial techniques. The predictive model is built on logistic regression and decision tree algorithms and produces an estimated default probability of the applicant. Models are built on normalised data to cover all possible scenarios from real life. The predictive probability will determine good and bad customers by classifying them into four categories. The output from predictive models is used on tableau to generate business dashboard. Models ability to classify and performance measurements were measured by using statistical metrics: Gini, KS and AUROC.

credit-card-approval-using-machine-learning

Commercial banks receive a lot of applications for credit cards. Many of them get rejected for many reasons, like high loan balances, low income levels, or too many inquiries on an individual's credit report, for example. Manually analyzing these applications is mundane, error-prone, and time-consuming (and time is money!). Luckily, this task can be automated with the power of machine learning and pretty much every commercial bank does so nowadays. In this project, we will build an automatic credit card approval predictor using machine learning techniques, just like the real banks do.

credit-card-attrition

Analysis of a bank dataset to predict credit card attrition.

credit-card-customer-acquisition-risk-analytics-

CredX is a leading credit card provider that gets thousands of credit card applicants every year. But in the past few years, it has experienced an increase in credit loss. The CEO believes that the best strategy to mitigate credit risk is to ‘acquire the right customers’. In this project, you will help CredX identify the right customers using predictive models. Using past data of the bank’s applicants, you need to determine the factors affecting credit risk, create strategies to mitigate the acquisition risk and assess the financial benefit of your project.

credit-card-default

Conducted exploratory data analysis & built predictive model of credit card default rate from 25 variables through R programming, using methods like linear and logistic regression, Lasso, K-means Clustering, Principal Component analysis, factor analysis & machine learning techniques. Defined performance metrics and Improved OOS accuracy of predictions by nearly 3%. Translated complex data insights creatively

credit-card-risk-dataset

Comparing Binary Classification and Numerical Prediction Models While Pricing New Information

credit-card-risk-prediction

This model predicts the risk of your credit account based on the set of values enteres

credit-classifier

German Credit Classifier

credit-default

Performed EDA and Model Building on predicting credit card defaulters in the Taiwan. Used Logistic Regression, Random Forest, and Boosting Algorithms and evaluated them based on ROC and AUC metrics.

credit-default-

This project is to predict the rate of return and the probability of default using Python

credit-default-swap-search-engine

Credit Default Swap Search Engine is an efficient search engine based on RESTful APIs to allow you to search and navigate historical mentions of credit default swaps all the way from 2004 - 2017.

credit-default-swaps

A series of applications for pricing CDSs

credit-metric-scraping

Python- Uses HTTP requests to automate the retrieval and graphing of credit metrics for a list of stocks

credit-pricing

Repository for the strategic credit reporting project.

credit-risk

Credit Risk - IRB Model Validation - BASEL Requirement

credit-risk-1

Decision tree and svm model-to predict expected loss

credit-risk-2

The issue in hand pertains building a model that will help to predict loan defaults form past data. Challenges that were addressed include Cleaning of data, Handling of unbalanced data, Buliding of various classfication models and Evaluation comparitive performances

credit-risk-3

Modeled default intensities with CIR model by R package “sde”; priced debatable zero-coupon bonds

credit-risk-analysis

To identify the right customers using predictive models. Using past data of the bank’s applicants to determine the factors affecting credit risk and to create strategies to mitigate the acquisition risk and assess the financial benefits.

credit-risk-analysis-1

It shows the complete credit risk modeling picture, from preprocessing, through probability of default (PD), loss given default (LGD) and exposure at default (EAD) modeling, and finally finishing off with calculating expected loss (EL). I've learned how to prepare credit application data,apply machine learning and business rules to reduce risk and ensure profitability.