Utilize autoencoders for anomaly detection and customer credit risk evaluation
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Updated
Mar 30, 2021 - HTML
Utilize autoencoders for anomaly detection and customer credit risk evaluation
This project explores credit card approval risk using machine learning. It covers data preprocessing, visualization, and comparisons of models like Logistic Regression and Random Forest. Key results show Random Forest and Logistic Regression achieving around 86% accuracy. 🚀📊
Comparison of models for credit risk purposes - logistic regression vs random forest. Empirical research.
A credit score classification is a system used by lenders and financial institutions to assess an individual's creditworthiness. A credit score is a numerical representation of a borrower's credit history, ranging from 300 to 850. The higher the score, the better the borrower's creditworthiness.
This is a Credit Analysis project developed by Felipe Solares da Silva and is part of his professional portfolio.
Course draft - bank corporate lending
By the data set from 'Give Me Some Credit' (2012), this work is to use it to illustrate some useful techniques in Credit Scoring Modelling, namely: GLM, SMOTE, CARET, CHAID, and MOB.
A short course on survival analysis applied to the financial industry
Fixed Income Analytics, Portfolio Construction Analytics, Transaction Cost Analytics, Counter Party Analytics, Asset Backed Analytics
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