A program to take in loan level data and create a model which can predict probability of default
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Updated
May 21, 2020 - Python
A program to take in loan level data and create a model which can predict probability of default
Supporting material for the Open Risk Academy course: "Loan Level Templates Using Python"
📊 Predict loan defaults reliably using a hybrid ensemble of machine learning models for enhanced accuracy and real-time insights in credit risk assessment.
ETL pipeline for loan data — missing value imputation, IQR outlier removal, and format normalization.
This program is mainly used to figure out loan schedules for clients and can be used both by clients and loan providers to both show and save a document that has the loan schedule on it.
Predicting Loan Defaulters using various Classification Algorithms using Python (Numpy, Pandas, Sklearn, Matplotlib, Seabon)
🏦 Predict loan approval outcomes using this Machine Learning app with user authentication, analytics, and real-time insights for informed decisions.
This is a one layer neural network that predict whether a loan will be defaulted.
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