Credit Risk Modelling & Credit Scoring with Machine Learning
Building credit risk assessment model and predicting credit score with logistic regression, random forest, and KNN
3.92 (40 reviews)
1 816
students
3.5 hours
content
Jul 2024
last update
$19.99
regular price
What you will learn
Learn how to build credit risk assessment model using logistic regression
Learn how to build credit risk assessment model using random forest
Learn how to build credit risk assessment model using K Nearest Neighbor
Learn how to predict credit score using decision tree regressor
Learn how to find correlation between debt to income ratio and default rate
Learn how to analyze relationship between loan intent, loan amount, and default rate
Learn how to analyze relationship between outstanding debt and credit score
Learn how to deploy machine learning model using Gradio
Learn the basic fundamentals of credit risk analysis, technical limitations in credit risk modelling, and credit risk assessment use cases in banking industries
Learn how credit risk assessment models work. This section will cover data collection, preprocessing, feature selection, train test split, and model training
Learn about factors that affect credit score, such as payment history, credit utilization ratio, length of credit history, outstanding debt, and credit mix
Learn how to evaluate the accuracy and performance of the model using precision, recall, and cross validation
Learn how to find and download credit dataset from Kaggle
Learn how to clean dataset by removing missing values and duplicates
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6094499
udemy ID
26/07/2024
course created date
31/07/2024
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