Building credit risk assessment model and predicting credit score with logistic regression, random forest, and KNN
Sub Category
- Financial Modeling & Analysis
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Objectives
- 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
Pre Requisites
- No previous experience in credit risk modelling is required
- Basic knowledge in Python and finance
FAQ
- Q. How long do I have access to the course materials?
- A. You can view and review the lecture materials indefinitely, like an on-demand channel.
- Q. Can I take my courses with me wherever I go?
- A. Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don't have an internet connection, some instructors also let their students download course lectures. That's up to the instructor though, so make sure you get on their good side!
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