Welcome to Credit Risk Modelling & Credit Scoring with Machine Learning course. This is a comprehensive project based course where you will learn step by step on how to build a credit risk assessment and credit scoring model using logistic regression, random forest, and K Nearest Neighbors. This course is a perfect combination between machine learning and credit risk analysis, making it an ideal opportunity to level up your data science skills while improving your technical knowledge in risk management. The course will be mainly concentrating on three major aspects, the first one is data analysis where you will explore the credit dataset from multiple angles, the second one is predictive modeling where you will learn how to build credit risk assessment and credit scoring system using machine learning, and the third one is to evaluate the accuracy and performance of the model. In the introduction session, you will learn the basic fundamentals of credit risk analysis, such as getting to know its use cases in banking and financial industries, getting to know more about machine learning models that will be used, and you will also learn about technical challenges and limitations in credit risk modeling. Then, in the next section, you will learn how credit risk assessment model works.This section will cover data collection, data preprocessing, feature selection, splitting the data into training and testing sets, model selection, model training, assessing credit risk, assigning credit score, model evaluation, and model deployment. Afterward, you will also learn about several factors that contribute to credit score, for example like payment history, credit utilization ratio, length of credit history, outstanding debt, credit mix, and new credit inquiries. After you have learnt all necessary knowledge about credit risk analysis, we will start the project. Firstly you will be guided step by step on how to set up Google Colab IDE. In addition to that, you will also learn how to find and download credit dataset from Kaggle. Once everything is all set, we will enter the first project section where you will explore the credit dataset from various angles, not only that, you will also visualize the data and try to identify the patterns. In the second part, you will learn step by step on how to build credit risk assessment models and credit scoring systems using logistic regression, random forest, and K Nearest Neighbour. Meanwhile, in the third part, you will learn how to evaluate the accuracy and performance of the model using several methods like cross validation, precision, and recall. Lastly, at the end of the course, we will deploy this machine learning model using Gradio and we will conduct testing to make sure that the model has been fully functioning and produces accurate results.

First of all, before getting into the course, we need to ask ourselves this question: why should we build a credit risk assessment model and credit scoring system? Well, here is my answer. In today's financial ecosystem, accurate credit risk assessment and scoring are essential for banks and financial institutions to make informed lending decisions. With the increasing complexity of financial markets and customer behavior, traditional methods alone may not suffice. By utilizing the power of machine learning algorithms and data-driven insights, we can enhance decision-making accuracy, mitigate credit risks effectively, and optimize lending practices. Moreover, mastering the skills in building sophisticated credit risk models and scoring systems can potentially lead to numerous career opportunities in financial technology sectors.

Below are things that you can expect to learn from this course:

• Learn the basic fundamentals of credit risk analysis, technical challenges and limitations in credit risk modeling, and credit risk assessment use cases in banking and financial industries

• Learn how credit risk assessment models work. This section will cover data collection, preprocessing, feature selection, train test split, model selection, model training. assessing credit risk and score, model evaluation, and model deployment

• Learn about factors that affect credit score, such as payment history, credit utilization ratio, length of credit history, outstanding debt, credit mix, and new credit inquiries

• Learn how to find and download credit dataset from Kaggle

• Learn how to clean dataset by removing missing values and duplicates

• 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 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 analyze relationship between outstanding debt and credit score

• Learn how to predict credit score using decision tree regressor

• Learn how to deploy machine learning model using Gradio

• Learn how to evaluate the accuracy and performance of the model using precision, recall, and cross validation