Loan Default Prediction with Machine Learning course overview
Machine Learning is about making predictions using data. In this course, you’ll learn to use basic Machine Learning skills to predict which customers are likely to default on their loans.
Once your model classifies each loan, you’ll learn to visualize your predictions to see how well the model performed.
Predicting defaults and creditworthiness is hugely valuable to risk management and pricing decisions.
We will cover the entire Machine Learning process in Python, reinforcing concepts from Python fundamentals. You’ll learn how to create predictive classification models, fine-tune and test your process, and importantly how to interpret the results.
Machine Learning is a hot topic in the world of data, and in particular data science. At a basic level, Machine Learning is not as complex as it may sound. If you’ve ever done linear regression, you may be surprised to learn that you’ve already taken steps towards this exciting world.
Join Andrew for a comprehensive step-by-step walkthrough of the Machine Learning process.
Loan Default Prediction with Machine Learning objectives
Upon completing this course, you will be able to:
- Explain and discuss the main steps of the Machine Learning cycle
- Load and clean data into a python notebook
- Use Exploratory Data Analysis to identify variables with likely predictive power
- Use Feature Engineering to transform data into a more useful format
- Build a logistic regression and random forest prediction model
- Evaluate and compare model performance using common evaluation metrics
Loan Default Prediction with Machine Learning is a course from CFI’s BIDA™ program
The Business Intelligence & Data Analyst (BIDA)™ program will take you on a highly interactive and hands-on journey through the world of data science and business intelligence.
From theory and interactive questions to applied Python workbooks, data science models, and interactive dashboards in Tableau, our instructors will walk you through countless real-world scenarios and exercises.
The program is ideal for students who want to learn how to analyze data more effectively, save time with automation, or be a general data pro at work. With a well-rounded knowledge of both BI and data science, learners are well-equipped to take on roles in Business Intelligence, Data Science, Data Analysis, Quantitative Analysis, and other data careers.
Who should take this course?
The Machine Learning cycle is one of the most foundational aspects of Data Science. Using this process, we can learn to make predictions using all types of data and variables. Anyone looking to make predictions in a practical Python environment should absolutely be doing this course.