How Do You Handle Missing Data In Loan Default Prediction? - Next LVL Programming

How Do You Handle Missing Data In Loan Default Prediction? In this informative video, we will address the challenges of missing data in the context of loan default prediction. Understanding how to handle missing data is essential for developing reliable models that can accurately assess the risk of loan defaults. We'll cover various strategies for identifying and managing missing values in your dataset, ensuring you have a solid foundation to work from.

We'll discuss different methods such as listwise and pairwise deletion, along with imputation techniques like mean, median, and mode. Additionally, we will touch on more advanced approaches, including regression imputation and K-Nearest Neighbors imputation, providing you with a range of options to choose from based on your dataset's characteristics.

Proper preprocessing of your data is just as important, and we will highlight key steps like scaling, encoding, and transforming your dataset. Furthermore, we will explore various machine learning models suitable for loan default prediction and how to evaluate their performance using important metrics.

Join us as we navigate the intricacies of missing data and its impact on loan default prediction. Subscribe to our channel for more insightful discussions on programming and data science!

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