This talk will demonstrate the use of the MLJ.jl package in building a machine-learning pipeline for a dataset of property loans. The goal is to predict which loans might default and build a strategy to minimize losses. Several machine learning models such as ElasticNet, XGBoost, and KNN will be explored, and then combined into a stacked model. I will also show how the output of these models can be used to drive investment decisions and the final results of the strategy. This talk will provide a
In this talk, I'll demonstrate the MLJ.jl package and how to build a machine-learning pipeline for a dataset of property loans. By trying to predict what loans might default, we can build a strategy driven by machine learning to try and better predict which loans might default and lose us money.
I'll explore several different machine learning models, such as ElasticNet XGBoost and KNN before combing them all into a stacked model that attempts to use all of the different techniques. I'll also show how we can use the output of all the models to drive the investment decision and the final results of this strategy.
Overall this talk will give a practical example of using machine learning in Julia and how MLJ.jl provides a comprehensive API for everything machine learning.