About this document

Machine Learning Risk Profile Analysis by victorjoseij is a document available to read on EtoBox.

The project aimed to develop a machine learning model to predict individual risk profiles using financial, demographic, and behavioral data, but all models achieved around 33% accuracy, indicating minimal learning and poor generalization. Despite exploring various algorithms, including Random Forest, XGBoost, and deep learning, the models struggled due to a lack of strong separability in the features. The conclusion emphasizes the need for improved data quality and feature richness rather than focusing on m

Author

victorjoseij

Language

EN