Projects
Auto Insurance Fraud Detection with Machine Learning
A practical actuarial data science project applying supervised machine learning models such as Logistic Regression, Random Forest, and Gradient Boosting, to automatically detect fraudulent auto insurance claims, with SMOTE used to address class imbalance.
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