Predict insurance claim amount, build insurance risk assessment model, and detect claim fraud with machine learning
What you will learn
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Learn about machine learning applications in insurance and its technical limitations
Learn how to predict insurance claim amount using XGBoost
Learn how to build insurance risk assessment model using Logistic Regression
Learn how to detect insurance claim fraud using Support Vector Machine
Learn how to predict insurance claim amount using LightGBM
Learn how to build insurance risk assessment model using Random Forest Classifier
Learn how to detect insurance claim fraud using K Nearest Neighbor
Learn how to test machine learning model using synthetic data
Learn how to handle class imbalance using Synthetic Minority Oversampling Technique
Learn how to conduct feature importance analysis using Random Forest Regressor
Learn how to analyze relationship between age, gender, and insurance claim amount
Learn how to find correlation between body mass index and blood pressure with insurance claim amount
Learn how to find correlation between smoking status and insurance claim amount
Learn how insurance risk assessment models work. This section covers data preprocessing, feature selection, train test split, model training, and assessing risk
Learn how to clean dataset by removing missing values and duplicates
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