Predict insurance claim amount, build insurance risk assessment model, and detect claim fraud with machine learning
Sub Category
- Operating Systems & Servers
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Objectives
- 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
Pre Requisites
- No previous experience in machine learning is required
- Basic knowledge in Python and insurance
FAQ
- Q. How long do I have access to the course materials?
- A. You can view and review the lecture materials indefinitely, like an on-demand channel.
- Q. Can I take my courses with me wherever I go?
- A. Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don't have an internet connection, some instructors also let their students download course lectures. That's up to the instructor though, so make sure you get on their good side!
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