This project aims to develop a predictive model estimating insurance coverage costs for customers based on their attributes and product choices. The dataset includes transaction and quote details for policy purchasers. The objective is to predict quoted coverage costs, considering customer traits and 7 customizable product options.
machine-learning data-preprocessing evaluation-metrics decision-tree-regression random-forest-regression cost-prediction neural-network-regression ml-models gradient-boosting-regression insurance-product customer-characteristics product-options
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Updated
Aug 20, 2023 - Jupyter Notebook