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