Insurance amount prediction based on accidental car damage level using AI

Accurately estimating insurance payouts for car damage is crucial for fair and efficient claim processing. This project uses Convolutional Neural Networks (CNNs) to predict insurance amounts based on vehicle damage images. The system combines visual features extracted by CNNs with structured data like vehicle details and repair costs to improve prediction accuracy. Data augmentation techniques enhance model performance and generalization. This AI-driven approach automates damage assessment, reduces fraud, and streamlines the insurance claim process.