Optimize Car Insurance with Data Analysis & Machine LearningOptimize Car Insurance with Data Analysis & Machine Learning
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This project analyzes historical car insurance data to identify low-risk customer segments and build predictive models for claim severity and premium optimization. Using Python, I performed exploratory data analysis, hypothesis testing, and machine learning modeling to uncover patterns in customer behavior, vehicle characteristics, and insurance claims.
Multiple predictive models, including Random Forest and XGBoost, were developed and evaluated to improve risk prediction accuracy. I also implemented data version control using DVC to ensure reproducible experiments and proper dataset management.
The analysis provides insights that can help insurance companies refine pricing strategies, reduce risk exposure, and design targeted marketing strategies for low-risk customers.
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