Project Overview
CarRiskPro is a data-driven project focused on analyzing a car insurance dataset to assess risk factors and provide informed recommendations. The project utilizes predictive modeling techniques to enhance decision-making in the car insurance industry.
Key Features:
- Data Preprocessing: Removal of unnecessary variables (ID, Postal code, Education) and imputation of missing values using the mode.
- Variable Significance: Statistical analysis identifies significant variables, with Credit Score standing out as a crucial factor in insurance decisions.
- Model Evaluation: Comparison of Logistic Regression, Gradient Boosting, and Random Forest models for accuracy, precision, recall, and F1 score.
- Confusion Matrices: Analysis of true positives, true negatives, false positives, and false negatives to assess model performance.
- Recommendations: Tailored premiums and coverage, credit score analysis, and safe driving incentives for risk mitigation and fair pricing.
Outcome: CarRiskPro provides valuable insights into risk assessment and pricing in the car insurance industry. By considering age, driving history, and credit score, insurance companies can make informed decisions, reduce risks, and optimize customer premiums. The project demonstrates the use of data analysis and predictive modeling to enhance decision-making processes.
Impact: CarRiskPro’s approach empowers insurance companies to accurately assess risk factors and make data-driven decisions. It promotes fair pricing, reduces false positives and false negatives, and enhances customer satisfaction by tailoring policies to individual risk profiles. Ultimately, CarRiskPro contributes to a more efficient and effective car insurance industry.