A Study on a Car Insurance Purchase Prediction Using Machine Learning
This paper predicted a model that indicates whether to buy a car insurance based on primary health insurance customer data. Currently, automobiles are being used to land transportation and living, and the scope of use and equipment is expanding. This rapid increase in automobiles has caused automobile insurance to emerge as an essential business target for insurance companies. Therefore, if the car insurance sales are predicted and sold using the information of existing health insurance customers, it can generate continuous profits in the insurance company’s operating performance. Therefore, this paper aims to analyze existing customer characteristics and implement a predictive model to activate advertisements for customers interested in such auto insurance. The goal of this study is to maximize the profits of insurance companies by devising communication strategies that can optimize business models and profits for customers. This study was conducted through an automobile insurance purchase prediction model was implemented using Health Insurance Cross-sell Prediction data. The proposed system uses Random Forest Classifier. According to the results of this study, Train Accuracy is 0.992 and test Accuracy is 0.936, which has high accuracy. Therefore, the result was that customers with health insurance could induce a positive reaction to auto insurance purchases.
- System : Pentium i3 Processor.
- Hard Disk : 500 GB.
- Monitor : 15’’ LED
- Input Devices : Keyboard, Mouse
- Ram : 4 GB
- Operating system : Windows 10.
- Coding Language : Python 3.8
- Web Framework : Flask
Su Hyun AN, Seong Hee YEO, Minsoo KANG, “A Study on a car Insurance purchase Prediction Using Two-Class Logistic Regression and Two-Class Boosted Decision Tree”, Korean Journal of Artificial Intelligence, 2021.
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