Original project on my Kaggle: Health Insurance Project

The customer is an insurance company that has provided health insurance to its customers. The company would like to know how many customers would accept to purchase a vehicle insurance.

The dataset consists of the following properties:

• id: unique id of the buyer;
• Gender: gender of the buyer;
• Age: age of the buyer;
• Driving_License: 1 if the user has a driving license, 0 otherwise;
• Region_Code: unique code of the buyer's region;
• Previously_Insured: 1 if the user already has an insured vehicle, 0 otherwise;
• Vehicle_Age: age of the vehicle;
• Vehicle_Damage: 1 if the user has damaged the vehicle in the past, 0 otherwise;
• Annual_Premium: the amount that the user must pay as a premium during the year;
• Policy_Sales_Channel: anonymized code of the channel used for the proposal (e.g. by email, by phone, in person, etc ...);
• Vintage: number of days from which the user is a customer of the company;
• Response: 1 if the buyer has responded positively to the sales proposal, 0 otherwise.

The goal of this project is the following:

Predict the value of Response providing the insurance company with a predictive model that can predict whether past year's policyholders might be interested in purchasing an insurance for their vehicle as well.