Travel insurance is a type of insurance that covers the costs and losses associated with traveling. It is useful protection for those traveling domestically or abroad.
Many companies selling tickets or travel packages, give consumers the option to purchase travel insurance, also known as travelers insurance. Some travel policies cover damage to personal property, rented equipment, such as rental cars, or even the cost of paying a ransom.
Imagine you are working as a data scientist in an insurance company in the USA. The company has collected the data of earlier travel insurance buyers. In this season of vacation, the company wants to know which person will claim their travel insurance and who will not. The company has chosen you to apply your Machine Learning knowledge and provide them with a model that achieves this vision.
You are responsible for building a machine learning model for the insurance company to predict if the insurance buyer will claim their travel insurance or not.
Evaluation Criteria Submissions are evaluated using F1 Score.
Once we release the data, anyone can download it, build a model, and make a submission. We give competitors a set of data (training data) with both the independent and dependent variables.
We also release another set of data (test dataset) with just the independent variables, and we hide the dependent variable that corresponds with this set. You submit the predicted values of the dependent variable for this set and we compare it against the actual values.
The predictions are evaluated based on the evaluation metric defined in the datathon.