Nothing ruins the thrill of buying a brand new car more quickly than seeing your new insurance bill. The sting’s even more painful when you know you’re a good driver. It doesn’t seem fair that you have to pay so much if you’ve been cautious on the road for years.
Porto Seguro, one of Brazil’s largest auto and homeowner insurance companies, completely agrees. Inaccuracies in car insurance company’s claim predictions raise the cost of insurance for good drivers and reduce the price for bad ones.
In this competition, you’re challenged to build a model that predicts the probability that a driver will initiate an auto insurance claim in the next year. While Porto Seguro has used machine learning for the past 20 years, they’re looking to Kaggle’s machine learning community to explore new, more powerful methods. A more accurate prediction will allow them to further tailor their prices, and hopefully make auto insurance coverage more accessible to more drivers.
The objective is use supervised learning technical for understend how severe is an insurance claim.
Kaggle competition: Porto Seguro Safe Driver Prediction
- Random Forest
- XGBoost
This project is tested with:
| Requisite | Version |
|---|---|
| Python | 3.9.7 |
| Pip | 21.2.4 |
I recommend using Python venv.
pip install --require-hashes -r requirements.txt
