A machine learning case study on a real world data science problem.
Overview:
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. A more accurate prediction will allow them to further tailor their prices, and hopefully make auto insurance coverage more accessible to more drivers.
Kaggle link: https://www.kaggle.com/c/porto-seguro-safe-driver-prediction Data : https://www.kaggle.com/c/porto-seguro-safe-driver-prediction/data