Porto Seguro Safe's Driver Prediction

Describe Project

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.

Objectives

The objective is use supervised learning technical for understend how severe is an insurance claim.

Data Source

Kaggle competition: Porto Seguro Safe Driver Prediction

Algorithms

• Random Forest
• XGBoost

Quickstart

• Data Exploration, Data Cleansing, Feature Engineering, Modeling and Evaluation

Requirements

This project is tested with:

Requisite	Version
Python	3.9.7
Pip	21.2.4

I recommend using Python venv.

Install Libraries

pip install --require-hashes -r requirements.txt

---