HackOne_TeamA

Scenario:

In a one-click shopping world with on-demand everything, the life insurance application process is antiquated. Customers provide extensive information to identify risk classification and eligibility, including scheduling medical exams, a process that takes an average of 30 days.

The result? People are turned off. That’s why only 40% of U.S. households own individual life insurance. Prudential wants to make it quicker and less labor intensive for new and existing customers to get a quote while maintaining privacy boundaries. By developing a predictive model that accurately classifies risk using a more automated approach, you can greatly impact public perception of the industry.

Business Objective:

The objective is to develop a predictive model that accurately classifies risk using a more automated approach. The Risk denotes the chances for a person to claiming his/her life insurance policy from the company. The Risk level helps Prudential Life insurance in providing an exact Quote of Life Insurance for each individual.

This will greatly help in public perception of the industry. The results will help Prudential better understand the predictive power of the data points in the existing assessment, and make the Life insurance process quicker and less labor intensive.

Dataset source:

File Name : Train.csv Source of Data : https://www.kaggle.com/c/prudential-life-insurance-assessment/data Total No of Rows : 59381 Total No of Variables : 128 Time Period : NA (The competition is closed)

Our Solution:

https://gallery.cortanaintelligence.com/Experiment/Health-Insurance-Data-Final2