Team Members: Bharat Sharman, Dylan Li, Mingdao Li, Leonie Lu

In this project, we aim to replicate and possibly improve upon the work of Jayabalan et al. in their paper “Risk prediction in life insurance industry using supervised learning algorithms”.

We will be using the Prudential Life Insurance Data Set that the authors have used and have shared with us. We will be pre-processing the data to replace missing values, using feature selection using CFS and feature reduction using PCA use this processed data to perform Classification via four algorithms – Neural Networks, Random Tree, REPTree and Multiple Linear Regression. We will compare the performance of these Algorithms using MAE and RMSE metrics and come up with visualizations that can explain the results easily even to a non-quantitative audience. Our goal behind this project is to learn applying the algorithms that we learned in our class to an industry dataset and come up with results that we can aid better, data-driven decision making.