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CPS1966 Jessa L. S. C. et al.
                  for clients looking for protection. They can be offered to the dependents of a
                  breadwinner or to the owner himself especially if he is earning much and has
                  a lot to lose. Other Traditional is also an option for protection push but it caters
                  to  the  extreme  segments  in  terms  of  income  earnings.  Hence,  Unit-linked
                  product as a protection solution can be used to target the middle class.
                      These results can be supplemented by qualitative studies that can validate
                  the targeting strategy. For  example, actual clients can be asked through a
                  survey if they will be interested to purchase a Unit-linked product. Similarly, it
                  can  be  checked  if  indeed  the  reason  why  higher  life  insurance  cover  is
                  purchased is because of protection and not for higher premium which can
                  generate higher investment. Either way, this quantitative study can be used as
                  a basis and a starting point for a qualitative study.

                  References
                  1.  Breiman, L. (October 2001). Random Forest. Machine Learning, 45.
                      Retrieved from:
                      https://www.stat.berkeley.edu/~breiman/randomforest2001.pdf
                  2.  Breiman, L. (1996). Bagging Predictors. Machine Learning, 24. Retrieved
                      from: https://link.springer.com/article/10.1023/A:1018054314350
                  3.  Fawagreh, K., et al. (August 2014). Random forests: from early
                      developments to recent advancements. Systems Science & Control
                      Engineering: An Open Access Journal, 2. Retrieved from: https:
                      https://www.tandfonline.com/doi/abs/10.1080/21642583.2014.956265
                  4.  Gass, K., et al. (2014). Classification and regression trees for
                      epidemiologic research: an air pollution example. Environment Health
                      Journal, 13. Retrieved from: http://www.ehjournal.net/content/13/1/17
                  5.  Gromping, U. (November 2009). Variable Importance Assessment in
                      Regression: Linear Regression versus Random Forest. American
                      Statistical Association, 63. Retrieved from:
                      https://www.tandfonline.com/doi/abs/10.1198/tast.2009.08199
                  6.  Hosmer, D., & Lemeshow, S. (2000). Applied Logistic Regression (2  ed.).
                                                                                      nd
                      Hoboken, New Jersey: John Wiley & Sons, Inc.
                  7.  Izenman, A. (2008). Modern Multivariate Statistical Techniques. New
                      York: Springer Science+Business Media
                  8.  Jay, M. (December 2017). Goodness of Fit Tests for Logistic Regression
                      Models. Retrieved from: https://cran.r-
                      project.org/web/packages/generalhoslem/generalhoslem.pdf
                  9.  Loh, W. (January 2011). Classification and regression trees. WIREs Data
                      Mining and Knowledge Discovery, 1. Retrieved from:
                      https://www.stat.wisc.edu/~loh/treeprogs/guide/wires11.pdf





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