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IJSTR >> Volume 9 - Issue 1, January 2020 Edition



International Journal of Scientific & Technology Research  
International Journal of Scientific & Technology Research

Website: http://www.ijstr.org

ISSN 2277-8616



Risk Prediction Assessment In Life Insurance Company Through Dimensionality

[Full Text]

 

AUTHOR(S)

Reduction Method Sandeep Kumar Dwivedi, Ashish Mishra, Rajeev Kumar Gupta

 

KEYWORDS

Big data, PCA, RMSE, classification, backward elimination, random forest, feature selection

 

ABSTRACT

Risk assessment is one of the major components in life insurance organization through which customers are grouped. These type of life insurance organization has to perform different operations so that they can settle on different choices bases on applications and to keep proper management. But nowadays there is major expansion in data collection due to large number of customers and advances in investigation process. This is the reason these analysis process has been automated for faster process. Through this automation process many updation can be done although it also helps to include the different new plans by predictive analysis approach. Although real world dataset consist of large numbers of features that are used for examination, that’s why dimensionality reduction has been applied to pick the selective attributes or features by which the power of the model can be increased. The dimensionality reduction can be done by strategies like Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), Correlation-Based Feature Selection (CFS), etc. Various machine learning classification methods like Artificial Neural Network, Multiple Linear Regression, Random Tree and the proposed Random Forest are applied on the dataset to predict the risk level of candidates. This work has shown that Backward Elimination Calculation has shown the most prominent result with least root mean square error (RMSE) OF 0.384 using the random forest strategy. This paper has also shown the training accuracy and testing accuracy on the basis of Random forest model.

 

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