<commit_before>---
layout: post
title: Prudential Insurance Kaggle
published: true
---

### Business 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](https://www.kaggle.com/c/prudential-life-insurance-assessment/data) 
- Total No of Rows		: 59381
- Total No of Variables	: 128
- Time Period			: NA (The time period is not mentioned on the source website)

### Identifying Variable 
#### Dependant Variable (Y): 

The ‘Response’ field in the dataset is the dependant variable. ‘Response’ variable denotes the level of risk associated with a person’s chances of claiming his/her life insurance, in order to get a life Insurance Quote. This helps the Insurance company in assessing the application and denoting the right quote for the applicant. The several levels for ‘Response’ variable are: 1,2,3,4,5,6,7,8, where 8 means the highest level of risk. For example, if insurance risk level is calculated as 8 for an individual, he/she has maximum chances of claiming insurance from the company, Therefore a level of 8 is bad for the insurance company

![prudential_1.png](/images/prudential_1.png)


#### Predictor Variable (X): 

Following is the list of possible predictor variable

![Screen Shot 2016-05-23 at 7.49.36 PM.png](Screen Shot 2016-05-23 at 7.49.36 PM.png)

### R Libraries

Following are some of the libraries which will be used in this analysis
- library(nnet)
  - Used to build the multinomial logistic model
  - Uses the predictor variables to build a model which fits value for the Response variable
- library(xlsx)
  - To extract xls file into R
  - Use to create the final output file in an excel sheet<commit_msg>Update 2016-3-22-Prudential-Insurance.md<commit_after>---
layout: post
title: Prudential Insurance Kaggle
published: true
---

### Business 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](https://www.kaggle.com/c/prudential-life-insurance-assessment/data) 
- Total No of Rows		: 59381
- Total No of Variables	: 128
- Time Period			: NA (The time period is not mentioned on the source website)

### Identifying Variable 
#### Dependant Variable (Y): 

The ‘Response’ field in the dataset is the dependant variable. ‘Response’ variable denotes the level of risk associated with a person’s chances of claiming his/her life insurance, in order to get a life Insurance Quote. This helps the Insurance company in assessing the application and denoting the right quote for the applicant. The several levels for ‘Response’ variable are: 1,2,3,4,5,6,7,8, where 8 means the highest level of risk. For example, if insurance risk level is calculated as 8 for an individual, he/she has maximum chances of claiming insurance from the company, Therefore a level of 8 is bad for the insurance company

![prudential_1.png]({{ site.baseurl }}/images/prudential_1.png)


#### Predictor Variable (X): 

Following is the list of possible predictor variable

![Screen Shot 2016-05-23 at 7.49.36 PM.png](Screen Shot 2016-05-23 at 7.49.36 PM.png)

### R Libraries

Following are some of the libraries which will be used in this analysis
- library(nnet)
  - Used to build the multinomial logistic model
  - Uses the predictor variables to build a model which fits value for the Response variable
- library(xlsx)
  - To extract xls file into R
  - Use to create the final output file in an excel sheet
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