Introduction

This data set is about a direct marketing case from the insurance sector which was to predict and explain policy ownership. It is about predicting who would be interested in buying a caravan insurance policy and to give a relevant explanation. This data set was used in the second edition of the Computational intelligence and Learning(CoIL) competition Challenge in the Year 2000, organized by CoIL cluster, which is a cooperation between four EU funded Networks of Excellence which represent the areas of neural networks (NeuroNet), fuzzy systems (ERUDIT), evolutionary computing (EvoNet) and machine learning (MLNet) and it is owned and donated by Peter van der Putten of the Dutch data mining company Sentient Machine Research, Baarsjesweg 224 1058 AA Amsterdam The Netherlands +31 20 6186927 putten@liacs.nl and is based on real world business problem. TIC (The Insurance Company) Benchmark Homepage (http://www.liacs.nl/~putten/library/cc2000) was donated on March 7, 2000.

Relevant Papers

P. van der Putten and M. van Someren (eds). CoIL Challenge 2000: The Insurance Company Case. Published by Sentient Machine Research, Amsterdam. Also a Leiden Institute of Advanced Computer Science Technical Report 2000-09. June 22, 2000.

SUMMARY ABOUT DATASET

NO OF OBSERVATIONS: 5822 real customer records

NO OF VARIABLES: 86 Nos.

Each real customer record consists of 86 variables, containing sociodemographic data (variables 1-43) and product ownership data (variables 44-86). The sociodemographic data is derived from zip codes. All customers living in areas with the same zip code have the same sociodemographic attributes. Variable 86 (Purchase), “CARAVAN: Number of mobile home policies”, is the target variable which indicates whether the customer purchased a caravan insurance policy or not.

TASK

Predict which customers are potentially interested in a caravan insurance policy (Prediction or Regression).

PREDICTION TASK

To predict whether a customer is interested in a caravan insurance policy from other data about the customer. Information about customers consists of 86 variables and includes product usage data and socio-demographic data derived from zip area codes. The data was supplied by the Dutch data mining company Sentient Machine Research and is based on a real world business problem. The training set contains over 5000 descriptions of customers, including the information of whether or not they have a caravan insurance policy. A test set contains 4000 customers. In the prediction task, the underlying problem is to the find the subset of customers with a probability of having a caravan insurance policy above some boundary probability. The known policyholders can then be removed and the rest receives a mailing. The boundary depends on the costs and benefits such as of the costs of mailing and benefit of selling insurance policies. To approximate this problem, we want to find the set of 800 customers in the test set of 4000 customers that contains the most caravan policy owners. For each solution submitted, the number of actual policyholders will be counted and this gives the score of a solution.

library(ISLR)

## PIE CHART OF YES/NO FOR PURCHASE OF CARAVAN POLICY BY CUSTOMERS

a<-table(Caravan$Purchase)
a
## 
##   No  Yes 
## 5474  348
colors=c("red","green")
col=colors
pie(a,main = "CUSTOMERS OF CARAVAN POLICY",col=colors)
box()

OBSERVATION FOR PIE CHART

The above piechart shows the number of customers who purchased(Yes) the Caravan policy which is 348 and who have not purchased(NO) the Caravan policy which is 5474

# BAR AND PIE CHARTS SHOWING CORRELATION OF CUSTOMERS WHO PURCHASED CARAVAN POLICY AND VARIOUS VARIABLES

## CHARTS SHOWING PURCHASE OF CARAVAN POLICY BY CUSTOMERS AGAINST SOCIODEMOGRAPHIC DATA VARIABLES

### 1. VARIABLE - CUSTOMER SUBTYPE

a<-table(Caravan$MOSTYPE[Caravan$Purchase=="Yes"])
a
## 
##  1  2  3  4  5  6  7  8  9 10 11 12 13 20 22 23 24 25 26 27 29 30 31 32 33 
## 13  6 25  2  2 12  3 51 12  9  9 16 13  2  4  4  5  2  1  1  2  4  6  8 46 
## 34 35 36 37 38 39 41 
##  9  8 16 10 23 19  5
barplot(a,border="dark blue",main = "PURCHASE OF CARAVAN POLICY vs CUSTOMER SUBTYPE",xlab="Customer subtype",ylab="Number of customers")

OBSERVATION OF CUSTOMER TYPE

In the above barplot, Customers of various subtype of about 41 labels are taken.  Customers belonging to subtype 8(Middle class families) & subtype 33(lower class with large families) are more likely to purchase the Caravan policy

### 2. VARIABLE - AVG AGE (Age group)

a<-table(Caravan$MGEMLEEF[Caravan$Purchase=="Yes"])
a
## 
##   1   2   3   4   5   6 
##   1  87 183  64  12   1
names(a)=c("20 to 30","30 to 40","40 to 50","50 to 60","60 to 70","70 to 80")
barplot(a,col=rainbow(6),main = "PURCHASE OF CARAVAN POLICY vs AVE AGE",xlab="Avg age or Age group",ylab="Number of customers")

OBSERVATION FOR AVG AGE 

In the above barplot, customers of various age group is taken and it is plotted against the customers who have said yes to buy caravan policy. The customers belonging to age group of 40-50 are more likely to purchase the caravan policy

### 3. VARIABLE - PURCHASING POWER CLASS

a<-table(Caravan$MKOOPKLA[Caravan$Purchase=="Yes"])
a
## 
##  1  2  3  4  5  6  7  8 
## 18 15 71 46 30 66 67 35
barplot(a,col=rainbow(7),main = "PURCHASE OF CARAVAN POLICY vs PURCHASING POWER CLASS",xlab = "Purchasing power class",ylab = "Number of customers")

OBSERVATION OF CUSTOMER TYPE

In the above barplot, We come to know that the Customers who are of High status seniors(3) are more likely to purchase the Caravan policy with Dinki's (double income no kids)(7) people and Career and childcare class(6) coming a close second and third respectively

### 4. VARIABLE - AVERAGE INCOME

a<-table(Caravan$MINKGEM[Caravan$Purchase=="Yes"])
a
## 
##   1   2   3   4   5   6   7   8 
##   1  20  69 139  70  24  17   8
pie(a,col=rainbow(7),main ="PURCHASE OF CARAVAN POLICY vs AVERAGE INCOME")
box()

OBSERVATION OF CUSTOMER TYPE

In the above piechart, We come to know that the middle income Customers who are of the average income between $200 to $499(4) are more likely and the Customers who are of the average income between $100 to $199(3) and between $500 to $999(5) are likely to purchase the Caravan policy

### 5. VARIABLE - CUSTOMER MAIN TYPE

b<-table(Caravan$MOSHOOFD[Caravan$Purchase=="Yes"])
b
## 
##  1  2  3  5  6  7  8  9 10 
## 48 66 59 15  4 20 89 42  5
colors=c("violet","yellow","blue","red","brown","orange","green")
color=colors
pie(b,col=colors,main ="PURCHASE OF CARAVAN POLICY vs CUSTOMER MAIN TYPE")
box()

OBSERVATION OF CUSTOMER TYPE

In the above Pie chart, Customers of various maintype of about 10 labels are taken. Customers belonging to maintype 8(Family with grown ups) & maintype 2(Driven Growers) are more likely to purchase the Caravan policy

## BAR CHARTS AND PIE CHARTS SHOWING PURCHASE OF CARAVAN POLICY BY CUSTOMERS AGAINST PRODUCT USAGE(POLICY OWNERSHIP) DATA VARIABLES

### 1.VARIABLE - NUMBER OF BOAT POLICIES

a<-table(Caravan$APLEZIER[Caravan$Purchase=="Yes"])
a
## 
##   0   1   2 
## 335  12   1
barplot(a,border="dark blue",main = "PURCHASE OF CARAVAN POLICY vs NUMBER OF BOAT POLICIES",xlab = "Number of boat policies",ylab = "Number of customers")

OBSERVATION OF CUSTOMER TYPE

In the above barplot, We come to know that the Customers who have not purchased the boat policy(0) are more likely to purchase the Caravan policy

### 2. VARIABLE - NUMBER OF SOCIAL SECURITY INSURANCE POLICIES

a<-table(Caravan$ABYSTAND[Caravan$Purchase=="Yes"])
a
## 
##   0   1 
## 332  16
barplot(a,border="dark blue",main = "PURCHASE OF CARAVAN POLICY vs NO. OF SS INSURANCE POLICIES",xlab = "Number of social security insurance policies",ylab = "Number of customers")

OBSERVATION OF CUSTOMER TYPE

In the above barplot, We come to know that the Customers who have not purchased social security insurance policy(0) are more likely to purchase the Caravan policy

### 3. VARIABLE - CONTRIBUTION CAR POLICIES

a<-table(Caravan$PPERSAUT[Caravan$Purchase=="Yes"])
a
## 
##   0   5   6 
##  72  14 262
colors=c("blue","red","green")
col=colors
pie(a,main ="PURCHASE OF CARAVAN POLICY vs CONTRIBUTION CAR POLICIES",col=colors)
box()

OBSERVATION OF CUSTOMER TYPE

In the above piechart, We come to know that the Customers who pay car policy premium averagely from $1000 to $4999(6)  are more likely to purchase the Caravan policy

### 4. VARIABLE - Number of fire policies

a<-table(Caravan$ABRAND[Caravan$Purchase=="Yes"])
a
## 
##   0   1   2 
## 109 232   7
colors=c("orange","violet","yellow")
col=colors
pie(a,main ="PURCHASE OF CARAVAN POLICY vs NUMBER OF FIRE POLICIES",col=colors)
box()

OBSERVATION OF CUSTOMER TYPE

In the above piechart, We come to know that the Customers who purchase only one fire policy are more likely to purchase the Caravan policy