IntroductionThis 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 pvdputten@hotmail.com, 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 PapersP. 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 DATASETNO 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.
TASKSPredict which customers are potentially interested in a caravan insurance policy (Prediction or Regression).
Describe the actual or potential customers; and possibly explain why these customers buy a caravan policy (Description).
PREDICTION TASKTo 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.
DESCRIPTION TASKThe purpose of the description task is to give a clear insight to why customers have a caravan insurance policy and how these customers are different from other customers. Descriptions can be based on regression equations, decision trees, neural network weights, linguistic descriptions, evolutionary programs, graphical representations or any other form of solutions (e.g. minimize a loss function, maximize comprehensibility, minimize response time, etc.). The descriptions and accompanying interpretation must be comprehensible, useful and actionable for a marketing professional with no prior knowledge of computational learning technology. The value of a description is inherently subjective.
INPUT VARIABLESNr Name Description Domain
1 MOSTYPE Customer Subtype see L0
2 MAANTHUI Number of houses 1 ??? 10
3 MGEMOMV Avg size household 1 ??? 6
4 MGEMLEEF Avg age see L1
5 MOSHOOFD Customer main type see L2
6 MGODRK Roman catholic see L3
7 MGODPR Protestant ...
8 MGODOV Other religion
9 MGODGE No religion
10 MRELGE Married
11 MRELSA Living together
12 MRELOV Other relation
13 MFALLEEN Singles
14 MFGEKIND Household without children
15 MFWEKIND Household with children
16 MOPLHOOG G High level education
17 MOPLMIDD Medium level education
18 MOPLLAAG Lower level education
19 MBERHOOG High status
20 MBERZELF Entrepreneur
21 MBERBOER Farmer
22 MBERMIDD Middle management
23 MBERARBG Skilled labourers
24 MBERARBO Unskilled labourers
25 MSKA Social class A
26 MSKB1 Social class B1
27 MSKB2 Social class B2
28 MSKC Social class C
29 MSKD Social class D
30 MHHUUR Rented house
31 MHKOOP Home owners
32 MAUT1 1 car
33 MAUT2 2 cars
34 MAUT0 No car
35 MZFONDS National Health Service
36 MZPART Private health insurance
37 MINKM30 Income < 30.000
38 MINK3045 Income 30-45.000
39 MINK4575 Income 45-75.000
40 MINK7512 Income 75-122.000
41 MINK123M Income >123.000
42 MINKGEM Average income
43 MKOOPKLA Purchasing power class
44 PWAPART Contribution private third party insurance see L4
45 PWABEDR Contribution third party insurance (firms) ...
46 PWALAND Contribution third party insurane (agriculture)
47 PPERSAUT Contribution car policies
48 PBESAUT Contribution delivery van policies
49 PMOTSCO Contribution motorcycle/scooter policies
50 PVRAAUT Contribution lorry policies
51 PAANHANG Contribution trailer policies
52 PTRACTOR Contribution tractor policies
53 PWERKT Contribution agricultural machines policies
54 PBROM C ontribution moped policies
55 PLEVEN Contribution life insurances
56 PPERSONG Contribution private accident insurance policies
57 PGEZONG Contribution family accidents insurance policies
58 PWAOREG Contribution disability insurance policies
59 PBRAND Contribution fire policies
60 PZEILPL Contribution surfboard policies
61 PPLEZIER Contribution boat policies
62 PFIETS Contribution bicycle policies
63 PINBOED Contribution property insurance policies
64 PBYSTAND Contribution social security insurance policies
65 AWAPART Number of private third party insurance 1 - 12
66 AWABEDR Number of third party insurance (firms) ...
67 AWALAND Number of third party insurane (agriculture)
68 APERSAUT Number of car policies
69 ABESAUT Number of delivery van policies
70 AMOTSCO Number of motorcycle/scooter policies
71 AVRAAUT Number of lorry policies
72 AAANHANG Number of trailer policies
73 ATRACTOR Number of tractor policies
74 AWERKT Number of agricultural machines policies
75 ABROM Number of moped policies
76 ALEVEN Number of life insurances
77 APERSONG Number of private accident insurance policies
78 AGEZONG Number of family accidents insurance policies
79 AWAOREG Number of disability insurance policies
80 ABRAND Number of fire policies
81 AZEILPL Number of surfboard policies
82 APLEZIER Number of boat policies
83 AFIETS Number of bicycle policies
84 AINBOED Number of property insurance policies
85 ABYSTAND Number of social security insurance policies
86 CARAVAN Number of mobile home policies 0 - 1LO:Value Label1 1 High Income, expensive child
2 2 Very Important Provincials
3 3 High status seniors
4 4 Affluent senior apartments
5 5 Mixed seniors
6 6 Career and childcare
7 7 Dinki’s (double income no kids)
8 8 Middle class families
9 9 Modern, complete families
10 10 Stable family
11 11 Family starters
12 12 Affluent young families
13 13 Young all american family
14 14 Junior cosmopolitan
15 15 Senior cosmopolitans
16 16 Students in apartments
17 17 Fresh masters in the city
18 18 Single youth
19 19 Suburban youth
20 20 Etnically diverse
21 21 Young urban have-nots
22 22 Mixed apartment dwellers
23 23 Young and rising
24 24 Young, low educated
25 25 Young seniors in the city
26 26 Own home elderly
27 27 Seniors in apartments
28 28 Residential elderly
29 29 Porchless seniors: no front yard
30 30 Religious elderly singles
31 31 Low income catholics
32 32 Mixed seniors
33 33 Lower class large families
34 34 Large family, employed child
35 35 Village families
36 36 Couples with teens ‘Married with children’
37 37 Mixed small town dwellers
38 38 Traditional families
39 39 Large religous families
40 40 Large family farms
41 41 Mixed rurals
L3:0 0%
1 1 - 10%
2 11 - 23%
3 24 - 36%
4 37 - 49%
5 50 - 62%
6 63 - 75%
7 76 - 88%
8 89 - 99%
9 100%
L4:0 f 0
1 f 1 ??? 49
2 f 50 ??? 99
3 f 100 ??? 199
4 f 200 ??? 499
5 f 500 ??? 999
6 f 1000 ??? 4999
7 f 5000 ??? 9999
8 f 10.000 - 19.999
9 f 20.000 - ?
library(ISLR)summary(Caravan) MOSTYPE MAANTHUI MGEMOMV MGEMLEEF MOSHOOFD
Min. : 1.00 Min. : 1.000 Min. :1.000 Min. :1.000 Min. : 1.000
1st Qu.:10.00 1st Qu.: 1.000 1st Qu.:2.000 1st Qu.:2.000 1st Qu.: 3.000
Median :30.00 Median : 1.000 Median :3.000 Median :3.000 Median : 7.000
Mean :24.25 Mean : 1.111 Mean :2.679 Mean :2.991 Mean : 5.774
3rd Qu.:35.00 3rd Qu.: 1.000 3rd Qu.:3.000 3rd Qu.:3.000 3rd Qu.: 8.000
Max. :41.00 Max. :10.000 Max. :5.000 Max. :6.000 Max. :10.000
MGODRK MGODPR MGODOV MGODGE MRELGE
Min. :0.0000 Min. :0.000 Min. :0.00 Min. :0.000 Min. :0.000
1st Qu.:0.0000 1st Qu.:4.000 1st Qu.:0.00 1st Qu.:2.000 1st Qu.:5.000
Median :0.0000 Median :5.000 Median :1.00 Median :3.000 Median :6.000
Mean :0.6965 Mean :4.627 Mean :1.07 Mean :3.259 Mean :6.183
3rd Qu.:1.0000 3rd Qu.:6.000 3rd Qu.:2.00 3rd Qu.:4.000 3rd Qu.:7.000
Max. :9.0000 Max. :9.000 Max. :5.00 Max. :9.000 Max. :9.000
MRELSA MRELOV MFALLEEN MFGEKIND MFWEKIND
Min. :0.0000 Min. :0.00 Min. :0.000 Min. :0.00 Min. :0.0
1st Qu.:0.0000 1st Qu.:1.00 1st Qu.:0.000 1st Qu.:2.00 1st Qu.:3.0
Median :1.0000 Median :2.00 Median :2.000 Median :3.00 Median :4.0
Mean :0.8835 Mean :2.29 Mean :1.888 Mean :3.23 Mean :4.3
3rd Qu.:1.0000 3rd Qu.:3.00 3rd Qu.:3.000 3rd Qu.:4.00 3rd Qu.:6.0
Max. :7.0000 Max. :9.00 Max. :9.000 Max. :9.00 Max. :9.0
MOPLHOOG MOPLMIDD MOPLLAAG MBERHOOG MBERZELF
Min. :0.000 Min. :0.000 Min. :0.000 Min. :0.000 Min. :0.000
1st Qu.:0.000 1st Qu.:2.000 1st Qu.:3.000 1st Qu.:0.000 1st Qu.:0.000
Median :1.000 Median :3.000 Median :5.000 Median :2.000 Median :0.000
Mean :1.461 Mean :3.351 Mean :4.572 Mean :1.895 Mean :0.398
3rd Qu.:2.000 3rd Qu.:4.000 3rd Qu.:6.000 3rd Qu.:3.000 3rd Qu.:1.000
Max. :9.000 Max. :9.000 Max. :9.000 Max. :9.000 Max. :5.000
MBERBOER MBERMIDD MBERARBG MBERARBO MSKA
Min. :0.0000 Min. :0.000 Min. :0.00 Min. :0.000 Min. :0.000
1st Qu.:0.0000 1st Qu.:2.000 1st Qu.:1.00 1st Qu.:1.000 1st Qu.:0.000
Median :0.0000 Median :3.000 Median :2.00 Median :2.000 Median :1.000
Mean :0.5223 Mean :2.899 Mean :2.22 Mean :2.306 Mean :1.621
3rd Qu.:1.0000 3rd Qu.:4.000 3rd Qu.:3.00 3rd Qu.:3.000 3rd Qu.:2.000
Max. :9.0000 Max. :9.000 Max. :9.00 Max. :9.000 Max. :9.000
MSKB1 MSKB2 MSKC MSKD MHHUUR
Min. :0.000 Min. :0.000 Min. :0.000 Min. :0.000 Min. :0.000
1st Qu.:1.000 1st Qu.:1.000 1st Qu.:2.000 1st Qu.:0.000 1st Qu.:2.000
Median :2.000 Median :2.000 Median :4.000 Median :1.000 Median :4.000
Mean :1.607 Mean :2.203 Mean :3.759 Mean :1.067 Mean :4.237
3rd Qu.:2.000 3rd Qu.:3.000 3rd Qu.:5.000 3rd Qu.:2.000 3rd Qu.:7.000
Max. :9.000 Max. :9.000 Max. :9.000 Max. :9.000 Max. :9.000
MHKOOP MAUT1 MAUT2 MAUT0 MZFONDS
Min. :0.000 Min. :0.00 Min. :0.000 Min. :0.000 Min. :0.000
1st Qu.:2.000 1st Qu.:5.00 1st Qu.:0.000 1st Qu.:1.000 1st Qu.:5.000
Median :5.000 Median :6.00 Median :1.000 Median :2.000 Median :7.000
Mean :4.772 Mean :6.04 Mean :1.316 Mean :1.959 Mean :6.277
3rd Qu.:7.000 3rd Qu.:7.00 3rd Qu.:2.000 3rd Qu.:3.000 3rd Qu.:8.000
Max. :9.000 Max. :9.00 Max. :7.000 Max. :9.000 Max. :9.000
MZPART MINKM30 MINK3045 MINK4575 MINK7512
Min. :0.000 Min. :0.000 Min. :0.000 Min. :0.000 Min. :0.0000
1st Qu.:1.000 1st Qu.:1.000 1st Qu.:2.000 1st Qu.:1.000 1st Qu.:0.0000
Median :2.000 Median :2.000 Median :4.000 Median :3.000 Median :0.0000
Mean :2.729 Mean :2.574 Mean :3.536 Mean :2.731 Mean :0.7961
3rd Qu.:4.000 3rd Qu.:4.000 3rd Qu.:5.000 3rd Qu.:4.000 3rd Qu.:1.0000
Max. :9.000 Max. :9.000 Max. :9.000 Max. :9.000 Max. :9.0000
MINK123M MINKGEM MKOOPKLA PWAPART PWABEDR
Min. :0.0000 Min. :0.000 Min. :1.000 Min. :0.0000 Min. :0.00000
1st Qu.:0.0000 1st Qu.:3.000 1st Qu.:3.000 1st Qu.:0.0000 1st Qu.:0.00000
Median :0.0000 Median :4.000 Median :4.000 Median :0.0000 Median :0.00000
Mean :0.2027 Mean :3.784 Mean :4.236 Mean :0.7712 Mean :0.04002
3rd Qu.:0.0000 3rd Qu.:4.000 3rd Qu.:6.000 3rd Qu.:2.0000 3rd Qu.:0.00000
Max. :9.0000 Max. :9.000 Max. :8.000 Max. :3.0000 Max. :6.00000
PWALAND PPERSAUT PBESAUT PMOTSCO
Min. :0.00000 Min. :0.00 Min. :0.00000 Min. :0.0000
1st Qu.:0.00000 1st Qu.:0.00 1st Qu.:0.00000 1st Qu.:0.0000
Median :0.00000 Median :5.00 Median :0.00000 Median :0.0000
Mean :0.07162 Mean :2.97 Mean :0.04827 Mean :0.1754
3rd Qu.:0.00000 3rd Qu.:6.00 3rd Qu.:0.00000 3rd Qu.:0.0000
Max. :4.00000 Max. :8.00 Max. :7.00000 Max. :7.0000
PVRAAUT PAANHANG PTRACTOR PWERKT
Min. :0.000000 Min. :0.00000 Min. :0.00000 Min. :0.00000
1st Qu.:0.000000 1st Qu.:0.00000 1st Qu.:0.00000 1st Qu.:0.00000
Median :0.000000 Median :0.00000 Median :0.00000 Median :0.00000
Mean :0.009447 Mean :0.02096 Mean :0.09258 Mean :0.01305
3rd Qu.:0.000000 3rd Qu.:0.00000 3rd Qu.:0.00000 3rd Qu.:0.00000
Max. :9.000000 Max. :5.00000 Max. :6.00000 Max. :6.00000
PBROM PLEVEN PPERSONG PGEZONG
Min. :0.000 Min. :0.0000 Min. :0.00000 Min. :0.00000
1st Qu.:0.000 1st Qu.:0.0000 1st Qu.:0.00000 1st Qu.:0.00000
Median :0.000 Median :0.0000 Median :0.00000 Median :0.00000
Mean :0.215 Mean :0.1948 Mean :0.01374 Mean :0.01529
3rd Qu.:0.000 3rd Qu.:0.0000 3rd Qu.:0.00000 3rd Qu.:0.00000
Max. :6.000 Max. :9.0000 Max. :6.00000 Max. :3.00000
PWAOREG PBRAND PZEILPL PPLEZIER
Min. :0.00000 Min. :0.000 Min. :0.0000000 Min. :0.00000
1st Qu.:0.00000 1st Qu.:0.000 1st Qu.:0.0000000 1st Qu.:0.00000
Median :0.00000 Median :2.000 Median :0.0000000 Median :0.00000
Mean :0.02353 Mean :1.828 Mean :0.0008588 Mean :0.01889
3rd Qu.:0.00000 3rd Qu.:4.000 3rd Qu.:0.0000000 3rd Qu.:0.00000
Max. :7.00000 Max. :8.000 Max. :3.0000000 Max. :6.00000
PFIETS PINBOED PBYSTAND AWAPART
Min. :0.00000 Min. :0.00000 Min. :0.00000 Min. :0.000
1st Qu.:0.00000 1st Qu.:0.00000 1st Qu.:0.00000 1st Qu.:0.000
Median :0.00000 Median :0.00000 Median :0.00000 Median :0.000
Mean :0.02525 Mean :0.01563 Mean :0.04758 Mean :0.403
3rd Qu.:0.00000 3rd Qu.:0.00000 3rd Qu.:0.00000 3rd Qu.:1.000
Max. :1.00000 Max. :6.00000 Max. :5.00000 Max. :2.000
AWABEDR AWALAND APERSAUT ABESAUT
Min. :0.00000 Min. :0.00000 Min. :0.0000 Min. :0.00000
1st Qu.:0.00000 1st Qu.:0.00000 1st Qu.:0.0000 1st Qu.:0.00000
Median :0.00000 Median :0.00000 Median :1.0000 Median :0.00000
Mean :0.01477 Mean :0.02061 Mean :0.5622 Mean :0.01048
3rd Qu.:0.00000 3rd Qu.:0.00000 3rd Qu.:1.0000 3rd Qu.:0.00000
Max. :5.00000 Max. :1.00000 Max. :7.0000 Max. :4.00000
AMOTSCO AVRAAUT AAANHANG ATRACTOR
Min. :0.00000 Min. :0.000000 Min. :0.00000 Min. :0.00000
1st Qu.:0.00000 1st Qu.:0.000000 1st Qu.:0.00000 1st Qu.:0.00000
Median :0.00000 Median :0.000000 Median :0.00000 Median :0.00000
Mean :0.04105 Mean :0.002233 Mean :0.01254 Mean :0.03367
3rd Qu.:0.00000 3rd Qu.:0.000000 3rd Qu.:0.00000 3rd Qu.:0.00000
Max. :8.00000 Max. :3.000000 Max. :3.00000 Max. :4.00000
AWERKT ABROM ALEVEN APERSONG
Min. :0.000000 Min. :0.00000 Min. :0.00000 Min. :0.000000
1st Qu.:0.000000 1st Qu.:0.00000 1st Qu.:0.00000 1st Qu.:0.000000
Median :0.000000 Median :0.00000 Median :0.00000 Median :0.000000
Mean :0.006183 Mean :0.07042 Mean :0.07661 Mean :0.005325
3rd Qu.:0.000000 3rd Qu.:0.00000 3rd Qu.:0.00000 3rd Qu.:0.000000
Max. :6.000000 Max. :2.00000 Max. :8.00000 Max. :1.000000
AGEZONG AWAOREG ABRAND AZEILPL
Min. :0.000000 Min. :0.000000 Min. :0.0000 Min. :0.0000000
1st Qu.:0.000000 1st Qu.:0.000000 1st Qu.:0.0000 1st Qu.:0.0000000
Median :0.000000 Median :0.000000 Median :1.0000 Median :0.0000000
Mean :0.006527 Mean :0.004638 Mean :0.5701 Mean :0.0005153
3rd Qu.:0.000000 3rd Qu.:0.000000 3rd Qu.:1.0000 3rd Qu.:0.0000000
Max. :1.000000 Max. :2.000000 Max. :7.0000 Max. :1.0000000
APLEZIER AFIETS AINBOED ABYSTAND Purchase
Min. :0.000000 Min. :0.00000 Min. :0.000000 Min. :0.00000 No :5474
1st Qu.:0.000000 1st Qu.:0.00000 1st Qu.:0.000000 1st Qu.:0.00000 Yes: 348
Median :0.000000 Median :0.00000 Median :0.000000 Median :0.00000
Mean :0.006012 Mean :0.03178 Mean :0.007901 Mean :0.01426
3rd Qu.:0.000000 3rd Qu.:0.00000 3rd Qu.:0.000000 3rd Qu.:0.00000
Max. :2.000000 Max. :3.00000 Max. :2.000000 Max. :2.00000
str(Caravan)'data.frame': 5822 obs. of 86 variables: $ MOSTYPE : num 33 37 37 9 40 23 39 33 33 11 ...
$ MAANTHUI: num 1 1 1 1 1 1 2 1 1 2 ...
$ MGEMOMV : num 3 2 2 3 4 2 3 2 2 3 ...
$ MGEMLEEF: num 2 2 2 3 2 1 2 3 4 3 ...
$ MOSHOOFD: num 8 8 8 3 10 5 9 8 8 3 ...
$ MGODRK : num 0 1 0 2 1 0 2 0 0 3 ...
$ MGODPR : num 5 4 4 3 4 5 2 7 1 5 ...
$ MGODOV : num 1 1 2 2 1 0 0 0 3 0 ...
$ MGODGE : num 3 4 4 4 4 5 5 2 6 2 ...
$ MRELGE : num 7 6 3 5 7 0 7 7 6 7 ...
$ MRELSA : num 0 2 2 2 1 6 2 2 0 0 ...
$ MRELOV : num 2 2 4 2 2 3 0 0 3 2 ...
$ MFALLEEN: num 1 0 4 2 2 3 0 0 3 2 ...
$ MFGEKIND: num 2 4 4 3 4 5 3 5 3 2 ...
$ MFWEKIND: num 6 5 2 4 4 2 6 4 3 6 ...
$ MOPLHOOG: num 1 0 0 3 5 0 0 0 0 0 ...
$ MOPLMIDD: num 2 5 5 4 4 5 4 3 1 4 ...
$ MOPLLAAG: num 7 4 4 2 0 4 5 6 8 5 ...
$ MBERHOOG: num 1 0 0 4 0 2 0 2 1 2 ...
$ MBERZELF: num 0 0 0 0 5 0 0 0 1 0 ...
$ MBERBOER: num 1 0 0 0 4 0 0 0 0 0 ...
$ MBERMIDD: num 2 5 7 3 0 4 4 2 1 3 ...
$ MBERARBG: num 5 0 0 1 0 2 1 5 8 3 ...
$ MBERARBO: num 2 4 2 2 0 2 5 2 1 3 ...
$ MSKA : num 1 0 0 3 9 2 0 2 1 1 ...
$ MSKB1 : num 1 2 5 2 0 2 1 1 1 2 ...
$ MSKB2 : num 2 3 0 1 0 2 4 2 0 1 ...
$ MSKC : num 6 5 4 4 0 4 5 5 8 4 ...
$ MSKD : num 1 0 0 0 0 2 0 2 1 2 ...
$ MHHUUR : num 1 2 7 5 4 9 6 0 9 0 ...
$ MHKOOP : num 8 7 2 4 5 0 3 9 0 9 ...
$ MAUT1 : num 8 7 7 9 6 5 8 4 5 6 ...
$ MAUT2 : num 0 1 0 0 2 3 0 4 2 1 ...
$ MAUT0 : num 1 2 2 0 1 3 1 2 3 2 ...
$ MZFONDS : num 8 6 9 7 5 9 9 6 7 6 ...
$ MZPART : num 1 3 0 2 4 0 0 3 2 3 ...
$ MINKM30 : num 0 2 4 1 0 5 4 2 7 2 ...
$ MINK3045: num 4 0 5 5 0 2 3 5 2 3 ...
$ MINK4575: num 5 5 0 3 9 3 3 3 1 3 ...
$ MINK7512: num 0 2 0 0 0 0 0 0 0 1 ...
$ MINK123M: num 0 0 0 0 0 0 0 0 0 0 ...
$ MINKGEM : num 4 5 3 4 6 3 3 3 2 4 ...
$ MKOOPKLA: num 3 4 4 4 3 3 5 3 3 7 ...
$ PWAPART : num 0 2 2 0 0 0 0 0 0 2 ...
$ PWABEDR : num 0 0 0 0 0 0 0 0 0 0 ...
$ PWALAND : num 0 0 0 0 0 0 0 0 0 0 ...
$ PPERSAUT: num 6 0 6 6 0 6 6 0 5 0 ...
$ PBESAUT : num 0 0 0 0 0 0 0 0 0 0 ...
$ PMOTSCO : num 0 0 0 0 0 0 0 0 0 0 ...
$ PVRAAUT : num 0 0 0 0 0 0 0 0 0 0 ...
$ PAANHANG: num 0 0 0 0 0 0 0 0 0 0 ...
$ PTRACTOR: num 0 0 0 0 0 0 0 0 0 0 ...
$ PWERKT : num 0 0 0 0 0 0 0 0 0 0 ...
$ PBROM : num 0 0 0 0 0 0 0 3 0 0 ...
$ PLEVEN : num 0 0 0 0 0 0 0 0 0 0 ...
$ PPERSONG: num 0 0 0 0 0 0 0 0 0 0 ...
$ PGEZONG : num 0 0 0 0 0 0 0 0 0 0 ...
$ PWAOREG : num 0 0 0 0 0 0 0 0 0 0 ...
$ PBRAND : num 5 2 2 2 6 0 0 0 0 3 ...
$ PZEILPL : num 0 0 0 0 0 0 0 0 0 0 ...
$ PPLEZIER: num 0 0 0 0 0 0 0 0 0 0 ...
$ PFIETS : num 0 0 0 0 0 0 0 0 0 0 ...
$ PINBOED : num 0 0 0 0 0 0 0 0 0 0 ...
$ PBYSTAND: num 0 0 0 0 0 0 0 0 0 0 ...
$ AWAPART : num 0 2 1 0 0 0 0 0 0 1 ...
$ AWABEDR : num 0 0 0 0 0 0 0 0 0 0 ...
$ AWALAND : num 0 0 0 0 0 0 0 0 0 0 ...
$ APERSAUT: num 1 0 1 1 0 1 1 0 1 0 ...
$ ABESAUT : num 0 0 0 0 0 0 0 0 0 0 ...
$ AMOTSCO : num 0 0 0 0 0 0 0 0 0 0 ...
$ AVRAAUT : num 0 0 0 0 0 0 0 0 0 0 ...
$ AAANHANG: num 0 0 0 0 0 0 0 0 0 0 ...
$ ATRACTOR: num 0 0 0 0 0 0 0 0 0 0 ...
$ AWERKT : num 0 0 0 0 0 0 0 0 0 0 ...
$ ABROM : num 0 0 0 0 0 0 0 1 0 0 ...
$ ALEVEN : num 0 0 0 0 0 0 0 0 0 0 ...
$ APERSONG: num 0 0 0 0 0 0 0 0 0 0 ...
$ AGEZONG : num 0 0 0 0 0 0 0 0 0 0 ...
$ AWAOREG : num 0 0 0 0 0 0 0 0 0 0 ...
$ ABRAND : num 1 1 1 1 1 0 0 0 0 1 ...
$ AZEILPL : num 0 0 0 0 0 0 0 0 0 0 ...
$ APLEZIER: num 0 0 0 0 0 0 0 0 0 0 ...
$ AFIETS : num 0 0 0 0 0 0 0 0 0 0 ...
$ AINBOED : num 0 0 0 0 0 0 0 0 0 0 ...
$ ABYSTAND: num 0 0 0 0 0 0 0 0 0 0 ...
$ Purchase: Factor w/ 2 levels "No","Yes": 1 1 1 1 1 1 1 1 1 1 ...