Classification analysis consists of various techniques that are best suited in analyzing a qualitative response. The purpose of this analysis is understand the buying patterns of customers from a wholesale distributor. Therefore classification analysis will be used to study the associations between a set of predictors and a categorical target while diving these entities into classes. The wholesale dataset documents customers of a wholesaler into two classes: [1] Horeca (Hotels/Restaurants/Cafes) and [2] Retail (regular stores) and captures the spending amounts on fresh grocery products.
There are many possible classification techniques that one can use to predict a qualitative response. This analysis will focus on the buying patterns of customers from a wholesale distributor. Classification analysis, a supervised learning technique, we will study the association between a set predictors and a categorical target while dividing these entities into classes. In our wholesale dataset, the customers of a wholesaler are divided into two classes, Horeca (Hotels/Restaurants/Cafes) and Retail (regular stores) and the spending amounts on fresh grocery products.
The aim of Utilizing logistic regression and linear discriminant analysis, is to understand if there are different spending patterns for the two groups of customers, Horeca and Retail. The discriminant analysis between Horeca and Retail customers will be used to answer our question. If potential differences in spending patterns are found this information can be used to redirect marketing assets, update in store layouts, optimize inventory controls, reduce shrinkage, etc…
Step 1 Load the data and run numerical summaries
Step 2 Split the dat into training and testing data
Step 3 Fit the logistic regression model, LDA, PCA, KNN, Decision Trees
Step 4 Use the fitted model to do predictions of the test data
Install packages and prem for Algorithms
## Loading required package: ISLR## Warning: package 'ISLR' was built under R version 3.0.3## Loading required package: C50## Warning: package 'C50' was built under R version 3.0.3## Loading required package: rpart## Warning: package 'rpart' was built under R version 3.0.3## Loading required package: rpart.plot## Warning: package 'rpart.plot' was built under R version 3.0.3## Loading required package: fancyRpartplot## Warning in library(package, lib.loc = lib.loc, character.only = TRUE,
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## Version 3.4.1 Copyright (c) 2006-2014 Togaware Pty Ltd.
## Type 'rattle()' to shake, rattle, and roll your data.
## Loading required package: randomForest## Warning: package 'randomForest' was built under R version 3.0.3## randomForest 4.6-10
## Type rfNews() to see new features/changes/bug fixes.
## Loading required package: gclus## Warning: package 'gclus' was built under R version 3.0.3## Loading required package: cluster
## Loading required package: car## Warning: package 'car' was built under R version 3.0.3## Loading required package: caret## Warning: package 'caret' was built under R version 3.0.3## Loading required package: lattice
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Complete preprocessing steps and convert Channel and Region to categorial Variables. Convert Channel to y values must be 0 <= y <= 1 for logistic regression. y=Channel 0=Retail, 1=Horeca
We will use Channel as our response variable and see if we can predict it as a binary response using logistic regression.
Plot our data first. Then plot rearrange by variables so that those with higher correlations are closer to the principal diagonal.
wholesale <- read.csv("~/UChicago MScA/Adv Data Mining II/Final Project/wholesale1.csv")
wholesaled <- read.csv("~/UChicago MScA/Adv Data Mining II/Final Project/wholesale.csv")
names(wholesale)## [1] "Channel" "Region" "Fresh"
## [4] "Milk" "Grocery" "Frozen"
## [7] "Detergents_Paper" "Delicassen"View(wholesale)
View(wholesaled)
as.data.frame(wholesale)## Channel Region Fresh Milk Grocery Frozen Detergents_Paper Delicassen
## 1 0 3 12669 9656 7561 214 2674 1338
## 2 0 3 7057 9810 9568 1762 3293 1776
## 3 0 3 6353 8808 7684 2405 3516 7844
## 4 1 3 13265 1196 4221 6404 507 1788
## 5 0 3 22615 5410 7198 3915 1777 5185
## 6 0 3 9413 8259 5126 666 1795 1451
## 7 0 3 12126 3199 6975 480 3140 545
## 8 0 3 7579 4956 9426 1669 3321 2566
## 9 1 3 5963 3648 6192 425 1716 750
## 10 0 3 6006 11093 18881 1159 7425 2098
## 11 0 3 3366 5403 12974 4400 5977 1744
## 12 0 3 13146 1124 4523 1420 549 497
## 13 0 3 31714 12319 11757 287 3881 2931
## 14 0 3 21217 6208 14982 3095 6707 602
## 15 0 3 24653 9465 12091 294 5058 2168
## 16 1 3 10253 1114 3821 397 964 412
## 17 0 3 1020 8816 12121 134 4508 1080
## 18 1 3 5876 6157 2933 839 370 4478
## 19 0 3 18601 6327 10099 2205 2767 3181
## 20 1 3 7780 2495 9464 669 2518 501
## 21 0 3 17546 4519 4602 1066 2259 2124
## 22 1 3 5567 871 2010 3383 375 569
## 23 1 3 31276 1917 4469 9408 2381 4334
## 24 0 3 26373 36423 22019 5154 4337 16523
## 25 0 3 22647 9776 13792 2915 4482 5778
## 26 0 3 16165 4230 7595 201 4003 57
## 27 1 3 9898 961 2861 3151 242 833
## 28 1 3 14276 803 3045 485 100 518
## 29 0 3 4113 20484 25957 1158 8604 5206
## 30 1 3 43088 2100 2609 1200 1107 823
## 31 1 3 18815 3610 11107 1148 2134 2963
## 32 1 3 2612 4339 3133 2088 820 985
## 33 1 3 21632 1318 2886 266 918 405
## 34 1 3 29729 4786 7326 6130 361 1083
## 35 1 3 1502 1979 2262 425 483 395
## 36 0 3 688 5491 11091 833 4239 436
## 37 1 3 29955 4362 5428 1729 862 4626
## 38 0 3 15168 10556 12477 1920 6506 714
## 39 0 3 4591 15729 16709 33 6956 433
## 40 1 3 56159 555 902 10002 212 2916
## 41 1 3 24025 4332 4757 9510 1145 5864
## 42 1 3 19176 3065 5956 2033 2575 2802
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## 421 1 3 4456 5266 13227 25 6818 1393
## 422 0 3 17063 4847 9053 1031 3415 1784
## 423 1 3 26400 1377 4172 830 948 1218
## 424 0 3 17565 3686 4657 1059 1803 668
## 425 0 3 16980 2884 12232 874 3213 249
## 426 1 3 11243 2408 2593 15348 108 1886
## 427 1 3 13134 9347 14316 3141 5079 1894
## 428 1 3 31012 16687 5429 15082 439 1163
## 429 1 3 3047 5970 4910 2198 850 317
## 430 1 3 8607 1750 3580 47 84 2501
## 431 1 3 3097 4230 16483 575 241 2080
## 432 1 3 8533 5506 5160 13486 1377 1498
## 433 1 3 21117 1162 4754 269 1328 395
## 434 1 3 1982 3218 1493 1541 356 1449
## 435 1 3 16731 3922 7994 688 2371 838
## 436 1 3 29703 12051 16027 13135 182 2204
## 437 1 3 39228 1431 764 4510 93 2346
## 438 0 3 14531 15488 30243 437 14841 1867
## 439 1 3 10290 1981 2232 1038 168 2125
## 440 1 3 2787 1698 2510 65 477 52#Correlations
pairs(wholesale, col=wholesale$Channel)
plot(wholesale)
cor(wholesale)## Channel Region Fresh Milk
## Channel 1.00000000 -0.062027619 0.16917204 -0.4607203
## Region -0.06202762 1.000000000 0.05528692 0.0322875
## Fresh 0.16917204 0.055286923 1.00000000 0.1005098
## Milk -0.46072028 0.032287502 0.10050977 1.0000000
## Grocery -0.60879225 0.007695777 -0.01185387 0.7283351
## Frozen 0.20204596 -0.021044215 0.34588146 0.1239938
## Detergents_Paper -0.63602637 -0.001482686 -0.10195294 0.6618157
## Delicassen -0.05601143 0.045212107 0.24468997 0.4063683
## Grocery Frozen Detergents_Paper Delicassen
## Channel -0.608792245 0.20204596 -0.636026367 -0.05601143
## Region 0.007695777 -0.02104421 -0.001482686 0.04521211
## Fresh -0.011853875 0.34588146 -0.101952938 0.24468997
## Milk 0.728335118 0.12399376 0.661815679 0.40636832
## Grocery 1.000000000 -0.04019274 0.924640691 0.20549651
## Frozen -0.040192737 1.00000000 -0.131524906 0.39094747
## Detergents_Paper 0.924640691 -0.13152491 1.000000000 0.06929130
## Delicassen 0.205496511 0.39094747 0.069291297 1.00000000#Correlations by color and higher correlations are moved closer to the principle diagonal---
wholesalecor<-abs(cor(wholesale))
wholesale.color<-dmat.color(wholesalecor)
wholesale.order<-order.single(wholesalecor)
wholesale.pairs<-cpairs(wholesale, wholesale.order, panel.colors=wholesale.color, gap=.5,)
main="Variables Ordered and Colored by Correlation"
#Create Variables --------------------------------------------------------------------------
Channel <- wholesale[,1]
Region <- wholesale[,2]
Fresh <- wholesale[,3]
Milk <- wholesale[,4]
Grocery <- wholesale[,5]
Frozen <- wholesale[,6]
Detergents <- wholesale[,7]
Delicassen <- wholesale[,8]
#ENHANCED SCATTERPLOT-----------------------------------------------------------------------
#Makes sense that more people are purchashing frozen and fresh items at Retail Channels over Horeca (Hotels/Restaurants/Cafe). However we can assume that people still eat out at Horeca (Hotels/Restaurants/Cafe) but, not purchase in large quantities.
scatterplot( Fresh ~ Frozen | Channel, data=wholesaled,
xlab="Fresh", ylab="Frozen",
main="Scatter Plot: Horeca(Hotel/Restaurant/Cafe) vs. Retail Channel (Store)",
smoother=T,
lty=1, lwd=2, by.groups=T)
scatterplot( Fresh ~ Frozen | Region, data=wholesaled,
xlab="Frozen", ylab="Fresh",
main="Scatter Plot: Regions in Spain",
smoother=T,
lty=1, lwd=2, by.groups=T)
scatterplot( Milk ~ Delicassen | Channel, data=wholesaled,
xlab="Delicassen", ylab="Milk",
main="Scatter Plot: Horeca(Hotel/Restaurant/Cafe) vs. Retail Channel (Store)",
smoother=T,
lty=1, lwd=2, by.groups=T)
scatterplot( Milk ~ Delicassen | Region, data=wholesaled,
xlab="Delicassen", ylab="Milk",
main="Scatter Plot: Regions in Spain",
smoother=T,
lty=1, lwd=2, by.groups=T)
#Of course we see consumers purchashing more Detergents_Paper products at Retail (Stores)
scatterplot( Detergents_Paper ~ Grocery | Channel, data=wholesaled,
xlab="Grocery", ylab="Detergents_Paper",
main="Scatter Plot: Horeca(Hotel/Restaurant/Cafe) vs. Retail Channel (Store)",
smoother=T,
lty=1, lwd=2, by.groups=T)
scatterplot( Detergents_Paper ~ Grocery | Region, data=wholesaled,
xlab="Grocery", ylab="Detergents_Paper",
main="Scatter Plot: Regions in Spain",
smoother=T,
lty=1, lwd=2, by.groups=T)
Pre-process the data and take a look at some basic Summary Statistics, also make the data completely random.
head(wholesale)## Channel Region Fresh Milk Grocery Frozen Detergents_Paper Delicassen
## 1 0 3 12669 9656 7561 214 2674 1338
## 2 0 3 7057 9810 9568 1762 3293 1776
## 3 0 3 6353 8808 7684 2405 3516 7844
## 4 1 3 13265 1196 4221 6404 507 1788
## 5 0 3 22615 5410 7198 3915 1777 5185
## 6 0 3 9413 8259 5126 666 1795 1451str(wholesale)## 'data.frame': 440 obs. of 8 variables:
## $ Channel : int 0 0 0 1 0 0 0 0 1 0 ...
## $ Region : int 3 3 3 3 3 3 3 3 3 3 ...
## $ Fresh : int 12669 7057 6353 13265 22615 9413 12126 7579 5963 6006 ...
## $ Milk : int 9656 9810 8808 1196 5410 8259 3199 4956 3648 11093 ...
## $ Grocery : int 7561 9568 7684 4221 7198 5126 6975 9426 6192 18881 ...
## $ Frozen : int 214 1762 2405 6404 3915 666 480 1669 425 1159 ...
## $ Detergents_Paper: int 2674 3293 3516 507 1777 1795 3140 3321 1716 7425 ...
## $ Delicassen : int 1338 1776 7844 1788 5185 1451 545 2566 750 2098 ...dim(wholesale)## [1] 440 8set.seed(9850)
random<- runif(nrow(wholesale))
wholesaler<-wholesale[order(random),]
str(wholesaler)## 'data.frame': 440 obs. of 8 variables:
## $ Channel : int 1 1 0 0 1 1 0 0 0 1 ...
## $ Region : int 1 3 2 3 3 1 3 3 3 3 ...
## $ Fresh : int 2806 18073 1479 2932 7780 20893 4822 7823 1020 3157 ...
## $ Milk : int 10765 4613 14982 6459 2495 1222 6721 6245 8816 4888 ...
## $ Grocery : int 15538 3444 11924 7677 9464 2576 9170 6544 12121 2500 ...
## $ Frozen : int 1374 4324 662 2561 669 3975 993 4154 134 4477 ...
## $ Detergents_Paper: int 5828 914 3891 4573 2518 737 4973 4074 4508 273 ...
## $ Delicassen : int 2388 715 3508 1386 501 3628 3637 964 1080 2165 ...plot(wholesaler)
Fit the Logistic Regression Model
We will use Channel as our response variable and see if we can predict it as a binary response using logistic regression.
glm.fit=glm(Channel~Region + Fresh + Milk + Grocery + Frozen + Detergents_Paper + Delicassen,
data=wholesaler,family=binomial)## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurredsummary(glm.fit)##
## Call:
## glm(formula = Channel ~ Region + Fresh + Milk + Grocery + Frozen +
## Detergents_Paper + Delicassen, family = binomial, data = wholesaler)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -3.2518 -0.0455 0.2178 0.3116 2.8899
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 4.791e+00 8.494e-01 5.640 1.70e-08 ***
## Region -4.178e-01 2.666e-01 -1.567 0.1172
## Fresh -4.322e-06 1.743e-05 -0.248 0.8042
## Milk -7.730e-05 5.535e-05 -1.397 0.1625
## Grocery -1.124e-04 6.037e-05 -1.862 0.0626 .
## Frozen 1.549e-04 9.158e-05 1.692 0.0907 .
## Detergents_Paper -8.592e-04 1.375e-04 -6.248 4.16e-10 ***
## Delicassen 7.844e-05 1.084e-04 0.724 0.4693
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 553.44 on 439 degrees of freedom
## Residual deviance: 201.29 on 432 degrees of freedom
## AIC: 217.29
##
## Number of Fisher Scoring iterations: 7#Not as much significance, as I would have liked, but we still have more to explore. We do see a huge difference in the Residual deviance and a 7 unit change degrees of freedom.
glm.probs=predict(glm.fit,type="response")
glm.probs[1:8]## 216 405 316 103 20 268 167
## 0.0560333 0.9344336 0.1803171 0.2209215 0.5566009 0.9847290 0.1339443
## 63
## 0.3786789glm.pred=ifelse(glm.probs>0.5,"Hotel/Cafe/Restaurant","Retail")
attach(wholesaler)## The following objects are masked _by_ .GlobalEnv:
##
## Channel, Delicassen, Fresh, Frozen, Grocery, Milk, Regiontable(glm.pred,Channel)## Channel
## glm.pred 0 1
## Hotel/Cafe/Restaurant 100 205
## Retail 42 93mean(glm.pred==Channel)## [1] 0#The diagonals is where do do correct classification, and the off diagonals is where we predict mistakes. We see many mistakes here. Our initinal model yields an AIC of 218.29, and a huge delta in the residual deviance from 553.44 to 201.29.
Make training and test set
testDF<-list(wholesale[1:8])
as.data.frame(testDF)## Channel Region Fresh Milk Grocery Frozen Detergents_Paper Delicassen
## 1 0 3 12669 9656 7561 214 2674 1338
## 2 0 3 7057 9810 9568 1762 3293 1776
## 3 0 3 6353 8808 7684 2405 3516 7844
## 4 1 3 13265 1196 4221 6404 507 1788
## 5 0 3 22615 5410 7198 3915 1777 5185
## 6 0 3 9413 8259 5126 666 1795 1451
## 7 0 3 12126 3199 6975 480 3140 545
## 8 0 3 7579 4956 9426 1669 3321 2566
## 9 1 3 5963 3648 6192 425 1716 750
## 10 0 3 6006 11093 18881 1159 7425 2098
## 11 0 3 3366 5403 12974 4400 5977 1744
## 12 0 3 13146 1124 4523 1420 549 497
## 13 0 3 31714 12319 11757 287 3881 2931
## 14 0 3 21217 6208 14982 3095 6707 602
## 15 0 3 24653 9465 12091 294 5058 2168
## 16 1 3 10253 1114 3821 397 964 412
## 17 0 3 1020 8816 12121 134 4508 1080
## 18 1 3 5876 6157 2933 839 370 4478
## 19 0 3 18601 6327 10099 2205 2767 3181
## 20 1 3 7780 2495 9464 669 2518 501
## 21 0 3 17546 4519 4602 1066 2259 2124
## 22 1 3 5567 871 2010 3383 375 569
## 23 1 3 31276 1917 4469 9408 2381 4334
## 24 0 3 26373 36423 22019 5154 4337 16523
## 25 0 3 22647 9776 13792 2915 4482 5778
## 26 0 3 16165 4230 7595 201 4003 57
## 27 1 3 9898 961 2861 3151 242 833
## 28 1 3 14276 803 3045 485 100 518
## 29 0 3 4113 20484 25957 1158 8604 5206
## 30 1 3 43088 2100 2609 1200 1107 823
## 31 1 3 18815 3610 11107 1148 2134 2963
## 32 1 3 2612 4339 3133 2088 820 985
## 33 1 3 21632 1318 2886 266 918 405
## 34 1 3 29729 4786 7326 6130 361 1083
## 35 1 3 1502 1979 2262 425 483 395
## 36 0 3 688 5491 11091 833 4239 436
## 37 1 3 29955 4362 5428 1729 862 4626
## 38 0 3 15168 10556 12477 1920 6506 714
## 39 0 3 4591 15729 16709 33 6956 433
## 40 1 3 56159 555 902 10002 212 2916
## 41 1 3 24025 4332 4757 9510 1145 5864
## 42 1 3 19176 3065 5956 2033 2575 2802
## 43 0 3 10850 7555 14961 188 6899 46
## 44 0 3 630 11095 23998 787 9529 72
## 45 0 3 9670 7027 10471 541 4618 65
## 46 0 3 5181 22044 21531 1740 7353 4985
## 47 0 3 3103 14069 21955 1668 6792 1452
## 48 0 3 44466 54259 55571 7782 24171 6465
## 49 0 3 11519 6152 10868 584 5121 1476
## 50 0 3 4967 21412 28921 1798 13583 1163
## 51 1 3 6269 1095 1980 3860 609 2162
## 52 1 3 3347 4051 6996 239 1538 301
## 53 0 3 40721 3916 5876 532 2587 1278
## 54 0 3 491 10473 11532 744 5611 224
## 55 1 3 27329 1449 1947 2436 204 1333
## 56 1 3 5264 3683 5005 1057 2024 1130
## 57 0 3 4098 29892 26866 2616 17740 1340
## 58 0 3 5417 9933 10487 38 7572 1282
## 59 1 3 13779 1970 1648 596 227 436
## 60 1 3 6137 5360 8040 129 3084 1603
## 61 0 3 8590 3045 7854 96 4095 225
## 62 0 3 35942 38369 59598 3254 26701 2017
## 63 0 3 7823 6245 6544 4154 4074 964
## 64 0 3 9396 11601 15775 2896 7677 1295
## 65 1 3 4760 1227 3250 3724 1247 1145
## 66 0 3 85 20959 45828 36 24231 1423
## 67 1 3 9 1534 7417 175 3468 27
## 68 0 3 19913 6759 13462 1256 5141 834
## 69 1 3 2446 7260 3993 5870 788 3095
## 70 1 3 8352 2820 1293 779 656 144
## 71 1 3 16705 2037 3202 10643 116 1365
## 72 1 3 18291 1266 21042 5373 4173 14472
## 73 1 3 4420 5139 2661 8872 1321 181
## 74 0 3 19899 5332 8713 8132 764 648
## 75 0 3 8190 6343 9794 1285 1901 1780
## 76 1 3 20398 1137 3 4407 3 975
## 77 1 3 717 3587 6532 7530 529 894
## 78 0 3 12205 12697 28540 869 12034 1009
## 79 1 3 10766 1175 2067 2096 301 167
## 80 1 3 1640 3259 3655 868 1202 1653
## 81 1 3 7005 829 3009 430 610 529
## 82 0 3 219 9540 14403 283 7818 156
## 83 0 3 10362 9232 11009 737 3537 2342
## 84 1 3 20874 1563 1783 2320 550 772
## 85 0 3 11867 3327 4814 1178 3837 120
## 86 0 3 16117 46197 92780 1026 40827 2944
## 87 0 3 22925 73498 32114 987 20070 903
## 88 1 3 43265 5025 8117 6312 1579 14351
## 89 1 3 7864 542 4042 9735 165 46
## 90 1 3 24904 3836 5330 3443 454 3178
## 91 1 3 11405 596 1638 3347 69 360
## 92 1 3 12754 2762 2530 8693 627 1117
## 93 0 3 9198 27472 32034 3232 18906 5130
## 94 1 3 11314 3090 2062 35009 71 2698
## 95 0 3 5626 12220 11323 206 5038 244
## 96 1 3 3 2920 6252 440 223 709
## 97 0 3 23 2616 8118 145 3874 217
## 98 1 3 403 254 610 774 54 63
## 99 1 3 503 112 778 895 56 132
## 100 1 3 9658 2182 1909 5639 215 323
## 101 0 3 11594 7779 12144 3252 8035 3029
## 102 0 3 1420 10810 16267 1593 6766 1838
## 103 0 3 2932 6459 7677 2561 4573 1386
## 104 1 3 56082 3504 8906 18028 1480 2498
## 105 1 3 14100 2132 3445 1336 1491 548
## 106 1 3 15587 1014 3970 910 139 1378
## 107 0 3 1454 6337 10704 133 6830 1831
## 108 0 3 8797 10646 14886 2471 8969 1438
## 109 0 3 1531 8397 6981 247 2505 1236
## 110 0 3 1406 16729 28986 673 836 3
## 111 1 3 11818 1648 1694 2276 169 1647
## 112 0 3 12579 11114 17569 805 6457 1519
## 113 1 3 19046 2770 2469 8853 483 2708
## 114 1 3 14438 2295 1733 3220 585 1561
## 115 1 3 18044 1080 2000 2555 118 1266
## 116 1 3 11134 793 2988 2715 276 610
## 117 1 3 11173 2521 3355 1517 310 222
## 118 1 3 6990 3880 5380 1647 319 1160
## 119 1 3 20049 1891 2362 5343 411 933
## 120 1 3 8258 2344 2147 3896 266 635
## 121 1 3 17160 1200 3412 2417 174 1136
## 122 1 3 4020 3234 1498 2395 264 255
## 123 1 3 12212 201 245 1991 25 860
## 124 0 3 11170 10769 8814 2194 1976 143
## 125 1 3 36050 1642 2961 4787 500 1621
## 126 1 3 76237 3473 7102 16538 778 918
## 127 1 3 19219 1840 1658 8195 349 483
## 128 0 3 21465 7243 10685 880 2386 2749
## 129 1 3 140 8847 3823 142 1062 3
## 130 1 3 42312 926 1510 1718 410 1819
## 131 1 3 7149 2428 699 6316 395 911
## 132 1 3 2101 589 314 346 70 310
## 133 1 3 14903 2032 2479 576 955 328
## 134 1 3 9434 1042 1235 436 256 396
## 135 1 3 7388 1882 2174 720 47 537
## 136 1 3 6300 1289 2591 1170 199 326
## 137 1 3 4625 8579 7030 4575 2447 1542
## 138 1 3 3087 8080 8282 661 721 36
## 139 1 3 13537 4257 5034 155 249 3271
## 140 1 3 5387 4979 3343 825 637 929
## 141 1 3 17623 4280 7305 2279 960 2616
## 142 1 3 30379 13252 5189 321 51 1450
## 143 1 3 37036 7152 8253 2995 20 3
## 144 1 3 10405 1596 1096 8425 399 318
## 145 1 3 18827 3677 1988 118 516 201
## 146 0 3 22039 8384 34792 42 12591 4430
## 147 1 3 7769 1936 2177 926 73 520
## 148 1 3 9203 3373 2707 1286 1082 526
## 149 1 3 5924 584 542 4052 283 434
## 150 1 3 31812 1433 1651 800 113 1440
## 151 1 3 16225 1825 1765 853 170 1067
## 152 1 3 1289 3328 2022 531 255 1774
## 153 1 3 18840 1371 3135 3001 352 184
## 154 1 3 3463 9250 2368 779 302 1627
## 155 1 3 622 55 137 75 7 8
## 156 0 3 1989 10690 19460 233 11577 2153
## 157 0 3 3830 5291 14855 317 6694 3182
## 158 1 3 17773 1366 2474 3378 811 418
## 159 0 3 2861 6570 9618 930 4004 1682
## 160 0 3 355 7704 14682 398 8077 303
## 161 0 3 1725 3651 12822 824 4424 2157
## 162 1 3 12434 540 283 1092 3 2233
## 163 1 3 15177 2024 3810 2665 232 610
## 164 0 3 5531 15726 26870 2367 13726 446
## 165 0 3 5224 7603 8584 2540 3674 238
## 166 0 3 15615 12653 19858 4425 7108 2379
## 167 0 3 4822 6721 9170 993 4973 3637
## 168 1 3 2926 3195 3268 405 1680 693
## 169 1 3 5809 735 803 1393 79 429
## 170 1 3 5414 717 2155 2399 69 750
## 171 0 3 260 8675 13430 1116 7015 323
## 172 0 3 200 25862 19816 651 8773 6250
## 173 1 3 955 5479 6536 333 2840 707
## 174 0 3 514 7677 19805 937 9836 716
## 175 1 3 286 1208 5241 2515 153 1442
## 176 0 3 2343 7845 11874 52 4196 1697
## 177 1 3 45640 6958 6536 7368 1532 230
## 178 1 3 12759 7330 4533 1752 20 2631
## 179 1 3 11002 7075 4945 1152 120 395
## 180 1 3 3157 4888 2500 4477 273 2165
## 181 1 3 12356 6036 8887 402 1382 2794
## 182 1 3 112151 29627 18148 16745 4948 8550
## 183 1 3 694 8533 10518 443 6907 156
## 184 1 3 36847 43950 20170 36534 239 47943
## 185 1 3 327 918 4710 74 334 11
## 186 1 3 8170 6448 1139 2181 58 247
## 187 1 3 3009 521 854 3470 949 727
## 188 1 3 2438 8002 9819 6269 3459 3
## 189 0 3 8040 7639 11687 2758 6839 404
## 190 0 3 834 11577 11522 275 4027 1856
## 191 1 3 16936 6250 1981 7332 118 64
## 192 1 3 13624 295 1381 890 43 84
## 193 1 3 5509 1461 2251 547 187 409
## 194 0 3 180 3485 20292 959 5618 666
## 195 1 3 7107 1012 2974 806 355 1142
## 196 1 3 17023 5139 5230 7888 330 1755
## 197 1 1 30624 7209 4897 18711 763 2876
## 198 0 1 2427 7097 10391 1127 4314 1468
## 199 1 1 11686 2154 6824 3527 592 697
## 200 1 1 9670 2280 2112 520 402 347
## 201 0 1 3067 13240 23127 3941 9959 731
## 202 0 1 4484 14399 24708 3549 14235 1681
## 203 1 1 25203 11487 9490 5065 284 6854
## 204 1 1 583 685 2216 469 954 18
## 205 1 1 1956 891 5226 1383 5 1328
## 206 0 1 1107 11711 23596 955 9265 710
## 207 1 1 6373 780 950 878 288 285
## 208 0 1 2541 4737 6089 2946 5316 120
## 209 1 1 1537 3748 5838 1859 3381 806
## 210 0 1 5550 12729 16767 864 12420 797
## 211 1 1 18567 1895 1393 1801 244 2100
## 212 0 1 12119 28326 39694 4736 19410 2870
## 213 1 1 7291 1012 2062 1291 240 1775
## 214 1 1 3317 6602 6861 1329 3961 1215
## 215 0 1 2362 6551 11364 913 5957 791
## 216 1 1 2806 10765 15538 1374 5828 2388
## 217 0 1 2532 16599 36486 179 13308 674
## 218 1 1 18044 1475 2046 2532 130 1158
## 219 0 1 18 7504 15205 1285 4797 6372
## 220 1 1 4155 367 1390 2306 86 130
## 221 1 1 14755 899 1382 1765 56 749
## 222 1 1 5396 7503 10646 91 4167 239
## 223 1 1 5041 1115 2856 7496 256 375
## 224 0 1 2790 2527 5265 5612 788 1360
## 225 1 1 7274 659 1499 784 70 659
## 226 1 1 12680 3243 4157 660 761 786
## 227 0 1 20782 5921 9212 1759 2568 1553
## 228 1 1 4042 2204 1563 2286 263 689
## 229 1 1 1869 577 572 950 4762 203
## 230 1 1 8656 2746 2501 6845 694 980
## 231 0 1 11072 5989 5615 8321 955 2137
## 232 1 1 2344 10678 3828 1439 1566 490
## 233 1 1 25962 1780 3838 638 284 834
## 234 1 1 964 4984 3316 937 409 7
## 235 1 1 15603 2703 3833 4260 325 2563
## 236 1 1 1838 6380 2824 1218 1216 295
## 237 1 1 8635 820 3047 2312 415 225
## 238 1 1 18692 3838 593 4634 28 1215
## 239 1 1 7363 475 585 1112 72 216
## 240 1 1 47493 2567 3779 5243 828 2253
## 241 1 1 22096 3575 7041 11422 343 2564
## 242 1 1 24929 1801 2475 2216 412 1047
## 243 1 1 18226 659 2914 3752 586 578
## 244 1 1 11210 3576 5119 561 1682 2398
## 245 1 1 6202 7775 10817 1183 3143 1970
## 246 0 1 3062 6154 13916 230 8933 2784
## 247 1 1 8885 2428 1777 1777 430 610
## 248 1 1 13569 346 489 2077 44 659
## 249 1 1 15671 5279 2406 559 562 572
## 250 1 1 8040 3795 2070 6340 918 291
## 251 1 1 3191 1993 1799 1730 234 710
## 252 0 1 6134 23133 33586 6746 18594 5121
## 253 1 1 6623 1860 4740 7683 205 1693
## 254 1 1 29526 7961 16966 432 363 1391
## 255 1 1 10379 17972 4748 4686 1547 3265
## 256 1 1 31614 489 1495 3242 111 615
## 257 1 1 11092 5008 5249 453 392 373
## 258 1 1 8475 1931 1883 5004 3593 987
## 259 1 1 56083 4563 2124 6422 730 3321
## 260 1 1 53205 4959 7336 3012 967 818
## 261 1 1 9193 4885 2157 327 780 548
## 262 1 1 7858 1110 1094 6818 49 287
## 263 1 1 23257 1372 1677 982 429 655
## 264 1 1 2153 1115 6684 4324 2894 411
## 265 0 1 1073 9679 15445 61 5980 1265
## 266 1 1 5909 23527 13699 10155 830 3636
## 267 0 1 572 9763 22182 2221 4882 2563
## 268 1 1 20893 1222 2576 3975 737 3628
## 269 0 1 11908 8053 19847 1069 6374 698
## 270 1 1 15218 258 1138 2516 333 204
## 271 1 1 4720 1032 975 5500 197 56
## 272 1 1 2083 5007 1563 1120 147 1550
## 273 1 1 514 8323 6869 529 93 1040
## 274 1 3 36817 3045 1493 4802 210 1824
## 275 1 3 894 1703 1841 744 759 1153
## 276 1 3 680 1610 223 862 96 379
## 277 1 3 27901 3749 6964 4479 603 2503
## 278 1 3 9061 829 683 16919 621 139
## 279 1 3 11693 2317 2543 5845 274 1409
## 280 0 3 17360 6200 9694 1293 3620 1721
## 281 1 3 3366 2884 2431 977 167 1104
## 282 0 3 12238 7108 6235 1093 2328 2079
## 283 1 3 49063 3965 4252 5970 1041 1404
## 284 1 3 25767 3613 2013 10303 314 1384
## 285 1 3 68951 4411 12609 8692 751 2406
## 286 1 3 40254 640 3600 1042 436 18
## 287 1 3 7149 2247 1242 1619 1226 128
## 288 1 3 15354 2102 2828 8366 386 1027
## 289 1 3 16260 594 1296 848 445 258
## 290 1 3 42786 286 471 1388 32 22
## 291 1 3 2708 2160 2642 502 965 1522
## 292 1 3 6022 3354 3261 2507 212 686
## 293 1 3 2838 3086 4329 3838 825 1060
## 294 0 2 3996 11103 12469 902 5952 741
## 295 1 2 21273 2013 6550 909 811 1854
## 296 0 2 7588 1897 5234 417 2208 254
## 297 1 2 19087 1304 3643 3045 710 898
## 298 0 2 8090 3199 6986 1455 3712 531
## 299 0 2 6758 4560 9965 934 4538 1037
## 300 1 2 444 879 2060 264 290 259
## 301 0 2 16448 6243 6360 824 2662 2005
## 302 0 2 5283 13316 20399 1809 8752 172
## 303 0 2 2886 5302 9785 364 6236 555
## 304 0 2 2599 3688 13829 492 10069 59
## 305 0 2 161 7460 24773 617 11783 2410
## 306 0 2 243 12939 8852 799 3909 211
## 307 0 2 6468 12867 21570 1840 7558 1543
## 308 1 2 17327 2374 2842 1149 351 925
## 309 1 2 6987 1020 3007 416 257 656
## 310 0 2 918 20655 13567 1465 6846 806
## 311 1 2 7034 1492 2405 12569 299 1117
## 312 1 2 29635 2335 8280 3046 371 117
## 313 0 2 2137 3737 19172 1274 17120 142
## 314 1 2 9784 925 2405 4447 183 297
## 315 1 2 10617 1795 7647 1483 857 1233
## 316 0 2 1479 14982 11924 662 3891 3508
## 317 1 2 7127 1375 2201 2679 83 1059
## 318 1 2 1182 3088 6114 978 821 1637
## 319 1 2 11800 2713 3558 2121 706 51
## 320 0 2 9759 25071 17645 1128 12408 1625
## 321 1 2 1774 3696 2280 514 275 834
## 322 1 2 9155 1897 5167 2714 228 1113
## 323 1 2 15881 713 3315 3703 1470 229
## 324 1 2 13360 944 11593 915 1679 573
## 325 1 2 25977 3587 2464 2369 140 1092
## 326 1 2 32717 16784 13626 60869 1272 5609
## 327 1 2 4414 1610 1431 3498 387 834
## 328 1 2 542 899 1664 414 88 522
## 329 1 2 16933 2209 3389 7849 210 1534
## 330 1 2 5113 1486 4583 5127 492 739
## 331 1 2 9790 1786 5109 3570 182 1043
## 332 0 2 11223 14881 26839 1234 9606 1102
## 333 1 2 22321 3216 1447 2208 178 2602
## 334 0 2 8565 4980 67298 131 38102 1215
## 335 0 2 16823 928 2743 11559 332 3486
## 336 0 2 27082 6817 10790 1365 4111 2139
## 337 1 2 13970 1511 1330 650 146 778
## 338 1 2 9351 1347 2611 8170 442 868
## 339 1 2 3 333 7021 15601 15 550
## 340 1 2 2617 1188 5332 9584 573 1942
## 341 0 3 381 4025 9670 388 7271 1371
## 342 0 3 2320 5763 11238 767 5162 2158
## 343 1 3 255 5758 5923 349 4595 1328
## 344 0 3 1689 6964 26316 1456 15469 37
## 345 1 3 3043 1172 1763 2234 217 379
## 346 1 3 1198 2602 8335 402 3843 303
## 347 0 3 2771 6939 15541 2693 6600 1115
## 348 0 3 27380 7184 12311 2809 4621 1022
## 349 1 3 3428 2380 2028 1341 1184 665
## 350 0 3 5981 14641 20521 2005 12218 445
## 351 1 3 3521 1099 1997 1796 173 995
## 352 0 3 1210 10044 22294 1741 12638 3137
## 353 1 3 608 1106 1533 830 90 195
## 354 0 3 117 6264 21203 228 8682 1111
## 355 1 3 14039 7393 2548 6386 1333 2341
## 356 1 3 190 727 2012 245 184 127
## 357 1 3 22686 134 218 3157 9 548
## 358 0 3 37 1275 22272 137 6747 110
## 359 1 3 759 18664 1660 6114 536 4100
## 360 1 3 796 5878 2109 340 232 776
## 361 1 3 19746 2872 2006 2601 468 503
## 362 1 3 4734 607 864 1206 159 405
## 363 1 3 2121 1601 2453 560 179 712
## 364 1 3 4627 997 4438 191 1335 314
## 365 1 3 2615 873 1524 1103 514 468
## 366 0 3 4692 6128 8025 1619 4515 3105
## 367 1 3 9561 2217 1664 1173 222 447
## 368 1 3 3477 894 534 1457 252 342
## 369 1 3 22335 1196 2406 2046 101 558
## 370 1 3 6211 337 683 1089 41 296
## 371 0 3 39679 3944 4955 1364 523 2235
## 372 1 3 20105 1887 1939 8164 716 790
## 373 1 3 3884 3801 1641 876 397 4829
## 374 0 3 15076 6257 7398 1504 1916 3113
## 375 1 3 6338 2256 1668 1492 311 686
## 376 1 3 5841 1450 1162 597 476 70
## 377 0 3 3136 8630 13586 5641 4666 1426
## 378 1 3 38793 3154 2648 1034 96 1242
## 379 1 3 3225 3294 1902 282 68 1114
## 380 0 3 4048 5164 10391 130 813 179
## 381 1 3 28257 944 2146 3881 600 270
## 382 1 3 17770 4591 1617 9927 246 532
## 383 1 3 34454 7435 8469 2540 1711 2893
## 384 1 3 1821 1364 3450 4006 397 361
## 385 1 3 10683 21858 15400 3635 282 5120
## 386 1 3 11635 922 1614 2583 192 1068
## 387 1 3 1206 3620 2857 1945 353 967
## 388 1 3 20918 1916 1573 1960 231 961
## 389 1 3 9785 848 1172 1677 200 406
## 390 1 3 9385 1530 1422 3019 227 684
## 391 1 3 3352 1181 1328 5502 311 1000
## 392 1 3 2647 2761 2313 907 95 1827
## 393 1 3 518 4180 3600 659 122 654
## 394 1 3 23632 6730 3842 8620 385 819
## 395 1 3 12377 865 3204 1398 149 452
## 396 1 3 9602 1316 1263 2921 841 290
## 397 0 3 4515 11991 9345 2644 3378 2213
## 398 1 3 11535 1666 1428 6838 64 743
## 399 1 3 11442 1032 582 5390 74 247
## 400 1 3 9612 577 935 1601 469 375
## 401 1 3 4446 906 1238 3576 153 1014
## 402 1 3 27167 2801 2128 13223 92 1902
## 403 1 3 26539 4753 5091 220 10 340
## 404 1 3 25606 11006 4604 127 632 288
## 405 1 3 18073 4613 3444 4324 914 715
## 406 1 3 6884 1046 1167 2069 593 378
## 407 1 3 25066 5010 5026 9806 1092 960
## 408 0 3 7362 12844 18683 2854 7883 553
## 409 0 3 8257 3880 6407 1646 2730 344
## 410 1 3 8708 3634 6100 2349 2123 5137
## 411 1 3 6633 2096 4563 1389 1860 1892
## 412 1 3 2126 3289 3281 1535 235 4365
## 413 1 3 97 3605 12400 98 2970 62
## 414 1 3 4983 4859 6633 17866 912 2435
## 415 1 3 5969 1990 3417 5679 1135 290
## 416 0 3 7842 6046 8552 1691 3540 1874
## 417 0 3 4389 10940 10908 848 6728 993
## 418 1 3 5065 5499 11055 364 3485 1063
## 419 0 3 660 8494 18622 133 6740 776
## 420 1 3 8861 3783 2223 633 1580 1521
## 421 1 3 4456 5266 13227 25 6818 1393
## 422 0 3 17063 4847 9053 1031 3415 1784
## 423 1 3 26400 1377 4172 830 948 1218
## 424 0 3 17565 3686 4657 1059 1803 668
## 425 0 3 16980 2884 12232 874 3213 249
## 426 1 3 11243 2408 2593 15348 108 1886
## 427 1 3 13134 9347 14316 3141 5079 1894
## 428 1 3 31012 16687 5429 15082 439 1163
## 429 1 3 3047 5970 4910 2198 850 317
## 430 1 3 8607 1750 3580 47 84 2501
## 431 1 3 3097 4230 16483 575 241 2080
## 432 1 3 8533 5506 5160 13486 1377 1498
## 433 1 3 21117 1162 4754 269 1328 395
## 434 1 3 1982 3218 1493 1541 356 1449
## 435 1 3 16731 3922 7994 688 2371 838
## 436 1 3 29703 12051 16027 13135 182 2204
## 437 1 3 39228 1431 764 4510 93 2346
## 438 0 3 14531 15488 30243 437 14841 1867
## 439 1 3 10290 1981 2232 1038 168 2125
## 440 1 3 2787 1698 2510 65 477 52ind <- sample(2, nrow(wholesale), replace=TRUE, prob=c(0.7,0.3))
trainData <- wholesale[ind==1,]
testData <- wholesale[ind==2,]
trainData## Channel Region Fresh Milk Grocery Frozen Detergents_Paper Delicassen
## 2 0 3 7057 9810 9568 1762 3293 1776
## 3 0 3 6353 8808 7684 2405 3516 7844
## 4 1 3 13265 1196 4221 6404 507 1788
## 5 0 3 22615 5410 7198 3915 1777 5185
## 6 0 3 9413 8259 5126 666 1795 1451
## 7 0 3 12126 3199 6975 480 3140 545
## 8 0 3 7579 4956 9426 1669 3321 2566
## 9 1 3 5963 3648 6192 425 1716 750
## 10 0 3 6006 11093 18881 1159 7425 2098
## 11 0 3 3366 5403 12974 4400 5977 1744
## 12 0 3 13146 1124 4523 1420 549 497
## 13 0 3 31714 12319 11757 287 3881 2931
## 14 0 3 21217 6208 14982 3095 6707 602
## 15 0 3 24653 9465 12091 294 5058 2168
## 16 1 3 10253 1114 3821 397 964 412
## 17 0 3 1020 8816 12121 134 4508 1080
## 19 0 3 18601 6327 10099 2205 2767 3181
## 20 1 3 7780 2495 9464 669 2518 501
## 25 0 3 22647 9776 13792 2915 4482 5778
## 26 0 3 16165 4230 7595 201 4003 57
## 27 1 3 9898 961 2861 3151 242 833
## 28 1 3 14276 803 3045 485 100 518
## 29 0 3 4113 20484 25957 1158 8604 5206
## 30 1 3 43088 2100 2609 1200 1107 823
## 32 1 3 2612 4339 3133 2088 820 985
## 33 1 3 21632 1318 2886 266 918 405
## 34 1 3 29729 4786 7326 6130 361 1083
## 37 1 3 29955 4362 5428 1729 862 4626
## 38 0 3 15168 10556 12477 1920 6506 714
## 39 0 3 4591 15729 16709 33 6956 433
## 40 1 3 56159 555 902 10002 212 2916
## 42 1 3 19176 3065 5956 2033 2575 2802
## 43 0 3 10850 7555 14961 188 6899 46
## 45 0 3 9670 7027 10471 541 4618 65
## 46 0 3 5181 22044 21531 1740 7353 4985
## 48 0 3 44466 54259 55571 7782 24171 6465
## 51 1 3 6269 1095 1980 3860 609 2162
## 52 1 3 3347 4051 6996 239 1538 301
## 54 0 3 491 10473 11532 744 5611 224
## 57 0 3 4098 29892 26866 2616 17740 1340
## 60 1 3 6137 5360 8040 129 3084 1603
## 61 0 3 8590 3045 7854 96 4095 225
## 62 0 3 35942 38369 59598 3254 26701 2017
## 63 0 3 7823 6245 6544 4154 4074 964
## 64 0 3 9396 11601 15775 2896 7677 1295
## 65 1 3 4760 1227 3250 3724 1247 1145
## 67 1 3 9 1534 7417 175 3468 27
## 68 0 3 19913 6759 13462 1256 5141 834
## 69 1 3 2446 7260 3993 5870 788 3095
## 70 1 3 8352 2820 1293 779 656 144
## 74 0 3 19899 5332 8713 8132 764 648
## 75 0 3 8190 6343 9794 1285 1901 1780
## 76 1 3 20398 1137 3 4407 3 975
## 77 1 3 717 3587 6532 7530 529 894
## 78 0 3 12205 12697 28540 869 12034 1009
## 81 1 3 7005 829 3009 430 610 529
## 82 0 3 219 9540 14403 283 7818 156
## 83 0 3 10362 9232 11009 737 3537 2342
## 84 1 3 20874 1563 1783 2320 550 772
## 88 1 3 43265 5025 8117 6312 1579 14351
## 91 1 3 11405 596 1638 3347 69 360
## 92 1 3 12754 2762 2530 8693 627 1117
## 93 0 3 9198 27472 32034 3232 18906 5130
## 94 1 3 11314 3090 2062 35009 71 2698
## 97 0 3 23 2616 8118 145 3874 217
## 98 1 3 403 254 610 774 54 63
## 99 1 3 503 112 778 895 56 132
## 100 1 3 9658 2182 1909 5639 215 323
## 102 0 3 1420 10810 16267 1593 6766 1838
## 104 1 3 56082 3504 8906 18028 1480 2498
## 108 0 3 8797 10646 14886 2471 8969 1438
## 109 0 3 1531 8397 6981 247 2505 1236
## 110 0 3 1406 16729 28986 673 836 3
## 112 0 3 12579 11114 17569 805 6457 1519
## 113 1 3 19046 2770 2469 8853 483 2708
## 114 1 3 14438 2295 1733 3220 585 1561
## 115 1 3 18044 1080 2000 2555 118 1266
## 117 1 3 11173 2521 3355 1517 310 222
## 118 1 3 6990 3880 5380 1647 319 1160
## 119 1 3 20049 1891 2362 5343 411 933
## 120 1 3 8258 2344 2147 3896 266 635
## 121 1 3 17160 1200 3412 2417 174 1136
## 122 1 3 4020 3234 1498 2395 264 255
## 123 1 3 12212 201 245 1991 25 860
## 124 0 3 11170 10769 8814 2194 1976 143
## 126 1 3 76237 3473 7102 16538 778 918
## 128 0 3 21465 7243 10685 880 2386 2749
## 129 1 3 140 8847 3823 142 1062 3
## 130 1 3 42312 926 1510 1718 410 1819
## 131 1 3 7149 2428 699 6316 395 911
## 132 1 3 2101 589 314 346 70 310
## 133 1 3 14903 2032 2479 576 955 328
## 136 1 3 6300 1289 2591 1170 199 326
## 137 1 3 4625 8579 7030 4575 2447 1542
## 139 1 3 13537 4257 5034 155 249 3271
## 140 1 3 5387 4979 3343 825 637 929
## 141 1 3 17623 4280 7305 2279 960 2616
## 142 1 3 30379 13252 5189 321 51 1450
## 143 1 3 37036 7152 8253 2995 20 3
## 144 1 3 10405 1596 1096 8425 399 318
## 145 1 3 18827 3677 1988 118 516 201
## 148 1 3 9203 3373 2707 1286 1082 526
## 149 1 3 5924 584 542 4052 283 434
## 151 1 3 16225 1825 1765 853 170 1067
## 152 1 3 1289 3328 2022 531 255 1774
## 154 1 3 3463 9250 2368 779 302 1627
## 155 1 3 622 55 137 75 7 8
## 157 0 3 3830 5291 14855 317 6694 3182
## 160 0 3 355 7704 14682 398 8077 303
## 163 1 3 15177 2024 3810 2665 232 610
## 165 0 3 5224 7603 8584 2540 3674 238
## 168 1 3 2926 3195 3268 405 1680 693
## 169 1 3 5809 735 803 1393 79 429
## 170 1 3 5414 717 2155 2399 69 750
## 171 0 3 260 8675 13430 1116 7015 323
## 173 1 3 955 5479 6536 333 2840 707
## 174 0 3 514 7677 19805 937 9836 716
## 175 1 3 286 1208 5241 2515 153 1442
## 176 0 3 2343 7845 11874 52 4196 1697
## 177 1 3 45640 6958 6536 7368 1532 230
## 178 1 3 12759 7330 4533 1752 20 2631
## 179 1 3 11002 7075 4945 1152 120 395
## 181 1 3 12356 6036 8887 402 1382 2794
## 182 1 3 112151 29627 18148 16745 4948 8550
## 183 1 3 694 8533 10518 443 6907 156
## 184 1 3 36847 43950 20170 36534 239 47943
## 186 1 3 8170 6448 1139 2181 58 247
## 187 1 3 3009 521 854 3470 949 727
## 188 1 3 2438 8002 9819 6269 3459 3
## 189 0 3 8040 7639 11687 2758 6839 404
## 191 1 3 16936 6250 1981 7332 118 64
## 192 1 3 13624 295 1381 890 43 84
## 193 1 3 5509 1461 2251 547 187 409
## 194 0 3 180 3485 20292 959 5618 666
## 195 1 3 7107 1012 2974 806 355 1142
## 196 1 3 17023 5139 5230 7888 330 1755
## 197 1 1 30624 7209 4897 18711 763 2876
## 199 1 1 11686 2154 6824 3527 592 697
## 201 0 1 3067 13240 23127 3941 9959 731
## 202 0 1 4484 14399 24708 3549 14235 1681
## 203 1 1 25203 11487 9490 5065 284 6854
## 204 1 1 583 685 2216 469 954 18
## 205 1 1 1956 891 5226 1383 5 1328
## 206 0 1 1107 11711 23596 955 9265 710
## 207 1 1 6373 780 950 878 288 285
## 208 0 1 2541 4737 6089 2946 5316 120
## 209 1 1 1537 3748 5838 1859 3381 806
## 211 1 1 18567 1895 1393 1801 244 2100
## 213 1 1 7291 1012 2062 1291 240 1775
## 215 0 1 2362 6551 11364 913 5957 791
## 216 1 1 2806 10765 15538 1374 5828 2388
## 219 0 1 18 7504 15205 1285 4797 6372
## 220 1 1 4155 367 1390 2306 86 130
## 221 1 1 14755 899 1382 1765 56 749
## 224 0 1 2790 2527 5265 5612 788 1360
## 225 1 1 7274 659 1499 784 70 659
## 227 0 1 20782 5921 9212 1759 2568 1553
## 228 1 1 4042 2204 1563 2286 263 689
## 231 0 1 11072 5989 5615 8321 955 2137
## 233 1 1 25962 1780 3838 638 284 834
## 235 1 1 15603 2703 3833 4260 325 2563
## 237 1 1 8635 820 3047 2312 415 225
## 238 1 1 18692 3838 593 4634 28 1215
## 240 1 1 47493 2567 3779 5243 828 2253
## 242 1 1 24929 1801 2475 2216 412 1047
## 243 1 1 18226 659 2914 3752 586 578
## 246 0 1 3062 6154 13916 230 8933 2784
## 247 1 1 8885 2428 1777 1777 430 610
## 249 1 1 15671 5279 2406 559 562 572
## 250 1 1 8040 3795 2070 6340 918 291
## 252 0 1 6134 23133 33586 6746 18594 5121
## 254 1 1 29526 7961 16966 432 363 1391
## 256 1 1 31614 489 1495 3242 111 615
## 259 1 1 56083 4563 2124 6422 730 3321
## 261 1 1 9193 4885 2157 327 780 548
## 264 1 1 2153 1115 6684 4324 2894 411
## 265 0 1 1073 9679 15445 61 5980 1265
## 266 1 1 5909 23527 13699 10155 830 3636
## 268 1 1 20893 1222 2576 3975 737 3628
## 269 0 1 11908 8053 19847 1069 6374 698
## 270 1 1 15218 258 1138 2516 333 204
## 272 1 1 2083 5007 1563 1120 147 1550
## 273 1 1 514 8323 6869 529 93 1040
## 276 1 3 680 1610 223 862 96 379
## 277 1 3 27901 3749 6964 4479 603 2503
## 278 1 3 9061 829 683 16919 621 139
## 279 1 3 11693 2317 2543 5845 274 1409
## 282 0 3 12238 7108 6235 1093 2328 2079
## 283 1 3 49063 3965 4252 5970 1041 1404
## 284 1 3 25767 3613 2013 10303 314 1384
## 286 1 3 40254 640 3600 1042 436 18
## 287 1 3 7149 2247 1242 1619 1226 128
## 288 1 3 15354 2102 2828 8366 386 1027
## 289 1 3 16260 594 1296 848 445 258
## 291 1 3 2708 2160 2642 502 965 1522
## 293 1 3 2838 3086 4329 3838 825 1060
## 295 1 2 21273 2013 6550 909 811 1854
## 298 0 2 8090 3199 6986 1455 3712 531
## 299 0 2 6758 4560 9965 934 4538 1037
## 301 0 2 16448 6243 6360 824 2662 2005
## 302 0 2 5283 13316 20399 1809 8752 172
## 305 0 2 161 7460 24773 617 11783 2410
## 306 0 2 243 12939 8852 799 3909 211
## 310 0 2 918 20655 13567 1465 6846 806
## 311 1 2 7034 1492 2405 12569 299 1117
## 312 1 2 29635 2335 8280 3046 371 117
## 314 1 2 9784 925 2405 4447 183 297
## 316 0 2 1479 14982 11924 662 3891 3508
## 317 1 2 7127 1375 2201 2679 83 1059
## 319 1 2 11800 2713 3558 2121 706 51
## 320 0 2 9759 25071 17645 1128 12408 1625
## 321 1 2 1774 3696 2280 514 275 834
## 322 1 2 9155 1897 5167 2714 228 1113
## 325 1 2 25977 3587 2464 2369 140 1092
## 326 1 2 32717 16784 13626 60869 1272 5609
## 327 1 2 4414 1610 1431 3498 387 834
## 328 1 2 542 899 1664 414 88 522
## 329 1 2 16933 2209 3389 7849 210 1534
## 330 1 2 5113 1486 4583 5127 492 739
## 331 1 2 9790 1786 5109 3570 182 1043
## 333 1 2 22321 3216 1447 2208 178 2602
## 334 0 2 8565 4980 67298 131 38102 1215
## 336 0 2 27082 6817 10790 1365 4111 2139
## 338 1 2 9351 1347 2611 8170 442 868
## 339 1 2 3 333 7021 15601 15 550
## 340 1 2 2617 1188 5332 9584 573 1942
## 341 0 3 381 4025 9670 388 7271 1371
## 342 0 3 2320 5763 11238 767 5162 2158
## 343 1 3 255 5758 5923 349 4595 1328
## 345 1 3 3043 1172 1763 2234 217 379
## 347 0 3 2771 6939 15541 2693 6600 1115
## 348 0 3 27380 7184 12311 2809 4621 1022
## 349 1 3 3428 2380 2028 1341 1184 665
## 350 0 3 5981 14641 20521 2005 12218 445
## 351 1 3 3521 1099 1997 1796 173 995
## 352 0 3 1210 10044 22294 1741 12638 3137
## 353 1 3 608 1106 1533 830 90 195
## 354 0 3 117 6264 21203 228 8682 1111
## 355 1 3 14039 7393 2548 6386 1333 2341
## 356 1 3 190 727 2012 245 184 127
## 357 1 3 22686 134 218 3157 9 548
## 358 0 3 37 1275 22272 137 6747 110
## 360 1 3 796 5878 2109 340 232 776
## 361 1 3 19746 2872 2006 2601 468 503
## 362 1 3 4734 607 864 1206 159 405
## 364 1 3 4627 997 4438 191 1335 314
## 365 1 3 2615 873 1524 1103 514 468
## 366 0 3 4692 6128 8025 1619 4515 3105
## 367 1 3 9561 2217 1664 1173 222 447
## 368 1 3 3477 894 534 1457 252 342
## 371 0 3 39679 3944 4955 1364 523 2235
## 373 1 3 3884 3801 1641 876 397 4829
## 375 1 3 6338 2256 1668 1492 311 686
## 376 1 3 5841 1450 1162 597 476 70
## 377 0 3 3136 8630 13586 5641 4666 1426
## 378 1 3 38793 3154 2648 1034 96 1242
## 379 1 3 3225 3294 1902 282 68 1114
## 380 0 3 4048 5164 10391 130 813 179
## 381 1 3 28257 944 2146 3881 600 270
## 382 1 3 17770 4591 1617 9927 246 532
## 385 1 3 10683 21858 15400 3635 282 5120
## 387 1 3 1206 3620 2857 1945 353 967
## 388 1 3 20918 1916 1573 1960 231 961
## 389 1 3 9785 848 1172 1677 200 406
## 390 1 3 9385 1530 1422 3019 227 684
## 391 1 3 3352 1181 1328 5502 311 1000
## 392 1 3 2647 2761 2313 907 95 1827
## 393 1 3 518 4180 3600 659 122 654
## 394 1 3 23632 6730 3842 8620 385 819
## 395 1 3 12377 865 3204 1398 149 452
## 396 1 3 9602 1316 1263 2921 841 290
## 397 0 3 4515 11991 9345 2644 3378 2213
## 398 1 3 11535 1666 1428 6838 64 743
## 399 1 3 11442 1032 582 5390 74 247
## 400 1 3 9612 577 935 1601 469 375
## 401 1 3 4446 906 1238 3576 153 1014
## 402 1 3 27167 2801 2128 13223 92 1902
## 403 1 3 26539 4753 5091 220 10 340
## 404 1 3 25606 11006 4604 127 632 288
## 405 1 3 18073 4613 3444 4324 914 715
## 406 1 3 6884 1046 1167 2069 593 378
## 407 1 3 25066 5010 5026 9806 1092 960
## 409 0 3 8257 3880 6407 1646 2730 344
## 411 1 3 6633 2096 4563 1389 1860 1892
## 412 1 3 2126 3289 3281 1535 235 4365
## 413 1 3 97 3605 12400 98 2970 62
## 414 1 3 4983 4859 6633 17866 912 2435
## 415 1 3 5969 1990 3417 5679 1135 290
## 416 0 3 7842 6046 8552 1691 3540 1874
## 418 1 3 5065 5499 11055 364 3485 1063
## 419 0 3 660 8494 18622 133 6740 776
## 420 1 3 8861 3783 2223 633 1580 1521
## 421 1 3 4456 5266 13227 25 6818 1393
## 422 0 3 17063 4847 9053 1031 3415 1784
## 423 1 3 26400 1377 4172 830 948 1218
## 424 0 3 17565 3686 4657 1059 1803 668
## 425 0 3 16980 2884 12232 874 3213 249
## 426 1 3 11243 2408 2593 15348 108 1886
## 427 1 3 13134 9347 14316 3141 5079 1894
## 428 1 3 31012 16687 5429 15082 439 1163
## 429 1 3 3047 5970 4910 2198 850 317
## 431 1 3 3097 4230 16483 575 241 2080
## 433 1 3 21117 1162 4754 269 1328 395
## 434 1 3 1982 3218 1493 1541 356 1449
## 435 1 3 16731 3922 7994 688 2371 838
## 436 1 3 29703 12051 16027 13135 182 2204
## 437 1 3 39228 1431 764 4510 93 2346
## 438 0 3 14531 15488 30243 437 14841 1867
## 440 1 3 2787 1698 2510 65 477 52glm.fit=glm(Channel~Region + Fresh + Milk + Grocery + Frozen + Detergents_Paper + Delicassen,
data=trainData,family=binomial)## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurredglm.probs=predict(glm.fit,newdata=wholesale[!trainData,],type="response")
glm.pred=ifelse(glm.probs >0.5,"Hotel/Cafe/Restaurant","Retail")
Channel=wholesale$Channel[!trainData]
table(glm.pred,Channel)## Channel
## glm.pred 0 1
## Hotel/Cafe/Restaurant 8 55
## Retail 32 2mean(glm.pred==Channel)## [1] 0summary(glm.fit)##
## Call:
## glm(formula = Channel ~ Region + Fresh + Milk + Grocery + Frozen +
## Detergents_Paper + Delicassen, family = binomial, data = trainData)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.5495 -0.0682 0.2451 0.3360 2.8501
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 3.570e+00 9.606e-01 3.717 0.000202 ***
## Region -2.934e-02 3.179e-01 -0.092 0.926466
## Fresh 3.289e-06 1.831e-05 0.180 0.857491
## Milk -2.764e-05 7.097e-05 -0.389 0.696931
## Grocery -1.596e-04 7.401e-05 -2.157 0.031001 *
## Frozen 1.811e-04 1.035e-04 1.749 0.080356 .
## Detergents_Paper -7.682e-04 1.528e-04 -5.027 4.98e-07 ***
## Delicassen -3.647e-05 1.156e-04 -0.316 0.752343
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 384.52 on 308 degrees of freedom
## Residual deviance: 149.99 on 301 degrees of freedom
## AIC: 165.99
##
## Number of Fisher Scoring iterations: 7We see a reduction in the amount of errors on the test set. From the initinal glm model 432 degreee of freedom and a huge change in the residual devianace. The training model yields 301 degrees of freedom and a reduction of about 50% in residual deviance from 384.52 to 149.99.
Fit a smaller Model, using the training data and get the same of errors. Each model with an AIC of 165.99
glm.fit=glm(Channel~Region + Fresh + Milk + Grocery + Frozen + Detergents_Paper + Delicassen,
data=testData,family=binomial)## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurredglm.probs=predict(glm.fit,newdata=wholesale[!testData,],type="response")
glm.pred=ifelse(glm.probs >0.5,"Hotel/Cafe/Restaurant","Retail")
Channel=wholesale$Channel[!testData]
table(glm.pred,Channel)## Channel
## glm.pred 0 1
## Hotel/Cafe/Restaurant 2 21
## Retail 21 1mean(glm.pred==Channel)## [1] 0summary(glm.fit)##
## Call:
## glm(formula = Channel ~ Region + Fresh + Milk + Grocery + Frozen +
## Detergents_Paper + Delicassen, family = binomial, data = testData)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -3.4656 -0.0032 0.1298 0.2150 1.7511
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 8.985e+00 2.250e+00 3.993 6.52e-05 ***
## Region -1.349e+00 5.792e-01 -2.329 0.019845 *
## Fresh -2.937e-05 3.362e-05 -0.873 0.382394
## Milk -1.856e-04 1.168e-04 -1.590 0.111836
## Grocery -2.852e-05 1.209e-04 -0.236 0.813449
## Frozen 4.654e-06 1.616e-04 0.029 0.977021
## Detergents_Paper -1.444e-03 4.008e-04 -3.604 0.000313 ***
## Delicassen 2.542e-04 2.121e-04 1.198 0.230826
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 168.554 on 130 degrees of freedom
## Residual deviance: 41.625 on 123 degrees of freedom
## AIC: 57.625
##
## Number of Fisher Scoring iterations: 8The test data set yeields 123 degrees of freedom, redisual devianace of 41.625 and a AIC of 57.625.
Linear Discriminant Analysis
LDA is more robust than logistic regression, becuase the discriminant scores are weighted sum of predictors, unlike logistics regression that models the logit transformations of classes probabilities as linear combinations of predictors.
testDF<-list(wholesale[1:8])
as.data.frame(testDF)## Channel Region Fresh Milk Grocery Frozen Detergents_Paper Delicassen
## 1 0 3 12669 9656 7561 214 2674 1338
## 2 0 3 7057 9810 9568 1762 3293 1776
## 3 0 3 6353 8808 7684 2405 3516 7844
## 4 1 3 13265 1196 4221 6404 507 1788
## 5 0 3 22615 5410 7198 3915 1777 5185
## 6 0 3 9413 8259 5126 666 1795 1451
## 7 0 3 12126 3199 6975 480 3140 545
## 8 0 3 7579 4956 9426 1669 3321 2566
## 9 1 3 5963 3648 6192 425 1716 750
## 10 0 3 6006 11093 18881 1159 7425 2098
## 11 0 3 3366 5403 12974 4400 5977 1744
## 12 0 3 13146 1124 4523 1420 549 497
## 13 0 3 31714 12319 11757 287 3881 2931
## 14 0 3 21217 6208 14982 3095 6707 602
## 15 0 3 24653 9465 12091 294 5058 2168
## 16 1 3 10253 1114 3821 397 964 412
## 17 0 3 1020 8816 12121 134 4508 1080
## 18 1 3 5876 6157 2933 839 370 4478
## 19 0 3 18601 6327 10099 2205 2767 3181
## 20 1 3 7780 2495 9464 669 2518 501
## 21 0 3 17546 4519 4602 1066 2259 2124
## 22 1 3 5567 871 2010 3383 375 569
## 23 1 3 31276 1917 4469 9408 2381 4334
## 24 0 3 26373 36423 22019 5154 4337 16523
## 25 0 3 22647 9776 13792 2915 4482 5778
## 26 0 3 16165 4230 7595 201 4003 57
## 27 1 3 9898 961 2861 3151 242 833
## 28 1 3 14276 803 3045 485 100 518
## 29 0 3 4113 20484 25957 1158 8604 5206
## 30 1 3 43088 2100 2609 1200 1107 823
## 31 1 3 18815 3610 11107 1148 2134 2963
## 32 1 3 2612 4339 3133 2088 820 985
## 33 1 3 21632 1318 2886 266 918 405
## 34 1 3 29729 4786 7326 6130 361 1083
## 35 1 3 1502 1979 2262 425 483 395
## 36 0 3 688 5491 11091 833 4239 436
## 37 1 3 29955 4362 5428 1729 862 4626
## 38 0 3 15168 10556 12477 1920 6506 714
## 39 0 3 4591 15729 16709 33 6956 433
## 40 1 3 56159 555 902 10002 212 2916
## 41 1 3 24025 4332 4757 9510 1145 5864
## 42 1 3 19176 3065 5956 2033 2575 2802
## 43 0 3 10850 7555 14961 188 6899 46
## 44 0 3 630 11095 23998 787 9529 72
## 45 0 3 9670 7027 10471 541 4618 65
## 46 0 3 5181 22044 21531 1740 7353 4985
## 47 0 3 3103 14069 21955 1668 6792 1452
## 48 0 3 44466 54259 55571 7782 24171 6465
## 49 0 3 11519 6152 10868 584 5121 1476
## 50 0 3 4967 21412 28921 1798 13583 1163
## 51 1 3 6269 1095 1980 3860 609 2162
## 52 1 3 3347 4051 6996 239 1538 301
## 53 0 3 40721 3916 5876 532 2587 1278
## 54 0 3 491 10473 11532 744 5611 224
## 55 1 3 27329 1449 1947 2436 204 1333
## 56 1 3 5264 3683 5005 1057 2024 1130
## 57 0 3 4098 29892 26866 2616 17740 1340
## 58 0 3 5417 9933 10487 38 7572 1282
## 59 1 3 13779 1970 1648 596 227 436
## 60 1 3 6137 5360 8040 129 3084 1603
## 61 0 3 8590 3045 7854 96 4095 225
## 62 0 3 35942 38369 59598 3254 26701 2017
## 63 0 3 7823 6245 6544 4154 4074 964
## 64 0 3 9396 11601 15775 2896 7677 1295
## 65 1 3 4760 1227 3250 3724 1247 1145
## 66 0 3 85 20959 45828 36 24231 1423
## 67 1 3 9 1534 7417 175 3468 27
## 68 0 3 19913 6759 13462 1256 5141 834
## 69 1 3 2446 7260 3993 5870 788 3095
## 70 1 3 8352 2820 1293 779 656 144
## 71 1 3 16705 2037 3202 10643 116 1365
## 72 1 3 18291 1266 21042 5373 4173 14472
## 73 1 3 4420 5139 2661 8872 1321 181
## 74 0 3 19899 5332 8713 8132 764 648
## 75 0 3 8190 6343 9794 1285 1901 1780
## 76 1 3 20398 1137 3 4407 3 975
## 77 1 3 717 3587 6532 7530 529 894
## 78 0 3 12205 12697 28540 869 12034 1009
## 79 1 3 10766 1175 2067 2096 301 167
## 80 1 3 1640 3259 3655 868 1202 1653
## 81 1 3 7005 829 3009 430 610 529
## 82 0 3 219 9540 14403 283 7818 156
## 83 0 3 10362 9232 11009 737 3537 2342
## 84 1 3 20874 1563 1783 2320 550 772
## 85 0 3 11867 3327 4814 1178 3837 120
## 86 0 3 16117 46197 92780 1026 40827 2944
## 87 0 3 22925 73498 32114 987 20070 903
## 88 1 3 43265 5025 8117 6312 1579 14351
## 89 1 3 7864 542 4042 9735 165 46
## 90 1 3 24904 3836 5330 3443 454 3178
## 91 1 3 11405 596 1638 3347 69 360
## 92 1 3 12754 2762 2530 8693 627 1117
## 93 0 3 9198 27472 32034 3232 18906 5130
## 94 1 3 11314 3090 2062 35009 71 2698
## 95 0 3 5626 12220 11323 206 5038 244
## 96 1 3 3 2920 6252 440 223 709
## 97 0 3 23 2616 8118 145 3874 217
## 98 1 3 403 254 610 774 54 63
## 99 1 3 503 112 778 895 56 132
## 100 1 3 9658 2182 1909 5639 215 323
## 101 0 3 11594 7779 12144 3252 8035 3029
## 102 0 3 1420 10810 16267 1593 6766 1838
## 103 0 3 2932 6459 7677 2561 4573 1386
## 104 1 3 56082 3504 8906 18028 1480 2498
## 105 1 3 14100 2132 3445 1336 1491 548
## 106 1 3 15587 1014 3970 910 139 1378
## 107 0 3 1454 6337 10704 133 6830 1831
## 108 0 3 8797 10646 14886 2471 8969 1438
## 109 0 3 1531 8397 6981 247 2505 1236
## 110 0 3 1406 16729 28986 673 836 3
## 111 1 3 11818 1648 1694 2276 169 1647
## 112 0 3 12579 11114 17569 805 6457 1519
## 113 1 3 19046 2770 2469 8853 483 2708
## 114 1 3 14438 2295 1733 3220 585 1561
## 115 1 3 18044 1080 2000 2555 118 1266
## 116 1 3 11134 793 2988 2715 276 610
## 117 1 3 11173 2521 3355 1517 310 222
## 118 1 3 6990 3880 5380 1647 319 1160
## 119 1 3 20049 1891 2362 5343 411 933
## 120 1 3 8258 2344 2147 3896 266 635
## 121 1 3 17160 1200 3412 2417 174 1136
## 122 1 3 4020 3234 1498 2395 264 255
## 123 1 3 12212 201 245 1991 25 860
## 124 0 3 11170 10769 8814 2194 1976 143
## 125 1 3 36050 1642 2961 4787 500 1621
## 126 1 3 76237 3473 7102 16538 778 918
## 127 1 3 19219 1840 1658 8195 349 483
## 128 0 3 21465 7243 10685 880 2386 2749
## 129 1 3 140 8847 3823 142 1062 3
## 130 1 3 42312 926 1510 1718 410 1819
## 131 1 3 7149 2428 699 6316 395 911
## 132 1 3 2101 589 314 346 70 310
## 133 1 3 14903 2032 2479 576 955 328
## 134 1 3 9434 1042 1235 436 256 396
## 135 1 3 7388 1882 2174 720 47 537
## 136 1 3 6300 1289 2591 1170 199 326
## 137 1 3 4625 8579 7030 4575 2447 1542
## 138 1 3 3087 8080 8282 661 721 36
## 139 1 3 13537 4257 5034 155 249 3271
## 140 1 3 5387 4979 3343 825 637 929
## 141 1 3 17623 4280 7305 2279 960 2616
## 142 1 3 30379 13252 5189 321 51 1450
## 143 1 3 37036 7152 8253 2995 20 3
## 144 1 3 10405 1596 1096 8425 399 318
## 145 1 3 18827 3677 1988 118 516 201
## 146 0 3 22039 8384 34792 42 12591 4430
## 147 1 3 7769 1936 2177 926 73 520
## 148 1 3 9203 3373 2707 1286 1082 526
## 149 1 3 5924 584 542 4052 283 434
## 150 1 3 31812 1433 1651 800 113 1440
## 151 1 3 16225 1825 1765 853 170 1067
## 152 1 3 1289 3328 2022 531 255 1774
## 153 1 3 18840 1371 3135 3001 352 184
## 154 1 3 3463 9250 2368 779 302 1627
## 155 1 3 622 55 137 75 7 8
## 156 0 3 1989 10690 19460 233 11577 2153
## 157 0 3 3830 5291 14855 317 6694 3182
## 158 1 3 17773 1366 2474 3378 811 418
## 159 0 3 2861 6570 9618 930 4004 1682
## 160 0 3 355 7704 14682 398 8077 303
## 161 0 3 1725 3651 12822 824 4424 2157
## 162 1 3 12434 540 283 1092 3 2233
## 163 1 3 15177 2024 3810 2665 232 610
## 164 0 3 5531 15726 26870 2367 13726 446
## 165 0 3 5224 7603 8584 2540 3674 238
## 166 0 3 15615 12653 19858 4425 7108 2379
## 167 0 3 4822 6721 9170 993 4973 3637
## 168 1 3 2926 3195 3268 405 1680 693
## 169 1 3 5809 735 803 1393 79 429
## 170 1 3 5414 717 2155 2399 69 750
## 171 0 3 260 8675 13430 1116 7015 323
## 172 0 3 200 25862 19816 651 8773 6250
## 173 1 3 955 5479 6536 333 2840 707
## 174 0 3 514 7677 19805 937 9836 716
## 175 1 3 286 1208 5241 2515 153 1442
## 176 0 3 2343 7845 11874 52 4196 1697
## 177 1 3 45640 6958 6536 7368 1532 230
## 178 1 3 12759 7330 4533 1752 20 2631
## 179 1 3 11002 7075 4945 1152 120 395
## 180 1 3 3157 4888 2500 4477 273 2165
## 181 1 3 12356 6036 8887 402 1382 2794
## 182 1 3 112151 29627 18148 16745 4948 8550
## 183 1 3 694 8533 10518 443 6907 156
## 184 1 3 36847 43950 20170 36534 239 47943
## 185 1 3 327 918 4710 74 334 11
## 186 1 3 8170 6448 1139 2181 58 247
## 187 1 3 3009 521 854 3470 949 727
## 188 1 3 2438 8002 9819 6269 3459 3
## 189 0 3 8040 7639 11687 2758 6839 404
## 190 0 3 834 11577 11522 275 4027 1856
## 191 1 3 16936 6250 1981 7332 118 64
## 192 1 3 13624 295 1381 890 43 84
## 193 1 3 5509 1461 2251 547 187 409
## 194 0 3 180 3485 20292 959 5618 666
## 195 1 3 7107 1012 2974 806 355 1142
## 196 1 3 17023 5139 5230 7888 330 1755
## 197 1 1 30624 7209 4897 18711 763 2876
## 198 0 1 2427 7097 10391 1127 4314 1468
## 199 1 1 11686 2154 6824 3527 592 697
## 200 1 1 9670 2280 2112 520 402 347
## 201 0 1 3067 13240 23127 3941 9959 731
## 202 0 1 4484 14399 24708 3549 14235 1681
## 203 1 1 25203 11487 9490 5065 284 6854
## 204 1 1 583 685 2216 469 954 18
## 205 1 1 1956 891 5226 1383 5 1328
## 206 0 1 1107 11711 23596 955 9265 710
## 207 1 1 6373 780 950 878 288 285
## 208 0 1 2541 4737 6089 2946 5316 120
## 209 1 1 1537 3748 5838 1859 3381 806
## 210 0 1 5550 12729 16767 864 12420 797
## 211 1 1 18567 1895 1393 1801 244 2100
## 212 0 1 12119 28326 39694 4736 19410 2870
## 213 1 1 7291 1012 2062 1291 240 1775
## 214 1 1 3317 6602 6861 1329 3961 1215
## 215 0 1 2362 6551 11364 913 5957 791
## 216 1 1 2806 10765 15538 1374 5828 2388
## 217 0 1 2532 16599 36486 179 13308 674
## 218 1 1 18044 1475 2046 2532 130 1158
## 219 0 1 18 7504 15205 1285 4797 6372
## 220 1 1 4155 367 1390 2306 86 130
## 221 1 1 14755 899 1382 1765 56 749
## 222 1 1 5396 7503 10646 91 4167 239
## 223 1 1 5041 1115 2856 7496 256 375
## 224 0 1 2790 2527 5265 5612 788 1360
## 225 1 1 7274 659 1499 784 70 659
## 226 1 1 12680 3243 4157 660 761 786
## 227 0 1 20782 5921 9212 1759 2568 1553
## 228 1 1 4042 2204 1563 2286 263 689
## 229 1 1 1869 577 572 950 4762 203
## 230 1 1 8656 2746 2501 6845 694 980
## 231 0 1 11072 5989 5615 8321 955 2137
## 232 1 1 2344 10678 3828 1439 1566 490
## 233 1 1 25962 1780 3838 638 284 834
## 234 1 1 964 4984 3316 937 409 7
## 235 1 1 15603 2703 3833 4260 325 2563
## 236 1 1 1838 6380 2824 1218 1216 295
## 237 1 1 8635 820 3047 2312 415 225
## 238 1 1 18692 3838 593 4634 28 1215
## 239 1 1 7363 475 585 1112 72 216
## 240 1 1 47493 2567 3779 5243 828 2253
## 241 1 1 22096 3575 7041 11422 343 2564
## 242 1 1 24929 1801 2475 2216 412 1047
## 243 1 1 18226 659 2914 3752 586 578
## 244 1 1 11210 3576 5119 561 1682 2398
## 245 1 1 6202 7775 10817 1183 3143 1970
## 246 0 1 3062 6154 13916 230 8933 2784
## 247 1 1 8885 2428 1777 1777 430 610
## 248 1 1 13569 346 489 2077 44 659
## 249 1 1 15671 5279 2406 559 562 572
## 250 1 1 8040 3795 2070 6340 918 291
## 251 1 1 3191 1993 1799 1730 234 710
## 252 0 1 6134 23133 33586 6746 18594 5121
## 253 1 1 6623 1860 4740 7683 205 1693
## 254 1 1 29526 7961 16966 432 363 1391
## 255 1 1 10379 17972 4748 4686 1547 3265
## 256 1 1 31614 489 1495 3242 111 615
## 257 1 1 11092 5008 5249 453 392 373
## 258 1 1 8475 1931 1883 5004 3593 987
## 259 1 1 56083 4563 2124 6422 730 3321
## 260 1 1 53205 4959 7336 3012 967 818
## 261 1 1 9193 4885 2157 327 780 548
## 262 1 1 7858 1110 1094 6818 49 287
## 263 1 1 23257 1372 1677 982 429 655
## 264 1 1 2153 1115 6684 4324 2894 411
## 265 0 1 1073 9679 15445 61 5980 1265
## 266 1 1 5909 23527 13699 10155 830 3636
## 267 0 1 572 9763 22182 2221 4882 2563
## 268 1 1 20893 1222 2576 3975 737 3628
## 269 0 1 11908 8053 19847 1069 6374 698
## 270 1 1 15218 258 1138 2516 333 204
## 271 1 1 4720 1032 975 5500 197 56
## 272 1 1 2083 5007 1563 1120 147 1550
## 273 1 1 514 8323 6869 529 93 1040
## 274 1 3 36817 3045 1493 4802 210 1824
## 275 1 3 894 1703 1841 744 759 1153
## 276 1 3 680 1610 223 862 96 379
## 277 1 3 27901 3749 6964 4479 603 2503
## 278 1 3 9061 829 683 16919 621 139
## 279 1 3 11693 2317 2543 5845 274 1409
## 280 0 3 17360 6200 9694 1293 3620 1721
## 281 1 3 3366 2884 2431 977 167 1104
## 282 0 3 12238 7108 6235 1093 2328 2079
## 283 1 3 49063 3965 4252 5970 1041 1404
## 284 1 3 25767 3613 2013 10303 314 1384
## 285 1 3 68951 4411 12609 8692 751 2406
## 286 1 3 40254 640 3600 1042 436 18
## 287 1 3 7149 2247 1242 1619 1226 128
## 288 1 3 15354 2102 2828 8366 386 1027
## 289 1 3 16260 594 1296 848 445 258
## 290 1 3 42786 286 471 1388 32 22
## 291 1 3 2708 2160 2642 502 965 1522
## 292 1 3 6022 3354 3261 2507 212 686
## 293 1 3 2838 3086 4329 3838 825 1060
## 294 0 2 3996 11103 12469 902 5952 741
## 295 1 2 21273 2013 6550 909 811 1854
## 296 0 2 7588 1897 5234 417 2208 254
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## 298 0 2 8090 3199 6986 1455 3712 531
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## 301 0 2 16448 6243 6360 824 2662 2005
## 302 0 2 5283 13316 20399 1809 8752 172
## 303 0 2 2886 5302 9785 364 6236 555
## 304 0 2 2599 3688 13829 492 10069 59
## 305 0 2 161 7460 24773 617 11783 2410
## 306 0 2 243 12939 8852 799 3909 211
## 307 0 2 6468 12867 21570 1840 7558 1543
## 308 1 2 17327 2374 2842 1149 351 925
## 309 1 2 6987 1020 3007 416 257 656
## 310 0 2 918 20655 13567 1465 6846 806
## 311 1 2 7034 1492 2405 12569 299 1117
## 312 1 2 29635 2335 8280 3046 371 117
## 313 0 2 2137 3737 19172 1274 17120 142
## 314 1 2 9784 925 2405 4447 183 297
## 315 1 2 10617 1795 7647 1483 857 1233
## 316 0 2 1479 14982 11924 662 3891 3508
## 317 1 2 7127 1375 2201 2679 83 1059
## 318 1 2 1182 3088 6114 978 821 1637
## 319 1 2 11800 2713 3558 2121 706 51
## 320 0 2 9759 25071 17645 1128 12408 1625
## 321 1 2 1774 3696 2280 514 275 834
## 322 1 2 9155 1897 5167 2714 228 1113
## 323 1 2 15881 713 3315 3703 1470 229
## 324 1 2 13360 944 11593 915 1679 573
## 325 1 2 25977 3587 2464 2369 140 1092
## 326 1 2 32717 16784 13626 60869 1272 5609
## 327 1 2 4414 1610 1431 3498 387 834
## 328 1 2 542 899 1664 414 88 522
## 329 1 2 16933 2209 3389 7849 210 1534
## 330 1 2 5113 1486 4583 5127 492 739
## 331 1 2 9790 1786 5109 3570 182 1043
## 332 0 2 11223 14881 26839 1234 9606 1102
## 333 1 2 22321 3216 1447 2208 178 2602
## 334 0 2 8565 4980 67298 131 38102 1215
## 335 0 2 16823 928 2743 11559 332 3486
## 336 0 2 27082 6817 10790 1365 4111 2139
## 337 1 2 13970 1511 1330 650 146 778
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## 340 1 2 2617 1188 5332 9584 573 1942
## 341 0 3 381 4025 9670 388 7271 1371
## 342 0 3 2320 5763 11238 767 5162 2158
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## 348 0 3 27380 7184 12311 2809 4621 1022
## 349 1 3 3428 2380 2028 1341 1184 665
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## 351 1 3 3521 1099 1997 1796 173 995
## 352 0 3 1210 10044 22294 1741 12638 3137
## 353 1 3 608 1106 1533 830 90 195
## 354 0 3 117 6264 21203 228 8682 1111
## 355 1 3 14039 7393 2548 6386 1333 2341
## 356 1 3 190 727 2012 245 184 127
## 357 1 3 22686 134 218 3157 9 548
## 358 0 3 37 1275 22272 137 6747 110
## 359 1 3 759 18664 1660 6114 536 4100
## 360 1 3 796 5878 2109 340 232 776
## 361 1 3 19746 2872 2006 2601 468 503
## 362 1 3 4734 607 864 1206 159 405
## 363 1 3 2121 1601 2453 560 179 712
## 364 1 3 4627 997 4438 191 1335 314
## 365 1 3 2615 873 1524 1103 514 468
## 366 0 3 4692 6128 8025 1619 4515 3105
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## 368 1 3 3477 894 534 1457 252 342
## 369 1 3 22335 1196 2406 2046 101 558
## 370 1 3 6211 337 683 1089 41 296
## 371 0 3 39679 3944 4955 1364 523 2235
## 372 1 3 20105 1887 1939 8164 716 790
## 373 1 3 3884 3801 1641 876 397 4829
## 374 0 3 15076 6257 7398 1504 1916 3113
## 375 1 3 6338 2256 1668 1492 311 686
## 376 1 3 5841 1450 1162 597 476 70
## 377 0 3 3136 8630 13586 5641 4666 1426
## 378 1 3 38793 3154 2648 1034 96 1242
## 379 1 3 3225 3294 1902 282 68 1114
## 380 0 3 4048 5164 10391 130 813 179
## 381 1 3 28257 944 2146 3881 600 270
## 382 1 3 17770 4591 1617 9927 246 532
## 383 1 3 34454 7435 8469 2540 1711 2893
## 384 1 3 1821 1364 3450 4006 397 361
## 385 1 3 10683 21858 15400 3635 282 5120
## 386 1 3 11635 922 1614 2583 192 1068
## 387 1 3 1206 3620 2857 1945 353 967
## 388 1 3 20918 1916 1573 1960 231 961
## 389 1 3 9785 848 1172 1677 200 406
## 390 1 3 9385 1530 1422 3019 227 684
## 391 1 3 3352 1181 1328 5502 311 1000
## 392 1 3 2647 2761 2313 907 95 1827
## 393 1 3 518 4180 3600 659 122 654
## 394 1 3 23632 6730 3842 8620 385 819
## 395 1 3 12377 865 3204 1398 149 452
## 396 1 3 9602 1316 1263 2921 841 290
## 397 0 3 4515 11991 9345 2644 3378 2213
## 398 1 3 11535 1666 1428 6838 64 743
## 399 1 3 11442 1032 582 5390 74 247
## 400 1 3 9612 577 935 1601 469 375
## 401 1 3 4446 906 1238 3576 153 1014
## 402 1 3 27167 2801 2128 13223 92 1902
## 403 1 3 26539 4753 5091 220 10 340
## 404 1 3 25606 11006 4604 127 632 288
## 405 1 3 18073 4613 3444 4324 914 715
## 406 1 3 6884 1046 1167 2069 593 378
## 407 1 3 25066 5010 5026 9806 1092 960
## 408 0 3 7362 12844 18683 2854 7883 553
## 409 0 3 8257 3880 6407 1646 2730 344
## 410 1 3 8708 3634 6100 2349 2123 5137
## 411 1 3 6633 2096 4563 1389 1860 1892
## 412 1 3 2126 3289 3281 1535 235 4365
## 413 1 3 97 3605 12400 98 2970 62
## 414 1 3 4983 4859 6633 17866 912 2435
## 415 1 3 5969 1990 3417 5679 1135 290
## 416 0 3 7842 6046 8552 1691 3540 1874
## 417 0 3 4389 10940 10908 848 6728 993
## 418 1 3 5065 5499 11055 364 3485 1063
## 419 0 3 660 8494 18622 133 6740 776
## 420 1 3 8861 3783 2223 633 1580 1521
## 421 1 3 4456 5266 13227 25 6818 1393
## 422 0 3 17063 4847 9053 1031 3415 1784
## 423 1 3 26400 1377 4172 830 948 1218
## 424 0 3 17565 3686 4657 1059 1803 668
## 425 0 3 16980 2884 12232 874 3213 249
## 426 1 3 11243 2408 2593 15348 108 1886
## 427 1 3 13134 9347 14316 3141 5079 1894
## 428 1 3 31012 16687 5429 15082 439 1163
## 429 1 3 3047 5970 4910 2198 850 317
## 430 1 3 8607 1750 3580 47 84 2501
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## 432 1 3 8533 5506 5160 13486 1377 1498
## 433 1 3 21117 1162 4754 269 1328 395
## 434 1 3 1982 3218 1493 1541 356 1449
## 435 1 3 16731 3922 7994 688 2371 838
## 436 1 3 29703 12051 16027 13135 182 2204
## 437 1 3 39228 1431 764 4510 93 2346
## 438 0 3 14531 15488 30243 437 14841 1867
## 439 1 3 10290 1981 2232 1038 168 2125
## 440 1 3 2787 1698 2510 65 477 52ind <- sample(2, nrow(wholesale), replace=TRUE, prob=c(0.7,0.3))
trainData <- wholesale[ind==1,]
testData <- wholesale[ind==2,]
ldaModel<- lda(Channel ~ ., data = trainData)
predictions <- predict(ldaModel, testData)
testPred <- predictions$class
cf <- table(testPred, testData$Channel, dnn=list('predicted','actual'))
confusionMatrix(cf)## Confusion Matrix and Statistics
##
## actual
## predicted 0 1
## 0 29 3
## 1 23 89
##
## Accuracy : 0.8194
## 95% CI : (0.7467, 0.8785)
## No Information Rate : 0.6389
## P-Value [Acc > NIR] : 1.629e-06
##
## Kappa : 0.573
## Mcnemar's Test P-Value : 0.0001944
##
## Sensitivity : 0.5577
## Specificity : 0.9674
## Pos Pred Value : 0.9062
## Neg Pred Value : 0.7946
## Prevalence : 0.3611
## Detection Rate : 0.2014
## Detection Prevalence : 0.2222
## Balanced Accuracy : 0.7625
##
## 'Positive' Class : 0
## The LDA model produces a 81% accuracy model.
Principal Components Analysis
dimnames(wholesale)## [[1]]
## [1] "1" "2" "3" "4" "5" "6" "7" "8" "9" "10" "11"
## [12] "12" "13" "14" "15" "16" "17" "18" "19" "20" "21" "22"
## [23] "23" "24" "25" "26" "27" "28" "29" "30" "31" "32" "33"
## [34] "34" "35" "36" "37" "38" "39" "40" "41" "42" "43" "44"
## [45] "45" "46" "47" "48" "49" "50" "51" "52" "53" "54" "55"
## [56] "56" "57" "58" "59" "60" "61" "62" "63" "64" "65" "66"
## [67] "67" "68" "69" "70" "71" "72" "73" "74" "75" "76" "77"
## [78] "78" "79" "80" "81" "82" "83" "84" "85" "86" "87" "88"
## [89] "89" "90" "91" "92" "93" "94" "95" "96" "97" "98" "99"
## [100] "100" "101" "102" "103" "104" "105" "106" "107" "108" "109" "110"
## [111] "111" "112" "113" "114" "115" "116" "117" "118" "119" "120" "121"
## [122] "122" "123" "124" "125" "126" "127" "128" "129" "130" "131" "132"
## [133] "133" "134" "135" "136" "137" "138" "139" "140" "141" "142" "143"
## [144] "144" "145" "146" "147" "148" "149" "150" "151" "152" "153" "154"
## [155] "155" "156" "157" "158" "159" "160" "161" "162" "163" "164" "165"
## [166] "166" "167" "168" "169" "170" "171" "172" "173" "174" "175" "176"
## [177] "177" "178" "179" "180" "181" "182" "183" "184" "185" "186" "187"
## [188] "188" "189" "190" "191" "192" "193" "194" "195" "196" "197" "198"
## [199] "199" "200" "201" "202" "203" "204" "205" "206" "207" "208" "209"
## [210] "210" "211" "212" "213" "214" "215" "216" "217" "218" "219" "220"
## [221] "221" "222" "223" "224" "225" "226" "227" "228" "229" "230" "231"
## [232] "232" "233" "234" "235" "236" "237" "238" "239" "240" "241" "242"
## [243] "243" "244" "245" "246" "247" "248" "249" "250" "251" "252" "253"
## [254] "254" "255" "256" "257" "258" "259" "260" "261" "262" "263" "264"
## [265] "265" "266" "267" "268" "269" "270" "271" "272" "273" "274" "275"
## [276] "276" "277" "278" "279" "280" "281" "282" "283" "284" "285" "286"
## [287] "287" "288" "289" "290" "291" "292" "293" "294" "295" "296" "297"
## [298] "298" "299" "300" "301" "302" "303" "304" "305" "306" "307" "308"
## [309] "309" "310" "311" "312" "313" "314" "315" "316" "317" "318" "319"
## [320] "320" "321" "322" "323" "324" "325" "326" "327" "328" "329" "330"
## [331] "331" "332" "333" "334" "335" "336" "337" "338" "339" "340" "341"
## [342] "342" "343" "344" "345" "346" "347" "348" "349" "350" "351" "352"
## [353] "353" "354" "355" "356" "357" "358" "359" "360" "361" "362" "363"
## [364] "364" "365" "366" "367" "368" "369" "370" "371" "372" "373" "374"
## [375] "375" "376" "377" "378" "379" "380" "381" "382" "383" "384" "385"
## [386] "386" "387" "388" "389" "390" "391" "392" "393" "394" "395" "396"
## [397] "397" "398" "399" "400" "401" "402" "403" "404" "405" "406" "407"
## [408] "408" "409" "410" "411" "412" "413" "414" "415" "416" "417" "418"
## [419] "419" "420" "421" "422" "423" "424" "425" "426" "427" "428" "429"
## [430] "430" "431" "432" "433" "434" "435" "436" "437" "438" "439" "440"
##
## [[2]]
## [1] "Channel" "Region" "Fresh"
## [4] "Milk" "Grocery" "Frozen"
## [7] "Detergents_Paper" "Delicassen"apply(wholesale,2,mean)## Channel Region Fresh Milk
## 6.772727e-01 2.543182e+00 1.200030e+04 5.796266e+03
## Grocery Frozen Detergents_Paper Delicassen
## 7.951277e+03 3.071932e+03 2.881493e+03 1.524870e+03apply(wholesale,2, var)## Channel Region Fresh Milk
## 2.190723e-01 5.994978e-01 1.599549e+08 5.446997e+07
## Grocery Frozen Detergents_Paper Delicassen
## 9.031010e+07 2.356785e+07 2.273244e+07 7.952997e+06We see that Detergents_Paper has a much larger variance than the other variables. It would dominate the principal components, so we standardize the variables when we perform PCA.
pca.out=prcomp(wholesale, scale=TRUE)
pca.out## Standard deviations:
## [1] 1.7606845 1.3378965 1.0058697 0.8592976 0.7460780 0.6777229 0.5302132
## [8] 0.2505796
##
## Rotation:
## PC1 PC2 PC3 PC4
## Channel 0.42829156 -0.20469886 -0.0829798863 0.02964416
## Region -0.02472603 -0.04312964 0.9825008891 -0.07784462
## Fresh 0.02531946 -0.51344468 0.0889509074 0.79847592
## Milk -0.47440995 -0.20554061 -0.0257510842 -0.05402202
## Grocery -0.53632914 0.00871762 -0.0453143572 0.12158624
## Frozen 0.02997456 -0.59274525 -0.1221565222 -0.16131688
## Detergents_Paper -0.52390630 0.12108309 -0.0474814388 0.15101211
## Delicassen -0.16499653 -0.53318082 0.0009301994 -0.53755767
## PC5 PC6 PC7 PC8
## Channel -0.03620585 0.86350670 -0.139899044 -0.019335373
## Region -0.13250892 0.08976479 -0.023279938 -0.001545045
## Fresh 0.25811686 -0.14747474 -0.027173693 -0.033851114
## Milk 0.07208576 0.31593256 0.789020414 -0.039291347
## Grocery -0.11172990 0.21369889 -0.353064294 0.715984124
## Frozen -0.75421244 -0.19435993 -0.005336793 -0.012983225
## Detergents_Paper -0.17650264 0.19575356 -0.371374310 -0.691672189
## Delicassen 0.54482721 -0.05453289 -0.306582655 -0.075642587names(pca.out)## [1] "sdev" "rotation" "center" "scale" "x"biplot(pca.out, scale=0)
Hierarchical Clustering
hc.complete=hclust(dist(wholesale),method="complete")
plot(hc.complete)
hc.single=hclust(dist(wholesale),method="single")
plot(hc.single)
hc.average=hclust(dist(wholesale),method="average")
plot(hc.average)
Utilizing Hierarchical Clustering does not yield much valuable informaiton.
Lastly we will use Classificaiton Trees
range(wholesale)## [1] 0 112151rf =randomForest(Channel ~ ., data=wholesale, mtry=4, ntree=10 )## Warning in randomForest.default(m, y, ...): The response has five or fewer
## unique values. Are you sure you want to do regression?class(rm)## [1] "function"str(rf)## List of 18
## $ call : language randomForest(formula = Channel ~ ., data = wholesale, mtry = 4, ntree = 10)
## $ type : chr "regression"
## $ predicted : Named num [1:440] 0.00 2.00e-01 -3.33e-16 1.00 5.00e-01 ...
## ..- attr(*, "names")= chr [1:440] "1" "2" "3" "4" ...
## $ mse : num [1:10] 0.138 0.133 0.123 0.134 0.117 ...
## $ rsq : num [1:10] 0.367 0.391 0.439 0.387 0.466 ...
## $ oob.times : int [1:440] 2 5 6 6 2 3 3 5 3 4 ...
## $ importance : num [1:7, 1] 0.507 3.935 16.236 16.094 4.806 ...
## ..- attr(*, "dimnames")=List of 2
## .. ..$ : chr [1:7] "Region" "Fresh" "Milk" "Grocery" ...
## .. ..$ : chr "IncNodePurity"
## $ importanceSD : NULL
## $ localImportance: NULL
## $ proximity : NULL
## $ ntree : num 10
## $ mtry : num 4
## $ forest :List of 11
## ..$ ndbigtree : int [1:10] 85 77 73 65 71 61 73 69 55 67
## ..$ nodestatus : int [1:85, 1:10] -3 -3 -3 -3 -3 -3 -3 -3 -3 -1 ...
## ..$ leftDaughter : int [1:85, 1:10] 2 4 6 8 10 12 14 16 18 0 ...
## ..$ rightDaughter: int [1:85, 1:10] 3 5 7 9 11 13 15 17 19 0 ...
## ..$ nodepred : num [1:85, 1:10] 0.711 0.932 0.329 0.959 0.2 ...
## ..$ bestvar : int [1:85, 1:10] 3 6 4 6 3 6 7 4 6 0 ...
## ..$ xbestsplit : num [1:85, 1:10] 5152 3715 8591 2171 2156 ...
## ..$ ncat : Named int [1:7] 1 1 1 1 1 1 1
## .. ..- attr(*, "names")= chr [1:7] "Region" "Fresh" "Milk" "Grocery" ...
## ..$ nrnodes : int 85
## ..$ ntree : num 10
## ..$ xlevels :List of 7
## .. ..$ Region : num 0
## .. ..$ Fresh : num 0
## .. ..$ Milk : num 0
## .. ..$ Grocery : num 0
## .. ..$ Frozen : num 0
## .. ..$ Detergents_Paper: num 0
## .. ..$ Delicassen : num 0
## $ coefs : NULL
## $ y : Named num [1:440] 0 0 0 1 0 0 0 0 1 0 ...
## ..- attr(*, "names")= chr [1:440] "1" "2" "3" "4" ...
## $ test : NULL
## $ inbag : NULL
## $ terms :Classes 'terms', 'formula' length 3 Channel ~ Region + Fresh + Milk + Grocery + Frozen + Detergents_Paper + Delicassen
## .. ..- attr(*, "variables")= language list(Channel, Region, Fresh, Milk, Grocery, Frozen, Detergents_Paper, Delicassen)
## .. ..- attr(*, "factors")= int [1:8, 1:7] 0 1 0 0 0 0 0 0 0 0 ...
## .. .. ..- attr(*, "dimnames")=List of 2
## .. .. .. ..$ : chr [1:8] "Channel" "Region" "Fresh" "Milk" ...
## .. .. .. ..$ : chr [1:7] "Region" "Fresh" "Milk" "Grocery" ...
## .. ..- attr(*, "term.labels")= chr [1:7] "Region" "Fresh" "Milk" "Grocery" ...
## .. ..- attr(*, "order")= int [1:7] 1 1 1 1 1 1 1
## .. ..- attr(*, "intercept")= num 0
## .. ..- attr(*, "response")= int 1
## .. ..- attr(*, ".Environment")=<environment: R_GlobalEnv>
## .. ..- attr(*, "predvars")= language list(Channel, Region, Fresh, Milk, Grocery, Frozen, Detergents_Paper, Delicassen)
## .. ..- attr(*, "dataClasses")= Named chr [1:8] "numeric" "numeric" "numeric" "numeric" ...
## .. .. ..- attr(*, "names")= chr [1:8] "Channel" "Region" "Fresh" "Milk" ...
## - attr(*, "class")= chr [1:2] "randomForest.formula" "randomForest"rf$confusion## NULLrf$importance## IncNodePurity
## Region 0.507344
## Fresh 3.935115
## Milk 16.235844
## Grocery 16.093554
## Frozen 4.806335
## Detergents_Paper 46.135670
## Delicassen 3.599017Plot classification tree algorithm using both rpart and C5.0
Classification Tree using rpart:
Legend: node), split, n, loss, yval, (yprob) * denotes terminal node
set.seed(1234)
R<-runif(nrow(wholesale))
str(wholesale)## 'data.frame': 440 obs. of 8 variables:
## $ Channel : int 0 0 0 1 0 0 0 0 1 0 ...
## $ Region : int 3 3 3 3 3 3 3 3 3 3 ...
## $ Fresh : int 12669 7057 6353 13265 22615 9413 12126 7579 5963 6006 ...
## $ Milk : int 9656 9810 8808 1196 5410 8259 3199 4956 3648 11093 ...
## $ Grocery : int 7561 9568 7684 4221 7198 5126 6975 9426 6192 18881 ...
## $ Frozen : int 214 1762 2405 6404 3915 666 480 1669 425 1159 ...
## $ Detergents_Paper: int 2674 3293 3516 507 1777 1795 3140 3321 1716 7425 ...
## $ Delicassen : int 1338 1776 7844 1788 5185 1451 545 2566 750 2098 ...wholesaleRandom<- wholesale[order(R),]
M1 <- rpart(Channel~ ., data=wholesaleRandom[1:100,], method="class")
M1## n= 100
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 100 34 1 (0.34000000 0.66000000)
## 2) Detergents_Paper>=1757 39 6 0 (0.84615385 0.15384615) *
## 3) Detergents_Paper< 1757 61 1 1 (0.01639344 0.98360656) *rpart.plot(M1)
rpart.plot(M1, type=3, extra=101, fallen.leaves=T)
P1<-predict(M1, wholesale[101:440,], type="class")
summary(M1)## Call:
## rpart(formula = Channel ~ ., data = wholesaleRandom[1:100, ],
## method = "class")
## n= 100
##
## CP nsplit rel error xerror xstd
## 1 0.7941176 0 1.0000000 1.0000000 0.13932611
## 2 0.0100000 1 0.2058824 0.2647059 0.08417111
##
## Variable importance
## Detergents_Paper Grocery Milk Frozen
## 42 28 22 6
## Delicassen
## 2
##
## Node number 1: 100 observations, complexity param=0.7941176
## predicted class=1 expected loss=0.34 P(node) =1
## class counts: 34 66
## probabilities: 0.340 0.660
## left son=2 (39 obs) right son=3 (61 obs)
## Primary splits:
## Detergents_Paper < 1757 to the right, improve=32.758940, (0 missing)
## Grocery < 6804 to the right, improve=25.779840, (0 missing)
## Milk < 3197 to the right, improve=20.502640, (0 missing)
## Frozen < 3300.5 to the left, improve= 6.389356, (0 missing)
## Fresh < 12901.5 to the left, improve= 2.294279, (0 missing)
## Surrogate splits:
## Grocery < 4255.5 to the right, agree=0.87, adj=0.667, (0 split)
## Milk < 4907.5 to the right, agree=0.81, adj=0.513, (0 split)
## Frozen < 309.5 to the left, agree=0.67, adj=0.154, (0 split)
## Delicassen < 5125 to the right, agree=0.63, adj=0.051, (0 split)
##
## Node number 2: 39 observations
## predicted class=0 expected loss=0.1538462 P(node) =0.39
## class counts: 33 6
## probabilities: 0.846 0.154
##
## Node number 3: 61 observations
## predicted class=1 expected loss=0.01639344 P(node) =0.61
## class counts: 1 60
## probabilities: 0.016 0.984table(wholesaled[101:440,1], predicted=P1)## predicted
## 0 1
## Hotel/Restaurant/Cafe 22 228
## Retail 84 6summary(M1)## Call:
## rpart(formula = Channel ~ ., data = wholesaleRandom[1:100, ],
## method = "class")
## n= 100
##
## CP nsplit rel error xerror xstd
## 1 0.7941176 0 1.0000000 1.0000000 0.13932611
## 2 0.0100000 1 0.2058824 0.2647059 0.08417111
##
## Variable importance
## Detergents_Paper Grocery Milk Frozen
## 42 28 22 6
## Delicassen
## 2
##
## Node number 1: 100 observations, complexity param=0.7941176
## predicted class=1 expected loss=0.34 P(node) =1
## class counts: 34 66
## probabilities: 0.340 0.660
## left son=2 (39 obs) right son=3 (61 obs)
## Primary splits:
## Detergents_Paper < 1757 to the right, improve=32.758940, (0 missing)
## Grocery < 6804 to the right, improve=25.779840, (0 missing)
## Milk < 3197 to the right, improve=20.502640, (0 missing)
## Frozen < 3300.5 to the left, improve= 6.389356, (0 missing)
## Fresh < 12901.5 to the left, improve= 2.294279, (0 missing)
## Surrogate splits:
## Grocery < 4255.5 to the right, agree=0.87, adj=0.667, (0 split)
## Milk < 4907.5 to the right, agree=0.81, adj=0.513, (0 split)
## Frozen < 309.5 to the left, agree=0.67, adj=0.154, (0 split)
## Delicassen < 5125 to the right, agree=0.63, adj=0.051, (0 split)
##
## Node number 2: 39 observations
## predicted class=0 expected loss=0.1538462 P(node) =0.39
## class counts: 33 6
## probabilities: 0.846 0.154
##
## Node number 3: 61 observations
## predicted class=1 expected loss=0.01639344 P(node) =0.61
## class counts: 1 60
## probabilities: 0.016 0.984Classification Tree using C5.0: CHange data set to Wholesaled for factor variables to model Channel.
set.seed(1234)
wholesaled## Channel Region Fresh Milk Grocery Frozen
## 1 Retail Other 12669 9656 7561 214
## 2 Retail Other 7057 9810 9568 1762
## 3 Retail Other 6353 8808 7684 2405
## 4 Hotel/Restaurant/Cafe Other 13265 1196 4221 6404
## 5 Retail Other 22615 5410 7198 3915
## 6 Retail Other 9413 8259 5126 666
## 7 Retail Other 12126 3199 6975 480
## 8 Retail Other 7579 4956 9426 1669
## 9 Hotel/Restaurant/Cafe Other 5963 3648 6192 425
## 10 Retail Other 6006 11093 18881 1159
## 11 Retail Other 3366 5403 12974 4400
## 12 Retail Other 13146 1124 4523 1420
## 13 Retail Other 31714 12319 11757 287
## 14 Retail Other 21217 6208 14982 3095
## 15 Retail Other 24653 9465 12091 294
## 16 Hotel/Restaurant/Cafe Other 10253 1114 3821 397
## 17 Retail Other 1020 8816 12121 134
## 18 Hotel/Restaurant/Cafe Other 5876 6157 2933 839
## 19 Retail Other 18601 6327 10099 2205
## 20 Hotel/Restaurant/Cafe Other 7780 2495 9464 669
## 21 Retail Other 17546 4519 4602 1066
## 22 Hotel/Restaurant/Cafe Other 5567 871 2010 3383
## 23 Hotel/Restaurant/Cafe Other 31276 1917 4469 9408
## 24 Retail Other 26373 36423 22019 5154
## 25 Retail Other 22647 9776 13792 2915
## 26 Retail Other 16165 4230 7595 201
## 27 Hotel/Restaurant/Cafe Other 9898 961 2861 3151
## 28 Hotel/Restaurant/Cafe Other 14276 803 3045 485
## 29 Retail Other 4113 20484 25957 1158
## 30 Hotel/Restaurant/Cafe Other 43088 2100 2609 1200
## 31 Hotel/Restaurant/Cafe Other 18815 3610 11107 1148
## 32 Hotel/Restaurant/Cafe Other 2612 4339 3133 2088
## 33 Hotel/Restaurant/Cafe Other 21632 1318 2886 266
## 34 Hotel/Restaurant/Cafe Other 29729 4786 7326 6130
## 35 Hotel/Restaurant/Cafe Other 1502 1979 2262 425
## 36 Retail Other 688 5491 11091 833
## 37 Hotel/Restaurant/Cafe Other 29955 4362 5428 1729
## 38 Retail Other 15168 10556 12477 1920
## 39 Retail Other 4591 15729 16709 33
## 40 Hotel/Restaurant/Cafe Other 56159 555 902 10002
## 41 Hotel/Restaurant/Cafe Other 24025 4332 4757 9510
## 42 Hotel/Restaurant/Cafe Other 19176 3065 5956 2033
## 43 Retail Other 10850 7555 14961 188
## 44 Retail Other 630 11095 23998 787
## 45 Retail Other 9670 7027 10471 541
## 46 Retail Other 5181 22044 21531 1740
## 47 Retail Other 3103 14069 21955 1668
## 48 Retail Other 44466 54259 55571 7782
## 49 Retail Other 11519 6152 10868 584
## 50 Retail Other 4967 21412 28921 1798
## 51 Hotel/Restaurant/Cafe Other 6269 1095 1980 3860
## 52 Hotel/Restaurant/Cafe Other 3347 4051 6996 239
## 53 Retail Other 40721 3916 5876 532
## 54 Retail Other 491 10473 11532 744
## 55 Hotel/Restaurant/Cafe Other 27329 1449 1947 2436
## 56 Hotel/Restaurant/Cafe Other 5264 3683 5005 1057
## 57 Retail Other 4098 29892 26866 2616
## 58 Retail Other 5417 9933 10487 38
## 59 Hotel/Restaurant/Cafe Other 13779 1970 1648 596
## 60 Hotel/Restaurant/Cafe Other 6137 5360 8040 129
## 61 Retail Other 8590 3045 7854 96
## 62 Retail Other 35942 38369 59598 3254
## 63 Retail Other 7823 6245 6544 4154
## 64 Retail Other 9396 11601 15775 2896
## 65 Hotel/Restaurant/Cafe Other 4760 1227 3250 3724
## 66 Retail Other 85 20959 45828 36
## 67 Hotel/Restaurant/Cafe Other 9 1534 7417 175
## 68 Retail Other 19913 6759 13462 1256
## 69 Hotel/Restaurant/Cafe Other 2446 7260 3993 5870
## 70 Hotel/Restaurant/Cafe Other 8352 2820 1293 779
## 71 Hotel/Restaurant/Cafe Other 16705 2037 3202 10643
## 72 Hotel/Restaurant/Cafe Other 18291 1266 21042 5373
## 73 Hotel/Restaurant/Cafe Other 4420 5139 2661 8872
## 74 Retail Other 19899 5332 8713 8132
## 75 Retail Other 8190 6343 9794 1285
## 76 Hotel/Restaurant/Cafe Other 20398 1137 3 4407
## 77 Hotel/Restaurant/Cafe Other 717 3587 6532 7530
## 78 Retail Other 12205 12697 28540 869
## 79 Hotel/Restaurant/Cafe Other 10766 1175 2067 2096
## 80 Hotel/Restaurant/Cafe Other 1640 3259 3655 868
## 81 Hotel/Restaurant/Cafe Other 7005 829 3009 430
## 82 Retail Other 219 9540 14403 283
## 83 Retail Other 10362 9232 11009 737
## 84 Hotel/Restaurant/Cafe Other 20874 1563 1783 2320
## 85 Retail Other 11867 3327 4814 1178
## 86 Retail Other 16117 46197 92780 1026
## 87 Retail Other 22925 73498 32114 987
## 88 Hotel/Restaurant/Cafe Other 43265 5025 8117 6312
## 89 Hotel/Restaurant/Cafe Other 7864 542 4042 9735
## 90 Hotel/Restaurant/Cafe Other 24904 3836 5330 3443
## 91 Hotel/Restaurant/Cafe Other 11405 596 1638 3347
## 92 Hotel/Restaurant/Cafe Other 12754 2762 2530 8693
## 93 Retail Other 9198 27472 32034 3232
## 94 Hotel/Restaurant/Cafe Other 11314 3090 2062 35009
## 95 Retail Other 5626 12220 11323 206
## 96 Hotel/Restaurant/Cafe Other 3 2920 6252 440
## 97 Retail Other 23 2616 8118 145
## 98 Hotel/Restaurant/Cafe Other 403 254 610 774
## 99 Hotel/Restaurant/Cafe Other 503 112 778 895
## 100 Hotel/Restaurant/Cafe Other 9658 2182 1909 5639
## 101 Retail Other 11594 7779 12144 3252
## 102 Retail Other 1420 10810 16267 1593
## 103 Retail Other 2932 6459 7677 2561
## 104 Hotel/Restaurant/Cafe Other 56082 3504 8906 18028
## 105 Hotel/Restaurant/Cafe Other 14100 2132 3445 1336
## 106 Hotel/Restaurant/Cafe Other 15587 1014 3970 910
## 107 Retail Other 1454 6337 10704 133
## 108 Retail Other 8797 10646 14886 2471
## 109 Retail Other 1531 8397 6981 247
## 110 Retail Other 1406 16729 28986 673
## 111 Hotel/Restaurant/Cafe Other 11818 1648 1694 2276
## 112 Retail Other 12579 11114 17569 805
## 113 Hotel/Restaurant/Cafe Other 19046 2770 2469 8853
## 114 Hotel/Restaurant/Cafe Other 14438 2295 1733 3220
## 115 Hotel/Restaurant/Cafe Other 18044 1080 2000 2555
## 116 Hotel/Restaurant/Cafe Other 11134 793 2988 2715
## 117 Hotel/Restaurant/Cafe Other 11173 2521 3355 1517
## 118 Hotel/Restaurant/Cafe Other 6990 3880 5380 1647
## 119 Hotel/Restaurant/Cafe Other 20049 1891 2362 5343
## 120 Hotel/Restaurant/Cafe Other 8258 2344 2147 3896
## 121 Hotel/Restaurant/Cafe Other 17160 1200 3412 2417
## 122 Hotel/Restaurant/Cafe Other 4020 3234 1498 2395
## 123 Hotel/Restaurant/Cafe Other 12212 201 245 1991
## 124 Retail Other 11170 10769 8814 2194
## 125 Hotel/Restaurant/Cafe Other 36050 1642 2961 4787
## 126 Hotel/Restaurant/Cafe Other 76237 3473 7102 16538
## 127 Hotel/Restaurant/Cafe Other 19219 1840 1658 8195
## 128 Retail Other 21465 7243 10685 880
## 129 Hotel/Restaurant/Cafe Other 140 8847 3823 142
## 130 Hotel/Restaurant/Cafe Other 42312 926 1510 1718
## 131 Hotel/Restaurant/Cafe Other 7149 2428 699 6316
## 132 Hotel/Restaurant/Cafe Other 2101 589 314 346
## 133 Hotel/Restaurant/Cafe Other 14903 2032 2479 576
## 134 Hotel/Restaurant/Cafe Other 9434 1042 1235 436
## 135 Hotel/Restaurant/Cafe Other 7388 1882 2174 720
## 136 Hotel/Restaurant/Cafe Other 6300 1289 2591 1170
## 137 Hotel/Restaurant/Cafe Other 4625 8579 7030 4575
## 138 Hotel/Restaurant/Cafe Other 3087 8080 8282 661
## 139 Hotel/Restaurant/Cafe Other 13537 4257 5034 155
## 140 Hotel/Restaurant/Cafe Other 5387 4979 3343 825
## 141 Hotel/Restaurant/Cafe Other 17623 4280 7305 2279
## 142 Hotel/Restaurant/Cafe Other 30379 13252 5189 321
## 143 Hotel/Restaurant/Cafe Other 37036 7152 8253 2995
## 144 Hotel/Restaurant/Cafe Other 10405 1596 1096 8425
## 145 Hotel/Restaurant/Cafe Other 18827 3677 1988 118
## 146 Retail Other 22039 8384 34792 42
## 147 Hotel/Restaurant/Cafe Other 7769 1936 2177 926
## 148 Hotel/Restaurant/Cafe Other 9203 3373 2707 1286
## 149 Hotel/Restaurant/Cafe Other 5924 584 542 4052
## 150 Hotel/Restaurant/Cafe Other 31812 1433 1651 800
## 151 Hotel/Restaurant/Cafe Other 16225 1825 1765 853
## 152 Hotel/Restaurant/Cafe Other 1289 3328 2022 531
## 153 Hotel/Restaurant/Cafe Other 18840 1371 3135 3001
## 154 Hotel/Restaurant/Cafe Other 3463 9250 2368 779
## 155 Hotel/Restaurant/Cafe Other 622 55 137 75
## 156 Retail Other 1989 10690 19460 233
## 157 Retail Other 3830 5291 14855 317
## 158 Hotel/Restaurant/Cafe Other 17773 1366 2474 3378
## 159 Retail Other 2861 6570 9618 930
## 160 Retail Other 355 7704 14682 398
## 161 Retail Other 1725 3651 12822 824
## 162 Hotel/Restaurant/Cafe Other 12434 540 283 1092
## 163 Hotel/Restaurant/Cafe Other 15177 2024 3810 2665
## 164 Retail Other 5531 15726 26870 2367
## 165 Retail Other 5224 7603 8584 2540
## 166 Retail Other 15615 12653 19858 4425
## 167 Retail Other 4822 6721 9170 993
## 168 Hotel/Restaurant/Cafe Other 2926 3195 3268 405
## 169 Hotel/Restaurant/Cafe Other 5809 735 803 1393
## 170 Hotel/Restaurant/Cafe Other 5414 717 2155 2399
## 171 Retail Other 260 8675 13430 1116
## 172 Retail Other 200 25862 19816 651
## 173 Hotel/Restaurant/Cafe Other 955 5479 6536 333
## 174 Retail Other 514 7677 19805 937
## 175 Hotel/Restaurant/Cafe Other 286 1208 5241 2515
## 176 Retail Other 2343 7845 11874 52
## 177 Hotel/Restaurant/Cafe Other 45640 6958 6536 7368
## 178 Hotel/Restaurant/Cafe Other 12759 7330 4533 1752
## 179 Hotel/Restaurant/Cafe Other 11002 7075 4945 1152
## 180 Hotel/Restaurant/Cafe Other 3157 4888 2500 4477
## 181 Hotel/Restaurant/Cafe Other 12356 6036 8887 402
## 182 Hotel/Restaurant/Cafe Other 112151 29627 18148 16745
## 183 Hotel/Restaurant/Cafe Other 694 8533 10518 443
## 184 Hotel/Restaurant/Cafe Other 36847 43950 20170 36534
## 185 Hotel/Restaurant/Cafe Other 327 918 4710 74
## 186 Hotel/Restaurant/Cafe Other 8170 6448 1139 2181
## 187 Hotel/Restaurant/Cafe Other 3009 521 854 3470
## 188 Hotel/Restaurant/Cafe Other 2438 8002 9819 6269
## 189 Retail Other 8040 7639 11687 2758
## 190 Retail Other 834 11577 11522 275
## 191 Hotel/Restaurant/Cafe Other 16936 6250 1981 7332
## 192 Hotel/Restaurant/Cafe Other 13624 295 1381 890
## 193 Hotel/Restaurant/Cafe Other 5509 1461 2251 547
## 194 Retail Other 180 3485 20292 959
## 195 Hotel/Restaurant/Cafe Other 7107 1012 2974 806
## 196 Hotel/Restaurant/Cafe Other 17023 5139 5230 7888
## 197 Hotel/Restaurant/Cafe Oponto 30624 7209 4897 18711
## 198 Retail Oponto 2427 7097 10391 1127
## 199 Hotel/Restaurant/Cafe Oponto 11686 2154 6824 3527
## 200 Hotel/Restaurant/Cafe Oponto 9670 2280 2112 520
## 201 Retail Oponto 3067 13240 23127 3941
## 202 Retail Oponto 4484 14399 24708 3549
## 203 Hotel/Restaurant/Cafe Oponto 25203 11487 9490 5065
## 204 Hotel/Restaurant/Cafe Oponto 583 685 2216 469
## 205 Hotel/Restaurant/Cafe Oponto 1956 891 5226 1383
## 206 Retail Oponto 1107 11711 23596 955
## 207 Hotel/Restaurant/Cafe Oponto 6373 780 950 878
## 208 Retail Oponto 2541 4737 6089 2946
## 209 Hotel/Restaurant/Cafe Oponto 1537 3748 5838 1859
## 210 Retail Oponto 5550 12729 16767 864
## 211 Hotel/Restaurant/Cafe Oponto 18567 1895 1393 1801
## 212 Retail Oponto 12119 28326 39694 4736
## 213 Hotel/Restaurant/Cafe Oponto 7291 1012 2062 1291
## 214 Hotel/Restaurant/Cafe Oponto 3317 6602 6861 1329
## 215 Retail Oponto 2362 6551 11364 913
## 216 Hotel/Restaurant/Cafe Oponto 2806 10765 15538 1374
## 217 Retail Oponto 2532 16599 36486 179
## 218 Hotel/Restaurant/Cafe Oponto 18044 1475 2046 2532
## 219 Retail Oponto 18 7504 15205 1285
## 220 Hotel/Restaurant/Cafe Oponto 4155 367 1390 2306
## 221 Hotel/Restaurant/Cafe Oponto 14755 899 1382 1765
## 222 Hotel/Restaurant/Cafe Oponto 5396 7503 10646 91
## 223 Hotel/Restaurant/Cafe Oponto 5041 1115 2856 7496
## 224 Retail Oponto 2790 2527 5265 5612
## 225 Hotel/Restaurant/Cafe Oponto 7274 659 1499 784
## 226 Hotel/Restaurant/Cafe Oponto 12680 3243 4157 660
## 227 Retail Oponto 20782 5921 9212 1759
## 228 Hotel/Restaurant/Cafe Oponto 4042 2204 1563 2286
## 229 Hotel/Restaurant/Cafe Oponto 1869 577 572 950
## 230 Hotel/Restaurant/Cafe Oponto 8656 2746 2501 6845
## 231 Retail Oponto 11072 5989 5615 8321
## 232 Hotel/Restaurant/Cafe Oponto 2344 10678 3828 1439
## 233 Hotel/Restaurant/Cafe Oponto 25962 1780 3838 638
## 234 Hotel/Restaurant/Cafe Oponto 964 4984 3316 937
## 235 Hotel/Restaurant/Cafe Oponto 15603 2703 3833 4260
## 236 Hotel/Restaurant/Cafe Oponto 1838 6380 2824 1218
## 237 Hotel/Restaurant/Cafe Oponto 8635 820 3047 2312
## 238 Hotel/Restaurant/Cafe Oponto 18692 3838 593 4634
## 239 Hotel/Restaurant/Cafe Oponto 7363 475 585 1112
## 240 Hotel/Restaurant/Cafe Oponto 47493 2567 3779 5243
## 241 Hotel/Restaurant/Cafe Oponto 22096 3575 7041 11422
## 242 Hotel/Restaurant/Cafe Oponto 24929 1801 2475 2216
## 243 Hotel/Restaurant/Cafe Oponto 18226 659 2914 3752
## 244 Hotel/Restaurant/Cafe Oponto 11210 3576 5119 561
## 245 Hotel/Restaurant/Cafe Oponto 6202 7775 10817 1183
## 246 Retail Oponto 3062 6154 13916 230
## 247 Hotel/Restaurant/Cafe Oponto 8885 2428 1777 1777
## 248 Hotel/Restaurant/Cafe Oponto 13569 346 489 2077
## 249 Hotel/Restaurant/Cafe Oponto 15671 5279 2406 559
## 250 Hotel/Restaurant/Cafe Oponto 8040 3795 2070 6340
## 251 Hotel/Restaurant/Cafe Oponto 3191 1993 1799 1730
## 252 Retail Oponto 6134 23133 33586 6746
## 253 Hotel/Restaurant/Cafe Oponto 6623 1860 4740 7683
## 254 Hotel/Restaurant/Cafe Oponto 29526 7961 16966 432
## 255 Hotel/Restaurant/Cafe Oponto 10379 17972 4748 4686
## 256 Hotel/Restaurant/Cafe Oponto 31614 489 1495 3242
## 257 Hotel/Restaurant/Cafe Oponto 11092 5008 5249 453
## 258 Hotel/Restaurant/Cafe Oponto 8475 1931 1883 5004
## 259 Hotel/Restaurant/Cafe Oponto 56083 4563 2124 6422
## 260 Hotel/Restaurant/Cafe Oponto 53205 4959 7336 3012
## 261 Hotel/Restaurant/Cafe Oponto 9193 4885 2157 327
## 262 Hotel/Restaurant/Cafe Oponto 7858 1110 1094 6818
## 263 Hotel/Restaurant/Cafe Oponto 23257 1372 1677 982
## 264 Hotel/Restaurant/Cafe Oponto 2153 1115 6684 4324
## 265 Retail Oponto 1073 9679 15445 61
## 266 Hotel/Restaurant/Cafe Oponto 5909 23527 13699 10155
## 267 Retail Oponto 572 9763 22182 2221
## 268 Hotel/Restaurant/Cafe Oponto 20893 1222 2576 3975
## 269 Retail Oponto 11908 8053 19847 1069
## 270 Hotel/Restaurant/Cafe Oponto 15218 258 1138 2516
## 271 Hotel/Restaurant/Cafe Oponto 4720 1032 975 5500
## 272 Hotel/Restaurant/Cafe Oponto 2083 5007 1563 1120
## 273 Hotel/Restaurant/Cafe Oponto 514 8323 6869 529
## 274 Hotel/Restaurant/Cafe Other 36817 3045 1493 4802
## 275 Hotel/Restaurant/Cafe Other 894 1703 1841 744
## 276 Hotel/Restaurant/Cafe Other 680 1610 223 862
## 277 Hotel/Restaurant/Cafe Other 27901 3749 6964 4479
## 278 Hotel/Restaurant/Cafe Other 9061 829 683 16919
## 279 Hotel/Restaurant/Cafe Other 11693 2317 2543 5845
## 280 Retail Other 17360 6200 9694 1293
## 281 Hotel/Restaurant/Cafe Other 3366 2884 2431 977
## 282 Retail Other 12238 7108 6235 1093
## 283 Hotel/Restaurant/Cafe Other 49063 3965 4252 5970
## 284 Hotel/Restaurant/Cafe Other 25767 3613 2013 10303
## 285 Hotel/Restaurant/Cafe Other 68951 4411 12609 8692
## 286 Hotel/Restaurant/Cafe Other 40254 640 3600 1042
## 287 Hotel/Restaurant/Cafe Other 7149 2247 1242 1619
## 288 Hotel/Restaurant/Cafe Other 15354 2102 2828 8366
## 289 Hotel/Restaurant/Cafe Other 16260 594 1296 848
## 290 Hotel/Restaurant/Cafe Other 42786 286 471 1388
## 291 Hotel/Restaurant/Cafe Other 2708 2160 2642 502
## 292 Hotel/Restaurant/Cafe Other 6022 3354 3261 2507
## 293 Hotel/Restaurant/Cafe Other 2838 3086 4329 3838
## 294 Retail Lisbon 3996 11103 12469 902
## 295 Hotel/Restaurant/Cafe Lisbon 21273 2013 6550 909
## 296 Retail Lisbon 7588 1897 5234 417
## 297 Hotel/Restaurant/Cafe Lisbon 19087 1304 3643 3045
## 298 Retail Lisbon 8090 3199 6986 1455
## 299 Retail Lisbon 6758 4560 9965 934
## 300 Hotel/Restaurant/Cafe Lisbon 444 879 2060 264
## 301 Retail Lisbon 16448 6243 6360 824
## 302 Retail Lisbon 5283 13316 20399 1809
## 303 Retail Lisbon 2886 5302 9785 364
## 304 Retail Lisbon 2599 3688 13829 492
## 305 Retail Lisbon 161 7460 24773 617
## 306 Retail Lisbon 243 12939 8852 799
## 307 Retail Lisbon 6468 12867 21570 1840
## 308 Hotel/Restaurant/Cafe Lisbon 17327 2374 2842 1149
## 309 Hotel/Restaurant/Cafe Lisbon 6987 1020 3007 416
## 310 Retail Lisbon 918 20655 13567 1465
## 311 Hotel/Restaurant/Cafe Lisbon 7034 1492 2405 12569
## 312 Hotel/Restaurant/Cafe Lisbon 29635 2335 8280 3046
## 313 Retail Lisbon 2137 3737 19172 1274
## 314 Hotel/Restaurant/Cafe Lisbon 9784 925 2405 4447
## 315 Hotel/Restaurant/Cafe Lisbon 10617 1795 7647 1483
## 316 Retail Lisbon 1479 14982 11924 662
## 317 Hotel/Restaurant/Cafe Lisbon 7127 1375 2201 2679
## 318 Hotel/Restaurant/Cafe Lisbon 1182 3088 6114 978
## 319 Hotel/Restaurant/Cafe Lisbon 11800 2713 3558 2121
## 320 Retail Lisbon 9759 25071 17645 1128
## 321 Hotel/Restaurant/Cafe Lisbon 1774 3696 2280 514
## 322 Hotel/Restaurant/Cafe Lisbon 9155 1897 5167 2714
## 323 Hotel/Restaurant/Cafe Lisbon 15881 713 3315 3703
## 324 Hotel/Restaurant/Cafe Lisbon 13360 944 11593 915
## 325 Hotel/Restaurant/Cafe Lisbon 25977 3587 2464 2369
## 326 Hotel/Restaurant/Cafe Lisbon 32717 16784 13626 60869
## 327 Hotel/Restaurant/Cafe Lisbon 4414 1610 1431 3498
## 328 Hotel/Restaurant/Cafe Lisbon 542 899 1664 414
## 329 Hotel/Restaurant/Cafe Lisbon 16933 2209 3389 7849
## 330 Hotel/Restaurant/Cafe Lisbon 5113 1486 4583 5127
## 331 Hotel/Restaurant/Cafe Lisbon 9790 1786 5109 3570
## 332 Retail Lisbon 11223 14881 26839 1234
## 333 Hotel/Restaurant/Cafe Lisbon 22321 3216 1447 2208
## 334 Retail Lisbon 8565 4980 67298 131
## 335 Retail Lisbon 16823 928 2743 11559
## 336 Retail Lisbon 27082 6817 10790 1365
## 337 Hotel/Restaurant/Cafe Lisbon 13970 1511 1330 650
## 338 Hotel/Restaurant/Cafe Lisbon 9351 1347 2611 8170
## 339 Hotel/Restaurant/Cafe Lisbon 3 333 7021 15601
## 340 Hotel/Restaurant/Cafe Lisbon 2617 1188 5332 9584
## 341 Retail Other 381 4025 9670 388
## 342 Retail Other 2320 5763 11238 767
## 343 Hotel/Restaurant/Cafe Other 255 5758 5923 349
## 344 Retail Other 1689 6964 26316 1456
## 345 Hotel/Restaurant/Cafe Other 3043 1172 1763 2234
## 346 Hotel/Restaurant/Cafe Other 1198 2602 8335 402
## 347 Retail Other 2771 6939 15541 2693
## 348 Retail Other 27380 7184 12311 2809
## 349 Hotel/Restaurant/Cafe Other 3428 2380 2028 1341
## 350 Retail Other 5981 14641 20521 2005
## 351 Hotel/Restaurant/Cafe Other 3521 1099 1997 1796
## 352 Retail Other 1210 10044 22294 1741
## 353 Hotel/Restaurant/Cafe Other 608 1106 1533 830
## 354 Retail Other 117 6264 21203 228
## 355 Hotel/Restaurant/Cafe Other 14039 7393 2548 6386
## 356 Hotel/Restaurant/Cafe Other 190 727 2012 245
## 357 Hotel/Restaurant/Cafe Other 22686 134 218 3157
## 358 Retail Other 37 1275 22272 137
## 359 Hotel/Restaurant/Cafe Other 759 18664 1660 6114
## 360 Hotel/Restaurant/Cafe Other 796 5878 2109 340
## 361 Hotel/Restaurant/Cafe Other 19746 2872 2006 2601
## 362 Hotel/Restaurant/Cafe Other 4734 607 864 1206
## 363 Hotel/Restaurant/Cafe Other 2121 1601 2453 560
## 364 Hotel/Restaurant/Cafe Other 4627 997 4438 191
## 365 Hotel/Restaurant/Cafe Other 2615 873 1524 1103
## 366 Retail Other 4692 6128 8025 1619
## 367 Hotel/Restaurant/Cafe Other 9561 2217 1664 1173
## 368 Hotel/Restaurant/Cafe Other 3477 894 534 1457
## 369 Hotel/Restaurant/Cafe Other 22335 1196 2406 2046
## 370 Hotel/Restaurant/Cafe Other 6211 337 683 1089
## 371 Retail Other 39679 3944 4955 1364
## 372 Hotel/Restaurant/Cafe Other 20105 1887 1939 8164
## 373 Hotel/Restaurant/Cafe Other 3884 3801 1641 876
## 374 Retail Other 15076 6257 7398 1504
## 375 Hotel/Restaurant/Cafe Other 6338 2256 1668 1492
## 376 Hotel/Restaurant/Cafe Other 5841 1450 1162 597
## 377 Retail Other 3136 8630 13586 5641
## 378 Hotel/Restaurant/Cafe Other 38793 3154 2648 1034
## 379 Hotel/Restaurant/Cafe Other 3225 3294 1902 282
## 380 Retail Other 4048 5164 10391 130
## 381 Hotel/Restaurant/Cafe Other 28257 944 2146 3881
## 382 Hotel/Restaurant/Cafe Other 17770 4591 1617 9927
## 383 Hotel/Restaurant/Cafe Other 34454 7435 8469 2540
## 384 Hotel/Restaurant/Cafe Other 1821 1364 3450 4006
## 385 Hotel/Restaurant/Cafe Other 10683 21858 15400 3635
## 386 Hotel/Restaurant/Cafe Other 11635 922 1614 2583
## 387 Hotel/Restaurant/Cafe Other 1206 3620 2857 1945
## 388 Hotel/Restaurant/Cafe Other 20918 1916 1573 1960
## 389 Hotel/Restaurant/Cafe Other 9785 848 1172 1677
## 390 Hotel/Restaurant/Cafe Other 9385 1530 1422 3019
## 391 Hotel/Restaurant/Cafe Other 3352 1181 1328 5502
## 392 Hotel/Restaurant/Cafe Other 2647 2761 2313 907
## 393 Hotel/Restaurant/Cafe Other 518 4180 3600 659
## 394 Hotel/Restaurant/Cafe Other 23632 6730 3842 8620
## 395 Hotel/Restaurant/Cafe Other 12377 865 3204 1398
## 396 Hotel/Restaurant/Cafe Other 9602 1316 1263 2921
## 397 Retail Other 4515 11991 9345 2644
## 398 Hotel/Restaurant/Cafe Other 11535 1666 1428 6838
## 399 Hotel/Restaurant/Cafe Other 11442 1032 582 5390
## 400 Hotel/Restaurant/Cafe Other 9612 577 935 1601
## 401 Hotel/Restaurant/Cafe Other 4446 906 1238 3576
## 402 Hotel/Restaurant/Cafe Other 27167 2801 2128 13223
## 403 Hotel/Restaurant/Cafe Other 26539 4753 5091 220
## 404 Hotel/Restaurant/Cafe Other 25606 11006 4604 127
## 405 Hotel/Restaurant/Cafe Other 18073 4613 3444 4324
## 406 Hotel/Restaurant/Cafe Other 6884 1046 1167 2069
## 407 Hotel/Restaurant/Cafe Other 25066 5010 5026 9806
## 408 Retail Other 7362 12844 18683 2854
## 409 Retail Other 8257 3880 6407 1646
## 410 Hotel/Restaurant/Cafe Other 8708 3634 6100 2349
## 411 Hotel/Restaurant/Cafe Other 6633 2096 4563 1389
## 412 Hotel/Restaurant/Cafe Other 2126 3289 3281 1535
## 413 Hotel/Restaurant/Cafe Other 97 3605 12400 98
## 414 Hotel/Restaurant/Cafe Other 4983 4859 6633 17866
## 415 Hotel/Restaurant/Cafe Other 5969 1990 3417 5679
## 416 Retail Other 7842 6046 8552 1691
## 417 Retail Other 4389 10940 10908 848
## 418 Hotel/Restaurant/Cafe Other 5065 5499 11055 364
## 419 Retail Other 660 8494 18622 133
## 420 Hotel/Restaurant/Cafe Other 8861 3783 2223 633
## 421 Hotel/Restaurant/Cafe Other 4456 5266 13227 25
## 422 Retail Other 17063 4847 9053 1031
## 423 Hotel/Restaurant/Cafe Other 26400 1377 4172 830
## 424 Retail Other 17565 3686 4657 1059
## 425 Retail Other 16980 2884 12232 874
## 426 Hotel/Restaurant/Cafe Other 11243 2408 2593 15348
## 427 Hotel/Restaurant/Cafe Other 13134 9347 14316 3141
## 428 Hotel/Restaurant/Cafe Other 31012 16687 5429 15082
## 429 Hotel/Restaurant/Cafe Other 3047 5970 4910 2198
## 430 Hotel/Restaurant/Cafe Other 8607 1750 3580 47
## 431 Hotel/Restaurant/Cafe Other 3097 4230 16483 575
## 432 Hotel/Restaurant/Cafe Other 8533 5506 5160 13486
## 433 Hotel/Restaurant/Cafe Other 21117 1162 4754 269
## 434 Hotel/Restaurant/Cafe Other 1982 3218 1493 1541
## 435 Hotel/Restaurant/Cafe Other 16731 3922 7994 688
## 436 Hotel/Restaurant/Cafe Other 29703 12051 16027 13135
## 437 Hotel/Restaurant/Cafe Other 39228 1431 764 4510
## 438 Retail Other 14531 15488 30243 437
## 439 Hotel/Restaurant/Cafe Other 10290 1981 2232 1038
## 440 Hotel/Restaurant/Cafe Other 2787 1698 2510 65
## Detergents_Paper Delicassen
## 1 2674 1338
## 2 3293 1776
## 3 3516 7844
## 4 507 1788
## 5 1777 5185
## 6 1795 1451
## 7 3140 545
## 8 3321 2566
## 9 1716 750
## 10 7425 2098
## 11 5977 1744
## 12 549 497
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## 15 5058 2168
## 16 964 412
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## 18 370 4478
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## 20 2518 501
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## 25 4482 5778
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## 27 242 833
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## 35 483 395
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## 40 212 2916
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## 43 6899 46
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## 436 182 2204
## 437 93 2346
## 438 14841 1867
## 439 168 2125
## 440 477 52str(wholesaled)## 'data.frame': 440 obs. of 8 variables:
## $ Channel : Factor w/ 2 levels "Hotel/Restaurant/Cafe",..: 2 2 2 1 2 2 2 2 1 2 ...
## $ Region : Factor w/ 3 levels "Lisbon","Oponto",..: 3 3 3 3 3 3 3 3 3 3 ...
## $ Fresh : int 12669 7057 6353 13265 22615 9413 12126 7579 5963 6006 ...
## $ Milk : int 9656 9810 8808 1196 5410 8259 3199 4956 3648 11093 ...
## $ Grocery : int 7561 9568 7684 4221 7198 5126 6975 9426 6192 18881 ...
## $ Frozen : int 214 1762 2405 6404 3915 666 480 1669 425 1159 ...
## $ Detergents_Paper: int 2674 3293 3516 507 1777 1795 3140 3321 1716 7425 ...
## $ Delicassen : int 1338 1776 7844 1788 5185 1451 545 2566 750 2098 ...random<- runif(nrow(wholesaled))
wholesaleF<-wholesaled[order(random),]
wholesaleF## Channel Region Fresh Milk Grocery Frozen
## 383 Hotel/Restaurant/Cafe Other 34454 7435 8469 2540
## 361 Hotel/Restaurant/Cafe Other 19746 2872 2006 2601
## 258 Hotel/Restaurant/Cafe Oponto 8475 1931 1883 5004
## 7 Retail Other 12126 3199 6975 480
## 64 Retail Other 9396 11601 15775 2896
## 73 Hotel/Restaurant/Cafe Other 4420 5139 2661 8872
## 268 Hotel/Restaurant/Cafe Oponto 20893 1222 2576 3975
## 399 Hotel/Restaurant/Cafe Other 11442 1032 582 5390
## 186 Hotel/Restaurant/Cafe Other 8170 6448 1139 2181
## 275 Hotel/Restaurant/Cafe Other 894 1703 1841 744
## 98 Hotel/Restaurant/Cafe Other 403 254 610 774
## 338 Hotel/Restaurant/Cafe Lisbon 9351 1347 2611 8170
## 262 Hotel/Restaurant/Cafe Oponto 7858 1110 1094 6818
## 222 Hotel/Restaurant/Cafe Oponto 5396 7503 10646 91
## 347 Retail Other 2771 6939 15541 2693
## 101 Retail Other 11594 7779 12144 3252
## 110 Retail Other 1406 16729 28986 673
## 24 Retail Other 26373 36423 22019 5154
## 62 Retail Other 35942 38369 59598 3254
## 350 Retail Other 5981 14641 20521 2005
## 30 Hotel/Restaurant/Cafe Other 43088 2100 2609 1200
## 269 Retail Oponto 11908 8053 19847 1069
## 69 Hotel/Restaurant/Cafe Other 2446 7260 3993 5870
## 427 Hotel/Restaurant/Cafe Other 13134 9347 14316 3141
## 255 Hotel/Restaurant/Cafe Oponto 10379 17972 4748 4686
## 78 Retail Other 12205 12697 28540 869
## 168 Hotel/Restaurant/Cafe Other 2926 3195 3268 405
## 51 Hotel/Restaurant/Cafe Other 6269 1095 1980 3860
## 184 Hotel/Restaurant/Cafe Other 36847 43950 20170 36534
## 153 Hotel/Restaurant/Cafe Other 18840 1371 3135 3001
## 165 Retail Other 5224 7603 8584 2540
## 75 Retail Other 8190 6343 9794 1285
## 408 Retail Other 7362 12844 18683 2854
## 266 Hotel/Restaurant/Cafe Oponto 5909 23527 13699 10155
## 112 Retail Other 12579 11114 17569 805
## 378 Hotel/Restaurant/Cafe Other 38793 3154 2648 1034
## 424 Retail Other 17565 3686 4657 1059
## 95 Retail Other 5626 12220 11323 206
## 233 Hotel/Restaurant/Cafe Oponto 25962 1780 3838 638
## 384 Hotel/Restaurant/Cafe Other 1821 1364 3450 4006
## 1 Retail Other 12669 9656 7561 214
## 386 Hotel/Restaurant/Cafe Other 11635 922 1614 2583
## 298 Retail Lisbon 8090 3199 6986 1455
## 179 Hotel/Restaurant/Cafe Other 11002 7075 4945 1152
## 265 Retail Oponto 1073 9679 15445 61
## 71 Hotel/Restaurant/Cafe Other 16705 2037 3202 10643
## 114 Hotel/Restaurant/Cafe Other 14438 2295 1733 3220
## 119 Hotel/Restaurant/Cafe Other 20049 1891 2362 5343
## 133 Hotel/Restaurant/Cafe Other 14903 2032 2479 576
## 193 Hotel/Restaurant/Cafe Other 5509 1461 2251 547
## 108 Retail Other 8797 10646 14886 2471
## 353 Hotel/Restaurant/Cafe Other 608 1106 1533 830
## 94 Hotel/Restaurant/Cafe Other 11314 3090 2062 35009
## 419 Retail Other 660 8494 18622 133
## 225 Hotel/Restaurant/Cafe Oponto 7274 659 1499 784
## 105 Hotel/Restaurant/Cafe Other 14100 2132 3445 1336
## 93 Retail Other 9198 27472 32034 3232
## 83 Retail Other 10362 9232 11009 737
## 315 Hotel/Restaurant/Cafe Lisbon 10617 1795 7647 1483
## 55 Hotel/Restaurant/Cafe Other 27329 1449 1947 2436
## 324 Hotel/Restaurant/Cafe Lisbon 13360 944 11593 915
## 107 Retail Other 1454 6337 10704 133
## 23 Hotel/Restaurant/Cafe Other 31276 1917 4469 9408
## 89 Hotel/Restaurant/Cafe Other 7864 542 4042 9735
## 91 Hotel/Restaurant/Cafe Other 11405 596 1638 3347
## 395 Hotel/Restaurant/Cafe Other 12377 865 3204 1398
## 59 Hotel/Restaurant/Cafe Other 13779 1970 1648 596
## 35 Hotel/Restaurant/Cafe Other 1502 1979 2262 425
## 213 Hotel/Restaurant/Cafe Oponto 7291 1012 2062 1291
## 328 Hotel/Restaurant/Cafe Lisbon 542 899 1664 414
## 19 Retail Other 18601 6327 10099 2205
## 406 Hotel/Restaurant/Cafe Other 6884 1046 1167 2069
## 307 Retail Lisbon 6468 12867 21570 1840
## 317 Hotel/Restaurant/Cafe Lisbon 7127 1375 2201 2679
## 388 Hotel/Restaurant/Cafe Other 20918 1916 1573 1960
## 334 Retail Lisbon 8565 4980 67298 131
## 85 Retail Other 11867 3327 4814 1178
## 316 Retail Lisbon 1479 14982 11924 662
## 321 Hotel/Restaurant/Cafe Lisbon 1774 3696 2280 514
## 426 Hotel/Restaurant/Cafe Other 11243 2408 2593 15348
## 351 Hotel/Restaurant/Cafe Other 3521 1099 1997 1796
## 37 Hotel/Restaurant/Cafe Other 29955 4362 5428 1729
## 104 Hotel/Restaurant/Cafe Other 56082 3504 8906 18028
## 209 Hotel/Restaurant/Cafe Oponto 1537 3748 5838 1859
## 382 Hotel/Restaurant/Cafe Other 17770 4591 1617 9927
## 236 Hotel/Restaurant/Cafe Oponto 1838 6380 2824 1218
## 385 Hotel/Restaurant/Cafe Other 10683 21858 15400 3635
## 414 Hotel/Restaurant/Cafe Other 4983 4859 6633 17866
## 243 Hotel/Restaurant/Cafe Oponto 18226 659 2914 3752
## 25 Retail Other 22647 9776 13792 2915
## 115 Hotel/Restaurant/Cafe Other 18044 1080 2000 2555
## 164 Retail Other 5531 15726 26870 2367
## 152 Hotel/Restaurant/Cafe Other 1289 3328 2022 531
## 20 Hotel/Restaurant/Cafe Other 7780 2495 9464 669
## 8 Retail Other 7579 4956 9426 1669
## 368 Hotel/Restaurant/Cafe Other 3477 894 534 1457
## 155 Hotel/Restaurant/Cafe Other 622 55 137 75
## 65 Hotel/Restaurant/Cafe Other 4760 1227 3250 3724
## 294 Retail Lisbon 3996 11103 12469 902
## 49 Retail Other 11519 6152 10868 584
## 242 Hotel/Restaurant/Cafe Oponto 24929 1801 2475 2216
## 127 Hotel/Restaurant/Cafe Other 19219 1840 1658 8195
## 223 Hotel/Restaurant/Cafe Oponto 5041 1115 2856 7496
## 38 Retail Other 15168 10556 12477 1920
## 257 Hotel/Restaurant/Cafe Oponto 11092 5008 5249 453
## 253 Hotel/Restaurant/Cafe Oponto 6623 1860 4740 7683
## 372 Hotel/Restaurant/Cafe Other 20105 1887 1939 8164
## 32 Hotel/Restaurant/Cafe Other 2612 4339 3133 2088
## 279 Hotel/Restaurant/Cafe Other 11693 2317 2543 5845
## 18 Hotel/Restaurant/Cafe Other 5876 6157 2933 839
## 264 Hotel/Restaurant/Cafe Oponto 2153 1115 6684 4324
## 342 Retail Other 2320 5763 11238 767
## 435 Hotel/Restaurant/Cafe Other 16731 3922 7994 688
## 430 Hotel/Restaurant/Cafe Other 8607 1750 3580 47
## 118 Hotel/Restaurant/Cafe Other 6990 3880 5380 1647
## 254 Hotel/Restaurant/Cafe Oponto 29526 7961 16966 432
## 103 Retail Other 2932 6459 7677 2561
## 212 Retail Oponto 12119 28326 39694 4736
## 286 Hotel/Restaurant/Cafe Other 40254 640 3600 1042
## 13 Retail Other 31714 12319 11757 287
## 126 Hotel/Restaurant/Cafe Other 76237 3473 7102 16538
## 17 Retail Other 1020 8816 12121 134
## 288 Hotel/Restaurant/Cafe Other 15354 2102 2828 8366
## 15 Retail Other 24653 9465 12091 294
## 381 Hotel/Restaurant/Cafe Other 28257 944 2146 3881
## 97 Retail Other 23 2616 8118 145
## 22 Hotel/Restaurant/Cafe Other 5567 871 2010 3383
## 180 Hotel/Restaurant/Cafe Other 3157 4888 2500 4477
## 33 Hotel/Restaurant/Cafe Other 21632 1318 2886 266
## 375 Hotel/Restaurant/Cafe Other 6338 2256 1668 1492
## 238 Hotel/Restaurant/Cafe Oponto 18692 3838 593 4634
## 207 Hotel/Restaurant/Cafe Oponto 6373 780 950 878
## 67 Hotel/Restaurant/Cafe Other 9 1534 7417 175
## 300 Hotel/Restaurant/Cafe Lisbon 444 879 2060 264
## 99 Hotel/Restaurant/Cafe Other 503 112 778 895
## 52 Hotel/Restaurant/Cafe Other 3347 4051 6996 239
## 189 Retail Other 8040 7639 11687 2758
## 346 Hotel/Restaurant/Cafe Other 1198 2602 8335 402
## 88 Hotel/Restaurant/Cafe Other 43265 5025 8117 6312
## 43 Retail Other 10850 7555 14961 188
## 21 Retail Other 17546 4519 4602 1066
## 63 Retail Other 7823 6245 6544 4154
## 203 Hotel/Restaurant/Cafe Oponto 25203 11487 9490 5065
## 130 Hotel/Restaurant/Cafe Other 42312 926 1510 1718
## 79 Hotel/Restaurant/Cafe Other 10766 1175 2067 2096
## 143 Hotel/Restaurant/Cafe Other 37036 7152 8253 2995
## 170 Hotel/Restaurant/Cafe Other 5414 717 2155 2399
## 106 Hotel/Restaurant/Cafe Other 15587 1014 3970 910
## 364 Hotel/Restaurant/Cafe Other 4627 997 4438 191
## 45 Retail Other 9670 7027 10471 541
## 224 Retail Oponto 2790 2527 5265 5612
## 228 Hotel/Restaurant/Cafe Oponto 4042 2204 1563 2286
## 429 Hotel/Restaurant/Cafe Other 3047 5970 4910 2198
## 325 Hotel/Restaurant/Cafe Lisbon 25977 3587 2464 2369
## 175 Hotel/Restaurant/Cafe Other 286 1208 5241 2515
## 423 Hotel/Restaurant/Cafe Other 26400 1377 4172 830
## 280 Retail Other 17360 6200 9694 1293
## 182 Hotel/Restaurant/Cafe Other 112151 29627 18148 16745
## 319 Hotel/Restaurant/Cafe Lisbon 11800 2713 3558 2121
## 301 Retail Lisbon 16448 6243 6360 824
## 145 Hotel/Restaurant/Cafe Other 18827 3677 1988 118
## 271 Hotel/Restaurant/Cafe Oponto 4720 1032 975 5500
## 400 Hotel/Restaurant/Cafe Other 9612 577 935 1601
## 326 Hotel/Restaurant/Cafe Lisbon 32717 16784 13626 60869
## 281 Hotel/Restaurant/Cafe Other 3366 2884 2431 977
## 345 Hotel/Restaurant/Cafe Other 3043 1172 1763 2234
## 337 Hotel/Restaurant/Cafe Lisbon 13970 1511 1330 650
## 159 Retail Other 2861 6570 9618 930
## 273 Hotel/Restaurant/Cafe Oponto 514 8323 6869 529
## 437 Hotel/Restaurant/Cafe Other 39228 1431 764 4510
## 77 Hotel/Restaurant/Cafe Other 717 3587 6532 7530
## 336 Retail Lisbon 27082 6817 10790 1365
## 87 Retail Other 22925 73498 32114 987
## 376 Hotel/Restaurant/Cafe Other 5841 1450 1162 597
## 323 Hotel/Restaurant/Cafe Lisbon 15881 713 3315 3703
## 208 Retail Oponto 2541 4737 6089 2946
## 313 Retail Lisbon 2137 3737 19172 1274
## 314 Hotel/Restaurant/Cafe Lisbon 9784 925 2405 4447
## 318 Hotel/Restaurant/Cafe Lisbon 1182 3088 6114 978
## 340 Hotel/Restaurant/Cafe Lisbon 2617 1188 5332 9584
## 420 Hotel/Restaurant/Cafe Other 8861 3783 2223 633
## 292 Hotel/Restaurant/Cafe Other 6022 3354 3261 2507
## 410 Hotel/Restaurant/Cafe Other 8708 3634 6100 2349
## 402 Hotel/Restaurant/Cafe Other 27167 2801 2128 13223
## 416 Retail Other 7842 6046 8552 1691
## 379 Hotel/Restaurant/Cafe Other 3225 3294 1902 282
## 221 Hotel/Restaurant/Cafe Oponto 14755 899 1382 1765
## 134 Hotel/Restaurant/Cafe Other 9434 1042 1235 436
## 177 Hotel/Restaurant/Cafe Other 45640 6958 6536 7368
## 241 Hotel/Restaurant/Cafe Oponto 22096 3575 7041 11422
## 428 Hotel/Restaurant/Cafe Other 31012 16687 5429 15082
## 174 Retail Other 514 7677 19805 937
## 327 Hotel/Restaurant/Cafe Lisbon 4414 1610 1431 3498
## 282 Retail Other 12238 7108 6235 1093
## 161 Retail Other 1725 3651 12822 824
## 167 Retail Other 4822 6721 9170 993
## 163 Hotel/Restaurant/Cafe Other 15177 2024 3810 2665
## 109 Retail Other 1531 8397 6981 247
## 151 Hotel/Restaurant/Cafe Other 16225 1825 1765 853
## 433 Hotel/Restaurant/Cafe Other 21117 1162 4754 269
## 304 Retail Lisbon 2599 3688 13829 492
## 306 Retail Lisbon 243 12939 8852 799
## 31 Hotel/Restaurant/Cafe Other 18815 3610 11107 1148
## 287 Hotel/Restaurant/Cafe Other 7149 2247 1242 1619
## 363 Hotel/Restaurant/Cafe Other 2121 1601 2453 560
## 136 Hotel/Restaurant/Cafe Other 6300 1289 2591 1170
## 82 Retail Other 219 9540 14403 283
## 335 Retail Lisbon 16823 928 2743 11559
## 246 Retail Oponto 3062 6154 13916 230
## 181 Hotel/Restaurant/Cafe Other 12356 6036 8887 402
## 219 Retail Oponto 18 7504 15205 1285
## 144 Hotel/Restaurant/Cafe Other 10405 1596 1096 8425
## 48 Retail Other 44466 54259 55571 7782
## 125 Hotel/Restaurant/Cafe Other 36050 1642 2961 4787
## 359 Hotel/Restaurant/Cafe Other 759 18664 1660 6114
## 374 Retail Other 15076 6257 7398 1504
## 239 Hotel/Restaurant/Cafe Oponto 7363 475 585 1112
## 57 Retail Other 4098 29892 26866 2616
## 230 Hotel/Restaurant/Cafe Oponto 8656 2746 2501 6845
## 129 Hotel/Restaurant/Cafe Other 140 8847 3823 142
## 226 Hotel/Restaurant/Cafe Oponto 12680 3243 4157 660
## 46 Retail Other 5181 22044 21531 1740
## 141 Hotel/Restaurant/Cafe Other 17623 4280 7305 2279
## 128 Retail Other 21465 7243 10685 880
## 56 Hotel/Restaurant/Cafe Other 5264 3683 5005 1057
## 54 Retail Other 491 10473 11532 744
## 276 Hotel/Restaurant/Cafe Other 680 1610 223 862
## 34 Hotel/Restaurant/Cafe Other 29729 4786 7326 6130
## 68 Retail Other 19913 6759 13462 1256
## 229 Hotel/Restaurant/Cafe Oponto 1869 577 572 950
## 96 Hotel/Restaurant/Cafe Other 3 2920 6252 440
## 417 Retail Other 4389 10940 10908 848
## 10 Retail Other 6006 11093 18881 1159
## 343 Hotel/Restaurant/Cafe Other 255 5758 5923 349
## 76 Hotel/Restaurant/Cafe Other 20398 1137 3 4407
## 260 Hotel/Restaurant/Cafe Oponto 53205 4959 7336 3012
## 27 Hotel/Restaurant/Cafe Other 9898 961 2861 3151
## 205 Hotel/Restaurant/Cafe Oponto 1956 891 5226 1383
## 202 Retail Oponto 4484 14399 24708 3549
## 251 Hotel/Restaurant/Cafe Oponto 3191 1993 1799 1730
## 398 Hotel/Restaurant/Cafe Other 11535 1666 1428 6838
## 303 Retail Lisbon 2886 5302 9785 364
## 84 Hotel/Restaurant/Cafe Other 20874 1563 1783 2320
## 278 Hotel/Restaurant/Cafe Other 9061 829 683 16919
## 12 Retail Other 13146 1124 4523 1420
## 309 Hotel/Restaurant/Cafe Lisbon 6987 1020 3007 416
## 41 Hotel/Restaurant/Cafe Other 24025 4332 4757 9510
## 160 Retail Other 355 7704 14682 398
## 362 Hotel/Restaurant/Cafe Other 4734 607 864 1206
## 178 Hotel/Restaurant/Cafe Other 12759 7330 4533 1752
## 256 Hotel/Restaurant/Cafe Oponto 31614 489 1495 3242
## 70 Hotel/Restaurant/Cafe Other 8352 2820 1293 779
## 397 Retail Other 4515 11991 9345 2644
## 102 Retail Other 1420 10810 16267 1593
## 148 Hotel/Restaurant/Cafe Other 9203 3373 2707 1286
## 211 Hotel/Restaurant/Cafe Oponto 18567 1895 1393 1801
## 215 Retail Oponto 2362 6551 11364 913
## 232 Hotel/Restaurant/Cafe Oponto 2344 10678 3828 1439
## 235 Hotel/Restaurant/Cafe Oponto 15603 2703 3833 4260
## 405 Hotel/Restaurant/Cafe Other 18073 4613 3444 4324
## 248 Hotel/Restaurant/Cafe Oponto 13569 346 489 2077
## 162 Hotel/Restaurant/Cafe Other 12434 540 283 1092
## 150 Hotel/Restaurant/Cafe Other 31812 1433 1651 800
## 172 Retail Other 200 25862 19816 651
## 259 Hotel/Restaurant/Cafe Oponto 56083 4563 2124 6422
## 440 Hotel/Restaurant/Cafe Other 2787 1698 2510 65
## 357 Hotel/Restaurant/Cafe Other 22686 134 218 3157
## 252 Retail Oponto 6134 23133 33586 6746
## 138 Hotel/Restaurant/Cafe Other 3087 8080 8282 661
## 263 Hotel/Restaurant/Cafe Oponto 23257 1372 1677 982
## 183 Hotel/Restaurant/Cafe Other 694 8533 10518 443
## 157 Retail Other 3830 5291 14855 317
## 295 Hotel/Restaurant/Cafe Lisbon 21273 2013 6550 909
## 3 Retail Other 6353 8808 7684 2405
## 44 Retail Other 630 11095 23998 787
## 2 Retail Other 7057 9810 9568 1762
## 409 Retail Other 8257 3880 6407 1646
## 4 Hotel/Restaurant/Cafe Other 13265 1196 4221 6404
## 146 Retail Other 22039 8384 34792 42
## 139 Hotel/Restaurant/Cafe Other 13537 4257 5034 155
## 188 Hotel/Restaurant/Cafe Other 2438 8002 9819 6269
## 132 Hotel/Restaurant/Cafe Other 2101 589 314 346
## 299 Retail Lisbon 6758 4560 9965 934
## 166 Retail Other 15615 12653 19858 4425
## 217 Retail Oponto 2532 16599 36486 179
## 191 Hotel/Restaurant/Cafe Other 16936 6250 1981 7332
## 200 Hotel/Restaurant/Cafe Oponto 9670 2280 2112 520
## 6 Retail Other 9413 8259 5126 666
## 42 Hotel/Restaurant/Cafe Other 19176 3065 5956 2033
## 394 Hotel/Restaurant/Cafe Other 23632 6730 3842 8620
## 291 Hotel/Restaurant/Cafe Other 2708 2160 2642 502
## 380 Retail Other 4048 5164 10391 130
## 329 Hotel/Restaurant/Cafe Lisbon 16933 2209 3389 7849
## 436 Hotel/Restaurant/Cafe Other 29703 12051 16027 13135
## 367 Hotel/Restaurant/Cafe Other 9561 2217 1664 1173
## 201 Retail Oponto 3067 13240 23127 3941
## 421 Hotel/Restaurant/Cafe Other 4456 5266 13227 25
## 431 Hotel/Restaurant/Cafe Other 3097 4230 16483 575
## 9 Hotel/Restaurant/Cafe Other 5963 3648 6192 425
## 348 Retail Other 27380 7184 12311 2809
## 80 Hotel/Restaurant/Cafe Other 1640 3259 3655 868
## 47 Retail Other 3103 14069 21955 1668
## 312 Hotel/Restaurant/Cafe Lisbon 29635 2335 8280 3046
## 401 Hotel/Restaurant/Cafe Other 4446 906 1238 3576
## 387 Hotel/Restaurant/Cafe Other 1206 3620 2857 1945
## 244 Hotel/Restaurant/Cafe Oponto 11210 3576 5119 561
## 391 Hotel/Restaurant/Cafe Other 3352 1181 1328 5502
## 11 Retail Other 3366 5403 12974 4400
## 297 Hotel/Restaurant/Cafe Lisbon 19087 1304 3643 3045
## 289 Hotel/Restaurant/Cafe Other 16260 594 1296 848
## 218 Hotel/Restaurant/Cafe Oponto 18044 1475 2046 2532
## 66 Retail Other 85 20959 45828 36
## 173 Hotel/Restaurant/Cafe Other 955 5479 6536 333
## 354 Retail Other 117 6264 21203 228
## 111 Hotel/Restaurant/Cafe Other 11818 1648 1694 2276
## 53 Retail Other 40721 3916 5876 532
## 370 Hotel/Restaurant/Cafe Other 6211 337 683 1089
## 358 Retail Other 37 1275 22272 137
## 206 Retail Oponto 1107 11711 23596 955
## 393 Hotel/Restaurant/Cafe Other 518 4180 3600 659
## 198 Retail Oponto 2427 7097 10391 1127
## 331 Hotel/Restaurant/Cafe Lisbon 9790 1786 5109 3570
## 439 Hotel/Restaurant/Cafe Other 10290 1981 2232 1038
## 285 Hotel/Restaurant/Cafe Other 68951 4411 12609 8692
## 392 Hotel/Restaurant/Cafe Other 2647 2761 2313 907
## 147 Hotel/Restaurant/Cafe Other 7769 1936 2177 926
## 100 Hotel/Restaurant/Cafe Other 9658 2182 1909 5639
## 190 Retail Other 834 11577 11522 275
## 352 Retail Other 1210 10044 22294 1741
## 121 Hotel/Restaurant/Cafe Other 17160 1200 3412 2417
## 270 Hotel/Restaurant/Cafe Oponto 15218 258 1138 2516
## 267 Retail Oponto 572 9763 22182 2221
## 237 Hotel/Restaurant/Cafe Oponto 8635 820 3047 2312
## 58 Retail Other 5417 9933 10487 38
## 403 Hotel/Restaurant/Cafe Other 26539 4753 5091 220
## 171 Retail Other 260 8675 13430 1116
## 296 Retail Lisbon 7588 1897 5234 417
## 214 Hotel/Restaurant/Cafe Oponto 3317 6602 6861 1329
## 407 Hotel/Restaurant/Cafe Other 25066 5010 5026 9806
## 272 Hotel/Restaurant/Cafe Oponto 2083 5007 1563 1120
## 176 Retail Other 2343 7845 11874 52
## 377 Retail Other 3136 8630 13586 5641
## 36 Retail Other 688 5491 11091 833
## 50 Retail Other 4967 21412 28921 1798
## 204 Hotel/Restaurant/Cafe Oponto 583 685 2216 469
## 360 Hotel/Restaurant/Cafe Other 796 5878 2109 340
## 310 Retail Lisbon 918 20655 13567 1465
## 247 Hotel/Restaurant/Cafe Oponto 8885 2428 1777 1777
## 418 Hotel/Restaurant/Cafe Other 5065 5499 11055 364
## 404 Hotel/Restaurant/Cafe Other 25606 11006 4604 127
## 411 Hotel/Restaurant/Cafe Other 6633 2096 4563 1389
## 74 Retail Other 19899 5332 8713 8132
## 432 Hotel/Restaurant/Cafe Other 8533 5506 5160 13486
## 284 Hotel/Restaurant/Cafe Other 25767 3613 2013 10303
## 250 Hotel/Restaurant/Cafe Oponto 8040 3795 2070 6340
## 231 Retail Oponto 11072 5989 5615 8321
## 120 Hotel/Restaurant/Cafe Other 8258 2344 2147 3896
## 274 Hotel/Restaurant/Cafe Other 36817 3045 1493 4802
## 227 Retail Oponto 20782 5921 9212 1759
## 169 Hotel/Restaurant/Cafe Other 5809 735 803 1393
## 40 Hotel/Restaurant/Cafe Other 56159 555 902 10002
## 234 Hotel/Restaurant/Cafe Oponto 964 4984 3316 937
## 412 Hotel/Restaurant/Cafe Other 2126 3289 3281 1535
## 195 Hotel/Restaurant/Cafe Other 7107 1012 2974 806
## 26 Retail Other 16165 4230 7595 201
## 369 Hotel/Restaurant/Cafe Other 22335 1196 2406 2046
## 290 Hotel/Restaurant/Cafe Other 42786 286 471 1388
## 277 Hotel/Restaurant/Cafe Other 27901 3749 6964 4479
## 196 Hotel/Restaurant/Cafe Other 17023 5139 5230 7888
## 29 Retail Other 4113 20484 25957 1158
## 320 Retail Lisbon 9759 25071 17645 1128
## 197 Hotel/Restaurant/Cafe Oponto 30624 7209 4897 18711
## 365 Hotel/Restaurant/Cafe Other 2615 873 1524 1103
## 16 Hotel/Restaurant/Cafe Other 10253 1114 3821 397
## 187 Hotel/Restaurant/Cafe Other 3009 521 854 3470
## 261 Hotel/Restaurant/Cafe Oponto 9193 4885 2157 327
## 60 Hotel/Restaurant/Cafe Other 6137 5360 8040 129
## 154 Hotel/Restaurant/Cafe Other 3463 9250 2368 779
## 220 Hotel/Restaurant/Cafe Oponto 4155 367 1390 2306
## 5 Retail Other 22615 5410 7198 3915
## 322 Hotel/Restaurant/Cafe Lisbon 9155 1897 5167 2714
## 61 Retail Other 8590 3045 7854 96
## 415 Hotel/Restaurant/Cafe Other 5969 1990 3417 5679
## 140 Hotel/Restaurant/Cafe Other 5387 4979 3343 825
## 373 Hotel/Restaurant/Cafe Other 3884 3801 1641 876
## 332 Retail Lisbon 11223 14881 26839 1234
## 194 Retail Other 180 3485 20292 959
## 72 Hotel/Restaurant/Cafe Other 18291 1266 21042 5373
## 90 Hotel/Restaurant/Cafe Other 24904 3836 5330 3443
## 86 Retail Other 16117 46197 92780 1026
## 92 Hotel/Restaurant/Cafe Other 12754 2762 2530 8693
## 422 Retail Other 17063 4847 9053 1031
## 137 Hotel/Restaurant/Cafe Other 4625 8579 7030 4575
## 116 Hotel/Restaurant/Cafe Other 11134 793 2988 2715
## 311 Hotel/Restaurant/Cafe Lisbon 7034 1492 2405 12569
## 135 Hotel/Restaurant/Cafe Other 7388 1882 2174 720
## 28 Hotel/Restaurant/Cafe Other 14276 803 3045 485
## 122 Hotel/Restaurant/Cafe Other 4020 3234 1498 2395
## 283 Hotel/Restaurant/Cafe Other 49063 3965 4252 5970
## 330 Hotel/Restaurant/Cafe Lisbon 5113 1486 4583 5127
## 349 Hotel/Restaurant/Cafe Other 3428 2380 2028 1341
## 396 Hotel/Restaurant/Cafe Other 9602 1316 1263 2921
## 14 Retail Other 21217 6208 14982 3095
## 81 Hotel/Restaurant/Cafe Other 7005 829 3009 430
## 339 Hotel/Restaurant/Cafe Lisbon 3 333 7021 15601
## 434 Hotel/Restaurant/Cafe Other 1982 3218 1493 1541
## 216 Hotel/Restaurant/Cafe Oponto 2806 10765 15538 1374
## 425 Retail Other 16980 2884 12232 874
## 438 Retail Other 14531 15488 30243 437
## 124 Retail Other 11170 10769 8814 2194
## 356 Hotel/Restaurant/Cafe Other 190 727 2012 245
## 117 Hotel/Restaurant/Cafe Other 11173 2521 3355 1517
## 390 Hotel/Restaurant/Cafe Other 9385 1530 1422 3019
## 305 Retail Lisbon 161 7460 24773 617
## 113 Hotel/Restaurant/Cafe Other 19046 2770 2469 8853
## 293 Hotel/Restaurant/Cafe Other 2838 3086 4329 3838
## 333 Hotel/Restaurant/Cafe Lisbon 22321 3216 1447 2208
## 341 Retail Other 381 4025 9670 388
## 185 Hotel/Restaurant/Cafe Other 327 918 4710 74
## 131 Hotel/Restaurant/Cafe Other 7149 2428 699 6316
## 249 Hotel/Restaurant/Cafe Oponto 15671 5279 2406 559
## 413 Hotel/Restaurant/Cafe Other 97 3605 12400 98
## 371 Retail Other 39679 3944 4955 1364
## 366 Retail Other 4692 6128 8025 1619
## 344 Retail Other 1689 6964 26316 1456
## 245 Hotel/Restaurant/Cafe Oponto 6202 7775 10817 1183
## 149 Hotel/Restaurant/Cafe Other 5924 584 542 4052
## 302 Retail Lisbon 5283 13316 20399 1809
## 199 Hotel/Restaurant/Cafe Oponto 11686 2154 6824 3527
## 142 Hotel/Restaurant/Cafe Other 30379 13252 5189 321
## 389 Hotel/Restaurant/Cafe Other 9785 848 1172 1677
## 210 Retail Oponto 5550 12729 16767 864
## 156 Retail Other 1989 10690 19460 233
## 240 Hotel/Restaurant/Cafe Oponto 47493 2567 3779 5243
## 308 Hotel/Restaurant/Cafe Lisbon 17327 2374 2842 1149
## 39 Retail Other 4591 15729 16709 33
## 192 Hotel/Restaurant/Cafe Other 13624 295 1381 890
## 123 Hotel/Restaurant/Cafe Other 12212 201 245 1991
## 158 Hotel/Restaurant/Cafe Other 17773 1366 2474 3378
## 355 Hotel/Restaurant/Cafe Other 14039 7393 2548 6386
## Detergents_Paper Delicassen
## 383 1711 2893
## 361 468 503
## 258 3593 987
## 7 3140 545
## 64 7677 1295
## 73 1321 181
## 268 737 3628
## 399 74 247
## 186 58 247
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## 101 8035 3029
## 110 836 3
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## 30 1107 823
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## 1 2674 1338
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## 94 71 2698
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## 105 1491 548
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## 55 204 1333
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## 107 6830 1831
## 23 2381 4334
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## 355 1333 2341#Utilizing Channel as our target feature, and all other variables as our target predictors. Create a training data set and begin to build my algorithm.
M2 <- C5.0(wholesaleF[1:100,-1], wholesaleF[1:100, 1])
summary(M2)##
## Call:
## C5.0.default(x = wholesaleF[1:100, -1], y = wholesaleF[1:100, 1])
##
##
## C5.0 [Release 2.07 GPL Edition] Wed Jun 10 15:53:03 2015
## -------------------------------
##
## Class specified by attribute `outcome'
##
## Read 100 cases (8 attributes) from undefined.data
##
## Decision tree:
##
## Detergents_Paper <= 1711: Hotel/Restaurant/Cafe (61/1)
## Detergents_Paper > 1711:
## :...Milk <= 2495: Hotel/Restaurant/Cafe (3)
## Milk > 2495:
## :...Region in {Lisbon,Other}: Retail (32/1)
## Region = Oponto:
## :...Milk <= 7603: Hotel/Restaurant/Cafe (2)
## Milk > 7603: Retail (2)
##
##
## Evaluation on training data (100 cases):
##
## Decision Tree
## ----------------
## Size Errors
##
## 5 2( 2.0%) <<
##
##
## (a) (b) <-classified as
## ---- ----
## 65 1 (a): class Hotel/Restaurant/Cafe
## 1 33 (b): class Retail
##
##
## Attribute usage:
##
## 100.00% Detergents_Paper
## 39.00% Milk
## 36.00% Region
##
##
## Time: 0.0 secs#Model M2 only yields 4 total errors.
#Now let test our remaining data set.
P2 <-predict(M2, wholesaleF[101:440,])
P2## [1] Hotel/Restaurant/Cafe Hotel/Restaurant/Cafe Hotel/Restaurant/Cafe
## [4] Retail Hotel/Restaurant/Cafe Hotel/Restaurant/Cafe
## [7] Hotel/Restaurant/Cafe Hotel/Restaurant/Cafe Hotel/Restaurant/Cafe
## [10] Hotel/Restaurant/Cafe Hotel/Restaurant/Cafe Retail
## [13] Retail Hotel/Restaurant/Cafe Hotel/Restaurant/Cafe
## [16] Hotel/Restaurant/Cafe Retail Retail
## [19] Hotel/Restaurant/Cafe Retail Hotel/Restaurant/Cafe
## [22] Retail Hotel/Restaurant/Cafe Retail
## [25] Hotel/Restaurant/Cafe Retail Hotel/Restaurant/Cafe
## [28] Hotel/Restaurant/Cafe Hotel/Restaurant/Cafe Hotel/Restaurant/Cafe
## [31] Hotel/Restaurant/Cafe Hotel/Restaurant/Cafe Hotel/Restaurant/Cafe
## [34] Hotel/Restaurant/Cafe Hotel/Restaurant/Cafe Hotel/Restaurant/Cafe
## [37] Retail Retail Hotel/Restaurant/Cafe
## [40] Retail Retail Retail
## [43] Hotel/Restaurant/Cafe Hotel/Restaurant/Cafe Hotel/Restaurant/Cafe
## [46] Hotel/Restaurant/Cafe Hotel/Restaurant/Cafe Hotel/Restaurant/Cafe
## [49] Hotel/Restaurant/Cafe Retail Hotel/Restaurant/Cafe
## [52] Hotel/Restaurant/Cafe Hotel/Restaurant/Cafe Hotel/Restaurant/Cafe
## [55] Hotel/Restaurant/Cafe Hotel/Restaurant/Cafe Retail
## [58] Retail Hotel/Restaurant/Cafe Retail
## [61] Hotel/Restaurant/Cafe Hotel/Restaurant/Cafe Hotel/Restaurant/Cafe
## [64] Hotel/Restaurant/Cafe Hotel/Restaurant/Cafe Hotel/Restaurant/Cafe
## [67] Hotel/Restaurant/Cafe Retail Hotel/Restaurant/Cafe
## [70] Hotel/Restaurant/Cafe Hotel/Restaurant/Cafe Retail
## [73] Retail Hotel/Restaurant/Cafe Hotel/Restaurant/Cafe
## [76] Hotel/Restaurant/Cafe Retail Hotel/Restaurant/Cafe
## [79] Hotel/Restaurant/Cafe Hotel/Restaurant/Cafe Hotel/Restaurant/Cafe
## [82] Hotel/Restaurant/Cafe Retail Hotel/Restaurant/Cafe
## [85] Retail Hotel/Restaurant/Cafe Hotel/Restaurant/Cafe
## [88] Hotel/Restaurant/Cafe Hotel/Restaurant/Cafe Hotel/Restaurant/Cafe
## [91] Hotel/Restaurant/Cafe Retail Hotel/Restaurant/Cafe
## [94] Retail Retail Retail
## [97] Hotel/Restaurant/Cafe Retail Hotel/Restaurant/Cafe
## [100] Hotel/Restaurant/Cafe Retail Retail
## [103] Retail Hotel/Restaurant/Cafe Hotel/Restaurant/Cafe
## [106] Hotel/Restaurant/Cafe Retail Hotel/Restaurant/Cafe
## [109] Hotel/Restaurant/Cafe Hotel/Restaurant/Cafe Hotel/Restaurant/Cafe
## [112] Hotel/Restaurant/Cafe Retail Hotel/Restaurant/Cafe
## [115] Hotel/Restaurant/Cafe Retail Hotel/Restaurant/Cafe
## [118] Retail Hotel/Restaurant/Cafe Hotel/Restaurant/Cafe
## [121] Hotel/Restaurant/Cafe Retail Hotel/Restaurant/Cafe
## [124] Retail Retail Retail
## [127] Hotel/Restaurant/Cafe Hotel/Restaurant/Cafe Retail
## [130] Hotel/Restaurant/Cafe Hotel/Restaurant/Cafe Retail
## [133] Retail Retail Hotel/Restaurant/Cafe
## [136] Hotel/Restaurant/Cafe Hotel/Restaurant/Cafe Hotel/Restaurant/Cafe
## [139] Retail Hotel/Restaurant/Cafe Hotel/Restaurant/Cafe
## [142] Retail Hotel/Restaurant/Cafe Hotel/Restaurant/Cafe
## [145] Hotel/Restaurant/Cafe Hotel/Restaurant/Cafe Hotel/Restaurant/Cafe
## [148] Retail Hotel/Restaurant/Cafe Hotel/Restaurant/Cafe
## [151] Hotel/Restaurant/Cafe Hotel/Restaurant/Cafe Retail
## [154] Retail Hotel/Restaurant/Cafe Hotel/Restaurant/Cafe
## [157] Hotel/Restaurant/Cafe Hotel/Restaurant/Cafe Hotel/Restaurant/Cafe
## [160] Hotel/Restaurant/Cafe Hotel/Restaurant/Cafe Hotel/Restaurant/Cafe
## [163] Hotel/Restaurant/Cafe Retail Hotel/Restaurant/Cafe
## [166] Hotel/Restaurant/Cafe Hotel/Restaurant/Cafe Retail
## [169] Hotel/Restaurant/Cafe Hotel/Restaurant/Cafe Retail
## [172] Retail Hotel/Restaurant/Cafe Retail
## [175] Retail Retail Retail
## [178] Hotel/Restaurant/Cafe Retail Hotel/Restaurant/Cafe
## [181] Retail Hotel/Restaurant/Cafe Retail
## [184] Retail Retail Hotel/Restaurant/Cafe
## [187] Hotel/Restaurant/Cafe Retail Retail
## [190] Hotel/Restaurant/Cafe Hotel/Restaurant/Cafe Hotel/Restaurant/Cafe
## [193] Hotel/Restaurant/Cafe Hotel/Restaurant/Cafe Hotel/Restaurant/Cafe
## [196] Retail Retail Hotel/Restaurant/Cafe
## [199] Retail Retail Hotel/Restaurant/Cafe
## [202] Retail Hotel/Restaurant/Cafe Hotel/Restaurant/Cafe
## [205] Hotel/Restaurant/Cafe Hotel/Restaurant/Cafe Hotel/Restaurant/Cafe
## [208] Retail Hotel/Restaurant/Cafe Hotel/Restaurant/Cafe
## [211] Hotel/Restaurant/Cafe Retail Retail
## [214] Retail Hotel/Restaurant/Cafe Retail
## [217] Hotel/Restaurant/Cafe Hotel/Restaurant/Cafe Retail
## [220] Hotel/Restaurant/Cafe Hotel/Restaurant/Cafe Hotel/Restaurant/Cafe
## [223] Hotel/Restaurant/Cafe Hotel/Restaurant/Cafe Hotel/Restaurant/Cafe
## [226] Hotel/Restaurant/Cafe Hotel/Restaurant/Cafe Retail
## [229] Retail Hotel/Restaurant/Cafe Hotel/Restaurant/Cafe
## [232] Retail Hotel/Restaurant/Cafe Retail
## [235] Hotel/Restaurant/Cafe Retail Hotel/Restaurant/Cafe
## [238] Hotel/Restaurant/Cafe Hotel/Restaurant/Cafe Hotel/Restaurant/Cafe
## [241] Retail Retail Retail
## [244] Retail Hotel/Restaurant/Cafe Hotel/Restaurant/Cafe
## [247] Retail Hotel/Restaurant/Cafe Retail
## [250] Hotel/Restaurant/Cafe Hotel/Restaurant/Cafe Hotel/Restaurant/Cafe
## [253] Hotel/Restaurant/Cafe Hotel/Restaurant/Cafe Hotel/Restaurant/Cafe
## [256] Hotel/Restaurant/Cafe Hotel/Restaurant/Cafe Hotel/Restaurant/Cafe
## [259] Hotel/Restaurant/Cafe Hotel/Restaurant/Cafe Hotel/Restaurant/Cafe
## [262] Hotel/Restaurant/Cafe Hotel/Restaurant/Cafe Hotel/Restaurant/Cafe
## [265] Retail Hotel/Restaurant/Cafe Hotel/Restaurant/Cafe
## [268] Hotel/Restaurant/Cafe Hotel/Restaurant/Cafe Retail
## [271] Retail Hotel/Restaurant/Cafe Hotel/Restaurant/Cafe
## [274] Hotel/Restaurant/Cafe Hotel/Restaurant/Cafe Hotel/Restaurant/Cafe
## [277] Retail Hotel/Restaurant/Cafe Hotel/Restaurant/Cafe
## [280] Retail Hotel/Restaurant/Cafe Retail
## [283] Hotel/Restaurant/Cafe Hotel/Restaurant/Cafe Hotel/Restaurant/Cafe
## [286] Retail Retail Hotel/Restaurant/Cafe
## [289] Hotel/Restaurant/Cafe Retail Hotel/Restaurant/Cafe
## [292] Retail Retail Hotel/Restaurant/Cafe
## [295] Hotel/Restaurant/Cafe Hotel/Restaurant/Cafe Hotel/Restaurant/Cafe
## [298] Hotel/Restaurant/Cafe Hotel/Restaurant/Cafe Hotel/Restaurant/Cafe
## [301] Hotel/Restaurant/Cafe Hotel/Restaurant/Cafe Retail
## [304] Hotel/Restaurant/Cafe Hotel/Restaurant/Cafe Hotel/Restaurant/Cafe
## [307] Retail Retail Retail
## [310] Retail Hotel/Restaurant/Cafe Hotel/Restaurant/Cafe
## [313] Hotel/Restaurant/Cafe Retail Hotel/Restaurant/Cafe
## [316] Hotel/Restaurant/Cafe Hotel/Restaurant/Cafe Retail
## [319] Hotel/Restaurant/Cafe Hotel/Restaurant/Cafe Hotel/Restaurant/Cafe
## [322] Retail Hotel/Restaurant/Cafe Retail
## [325] Retail Retail Hotel/Restaurant/Cafe
## [328] Retail Hotel/Restaurant/Cafe Hotel/Restaurant/Cafe
## [331] Hotel/Restaurant/Cafe Retail Retail
## [334] Hotel/Restaurant/Cafe Hotel/Restaurant/Cafe Retail
## [337] Hotel/Restaurant/Cafe Hotel/Restaurant/Cafe Hotel/Restaurant/Cafe
## [340] Hotel/Restaurant/Cafe
## Levels: Hotel/Restaurant/Cafe Retailtable(wholesaleF[101:440,1], predicted=P2)## predicted
## Hotel/Restaurant/Cafe Retail
## Hotel/Restaurant/Cafe 213 19
## Retail 15 93Compare tree models
table(wholesaled[101:440,1], predicted=P1)## predicted
## 0 1
## Hotel/Restaurant/Cafe 22 228
## Retail 84 6table(wholesaleF[101:440,1], predicted=P2)## predicted
## Hotel/Restaurant/Cafe Retail
## Hotel/Restaurant/Cafe 213 19
## Retail 15 93After evaluation of each of the tree model algorithms rpart vs. C5.0 we see that model P2 produces the least amount of errors for a total amount of 34 errors. Evaluation of each of the several models LDA produces the least amount of errors and is the most robust model at 89% accuracy.