Nursery Dataset
VARADARAJ KAMISETTY
2022-11-11
Aim/Objective
To Develop and Create a suitable machine learning model for the dataset assigned. Compare the results with atleast 3 machine learning algorihtm FOR classification.
Literature Survey of nursery dataset
Nursery Database was derived from a hierarchical decision model originally developed to rank applications for nursery schools. It was used during several years in 1980’s when there was excessive enrollment to these schools in Ljubljana, Slovenia, and the rejected applications frequently needed an objective explanation. The final decision depended on three subproblems: occupation of parents and child’s nursery, family structure and financial standing, and social and health picture of the family. The model was developed within expert system shell for decision making DEX (M. Bohanec, V. Rajkovic: Expert system for decision making. Sistemica 1(1), pp. 145-157, 1990.).
The hierarchical model ranks nursery-school applications according to the following concept structure:
NURSERY Evaluation of applications for nursery schools . EMPLOY Employment of parents and child’s nursery . . parents Parents’ occupation . . has_nurs Child’s nursery . STRUCT_FINAN Family structure and financial standings . . STRUCTURE Family structure . . . form Form of the family . . . children Number of children . . housing Housing conditions . . finance Financial standing of the family . SOC_HEALTH Social and health picture of the family . . social Social conditions . . health Health conditions
Input attributes are printed in lowercase. Besides the target concept (NURSERY) the model includes four intermediate concepts: EMPLOY, STRUCT_FINAN, STRUCTURE, SOC_HEALTH. Every concept is in the original model related to its lower level descendants by a set of examples (for these examples sets see [Web Link]).
The Nursery Database contains examples with the structural information removed, i.e., directly relates NURSERY to the eight input attributes: parents, has_nurs, form, children, housing, finance, social, health.
Because of known underlying concept structure, this database may be particularly useful for testing constructive induction and structure discovery methods.
Attribute Information:
parents: usual, pretentious, great_pret has_nurs: proper, less_proper, improper, critical, very_crit form: complete, completed, incomplete, foster children: 1, 2, 3, more housing: convenient, less_conv, critical finance: convenient, inconv social: non-prob, slightly_prob, problematic health: recommended, priority, not_recom
code:
Data manipulation:
getwd()## [1] "C:/Desktop/5-DATA SCIENCE PROGRAMMING/r studio files"setwd("C:/Desktop/5-DATA SCIENCE PROGRAMMING")
data=read.csv("nursery.csv")
summary(data)## parents has_nurs form children
## Length:12960 Length:12960 Length:12960 Length:12960
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
## housing finance social health
## Length:12960 Length:12960 Length:12960 Length:12960
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
## final.evaluation
## Length:12960
## Class :character
## Mode :characterstr(data)## 'data.frame': 12960 obs. of 9 variables:
## $ parents : chr "usual" "usual" "usual" "usual" ...
## $ has_nurs : chr "proper" "proper" "proper" "proper" ...
## $ form : chr "complete" "complete" "complete" "complete" ...
## $ children : chr "1" "1" "1" "1" ...
## $ housing : chr "convenient" "convenient" "convenient" "convenient" ...
## $ finance : chr "convenient" "convenient" "convenient" "convenient" ...
## $ social : chr "nonprob" "nonprob" "nonprob" "slightly_prob" ...
## $ health : chr "recommended" "priority" "not_recom" "recommended" ...
## $ final.evaluation: chr "recommend" "priority" "not_recom" "recommend" ...data=data[sample(1:nrow(data)), ]DATA CLEANING:
converting character data into numbers as factor data
data$parents=factor(data$parents,levels = c("usual","pretentious","great_pret"),labels=c(1,2,3))
data$has_nurs=factor(data$has_nurs,levels = c("proper","less_proper","improper","critical","very_crit"),labels=c(4,3,2,1,0))
data$form=factor(data$form,levels = c("complete","completed","incomplete","foster"),labels=c(4,3,2,1))
data$children<-as.numeric(sub('more',4,data$children))
data$housing=factor(data$housing, levels = c("convenient","less_conv","critical"),labels=c(2,1,0))
data$finance=factor(data$finance,levels = c("convenient","inconv"),labels=c(1,0))
data$social=factor(data$social,levels = c("nonprob","slightly_prob","problematic"),labels=c(3,2,1))
data$health=factor(data$health,levels = c("recommended","priority","not_recom"),labels=c(3,2,1))
data$final.evaluation=factor(data$final.evaluation,levels = c("spec_prior","very_recom","recommend","priority","not_recom"),labels=c(4,3,2,1,0))converting factor data into numerical except “final.evaluation” as it is the dependent attribute
data$parents=as.numeric(data$parents)
data$has_nurs=as.numeric(data$has_nurs)
data$form=as.numeric(data$form)
data$finance=as.numeric(data$finance)
data$housing=as.numeric(data$housing)
data$social=as.numeric(data$social)
data$health=as.numeric(data$health)Structure of data
str(data)## 'data.frame': 12960 obs. of 9 variables:
## $ parents : num 3 3 2 2 2 1 2 1 2 1 ...
## $ has_nurs : num 5 2 1 3 1 4 2 2 5 3 ...
## $ form : num 2 4 4 2 3 3 1 3 2 3 ...
## $ children : num 4 1 1 2 4 2 2 2 1 2 ...
## $ housing : num 2 2 3 1 1 1 2 1 2 1 ...
## $ finance : num 1 1 2 2 1 2 2 2 2 1 ...
## $ social : num 3 1 3 1 3 2 2 3 3 3 ...
## $ health : num 3 2 2 3 3 1 2 3 3 3 ...
## $ final.evaluation: Factor w/ 5 levels "4","3","2","1",..: 5 1 4 5 5 4 4 5 5 5 ...Summary of data
summary(data)## parents has_nurs form children housing
## Min. :1 Min. :1 Min. :1.00 Min. :1.00 Min. :1
## 1st Qu.:1 1st Qu.:2 1st Qu.:1.75 1st Qu.:1.75 1st Qu.:1
## Median :2 Median :3 Median :2.50 Median :2.50 Median :2
## Mean :2 Mean :3 Mean :2.50 Mean :2.50 Mean :2
## 3rd Qu.:3 3rd Qu.:4 3rd Qu.:3.25 3rd Qu.:3.25 3rd Qu.:3
## Max. :3 Max. :5 Max. :4.00 Max. :4.00 Max. :3
## finance social health final.evaluation
## Min. :1.0 Min. :1 Min. :1 4:4044
## 1st Qu.:1.0 1st Qu.:1 1st Qu.:1 3: 328
## Median :1.5 Median :2 Median :2 2: 2
## Mean :1.5 Mean :2 Mean :2 1:4266
## 3rd Qu.:2.0 3rd Qu.:3 3rd Qu.:3 0:4320
## Max. :2.0 Max. :3 Max. :3Plot of the whole data
plot(data)
Creation of Training and Testing data
library(caTools)## Warning: package 'caTools' was built under R version 4.1.3set.seed(500)
summary(data$final.evaluation)## 4 3 2 1 0
## 4044 328 2 4266 4320split= sample.split(data$final.evaluation,SplitRatio=0.8)
training=subset(data,split==T)
#View(training)
dim(training)## [1] 10368 9summary(training)## parents has_nurs form children housing
## Min. :1.000 Min. :1 Min. :1.000 Min. :1.000 Min. :1
## 1st Qu.:1.000 1st Qu.:2 1st Qu.:1.000 1st Qu.:2.000 1st Qu.:1
## Median :2.000 Median :3 Median :3.000 Median :2.000 Median :2
## Mean :1.999 Mean :3 Mean :2.495 Mean :2.502 Mean :2
## 3rd Qu.:3.000 3rd Qu.:4 3rd Qu.:3.000 3rd Qu.:4.000 3rd Qu.:3
## Max. :3.000 Max. :5 Max. :4.000 Max. :4.000 Max. :3
## finance social health final.evaluation
## Min. :1.000 Min. :1.000 Min. :1.000 4:3235
## 1st Qu.:1.000 1st Qu.:1.000 1st Qu.:1.000 3: 262
## Median :1.000 Median :2.000 Median :2.000 2: 2
## Mean :1.498 Mean :2.009 Mean :2.001 1:3413
## 3rd Qu.:2.000 3rd Qu.:3.000 3rd Qu.:3.000 0:3456
## Max. :2.000 Max. :3.000 Max. :3.000test=subset(data,split==F)
#View(test)
dim(test)## [1] 2592 9summary(test)## parents has_nurs form children
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:1.000 1st Qu.:2.000 1st Qu.:2.000 1st Qu.:1.000
## Median :2.000 Median :3.000 Median :2.000 Median :3.000
## Mean :2.005 Mean :3.002 Mean :2.518 Mean :2.494
## 3rd Qu.:3.000 3rd Qu.:4.000 3rd Qu.:4.000 3rd Qu.:3.000
## Max. :3.000 Max. :5.000 Max. :4.000 Max. :4.000
## housing finance social health
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:1.000 1st Qu.:1.000 1st Qu.:1.000 1st Qu.:1.000
## Median :2.000 Median :2.000 Median :2.000 Median :2.000
## Mean :2.002 Mean :1.508 Mean :1.963 Mean :1.998
## 3rd Qu.:3.000 3rd Qu.:2.000 3rd Qu.:3.000 3rd Qu.:3.000
## Max. :3.000 Max. :2.000 Max. :3.000 Max. :3.000
## final.evaluation
## 4:809
## 3: 66
## 2: 0
## 1:853
## 0:864
## Applying Decision tree algorithm to your dataset (Nursery dataset)
We consider the use of the Decision Tree, modelled using the rpart function. To get a large tree we make the complexity paramter really small (cp). We see in the output, all the trees that are considered in the model, giving the complexity parameter, number of splits, re-substitution error rate, cross-validated error rate and the associated standard error.
#decision tree
library(rpart)## Warning: package 'rpart' was built under R version 4.1.3library(rpart.plot)## Warning: package 'rpart.plot' was built under R version 4.1.3tree=rpart(formula=final.evaluation~.,training,method='class')Plotting tree
plot(tree)
rpart.plot(tree,type=4,extra = 101)
Model prediction
#Predicting the model
pred=predict(object=tree,test,type='class')
pred## 12501 4923 8028 8531 11535 2500 11151 3429 6449 7443 8365 3945 2505
## 0 0 0 4 0 1 0 0 1 0 4 0 0
## 8792 9165 7194 4174 10257 8913 1792 11899 4496 12358 1252 2865 7579
## 4 0 0 4 0 0 1 4 1 4 1 0 4
## 303 7804 8954 5882 5375 5315 8578 3435 12816 1950 4456 7776 9019
## 0 4 4 1 1 1 4 0 0 0 1 0 1
## 3628 10273 3518 9528 2537 12493 7564 469 1698 6994 3380 7500 4986
## 4 1 4 0 1 4 4 1 0 4 4 0 0
## 3182 12329 11861 5784 8476 9696 8099 12360 2332 9228 10264 3045 4267
## 4 4 4 0 4 0 4 0 1 0 1 0 4
## 8083 10209 12882 5709 5600 315 7497 5735 8389 4895 5080 3511 11761
## 4 0 0 0 1 0 0 1 4 1 1 4 4
## 12825 10938 5245 632 1420 4287 6726 9658 2216 9976 1556 8498 1505
## 0 0 1 1 1 0 0 1 1 1 1 4 1
## 1459 5130 8105 7340 12457 7739 12673 11989 3203 6701 3149 9098 8681
## 1 0 4 4 4 4 4 4 4 1 4 4 4
## 1111 12332 363 9447 7255 10213 10181 4390 12588 8832 11473 4831 2414
## 1 4 0 0 4 1 4 1 0 0 4 1 1
## 8292 11115 11241 3063 8301 5557 957 12530 11472 10655 2614 10333 9214
## 0 0 0 0 0 1 0 4 0 4 4 1 1
## 3062 9140 9020 2289 9114 8939 12649 2347 5816 1753 12281 1090 12017
## 4 4 4 0 0 4 4 1 1 1 4 1 4
## 7172 2576 988 8605 4315 2533 6415 1226 3861 11334 2046 4335 5430
## 4 1 1 4 4 1 1 1 0 0 0 0 0
## 1113 9754 701 5166 2021 5072 11346 1844 2371 8188 4324 9703 8963
## 0 1 1 0 1 1 0 1 1 4 1 1 4
## 1513 5641 809 12753 6825 5534 4988 739 1599 405 12452 9366 12561
## 1 1 1 0 0 1 1 1 0 0 4 0 0
## 3248 4434 4732 10835 319 8731 9677 4264 11338 12654 8078 2271 12758
## 4 0 1 4 1 1 4 4 4 0 4 0 4
## 3934 6751 3931 10232 9350 11750 7565 11704 7748 12888 7770 12497 12919
## 4 1 4 4 4 4 4 4 4 0 0 4 4
## 11011 6919 1407 4182 2066 7290 4452 8638 8185 7447 5196 6788 361
## 4 4 0 0 1 0 0 4 4 4 0 1 1
## 9430 1645 9974 10929 6347 109 11299 9416 2415 7291 9857 4285 9384
## 1 1 4 0 1 1 4 4 0 4 4 4 0
## 10748 6496 4513 3381 4647 3339 12295 6380 8967 4862 6629 8317 9556
## 4 1 1 0 0 0 4 1 0 1 1 4 1
## 11997 3633 6340 320 1956 6503 8546 539 516 12085 4576 12087 4718
## 0 0 1 1 0 1 4 1 0 4 1 0 1
## 2756 5756 7658 9406 6253 2760 1530 5051 5548 11715 10442 5414 2806
## 4 1 4 1 1 0 0 1 1 0 4 1 4
## 11953 12153 3169 2832 3624 3794 10094 10335 7709 10987 5470 2103 7366
## 4 0 4 0 0 4 4 0 4 4 1 0 4
## 819 9531 3340 4815 8895 1558 124 12038 5459 3281 5820 7149 12584
## 0 0 4 0 0 1 1 4 1 4 0 0 4
## 6587 11332 6135 1654 6620 582 7665 1638 11760 1765 7681 2916 7382
## 1 4 0 1 1 0 0 0 0 1 4 0 4
## 4243 10758 5107 10185 6125 4619 11866 7202 6310 9232 12656 4618 1273
## 4 0 1 0 1 1 4 4 1 1 4 1 1
## 1479 5876 3447 10947 8723 8835 3757 4724 5417 7746 12511 12448 11526
## 0 1 0 0 4 0 4 1 1 0 4 4 0
## 6914 8539 12838 7368 6174 6974 2640 794 3267 11596 10147 2164 8704
## 4 4 4 0 0 4 0 1 0 4 1 1 1
## 10450 3489 6235 9465 8101 2825 12265 8076 9268 5329 3865 4439 1595
## 4 0 1 0 4 4 4 0 1 1 4 1 1
## 12600 5625 6477 5736 11532 3509 1430 10586 5070 3968 12602 7245 5866
## 0 0 0 0 0 4 1 4 0 4 4 0 1
## 6640 6641 4849 236 7585 4935 8277 7064 10846 1778 11963 4779 7251
## 1 1 1 1 4 0 0 4 4 1 4 0 0
## 11586 5806 2044 4841 2656 10029 9340 1010 7731 7988 11912 4850 10453
## 0 1 1 1 4 0 1 1 0 4 4 1 4
## 6883 4303 12751 8296 8261 12754 3830 3674 6139 2075 10979 8162 4712
## 1 4 4 4 4 4 4 4 1 1 4 4 1
## 12509 9489 9686 12327 8038 9682 2900 12597 2323 1614 5726 1503 4801
## 4 0 4 0 4 1 4 0 1 0 1 0 1
## 3880 2476 9176 12181 1712 1893 10925 997 11375 9388 2995 2469 11168
## 4 1 4 4 1 0 4 1 4 1 4 0 4
## 4534 1685 6689 11344 5999 11936 1900 1041 9038 3852 4323 10964 7113
## 1 1 1 4 1 4 1 0 4 0 0 4 0
## 695 3924 8160 1144 10464 6686 12113 8435 989 8031 1325 12639 4629
## 1 0 0 1 0 1 4 4 1 0 1 0 0
## 2382 10710 2368 7154 12050 11606 2131 7597 6213 5850 2421 12207 4893
## 0 0 1 4 4 4 1 4 0 0 0 0 0
## 7945 6841 5264 133 10475 6465 3016 2950 12571 1776 5827 6515 274
## 4 1 1 1 4 0 4 4 4 0 1 1 1
## 9499 12856 5254 2524 4659 2679 8503 10437 12035 4367 3468 10445 4419
## 1 4 1 1 0 0 4 0 4 1 0 4 0
## 1892 10135 8062 699 12523 8401 7285 4720 2095 11297 9254 3394 3770
## 1 1 4 0 4 4 4 1 1 4 4 4 4
## 11147 560 6987 705 6520 12652 174 267 8537 629 454 4567 9933
## 4 1 0 0 1 4 0 0 4 1 1 1 0
## 7606 3686 3750 4946 171 6929 7583 7857 8964 8603 8920 9247 5730
## 4 4 0 1 0 4 4 0 0 4 1 1 0
## 12309 9872 1616 11498 8906 5341 5284 9259 11035 5444 6112 7029 891
## 0 4 1 4 4 1 1 1 4 1 1 0 0
## 9828 7923 8868 2889 11056 4620 7427 5575 4746 4021 4971 10219 8399
## 0 0 0 0 4 0 4 1 0 4 0 1 4
## 5553 2060 1920 10482 3102 7264 12480 3065 5334 8285 11935 89 252
## 0 1 0 0 0 4 0 4 0 4 4 1 0
## 11522 7693 10656 2484 1566 10154 6714 10931 11355 3272 7649 4944 9103
## 4 4 0 0 0 4 0 4 0 4 4 0 1
## 540 1066 12864 5887 12675 543 1732 7717 2028 12807 9278 275 3704
## 0 1 0 1 0 0 1 4 0 0 4 1 4
## 7510 8465 8654 5464 4082 4951 4578 1572 10269 8427 7123 2683 3204
## 4 4 4 1 4 1 0 0 0 0 4 4 0
## 2755 513 1576 852 3020 7851 10831 11390 5897 451 6167 1192 5818
## 4 0 1 0 4 0 4 4 1 1 1 1 1
## 8671 11569 9227 11951 186 2670 11023 11880 8370 146 5082 3647 6837
## 1 4 4 4 0 0 4 0 0 1 0 4 0
## 10807 9024 9400 10303 9054 56 7115 3224 12883 10975 12312 6164 11966
## 4 0 1 1 0 1 4 4 4 4 0 1 4
## 8570 11555 4186 4601 4071 4421 4206 9132 6366 7392 5793 11820 8903
## 4 4 4 1 0 1 0 0 0 0 0 0 4
## 3683 2144 7346 12729 8885 12859 584 5348 7575 5953 266 992 12873
## 4 1 4 0 4 4 1 1 0 1 1 1 0
## 5646 7525 5187 4129 12109 2062 5117 5327 3201 12863 2929 11788 12797
## 0 4 0 4 4 1 1 1 0 4 4 4 4
## 2343 10127 2467 2662 651 7261 10974 12102 8295 8968 3369 10643 9376
## 0 4 1 4 0 4 0 0 0 1 0 4 1
## 9776 381 5792 9252 10421 16 12426 9243 1828 1532 10332 2978 12735
## 4 0 1 0 4 1 0 0 1 1 0 4 0
## 1204 6026 4024 7999 6321 4867 12359 5622 7249 12678 5012 9158 12333
## 1 1 4 4 0 1 4 0 4 0 1 4 0
## 5787 2304 6317 12393 11325 10627 1448 8353 10076 1387 8225 12500 5378
## 0 0 1 0 0 4 1 4 4 1 4 4 1
## 8689 8507 4110 8879 5830 10965 2316 1021 2519 7530 5433 2546 8966
## 1 4 0 4 1 0 0 1 1 0 0 1 4
## 4093 6828 983 1537 7237 10231 10707 11785 12078 10849 7543 7214 11374
## 4 0 1 1 4 1 0 4 0 4 4 4 4
## 726 2882 2943 12784 1621 2746 2780 4443 550 10479 2192 8625 5836
## 0 4 0 4 1 4 4 0 1 0 1 0 1
## 5991 11218 5028 8889 1520 3420 3047 314 4885 11915 5252 4804 449
## 0 4 0 0 1 0 4 1 1 4 1 1 1
## 10279 1102 6501 7223 10310 12461 5718 895 12846 8549 6460 2715 11089
## 1 1 0 4 4 4 0 1 0 4 1 0 4
## 6291 1814 5731 8896 4169 2158 3166 5978 195 11060 9290 6921 2407
## 0 1 1 1 4 1 4 1 0 4 4 0 1
## 5003 12320 893 12884 3567 9803 12388 1655 10747 9790 874 4466 790
## 1 4 1 4 0 4 4 1 4 1 1 1 1
## 3973 5289 4525 2703 12942 10034 8184 1574 2233 9606 12475 329 8597
## 4 0 1 0 0 4 0 1 1 0 4 1 4
## 6738 463 1341 5209 4480 10080 4548 11028 7910 12143 4216 10341 11995
## 0 1 0 1 1 0 0 0 4 4 4 0 4
## 9589 7615 4693 5606 7248 12518 8373 3494 3680 10671 1565 3554 7675
## 1 4 1 1 0 4 0 4 4 0 1 4 4
## 1581 7596 9816 2822 11587 5753 339 4138 2881 11475 6761 6653 12385
## 0 0 0 4 4 1 0 4 4 0 1 1 4
## 2443 5105 6429 8251 7774 9820 1508 3581 2424 11968 4688 11084 2295
## 1 1 0 4 4 1 1 4 0 4 1 4 0
## 9093 7351 5996 9650 4241 12853 1868 12599 4696 1770 4343 4911 4848
## 0 4 1 4 4 4 1 4 1 0 1 0 0
## 6004 3018 3031 839 1818 7357 8229 6348 7451 2770 11228 4334 4533
## 1 0 4 1 0 4 0 0 4 4 4 1 0
## 6076 10282 11291 5345 10506 8808 3376 3457 8626 12814 6815 4656 10012
## 1 1 4 1 0 0 4 4 4 4 1 0 1
## 7433 12526 872 2922 634 11163 1557 1145 6931 2019 7468 5366 3544
## 4 4 1 0 1 0 0 1 4 0 4 1 4
## 2939 4073 12089 11798 2624 10373 11975 10417 6005 8818 11781 5436 8714
## 4 4 4 4 4 4 4 4 1 1 0 0 4
## 6962 1907 9936 4141 4485 5549 5525 4604 672 4892 587 10220 6669
## 4 1 0 4 0 1 1 1 0 1 1 4 0
## 11213 1626 12270 6846 6468 1327 8378 772 7507 11898 5573 2728 5613
## 4 0 0 0 0 1 4 1 4 0 1 4 0
## 6684 11387 6188 4104 5916 4048 9427 5225 3911 10733 2400 8821 6328
## 0 4 1 0 0 4 1 1 4 4 0 1 1
## 1208 216 1795 6407 11938 1501 11771 5598 9040 10744 5388 6803 6950
## 1 0 1 1 4 1 4 0 1 4 0 1 4
## 7400 439 3949 10668 12765 5515 2025 583 4291 3025 12552 5742 1910
## 4 1 4 0 0 1 0 1 4 4 0 0 1
## 5409 853 9407 2964 12560 2858 6495 2085 2930 6206 9424 32 7081
## 0 1 4 0 4 4 0 0 4 1 1 1 4
## 588 5335 2058 2141 8395 4743 5652 2622 8763 11808 5567 6814 11850
## 0 1 0 1 4 0 0 0 0 0 1 1 0
## 5113 4031 5693 10424 12815 6285 8014 8340 9945 117 5677 1216 10547
## 1 4 1 4 4 0 4 0 0 0 1 1 4
## 8619 11364 3583 5268 6311 6510 1100 8961 2116 7591 10906 528 10734
## 0 0 4 0 1 0 1 0 1 4 4 0 0
## 3286 5132 11230 11716 12760 687 5424 4924 10917 6033 6762 345 5283
## 4 1 4 4 4 0 0 1 0 0 0 0 0
## 5085 800 2625 3439 8096 11379 5108 423 6942 12086 4086 1664 7490
## 0 1 0 4 4 0 1 0 0 4 0 1 4
## 12795 3698 8059 11494 9670 3954 10687 10419 6630 10868 6481 6589 6845
## 0 4 4 4 1 0 4 0 0 4 1 1 1
## 1214 10811 3631 7753 6259 2436 2585 8128 12559 1642 11556 3315 8343
## 1 4 4 4 1 0 1 4 4 1 0 0 0
## 7189 11677 2206 4495 8615 734 737 12890 8925 5616 9601 5167 1619
## 4 4 1 1 4 1 1 4 0 0 1 1 1
## 4677 5629 1887 11646 7584 11693 10706 2508 4475 11487 3702 7899 3910
## 0 1 0 0 0 4 4 0 1 0 0 0 4
## 8917 3032 5896 10088 7986 12472 11197 2558 340 12507 6926 4984 8175
## 1 4 1 4 0 4 4 1 1 0 4 1 0
## 7252 6826 12294 378 5144 11698 9334 11471 12744 1563 7023 9506 1084
## 4 1 0 0 1 4 1 4 0 0 0 4 1
## 6085 10720 11517 4354 9090 3574 12623 1575 6051 2267 12470 8753 1833
## 1 4 0 1 0 4 4 0 0 1 4 4 0
## 11757 1384 5721 12845 1412 10716 8564 2088 3358 36 11165 1186 5845
## 0 1 0 4 1 0 4 0 4 0 4 1 1
## 8439 6904 3560 96 2008 8470 7914 12011 9913 7940 9791 7766 5914
## 0 1 4 0 1 4 0 4 1 4 4 4 1
## 10233 11264 8417 93 5204 4519 9386 12094 9141 11492 2562 309 5426
## 0 4 4 0 1 1 4 4 0 4 0 0 1
## 708 42 6634 1290 11534 2695 3993 6375 11080 7027 8659 1658 3088
## 0 0 1 0 4 4 0 0 4 4 1 1 4
## 10055 7593 10256 4805 12868 6851 6637 12138 7886 831 7571 5520 4203
## 4 0 4 1 4 1 1 0 4 0 4 0 0
## 1600 4001 152 12256 10941 28 10752 11314 6374 4754 7994 3070 10494
## 1 4 1 4 0 1 0 4 1 1 4 4 0
## 2373 9537 9675 6907 1859 3202 5127 11780 88 435 7557 323 7761
## 0 0 0 1 1 4 0 4 1 0 0 1 0
## 756 10103 976 5759 12214 9585 8894 4401 10911 762 6489 5835 7849
## 0 4 1 1 4 0 4 0 0 0 0 0 4
## 5206 10077 9577 2974 1653 8541 242 11053 1117 230 7762 5255 4706
## 1 0 1 4 0 0 1 4 1 1 4 1 1
## 12874 6010 3553 8860 7305 1499 5188 2696 4806 92 10423 8194 3367
## 4 1 4 1 0 1 1 4 0 1 4 4 4
## 3425 11559 8244 5579 11521 6060 9071 9008 5964 10724 7901 12111 7047
## 4 0 0 1 4 0 4 4 0 4 4 0 0
## 6408 3838 9664 6458 7322 9590 7799 8337 8933 3875 9825 12648 3727
## 0 4 1 1 4 4 4 0 4 4 0 0 4
## 11246 10097 11479 6582 5490 5161 4783 2809 8909 6453 6226 6794 370
## 4 4 4 0 0 1 1 4 4 0 1 1 1
## 1522 823 531 2480 9389 9065 11499 7090 8858 1825 5893 8609 10284
## 1 1 0 1 4 4 0 4 4 1 1 4 0
## 3386 8706 2912 4499 3669 2426 8886 525 3246 3251 3236 3951 4544
## 4 0 4 1 0 1 0 0 0 4 4 0 1
## 9028 11268 6355 10540 4707 8496 6319 7211 5947 9539 5214 1149 9815
## 1 0 1 4 0 0 1 4 1 4 0 0 4
## 11602 2324 3876 1766 8633 1504 9197 878 2724 2529 10871 11298 7409
## 4 1 0 1 4 1 4 1 0 0 4 0 4
## 2987 3828 6665 6146 1696 4934 8164 2810 12361 34 1832 8878 7124
## 4 0 1 1 1 1 4 4 4 1 1 1 4
## 6229 5223 8077 7163 3490 8891 3436 11566 8103 8456 7060 11795 4246
## 1 0 4 4 4 4 4 4 0 4 4 4 4
## 6955 6096 3540 2083 1257 1485 901 9210 10537 2163 1957 8298 8584
## 4 0 0 1 0 0 1 0 4 0 1 0 4
## 6691 1299 1971 5350 7229 12412 9012 4587 6879 10793 6294 5812 2652
## 1 0 0 1 4 4 0 0 0 4 0 1 0
## 5237 2549 10601 8063 7707 3007 10653 4753 12832 11877 6109 6122 6517
## 1 1 4 4 0 4 0 1 4 0 1 1 1
## 5686 6946 3798 440 1881 7997 8653 2412 8423 10538 5313 3385 10997
## 1 4 0 1 0 4 1 0 4 4 0 4 4
## 4348 3808 5472 3807 917 12935 7590 2893 4079 10591 5261 3694 7840
## 1 4 0 0 1 4 0 4 4 4 1 4 4
## 9457 4833 2456 9865 7306 4398 422 9855 2941 5920 3755 9470 10789
## 1 0 1 1 4 0 1 0 4 1 4 4 4
## 5630 1423 1919 11723 180 6890 2319 8308 4721 9586 11923 3568 3784
## 1 1 1 4 0 1 0 4 1 1 4 4 4
## 12449 7630 7280 10174 12396 10524 1639 6968 11967 3324 4101 4346 10739
## 4 4 4 1 0 0 1 4 0 0 0 1 4
## 10909 2860 1686 6428 2006 4902 4210 9742 2230 7274 8273 649 4462
## 4 4 0 1 1 0 4 1 1 4 4 1 1
## 3758 4638 1334 4284 12524 1514 1641 10527 9326 2965 7365 547 322
## 4 0 1 0 4 1 0 0 4 4 0 1 1
## 5045 9119 4757 9241 8533 5645 3465 10673 1127 11848 10812 12247 10145
## 1 4 1 1 4 1 0 4 1 4 0 4 4
## 9193 4261 10951 4402 7455 12357 2102 8851 1388 2582 10814 11579 12264
## 1 4 4 1 0 0 1 1 1 1 4 4 0
## 11635 11112 7470 9884 10473 5531 11851 10742 8927 10353 12666 197 6432
## 4 0 0 4 0 1 4 4 4 0 0 1 0
## 10631 3365 3273 10281 6573 1899 4342 11280 7767 9353 2106 4919 12491
## 4 4 0 0 0 0 1 0 0 4 0 1 4
## 6177 3255 11242 1453 6734 7240 775 630 112 11 4112 4365 4683
## 0 0 4 1 1 4 1 0 1 1 4 0 0
## 7135 2247 6821 2775 5597 9690 10046 7981 2839 960 356 3895 2509
## 4 0 1 0 1 0 4 4 4 0 1 4 1
## 10957 12512 2850 9554 4778 1138 384 9032 6419 8203 7348 6405 4991
## 4 4 0 4 1 1 0 4 1 4 4 0 1
## 9691 2719 11284 8952 431 4362 12150 6519 928 141 7283 7069 7698
## 1 4 4 0 1 0 0 0 1 0 4 4 0
## 10505 2653 7483 3543 11941 11410 11829 9755 2534 3584 1624 11638 2336
## 4 4 4 0 4 4 0 4 1 4 1 4 1
## 8741 4256 11423 12900 3305 7949 12604 9707 1829 1968 3666 1914 4737
## 4 4 4 0 4 4 4 4 1 0 0 0 0
## 7573 8114 9723 8080 10048 12913 8151 3552 3414 4517 6079 5298 9131
## 4 4 0 4 1 4 0 0 0 1 1 0 4
## 212 3655 4609 3661 4400 6610 591 5259 4254 2587 5665 1890 11614
## 1 4 1 4 1 1 0 0 0 1 1 0 4
## 1865 9286 7257 7951 4322 5607 10579 11003 8716 10654 2890 10943 3053
## 1 1 0 4 1 0 4 4 1 4 4 4 4
## 688 8557 9947 3299 4997 7659 11965 11523 11620 6 12520 10379 7599
## 1 4 4 4 1 0 4 0 4 0 4 4 0
## 2747 5277 12841 2826 1089 6491 2035 2969 5927 11567 6763 12400 5543
## 4 0 4 0 0 1 1 4 1 4 1 4 1
## 5870 2017 8288 11520 12179 8825 7098 12069 4357 10762 6180 2486 1760
## 1 1 4 0 4 4 0 0 1 4 0 1 1
## 7982 8007 8468 2279 8655 2853 3905 7126 6087 3505 7878 6765 4230
## 4 0 4 1 0 0 4 4 0 4 0 0 0
## 11087 9362 1636 5314 6284 8259 3384 1215 12933 11013 11000 3621 10803
## 4 4 1 1 1 0 0 0 0 0 4 0 0
## 1800 6795 9575 2487 1831 9836 5741 697 7574 9950 5691 6362 11083
## 0 0 4 0 1 4 1 1 4 4 0 1 4
## 10850 10460 11339 8255 6031 12324 787 9160 8863 7491 4488 7416 10133
## 4 4 4 4 1 0 1 1 1 0 0 0 4
## 7344 3444 4663 11478 406 2422 2168 11476 7129 3392 3105 776 12522
## 0 0 1 0 1 1 1 4 4 4 0 1 0
## 694 10682 1527 9758 3777 6258 2282 7685 6207 6148 7512 1788 8018
## 1 4 0 4 0 0 1 4 0 1 0 0 4
## 5708 10521 2067 7930 5068 7970 5425 4641 12389 18 4471 4742 12224
## 1 0 0 4 1 4 1 0 4 0 1 1 4
## 8011 8841 2968 6782 1329 12211 6099 848 10436 8955 3940 12662 9735
## 4 0 4 1 0 4 0 1 4 0 4 4 0
## 9978 5097 3586 1032 8232 1093 12537 10243 8838 9201 7385 5159 753
## 0 0 4 0 0 1 0 1 0 0 4 1 0
## 342 1673 3100 3745 1239 3914 11357 6506 6040 1122 12586 6769 3424
## 0 1 4 4 0 4 4 1 1 0 4 1 4
## 9199 11993 8142 2814 1594 6959 6580 8592 7097 3487 2331 12001 10121
## 1 4 0 0 1 4 1 0 4 4 0 4 4
## 818 8693 10819 4239 9745 6636 7783 3124 1748 4070 6053 10692 6050
## 1 4 4 0 1 0 4 4 1 4 1 0 1
## 7673 982 4819 8672 2465 2209 10031 984 5752 8932 7130 9622 12835
## 4 1 1 4 1 1 4 0 1 1 4 1 4
## 12829 7836 11918 270 6903 11078 5241 5683 8566 4207 10344 6812 12145
## 4 0 4 0 0 4 0 1 4 4 0 1 4
## 2375 12625 7822 6412 7612 3634 1179 102 8180 9294 1958 10167 10697
## 1 4 4 1 4 4 0 0 4 0 1 0 4
## 12253 2895 335 11480 5168 2516 1688 2580 4699 2449 8442 12771 9942
## 4 0 1 4 1 1 1 0 1 1 0 0 0
## 8213 2660 5267 5518 2759 2352 6181 11418 1834 7343 12540 9620 615
## 4 4 1 1 4 0 1 0 1 4 0 4 0
## 10383 3837 9984 8791 7350 3141 2442 2430 12093 8871 12723 10329 10039
## 0 0 0 1 0 0 0 0 0 0 0 0 1
## 4417 1366 11464 9977 10884 8013 4704 11138 11408 9382 1944 3341 4336
## 1 1 4 4 0 0 0 4 4 1 0 4 1
## 9439 10989 8052 9250 3765 9352 5109 4522 7254 8684 10904 1395 12826
## 1 0 0 1 0 1 0 1 0 4 4 0 4
## 3475 6393 4446 12420 4515 3231 4013 1432 4074 12076 10970 10728 9295
## 4 0 0 0 0 0 4 1 0 4 4 0 1
## 5581 11737 3134 2704 9768 5306 4232 6036 8772 5565 5890 3418 10848
## 1 4 4 4 0 1 4 0 0 0 1 4 0
## 10309 6722 8166 6729 11950 1283 523 6934 10578 4854 1727 7527 7120
## 1 1 0 0 4 1 1 4 0 0 1 0 4
## 6448 9874 480 8040 9411 2727 4317 1517 10155 10385 12217 3250 8404
## 1 1 0 0 0 0 0 1 0 4 4 4 4
## 8898 10583 12536 6537 4990 11437 8347 11018 6451 765 10598 3979 902
## 0 4 4 0 1 4 4 4 1 0 4 4 1
## 6394 12095 7484 7944 9075 535 894 1064 11897 11159 4957 8790 8069
## 1 4 4 0 0 1 0 1 4 4 1 0 4
## 10736 8287 12039 1224 3093 138 6208 8551 12837 12422 11872 7230 8722
## 4 4 0 0 0 0 1 4 0 4 4 0 1
## 1402 8033 1171 1981 6799 11699 8627 7345 7983 8460 12687 6864 7955
## 1 4 1 1 1 4 4 4 0 0 0 0 4
## 6626 2791 11286 5481 7224 5164 9557 2953 2845 9793 169 9567 6091
## 1 4 0 0 0 1 4 4 4 1 1 0 1
## 4809 10008 11875 6158 9481 4030 6209 9700 1684 8467 12433 10057 12592
## 0 0 4 1 1 4 1 1 1 4 4 1 4
## 6094 3325 6632 5822 6017 1650 206 10221 8109 4007 7158 1998 7939
## 1 4 1 1 1 0 1 0 0 4 0 0 4
## 7618 11136 1390 12240 1839 4821 10235 3423 11746 9096 2545 4493 12743
## 4 0 1 0 0 0 4 0 4 0 1 1 4
## 11273 5310 6834 3735 9473 3723 10348 2087 7363 11055 2239 1038 4320
## 4 0 0 0 4 0 1 1 4 0 1 0 0
## 240 11959 8810 6731 683 9580 6292 10879 2909 1251 6790 6877 833
## 0 4 4 1 1 1 1 4 4 0 1 1 1
## 11258 10280 10787 291 4989 3106 11589 11819 12562 11730 12 5046 1481
## 4 4 4 0 0 4 0 4 4 0 0 0 1
## 10081 1007 9064 6244 3620 7869 6742 10895 10897 7980 8004 11085 9634
## 1 1 1 1 4 0 1 4 4 0 0 0 1
## 4118 5157 10622 403 11608 1896 2733 6370 7127 8656 1535 3810 10934
## 4 0 4 1 4 0 0 1 4 1 1 0 4
## 8174 7292 6312 12187 1421 3328 9200 4967 6388 10874 8490 8274 11749
## 4 4 0 4 1 4 4 1 1 4 0 0 4
## 411 599 5542 8499 4925 8697 4741 8613 7838 4504 6558 1553 2372
## 0 1 1 0 1 0 1 0 4 1 0 1 1
## 7892 10295 1237 9219 425 10944 4201 1365 1372 4304 12062 11068 5146
## 4 4 1 0 1 0 4 0 1 4 4 4 1
## 10468 12077 11684 244 4891 10531 3375 3689 3722 10225 3077 2693 12637
## 4 4 4 1 1 4 0 4 4 1 4 4 4
## 3946 11810 2891 1238 2255 720 2615 11065 84 2665 8892 7655 7723
## 4 4 4 1 1 0 4 4 0 4 0 4 4
## 2778 8824 4351 10613 4370 1931 12820 8743 11343 5701 1982 1607 12020
## 0 1 1 4 1 1 4 1 0 1 1 1 4
## 5737 11806 8039 5275 8049 11687 5257 9263 12241 11397 12043 4092 3858
## 1 4 4 1 0 4 1 4 4 0 4 0 0
## 6801 10438 5355 87 1123 11358 12022 10502 2932 11855 3896 231 9371
## 0 4 0 0 1 0 4 4 4 4 4 0 4
## 1040 2764 9548 1790 12593 12048 11827 5993 11396 9081 926 10403 4066
## 1 4 4 1 4 0 4 1 4 0 1 4 4
## 8737 12834 10288 12585 945 10158 1936 5008 9007 9496 1898 4644 1902
## 1 0 1 0 0 0 1 1 1 1 1 0 0
## 946 472 5799 9967 9545 6250 2702 12908 5060 45 11655 12450 11095
## 1 1 0 1 4 1 4 4 1 0 0 0 4
## 8754 3360 5962 144 3469 5278 6923 9255 4045 10764 492 4122 5404
## 0 0 1 0 4 1 4 0 4 0 0 0 1
## 10770 2335 6001 11450 11876 3881 5292 5413 7933 10896 12875 9154 1417
## 0 1 1 4 4 4 0 1 4 0 4 1 1
## 6313 8953 10223 4181 8192 12680 2586 1218 8969 10136 7643 9419 8147
## 1 1 4 4 4 4 0 0 4 4 4 4 4
## 3763 11142 10160 6059 3682 7039 12160 10368 7963 325 1531 5690 1904
## 4 0 4 1 4 4 4 0 4 1 1 1 1
## 10261 1118 5699 3799 3115 1098 1304 5591 6446 10132 5855 11678 5136
## 1 1 1 4 4 0 1 1 1 1 1 4 0
## 6529 5680 9789 1246 142 9851 8923 6678 8199 5773 7213 2196 2179
## 1 1 0 1 1 4 1 0 0 1 4 0 1
## 73 365 4455 7660 4154 8843 11337 5484 4914 8646 12197 2868 1217
## 1 1 0 4 4 4 0 0 0 0 4 0 1
## 9698 8095 602 12346 2034 6805 10327 8861 5958 11550 10470 552 3225
## 4 4 1 4 0 1 1 4 0 0 0 0 0
## 3269 7438 12695 9560 2805 1671 11156 11033 1549 7015 5503 9063 7535
## 4 4 4 4 0 0 4 4 1 4 1 0 4
## 4382 8657 7877 8453 11020 3037 10334 1045 179 11613 9072 5066 12303
## 1 4 4 4 4 4 4 1 1 0 0 1 0
## 12538 6916 1053 11455 7937 12683 6296 8492 7435 11782 5675 6789 2150
## 4 4 0 4 4 4 1 4 4 4 1 0 1
## 11773 6431 11558 5309 4126 5886 6886 1306 1689 3284 10701 10189 3688
## 4 1 4 1 4 0 1 1 0 4 0 1 4
## 3417 8735 1133 6236 6238 1110 12710 7193 11960 3237 2241 7459 1195
## 0 4 1 1 1 0 4 4 4 0 0 4 1
## 4214 3815 12129 468 6075
## 4 4 0 0 0
## Levels: 4 3 2 1 0Confusion matrix: A confusion matrix in R is a table that will categorize the predictions against the actual values. It includes two dimensions, among them one will indicate the predicted values and another one will represent the actual values.
#confusion matrix
cm=table(test$final.evaluation,pred)
cm## pred
## 4 3 2 1 0
## 4 734 0 0 75 0
## 3 0 0 0 66 0
## 2 0 0 0 0 0
## 1 204 0 0 649 0
## 0 0 0 0 0 864Accuracy of the data model according to Decision tree algorithm
#Accuracy
dtacc=sum(diag(cm)/sum(cm))
dtacc## [1] 0.8668981Applying NAIVEBAYES algorithm to your dataset (Nursery dataset)
e1071 is a package for R programming that provides functions for statistic and probabilistic algorithms like a fuzzy classifier, naive Bayes classifier, bagged clustering, short-time Fourier transform, support vector machine, etc.. When it comes to SVM, there are many packages available in R to implement it
library(e1071) #Loading e1071 library for the usage of naiveBayes classification## Warning: package 'e1071' was built under R version 4.1.3classifier = naiveBayes(x= training[-9],y = training$final.evaluation )
classifier##
## Naive Bayes Classifier for Discrete Predictors
##
## Call:
## naiveBayes.default(x = training[-9], y = training$final.evaluation)
##
## A-priori probabilities:
## training$final.evaluation
## 4 3 2 1 0
## 0.3120177469 0.0252700617 0.0001929012 0.3291859568 0.3333333333
##
## Conditional probabilities:
## parents
## training$final.evaluation [,1] [,2]
## 4 2.307264 0.7706304
## 3 1.412214 0.4931753
## 2 1.000000 0.0000000
## 1 1.742748 0.7694590
## 0 2.007523 0.8172887
##
## has_nurs
## training$final.evaluation [,1] [,2]
## 4 3.876971 1.1665608
## 3 1.809160 0.7642842
## 2 1.000000 0.0000000
## 1 2.251978 1.1541885
## 0 3.008102 1.4100911
##
## form
## training$final.evaluation [,1] [,2]
## 4 2.590417 1.117730
## 3 2.106870 1.030210
## 2 1.000000 0.000000
## 1 2.429827 1.109804
## 0 2.501736 1.121296
##
## children
## training$final.evaluation [,1] [,2]
## 4 2.657496 1.087755
## 3 1.961832 1.042430
## 2 1.000000 0.000000
## 1 2.399648 1.127936
## 0 2.497975 1.118970
##
## housing
## training$final.evaluation [,1] [,2]
## 4 2.140031 0.7972522
## 3 1.423664 0.6126798
## 2 1.000000 0.0000000
## 1 1.905948 0.8140057
## 0 2.004630 0.8166016
##
## finance
## training$final.evaluation [,1] [,2]
## 4 1.535085 0.4988446
## 3 1.320611 0.4676046
## 2 1.000000 0.0000000
## 1 1.471433 0.4992564
## 0 1.502894 0.5000640
##
## social
## training$final.evaluation [,1] [,2]
## 4 2.109737 0.8292808
## 3 1.500000 0.5009569
## 2 1.500000 0.7071068
## 1 1.949604 0.7984694
## 0 2.013310 0.8193372
##
## health
## training$final.evaluation [,1] [,2]
## 4 1.612056 0.4873571
## 3 1.000000 0.0000000
## 2 1.000000 0.0000000
## 1 1.434222 0.4957270
## 0 3.000000 0.0000000summary(classifier) #Summary of the model## Length Class Mode
## apriori 5 table numeric
## tables 8 -none- list
## levels 5 -none- character
## isnumeric 8 -none- logical
## call 3 -none- callPrediction
#Predict the test_set result
y_pred= predict(object = classifier, newdata = test) #Prediting the model with help of testset
y_pred## [1] 0 0 0 4 0 3 0 0 4 0 4 0 0 1 0 0 4 0 0 3 4 1 4 3 0 3 0 4 1 1 1 1 4 0 0 0 3
## [38] 0 2 2 3 2 0 4 4 2 2 0 3 4 0 0 4 4 4 0 4 0 4 0 3 0 3 0 2 4 0 0 0 1 0 0 1 4
## [75] 1 3 2 4 0 0 3 1 3 0 0 1 1 1 1 4 1 3 0 4 4 4 4 4 4 4 1 4 1 2 3 4 0 0 3 1 1
## [112] 2 0 0 4 3 1 0 0 0 0 0 3 0 4 0 4 2 3 1 4 1 1 0 0 1 4 1 1 2 4 3 4 4 1 2 4 4
## [149] 3 1 1 0 0 0 0 0 0 1 1 0 1 1 0 2 3 4 2 1 1 2 3 1 0 0 1 1 2 0 0 4 0 0 2 0 1
## [186] 4 3 2 1 4 4 0 4 0 4 4 3 2 4 1 4 4 4 4 0 0 4 4 4 2 0 0 1 0 0 4 4 4 0 4 2 1
## [223] 3 1 0 1 2 4 1 0 3 1 4 0 4 3 3 0 0 0 4 1 0 1 4 4 1 0 0 3 1 0 1 4 1 0 4 2 0
## [260] 1 2 1 4 3 4 0 0 1 3 0 4 1 4 4 0 3 0 0 4 4 0 4 4 3 0 3 0 0 4 0 0 3 2 4 1 4
## [297] 0 0 4 4 4 0 3 4 0 0 0 0 2 4 0 4 4 0 3 0 1 1 4 4 3 1 4 3 3 0 1 0 0 1 0 4 1
## [334] 1 0 4 4 0 2 4 4 0 0 4 0 1 0 4 1 2 2 3 0 1 0 4 4 2 0 1 3 4 1 1 0 0 0 0 0 4
## [371] 1 1 0 4 4 0 3 4 4 1 2 4 0 0 4 4 1 4 0 0 0 3 3 1 2 0 1 1 0 4 4 1 4 4 4 2 4
## [408] 4 4 4 2 3 1 4 4 1 4 0 1 0 4 1 4 0 3 0 1 0 3 4 3 1 4 1 0 4 3 4 1 4 0 4 1 1
## [445] 4 2 1 4 2 0 1 0 2 4 0 1 0 0 3 0 4 4 4 1 0 1 0 0 0 0 3 4 4 4 3 4 0 0 0 0 0
## [482] 2 3 1 2 4 0 4 4 4 0 3 4 2 1 4 3 3 0 0 4 0 4 1 2 4 0 2 1 4 0 4 4 4 3 1 4 1
## [519] 4 4 4 1 0 0 3 4 2 0 4 1 2 3 0 4 4 0 1 2 4 4 0 0 4 3 3 0 0 4 1 4 1 3 3 1 3
## [556] 1 3 0 0 0 0 0 0 4 0 4 3 0 4 0 3 4 0 1 0 0 0 3 0 4 0 4 4 1 0 4 4 0 0 0 1 0
## [593] 4 0 4 4 0 3 0 2 0 3 0 0 2 4 0 0 1 2 4 3 4 2 3 4 3 0 0 0 0 4 2 0 2 0 3 0 4
## [630] 0 4 4 1 2 1 3 3 2 4 1 4 0 0 2 0 0 2 0 4 0 2 0 2 1 0 2 4 4 4 4 0 1 4 4 4 4
## [667] 1 0 1 0 0 0 0 0 0 1 4 1 4 0 1 4 1 1 0 3 1 1 0 0 3 0 2 2 3 1 1 0 4 3 4 4 0
## [704] 4 3 2 2 4 0 0 0 2 0 4 1 1 0 1 0 4 2 0 0 3 1 0 4 0 3 1 4 2 0 2 4 0 3 0 1 1
## [741] 0 0 0 4 0 0 4 1 4 4 3 4 4 1 2 4 0 1 1 0 0 3 1 0 0 1 1 4 0 1 2 3 1 0 4 0 4
## [778] 4 4 4 0 1 0 4 3 3 4 0 3 0 1 0 3 0 4 0 0 2 0 1 1 3 4 1 1 2 3 3 0 4 4 4 0 3
## [815] 0 4 4 0 3 0 1 3 2 4 1 4 1 0 4 2 0 3 1 4 1 4 0 1 4 1 3 1 2 1 3 4 0 2 0 0 4
## [852] 0 1 3 0 4 2 4 0 3 0 2 1 0 0 0 4 4 2 0 4 3 3 3 1 0 4 0 2 2 0 1 4 4 0 0 0 1
## [889] 4 1 0 4 3 0 1 4 4 3 1 0 4 4 1 1 4 0 4 1 4 0 0 2 1 1 4 4 1 4 1 0 2 0 0 3 0
## [926] 2 1 0 3 0 0 4 2 4 2 0 3 3 4 1 0 0 3 2 4 4 4 0 3 4 4 2 0 2 0 0 1 2 0 4 1 4
## [963] 4 4 4 4 4 2 4 4 1 2 0 0 1 4 1 0 2 0 1 1 1 0 1 1 1 0 4 0 0 0 0 3 4 2 3 0 1
## [1000] 3 0 0 4 4 0 0 4 1 1 4 4 0 2 3 1 0 3 4 4 1 4 0 3 4 0 4 4 4 2 2 0 0 3 0 2 4
## [1037] 2 0 0 1 0 2 1 0 4 4 0 0 4 4 3 2 2 0 1 0 1 4 0 0 0 0 0 1 3 0 3 4 1 4 4 0 4
## [1074] 0 0 2 3 3 4 0 0 2 0 4 0 1 0 3 4 4 0 0 1 1 4 4 4 2 0 3 0 0 0 0 0 0 1 0 4 4
## [1111] 0 1 0 0 4 0 1 4 0 4 4 4 2 0 4 0 0 4 3 3 4 1 4 2 4 3 0 1 4 4 3 0 0 0 4 4 3
## [1148] 2 4 1 1 4 0 0 1 3 1 0 3 0 0 0 4 4 0 1 0 0 0 2 2 2 3 4 0 4 4 1 2 0 4 2 0 4
## [1185] 3 0 0 1 4 1 4 0 0 0 2 2 2 4 0 2 0 2 4 0 0 1 4 2 0 0 3 0 4 1 0 4 0 3 0 4 1
## [1222] 3 0 4 4 0 3 4 0 4 1 4 1 4 3 0 4 4 0 1 2 1 4 0 4 0 0 1 0 2 3 0 4 1 0 0 3 2
## [1259] 2 1 3 1 0 4 1 4 4 3 0 4 0 4 0 0 3 4 2 4 0 2 0 4 1 2 4 3 0 0 0 0 4 1 4 0 4
## [1296] 2 2 0 1 0 0 1 2 1 4 0 1 0 0 0 0 0 4 3 0 3 2 0 0 2 4 3 2 4 1 1 4 1 2 2 0 1
## [1333] 2 4 0 2 2 4 3 4 0 0 1 4 0 1 1 0 4 4 0 0 0 2 1 4 4 1 4 0 1 4 0 0 2 4 4 4 0
## [1370] 0 3 3 2 1 0 1 4 3 3 2 0 1 1 1 0 4 2 2 3 4 0 4 0 4 1 0 1 0 0 0 1 4 0 2 3 0
## [1407] 3 3 0 0 3 4 3 1 0 0 1 4 1 0 2 4 3 1 2 0 0 4 0 4 4 0 4 4 3 1 4 2 4 2 1 2 4
## [1444] 3 0 4 4 2 1 4 4 0 4 4 4 4 2 0 0 3 0 0 2 0 2 0 3 0 4 4 0 0 3 4 4 0 0 0 4 0
## [1481] 1 0 1 1 4 4 0 3 0 2 4 0 2 1 3 3 4 0 2 0 4 2 0 4 4 0 4 4 3 4 0 0 1 4 0 3 4
## [1518] 2 1 2 4 2 0 1 1 4 0 1 0 1 3 4 1 4 1 3 1 4 0 4 0 4 1 3 4 2 2 4 4 4 3 0 0 3
## [1555] 4 0 0 0 2 4 3 4 0 1 1 0 4 3 3 4 4 2 3 4 0 1 0 4 2 0 0 1 4 0 2 1 1 1 2 2 4
## [1592] 1 2 4 1 4 0 4 4 3 4 4 3 0 0 1 1 1 4 4 4 0 4 0 0 1 0 1 4 4 1 0 0 1 0 4 4 0
## [1629] 0 0 0 2 0 0 1 0 1 4 0 0 2 3 4 3 3 0 2 2 2 0 0 2 0 4 0 1 0 1 4 3 0 1 2 3 4
## [1666] 4 0 1 1 3 0 1 4 4 2 0 1 1 2 4 0 1 0 0 0 3 0 4 4 0 4 2 4 0 4 4 0 1 1 4 3 4
## [1703] 1 1 4 4 0 4 4 4 4 1 0 0 0 0 3 4 0 4 3 4 0 0 0 1 3 0 1 1 2 3 2 1 3 0 0 0 1
## [1740] 3 0 4 1 1 0 4 2 0 4 4 2 4 3 4 4 2 4 1 4 1 0 4 0 4 2 4 4 0 4 0 4 0 0 1 3 4
## [1777] 1 4 3 4 1 1 3 4 0 4 1 0 0 2 4 0 1 1 4 0 4 1 0 0 4 4 0 2 0 0 0 4 1 3 3 1 0
## [1814] 0 0 0 0 4 2 0 0 0 1 0 3 1 1 2 4 1 0 4 4 4 4 4 4 3 0 3 1 2 0 0 0 4 0 0 3 0
## [1851] 3 3 2 4 2 4 0 1 0 2 4 0 1 0 0 1 4 0 3 0 0 4 1 0 0 4 3 4 3 0 4 2 2 1 4 4 0
## [1888] 4 4 0 2 0 1 4 0 4 4 0 0 0 2 0 0 3 0 1 0 0 4 1 0 0 1 3 4 0 4 4 4 3 0 4 3 3
## [1925] 3 4 0 0 3 4 3 0 4 2 0 4 4 2 1 3 0 1 0 2 3 2 4 2 0 2 4 3 3 1 1 3 4 0 3 3 4
## [1962] 3 4 4 0 4 0 0 4 0 3 4 4 0 4 4 4 4 4 3 4 2 0 0 4 0 1 0 4 4 0 1 4 1 1 1 0 3
## [1999] 3 0 0 0 4 1 1 3 2 0 3 0 1 4 0 1 0 0 0 0 2 0 0 0 0 0 0 0 0 1 2 3 4 1 0 0 0
## [2036] 4 4 1 0 4 2 1 0 0 1 0 3 0 2 0 2 4 0 4 2 0 0 0 0 0 4 3 0 4 4 0 2 3 4 1 2 0
## [2073] 1 4 0 0 0 3 4 0 1 4 0 0 4 1 2 2 0 0 1 0 4 3 1 0 0 0 0 0 2 0 4 4 2 4 0 4 4
## [2110] 0 3 4 4 4 1 0 4 4 2 3 4 4 0 0 3 0 1 4 4 1 0 4 4 4 0 0 0 0 4 4 0 4 4 0 3 1
## [2147] 4 3 2 3 4 4 2 0 0 0 0 4 4 2 0 0 0 1 4 3 2 3 2 0 2 0 0 4 1 1 4 4 1 3 4 4 3
## [2184] 4 3 3 4 1 1 0 2 0 0 4 0 0 2 3 0 3 0 0 0 1 0 4 0 2 1 4 4 0 0 0 1 0 4 1 3 0
## [2221] 3 0 0 0 4 2 4 1 3 3 4 4 0 3 3 1 4 1 4 0 0 3 0 4 4 0 2 0 1 1 1 1 1 2 0 3 4
## [2258] 4 0 0 0 3 4 0 4 2 4 0 0 4 4 2 1 0 4 4 4 0 4 1 4 1 1 3 4 0 0 4 0 2 3 0 1 0
## [2295] 1 0 4 2 0 1 4 4 4 3 0 1 0 4 0 3 4 4 4 1 4 4 4 3 3 2 0 4 4 3 4 4 4 2 4 4 1
## [2332] 1 0 2 4 0 2 0 4 3 0 2 3 4 1 1 4 1 0 3 1 1 4 3 4 4 3 0 4 3 1 4 0 4 0 0 0 4
## [2369] 0 0 2 0 4 4 1 4 2 0 1 1 2 1 2 4 0 4 4 4 0 2 4 4 2 0 3 0 0 0 3 2 1 1 2 0 0
## [2406] 3 2 0 3 1 3 2 4 1 2 0 0 4 0 0 3 0 2 3 4 0 4 0 0 0 3 0 3 3 4 4 4 0 3 4 0 4
## [2443] 3 3 3 1 4 4 4 4 0 0 1 4 4 1 4 4 0 1 1 2 3 4 0 4 2 3 1 1 3 1 1 4 3 0 2 1 4
## [2480] 1 1 4 0 3 3 0 3 2 1 3 0 0 3 3 0 3 2 1 0 4 4 1 0 0 0 2 4 0 1 4 4 2 4 0 3 1
## [2517] 2 0 0 0 0 0 4 4 4 1 0 0 4 4 2 4 3 0 4 2 2 4 4 2 2 4 3 2 0 0 1 0 4 2 0 2 4
## [2554] 4 1 4 4 4 1 0 1 4 1 4 1 2 0 3 3 0 4 0 1 2 0 1 1 4 3 0 4 4 4 0 0 4 3 4 4 0
## [2591] 0 0
## Levels: 4 3 2 1 0Confusion matrix: A confusion matrix in R is a table that will categorize the predictions against the actual values. It includes two dimensions, among them one will indicate the predicted values and another one will represent the actual values.
#confusion matrix
cm=table(test[,9],y_pred)
cm## y_pred
## 4 3 2 1 0
## 4 670 13 43 83 0
## 3 0 44 22 0 0
## 2 0 0 0 0 0
## 1 65 252 186 350 0
## 0 0 0 16 0 848Accuracy of the data model according to Naive base algorithm
#Accuracy
NBacc=sum(sum(diag(cm))/sum(cm))
NBacc## [1] 0.7376543Applying KNN algorithm to your dataset (Nursery dataset)
KNN is a Supervised Learning algorithm that uses labeled input data set to predict the output of the data points. It is one of the most simple Machine learning algorithms and it can be easily implemented for a varied set of problems. It is mainly based on feature similarity Loading packages
library(caTools)
library()Feature Scaling: Feature scaling is a method used to normalize the range of independent variables or features of data. In data processing, it is also known as data normalization and is generally performed during the data preprocessing step
#feature scaling
train_scale = scale(training[1:8])
test_scale = scale(test[1:8])Fitting a knn to the train_set
#fitting a knn to the train_set
library(class)
pred = knn(train_scale,test_scale,training$final.evaluation, k = 3)
pred## [1] 0 0 0 4 0 1 0 0 4 0 4 0 0 4 0 0 4 0 0 1 1 1 1 1 0 4 0 1 4 1 1 1 4 0 0 0 1
## [38] 0 1 1 4 1 0 1 4 1 1 0 1 4 0 0 4 4 4 0 4 0 4 0 1 0 1 0 1 4 0 0 0 1 0 0 1 4
## [75] 1 3 1 4 0 0 1 1 1 0 0 1 1 1 1 4 1 3 0 4 4 4 4 4 1 4 1 4 1 4 3 4 0 0 4 1 4
## [112] 1 0 0 4 1 1 0 0 0 0 0 1 0 4 0 4 1 1 4 4 4 1 0 0 4 4 1 1 1 4 3 4 4 1 1 4 1
## [149] 1 4 1 0 0 0 0 0 0 1 1 0 1 1 0 1 1 4 3 1 4 3 1 1 0 0 1 1 1 0 0 4 0 0 1 0 1
## [186] 4 1 1 4 4 4 0 4 0 4 4 1 4 4 1 4 4 4 4 0 0 4 4 4 4 0 0 1 0 0 4 4 4 0 4 1 4
## [223] 1 4 0 1 3 1 4 0 1 4 4 0 4 1 1 0 0 0 4 1 0 1 4 1 4 0 0 1 1 0 1 4 1 0 4 1 0
## [260] 1 1 1 4 1 4 0 0 1 1 0 4 1 4 4 0 1 0 0 4 4 0 4 4 1 0 1 0 0 4 0 0 1 1 4 1 4
## [297] 0 0 4 4 4 0 1 4 0 0 0 0 1 4 0 4 4 0 1 0 4 1 4 4 1 4 4 3 1 0 1 0 0 1 0 1 1
## [334] 1 0 4 4 0 1 4 4 0 0 1 0 1 0 4 1 3 1 1 0 1 0 1 1 4 0 4 1 4 1 1 0 0 0 0 0 4
## [371] 1 1 0 4 4 0 1 4 4 1 1 4 0 0 4 4 1 4 0 0 0 1 1 1 1 0 4 1 0 4 4 1 1 4 4 4 4
## [408] 4 4 4 4 4 1 4 4 1 4 0 4 0 4 4 4 0 3 0 1 0 1 4 1 4 1 1 0 4 1 4 1 4 0 4 1 1
## [445] 4 1 1 4 1 0 4 0 0 4 0 1 0 0 1 0 4 4 4 1 0 1 0 0 0 0 1 4 4 4 1 4 0 0 0 0 0
## [482] 4 1 1 1 4 0 1 4 4 0 1 1 3 1 1 1 1 0 0 4 0 4 1 0 4 0 1 1 4 0 4 4 4 1 1 4 4
## [519] 1 4 4 1 0 0 1 4 0 0 4 1 3 3 0 4 4 0 1 0 4 4 0 0 4 1 1 0 0 4 1 4 4 1 1 4 1
## [556] 1 1 0 0 0 0 0 0 4 0 4 1 0 4 0 1 4 0 1 0 0 0 4 0 4 0 4 1 1 0 4 4 0 0 0 1 0
## [593] 4 0 1 4 0 1 0 1 0 3 0 0 3 4 0 0 4 1 4 1 4 1 3 4 1 0 0 0 0 4 1 0 1 0 1 0 4
## [630] 0 1 4 1 3 4 3 1 1 4 4 4 0 0 4 0 0 1 0 4 0 4 0 1 4 0 1 4 4 4 4 0 1 4 4 4 4
## [667] 1 0 1 0 0 0 0 0 0 4 4 1 4 0 1 4 1 1 0 1 1 1 0 0 4 0 4 1 1 1 1 0 4 1 4 4 0
## [704] 4 1 4 0 4 0 0 0 1 0 4 4 1 0 1 0 4 1 0 0 1 1 0 1 0 1 1 4 4 0 1 4 0 4 0 1 4
## [741] 0 0 0 1 0 0 4 1 4 4 1 4 4 1 1 4 0 1 1 0 0 1 1 0 0 1 1 4 0 1 1 1 4 0 4 0 4
## [778] 4 4 4 0 1 0 4 3 1 4 0 1 0 1 0 3 0 4 0 0 1 0 1 1 1 4 1 1 1 1 3 0 4 4 4 0 3
## [815] 0 4 4 0 4 0 1 1 1 4 1 4 1 0 4 1 0 1 1 4 1 4 0 1 1 1 1 1 3 1 1 1 0 1 0 0 4
## [852] 0 1 1 0 1 1 4 0 1 0 1 1 0 0 0 4 4 1 0 4 1 1 1 1 0 4 0 4 1 0 1 4 4 0 0 0 1
## [889] 4 1 0 4 1 0 4 4 1 1 1 0 4 4 1 1 4 0 4 1 4 0 0 4 1 4 4 1 1 4 1 0 1 0 0 1 0
## [926] 1 1 0 1 0 0 4 4 4 1 0 1 1 4 1 0 0 1 1 4 4 4 0 1 4 4 1 0 1 0 0 1 1 0 4 1 4
## [963] 4 4 4 4 1 1 4 1 1 4 0 0 1 4 1 0 4 0 1 1 1 0 1 1 4 0 4 0 0 0 0 1 4 1 1 0 1
## [1000] 1 0 0 4 4 0 0 4 1 1 4 4 0 1 1 1 0 1 4 1 1 4 0 1 4 0 4 4 4 1 4 0 0 1 0 1 4
## [1037] 1 0 0 1 0 1 4 0 4 1 0 0 4 4 1 1 1 0 1 0 1 4 0 0 0 0 0 1 1 0 1 4 1 4 4 0 1
## [1074] 0 0 0 1 1 4 0 0 1 0 1 0 1 0 1 1 4 0 0 4 1 4 4 4 0 0 1 0 0 0 0 0 0 1 0 1 4
## [1111] 0 1 0 0 4 0 1 4 0 4 1 4 1 0 4 0 0 4 1 1 4 1 4 4 4 1 0 1 4 1 1 0 0 0 4 1 1
## [1148] 1 4 1 1 4 0 0 4 1 1 0 3 0 0 0 4 4 0 1 0 0 0 1 1 1 1 4 0 4 4 1 1 0 4 1 0 4
## [1185] 1 0 0 1 4 1 4 0 0 0 1 3 1 4 0 1 0 1 4 0 0 1 4 1 0 0 1 0 4 1 0 4 0 1 0 4 1
## [1222] 1 0 1 4 0 1 4 0 4 1 4 4 4 1 0 4 4 0 1 1 4 4 0 4 0 0 1 0 0 1 0 4 1 0 0 4 4
## [1259] 1 1 1 4 0 4 1 4 4 1 0 4 0 4 0 0 1 4 1 4 0 3 0 1 1 1 4 1 0 0 0 0 1 1 4 0 4
## [1296] 1 0 0 1 0 0 1 3 1 4 0 4 0 0 0 0 0 1 3 0 1 1 0 0 1 4 1 1 4 1 1 4 1 4 1 0 1
## [1333] 3 1 0 1 1 4 1 4 0 0 1 1 0 4 4 0 4 4 0 0 0 1 4 4 4 4 4 0 1 4 0 0 1 4 4 1 0
## [1370] 0 1 1 1 4 0 4 4 1 1 1 0 1 4 4 0 4 1 1 1 4 0 4 0 4 1 0 1 0 0 0 1 4 0 1 1 0
## [1407] 1 4 0 0 1 4 1 1 0 0 4 4 1 0 1 4 1 4 1 0 0 4 0 4 4 0 4 4 1 1 4 1 4 1 1 1 4
## [1444] 1 0 1 4 1 1 4 4 0 4 4 4 4 4 0 0 1 0 0 1 0 4 0 3 0 4 1 0 0 3 4 4 0 0 0 4 0
## [1481] 1 0 1 1 4 4 0 1 0 3 4 0 1 4 1 1 4 0 1 0 4 1 0 4 4 0 1 4 3 4 0 0 1 4 0 1 4
## [1518] 1 1 1 1 1 0 1 1 4 0 1 0 4 1 4 4 4 1 1 1 4 0 4 0 4 1 1 4 1 1 4 4 4 1 0 0 1
## [1555] 4 0 0 0 1 4 1 1 0 1 1 0 4 1 1 4 4 3 1 4 0 1 0 4 1 0 0 4 1 0 1 1 1 4 1 1 1
## [1592] 1 0 4 1 4 0 4 4 1 1 4 3 0 0 1 1 1 1 4 4 0 4 0 0 1 0 1 4 4 1 0 0 1 0 4 4 0
## [1629] 0 0 0 3 0 0 4 0 1 4 0 0 1 1 4 1 1 0 3 1 0 0 0 1 0 4 0 1 0 1 4 1 0 1 4 1 4
## [1666] 4 0 4 1 3 0 4 4 4 1 0 1 4 1 4 0 1 0 0 0 1 0 4 1 0 4 1 4 0 4 4 0 1 1 4 3 4
## [1703] 1 4 4 4 0 4 4 4 4 1 0 0 0 0 4 4 0 4 1 4 0 0 0 1 1 0 1 1 4 3 4 1 1 0 0 0 1
## [1740] 1 0 1 1 4 0 4 1 0 4 4 1 4 1 4 1 1 4 1 1 1 0 4 0 4 0 4 4 0 4 0 4 0 0 1 1 4
## [1777] 1 4 1 4 1 1 3 4 0 4 4 0 0 1 4 0 1 1 4 0 4 1 0 0 4 4 0 4 0 0 0 4 4 1 1 1 0
## [1814] 0 0 0 0 4 0 0 0 0 4 0 1 1 1 1 4 1 0 4 4 4 4 4 4 1 0 1 1 1 0 0 0 4 0 0 1 0
## [1851] 1 1 1 1 1 4 0 1 0 1 4 0 1 0 0 1 4 0 1 0 0 4 1 0 0 4 1 4 1 0 4 0 1 1 4 1 0
## [1888] 4 4 0 4 0 1 4 0 4 4 0 0 0 1 0 0 3 0 1 0 0 4 1 0 0 1 1 1 0 1 4 1 1 0 1 1 1
## [1925] 1 4 0 0 1 4 1 0 4 1 0 4 4 1 4 1 0 1 0 4 1 1 4 1 0 1 4 1 3 1 1 1 4 0 1 1 1
## [1962] 1 4 4 0 4 0 0 4 0 1 4 4 0 1 4 1 4 4 1 4 4 0 0 4 0 1 0 4 4 0 1 4 1 1 1 0 3
## [1999] 1 0 0 0 4 4 1 1 1 0 1 0 1 4 0 1 0 0 0 0 4 0 0 0 0 0 0 0 0 1 1 1 4 4 0 0 0
## [2036] 4 4 1 0 4 1 4 0 0 4 0 1 0 1 0 4 4 0 4 1 0 0 0 0 0 4 1 0 4 4 0 1 1 4 1 1 0
## [2073] 1 4 0 0 0 3 4 0 1 4 0 0 4 1 1 1 0 0 1 0 4 1 1 0 0 0 0 0 1 0 4 4 1 4 0 4 4
## [2110] 0 1 4 4 4 4 0 4 4 1 1 4 4 0 0 1 0 1 4 4 1 0 4 4 4 0 0 0 0 4 4 0 4 4 0 1 1
## [2147] 4 1 1 1 4 4 1 0 0 0 0 4 4 1 0 0 0 1 4 1 1 1 1 0 4 0 0 4 1 1 4 4 4 1 4 4 1
## [2184] 4 1 1 4 1 1 0 1 0 0 4 0 0 1 1 0 1 0 0 0 4 0 4 0 1 1 4 4 0 0 0 4 0 4 1 1 0
## [2221] 1 0 0 0 4 1 4 1 1 1 4 4 0 1 1 1 4 4 4 0 0 1 0 4 4 0 0 0 1 1 1 1 4 4 0 1 4
## [2258] 4 0 0 0 1 4 0 4 1 4 0 0 4 4 1 1 0 4 4 4 0 4 1 4 4 1 4 4 0 0 4 0 1 1 0 1 0
## [2295] 1 0 4 1 0 1 1 4 4 1 0 1 0 4 0 1 4 4 4 1 1 4 4 3 1 1 0 4 4 1 4 1 1 1 4 1 1
## [2332] 1 0 1 4 0 1 0 4 1 0 1 3 4 1 1 4 1 0 1 1 1 4 1 4 4 1 0 4 1 4 4 0 1 0 0 0 4
## [2369] 0 0 1 0 4 4 4 4 1 0 4 1 1 4 1 4 0 1 1 4 0 1 4 4 1 0 1 0 0 0 1 1 4 1 1 0 0
## [2406] 3 1 0 1 4 1 1 4 1 0 0 0 4 0 0 1 0 1 1 4 0 4 0 0 0 3 0 1 1 4 4 4 0 3 4 0 4
## [2443] 1 1 1 4 4 4 4 4 0 0 1 4 4 4 4 4 0 1 1 1 4 1 0 4 3 1 1 1 1 1 1 4 1 0 1 1 4
## [2480] 4 1 4 0 1 1 0 3 1 4 1 0 0 1 1 0 3 3 1 0 4 4 4 0 0 0 0 4 0 1 4 4 1 4 0 1 1
## [2517] 1 0 0 0 0 0 4 4 4 1 0 0 4 4 1 1 1 0 4 1 1 4 4 1 1 4 1 1 0 0 1 0 1 1 0 4 4
## [2554] 4 1 4 4 4 1 0 1 1 1 4 1 1 0 1 3 0 4 0 1 4 0 4 1 4 1 0 4 4 4 0 0 4 1 4 4 0
## [2591] 0 0
## Levels: 4 3 2 1 0Making the Confusion Matrix: A confusion matrix in R is a table that will categorize the predictions against the actual values. It includes two dimensions, among them one will indicate the predicted values and another one will represent the actual values.
# Making the Confusion Matrix
cm = table(test[,9], pred)
cm## pred
## 4 3 2 1 0
## 4 769 0 0 39 1
## 3 0 54 0 12 0
## 2 0 0 0 0 0
## 1 26 1 0 826 0
## 0 0 0 0 0 864Accuracy of the data model according to KNN algorithm
KNNacc=sum(sum(diag(cm))/sum(cm))
KNNacc## [1] 0.9695216Including Plots
#Graphs
BARPLOT:
count=table(data$final.evaluation)
count##
## 1 2 3 4 5
## 4044 328 2 4266 4320barplot(count)
legend(1,1000,legend=c("20MID0086"))
DENSITY PLOT
#density plot:
plot(density(data$final.evaluation))
legend(1,0.3,legend=c("20MID0086"))
HISTOGRAM
#HISTOGRAM
hist(data$final.evaluation,breaks = 3)
legend(1,1000,legend=c("20MID0086"))
LOLLIPOP
#LOLLIPOP
library(ggplot2)## Warning: package 'ggplot2' was built under R version 4.1.3ggplot(data,aes(x=final.evaluation,y=housing))+geom_segment(aes(x=final.evaluation,xend=final.evaluation,y=2,yend=housing))+geom_point()
SCATTER
#scatter
plot(x=data$final.evaluation,y=data$housing,main = "Scatter Plot")
legend(1,2,legend=c("20MID0086"))
HEATMAP
#HEATMAP
dataset=data[c(4,5,8)]
map<-as.matrix(dataset[])
heatmap(map)
Conclusion Result
For Set.seed(500) These are the accuracies Accuracy in Decision tree algorithm:
dtacc## [1] 0.8668981Accuracy in NaiveBayes algorithm:
NBacc## [1] 0.7376543Accuracy in KNN algorithm:
KNNacc## [1] 0.9695216Among these three algorithms: KNN got highest accuracy so The model created using KNN algorithm is more efficient with accuracy
KNNacc## [1] 0.9695216