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setwd("C:/Users/kherman002/Desktop/Analytics/ANLY 530/LabReport2")
creditData <- read_csv("creditData.csv")## Parsed with column specification:
## cols(
## .default = col_double()
## )## See spec(...) for full column specifications.View(creditData)
sum(is.na(creditData))## [1] 0creditData$Creditability <-as.factor(creditData$Creditability)
sum(is.na(creditData))## [1] 0set.seed(12345)
credit_rand <-creditData[order(runif(1000)),]
credit_train <-credit_rand[1:750,]
credit_test <-credit_rand[751:1000,]
prop.table(table(credit_train$Creditability))##
## 0 1
## 0.3146667 0.6853333We randomized before performing the split.
naive_model <- naive_bayes(Creditability ~. , data = credit_train)
naive_model##
## ================================ Naive Bayes =================================
##
## Call:
## naive_bayes.formula(formula = Creditability ~ ., data = credit_train)
##
## ------------------------------------------------------------------------------
##
## Laplace smoothing: 0
##
## ------------------------------------------------------------------------------
##
## A priori probabilities:
##
## 0 1
## 0.3146667 0.6853333
##
## ------------------------------------------------------------------------------
##
## Tables:
##
## ------------------------------------------------------------------------------
## ::: Account Balance (Gaussian)
## ------------------------------------------------------------------------------
##
## Account Balance 0 1
## mean 1.923729 2.793774
## sd 1.036826 1.252008
##
## ------------------------------------------------------------------------------
## ::: Duration of Credit (month) (Gaussian)
## ------------------------------------------------------------------------------
##
## Duration of Credit (month) 0 1
## mean 24.46610 19.20039
## sd 13.82208 11.13433
##
## ------------------------------------------------------------------------------
## ::: Payment Status of Previous Credit (Gaussian)
## ------------------------------------------------------------------------------
##
## Payment Status of Previous Credit 0 1
## mean 2.161017 2.665370
## sd 1.071649 1.045219
##
## ------------------------------------------------------------------------------
## ::: Purpose (Gaussian)
## ------------------------------------------------------------------------------
##
## Purpose 0 1
## mean 2.927966 2.803502
## sd 2.944722 2.633253
##
## ------------------------------------------------------------------------------
## ::: Credit Amount (Gaussian)
## ------------------------------------------------------------------------------
##
## Credit Amount 0 1
## mean 3964.195 2984.177
## sd 3597.093 2379.685
##
## ------------------------------------------------------------------------------
##
## # ... and 15 more tables
##
## ------------------------------------------------------------------------------(Model_nat <- table(predict(naive_model, credit_test), credit_test$Creditability))## Warning: predict.naive_bayes(): More features in the newdata are provided
## as there are probability tables in the object. Calculation is performed
## based on features to be found in the tables.##
## 0 1
## 0 42 35
## 1 22 151(Accuracy <- sum(diag(Model_nat))/sum(Model_nat)*100)## [1] 77.2The prior probabilities for 0 and 1 are, respectively, 0.3146667 0.6853333. The confusion matrix shows True Positve and True Negative values are 42 and 151 and
the False Positive and False Negative values are 22 and 35.
The accuracy for the above naive bayes model is 77.2%.
#Laboratory 2: Naïve Bayes Classifiers, Part 2
creditDataScaled <-scale(credit_rand[ ,2:ncol(credit_rand)],center = TRUE,scale = TRUE)
p <- cor(creditDataScaled)
(highlycor <- findCorrelation(p, 0.30))## [1] 5 12 19 15 3highlycor <- findCorrelation(p, 0.30)
filteredData <- credit_rand[, -(highlycor[5]+1)]
filteredTraining <- filteredData[1:750, ]
filteredTest <- filteredData[751:1000, ]nb_model <- naive_bayes(Creditability ~ ., data=filteredTraining)filteredTestPred <- predict(nb_model, newdata = filteredTest)## Warning: predict.naive_bayes(): More features in the newdata are provided
## as there are probability tables in the object. Calculation is performed
## based on features to be found in the tables.table(filteredTestPred, filteredTest$Creditability)##
## filteredTestPred 0 1
## 0 44 35
## 1 20 151Unfortunately, the results indicate that performance has not improved following the classifier’s improvment.
#Laboratory 2: Support Vector Machine, Part 3 ## Step 1: Collecting the Data 3
setwd("C:/Users/kherman002/Desktop/Analytics/ANLY 530/LabReport2")
letters <- read.csv("letterdata.csv")
str(letters)## 'data.frame': 20000 obs. of 17 variables:
## $ letter: Factor w/ 26 levels "A","B","C","D",..: 20 9 4 14 7 19 2 1 10 13 ...
## $ xbox : int 2 5 4 7 2 4 4 1 2 11 ...
## $ ybox : int 8 12 11 11 1 11 2 1 2 15 ...
## $ width : int 3 3 6 6 3 5 5 3 4 13 ...
## $ height: int 5 7 8 6 1 8 4 2 4 9 ...
## $ onpix : int 1 2 6 3 1 3 4 1 2 7 ...
## $ xbar : int 8 10 10 5 8 8 8 8 10 13 ...
## $ ybar : int 13 5 6 9 6 8 7 2 6 2 ...
## $ x2bar : int 0 5 2 4 6 6 6 2 2 6 ...
## $ y2bar : int 6 4 6 6 6 9 6 2 6 2 ...
## $ xybar : int 6 13 10 4 6 5 7 8 12 12 ...
## $ x2ybar: int 10 3 3 4 5 6 6 2 4 1 ...
## $ xy2bar: int 8 9 7 10 9 6 6 8 8 9 ...
## $ xedge : int 0 2 3 6 1 0 2 1 1 8 ...
## $ xedgey: int 8 8 7 10 7 8 8 6 6 1 ...
## $ yedge : int 0 4 3 2 5 9 7 2 1 1 ...
## $ yedgex: int 8 10 9 8 10 7 10 7 7 8 ...letters_train <- letters[1:18000, ]
letters_test <- letters[18001:20000, ]letter_classifier <- ksvm(letter ~ ., data = letters_train, kernel = "vanilladot")## Setting default kernel parametersletter_classifier## Support Vector Machine object of class "ksvm"
##
## SV type: C-svc (classification)
## parameter : cost C = 1
##
## Linear (vanilla) kernel function.
##
## Number of Support Vectors : 7886
##
## Objective Function Value : -15.3458 -21.3403 -25.7672 -6.8685 -8.8812 -35.9555 -59.5883 -18.1975 -65.6075 -41.5654 -18.8559 -39.3558 -36.9961 -60.3052 -15.1694 -42.144 -35.0941 -19.4069 -15.8234 -38.6718 -33.3013 -8.5298 -12.4387 -38.2194 -14.3682 -9.5508 -165.7154 -53.2778 -79.2163 -134.5053 -184.4809 -58.9285 -46.3252 -81.004 -28.1341 -29.6955 -27.5983 -38.1764 -47.2889 -137.0497 -208.1396 -239.2616 -23.8945 -10.9655 -64.228 -12.2139 -55.7818 -10.8001 -21.2407 -11.1795 -121.5639 -33.2229 -267.3926 -81.0708 -9.4937 -4.6577 -161.5171 -86.7114 -20.9146 -16.8272 -86.6582 -16.7205 -30.3036 -20.0054 -26.2331 -29.9289 -56.1072 -11.6335 -5.2564 -14.8153 -4.983 -4.8171 -8.5044 -43.2267 -55.9 -214.755 -47.0748 -49.6539 -50.2278 -18.3767 -19.1813 -97.6132 -113.6502 -42.4112 -32.5859 -127.4807 -33.7418 -30.7568 -40.0953 -18.6792 -5.4826 -49.3916 -10.6142 -20.0286 -63.8287 -183.8297 -57.0671 -43.3721 -35.2783 -85.4451 -145.9585 -11.8002 -6.1194 -12.5323 -33.5245 -155.2248 -57.2602 -194.0785 -111.0155 -10.8207 -16.7926 -3.7766 -77.3561 -7.9004 -106.5759 -52.523 -107.0402 -78.0148 -74.4773 -24.8166 -13.2372 -7.8706 -27.2788 -13.2342 -280.2869 -32.7288 -25.9531 -149.5447 -153.8495 -10.0146 -40.8917 -6.7333 -65.2053 -72.818 -35.1252 -246.7046 -38.0738 -16.9126 -158.18 -184.0021 -50.8427 -28.7686 -164.5969 -97.8359 -386.1426 -160.3188 -181.8759 -38.3648 -37.2272 -60.116 -28.2074 -53.7383 -7.8729 -12.3159 -37.8942 -72.6434 -211.8342 -58.5023 -105.1605 -176.7259 -685.8994 -142.8147 -159.635 -366.9437 -37.6409 -73.1357 -175.1906 -131.2833 -41.1464 -77.8404 -57.8131 -8.6365 -251.3728 -14.0836 -36.5144 -2.2292 -6.1598 -16.8011 -26.5165 -67.19 -21.3366 -221.4815 -22.9219 -4.2616 -4.7901 -0.8263 -134.7538 -8.8843 -83.1109 -23.1019 -14.4251 -5.7337 -17.5244 -29.7925 -23.9243 -88.9084 -28.6719 -106.0564 -16.4981 -10.6486 -7.9315 -1.5742 -91.1706 -7.3819 -118.2628 -117.5543 -48.5606 -26.6093 -71.2968 -30.4913 -63.5712 -279.2921 -46.3025 -50.4912 -37.9431 -21.5243 -11.6202 -134.9023 -7.516 -5.8131 -10.1595 -13.6329 -27.0293 -25.7282 -151.8511 -39.0524 -105.4861 -34.2434 -15.7051 -10.2304 -3.6687 -98.2094 -7.4666 -15.2668 -75.1283 -116.5382 -16.6429 -14.9215 -55.1062 -3.0636 -8.4262 -93.6829 -38.1162 -123.1859 -4.9078 -9.1612 -1.3077 -102.9021 -23.1138 -8.5262 -57.2623 -3.4297 -20.9579 -78.2019 -50.3741 -62.3531 -6.4908 -21.9308 -2.3736 -84.3835 -126.3997 -114.8723 -26.4109 -21.5589 -61.6405 -34.9162 -66.3243 -25.1148 -6.7203 -4.6695 -65.3518 -39.7924 -67.3505 -36.2154 -10.9031 -62.2195 -14.9491 -24.3238 -65.0847 -4.9657 -64.2797 -278.2873 -14.6902 -13.9198 -18.2059 -9.8972 -78.2645 -17.454 -49.5929 -55.7786 -28.7673 -15.9476 -47.531 -17.4379 -71.0516 -5.6899 -6.2519 -97.5508 -3.8196 -7.0502 -1.1238 -147.6952 -28.2018 -414.2586 -32.3275 -35.1191 -4.9605 -90.2307 -151.3409 -90.0329 -27.9491 -42.4688 -12.5118 -26.4828 -2.0045 -62.195 -9.1662 -178.4616 -1.9406 -1.9871 -11.3982 -0.5214 -29.6136 -35.0449 -6.7569
## Training error : 0.1335letter_predictions <- predict(letter_classifier, letters_test)
table(letter_predictions, letters_test$letter)##
## letter_predictions A B C D E F G H I J K L M N O P Q R
## A 73 0 0 0 0 0 0 0 0 1 0 0 0 0 3 0 4 0
## B 0 61 0 3 2 0 1 1 0 0 1 1 0 0 0 2 0 1
## C 0 0 64 0 2 0 4 2 1 0 1 2 0 0 1 0 0 0
## D 2 1 0 67 0 0 1 3 3 2 1 2 0 3 4 2 1 2
## E 0 0 1 0 64 1 1 0 0 0 2 2 0 0 0 0 2 0
## F 0 0 0 0 0 70 1 1 4 0 0 0 0 0 0 5 1 0
## G 1 1 2 1 3 2 68 1 0 0 0 1 0 0 0 0 4 1
## H 0 0 0 1 0 1 0 46 0 2 3 1 1 1 9 0 0 5
## I 0 0 0 0 0 0 0 0 65 3 0 0 0 0 0 0 0 0
## J 0 1 0 0 0 1 0 0 3 61 0 0 0 0 1 0 0 0
## K 0 1 4 0 0 0 0 5 0 0 56 0 0 2 0 0 0 4
## L 0 0 0 0 1 0 0 1 0 0 0 63 0 0 0 0 0 0
## M 0 0 1 0 0 0 1 0 0 0 0 0 70 2 0 0 0 0
## N 0 0 0 0 0 0 0 0 0 0 0 0 0 77 0 0 0 1
## O 0 0 1 1 0 0 0 1 0 1 0 0 0 0 49 1 2 0
## P 0 0 0 0 0 3 0 0 0 0 0 0 0 0 2 69 0 0
## Q 0 0 0 0 0 0 3 1 0 0 0 2 0 0 2 1 52 0
## R 0 4 0 0 1 0 0 3 0 0 3 0 0 0 1 0 0 64
## S 0 1 0 0 1 1 1 0 1 1 0 0 0 0 0 0 6 0
## T 0 0 0 0 1 1 0 0 0 0 1 0 0 0 0 0 0 0
## U 0 0 2 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0
## V 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0
## W 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0
## X 0 1 0 0 1 0 0 1 0 0 1 4 0 0 0 0 0 1
## Y 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 4 0 0
## Z 1 0 0 0 2 0 0 0 0 2 0 0 0 0 0 0 0 0
##
## letter_predictions S T U V W X Y Z
## A 0 1 2 0 1 0 0 0
## B 3 0 0 0 0 0 0 0
## C 0 0 0 0 0 0 0 0
## D 0 0 0 0 0 0 1 0
## E 6 0 0 0 0 1 0 0
## F 2 0 0 1 0 0 2 0
## G 3 2 0 0 0 0 0 0
## H 0 3 0 2 0 0 1 0
## I 2 0 0 0 0 2 1 0
## J 1 0 0 0 0 1 0 4
## K 0 1 2 0 0 4 0 0
## L 0 0 0 0 0 0 0 0
## M 0 0 1 0 6 0 0 0
## N 0 0 1 0 2 0 0 0
## O 0 0 1 0 0 0 0 0
## P 0 0 0 0 0 0 1 0
## Q 1 0 0 0 0 0 0 0
## R 0 1 0 1 0 0 0 0
## S 47 1 0 0 0 1 0 6
## T 1 83 1 0 0 0 2 2
## U 0 0 83 0 0 0 0 0
## V 0 0 0 64 1 0 1 0
## W 0 0 0 3 59 0 0 0
## X 0 0 0 0 0 76 1 0
## Y 0 1 0 0 0 1 58 0
## Z 5 1 0 0 0 0 0 70