Titanic Classification
George Gerogiannis
2017
- 1 Set my working directory and read in the training and test sets.
- 2 Have a quick look at the data to do quick and fast variable selection
- 3 Start by one hot coding sex and pclass for and SVM.
- 4 Quick clean up for the data and preperation for data splitting (cross-validation) - using ohe.Data2
- 5 Quick Linear SVM classification
- 6 Quick Polynomial SVM classification
- 7 Quick Radial SVM classification
- 8 KNN with the 1:150NN
- 9 Try trees with the one hot coding.
- 10 Get the test error rate for the best method.
- 11 Prep the actual test set that is to be predicted creating a csv. to submit.
- 12 Using the entire train,0he.Data6, to predict ohe.Data4
- 13 write the csv file
1 Set my working directory and read in the training and test sets.
setwd("~/Desktop/Titanic Dataset")
train <- read_csv("~/Desktop/Titanic Dataset/train.csv")names(train)## [1] "PassengerId" "Survived" "Pclass" "Name" "Sex"
## [6] "Age" "SibSp" "Parch" "Ticket" "Fare"
## [11] "Cabin" "Embarked"sum(is.na(train$PassengerId)) # PassengerId should be discarded## [1] 0sum(is.na(train$Survived))## [1] 0sum(is.na(train$Pclass))## [1] 0sum(is.na(train$Name)) # Name should be discarded## [1] 0sum(is.na(train$Sex))## [1] 0sum(is.na(train$Age)) # There are 177 missing ages## [1] 177sum(is.na(train$SibSp))## [1] 0sum(is.na(train$Parch))## [1] 0sum(is.na(train$Ticket))## [1] 0sum(is.na(train$Fare))## [1] 0sum(is.na(train$Cabin)) # There are 687 missing observations## [1] 687sum(is.na(train$Embarked)) # There are 2 missing observations## [1] 2train<-data.frame(train)
train<-data.frame(train[,c("Survived", "Pclass", "Sex", "Age", "SibSp", "Parch", "Ticket", "Fare", "Cabin", "Embarked")])
train$Survived<-factor(train$Survived)
train$Pclass<-factor(train$Pclass)
train$Sex<-factor(train$Sex)
train$SibSp<-factor(train$SibSp)
train$Parch<-factor(train$Parch)
train$Embarked<-factor(train$Embarked)
summary(train)## Survived Pclass Sex Age SibSp Parch
## 0:549 1:216 female:314 Min. : 0.42 0:608 0:678
## 1:342 2:184 male :577 1st Qu.:20.12 1:209 1:118
## 3:491 Median :28.00 2: 28 2: 80
## Mean :29.70 3: 16 3: 5
## 3rd Qu.:38.00 4: 18 4: 4
## Max. :80.00 5: 5 5: 5
## NA's :177 8: 7 6: 1
## Ticket Fare Cabin Embarked
## Length:891 Min. : 0.00 Length:891 C :168
## Class :character 1st Qu.: 7.91 Class :character Q : 77
## Mode :character Median : 14.45 Mode :character S :644
## Mean : 32.20 NA's: 2
## 3rd Qu.: 31.00
## Max. :512.33
## 2 Have a quick look at the data to do quick and fast variable selection
train<-data.frame(train[,c("Survived", "Pclass", "Sex", "Age", "SibSp", "Parch", "Fare", "Embarked")]) # Some variables have been removed to get a better picture
ggpairs(train, aes(colour=Survived))
# Plot each variable independently
ggpairs(train[, c("Survived", "Sex")], aes(colour=Survived))
ggpairs(train[, c("Survived", "Pclass")], aes(colour=Survived))
ggpairs(train[, c("Survived", "Age")], aes(colour=Survived))
ggpairs(train[, c("Survived", "SibSp")], aes(colour=Survived))
ggpairs(train[, c("Survived", "Parch")], aes(colour=Survived))
ggpairs(train[, c("Survived", "Fare")], aes(colour=Survived))
ggpairs(train[, c("Survived", "Embarked")], aes(colour=Survived))
3 Start by one hot coding sex and pclass for and SVM.
Data1<-data.frame(train[, c("Survived", "Sex", "Pclass")])
# One hot encode the Sex
cat.variable<-subset(Data1, select = -c(Survived, Pclass))
ohe.Data1<-data.frame(model.matrix(~Sex-1, cat.variable), Survived=Data1$Survived, Pclass=Data1$Pclass)
ohe.Data1## Sexfemale Sexmale Survived Pclass
## 1 0 1 0 3
## 2 1 0 1 1
## 3 1 0 1 3
## 4 1 0 1 1
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## 6 0 1 0 3
## 7 0 1 0 1
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## 827 0 1 0 3
## 828 0 1 1 2
## 829 0 1 1 3
## 830 1 0 1 1
## 831 1 0 1 3
## 832 0 1 1 2
## 833 0 1 0 3
## 834 0 1 0 3
## 835 0 1 0 3
## 836 1 0 1 1
## 837 0 1 0 3
## 838 0 1 0 3
## 839 0 1 1 3
## 840 0 1 1 1
## 841 0 1 0 3
## 842 0 1 0 2
## 843 1 0 1 1
## 844 0 1 0 3
## 845 0 1 0 3
## 846 0 1 0 3
## 847 0 1 0 3
## 848 0 1 0 3
## 849 0 1 0 2
## 850 1 0 1 1
## 851 0 1 0 3
## 852 0 1 0 3
## 853 1 0 0 3
## 854 1 0 1 1
## 855 1 0 0 2
## 856 1 0 1 3
## 857 1 0 1 1
## 858 0 1 1 1
## 859 1 0 1 3
## 860 0 1 0 3
## 861 0 1 0 3
## 862 0 1 0 2
## 863 1 0 1 1
## 864 1 0 0 3
## 865 0 1 0 2
## 866 1 0 1 2
## 867 1 0 1 2
## 868 0 1 0 1
## 869 0 1 0 3
## 870 0 1 1 3
## 871 0 1 0 3
## 872 1 0 1 1
## 873 0 1 0 1
## 874 0 1 0 3
## 875 1 0 1 2
## 876 1 0 1 3
## 877 0 1 0 3
## 878 0 1 0 3
## 879 0 1 0 3
## 880 1 0 1 1
## 881 1 0 1 2
## 882 0 1 0 3
## 883 1 0 0 3
## 884 0 1 0 2
## 885 0 1 0 3
## 886 1 0 0 3
## 887 0 1 0 2
## 888 1 0 1 1
## 889 1 0 0 3
## 890 0 1 1 1
## 891 0 1 0 3 # One hot encode the Pclass
cat.variable<-subset(ohe.Data1, select = -c(Survived, Sexfemale, Sexmale))
ohe.Data2<-data.frame(model.matrix(~Pclass-1, cat.variable), Sexfemale=ohe.Data1$Sexfemale, Sexmale=ohe.Data1$Sexmale, Survived=ohe.Data1$Survived)
ohe.Data2## Pclass1 Pclass2 Pclass3 Sexfemale Sexmale Survived
## 1 0 0 1 0 1 0
## 2 1 0 0 1 0 1
## 3 0 0 1 1 0 1
## 4 1 0 0 1 0 1
## 5 0 0 1 0 1 0
## 6 0 0 1 0 1 0
## 7 1 0 0 0 1 0
## 8 0 0 1 0 1 0
## 9 0 0 1 1 0 1
## 10 0 1 0 1 0 1
## 11 0 0 1 1 0 1
## 12 1 0 0 1 0 1
## 13 0 0 1 0 1 0
## 14 0 0 1 0 1 0
## 15 0 0 1 1 0 0
## 16 0 1 0 1 0 1
## 17 0 0 1 0 1 0
## 18 0 1 0 0 1 1
## 19 0 0 1 1 0 0
## 20 0 0 1 1 0 1
## 21 0 1 0 0 1 0
## 22 0 1 0 0 1 1
## 23 0 0 1 1 0 1
## 24 1 0 0 0 1 1
## 25 0 0 1 1 0 0
## 26 0 0 1 1 0 1
## 27 0 0 1 0 1 0
## 28 1 0 0 0 1 0
## 29 0 0 1 1 0 1
## 30 0 0 1 0 1 0
## 31 1 0 0 0 1 0
## 32 1 0 0 1 0 1
## 33 0 0 1 1 0 1
## 34 0 1 0 0 1 0
## 35 1 0 0 0 1 0
## 36 1 0 0 0 1 0
## 37 0 0 1 0 1 1
## 38 0 0 1 0 1 0
## 39 0 0 1 1 0 0
## 40 0 0 1 1 0 1
## 41 0 0 1 1 0 0
## 42 0 1 0 1 0 0
## 43 0 0 1 0 1 0
## 44 0 1 0 1 0 1
## 45 0 0 1 1 0 1
## 46 0 0 1 0 1 0
## 47 0 0 1 0 1 0
## 48 0 0 1 1 0 1
## 49 0 0 1 0 1 0
## 50 0 0 1 1 0 0
## 51 0 0 1 0 1 0
## 52 0 0 1 0 1 0
## 53 1 0 0 1 0 1
## 54 0 1 0 1 0 1
## 55 1 0 0 0 1 0
## 56 1 0 0 0 1 1
## 57 0 1 0 1 0 1
## 58 0 0 1 0 1 0
## 59 0 1 0 1 0 1
## 60 0 0 1 0 1 0
## 61 0 0 1 0 1 0
## 62 1 0 0 1 0 1
## 63 1 0 0 0 1 0
## 64 0 0 1 0 1 0
## 65 1 0 0 0 1 0
## 66 0 0 1 0 1 1
## 67 0 1 0 1 0 1
## 68 0 0 1 0 1 0
## 69 0 0 1 1 0 1
## 70 0 0 1 0 1 0
## 71 0 1 0 0 1 0
## 72 0 0 1 1 0 0
## 73 0 1 0 0 1 0
## 74 0 0 1 0 1 0
## 75 0 0 1 0 1 1
## 76 0 0 1 0 1 0
## 77 0 0 1 0 1 0
## 78 0 0 1 0 1 0
## 79 0 1 0 0 1 1
## 80 0 0 1 1 0 1
## 81 0 0 1 0 1 0
## 82 0 0 1 0 1 1
## 83 0 0 1 1 0 1
## 84 1 0 0 0 1 0
## 85 0 1 0 1 0 1
## 86 0 0 1 1 0 1
## 87 0 0 1 0 1 0
## 88 0 0 1 0 1 0
## 89 1 0 0 1 0 1
## 90 0 0 1 0 1 0
## 91 0 0 1 0 1 0
## 92 0 0 1 0 1 0
## 93 1 0 0 0 1 0
## 94 0 0 1 0 1 0
## 95 0 0 1 0 1 0
## 96 0 0 1 0 1 0
## 97 1 0 0 0 1 0
## 98 1 0 0 0 1 1
## 99 0 1 0 1 0 1
## 100 0 1 0 0 1 0
## 101 0 0 1 1 0 0
## 102 0 0 1 0 1 0
## 103 1 0 0 0 1 0
## 104 0 0 1 0 1 0
## 105 0 0 1 0 1 0
## 106 0 0 1 0 1 0
## 107 0 0 1 1 0 1
## 108 0 0 1 0 1 1
## 109 0 0 1 0 1 0
## 110 0 0 1 1 0 1
## 111 1 0 0 0 1 0
## 112 0 0 1 1 0 0
## 113 0 0 1 0 1 0
## 114 0 0 1 1 0 0
## 115 0 0 1 1 0 0
## 116 0 0 1 0 1 0
## 117 0 0 1 0 1 0
## 118 0 1 0 0 1 0
## 119 1 0 0 0 1 0
## 120 0 0 1 1 0 0
## 121 0 1 0 0 1 0
## 122 0 0 1 0 1 0
## 123 0 1 0 0 1 0
## 124 0 1 0 1 0 1
## 125 1 0 0 0 1 0
## 126 0 0 1 0 1 1
## 127 0 0 1 0 1 0
## 128 0 0 1 0 1 1
## 129 0 0 1 1 0 1
## 130 0 0 1 0 1 0
## 131 0 0 1 0 1 0
## 132 0 0 1 0 1 0
## 133 0 0 1 1 0 0
## 134 0 1 0 1 0 1
## 135 0 1 0 0 1 0
## 136 0 1 0 0 1 0
## 137 1 0 0 1 0 1
## 138 1 0 0 0 1 0
## 139 0 0 1 0 1 0
## 140 1 0 0 0 1 0
## 141 0 0 1 1 0 0
## 142 0 0 1 1 0 1
## 143 0 0 1 1 0 1
## 144 0 0 1 0 1 0
## 145 0 1 0 0 1 0
## 146 0 1 0 0 1 0
## 147 0 0 1 0 1 1
## 148 0 0 1 1 0 0
## 149 0 1 0 0 1 0
## 150 0 1 0 0 1 0
## 151 0 1 0 0 1 0
## 152 1 0 0 1 0 1
## 153 0 0 1 0 1 0
## 154 0 0 1 0 1 0
## 155 0 0 1 0 1 0
## 156 1 0 0 0 1 0
## 157 0 0 1 1 0 1
## 158 0 0 1 0 1 0
## 159 0 0 1 0 1 0
## 160 0 0 1 0 1 0
## 161 0 0 1 0 1 0
## 162 0 1 0 1 0 1
## 163 0 0 1 0 1 0
## 164 0 0 1 0 1 0
## 165 0 0 1 0 1 0
## 166 0 0 1 0 1 1
## 167 1 0 0 1 0 1
## 168 0 0 1 1 0 0
## 169 1 0 0 0 1 0
## 170 0 0 1 0 1 0
## 171 1 0 0 0 1 0
## 172 0 0 1 0 1 0
## 173 0 0 1 1 0 1
## 174 0 0 1 0 1 0
## 175 1 0 0 0 1 0
## 176 0 0 1 0 1 0
## 177 0 0 1 0 1 0
## 178 1 0 0 1 0 0
## 179 0 1 0 0 1 0
## 180 0 0 1 0 1 0
## 181 0 0 1 1 0 0
## 182 0 1 0 0 1 0
## 183 0 0 1 0 1 0
## 184 0 1 0 0 1 1
## 185 0 0 1 1 0 1
## 186 1 0 0 0 1 0
## 187 0 0 1 1 0 1
## 188 1 0 0 0 1 1
## 189 0 0 1 0 1 0
## 190 0 0 1 0 1 0
## 191 0 1 0 1 0 1
## 192 0 1 0 0 1 0
## 193 0 0 1 1 0 1
## 194 0 1 0 0 1 1
## 195 1 0 0 1 0 1
## 196 1 0 0 1 0 1
## 197 0 0 1 0 1 0
## 198 0 0 1 0 1 0
## 199 0 0 1 1 0 1
## 200 0 1 0 1 0 0
## 201 0 0 1 0 1 0
## 202 0 0 1 0 1 0
## 203 0 0 1 0 1 0
## 204 0 0 1 0 1 0
## 205 0 0 1 0 1 1
## 206 0 0 1 1 0 0
## 207 0 0 1 0 1 0
## 208 0 0 1 0 1 1
## 209 0 0 1 1 0 1
## 210 1 0 0 0 1 1
## 211 0 0 1 0 1 0
## 212 0 1 0 1 0 1
## 213 0 0 1 0 1 0
## 214 0 1 0 0 1 0
## 215 0 0 1 0 1 0
## 216 1 0 0 1 0 1
## 217 0 0 1 1 0 1
## 218 0 1 0 0 1 0
## 219 1 0 0 1 0 1
## 220 0 1 0 0 1 0
## 221 0 0 1 0 1 1
## 222 0 1 0 0 1 0
## 223 0 0 1 0 1 0
## 224 0 0 1 0 1 0
## 225 1 0 0 0 1 1
## 226 0 0 1 0 1 0
## 227 0 1 0 0 1 1
## 228 0 0 1 0 1 0
## 229 0 1 0 0 1 0
## 230 0 0 1 1 0 0
## 231 1 0 0 1 0 1
## 232 0 0 1 0 1 0
## 233 0 1 0 0 1 0
## 234 0 0 1 1 0 1
## 235 0 1 0 0 1 0
## 236 0 0 1 1 0 0
## 237 0 1 0 0 1 0
## 238 0 1 0 1 0 1
## 239 0 1 0 0 1 0
## 240 0 1 0 0 1 0
## 241 0 0 1 1 0 0
## 242 0 0 1 1 0 1
## 243 0 1 0 0 1 0
## 244 0 0 1 0 1 0
## 245 0 0 1 0 1 0
## 246 1 0 0 0 1 0
## 247 0 0 1 1 0 0
## 248 0 1 0 1 0 1
## 249 1 0 0 0 1 1
## 250 0 1 0 0 1 0
## 251 0 0 1 0 1 0
## 252 0 0 1 1 0 0
## 253 1 0 0 0 1 0
## 254 0 0 1 0 1 0
## 255 0 0 1 1 0 0
## 256 0 0 1 1 0 1
## 257 1 0 0 1 0 1
## 258 1 0 0 1 0 1
## 259 1 0 0 1 0 1
## 260 0 1 0 1 0 1
## 261 0 0 1 0 1 0
## 262 0 0 1 0 1 1
## 263 1 0 0 0 1 0
## 264 1 0 0 0 1 0
## 265 0 0 1 1 0 0
## 266 0 1 0 0 1 0
## 267 0 0 1 0 1 0
## 268 0 0 1 0 1 1
## 269 1 0 0 1 0 1
## 270 1 0 0 1 0 1
## 271 1 0 0 0 1 0
## 272 0 0 1 0 1 1
## 273 0 1 0 1 0 1
## 274 1 0 0 0 1 0
## 275 0 0 1 1 0 1
## 276 1 0 0 1 0 1
## 277 0 0 1 1 0 0
## 278 0 1 0 0 1 0
## 279 0 0 1 0 1 0
## 280 0 0 1 1 0 1
## 281 0 0 1 0 1 0
## 282 0 0 1 0 1 0
## 283 0 0 1 0 1 0
## 284 0 0 1 0 1 1
## 285 1 0 0 0 1 0
## 286 0 0 1 0 1 0
## 287 0 0 1 0 1 1
## 288 0 0 1 0 1 0
## 289 0 1 0 0 1 1
## 290 0 0 1 1 0 1
## 291 1 0 0 1 0 1
## 292 1 0 0 1 0 1
## 293 0 1 0 0 1 0
## 294 0 0 1 1 0 0
## 295 0 0 1 0 1 0
## 296 1 0 0 0 1 0
## 297 0 0 1 0 1 0
## 298 1 0 0 1 0 0
## 299 1 0 0 0 1 1
## 300 1 0 0 1 0 1
## 301 0 0 1 1 0 1
## 302 0 0 1 0 1 1
## 303 0 0 1 0 1 0
## 304 0 1 0 1 0 1
## 305 0 0 1 0 1 0
## 306 1 0 0 0 1 1
## 307 1 0 0 1 0 1
## 308 1 0 0 1 0 1
## 309 0 1 0 0 1 0
## 310 1 0 0 1 0 1
## 311 1 0 0 1 0 1
## 312 1 0 0 1 0 1
## 313 0 1 0 1 0 0
## 314 0 0 1 0 1 0
## 315 0 1 0 0 1 0
## 316 0 0 1 1 0 1
## 317 0 1 0 1 0 1
## 318 0 1 0 0 1 0
## 319 1 0 0 1 0 1
## 320 1 0 0 1 0 1
## 321 0 0 1 0 1 0
## 322 0 0 1 0 1 0
## 323 0 1 0 1 0 1
## 324 0 1 0 1 0 1
## 325 0 0 1 0 1 0
## 326 1 0 0 1 0 1
## 327 0 0 1 0 1 0
## 328 0 1 0 1 0 1
## 329 0 0 1 1 0 1
## 330 1 0 0 1 0 1
## 331 0 0 1 1 0 1
## 332 1 0 0 0 1 0
## 333 1 0 0 0 1 0
## 334 0 0 1 0 1 0
## 335 1 0 0 1 0 1
## 336 0 0 1 0 1 0
## 337 1 0 0 0 1 0
## 338 1 0 0 1 0 1
## 339 0 0 1 0 1 1
## 340 1 0 0 0 1 0
## 341 0 1 0 0 1 1
## 342 1 0 0 1 0 1
## 343 0 1 0 0 1 0
## 344 0 1 0 0 1 0
## 345 0 1 0 0 1 0
## 346 0 1 0 1 0 1
## 347 0 1 0 1 0 1
## 348 0 0 1 1 0 1
## 349 0 0 1 0 1 1
## 350 0 0 1 0 1 0
## 351 0 0 1 0 1 0
## 352 1 0 0 0 1 0
## 353 0 0 1 0 1 0
## 354 0 0 1 0 1 0
## 355 0 0 1 0 1 0
## 356 0 0 1 0 1 0
## 357 1 0 0 1 0 1
## 358 0 1 0 1 0 0
## 359 0 0 1 1 0 1
## 360 0 0 1 1 0 1
## 361 0 0 1 0 1 0
## 362 0 1 0 0 1 0
## 363 0 0 1 1 0 0
## 364 0 0 1 0 1 0
## 365 0 0 1 0 1 0
## 366 0 0 1 0 1 0
## 367 1 0 0 1 0 1
## 368 0 0 1 1 0 1
## 369 0 0 1 1 0 1
## 370 1 0 0 1 0 1
## 371 1 0 0 0 1 1
## 372 0 0 1 0 1 0
## 373 0 0 1 0 1 0
## 374 1 0 0 0 1 0
## 375 0 0 1 1 0 0
## 376 1 0 0 1 0 1
## 377 0 0 1 1 0 1
## 378 1 0 0 0 1 0
## 379 0 0 1 0 1 0
## 380 0 0 1 0 1 0
## 381 1 0 0 1 0 1
## 382 0 0 1 1 0 1
## 383 0 0 1 0 1 0
## 384 1 0 0 1 0 1
## 385 0 0 1 0 1 0
## 386 0 1 0 0 1 0
## 387 0 0 1 0 1 0
## 388 0 1 0 1 0 1
## 389 0 0 1 0 1 0
## 390 0 1 0 1 0 1
## 391 1 0 0 0 1 1
## 392 0 0 1 0 1 1
## 393 0 0 1 0 1 0
## 394 1 0 0 1 0 1
## 395 0 0 1 1 0 1
## 396 0 0 1 0 1 0
## 397 0 0 1 1 0 0
## 398 0 1 0 0 1 0
## 399 0 1 0 0 1 0
## 400 0 1 0 1 0 1
## 401 0 0 1 0 1 1
## 402 0 0 1 0 1 0
## 403 0 0 1 1 0 0
## 404 0 0 1 0 1 0
## 405 0 0 1 1 0 0
## 406 0 1 0 0 1 0
## 407 0 0 1 0 1 0
## 408 0 1 0 0 1 1
## 409 0 0 1 0 1 0
## 410 0 0 1 1 0 0
## 411 0 0 1 0 1 0
## 412 0 0 1 0 1 0
## 413 1 0 0 1 0 1
## 414 0 1 0 0 1 0
## 415 0 0 1 0 1 1
## 416 0 0 1 1 0 0
## 417 0 1 0 1 0 1
## 418 0 1 0 1 0 1
## 419 0 1 0 0 1 0
## 420 0 0 1 1 0 0
## 421 0 0 1 0 1 0
## 422 0 0 1 0 1 0
## 423 0 0 1 0 1 0
## 424 0 0 1 1 0 0
## 425 0 0 1 0 1 0
## 426 0 0 1 0 1 0
## 427 0 1 0 1 0 1
## 428 0 1 0 1 0 1
## 429 0 0 1 0 1 0
## 430 0 0 1 0 1 1
## 431 1 0 0 0 1 1
## 432 0 0 1 1 0 1
## 433 0 1 0 1 0 1
## 434 0 0 1 0 1 0
## 435 1 0 0 0 1 0
## 436 1 0 0 1 0 1
## 437 0 0 1 1 0 0
## 438 0 1 0 1 0 1
## 439 1 0 0 0 1 0
## 440 0 1 0 0 1 0
## 441 0 1 0 1 0 1
## 442 0 0 1 0 1 0
## 443 0 0 1 0 1 0
## 444 0 1 0 1 0 1
## 445 0 0 1 0 1 1
## 446 1 0 0 0 1 1
## 447 0 1 0 1 0 1
## 448 1 0 0 0 1 1
## 449 0 0 1 1 0 1
## 450 1 0 0 0 1 1
## 451 0 1 0 0 1 0
## 452 0 0 1 0 1 0
## 453 1 0 0 0 1 0
## 454 1 0 0 0 1 1
## 455 0 0 1 0 1 0
## 456 0 0 1 0 1 1
## 457 1 0 0 0 1 0
## 458 1 0 0 1 0 1
## 459 0 1 0 1 0 1
## 460 0 0 1 0 1 0
## 461 1 0 0 0 1 1
## 462 0 0 1 0 1 0
## 463 1 0 0 0 1 0
## 464 0 1 0 0 1 0
## 465 0 0 1 0 1 0
## 466 0 0 1 0 1 0
## 467 0 1 0 0 1 0
## 468 1 0 0 0 1 0
## 469 0 0 1 0 1 0
## 470 0 0 1 1 0 1
## 471 0 0 1 0 1 0
## 472 0 0 1 0 1 0
## 473 0 1 0 1 0 1
## 474 0 1 0 1 0 1
## 475 0 0 1 1 0 0
## 476 1 0 0 0 1 0
## 477 0 1 0 0 1 0
## 478 0 0 1 0 1 0
## 479 0 0 1 0 1 0
## 480 0 0 1 1 0 1
## 481 0 0 1 0 1 0
## 482 0 1 0 0 1 0
## 483 0 0 1 0 1 0
## 484 0 0 1 1 0 1
## 485 1 0 0 0 1 1
## 486 0 0 1 1 0 0
## 487 1 0 0 1 0 1
## 488 1 0 0 0 1 0
## 489 0 0 1 0 1 0
## 490 0 0 1 0 1 1
## 491 0 0 1 0 1 0
## 492 0 0 1 0 1 0
## 493 1 0 0 0 1 0
## 494 1 0 0 0 1 0
## 495 0 0 1 0 1 0
## 496 0 0 1 0 1 0
## 497 1 0 0 1 0 1
## 498 0 0 1 0 1 0
## 499 1 0 0 1 0 0
## 500 0 0 1 0 1 0
## 501 0 0 1 0 1 0
## 502 0 0 1 1 0 0
## 503 0 0 1 1 0 0
## 504 0 0 1 1 0 0
## 505 1 0 0 1 0 1
## 506 1 0 0 0 1 0
## 507 0 1 0 1 0 1
## 508 1 0 0 0 1 1
## 509 0 0 1 0 1 0
## 510 0 0 1 0 1 1
## 511 0 0 1 0 1 1
## 512 0 0 1 0 1 0
## 513 1 0 0 0 1 1
## 514 1 0 0 1 0 1
## 515 0 0 1 0 1 0
## 516 1 0 0 0 1 0
## 517 0 1 0 1 0 1
## 518 0 0 1 0 1 0
## 519 0 1 0 1 0 1
## 520 0 0 1 0 1 0
## 521 1 0 0 1 0 1
## 522 0 0 1 0 1 0
## 523 0 0 1 0 1 0
## 524 1 0 0 1 0 1
## 525 0 0 1 0 1 0
## 526 0 0 1 0 1 0
## 527 0 1 0 1 0 1
## 528 1 0 0 0 1 0
## 529 0 0 1 0 1 0
## 530 0 1 0 0 1 0
## 531 0 1 0 1 0 1
## 532 0 0 1 0 1 0
## 533 0 0 1 0 1 0
## 534 0 0 1 1 0 1
## 535 0 0 1 1 0 0
## 536 0 1 0 1 0 1
## 537 1 0 0 0 1 0
## 538 1 0 0 1 0 1
## 539 0 0 1 0 1 0
## 540 1 0 0 1 0 1
## 541 1 0 0 1 0 1
## 542 0 0 1 1 0 0
## 543 0 0 1 1 0 0
## 544 0 1 0 0 1 1
## 545 1 0 0 0 1 0
## 546 1 0 0 0 1 0
## 547 0 1 0 1 0 1
## 548 0 1 0 0 1 1
## 549 0 0 1 0 1 0
## 550 0 1 0 0 1 1
## 551 1 0 0 0 1 1
## 552 0 1 0 0 1 0
## 553 0 0 1 0 1 0
## 554 0 0 1 0 1 1
## 555 0 0 1 1 0 1
## 556 1 0 0 0 1 0
## 557 1 0 0 1 0 1
## 558 1 0 0 0 1 0
## 559 1 0 0 1 0 1
## 560 0 0 1 1 0 1
## 561 0 0 1 0 1 0
## 562 0 0 1 0 1 0
## 563 0 1 0 0 1 0
## 564 0 0 1 0 1 0
## 565 0 0 1 1 0 0
## 566 0 0 1 0 1 0
## 567 0 0 1 0 1 0
## 568 0 0 1 1 0 0
## 569 0 0 1 0 1 0
## 570 0 0 1 0 1 1
## 571 0 1 0 0 1 1
## 572 1 0 0 1 0 1
## 573 1 0 0 0 1 1
## 574 0 0 1 1 0 1
## 575 0 0 1 0 1 0
## 576 0 0 1 0 1 0
## 577 0 1 0 1 0 1
## 578 1 0 0 1 0 1
## 579 0 0 1 1 0 0
## 580 0 0 1 0 1 1
## 581 0 1 0 1 0 1
## 582 1 0 0 1 0 1
## 583 0 1 0 0 1 0
## 584 1 0 0 0 1 0
## 585 0 0 1 0 1 0
## 586 1 0 0 1 0 1
## 587 0 1 0 0 1 0
## 588 1 0 0 0 1 1
## 589 0 0 1 0 1 0
## 590 0 0 1 0 1 0
## 591 0 0 1 0 1 0
## 592 1 0 0 1 0 1
## 593 0 0 1 0 1 0
## 594 0 0 1 1 0 0
## 595 0 1 0 0 1 0
## 596 0 0 1 0 1 0
## 597 0 1 0 1 0 1
## 598 0 0 1 0 1 0
## 599 0 0 1 0 1 0
## 600 1 0 0 0 1 1
## 601 0 1 0 1 0 1
## 602 0 0 1 0 1 0
## 603 1 0 0 0 1 0
## 604 0 0 1 0 1 0
## 605 1 0 0 0 1 1
## 606 0 0 1 0 1 0
## 607 0 0 1 0 1 0
## 608 1 0 0 0 1 1
## 609 0 1 0 1 0 1
## 610 1 0 0 1 0 1
## 611 0 0 1 1 0 0
## 612 0 0 1 0 1 0
## 613 0 0 1 1 0 1
## 614 0 0 1 0 1 0
## 615 0 0 1 0 1 0
## 616 0 1 0 1 0 1
## 617 0 0 1 0 1 0
## 618 0 0 1 1 0 0
## 619 0 1 0 1 0 1
## 620 0 1 0 0 1 0
## 621 0 0 1 0 1 0
## 622 1 0 0 0 1 1
## 623 0 0 1 0 1 1
## 624 0 0 1 0 1 0
## 625 0 0 1 0 1 0
## 626 1 0 0 0 1 0
## 627 0 1 0 0 1 0
## 628 1 0 0 1 0 1
## 629 0 0 1 0 1 0
## 630 0 0 1 0 1 0
## 631 1 0 0 0 1 1
## 632 0 0 1 0 1 0
## 633 1 0 0 0 1 1
## 634 1 0 0 0 1 0
## 635 0 0 1 1 0 0
## 636 0 1 0 1 0 1
## 637 0 0 1 0 1 0
## 638 0 1 0 0 1 0
## 639 0 0 1 1 0 0
## 640 0 0 1 0 1 0
## 641 0 0 1 0 1 0
## 642 1 0 0 1 0 1
## 643 0 0 1 1 0 0
## 644 0 0 1 0 1 1
## 645 0 0 1 1 0 1
## 646 1 0 0 0 1 1
## 647 0 0 1 0 1 0
## 648 1 0 0 0 1 1
## 649 0 0 1 0 1 0
## 650 0 0 1 1 0 1
## 651 0 0 1 0 1 0
## 652 0 1 0 1 0 1
## 653 0 0 1 0 1 0
## 654 0 0 1 1 0 1
## 655 0 0 1 1 0 0
## 656 0 1 0 0 1 0
## 657 0 0 1 0 1 0
## 658 0 0 1 1 0 0
## 659 0 1 0 0 1 0
## 660 1 0 0 0 1 0
## 661 1 0 0 0 1 1
## 662 0 0 1 0 1 0
## 663 1 0 0 0 1 0
## 664 0 0 1 0 1 0
## 665 0 0 1 0 1 1
## 666 0 1 0 0 1 0
## 667 0 1 0 0 1 0
## 668 0 0 1 0 1 0
## 669 0 0 1 0 1 0
## 670 1 0 0 1 0 1
## 671 0 1 0 1 0 1
## 672 1 0 0 0 1 0
## 673 0 1 0 0 1 0
## 674 0 1 0 0 1 1
## 675 0 1 0 0 1 0
## 676 0 0 1 0 1 0
## 677 0 0 1 0 1 0
## 678 0 0 1 1 0 1
## 679 0 0 1 1 0 0
## 680 1 0 0 0 1 1
## 681 0 0 1 1 0 0
## 682 1 0 0 0 1 1
## 683 0 0 1 0 1 0
## 684 0 0 1 0 1 0
## 685 0 1 0 0 1 0
## 686 0 1 0 0 1 0
## 687 0 0 1 0 1 0
## 688 0 0 1 0 1 0
## 689 0 0 1 0 1 0
## 690 1 0 0 1 0 1
## 691 1 0 0 0 1 1
## 692 0 0 1 1 0 1
## 693 0 0 1 0 1 1
## 694 0 0 1 0 1 0
## 695 1 0 0 0 1 0
## 696 0 1 0 0 1 0
## 697 0 0 1 0 1 0
## 698 0 0 1 1 0 1
## 699 1 0 0 0 1 0
## 700 0 0 1 0 1 0
## 701 1 0 0 1 0 1
## 702 1 0 0 0 1 1
## 703 0 0 1 1 0 0
## 704 0 0 1 0 1 0
## 705 0 0 1 0 1 0
## 706 0 1 0 0 1 0
## 707 0 1 0 1 0 1
## 708 1 0 0 0 1 1
## 709 1 0 0 1 0 1
## 710 0 0 1 0 1 1
## 711 1 0 0 1 0 1
## 712 1 0 0 0 1 0
## 713 1 0 0 0 1 1
## 714 0 0 1 0 1 0
## 715 0 1 0 0 1 0
## 716 0 0 1 0 1 0
## 717 1 0 0 1 0 1
## 718 0 1 0 1 0 1
## 719 0 0 1 0 1 0
## 720 0 0 1 0 1 0
## 721 0 1 0 1 0 1
## 722 0 0 1 0 1 0
## 723 0 1 0 0 1 0
## 724 0 1 0 0 1 0
## 725 1 0 0 0 1 1
## 726 0 0 1 0 1 0
## 727 0 1 0 1 0 1
## 728 0 0 1 1 0 1
## 729 0 1 0 0 1 0
## 730 0 0 1 1 0 0
## 731 1 0 0 1 0 1
## 732 0 0 1 0 1 0
## 733 0 1 0 0 1 0
## 734 0 1 0 0 1 0
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## 889 0 0 1 1 0 0
## 890 1 0 0 0 1 1
## 891 0 0 1 0 1 04 Quick clean up for the data and preperation for data splitting (cross-validation) - using ohe.Data2
set.seed(1)
train.index<-createDataPartition(ohe.Data2$Survived, p=0.8, list = FALSE)
train.set<-ohe.Data2[train.index, ]
test.set<-ohe.Data2[-train.index, ]
# Stratify the training set into 5 folds
set.seed(1)
folds <- createFolds(y=factor(train.set$Survived), k = 5, list = FALSE)
train.set$fold <- folds5 Quick Linear SVM classification
CV.error<-NULL
for (i in 1:5) {
valid.data <- subset(train.set, fold == 1)
train.data <- subset(train.set, fold != 1)
svmfit<-svm(Survived ~ Pclass1 + Pclass2 + Pclass3 + Sexfemale + Sexmale ,data = train.data, kernel="linear", cost=100 ,scale=FALSE)
svm.y<-valid.data$Survived
svm.predy<-predict(svmfit, valid.data)
ith.test.error<- mean(svm.y!=svm.predy)
CV.error<-c(CV.error,(nrow(valid.data)/nrow(train.set))*ith.test.error)
}
sum(CV.error)## [1] 0.1890756Not bad for 20 mins work.
Optimization of the linear SVM is problematic.
6 Quick Polynomial SVM classification
CV.error<-NULL
for (i in 1:5) {
valid.data <- subset(train.set, fold == i)
train.data <- subset(train.set, fold != i)
svmfit<-svm(Survived ~ Pclass1 + Pclass2 + Pclass3 + Sexfemale + Sexmale ,data = train.data, kernel="polynomial", cost=100 , gamma = 1, degree = 1, scale= FALSE)
svm.y<-valid.data$Survived
svm.predy<-predict(svmfit, valid.data)
ith.test.error<- mean(svm.y!=svm.predy)
CV.error<-c(CV.error,(nrow(valid.data)/nrow(train.set))*ith.test.error)
}
sum(CV.error)## [1] 0.2086835svm_fit_bayes<-function(logCost, Degree){
CV.error<-NULL
for (i in 1:5) {
valid.data <- subset(train.set, fold == i)
train.data <- subset(train.set, fold != i)
svmfit<-svm(Survived ~ Pclass1 + Pclass2 + Pclass3 + Sexfemale + Sexmale, data = train.data, kernel="polynomial", cost=exp(logCost), gamma=1, degree=Degree)
svm.y<-valid.data$Survived
svm.predy<-predict(svmfit, valid.data)
ith.test.error<- mean(svm.y!=svm.predy)
CV.error<-c(CV.error,(nrow(valid.data)/nrow(train.set))*ith.test.error)
}
list(Score=-sum(CV.error), pred=0)
}
set.seed(1)
OPT_Res<- BayesianOptimization(svm_fit_bayes, bounds= list(logCost = c(-5, 20),
Degree = c(1L, 5L)),
init_grid_dt = NULL, init_points = 5,
n_iter = 5, acq = "ucb", kappa =2.576,
eps=0, verbose = TRUE)## elapsed = 0.09 Round = 1 logCost = 1.6377 Degree = 5.0000 Value = -0.2241
## elapsed = 0.09 Round = 2 logCost = 4.3031 Degree = 5.0000 Value = -0.2241
## elapsed = 0.09 Round = 3 logCost = 9.3213 Degree = 4.0000 Value = -0.2241
## elapsed = 0.75 Round = 4 logCost = 17.7052 Degree = 4.0000 Value = -0.2241
## elapsed = 0.08 Round = 5 logCost = 0.0420 Degree = 1.0000 Value = -0.2087
## elapsed = 0.09 Round = 6 logCost = 17.1810 Degree = 2.0000 Value = -0.2241
## elapsed = 0.08 Round = 7 logCost = 6.0544 Degree = 5.0000 Value = -0.2241
## elapsed = 71.84 Round = 8 logCost = 14.1719 Degree = 1.0000 Value = -0.2507
## elapsed = 0.07 Round = 9 logCost = 0.4755 Degree = 1.0000 Value = -0.2087
## elapsed = 0.08 Round = 10 logCost = 0.0063 Degree = 1.0000 Value = -0.2087
##
## Best Parameters Found:
## Round = 5 logCost = 0.0420 Degree = 1.0000 Value = -0.2087I’ve stopped the polynomial being reduced to degree 1.
7 Quick Radial SVM classification
CV.error<-NULL
for (i in 1:5) {
valid.data <- subset(train.set, fold == i)
train.data <- subset(train.set, fold != i)
svmfit<-svm(Survived ~ Pclass1 + Pclass2 + Pclass3 + Sexfemale + Sexmale ,data = train.data, kernel="radial", cost=1538065 , gamma = 0.5, scale= FALSE)
svm.y<-valid.data$Survived
svm.predy<-predict(svmfit, valid.data)
ith.test.error<- mean(svm.y!=svm.predy)
CV.error<-c(CV.error,(nrow(valid.data)/nrow(train.set))*ith.test.error)
}
sum(CV.error)## [1] 0.2240896svm_fit_bayes<-function(logCost, logGamma){
CV.error<-NULL
for (i in 1:5) {
valid.data <- subset(train.set, fold == i)
train.data <- subset(train.set, fold != i)
svmfit<-svm(Survived ~ Pclass1 + Pclass2 + Pclass3 + Sexfemale + Sexmale, data = train.data, kernel="radial", cost=exp(logCost), gamma=exp(logGamma), scale= FALSE)
svm.y<-valid.data$Survived
svm.predy<-predict(svmfit, valid.data)
ith.test.error<- mean(svm.y!=svm.predy)
CV.error<-c(CV.error,(nrow(valid.data)/nrow(train.set))*ith.test.error)
}
list(Score=-sum(CV.error), pred=0)
}
set.seed(1)
OPT_Res<- BayesianOptimization(svm_fit_bayes, bounds= list(logCost = c(-5, 20),
logGamma = c(-9, -0.75)),
init_grid_dt = NULL, init_points = 10,
n_iter = 5, acq = "ucb", kappa =2.576,
eps=0, verbose = TRUE)## elapsed = 0.14 Round = 1 logCost = 1.6377 logGamma = -7.3007 Value = -0.2087
## elapsed = 0.10 Round = 2 logCost = 4.3031 logGamma = -7.5434 Value = -0.2087
## elapsed = 0.09 Round = 3 logCost = 9.3213 logGamma = -3.3321 Value = -0.2241
## elapsed = 0.12 Round = 4 logCost = 17.7052 logGamma = -5.8311 Value = -0.2241
## elapsed = 0.10 Round = 5 logCost = 0.0420 logGamma = -2.6488 Value = -0.2087
## elapsed = 0.09 Round = 6 logCost = 17.4597 logGamma = -4.8940 Value = -0.2241
## elapsed = 0.09 Round = 7 logCost = 18.6169 logGamma = -3.0796 Value = -0.2241
## elapsed = 0.08 Round = 8 logCost = 11.5199 logGamma = -0.8168 Value = -0.2241
## elapsed = 0.11 Round = 9 logCost = 10.7279 logGamma = -5.8647 Value = -0.2241
## elapsed = 0.14 Round = 10 logCost = -3.4553 logGamma = -2.5861 Value = -0.3095
## elapsed = 0.11 Round = 11 logCost = 6.1946 logGamma = -8.2491 Value = -0.2087
## elapsed = 0.11 Round = 12 logCost = 0.7301 logGamma = -5.0042 Value = -0.2087
## elapsed = 0.19 Round = 13 logCost = 14.3672 logGamma = -8.5999 Value = -0.2241
## elapsed = 0.09 Round = 14 logCost = 3.1210 logGamma = -2.9035 Value = -0.2241
## elapsed = 5.35 Round = 15 logCost = 20.0000 logGamma = -9.0000 Value = -0.2927
##
## Best Parameters Found:
## Round = 1 logCost = 1.6377 logGamma = -7.3007 Value = -0.20878 KNN with the 1:150NN
K<-150
holder<-numeric(K)
for(j in 1:K){
CV.error<-NULL
for (i in 1:5) {
valid.data <- subset(train.set, fold == i)
train.data <- subset(train.set, fold != i)
knn.y<-valid.data$Survived
knn.predy<-knn(train = train.data[, c("Pclass1", "Pclass2", "Pclass3", "Sexfemale", "Sexmale")], test = valid.data[, c("Pclass1", "Pclass2", "Pclass3", "Sexfemale", "Sexmale")], cl = train.data$Survived, k = j)
knn.y<-valid.data$Survived
ith.test.error<- mean(knn.y!=knn.predy)
CV.error<-c(CV.error,(nrow(valid.data)/nrow(train.set))*ith.test.error)
}
holder[j]<-sum(CV.error)
}
Holder<-data.frame(holder, seq(1:K))
ggplot(Holder, aes(x=seq.1.K., y=holder)) + geom_point() + xlab("K") + ylab("Error Rate")
Holder[which.min(Holder$holder), ]## holder seq.1.K.
## 82 0.2058824 829 Try trees with the one hot coding.
CV.error<-NULL
for (i in 1:5) {
valid.data <- subset(train.set, fold == i)
train.data <- subset(train.set, fold != i)
treefit<-rpart(Survived ~ Pclass1 + Pclass2 + Pclass3 + Sexfemale + Sexmale, data = train.data, method = "class", control = rpart.control(minsplit = 1, cp = 0.004))
tree.y<-valid.data$Survived
tree.predy<-predict(treefit, newdata = valid.data[, c("Pclass1", "Pclass2", "Pclass3", "Sexfemale", "Sexmale")], type ="class")
ith.test.error<- mean(tree.y!=tree.predy)
CV.error<-c(CV.error,(nrow(valid.data)/nrow(train.set))*ith.test.error)
}
sum(CV.error)## [1] 0.2240896Optimizing the tree is problematic.
10 Get the test error rate for the best method.
svmfit<-svm(Survived ~ Pclass1 + Pclass2 + Pclass3 + Sexfemale + Sexmale ,data = train.set, kernel="linear", cost=100 ,scale=FALSE)
svm.y<-test.set$Survived
svm.predy<-predict(svmfit, test.set)
svmfit##
## Call:
## svm(formula = Survived ~ Pclass1 + Pclass2 + Pclass3 + Sexfemale +
## Sexmale, data = train.set, kernel = "linear", cost = 100,
## scale = FALSE)
##
##
## Parameters:
## SVM-Type: C-classification
## SVM-Kernel: linear
## cost: 100
## gamma: 0.2
##
## Number of Support Vectors: 300 mean(svm.y!=svm.predy)## [1] 0.2316384 table(svm.y, svm.predy)## svm.predy
## svm.y 0 1
## 0 89 20
## 1 21 47 length(svm.y)## [1] 177 svmfit<-svm(Survived ~ Pclass1 + Pclass2 + Pclass3 + Sexfemale + Sexmale, data = train.data, kernel="radial", cost=exp(1.6377), gamma=exp(-7.3007))
svm.y<-test.set$Survived
svm.predy<-predict(svmfit, test.set)
svmfit##
## Call:
## svm(formula = Survived ~ Pclass1 + Pclass2 + Pclass3 + Sexfemale +
## Sexmale, data = train.data, kernel = "radial", cost = exp(1.6377),
## gamma = exp(-7.3007))
##
##
## Parameters:
## SVM-Type: C-classification
## SVM-Kernel: radial
## cost: 5.143326
## gamma: 0.0006750661
##
## Number of Support Vectors: 320 mean(svm.y!=svm.predy)## [1] 0.2316384 table(svm.y, svm.predy)## svm.predy
## svm.y 0 1
## 0 89 20
## 1 21 47 length(svm.y)## [1] 17711 Prep the actual test set that is to be predicted creating a csv. to submit.
test <- read_csv("~/Desktop/Titanic Dataset/test.csv")
test <-data.frame(test)
test<-data.frame(test[,c("Pclass", "Sex", "Age", "SibSp", "Parch", "Ticket", "Fare", "Cabin", "Embarked")])
test$Pclass<-factor(test$Pclass)
test$Sex<-factor(test$Sex)
test$SibSp<-factor(test$SibSp)
test$Parch<-factor(test$Parch)
test$Embarked<-factor(test$Embarked)
sum(is.na(test$Pclass))## [1] 0sum(is.na(test$Sex))## [1] 0summary(test)## Pclass Sex Age SibSp Parch
## 1:107 female:152 Min. : 0.17 0:283 0 :324
## 2: 93 male :266 1st Qu.:21.00 1:110 1 : 52
## 3:218 Median :27.00 2: 14 2 : 33
## Mean :30.27 3: 4 3 : 3
## 3rd Qu.:39.00 4: 4 4 : 2
## Max. :76.00 5: 1 9 : 2
## NA's :86 8: 2 (Other): 2
## Ticket Fare Cabin Embarked
## Length:418 Min. : 0.000 Length:418 C:102
## Class :character 1st Qu.: 7.896 Class :character Q: 46
## Mode :character Median : 14.454 Mode :character S:270
## Mean : 35.627
## 3rd Qu.: 31.500
## Max. :512.329
## NA's :1 Data2<-data.frame(test[, c("Sex", "Pclass")])
# One hot encode the Sex
cat.variable<-subset(Data2, select = -c(Pclass))
ohe.Data3<-data.frame(model.matrix(~Sex-1, cat.variable), Pclass=Data2$Pclass)
ohe.Data3## Sexfemale Sexmale Pclass
## 1 0 1 3
## 2 1 0 3
## 3 0 1 2
## 4 0 1 3
## 5 1 0 3
## 6 0 1 3
## 7 1 0 3
## 8 0 1 2
## 9 1 0 3
## 10 0 1 3
## 11 0 1 3
## 12 0 1 1
## 13 1 0 1
## 14 0 1 2
## 15 1 0 1
## 16 1 0 2
## 17 0 1 2
## 18 0 1 3
## 19 1 0 3
## 20 1 0 3
## 21 0 1 1
## 22 0 1 3
## 23 1 0 1
## 24 0 1 1
## 25 1 0 1
## 26 0 1 3
## 27 1 0 1
## 28 0 1 3
## 29 0 1 1
## 30 0 1 3
## 31 0 1 2
## 32 0 1 2
## 33 1 0 3
## 34 1 0 3
## 35 0 1 1
## 36 0 1 3
## 37 1 0 3
## 38 1 0 3
## 39 0 1 3
## 40 0 1 3
## 41 0 1 3
## 42 0 1 1
## 43 0 1 3
## 44 1 0 2
## 45 1 0 1
## 46 0 1 3
## 47 0 1 1
## 48 0 1 3
## 49 1 0 1
## 50 1 0 3
## 51 0 1 1
## 52 0 1 2
## 53 1 0 2
## 54 1 0 1
## 55 0 1 2
## 56 0 1 3
## 57 0 1 3
## 58 0 1 3
## 59 0 1 3
## 60 1 0 1
## 61 0 1 3
## 62 0 1 2
## 63 0 1 3
## 64 1 0 3
## 65 0 1 1
## 66 1 0 2
## 67 1 0 3
## 68 0 1 1
## 69 0 1 1
## 70 1 0 1
## 71 1 0 3
## 72 0 1 3
## 73 1 0 3
## 74 0 1 1
## 75 1 0 1
## 76 0 1 1
## 77 0 1 3
## 78 1 0 1
## 79 0 1 2
## 80 1 0 3
## 81 0 1 3
## 82 0 1 1
## 83 0 1 1
## 84 0 1 3
## 85 0 1 2
## 86 0 1 3
## 87 1 0 3
## 88 1 0 3
## 89 1 0 3
## 90 0 1 2
## 91 1 0 3
## 92 0 1 3
## 93 1 0 1
## 94 0 1 3
## 95 0 1 1
## 96 0 1 3
## 97 1 0 1
## 98 0 1 3
## 99 1 0 3
## 100 0 1 3
## 101 1 0 1
## 102 0 1 2
## 103 0 1 3
## 104 0 1 3
## 105 1 0 3
## 106 0 1 3
## 107 0 1 3
## 108 0 1 3
## 109 0 1 3
## 110 0 1 2
## 111 0 1 2
## 112 1 0 3
## 113 1 0 1
## 114 1 0 3
## 115 1 0 1
## 116 0 1 3
## 117 0 1 3
## 118 1 0 3
## 119 0 1 1
## 120 1 0 2
## 121 1 0 2
## 122 0 1 3
## 123 1 0 1
## 124 0 1 3
## 125 0 1 3
## 126 1 0 3
## 127 0 1 3
## 128 1 0 3
## 129 0 1 2
## 130 0 1 3
## 131 0 1 3
## 132 0 1 1
## 133 1 0 3
## 134 0 1 3
## 135 0 1 3
## 136 0 1 3
## 137 0 1 3
## 138 0 1 2
## 139 1 0 3
## 140 0 1 3
## 141 1 0 3
## 142 1 0 1
## 143 0 1 1
## 144 0 1 2
## 145 0 1 1
## 146 0 1 3
## 147 0 1 1
## 148 0 1 3
## 149 0 1 1
## 150 0 1 2
## 151 1 0 1
## 152 0 1 3
## 153 0 1 3
## 154 1 0 3
## 155 0 1 3
## 156 0 1 3
## 157 1 0 1
## 158 1 0 3
## 159 0 1 1
## 160 1 0 3
## 161 1 0 3
## 162 0 1 3
## 163 1 0 2
## 164 0 1 3
## 165 0 1 2
## 166 1 0 3
## 167 0 1 1
## 168 0 1 3
## 169 1 0 1
## 170 1 0 3
## 171 0 1 3
## 172 0 1 3
## 173 0 1 3
## 174 0 1 3
## 175 0 1 3
## 176 1 0 2
## 177 1 0 2
## 178 0 1 1
## 179 1 0 2
## 180 1 0 1
## 181 0 1 2
## 182 0 1 1
## 183 1 0 1
## 184 0 1 3
## 185 1 0 1
## 186 0 1 2
## 187 1 0 2
## 188 0 1 3
## 189 1 0 3
## 190 0 1 2
## 191 0 1 2
## 192 0 1 1
## 193 0 1 3
## 194 0 1 2
## 195 0 1 2
## 196 0 1 3
## 197 0 1 1
## 198 1 0 3
## 199 0 1 2
## 200 1 0 3
## 201 1 0 3
## 202 0 1 3
## 203 0 1 1
## 204 1 0 2
## 205 0 1 2
## 206 0 1 1
## 207 1 0 3
## 208 0 1 2
## 209 1 0 1
## 210 0 1 3
## 211 0 1 3
## 212 0 1 3
## 213 0 1 2
## 214 1 0 2
## 215 1 0 3
## 216 0 1 1
## 217 1 0 3
## 218 0 1 1
## 219 1 0 1
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## 223 1 0 2
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## 225 1 0 1
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## 338 0 1 3
## 339 0 1 2
## 340 0 1 3
## 341 0 1 2
## 342 0 1 3
## 343 0 1 3
## 344 1 0 1
## 345 0 1 3
## 346 1 0 3
## 347 0 1 2
## 348 1 0 3
## 349 0 1 2
## 350 1 0 2
## 351 1 0 1
## 352 0 1 2
## 353 0 1 2
## 354 0 1 2
## 355 1 0 3
## 356 0 1 1
## 357 1 0 1
## 358 0 1 3
## 359 0 1 3
## 360 1 0 3
## 361 0 1 3
## 362 1 0 2
## 363 1 0 2
## 364 0 1 3
## 365 1 0 1
## 366 1 0 3
## 367 0 1 3
## 368 1 0 3
## 369 1 0 1
## 370 0 1 2
## 371 0 1 2
## 372 1 0 1
## 373 0 1 1
## 374 0 1 2
## 375 1 0 1
## 376 1 0 1
## 377 1 0 3
## 378 0 1 2
## 379 0 1 1
## 380 0 1 3
## 381 0 1 3
## 382 0 1 3
## 383 1 0 3
## 384 1 0 3
## 385 0 1 2
## 386 1 0 2
## 387 0 1 3
## 388 0 1 2
## 389 0 1 3
## 390 0 1 3
## 391 0 1 1
## 392 1 0 1
## 393 0 1 3
## 394 0 1 2
## 395 0 1 3
## 396 1 0 1
## 397 0 1 3
## 398 1 0 1
## 399 0 1 3
## 400 0 1 3
## 401 1 0 1
## 402 0 1 2
## 403 1 0 1
## 404 0 1 1
## 405 0 1 1
## 406 0 1 2
## 407 0 1 2
## 408 0 1 1
## 409 1 0 3
## 410 1 0 3
## 411 1 0 3
## 412 1 0 1
## 413 1 0 3
## 414 0 1 3
## 415 1 0 1
## 416 0 1 3
## 417 0 1 3
## 418 0 1 3 # One hot encode the Pclass
cat.variable<-subset(ohe.Data3, select = -c(Sexfemale, Sexmale))
ohe.Data4<-data.frame(model.matrix(~Pclass-1, cat.variable), Sexfemale=ohe.Data3$Sexfemale, Sexmale=ohe.Data3$Sexmale)
ohe.Data4## Pclass1 Pclass2 Pclass3 Sexfemale Sexmale
## 1 0 0 1 0 1
## 2 0 0 1 1 0
## 3 0 1 0 0 1
## 4 0 0 1 0 1
## 5 0 0 1 1 0
## 6 0 0 1 0 1
## 7 0 0 1 1 0
## 8 0 1 0 0 1
## 9 0 0 1 1 0
## 10 0 0 1 0 1
## 11 0 0 1 0 1
## 12 1 0 0 0 1
## 13 1 0 0 1 0
## 14 0 1 0 0 1
## 15 1 0 0 1 0
## 16 0 1 0 1 0
## 17 0 1 0 0 1
## 18 0 0 1 0 1
## 19 0 0 1 1 0
## 20 0 0 1 1 0
## 21 1 0 0 0 1
## 22 0 0 1 0 1
## 23 1 0 0 1 0
## 24 1 0 0 0 1
## 25 1 0 0 1 0
## 26 0 0 1 0 1
## 27 1 0 0 1 0
## 28 0 0 1 0 1
## 29 1 0 0 0 1
## 30 0 0 1 0 1
## 31 0 1 0 0 1
## 32 0 1 0 0 1
## 33 0 0 1 1 0
## 34 0 0 1 1 0
## 35 1 0 0 0 1
## 36 0 0 1 0 1
## 37 0 0 1 1 0
## 38 0 0 1 1 0
## 39 0 0 1 0 1
## 40 0 0 1 0 1
## 41 0 0 1 0 1
## 42 1 0 0 0 1
## 43 0 0 1 0 1
## 44 0 1 0 1 0
## 45 1 0 0 1 0
## 46 0 0 1 0 1
## 47 1 0 0 0 1
## 48 0 0 1 0 1
## 49 1 0 0 1 0
## 50 0 0 1 1 0
## 51 1 0 0 0 1
## 52 0 1 0 0 1
## 53 0 1 0 1 0
## 54 1 0 0 1 0
## 55 0 1 0 0 1
## 56 0 0 1 0 1
## 57 0 0 1 0 1
## 58 0 0 1 0 1
## 59 0 0 1 0 1
## 60 1 0 0 1 0
## 61 0 0 1 0 1
## 62 0 1 0 0 1
## 63 0 0 1 0 1
## 64 0 0 1 1 0
## 65 1 0 0 0 1
## 66 0 1 0 1 0
## 67 0 0 1 1 0
## 68 1 0 0 0 1
## 69 1 0 0 0 1
## 70 1 0 0 1 0
## 71 0 0 1 1 0
## 72 0 0 1 0 1
## 73 0 0 1 1 0
## 74 1 0 0 0 1
## 75 1 0 0 1 0
## 76 1 0 0 0 1
## 77 0 0 1 0 1
## 78 1 0 0 1 0
## 79 0 1 0 0 1
## 80 0 0 1 1 0
## 81 0 0 1 0 1
## 82 1 0 0 0 1
## 83 1 0 0 0 1
## 84 0 0 1 0 1
## 85 0 1 0 0 1
## 86 0 0 1 0 1
## 87 0 0 1 1 0
## 88 0 0 1 1 0
## 89 0 0 1 1 0
## 90 0 1 0 0 1
## 91 0 0 1 1 0
## 92 0 0 1 0 1
## 93 1 0 0 1 0
## 94 0 0 1 0 1
## 95 1 0 0 0 1
## 96 0 0 1 0 1
## 97 1 0 0 1 0
## 98 0 0 1 0 1
## 99 0 0 1 1 0
## 100 0 0 1 0 1
## 101 1 0 0 1 0
## 102 0 1 0 0 1
## 103 0 0 1 0 1
## 104 0 0 1 0 1
## 105 0 0 1 1 0
## 106 0 0 1 0 1
## 107 0 0 1 0 1
## 108 0 0 1 0 1
## 109 0 0 1 0 1
## 110 0 1 0 0 1
## 111 0 1 0 0 1
## 112 0 0 1 1 0
## 113 1 0 0 1 0
## 114 0 0 1 1 0
## 115 1 0 0 1 0
## 116 0 0 1 0 1
## 117 0 0 1 0 1
## 118 0 0 1 1 0
## 119 1 0 0 0 1
## 120 0 1 0 1 0
## 121 0 1 0 1 0
## 122 0 0 1 0 1
## 123 1 0 0 1 0
## 124 0 0 1 0 1
## 125 0 0 1 0 1
## 126 0 0 1 1 0
## 127 0 0 1 0 1
## 128 0 0 1 1 0
## 129 0 1 0 0 1
## 130 0 0 1 0 1
## 131 0 0 1 0 1
## 132 1 0 0 0 1
## 133 0 0 1 1 0
## 134 0 0 1 0 1
## 135 0 0 1 0 1
## 136 0 0 1 0 1
## 137 0 0 1 0 1
## 138 0 1 0 0 1
## 139 0 0 1 1 0
## 140 0 0 1 0 1
## 141 0 0 1 1 0
## 142 1 0 0 1 0
## 143 1 0 0 0 1
## 144 0 1 0 0 1
## 145 1 0 0 0 1
## 146 0 0 1 0 1
## 147 1 0 0 0 1
## 148 0 0 1 0 1
## 149 1 0 0 0 1
## 150 0 1 0 0 1
## 151 1 0 0 1 0
## 152 0 0 1 0 1
## 153 0 0 1 0 1
## 154 0 0 1 1 0
## 155 0 0 1 0 1
## 156 0 0 1 0 1
## 157 1 0 0 1 0
## 158 0 0 1 1 0
## 159 1 0 0 0 1
## 160 0 0 1 1 0
## 161 0 0 1 1 0
## 162 0 0 1 0 1
## 163 0 1 0 1 0
## 164 0 0 1 0 1
## 165 0 1 0 0 1
## 166 0 0 1 1 0
## 167 1 0 0 0 1
## 168 0 0 1 0 1
## 169 1 0 0 1 0
## 170 0 0 1 1 0
## 171 0 0 1 0 1
## 172 0 0 1 0 1
## 173 0 0 1 0 1
## 174 0 0 1 0 1
## 175 0 0 1 0 1
## 176 0 1 0 1 0
## 177 0 1 0 1 0
## 178 1 0 0 0 1
## 179 0 1 0 1 0
## 180 1 0 0 1 0
## 181 0 1 0 0 1
## 182 1 0 0 0 1
## 183 1 0 0 1 0
## 184 0 0 1 0 1
## 185 1 0 0 1 0
## 186 0 1 0 0 1
## 187 0 1 0 1 0
## 188 0 0 1 0 1
## 189 0 0 1 1 0
## 190 0 1 0 0 1
## 191 0 1 0 0 1
## 192 1 0 0 0 1
## 193 0 0 1 0 1
## 194 0 1 0 0 1
## 195 0 1 0 0 1
## 196 0 0 1 0 1
## 197 1 0 0 0 1
## 198 0 0 1 1 0
## 199 0 1 0 0 1
## 200 0 0 1 1 0
## 201 0 0 1 1 0
## 202 0 0 1 0 1
## 203 1 0 0 0 1
## 204 0 1 0 1 0
## 205 0 1 0 0 1
## 206 1 0 0 0 1
## 207 0 0 1 1 0
## 208 0 1 0 0 1
## 209 1 0 0 1 0
## 210 0 0 1 0 1
## 211 0 0 1 0 1
## 212 0 0 1 0 1
## 213 0 1 0 0 1
## 214 0 1 0 1 0
## 215 0 0 1 1 0
## 216 1 0 0 0 1
## 217 0 0 1 1 0
## 218 1 0 0 0 1
## 219 1 0 0 1 0
## 220 0 0 1 0 1
## 221 0 1 0 1 0
## 222 0 0 1 0 1
## 223 0 1 0 1 0
## 224 0 0 1 0 1
## 225 1 0 0 1 0
## 226 0 0 1 1 0
## 227 0 0 1 0 1
## 228 0 0 1 1 0
## 229 0 0 1 0 1
## 230 0 1 0 0 1
## 231 0 1 0 0 1
## 232 1 0 0 1 0
## 233 0 0 1 0 1
## 234 0 0 1 0 1
## 235 1 0 0 0 1
## 236 0 0 1 0 1
## 237 1 0 0 0 1
## 238 0 0 1 0 1
## 239 0 1 0 1 0
## 240 1 0 0 1 0
## 241 1 0 0 1 0
## 242 0 1 0 1 0
## 243 1 0 0 0 1
## 244 0 0 1 0 1
## 245 0 0 1 0 1
## 246 1 0 0 0 1
## 247 0 1 0 1 0
## 248 0 1 0 0 1
## 249 0 1 0 1 0
## 250 0 0 1 1 0
## 251 0 1 0 1 0
## 252 0 0 1 0 1
## 253 1 0 0 0 1
## 254 0 0 1 0 1
## 255 0 0 1 0 1
## 256 0 0 1 0 1
## 257 0 0 1 0 1
## 258 0 0 1 0 1
## 259 0 1 0 1 0
## 260 0 0 1 0 1
## 261 0 0 1 0 1
## 262 0 0 1 0 1
## 263 0 1 0 1 0
## 264 0 0 1 1 0
## 265 0 1 0 0 1
## 266 0 0 1 0 1
## 267 1 0 0 0 1
## 268 0 0 1 0 1
## 269 0 0 1 1 0
## 270 0 0 1 0 1
## 271 1 0 0 0 1
## 272 0 0 1 0 1
## 273 1 0 0 1 0
## 274 0 0 1 1 0
## 275 0 0 1 0 1
## 276 0 1 0 1 0
## 277 0 1 0 0 1
## 278 0 1 0 0 1
## 279 0 1 0 0 1
## 280 0 1 0 0 1
## 281 0 0 1 1 0
## 282 0 0 1 0 1
## 283 0 0 1 1 0
## 284 0 0 1 1 0
## 285 0 0 1 1 0
## 286 0 0 1 0 1
## 287 0 0 1 0 1
## 288 1 0 0 0 1
## 289 0 0 1 0 1
## 290 0 0 1 0 1
## 291 1 0 0 0 1
## 292 0 0 1 1 0
## 293 0 0 1 0 1
## 294 1 0 0 0 1
## 295 0 0 1 0 1
## 296 0 0 1 0 1
## 297 0 1 0 1 0
## 298 0 0 1 0 1
## 299 1 0 0 0 1
## 300 0 0 1 0 1
## 301 0 0 1 0 1
## 302 0 1 0 0 1
## 303 0 1 0 0 1
## 304 0 0 1 0 1
## 305 0 0 1 1 0
## 306 1 0 0 1 0
## 307 1 0 0 0 1
## 308 0 0 1 0 1
## 309 1 0 0 0 1
## 310 0 0 1 1 0
## 311 0 0 1 0 1
## 312 0 0 1 0 1
## 313 0 0 1 0 1
## 314 0 0 1 1 0
## 315 1 0 0 1 0
## 316 0 0 1 1 0
## 317 1 0 0 0 1
## 318 0 1 0 0 1
## 319 0 0 1 0 1
## 320 0 1 0 0 1
## 321 0 0 1 0 1
## 322 0 0 1 0 1
## 323 0 1 0 0 1
## 324 1 0 0 0 1
## 325 1 0 0 1 0
## 326 0 0 1 0 1
## 327 0 1 0 1 0
## 328 1 0 0 0 1
## 329 0 1 0 0 1
## 330 0 1 0 0 1
## 331 0 1 0 1 0
## 332 1 0 0 0 1
## 333 0 0 1 0 1
## 334 0 0 1 1 0
## 335 0 0 1 0 1
## 336 1 0 0 0 1
## 337 0 1 0 0 1
## 338 0 0 1 0 1
## 339 0 1 0 0 1
## 340 0 0 1 0 1
## 341 0 1 0 0 1
## 342 0 0 1 0 1
## 343 0 0 1 0 1
## 344 1 0 0 1 0
## 345 0 0 1 0 1
## 346 0 0 1 1 0
## 347 0 1 0 0 1
## 348 0 0 1 1 0
## 349 0 1 0 0 1
## 350 0 1 0 1 0
## 351 1 0 0 1 0
## 352 0 1 0 0 1
## 353 0 1 0 0 1
## 354 0 1 0 0 1
## 355 0 0 1 1 0
## 356 1 0 0 0 1
## 357 1 0 0 1 0
## 358 0 0 1 0 1
## 359 0 0 1 0 1
## 360 0 0 1 1 0
## 361 0 0 1 0 1
## 362 0 1 0 1 0
## 363 0 1 0 1 0
## 364 0 0 1 0 1
## 365 1 0 0 1 0
## 366 0 0 1 1 0
## 367 0 0 1 0 1
## 368 0 0 1 1 0
## 369 1 0 0 1 0
## 370 0 1 0 0 1
## 371 0 1 0 0 1
## 372 1 0 0 1 0
## 373 1 0 0 0 1
## 374 0 1 0 0 1
## 375 1 0 0 1 0
## 376 1 0 0 1 0
## 377 0 0 1 1 0
## 378 0 1 0 0 1
## 379 1 0 0 0 1
## 380 0 0 1 0 1
## 381 0 0 1 0 1
## 382 0 0 1 0 1
## 383 0 0 1 1 0
## 384 0 0 1 1 0
## 385 0 1 0 0 1
## 386 0 1 0 1 0
## 387 0 0 1 0 1
## 388 0 1 0 0 1
## 389 0 0 1 0 1
## 390 0 0 1 0 1
## 391 1 0 0 0 1
## 392 1 0 0 1 0
## 393 0 0 1 0 1
## 394 0 1 0 0 1
## 395 0 0 1 0 1
## 396 1 0 0 1 0
## 397 0 0 1 0 1
## 398 1 0 0 1 0
## 399 0 0 1 0 1
## 400 0 0 1 0 1
## 401 1 0 0 1 0
## 402 0 1 0 0 1
## 403 1 0 0 1 0
## 404 1 0 0 0 1
## 405 1 0 0 0 1
## 406 0 1 0 0 1
## 407 0 1 0 0 1
## 408 1 0 0 0 1
## 409 0 0 1 1 0
## 410 0 0 1 1 0
## 411 0 0 1 1 0
## 412 1 0 0 1 0
## 413 0 0 1 1 0
## 414 0 0 1 0 1
## 415 1 0 0 1 0
## 416 0 0 1 0 1
## 417 0 0 1 0 1
## 418 0 0 1 0 112 Using the entire train,0he.Data6, to predict ohe.Data4
svmfit<-svm(Survived ~ Pclass1 + Pclass2 + Pclass3 + Sexfemale + Sexmale ,data = ohe.Data2, kernel="linear", cost=100 ,scale=FALSE)
svm.predy<-predict(svmfit, ohe.Data4)
svmfit##
## Call:
## svm(formula = Survived ~ Pclass1 + Pclass2 + Pclass3 + Sexfemale +
## Sexmale, data = ohe.Data2, kernel = "linear", cost = 100,
## scale = FALSE)
##
##
## Parameters:
## SVM-Type: C-classification
## SVM-Kernel: linear
## cost: 100
## gamma: 0.2
##
## Number of Support Vectors: 38513 write the csv file
submission<-data.frame(PassengerId = seq(892, 1309), Survived = svm.predy)
submission## PassengerId Survived
## 1 892 0
## 2 893 1
## 3 894 0
## 4 895 0
## 5 896 1
## 6 897 0
## 7 898 1
## 8 899 0
## 9 900 1
## 10 901 0
## 11 902 0
## 12 903 0
## 13 904 1
## 14 905 0
## 15 906 1
## 16 907 1
## 17 908 0
## 18 909 0
## 19 910 1
## 20 911 1
## 21 912 0
## 22 913 0
## 23 914 1
## 24 915 0
## 25 916 1
## 26 917 0
## 27 918 1
## 28 919 0
## 29 920 0
## 30 921 0
## 31 922 0
## 32 923 0
## 33 924 1
## 34 925 1
## 35 926 0
## 36 927 0
## 37 928 1
## 38 929 1
## 39 930 0
## 40 931 0
## 41 932 0
## 42 933 0
## 43 934 0
## 44 935 1
## 45 936 1
## 46 937 0
## 47 938 0
## 48 939 0
## 49 940 1
## 50 941 1
## 51 942 0
## 52 943 0
## 53 944 1
## 54 945 1
## 55 946 0
## 56 947 0
## 57 948 0
## 58 949 0
## 59 950 0
## 60 951 1
## 61 952 0
## 62 953 0
## 63 954 0
## 64 955 1
## 65 956 0
## 66 957 1
## 67 958 1
## 68 959 0
## 69 960 0
## 70 961 1
## 71 962 1
## 72 963 0
## 73 964 1
## 74 965 0
## 75 966 1
## 76 967 0
## 77 968 0
## 78 969 1
## 79 970 0
## 80 971 1
## 81 972 0
## 82 973 0
## 83 974 0
## 84 975 0
## 85 976 0
## 86 977 0
## 87 978 1
## 88 979 1
## 89 980 1
## 90 981 0
## 91 982 1
## 92 983 0
## 93 984 1
## 94 985 0
## 95 986 0
## 96 987 0
## 97 988 1
## 98 989 0
## 99 990 1
## 100 991 0
## 101 992 1
## 102 993 0
## 103 994 0
## 104 995 0
## 105 996 1
## 106 997 0
## 107 998 0
## 108 999 0
## 109 1000 0
## 110 1001 0
## 111 1002 0
## 112 1003 1
## 113 1004 1
## 114 1005 1
## 115 1006 1
## 116 1007 0
## 117 1008 0
## 118 1009 1
## 119 1010 0
## 120 1011 1
## 121 1012 1
## 122 1013 0
## 123 1014 1
## 124 1015 0
## 125 1016 0
## 126 1017 1
## 127 1018 0
## 128 1019 1
## 129 1020 0
## 130 1021 0
## 131 1022 0
## 132 1023 0
## 133 1024 1
## 134 1025 0
## 135 1026 0
## 136 1027 0
## 137 1028 0
## 138 1029 0
## 139 1030 1
## 140 1031 0
## 141 1032 1
## 142 1033 1
## 143 1034 0
## 144 1035 0
## 145 1036 0
## 146 1037 0
## 147 1038 0
## 148 1039 0
## 149 1040 0
## 150 1041 0
## 151 1042 1
## 152 1043 0
## 153 1044 0
## 154 1045 1
## 155 1046 0
## 156 1047 0
## 157 1048 1
## 158 1049 1
## 159 1050 0
## 160 1051 1
## 161 1052 1
## 162 1053 0
## 163 1054 1
## 164 1055 0
## 165 1056 0
## 166 1057 1
## 167 1058 0
## 168 1059 0
## 169 1060 1
## 170 1061 1
## 171 1062 0
## 172 1063 0
## 173 1064 0
## 174 1065 0
## 175 1066 0
## 176 1067 1
## 177 1068 1
## 178 1069 0
## 179 1070 1
## 180 1071 1
## 181 1072 0
## 182 1073 0
## 183 1074 1
## 184 1075 0
## 185 1076 1
## 186 1077 0
## 187 1078 1
## 188 1079 0
## 189 1080 1
## 190 1081 0
## 191 1082 0
## 192 1083 0
## 193 1084 0
## 194 1085 0
## 195 1086 0
## 196 1087 0
## 197 1088 0
## 198 1089 1
## 199 1090 0
## 200 1091 1
## 201 1092 1
## 202 1093 0
## 203 1094 0
## 204 1095 1
## 205 1096 0
## 206 1097 0
## 207 1098 1
## 208 1099 0
## 209 1100 1
## 210 1101 0
## 211 1102 0
## 212 1103 0
## 213 1104 0
## 214 1105 1
## 215 1106 1
## 216 1107 0
## 217 1108 1
## 218 1109 0
## 219 1110 1
## 220 1111 0
## 221 1112 1
## 222 1113 0
## 223 1114 1
## 224 1115 0
## 225 1116 1
## 226 1117 1
## 227 1118 0
## 228 1119 1
## 229 1120 0
## 230 1121 0
## 231 1122 0
## 232 1123 1
## 233 1124 0
## 234 1125 0
## 235 1126 0
## 236 1127 0
## 237 1128 0
## 238 1129 0
## 239 1130 1
## 240 1131 1
## 241 1132 1
## 242 1133 1
## 243 1134 0
## 244 1135 0
## 245 1136 0
## 246 1137 0
## 247 1138 1
## 248 1139 0
## 249 1140 1
## 250 1141 1
## 251 1142 1
## 252 1143 0
## 253 1144 0
## 254 1145 0
## 255 1146 0
## 256 1147 0
## 257 1148 0
## 258 1149 0
## 259 1150 1
## 260 1151 0
## 261 1152 0
## 262 1153 0
## 263 1154 1
## 264 1155 1
## 265 1156 0
## 266 1157 0
## 267 1158 0
## 268 1159 0
## 269 1160 1
## 270 1161 0
## 271 1162 0
## 272 1163 0
## 273 1164 1
## 274 1165 1
## 275 1166 0
## 276 1167 1
## 277 1168 0
## 278 1169 0
## 279 1170 0
## 280 1171 0
## 281 1172 1
## 282 1173 0
## 283 1174 1
## 284 1175 1
## 285 1176 1
## 286 1177 0
## 287 1178 0
## 288 1179 0
## 289 1180 0
## 290 1181 0
## 291 1182 0
## 292 1183 1
## 293 1184 0
## 294 1185 0
## 295 1186 0
## 296 1187 0
## 297 1188 1
## 298 1189 0
## 299 1190 0
## 300 1191 0
## 301 1192 0
## 302 1193 0
## 303 1194 0
## 304 1195 0
## 305 1196 1
## 306 1197 1
## 307 1198 0
## 308 1199 0
## 309 1200 0
## 310 1201 1
## 311 1202 0
## 312 1203 0
## 313 1204 0
## 314 1205 1
## 315 1206 1
## 316 1207 1
## 317 1208 0
## 318 1209 0
## 319 1210 0
## 320 1211 0
## 321 1212 0
## 322 1213 0
## 323 1214 0
## 324 1215 0
## 325 1216 1
## 326 1217 0
## 327 1218 1
## 328 1219 0
## 329 1220 0
## 330 1221 0
## 331 1222 1
## 332 1223 0
## 333 1224 0
## 334 1225 1
## 335 1226 0
## 336 1227 0
## 337 1228 0
## 338 1229 0
## 339 1230 0
## 340 1231 0
## 341 1232 0
## 342 1233 0
## 343 1234 0
## 344 1235 1
## 345 1236 0
## 346 1237 1
## 347 1238 0
## 348 1239 1
## 349 1240 0
## 350 1241 1
## 351 1242 1
## 352 1243 0
## 353 1244 0
## 354 1245 0
## 355 1246 1
## 356 1247 0
## 357 1248 1
## 358 1249 0
## 359 1250 0
## 360 1251 1
## 361 1252 0
## 362 1253 1
## 363 1254 1
## 364 1255 0
## 365 1256 1
## 366 1257 1
## 367 1258 0
## 368 1259 1
## 369 1260 1
## 370 1261 0
## 371 1262 0
## 372 1263 1
## 373 1264 0
## 374 1265 0
## 375 1266 1
## 376 1267 1
## 377 1268 1
## 378 1269 0
## 379 1270 0
## 380 1271 0
## 381 1272 0
## 382 1273 0
## 383 1274 1
## 384 1275 1
## 385 1276 0
## 386 1277 1
## 387 1278 0
## 388 1279 0
## 389 1280 0
## 390 1281 0
## 391 1282 0
## 392 1283 1
## 393 1284 0
## 394 1285 0
## 395 1286 0
## 396 1287 1
## 397 1288 0
## 398 1289 1
## 399 1290 0
## 400 1291 0
## 401 1292 1
## 402 1293 0
## 403 1294 1
## 404 1295 0
## 405 1296 0
## 406 1297 0
## 407 1298 0
## 408 1299 0
## 409 1300 1
## 410 1301 1
## 411 1302 1
## 412 1303 1
## 413 1304 1
## 414 1305 0
## 415 1306 1
## 416 1307 0
## 417 1308 0
## 418 1309 0