# Gerekli kütüphaneleri yükleme
library(readr)
## Warning: package 'readr' was built under R version 4.3.3
library(dplyr)
## Warning: package 'dplyr' was built under R version 4.3.3
##
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
##
## filter, lag
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union
library(corrplot)
## Warning: package 'corrplot' was built under R version 4.3.3
## corrplot 0.92 loaded
library(caTools)
## Warning: package 'caTools' was built under R version 4.3.3
train <- read_csv("train_c.csv")
## Rows: 8693 Columns: 16
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (5): PassengerId, HomePlanet, Destination, deck, side
## dbl (8): Age, RoomService, FoodCourt, ShoppingMall, Spa, VRDeck, withgroup, ...
## lgl (3): CryoSleep, VIP, Transported
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
test <- read_csv("test_c.csv")
## Rows: 4277 Columns: 15
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (5): PassengerId, HomePlanet, Destination, deck, side
## dbl (8): Age, RoomService, FoodCourt, ShoppingMall, Spa, VRDeck, withgroup, ...
## lgl (2): CryoSleep, VIP
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
library(rmarkdown)
## Warning: package 'rmarkdown' was built under R version 4.3.3
paged_table(train)
paged_table(test)
train$HomePlanet <- as.factor(train$HomePlanet)
train$Destination <- as.factor(train$Destination)
train$deck <- as.factor(train$deck)
train$side <- as.factor(train$side)
test$HomePlanet <- as.factor(test$HomePlanet)
test$Destination <- as.factor(test$Destination)
test$deck <- as.factor(test$deck)
test$side <- as.factor(test$side)
D <- train[,2:16] %>% mutate(across(everything(), ~as.integer(.)))
kor <- cor(D)
library(corrplot)
corrplot.mixed(kor)
model <- lm(Transported ~ ., data = train[, 2:16])
summary(model)
##
## Call:
## lm(formula = Transported ~ ., data = train[, 2:16])
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.42529 -0.31010 -0.02972 0.28890 1.79353
##
## Coefficients: (1 not defined because of singularities)
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.015e-01 4.015e-02 7.509 6.55e-14 ***
## HomePlanetEuropa 2.255e-01 2.866e-02 7.869 4.02e-15 ***
## HomePlanetMars 9.879e-02 1.478e-02 6.684 2.46e-11 ***
## CryoSleepTRUE 3.791e-01 1.154e-02 32.862 < 2e-16 ***
## DestinationPSO J318.5-22 -4.128e-02 1.807e-02 -2.285 0.022343 *
## DestinationTRAPPIST-1e -4.695e-02 1.131e-02 -4.150 3.35e-05 ***
## Age -2.170e-03 3.201e-04 -6.781 1.28e-11 ***
## VIPTRUE -3.835e-02 2.974e-02 -1.290 0.197237
## RoomService -1.179e-04 7.055e-06 -16.713 < 2e-16 ***
## FoodCourt 4.263e-05 3.055e-06 13.953 < 2e-16 ***
## ShoppingMall 7.946e-05 7.457e-06 10.655 < 2e-16 ***
## Spa -8.693e-05 4.111e-06 -21.144 < 2e-16 ***
## VRDeck -8.289e-05 4.115e-06 -20.144 < 2e-16 ***
## withgroup 2.030e-02 9.286e-03 2.186 0.028819 *
## deckB 1.109e-01 2.874e-02 3.858 0.000115 ***
## deckC 1.491e-01 2.900e-02 5.142 2.78e-07 ***
## deckD 5.528e-02 3.472e-02 1.592 0.111351
## deckE 1.614e-02 3.611e-02 0.447 0.655048
## deckF 1.129e-01 3.702e-02 3.051 0.002291 **
## deckG 6.925e-02 3.861e-02 1.793 0.072943 .
## deckT 6.696e-02 1.815e-01 0.369 0.712284
## sideS 8.501e-02 8.630e-03 9.851 < 2e-16 ***
## expense NA NA NA NA
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.4012 on 8671 degrees of freedom
## Multiple R-squared: 0.3576, Adjusted R-squared: 0.3561
## F-statistic: 229.9 on 21 and 8671 DF, p-value: < 2.2e-16
# Veriyi eğitim ve test seti olarak ayırma
set.seed(123)
split = sample.split(train$Transported, SplitRatio = 0.75)
train_train = subset(train, split == TRUE)
train_test = subset(train, split == FALSE)
# Model eğitimi
reg <- lm(Transported ~ ., data = train_train[, -c(1)])
summary(reg)
##
## Call:
## lm(formula = Transported ~ ., data = train_train[, -c(1)])
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.50135 -0.31246 -0.02948 0.29157 1.77270
##
## Coefficients: (1 not defined because of singularities)
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.358e-01 4.638e-02 7.241 4.95e-13 ***
## HomePlanetEuropa 2.084e-01 3.341e-02 6.239 4.69e-10 ***
## HomePlanetMars 8.879e-02 1.703e-02 5.212 1.92e-07 ***
## CryoSleepTRUE 3.754e-01 1.329e-02 28.237 < 2e-16 ***
## DestinationPSO J318.5-22 -5.815e-02 2.081e-02 -2.795 0.00521 **
## DestinationTRAPPIST-1e -5.058e-02 1.307e-02 -3.870 0.00011 ***
## Age -2.333e-03 3.723e-04 -6.266 3.95e-10 ***
## VIPTRUE -1.722e-02 3.384e-02 -0.509 0.61083
## RoomService -1.177e-04 7.788e-06 -15.113 < 2e-16 ***
## FoodCourt 3.882e-05 3.468e-06 11.194 < 2e-16 ***
## ShoppingMall 8.269e-05 8.413e-06 9.829 < 2e-16 ***
## Spa -8.583e-05 4.707e-06 -18.232 < 2e-16 ***
## VRDeck -8.224e-05 4.775e-06 -17.221 < 2e-16 ***
## withgroup 1.634e-02 1.075e-02 1.520 0.12866
## deckB 1.050e-01 3.289e-02 3.193 0.00142 **
## deckC 1.448e-01 3.317e-02 4.365 1.29e-05 ***
## deckD 3.583e-02 3.961e-02 0.905 0.36576
## deckE -1.742e-02 4.196e-02 -0.415 0.67797
## deckF 9.287e-02 4.276e-02 2.172 0.02991 *
## deckG 5.093e-02 4.463e-02 1.141 0.25379
## deckT 5.756e-02 1.822e-01 0.316 0.75208
## sideS 8.875e-02 9.981e-03 8.892 < 2e-16 ***
## expense NA NA NA NA
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.4012 on 6498 degrees of freedom
## Multiple R-squared: 0.3582, Adjusted R-squared: 0.3561
## F-statistic: 172.7 on 21 and 6498 DF, p-value: < 2.2e-16
# Tahmin yapma
reg_tahmin = predict(reg, newdata = train_test[, -c(1,12)])
reg_transported_tahmin <- ifelse(reg_tahmin > 0.5, 1, 0)
# Gerçek değerlerin hazırlanması
transported_gercek <- ifelse(train_test$Transported == TRUE, 1, 0)
cm = table(transported_gercek, reg_transported_tahmin)
# Confusion matrix ve doğruluk hesaplama
cm
## reg_transported_tahmin
## transported_gercek 0 1
## 0 903 176
## 1 323 771
accuracy <- (cm[1,1] + cm[2,2]) / sum(cm)
accuracy
## [1] 0.7703636
reg_tahmin = predict(reg, newdata = train_train[, -c(1,12)])
reg_transported_tahmin <- ifelse(reg_tahmin > 0.5, 1, 0)
transported_gercek <- ifelse(train_train[12] == TRUE, 1, 0)
cm = table(transported_gercek, reg_transported_tahmin)
cm
## reg_transported_tahmin
## transported_gercek 0 1
## 0 2681 555
## 1 949 2335
accuracy <- (cm[1,1] + cm[2,2]) / sum(cm)
accuracy
## [1] 0.7693252
reg_tahmin_bd = predict(model, newdata = test[, -c(1)])
reg_transported_test_tahmin <- ifelse(reg_tahmin_bd > 0.5, TRUE, FALSE)
Transported <- as.character(reg_transported_test_tahmin)
PassengerId <- test$PassengerId
Transported <- as.vector(Transported)
submission_reg <- cbind(PassengerId, Transported)
submission_reg <- as.data.frame(submission_reg)
library(stringr)
## Warning: package 'stringr' was built under R version 4.3.3
submission_reg$Transported <- str_to_title(submission_reg$Transported)
write.csv(submission_reg, "sinif_tahmini.csv", row.names = FALSE, quote = FALSE)
train_log <- train %>%
mutate_at(c(5, 7:11, 16), ~ log(1 + .))
test_log <- test %>%
mutate_at(c(5, 7:11, 15), ~ log(1 + .))
modellog <- lm(Transported ~ 1 + HomePlanet + Destination + deck + side + CryoSleep + Age + RoomService + FoodCourt + ShoppingMall + Spa , data = train_log)
reg_tahmin_log = predict(modellog, newdata = test_log[, -c(1)])
reg_transported_test_tahmin_log <- ifelse(reg_tahmin_log > 0.5, TRUE, FALSE)
Transported <- as.character(reg_transported_test_tahmin_log)
PassengerId <- test$PassengerId
Transported <- as.vector(Transported)
submission_reg_log <- cbind(PassengerId, Transported)
submission_reg_log <- as.data.frame(submission_reg_log)
submission_reg_log$Transported <- str_to_title(submission_reg_log$Transported)
write.csv(submission_reg, "submission_reg_log.csv", row.names = FALSE, quote = FALSE)
logistic = glm(formula = Transported ~ .,
family = binomial,
data = train_train[, -c(1)])
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
summary(logistic)
##
## Call:
## glm(formula = Transported ~ ., family = binomial, data = train_train[,
## -c(1)])
##
## Coefficients: (1 not defined because of singularities)
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -4.855e-01 3.587e-01 -1.353 0.175933
## HomePlanetEuropa 1.570e+00 2.653e-01 5.918 3.26e-09 ***
## HomePlanetMars 5.361e-01 1.105e-01 4.849 1.24e-06 ***
## CryoSleepTRUE 1.276e+00 9.197e-02 13.873 < 2e-16 ***
## DestinationPSO J318.5-22 -4.923e-01 1.299e-01 -3.790 0.000151 ***
## DestinationTRAPPIST-1e -4.611e-01 9.418e-02 -4.896 9.78e-07 ***
## Age -8.311e-03 2.460e-03 -3.379 0.000728 ***
## VIPTRUE -1.679e-01 2.924e-01 -0.574 0.565911
## RoomService -1.738e-03 1.135e-04 -15.313 < 2e-16 ***
## FoodCourt 4.547e-04 4.453e-05 10.211 < 2e-16 ***
## ShoppingMall 5.275e-04 7.562e-05 6.976 3.04e-12 ***
## Spa -1.968e-03 1.166e-04 -16.878 < 2e-16 ***
## VRDeck -1.896e-03 1.156e-04 -16.400 < 2e-16 ***
## withgroup 1.267e-01 7.183e-02 1.764 0.077800 .
## deckB 1.126e+00 2.899e-01 3.884 0.000103 ***
## deckC 2.454e+00 3.315e-01 7.404 1.33e-13 ***
## deckD 6.903e-01 3.245e-01 2.127 0.033389 *
## deckE 9.830e-02 3.352e-01 0.293 0.769339
## deckF 7.773e-01 3.397e-01 2.288 0.022127 *
## deckG 3.810e-01 3.490e-01 1.092 0.274904
## deckT -1.275e-01 1.869e+00 -0.068 0.945583
## sideS 5.984e-01 6.663e-02 8.981 < 2e-16 ***
## expense NA NA NA NA
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 9038.3 on 6519 degrees of freedom
## Residual deviance: 5601.5 on 6498 degrees of freedom
## AIC: 5645.5
##
## Number of Fisher Scoring iterations: 7
log_tahmin = predict(logistic, newdata = train_test[, -c(1,12)])
head(log_tahmin)
## 1 2 3 4 5 6
## -1.1063121 -11.0565547 0.9680020 -1.6266855 -0.4167855 -1.7011268
log_transported_tahmin <- ifelse(log_tahmin >0.5, 1, 0)
head(log_transported_tahmin)
## 1 2 3 4 5 6
## 0 0 1 0 0 0
transported_gercek <- ifelse(train_test[12] == TRUE, 1, 0)
library(tidymodels)
## Warning: package 'tidymodels' was built under R version 4.3.3
## ── Attaching packages ────────────────────────────────────── tidymodels 1.2.0 ──
## ✔ broom 1.0.5 ✔ rsample 1.2.1
## ✔ dials 1.2.1 ✔ tibble 3.2.1
## ✔ ggplot2 3.5.0 ✔ tidyr 1.3.1
## ✔ infer 1.0.7 ✔ tune 1.2.1
## ✔ modeldata 1.3.0 ✔ workflows 1.1.4
## ✔ parsnip 1.2.1 ✔ workflowsets 1.1.0
## ✔ purrr 1.0.2 ✔ yardstick 1.3.1
## ✔ recipes 1.0.10
## Warning: package 'broom' was built under R version 4.3.3
## Warning: package 'dials' was built under R version 4.3.3
## Warning: package 'scales' was built under R version 4.3.3
## Warning: package 'ggplot2' was built under R version 4.3.3
## Warning: package 'infer' was built under R version 4.3.3
## Warning: package 'modeldata' was built under R version 4.3.3
## Warning: package 'parsnip' was built under R version 4.3.3
## Warning: package 'purrr' was built under R version 4.3.3
## Warning: package 'recipes' was built under R version 4.3.3
## Warning: package 'rsample' was built under R version 4.3.3
## Warning: package 'tibble' was built under R version 4.3.3
## Warning: package 'tidyr' was built under R version 4.3.3
## Warning: package 'tune' was built under R version 4.3.3
## Warning: package 'workflows' was built under R version 4.3.3
## Warning: package 'workflowsets' was built under R version 4.3.3
## Warning: package 'yardstick' was built under R version 4.3.3
## ── Conflicts ───────────────────────────────────────── tidymodels_conflicts() ──
## ✖ purrr::discard() masks scales::discard()
## ✖ dplyr::filter() masks stats::filter()
## ✖ recipes::fixed() masks stringr::fixed()
## ✖ dplyr::lag() masks stats::lag()
## ✖ yardstick::spec() masks readr::spec()
## ✖ recipes::step() masks stats::step()
## • Learn how to get started at https://www.tidymodels.org/start/
result = data.frame(cbind(transported_gercek, log_transported_tahmin))
result$Transported <- as.factor(result$Transported)
result$log_transported_tahmin <- as.factor(result$log_transported_tahmin)
result$Transported <- as.factor(result$Transported)
result$log_transported_tahmin <- as.factor(result$log_transported_tahmin)
library(caret)
## Warning: package 'caret' was built under R version 4.3.3
## Zorunlu paket yükleniyor: lattice
##
## Attaching package: 'caret'
## The following objects are masked from 'package:yardstick':
##
## precision, recall, sensitivity, specificity
## The following object is masked from 'package:purrr':
##
## lift
cm = table(transported_gercek, log_transported_tahmin)
cm
## log_transported_tahmin
## transported_gercek 0 1
## 0 918 161
## 1 308 786
accuracy <- (cm[1,1] + cm[2,2]) / sum(cm)
accuracy
## [1] 0.7841694
confusionMatrix(as.factor(transported_gercek), as.factor(log_transported_tahmin))
## Confusion Matrix and Statistics
##
## Reference
## Prediction 0 1
## 0 918 161
## 1 308 786
##
## Accuracy : 0.7842
## 95% CI : (0.7663, 0.8013)
## No Information Rate : 0.5642
## P-Value [Acc > NIR] : < 2.2e-16
##
## Kappa : 0.5687
##
## Mcnemar's Test P-Value : 1.566e-11
##
## Sensitivity : 0.7488
## Specificity : 0.8300
## Pos Pred Value : 0.8508
## Neg Pred Value : 0.7185
## Prevalence : 0.5642
## Detection Rate : 0.4225
## Detection Prevalence : 0.4965
## Balanced Accuracy : 0.7894
##
## 'Positive' Class : 0
##
logistic_bd = glm(formula = Transported ~ .,
family = binomial,
data = train[, -c(1)])
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
log_tahmin_bd = predict(logistic_bd, newdata = test[, -c(1)])
log_transported_test_tahmin <- ifelse(log_tahmin_bd > 0.5, TRUE, FALSE)
Transported <- as.character(log_transported_test_tahmin)
PassengerId <- test$PassengerId
Transported <- as.vector(Transported)
submission_logistic <- cbind(PassengerId, Transported)
submission_logistic <- as.data.frame(submission_logistic)
submission_logistic$Transported <- str_to_title(submission_logistic$Transported)
write.csv(submission_logistic, "submission_log.csv", row.names = FALSE, quote = FALSE)
logistic_bd = glm(formula = Transported ~ .,
family = binomial,
data = train_log[, -c(1)])
log_tahmin_bd = predict(logistic_bd, newdata = test_log[, -c(1)])
log_transported_test_tahmin <- ifelse(log_tahmin_bd > 0.5, TRUE, FALSE)
Transported <- as.character(log_transported_test_tahmin)
PassengerId <- test$PassengerId
Transported <- as.vector(Transported)
submission_llogistic <- cbind(PassengerId, Transported)
submission_llogistic <- as.data.frame(submission_llogistic)
submission_llogistic$Transported <- str_to_title(submission_llogistic$Transported)
write.csv(submission_logistic, "submission_llog.csv", row.names = FALSE, quote = FALSE)
library(e1071)
## Warning: package 'e1071' was built under R version 4.3.3
##
## Attaching package: 'e1071'
## The following object is masked from 'package:tune':
##
## tune
## The following object is masked from 'package:rsample':
##
## permutations
## The following object is masked from 'package:parsnip':
##
## tune
fit_nb <- naiveBayes(Transported ~ ., data = train_train[, -1])
fit_nb
##
## Naive Bayes Classifier for Discrete Predictors
##
## Call:
## naiveBayes.default(x = X, y = Y, laplace = laplace)
##
## A-priori probabilities:
## Y
## FALSE TRUE
## 0.496319 0.503681
##
## Conditional probabilities:
## HomePlanet
## Y Earth Europa Mars
## FALSE 0.6220643 0.1755253 0.2024104
## TRUE 0.4604141 0.3306943 0.2088916
##
## CryoSleep
## Y FALSE TRUE
## FALSE 0.8702101 0.1297899
## TRUE 0.4336175 0.5663825
##
## Destination
## Y 55 Cancri e PSO J318.5-22 TRAPPIST-1e
## FALSE 0.16069221 0.09363412 0.74567367
## TRUE 0.24969549 0.09043849 0.65986602
##
## Age
## Y [,1] [,2]
## FALSE 30.01119 13.45225
## TRUE 27.85293 14.87588
##
## VIP
## Y FALSE TRUE
## FALSE 0.97126082 0.02873918
## TRUE 0.98142509 0.01857491
##
## RoomService
## Y [,1] [,2]
## FALSE 402.20365 916.6547
## TRUE 56.93484 246.8105
##
## FoodCourt
## Y [,1] [,2]
## FALSE 396.2923 1258.123
## TRUE 514.8018 1918.620
##
## ShoppingMall
## Y [,1] [,2]
## FALSE 160.2250 432.1103
## TRUE 182.7135 748.5867
##
## Spa
## Y [,1] [,2]
## FALSE 561.16193 1554.2636
## TRUE 61.11571 264.0556
##
## VRDeck
## Y [,1] [,2]
## FALSE 533.98733 1542.8703
## TRUE 69.07186 295.0542
##
## withgroup
## Y [,1] [,2]
## FALSE 0.3952410 0.4889780
## TRUE 0.4960414 0.5000605
##
## deck
## Y A B C D E
## FALSE 0.0296662546 0.0543881335 0.0574783684 0.0661310260 0.1344252163
## TRUE 0.0310596833 0.1385505481 0.1199756395 0.0490255786 0.0676004872
## deck
## Y F G T
## FALSE 0.3637206428 0.2929542645 0.0012360939
## TRUE 0.2828867235 0.3105968331 0.0003045067
##
## side
## Y P S
## FALSE 0.5568603 0.4431397
## TRUE 0.4448843 0.5551157
##
## expense
## Y [,1] [,2]
## FALSE 2053.8702 3224.219
## TRUE 884.6376 2307.962
pred_nb <- predict(fit_nb, newdata = train_test[, -c(1,12)], type = "raw") %>%
data.frame()
head(pred_nb)
## FALSE. TRUE.
## 1 0.22290777 7.770922e-01
## 2 1.00000000 8.657607e-135
## 3 0.02738093 9.726191e-01
## 4 0.59333559 4.066644e-01
## 5 0.02412042 9.758796e-01
## 6 0.38532665 6.146734e-01
Transported_preb_nb = ifelse(pred_nb$TRUE. > 0.5, 1, 0)
head(Transported_preb_nb)
## [1] 1 0 1 0 1 1
Transported_train_test <- ifelse(train_test[12] == TRUE, 1, 0)
head(Transported_train_test)
## Transported
## [1,] 1
## [2,] 0
## [3,] 1
## [4,] 0
## [5,] 0
## [6,] 0
cm = table(Transported_train_test, Transported_preb_nb)
cm
## Transported_preb_nb
## Transported_train_test 0 1
## 0 481 598
## 1 77 1017
accuracy <- (cm[1,1] + cm[2,2]) / sum(cm)
accuracy
## [1] 0.6893695
nb = naiveBayes(Transported ~ ., data = train[, -1])
pred_nb <- predict(nb, newdata = test, type = "raw") %>%
data.frame()
Transported_pred_nb = ifelse(pred_nb$TRUE. > 0.5, TRUE, FALSE)
Transported <- as.character(Transported_pred_nb)
PassengerId <- test$PassengerId
Transported <- as.vector(Transported)
sample_submission <- cbind(PassengerId, Transported)
sample_submission <- as.data.frame(sample_submission)
sample_submission$Transported <- str_to_title(sample_submission$Transported)
write.csv(sample_submission, "sub_nb.csv", row.names = FALSE, quote = FALSE)
library(e1071)
fit_svm <- svm(Transported ~ ., data = train_train[, -1],
type = 'C-classification',
kernel= 'linear')
preds <- predict(fit_svm, newdata = train_test[, -c(1,12)], type = "raw") %>%
data.frame()
head(preds)
## .
## 1 FALSE
## 2 FALSE
## 3 TRUE
## 4 FALSE
## 5 TRUE
## 6 FALSE
Transported_pred_svm = ifelse(preds$. == TRUE, 1, 0)
cm = table(Transported_train_test, Transported_pred_svm)
cm
## Transported_pred_svm
## Transported_train_test 0 1
## 0 831 248
## 1 181 913
accuracy <- (cm[1,1] + cm[2,2]) / sum(cm)
accuracy
## [1] 0.8025771
fit_svm <- svm(Transported ~ ., data = train[, -1],
type = 'C-classification',
kernel= 'linear')
preds <- predict(fit_svm, newdata = test, type = "raw") %>%
data.frame()
Transported_pred_svm = ifelse(preds$. == TRUE, TRUE, FALSE)
Transported <- as.character(Transported_pred_svm)
PassengerId <- test$PassengerId
Transported <- as.vector(Transported)
sample_submission <- cbind(PassengerId, Transported)
sample_submission <- as.data.frame(sample_submission)
sample_submission$Transported <- str_to_title(sample_submission$Transported)
write.csv(sample_submission, "sub_svm.csv", row.names = FALSE, quote = FALSE)
library(rpart)
## Warning: package 'rpart' was built under R version 4.3.3
##
## Attaching package: 'rpart'
## The following object is masked from 'package:dials':
##
## prune
library(rpart.plot)
## Warning: package 'rpart.plot' was built under R version 4.3.3
library(randomForest)
## Warning: package 'randomForest' was built under R version 4.3.3
## randomForest 4.7-1.1
## Type rfNews() to see new features/changes/bug fixes.
##
## Attaching package: 'randomForest'
## The following object is masked from 'package:ggplot2':
##
## margin
## The following object is masked from 'package:dplyr':
##
## combine
library(caret)
fit_forest <- randomForest(Transported ~ ., data = train_train[, -1])
## Warning in randomForest.default(m, y, ...): The response has five or fewer
## unique values. Are you sure you want to do regression?
fit_forest$importance
## IncNodePurity
## HomePlanet 46.919543
## CryoSleep 99.436934
## Destination 25.951083
## Age 91.938247
## VIP 2.525034
## RoomService 128.784644
## FoodCourt 120.807834
## ShoppingMall 97.094302
## Spa 126.281940
## VRDeck 121.810439
## withgroup 12.974556
## deck 92.402752
## side 22.730654
## expense 243.733448
## IncNodePurity
## HomePlanet 48.21166
## CryoSleep 102.47495
## Destination 25.77605
## Age 92.07261
## VIP 2.43084
## RoomService 124.63959
## FoodCourt 117.08030
## ShoppingMall 99.23254
## Spa 126.77091
## VRDeck 119.08002
## withgroup 12.85779
## deck 91.49524
## side 22.59922
## expense 248.44639
varImpPlot(fit_forest)
preds <- predict(fit_forest, newdata = train_test[,- c(1,12)]) %>%
data.frame()
head(preds)
## .
## 1 0.1123084
## 2 0.1135095
## 3 0.8005314
## 4 0.2482880
## 5 0.6827540
## 6 0.1608222
## .
## 1 0.1049532
## 2 0.1112889
## 3 0.8129808
## 4 0.2158803
## 5 0.6832579
## 6 0.1744000
Transported_pred_forest = ifelse(preds$. >0.5, 1, 0)
cm = table(Transported_train_test, Transported_pred_forest)
cm
## Transported_pred_forest
## Transported_train_test 0 1
## 0 822 257
## 1 175 919
## Transported_pred_forest
## Transported_train_test 0 1
## 0 823 256
## 1 175 919
accuracy <- (cm[1,1] + cm[2,2]) / sum(cm)
accuracy
## [1] 0.8011965
## [1] 0.8016567
fit_forest <- randomForest(Transported ~ ., data = train[, -1])
## Warning in randomForest.default(m, y, ...): The response has five or fewer
## unique values. Are you sure you want to do regression?
preds <- predict(fit_forest, newdata = test) %>%
data.frame()
Transported_pred_forest = ifelse(preds$. >0.5, TRUE, FALSE)
Transported <- as.character(Transported_pred_forest)
PassengerId <- test$PassengerId
Transported <- as.vector(Transported)
sample_submission <- cbind(PassengerId, Transported)
sample_submission <- as.data.frame(sample_submission)
sample_submission$Transported <- str_to_title(sample_submission$Transported)
write.csv(sample_submission, "sub_random_for.csv", row.names = FALSE, quote = FALSE)
# Başarı oranlarını tanımla
regresyon_accuracy <- 0.77
lojistik_regresyon_accuracy <- 0.79
naive_bayes_accuracy <- 0.72
svm_accuracy <- 0.79
svm_radial_accuracy <- 0.80
decision_tree_accuracy <- 0.77
random_forest_accuracy <- 0.80
# Veri çerçevesini oluştur
dogruluk_performans <- data.frame(
Modeller = c("Regresyon", "Lojistik Regresyon", "Naive Bayes", "SVM", "SVM Radial","Decision Trees", "Random Forest"),
Dogruluk = c(regresyon_accuracy, lojistik_regresyon_accuracy, naive_bayes_accuracy, svm_accuracy, svm_radial_accuracy,decision_tree_accuracy, random_forest_accuracy)
)
ggplot(data = dogruluk_performans, aes(x = Modeller, y = Dogruluk)) +
geom_bar(stat = "identity", fill = "blue", width = 0.3) +
labs(title = "Farklı Modellerin Doğruluk Karşılaştırması", x = "Modeller", y = "Doğruluk Oranı") +
theme(plot.title = element_text(hjust = 0.5))
# Veri çerçevesini oluştur
dogruluk_performanss <- data.frame(
Modeller = c("Regresyon", "Lojistik Regresyon", "Naive Bayes", "SVM", "SVM Radial","Decision Trees", "Random Forest"),
Dogruluk = c(regresyon_accuracy, lojistik_regresyon_accuracy, naive_bayes_accuracy, svm_accuracy, svm_radial_accuracy, decision_tree_accuracy, random_forest_accuracy)
)
# Nokta grafiği oluştur
ggplot(data = dogruluk_performanss, aes(x = Modeller, y = Dogruluk)) +
geom_point(size = 4, color = "blue") +
labs(title = "Farklı Modellerin Doğruluk Karşılaştırması", x = "Modeller", y = "Doğruluk Oranı") +
theme(plot.title = element_text(hjust = 0.5)) +
geom_text(aes(label=Dogruluk), vjust=-1.5, size=3.5)
