EKONOMETRİ FİNAL PROJESİ

library(rmarkdown)
library(explore)
library(tidyverse)
## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr     1.1.4     ✔ readr     2.1.5
## ✔ forcats   1.0.0     ✔ stringr   1.5.1
## ✔ ggplot2   3.5.1     ✔ tibble    3.2.1
## ✔ lubridate 1.9.3     ✔ tidyr     1.3.1
## ✔ purrr     1.0.2     
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag()    masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors
library(readr)
library(readr)
train_c <- 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.
library(readr)
test_c <- 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(readr)
sample_submission <- read_csv("sample_submission.csv")
## Rows: 4277 Columns: 2
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (1): PassengerId
## lgl (1): 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.
describe_all(train_c)
## # A tibble: 16 × 8
##    variable     type     na na_pct unique   min    mean   max
##    <chr>        <chr> <int>  <dbl>  <int> <dbl>   <dbl> <dbl>
##  1 PassengerId  chr       0      0   8693    NA   NA       NA
##  2 HomePlanet   chr       0      0      3    NA   NA       NA
##  3 CryoSleep    lgl       0      0      2     0    0.35     1
##  4 Destination  chr       0      0      4    NA   NA       NA
##  5 Age          dbl       0      0     90     0   28.8     79
##  6 VIP          lgl       0      0      2     0    0.02     1
##  7 RoomService  dbl       0      0   1273     0  220.   14327
##  8 FoodCourt    dbl       0      0   1507     0  448.   29813
##  9 ShoppingMall dbl       0      0   1115     0  170.   23492
## 10 Spa          dbl       0      0   1327     0  305.   22408
## 11 VRDeck       dbl       0      0   1306     0  298.   24133
## 12 Transported  lgl       0      0      2     0    0.5      1
## 13 withgroup    dbl       0      0      2     0    0.45     1
## 14 deck         chr       0      0      8    NA   NA       NA
## 15 side         chr       0      0      2    NA   NA       NA
## 16 expense      dbl       0      0   2336     0 1441.   35987
describe_all(test_c)
## # A tibble: 15 × 8
##    variable     type     na na_pct unique   min    mean   max
##    <chr>        <chr> <int>  <dbl>  <int> <dbl>   <dbl> <dbl>
##  1 PassengerId  chr       0      0   4277    NA   NA       NA
##  2 HomePlanet   chr       0      0      3    NA   NA       NA
##  3 CryoSleep    lgl       0      0      2     0    0.36     1
##  4 Destination  chr       0      0      4    NA   NA       NA
##  5 Age          dbl       0      0     89     0   28.7     79
##  6 VIP          lgl       0      0      2     0    0.02     1
##  7 RoomService  dbl       0      0    842     0  215.   11567
##  8 FoodCourt    dbl       0      0    902     0  429.   25273
##  9 ShoppingMall dbl       0      0    715     0  173.    8292
## 10 Spa          dbl       0      0    833     0  296.   19844
## 11 VRDeck       dbl       0      0    796     0  305.   22272
## 12 withgroup    dbl       0      0      2     0    0.45     1
## 13 deck         chr       0      0      8    NA   NA       NA
## 14 side         chr       0      0      2    NA   NA       NA
## 15 expense      dbl       0      0   1437     0 1418.   33666

Açıklama

Bu Projeden, Uzay Gemisi Titanic’in uzay-zaman anormalliği ile çarpışması sırasında bir yolcunun alternatif bir boyuta taşınıp taşınmadığını tahmin etmektir. Bu tahminleri yapmanıza yardımcı olmak için, geminin hasar görmüş bilgisayar sisteminden kurtarılan bir dizi kişisel kayıt verilmiştir.

Dosya ve Veri Alanı Açıklamaları train.csv - Eğitim verisi olarak kullanılacak yaklaşık üçte ikisi (~8700) yolcunun kişisel kayıtları.

PassengerId - Her yolcu için benzersiz bir kimlik. Her kimlik, yolcunun seyahat ettiği bir grubu gösteren gggg ile başlar ve pp grup içindeki sırasını temsil eder. Bir gruptaki insanlar genellikle aile üyeleridir, ancak her zaman değil.

HomePlanet - Yolcunun hareket ettiği gezegen, genellikle kalıcı ikamet gezegenleri. CryoSleep - Yolcunun yolculuğun süresi boyunca dondurulmuş animasyona alınıp alınmayacağını gösterir. CryoSleep’te olan yolcular kabinlerine kapatılmıştır.

Cabin - Yolcunun konakladığı kabin numarası. deck/num/side şeklinde alınır, burada side, Port için P veya Starboard için S olabilir.

Destination - Yolcunun ineceği gezegen.

Age - Yolcunun yaşı.

VIP - Yolcunun yolculuk sırasında özel VIP hizmeti için ödeme yapılıp yapılmadığını belirtir. RoomService, FoodCourt, ShoppingMall, Spa, VRDeck - Yolcunun Spaceship Titanic’in birçok lüks olanaklarında ne kadar fatura ödediği.

Name - Yolcunun adı ve soyadı.

Transported - Yolcunun başka bir boyuta taşınıp taşınmadığı. Bu, hedef, tahmin etmeye çalıştığınız sütun.

test.csv - Geri kalan üçte biri (~4300) yolcunun kişisel kayıtları, test verisi olarak kullanılacaktır. Göreviniz, bu setteki yolcular için Transported değerini tahmin etmektir.

sample_submission.csv - Doğru formatta bir sunum dosyası.

PassengerId - Test setindeki her yolcu için kimlik.

Transported - Hedef. Her yolcu için, True veya False değerlerinden birini tahmin edin.

train_c$Homeplanet <- as.factor(train_c$HomePlanet)
train_c$Destination <- as.factor(train_c$Destination)
train_c$deck <- as.factor(train_c$deck)
train_c$side <- as.factor(train_c$side)
test_c$Homeplanet <- as.factor(test_c$HomePlanet)
test_c$Destination <- as.factor(test_c$Destination)
test_c$deck <- as.factor(test_c$deck)
test_c$side <- as.factor(test_c$side)
library(DataExplorer)
create_report(train_c)
## 
## 
## processing file: report.rmd
##   |                                             |                                     |   0%  |                                             |.                                    |   2%                                   |                                             |..                                   |   5% [global_options]                  |                                             |...                                  |   7%                                   |                                             |....                                 |  10% [introduce]                       |                                             |....                                 |  12%                                   |                                             |.....                                |  14% [plot_intro]                      |                                             |......                               |  17%                                   |                                             |.......                              |  19% [data_structure]                  |                                             |........                             |  21%                                   |                                             |.........                            |  24% [missing_profile]                 |                                             |..........                           |  26%                                   |                                             |...........                          |  29% [univariate_distribution_header]  |                                             |...........                          |  31%                                   |                                             |............                         |  33% [plot_histogram]                  |                                             |.............                        |  36%                                   |                             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## output file: C:/Users/USER/Documents/EKONOMETRİ FİNAL PROJESİ/report.knit.md
## "C:/Program Files/RStudio/resources/app/bin/quarto/bin/tools/pandoc" +RTS -K512m -RTS "C:\Users\USER\DOCUME~1\EKONOM~2\REPORT~1.MD" --to html4 --from markdown+autolink_bare_uris+tex_math_single_backslash --output pandoc5b820bb77c8.html --lua-filter "C:\Users\USER\AppData\Local\R\win-library\4.3\rmarkdown\rmarkdown\lua\pagebreak.lua" --lua-filter "C:\Users\USER\AppData\Local\R\win-library\4.3\rmarkdown\rmarkdown\lua\latex-div.lua" --embed-resources --standalone --variable bs3=TRUE --section-divs --table-of-contents --toc-depth 6 --template "C:\Users\USER\AppData\Local\R\win-library\4.3\rmarkdown\rmd\h\default.html" --no-highlight --variable highlightjs=1 --variable theme=yeti --mathjax --variable "mathjax-url=https://mathjax.rstudio.com/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML" --include-in-header "C:\Users\USER\AppData\Local\Temp\Rtmpw7I2W8\rmarkdown-str5b87042fcc.html"
## 
## Output created: report.html
model <- lm(Transported ~ . , data = train_c[, 2:16])
summary(model)
## 
## Call:
## lm(formula = Transported ~ ., data = train_c[, 2:16])
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.42802 -0.30952 -0.03188  0.28870  1.79172 
## 
## Coefficients: (1 not defined because of singularities)
##                            Estimate Std. Error t value Pr(>|t|)    
## (Intercept)               3.147e-01  4.000e-02   7.866 4.09e-15 ***
## HomePlanetEuropa          2.150e-01  2.883e-02   7.457 9.72e-14 ***
## HomePlanetMars            9.617e-02  1.475e-02   6.522 7.33e-11 ***
## CryoSleepTRUE             3.811e-01  1.152e-02  33.082  < 2e-16 ***
## DestinationPSO J318.5-22 -4.449e-02  1.803e-02  -2.468 0.013598 *  
## DestinationTRAPPIST-1e   -5.140e-02  1.133e-02  -4.538 5.76e-06 ***
## DestinationTRAPPIST-le    7.513e-03  3.142e-02   0.239 0.811033    
## Age                      -2.182e-03  3.201e-04  -6.815 1.00e-11 ***
## VIPTRUE                  -3.757e-02  2.974e-02  -1.263 0.206555    
## RoomService              -1.176e-04  7.055e-06 -16.674  < 2e-16 ***
## FoodCourt                 4.281e-05  3.054e-06  14.017  < 2e-16 ***
## ShoppingMall              7.969e-05  7.457e-06  10.686  < 2e-16 ***
## Spa                      -8.675e-05  4.109e-06 -21.110  < 2e-16 ***
## VRDeck                   -8.280e-05  4.115e-06 -20.120  < 2e-16 ***
## withgroup                 1.958e-02  9.293e-03   2.107 0.035134 *  
## deckB                     1.107e-01  2.874e-02   3.852 0.000118 ***
## deckC                     1.477e-01  2.900e-02   5.094 3.58e-07 ***
## deckD                     5.090e-02  3.478e-02   1.463 0.143372    
## deckE                     8.494e-03  3.622e-02   0.235 0.814567    
## deckF                     1.031e-01  3.709e-02   2.778 0.005475 ** 
## deckG                     5.791e-02  3.866e-02   1.498 0.134218    
## deckT                     6.610e-02  1.815e-01   0.364 0.715784    
## sideS                     8.542e-02  8.629e-03   9.899  < 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 8670 degrees of freedom
## Multiple R-squared:  0.3578, Adjusted R-squared:  0.3561 
## F-statistic: 219.5 on 22 and 8670 DF,  p-value: < 2.2e-16
library(caTools)
set.seed(123)
split = sample.split(train_c$Transported, SplitRatio = 0.75)
train_train = subset(train_c, split == TRUE)
train_test = subset(train_c, split == FALSE)

Regresyon

Regresyon istatistiksel bir yöntemdir ve değişkenler arasındaki ilişkiyi incelemek için kullanılır. Temel olarak, bağımlı değişkenin (bağımlı değişken, sonucu etkilenen değişkendir) bir veya daha fazla bağımsız değişkenle (bağımsız değişkenler, sonucu etkileyen değişkenlerdir) ilişkisini modellemeye çalışır. Regresyon analizi, bu ilişkiyi açıklamak ve gelecekteki değerleri tahmin etmek için kullanılabilir.

İki temel regresyon türü vardır:

  1. Doğrusal Regresyon: Bağımlı değişken ile bağımsız değişken(ler) arasındaki ilişkiyi doğrusal bir denklemle modellemeye çalışır. En basit haliyle, tek bir bağımsız değişken ile tek bir bağımlı değişken arasındaki ilişkiyi tanımlar.

  2. Çoklu Regresyon: Birden fazla bağımsız değişkenin olduğu regresyon türüdür. Bu, bağımlı değişkenin birden fazla bağımsız değişken tarafından etkilendiği durumlar için kullanılır.

Regresyon analizi, veri analizi, tahmin ve keşifsel amaçlar için yaygın olarak kullanılır. Örneğin, bir ürünün satışlarını hava durumu, fiyat ve pazarlama harcamaları gibi faktörlerle ilişkilendirmek veya bir öğrencinin sınav başarısını çalışma süresi, uyku ve beslenme alışkanlıkları gibi değişkenlerle açıklamak için kullanılabilir.

regresyon <- lm(Transported ~ . , data = train_train[, -c(1)])
summary(regresyon)
## 
## Call:
## lm(formula = Transported ~ ., data = train_train[, -c(1)])
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.50155 -0.31213 -0.03018  0.29171  1.76723 
## 
## Coefficients: (3 not defined because of singularities)
##                            Estimate Std. Error t value Pr(>|t|)    
## (Intercept)               3.482e-01  4.626e-02   7.527 5.89e-14 ***
## HomePlanetEuropa          1.971e-01  3.358e-02   5.871 4.55e-09 ***
## HomePlanetMars            8.600e-02  1.699e-02   5.061 4.29e-07 ***
## CryoSleepTRUE             3.772e-01  1.328e-02  28.402  < 2e-16 ***
## DestinationPSO J318.5-22 -6.107e-02  2.077e-02  -2.940  0.00330 ** 
## DestinationTRAPPIST-1e   -5.426e-02  1.309e-02  -4.144 3.46e-05 ***
## DestinationTRAPPIST-le   -9.053e-03  3.649e-02  -0.248  0.80407    
## Age                      -2.339e-03  3.725e-04  -6.279 3.63e-10 ***
## VIPTRUE                  -1.616e-02  3.384e-02  -0.478  0.63290    
## RoomService              -1.175e-04  7.790e-06 -15.088  < 2e-16 ***
## FoodCourt                 3.905e-05  3.467e-06  11.262  < 2e-16 ***
## ShoppingMall              8.282e-05  8.415e-06   9.842  < 2e-16 ***
## Spa                      -8.543e-05  4.704e-06 -18.160  < 2e-16 ***
## VRDeck                   -8.216e-05  4.777e-06 -17.197  < 2e-16 ***
## withgroup                 1.601e-02  1.076e-02   1.488  0.13688    
## deckB                     1.049e-01  3.290e-02   3.188  0.00144 ** 
## deckC                     1.441e-01  3.318e-02   4.343 1.43e-05 ***
## deckD                     3.223e-02  3.974e-02   0.811  0.41744    
## deckE                    -2.463e-02  4.213e-02  -0.585  0.55884    
## deckF                     8.346e-02  4.290e-02   1.945  0.05176 .  
## deckG                     4.007e-02  4.472e-02   0.896  0.37029    
## deckT                     5.680e-02  1.822e-01   0.312  0.75526    
## sideS                     8.895e-02  9.984e-03   8.909  < 2e-16 ***
## expense                          NA         NA      NA       NA    
## HomeplanetEuropa                 NA         NA      NA       NA    
## HomeplanetMars                   NA         NA      NA       NA    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.4013 on 6497 degrees of freedom
## Multiple R-squared:  0.358,  Adjusted R-squared:  0.3558 
## F-statistic: 164.7 on 22 and 6497 DF,  p-value: < 2.2e-16
reg_tahmin = predict(regresyon, newdata = train_test[, -c(1,12)])
reg_transported_tahmin = ifelse(reg_tahmin > 0.5, 1, 0)
transported_gercek = ifelse(train_test[12] == TRUE, 1, 0)
cm = table(transported_gercek, reg_transported_tahmin)
cm
##                   reg_transported_tahmin
## transported_gercek   0   1
##                  0 902 177
##                  1 323 771
(902+771)/(902+771+323+177)
## [1] 0.7699034
reg_tahmin_bd = predict(model, newdata = test_c[, -c(1)])
reg_transported_test_c_tahmin = ifelse(reg_tahmin_bd > 0.5, TRUE, FALSE)
transported = as.character(reg_transported_test_c_tahmin)
PassengerId = test_c$PassengerId
Transported=as.vector(transported)
submission_regresyon = cbind(PassengerId, Transported)
submission_regresyon = as.data.frame(submission_regresyon)
library(stringr)
submission_regresyon$transported <- str_to_title(submission_regresyon$Transported)
write.csv(submission_regresyon, "siniftahmini.csv", row.names = FALSE, quote = FALSE)

Logistic

Logistik, genellikle lojistik olarak da adlandırılır ve malzeme, envanter, taşıma, depolama, paketleme ve dağıtım gibi süreçlerin yönetimini içeren bir endüstriyi ifade eder. Ancak, tahmin ettiğiniz kelimenin “logistik” olduğunu düşünüyorsanız, muhtemelen “lojistik regresyon” veya “logistik model” anlamını kastediyorsunuz.

Logistik regresyon, bağımlı değişkenin kategorik olduğu ve bağımsız değişkenlerin bu kategorik değişkenin olasılığını belirlemek için kullanıldığı bir istatistiksel modelleme tekniğidir. Bağımlı değişken, iki kategori arasında (örneğin, evet/hayır, başarılı/başarısız, hasta/sağlıklı) ya da birden fazla kategori arasında (örneğin, kedi/köpek/kuş, A/B/C/D/F notları) olabilir.

Logistik regresyon, bağımsız değişkenlerin belirli bir kategorik sonuç üzerindeki etkisini tahmin etmek için kullanılır. Örneğin, bir ilaç firması bir ilacın bir hastalığı iyileştirip iyileştirmediğini belirlemek için lojistik regresyon kullanabilir. Bağımsız değişkenler, tedavi edilen kişilerin yaşları, cinsiyetleri, tedavi süresi vb. olabilir ve bağımlı değişken, tedavi edilen kişilerin hastalığın iyileşip iyileşmediğiyle ilgili bir kategorik sonuç olabilir.

Lojistik regresyon, sınıflandırma problemlerini çözmek için sıklıkla kullanılır ve özellikle tıp, pazarlama, ekonomi, sosyal bilimler ve makine öğrenimi gibi alanlarda yaygın olarak uygulanır.

train_c_log = train_c %>% mutate_at(c(5, 7:11, 16), ~ log(1 + .))
test_c_log = test_c %>% mutate_at(c(5, 7:11, 15), ~ log(1 + .))
modellog = lm(Transported ~ 1 + HomePlanet + Destination + deck + side + CryoSleep + Age + RoomService + FoodCourt + ShoppingMall + Spa +  VRDeck, data = train_c_log)
reg_tahmin_log = predict(modellog, newdata = test_c_log[, -c(1)])
reg_transported_test_c_tahmin_log = ifelse(reg_tahmin_log > 0.5, TRUE, FALSE)
head(reg_transported_test_c_tahmin_log)
##     1     2     3     4     5     6 
##  TRUE FALSE  TRUE  TRUE  TRUE FALSE
Transported = as.character(reg_transported_test_c_tahmin_log)
PassengerId = test_c_log$PassengerId
Transported = as.vector(Transported)
submission_regresyon_log = cbind(PassengerId, Transported)
submission_regresyon_log =as.data.frame(submission_regresyon_log)
submission_regresyon_log$Transported = str_to_title(submission_regresyon_log$Transported)
write.csv(submission_regresyon_log,"submissyon_regresyon_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: (3 not defined because of singularities)
##                            Estimate Std. Error z value Pr(>|z|)    
## (Intercept)              -4.607e-01  3.588e-01  -1.284  0.19917    
## HomePlanetEuropa          1.556e+00  2.674e-01   5.819 5.91e-09 ***
## HomePlanetMars            5.323e-01  1.103e-01   4.827 1.39e-06 ***
## CryoSleepTRUE             1.285e+00  9.185e-02  13.987  < 2e-16 ***
## DestinationPSO J318.5-22 -5.125e-01  1.297e-01  -3.950 7.80e-05 ***
## DestinationTRAPPIST-1e   -4.904e-01  9.441e-02  -5.194 2.05e-07 ***
## DestinationTRAPPIST-le   -1.471e-01  2.436e-01  -0.604  0.54580    
## Age                      -8.481e-03  2.461e-03  -3.446  0.00057 ***
## VIPTRUE                  -1.650e-01  2.916e-01  -0.566  0.57149    
## RoomService              -1.730e-03  1.136e-04 -15.225  < 2e-16 ***
## FoodCourt                 4.581e-04  4.460e-05  10.271  < 2e-16 ***
## ShoppingMall              5.289e-04  7.559e-05   6.997 2.61e-12 ***
## Spa                      -1.969e-03  1.164e-04 -16.913  < 2e-16 ***
## VRDeck                   -1.902e-03  1.161e-04 -16.392  < 2e-16 ***
## withgroup                 1.206e-01  7.192e-02   1.677  0.09346 .  
## deckB                     1.153e+00  2.912e-01   3.961 7.47e-05 ***
## deckC                     2.481e+00  3.325e-01   7.463 8.48e-14 ***
## deckD                     6.978e-01  3.262e-01   2.139  0.03242 *  
## deckE                     1.029e-01  3.378e-01   0.305  0.76065    
## deckF                     7.739e-01  3.420e-01   2.263  0.02363 *  
## deckG                     3.766e-01  3.511e-01   1.073  0.28345    
## deckT                    -1.184e-01  1.866e+00  -0.063  0.94942    
## sideS                     5.988e-01  6.663e-02   8.987  < 2e-16 ***
## expense                          NA         NA      NA       NA    
## HomeplanetEuropa                 NA         NA      NA       NA    
## HomeplanetMars                   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: 5600.2  on 6497  degrees of freedom
## AIC: 5646.2
## 
## Number of Fisher Scoring iterations: 7
logistic_tahmin = predict(logistic, newdata = train_test[, -c(1,12)])
head(logistic_tahmin)
##           1           2           3           4           5           6 
##  -1.1175750 -11.0810548   0.9556805  -1.6375160  -0.4294310  -1.6887335
logistic_transported_tahmin = ifelse(logistic_tahmin > 0.5, 1, 0)
head(logistic_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)
## ── Attaching packages ────────────────────────────────────── tidymodels 1.2.0 ──
## ✔ broom        1.0.5      ✔ rsample      1.2.1 
## ✔ dials        1.2.1      ✔ tune         1.2.1 
## ✔ infer        1.0.7      ✔ workflows    1.1.4 
## ✔ modeldata    1.3.0      ✔ workflowsets 1.1.0 
## ✔ parsnip      1.2.1      ✔ yardstick    1.3.1 
## ✔ recipes      1.0.10
## ── Conflicts ───────────────────────────────────────── tidymodels_conflicts() ──
## ✖ data.table::between()   masks dplyr::between()
## ✖ scales::discard()       masks purrr::discard()
## ✖ dplyr::filter()         masks stats::filter()
## ✖ data.table::first()     masks dplyr::first()
## ✖ recipes::fixed()        masks stringr::fixed()
## ✖ dplyr::lag()            masks stats::lag()
## ✖ data.table::last()      masks dplyr::last()
## ✖ yardstick::spec()       masks readr::spec()
## ✖ recipes::step()         masks stats::step()
## ✖ data.table::transpose() masks purrr::transpose()
## • Search for functions across packages at https://www.tidymodels.org/find/
result = data.frame(cbind(transported_gercek, logistic_transported_tahmin))
result$Transported = as.factor(result$Transported)
result$logistic_transported_tahmin = as.factor(result$logistic_transported_tahmin)
accuracy(result, truth = Transported, estimate = logistic_transported_tahmin)
## # A tibble: 1 × 3
##   .metric  .estimator .estimate
##   <chr>    <chr>          <dbl>
## 1 accuracy binary         0.786
conf_mat(result, truth = Transported, estimate = logistic_transported_tahmin)
##           Truth
## Prediction   0   1
##          0 918 305
##          1 161 789
(918+789)/(918+789+161+305)
## [1] 0.7855499
library(caret)
## Loading required package: 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, logistic_transported_tahmin)
cm
##                   logistic_transported_tahmin
## transported_gercek   0   1
##                  0 918 161
##                  1 305 789
confusionMatrix(as.factor(transported_gercek), as.factor(logistic_transported_tahmin))
## Confusion Matrix and Statistics
## 
##           Reference
## Prediction   0   1
##          0 918 161
##          1 305 789
##                                           
##                Accuracy : 0.7855          
##                  95% CI : (0.7677, 0.8026)
##     No Information Rate : 0.5628          
##     P-Value [Acc > NIR] : < 2.2e-16       
##                                           
##                   Kappa : 0.5715          
##                                           
##  Mcnemar's Test P-Value : 3.488e-11       
##                                           
##             Sensitivity : 0.7506          
##             Specificity : 0.8305          
##          Pos Pred Value : 0.8508          
##          Neg Pred Value : 0.7212          
##              Prevalence : 0.5628          
##          Detection Rate : 0.4225          
##    Detection Prevalence : 0.4965          
##       Balanced Accuracy : 0.7906          
##                                           
##        'Positive' Class : 0               
## 
logistic_bd = glm(formula = Transported ~ .,
                  family = binomial,
                  data = train_c[, -c(1)])
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
logistic_tahmin_bd = predict(logistic_bd, newdata = test_c[, -c(1)])
logistic_transported_test_c_tahmin = ifelse(logistic_tahmin_bd >0.5, TRUE, FALSE )
Transported = as.character(logistic_transported_test_c_tahmin)
PassengerId = test_c$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_logistic.csv", row.names =FALSE, quote=FALSE)
logistic_bd = glm(formula = Transported ~ .,
                  family = binomial,
                  data = train_c_log[, -c(1)])
logistic_tahmin_bd = predict(logistic_bd, newdata = test_c_log[, -c(1)])
logistic_transported_test_c_tahmin = ifelse(logistic_tahmin_bd >0.5, TRUE, FALSE )
Transported = as.character(logistic_transported_test_c_tahmin)
PassengerId = test_c$PassengerId
Transported=as.vector(Transported)
submission_llogistic = cbind(PassengerId, Transported)
submission_llogistic = as.data.frame(submission_logistic)
submission_llogistic$Transported = str_to_title(submission_logistic$Transported)
write.csv(submission_logistic, "submission_llogistic.csv", row.names =FALSE, quote=FALSE)

Naive Bayes

Naive Bayes, makine öğrenimi ve istatistikte sınıflandırma problemlerini çözmek için kullanılan bir algoritmadır. Adını, Bayes Teoremi’nden alır. Bu algoritma, basit, ancak etkili bir sınıflandırma yöntemidir ve özellikle doğal dil işleme gibi alanlarda sıklıkla kullanılır.

Naive Bayes sınıflandırıcısı, belirli bir örneğin bir sınıfa ait olma olasılığını hesaplamak için Bayes Teoremi’ni kullanır. Bu sınıflandırıcı, tüm özelliklerin birbirinden bağımsız olduğunu varsayar. Bu varsayım nedeniyle “naive” (saf) olarak adlandırılır. Özelliklerin bağımsız olduğu gerçek dünyadaki durumlar nadiren oluşsa da, Naive Bayes, çoğu durumda oldukça iyi çalışır ve hızlı bir şekilde eğitilebilir.

Naive Bayes sınıflandırıcısı genellikle metin sınıflandırması gibi doğal dil işleme görevlerinde kullanılır. Örneğin, bir e-postanın spam mı yoksa spam olmayan mı olduğunu belirlemek için kullanılabilir. Bu durumda, özellikler kelime frekansları olabilir ve sınıflar “spam” ve “spam değil” olabilir.

Naive Bayes sınıflandırıcısı, aynı zamanda çoklu sınıfların (örneğin, bir belgenin birden fazla kategoriye ait olabileceği çok etiketli sınıflandırma) tahmin edilmesi gereken çok sınıflı sınıflandırma problemlerinde de kullanılabilir.

Naive Bayes, basit yapısı ve hızlı eğitimi nedeniyle genellikle küçük veri setleri ve gerçek zamanlı uygulamalar için tercih edilir.

library(e1071)
## 
## 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 0.6236094 0.1739802 0.2024104
##   TRUE  0.4631547 0.3279537 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 TRAPPIST-le
##   FALSE  0.16069221    0.09363412  0.72404203  0.02163164
##   TRUE   0.24969549    0.09043849  0.64007308  0.01979294
## 
##        Age
## Y           [,1]     [,2]
##   FALSE 30.01092 13.45222
##   TRUE  27.85527 14.87646
## 
##        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 0.0540791100 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 0.2932632880 0.0012360939
##   TRUE  0.2828867235 0.3105968331 0.0003045067
## 
##        side
## Y               P         S
##   FALSE 0.5571693 0.4428307
##   TRUE  0.4448843 0.5551157
## 
##        expense
## Y            [,1]     [,2]
##   FALSE 2053.8702 3224.219
##   TRUE   884.6376 2307.962
## 
##        Homeplanet
## Y           Earth    Europa      Mars
##   FALSE 0.6236094 0.1739802 0.2024104
##   TRUE  0.4631547 0.3279537 0.2088916
pred_nb = predict(fit_nb, newdata = train_test[, -c(1, 12)], type = "raw") %>%
  data.frame()
head(pred_nb)
##       FALSE.         TRUE.
## 1 0.27800374  7.219963e-01
## 2 1.00000000 1.633093e-134
## 3 0.03641247  9.635875e-01
## 4 0.66214769  3.378523e-01
## 5 0.03218187  9.678181e-01
## 6 0.45704075  5.429592e-01
Transported_pred_nb = ifelse(pred_nb$TRUE. > 0.5, 1, 0)
head(Transported_pred_nb)
## [1] 1 0 1 0 1 1
Transported_train_test = ifelse(train_test[12] == TRUE, 1, 0)
head(Transported_train_test)
##      Transported
## [1,]           1
## [2,]           0
## [3,]           1
## [4,]           0
## [5,]           0
## [6,]           0
cm = table(Transported_train_test, Transported_pred_nb)
cm
##                       Transported_pred_nb
## Transported_train_test    0    1
##                      0  501  578
##                      1   79 1015
(578+1015)/(578+1015+79+501)
## [1] 0.7330879
nb = naiveBayes(Transported ~ ., data = train_c[, -1])
pred_nb = predict(nb, newdata = test_c, type = "raw") %>% data.frame()
Transported_pred_nb = ifelse(pred_nb$TRUE. > 0.5, TRUE, FALSE)
Transported = as.character(Transported_pred_nb)
PassengerId = test_c$PassengerId
Transported = as.vector(Transported)
sample_submission = cbind(PassengerId, Transported)
sample_submission = as.data.frame(sample_submission)
sample_submission$Transported = str_to_title(sample_submission$Transported)
write.csv(sample_submission, "sub_nb.csv", row.names = FALSE, quote = FALSE)

SVM

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 FALSE
## 6 FALSE
Transported_pred_svm = ifelse(preds$. == TRUE, 1, 0)
cm = table(Transported_train_test, Transported_pred_svm)
cm
##                       Transported_pred_svm
## Transported_train_test   0   1
##                      0 836 243
##                      1 180 914
(836+914)/(836+914+243+180)
## [1] 0.8053382
fit_svm = svm(Transported ~ ., data = train_c[, -1],
              type= 'C-classification',
              kernel = 'linear')
preds = predict(fit_svm, newdata = test_c, type ="raw")%>%
  data.frame()
head(preds)
##       .
## 1  TRUE
## 2 FALSE
## 3  TRUE
## 4  TRUE
## 5  TRUE
## 6  TRUE
Transported_pred_svm = ifelse(preds$. == TRUE, TRUE, FALSE)
Transported = as.character(Transported_pred_svm)
PassengerId = test_c$PassengerId
Transported = as.vector(Transported)
sample_submission = cbind(PassengerId, Transported)
sample_submission = as.data.frame(sample_submission)
sample_submission$Transported = str_to_title(sample_submission$Transported)
write.csv(sample_submission, "sub_svm.csv", row.names = FALSE, quote = FALSE)

Svm Radial

fit_svm = svm(Transported ~ ., data = train_train[, -1],
              type= 'C-classification',
              kernel = 'radial')

preds = predict(fit_svm, newdata = train_test[, -c(1, 12)], type ="raw")%>%
  data.frame()
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
##                      0 850 229
##                      1 203 891
(850+891)/(850+891+203+229)
## [1] 0.8011965
fit_svm = svm(Transported ~ ., data = train_c[, -1],
              type= 'C-classification',
              kernel = 'radial')

preds = predict(fit_svm, newdata = test_c, type ="raw")%>%
  data.frame()
Transported_pred_svm = ifelse(preds$. == TRUE, TRUE, FALSE)
Transported = as.character(Transported_pred_svm)
PassengerId = test_c$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_radial.csv", row.names = FALSE, quote = FALSE)
P = ggplot(train_train, aes(x=HomePlanet, y=deck, color=factor(Transported))) + 
  geom_point(aes(shape=factor(Transported)), size=3) +
  scale_color_viridis_d() + labs(title = "", x="HomePlanet", y="deck") + theme_minimal() + theme(legend.position ="top")
P

library(rpart)
## 
## Attaching package: 'rpart'
## The following object is masked from 'package:dials':
## 
##     prune
library(rpart.plot)
library(randomForest)
## randomForest 4.7-1.1
## Type rfNews() to see new features/changes/bug fixes.
## 
## Attaching package: 'randomForest'
## The following object is masked from 'package:dplyr':
## 
##     combine
## The following object is masked from 'package:ggplot2':
## 
##     margin
library(caret)
fit_tree = rpart::rpart(Transported ~ ., data = train_train[, -1])
summary(fit_tree)
## Call:
## rpart::rpart(formula = Transported ~ ., data = train_train[, 
##     -1])
##   n= 6520 
## 
##           CP nsplit rel error    xerror         xstd
## 1 0.23321359      0 1.0000000 1.0001811 0.0001977604
## 2 0.04905507      1 0.7667864 0.7671015 0.0103212807
## 3 0.02807107      2 0.7177313 0.7180892 0.0090936774
## 4 0.02784643      3 0.6896603 0.7039381 0.0095638265
## 5 0.01770479      4 0.6618138 0.6661699 0.0097007659
## 6 0.01381587      5 0.6441090 0.6496207 0.0100644975
## 7 0.01194576      6 0.6302932 0.6389139 0.0102048399
## 8 0.01164524      7 0.6183474 0.6310762 0.0102269497
## 9 0.01000000      8 0.6067022 0.6242632 0.0102752753
## 
## Variable importance
##      expense    CryoSleep    FoodCourt          Spa       VRDeck  RoomService 
##           22           17           12           12           11            8 
##         deck   Homeplanet   HomePlanet ShoppingMall          Age  Destination 
##            5            5            5            2            1            1 
## 
## Node number 1: 6520 observations,    complexity param=0.2332136
##   mean=0.503681, MSE=0.2499865 
##   left son=2 (3795 obs) right son=3 (2725 obs)
##   Primary splits:
##       expense     < 0.5     to the right, improve=0.2332136, (0 missing)
##       CryoSleep   < 0.5     to the left,  improve=0.2095384, (0 missing)
##       RoomService < 0.5     to the right, improve=0.1254121, (0 missing)
##       Spa         < 0.5     to the right, improve=0.1142180, (0 missing)
##       VRDeck      < 0.5     to the right, improve=0.1084074, (0 missing)
##   Surrogate splits:
##       CryoSleep   < 0.5     to the left,  agree=0.932, adj=0.837, (0 split)
##       Spa         < 0.5     to the right, agree=0.784, adj=0.484, (0 split)
##       FoodCourt   < 0.5     to the right, agree=0.770, adj=0.451, (0 split)
##       VRDeck      < 0.5     to the right, agree=0.768, adj=0.444, (0 split)
##       RoomService < 0.5     to the right, agree=0.757, adj=0.419, (0 split)
## 
## Node number 2: 3795 observations,    complexity param=0.02807107
##   mean=0.2990777, MSE=0.2096302 
##   left son=4 (3243 obs) right son=5 (552 obs)
##   Primary splits:
##       FoodCourt    < 1331    to the left,  improve=0.05751186, (0 missing)
##       ShoppingMall < 627.5   to the left,  improve=0.04530804, (0 missing)
##       RoomService  < 365.5   to the right, improve=0.04440387, (0 missing)
##       Spa          < 257.5   to the right, improve=0.03347453, (0 missing)
##       VRDeck       < 721     to the right, improve=0.02290457, (0 missing)
##   Surrogate splits:
##       expense    < 5981    to the left,  agree=0.885, adj=0.210, (0 split)
##       deck       splits as  RRRLLLLL,    agree=0.884, adj=0.203, (0 split)
##       HomePlanet splits as  LRL,         agree=0.878, adj=0.159, (0 split)
##       Homeplanet splits as  LRL,         agree=0.878, adj=0.159, (0 split)
##       Spa        < 8955.5  to the left,  agree=0.856, adj=0.009, (0 split)
## 
## Node number 3: 2725 observations,    complexity param=0.04905507
##   mean=0.7886239, MSE=0.1666963 
##   left son=6 (1448 obs) right son=7 (1277 obs)
##   Primary splits:
##       deck        splits as  RRRRLRL-,    improve=0.17601740, (0 missing)
##       HomePlanet  splits as  LRR,         improve=0.12704810, (0 missing)
##       Homeplanet  splits as  LRR,         improve=0.12704810, (0 missing)
##       Destination splits as  RLLR,        improve=0.02774089, (0 missing)
##       CryoSleep   < 0.5     to the left,  improve=0.02268236, (0 missing)
##   Surrogate splits:
##       HomePlanet  splits as  LRR,         agree=0.934, adj=0.860, (0 split)
##       Homeplanet  splits as  LRR,         agree=0.934, adj=0.860, (0 split)
##       Age         < 24.5    to the left,  agree=0.626, adj=0.201, (0 split)
##       Destination splits as  RLLR,        agree=0.596, adj=0.137, (0 split)
##       withgroup   < 0.5     to the left,  agree=0.583, adj=0.110, (0 split)
## 
## Node number 4: 3243 observations,    complexity param=0.02784643
##   mean=0.2537774, MSE=0.1893744 
##   left son=8 (2577 obs) right son=9 (666 obs)
##   Primary splits:
##       ShoppingMall < 541.5   to the left,  improve=0.07390355, (0 missing)
##       RoomService  < 365.5   to the right, improve=0.03464407, (0 missing)
##       Spa          < 240.5   to the right, improve=0.03327259, (0 missing)
##       VRDeck       < 114     to the right, improve=0.02784287, (0 missing)
##       expense      < 2867.5  to the right, improve=0.01811461, (0 missing)
##   Surrogate splits:
##       expense < 18644   to the left,  agree=0.795, adj=0.003, (0 split)
## 
## Node number 5: 552 observations,    complexity param=0.01770479
##   mean=0.5652174, MSE=0.2457467 
##   left son=10 (123 obs) right son=11 (429 obs)
##   Primary splits:
##       Spa       < 1372.5  to the right, improve=0.21272970, (0 missing)
##       VRDeck    < 1063.5  to the right, improve=0.17089500, (0 missing)
##       expense   < 5395    to the right, improve=0.06611166, (0 missing)
##       FoodCourt < 2513    to the left,  improve=0.02780045, (0 missing)
##       deck      splits as  LLRLLRRL,    improve=0.02705865, (0 missing)
##   Surrogate splits:
##       expense     < 12647   to the right, agree=0.790, adj=0.057, (0 split)
##       Age         < 13.5    to the left,  agree=0.779, adj=0.008, (0 split)
##       RoomService < 3895.5  to the right, agree=0.779, adj=0.008, (0 split)
## 
## Node number 6: 1448 observations
##   mean=0.6277624, MSE=0.2336768 
## 
## Node number 7: 1277 observations
##   mean=0.9710258, MSE=0.02813466 
## 
## Node number 8: 2577 observations,    complexity param=0.01194576
##   mean=0.193636, MSE=0.1561411 
##   left son=16 (2067 obs) right son=17 (510 obs)
##   Primary splits:
##       FoodCourt  < 456.5   to the left,  improve=0.04838893, (0 missing)
##       expense    < 1447.5  to the right, improve=0.04016842, (0 missing)
##       HomePlanet splits as  RLL,         improve=0.02539308, (0 missing)
##       Homeplanet splits as  RLL,         improve=0.02539308, (0 missing)
##       Spa        < 537.5   to the right, improve=0.01895890, (0 missing)
##   Surrogate splits:
##       expense < 12373   to the left,  agree=0.804, adj=0.008, (0 split)
##       Spa     < 13650   to the left,  agree=0.803, adj=0.006, (0 split)
##       VRDeck  < 10123.5 to the left,  agree=0.802, adj=0.002, (0 split)
##       deck    splits as  LLLLLLLR,    agree=0.802, adj=0.002, (0 split)
## 
## Node number 9: 666 observations
##   mean=0.4864865, MSE=0.2498174 
## 
## Node number 10: 123 observations
##   mean=0.1382114, MSE=0.119109 
## 
## Node number 11: 429 observations,    complexity param=0.01381587
##   mean=0.6876457, MSE=0.2147891 
##   left son=22 (143 obs) right son=23 (286 obs)
##   Primary splits:
##       VRDeck      < 611     to the right, improve=0.24438400, (0 missing)
##       Spa         < 225     to the right, improve=0.05300377, (0 missing)
##       FoodCourt   < 3119.5  to the left,  improve=0.05168044, (0 missing)
##       side        splits as  LR,          improve=0.04004566, (0 missing)
##       RoomService < 1719.5  to the right, improve=0.03930897, (0 missing)
##   Surrogate splits:
##       expense   < 6032    to the right, agree=0.702, adj=0.105, (0 split)
##       Age       < 53.5    to the right, agree=0.674, adj=0.021, (0 split)
##       FoodCourt < 12128.5 to the right, agree=0.671, adj=0.014, (0 split)
## 
## Node number 16: 2067 observations
##   mean=0.1504596, MSE=0.1278215 
## 
## Node number 17: 510 observations,    complexity param=0.01164524
##   mean=0.3686275, MSE=0.2327413 
##   left son=34 (204 obs) right son=35 (306 obs)
##   Primary splits:
##       expense    < 1447.5  to the right, improve=0.15990760, (0 missing)
##       VRDeck     < 86.5    to the right, improve=0.10060710, (0 missing)
##       HomePlanet splits as  RLL,         improve=0.07952538, (0 missing)
##       Homeplanet splits as  RLL,         improve=0.07952538, (0 missing)
##       Spa        < 500     to the right, improve=0.07353369, (0 missing)
##   Surrogate splits:
##       HomePlanet splits as  RLL,         agree=0.873, adj=0.681, (0 split)
##       Homeplanet splits as  RLL,         agree=0.873, adj=0.681, (0 split)
##       deck       splits as  LLLLRRRL,    agree=0.839, adj=0.598, (0 split)
##       VRDeck     < 213.5   to the right, agree=0.818, adj=0.544, (0 split)
##       Spa        < 219.5   to the right, agree=0.792, adj=0.480, (0 split)
## 
## Node number 22: 143 observations
##   mean=0.3636364, MSE=0.231405 
## 
## Node number 23: 286 observations
##   mean=0.8496503, MSE=0.1277446 
## 
## Node number 34: 204 observations
##   mean=0.1323529, MSE=0.1148356 
## 
## Node number 35: 306 observations
##   mean=0.5261438, MSE=0.2493165
rpart.plot(fit_tree)

preds= predict(fit_tree, newdata = train_test[, -c(1,12)]) %>%
  data.frame()
head(preds)
##           .
## 1 0.1504596
## 2 0.1382114
## 3 0.8496503
## 4 0.1504596
## 5 0.4864865
## 6 0.1504596
Transported_pred_tree = ifelse(preds$. >0.5, 1, 0)
cm = table(Transported_train_test, Transported_pred_tree)
cm
##                       Transported_pred_tree
## Transported_train_test   0   1
##                      0 807 272
##                      1 237 857
(807+857)/(807+857+237+272)
## [1] 0.7657616
fit_tree = rpart::rpart(Transported ~ ., data = train_c[, -1])

preds= predict(fit_tree, newdata = test_c) %>%
  data.frame()
Transported_pred_tree = ifelse(preds$. > 0.5, TRUE, FALSE)
Transported = as.character(Transported_pred_tree)
PassengerId = test_c$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_tree.csv", row.names = FALSE, quote = FALSE)

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