Ekonometri final ödevi
Train ve test
“Test” ve “train” terimleri genellikle makine öğrenimi ve yapay zeka gibi alanlarda kullanılır.
Train (Eğitim):
Makine öğrenimi veya yapay zeka modelinin belirli bir görevi öğrenmek için kullanılan süreçtir. Model, eğitim veri kümesi olarak adlandırılan bir veri seti üzerinde eğitilir. Eğitim veri kümesi, modelin istenen sonuçları üretmesi beklenen girdi-veri çiftlerinden oluşur. Eğitim sırasında model, bu verileri kullanarak belirli bir görevi gerçekleştirmeyi öğrenir. Örneğin, görüntü tanıma modeli birçok görüntü ve bunların etiketlerini (örneğin, içerdikleri nesnelerin tanımları) gördükten sonra bu nesneleri tanıma yeteneği geliştirir. Test (Sınama):
Modelin eğitim veri kümesi üzerinde öğrendiği bilgilerin doğruluğunu değerlendirmek için kullanılan süreçtir. Test veri kümesi, modelin daha önce görmediği veri örneklerinden oluşur. Model, test veri kümesindeki girdileri alır ve bu girdilere dayanarak beklenen sonuçları üretir. Bu üretilen sonuçlar, gerçek sonuçlarla karşılaştırılır ve modelin ne kadar iyi veya ne kadar kötü performans gösterdiği belirlenir. Test sonuçları, modelin genelleme yeteneğini ve gerçek dünya verileri üzerinde ne kadar iyi performans gösterebileceğini gösterir. Özetle, “train” eğitim sürecini ifade ederken, “test” ise eğitilen modelin performansının değerlendirildiği süreci ifade eder.
## Rows: 4277 Columns: 13
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (5): PassengerId, HomePlanet, Cabin, Destination, Name
## dbl (6): Age, RoomService, FoodCourt, ShoppingMall, Spa, VRDeck
## 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.## Rows: 8693 Columns: 14
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (5): PassengerId, HomePlanet, Cabin, Destination, Name
## dbl (6): Age, RoomService, FoodCourt, ShoppingMall, Spa, VRDeck
## 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.## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr 1.1.4 ✔ purrr 1.0.2
## ✔ forcats 1.0.0 ✔ stringr 1.5.1
## ✔ ggplot2 3.5.1 ✔ tibble 3.2.1
## ✔ lubridate 1.9.3 ✔ tidyr 1.3.1
## ── 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## # A tibble: 13 × 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 87 2 4 NA NA NA
## 3 CryoSleep lgl 93 2.2 3 0 0.37 1
## 4 Cabin chr 100 2.3 3266 NA NA NA
## 5 Destination chr 92 2.2 4 NA NA NA
## 6 Age dbl 91 2.1 80 0 28.7 79
## 7 VIP lgl 93 2.2 3 0 0.02 1
## 8 RoomService dbl 82 1.9 843 0 219. 11567
## 9 FoodCourt dbl 106 2.5 903 0 439. 25273
## 10 ShoppingMall dbl 98 2.3 716 0 177. 8292
## 11 Spa dbl 101 2.4 834 0 303. 19844
## 12 VRDeck dbl 80 1.9 797 0 311. 22272
## 13 Name chr 94 2.2 4177 NA NA NA## # A tibble: 14 × 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 201 2.3 4 NA NA NA
## 3 CryoSleep lgl 217 2.5 3 0 0.36 1
## 4 Cabin chr 199 2.3 6561 NA NA NA
## 5 Destination chr 182 2.1 4 NA NA NA
## 6 Age dbl 179 2.1 81 0 28.8 79
## 7 VIP lgl 203 2.3 3 0 0.02 1
## 8 RoomService dbl 181 2.1 1274 0 225. 14327
## 9 FoodCourt dbl 183 2.1 1508 0 458. 29813
## 10 ShoppingMall dbl 208 2.4 1116 0 174. 23492
## 11 Spa dbl 183 2.1 1328 0 311. 22408
## 12 VRDeck dbl 188 2.2 1307 0 305. 24133
## 13 Name chr 200 2.3 8474 NA NA NA
## 14 Transported lgl 0 0 2 0 0.5 1test[c('group', 'pp')] <- str_split_fixed(test$PassengerId,'-', 2)
train[c('group', 'pp')] <- str_split_fixed(train$PassengerId,'-', 2)## # A tibble: 6 × 3
## PassengerId group pp
## <chr> <chr> <chr>
## 1 0001_01 0001_01 ""
## 2 0002_01 0002_01 ""
## 3 0003_01 0003_01 ""
## 4 0003_02 0003_02 ""
## 5 0004_01 0004_01 ""
## 6 0005_01 0005_01 ""## # A tibble: 6 × 4
## PassengerId group pp withgroup
## <chr> <chr> <chr> <dbl>
## 1 0013_01 0013_01 "" 0
## 2 0018_01 0018_01 "" 0
## 3 0019_01 0019_01 "" 0
## 4 0021_01 0021_01 "" 0
## 5 0023_01 0023_01 "" 0
## 6 0027_01 0027_01 "" 0## # A tibble: 6 × 4
## PassengerId group pp withgroup
## <chr> <chr> <chr> <dbl>
## 1 0001_01 0001_01 "" 0
## 2 0002_01 0002_01 "" 0
## 3 0003_01 0003_01 "" 0
## 4 0003_02 0003_02 "" 0
## 5 0004_01 0004_01 "" 0
## 6 0005_01 0005_01 "" 0train[c('deck', 'num', 'side')] <- str_split_fixed(train$Cabin, '/', 3)
test[c('deck', 'num', 'side')] <- str_split_fixed(test$Cabin, '/', 3)## PassengerId HomePlanet CryoSleep Cabin
## 0013_01: 1 Earth :2263 Mode :logical G/160/P: 8
## 0018_01: 1 Europa:1002 FALSE:2640 B/31/P : 7
## 0019_01: 1 Mars : 925 TRUE :1544 D/273/S: 7
## 0021_01: 1 NA's : 87 NA's :93 E/228/S: 7
## 0023_01: 1 G/748/S: 7
## 0027_01: 1 (Other):4141
## (Other):4271 NA's : 100
## Destination Age VIP RoomService
## 55 Cancri e : 841 Min. : 0.00 Mode :logical Min. : 0.0
## PSO J318.5-22: 388 1st Qu.:19.00 FALSE:4110 1st Qu.: 0.0
## TRAPPIST-1e :2956 Median :26.00 TRUE :74 Median : 0.0
## NA's : 92 Mean :28.66 NA's :93 Mean : 219.3
## 3rd Qu.:37.00 3rd Qu.: 53.0
## Max. :79.00 Max. :11567.0
## NA's :91 NA's :82
## FoodCourt ShoppingMall Spa VRDeck
## Min. : 0.0 Min. : 0.0 Min. : 0.0 Min. : 0.0
## 1st Qu.: 0.0 1st Qu.: 0.0 1st Qu.: 0.0 1st Qu.: 0.0
## Median : 0.0 Median : 0.0 Median : 0.0 Median : 0.0
## Mean : 439.5 Mean : 177.3 Mean : 303.1 Mean : 310.7
## 3rd Qu.: 78.0 3rd Qu.: 33.0 3rd Qu.: 50.0 3rd Qu.: 36.0
## Max. :25273.0 Max. :8292.0 Max. :19844.0 Max. :22272.0
## NA's :106 NA's :98 NA's :101 NA's :80
## Name group pp withgroup deck
## Berta Barnolderg: 2 0013_01: 1 :4277 Min. :0 F :1445
## Chrey Colte : 2 0018_01: 1 1st Qu.:0 G :1222
## Cints Erle : 2 0019_01: 1 Median :0 E : 447
## Cocors Cola : 2 0021_01: 1 Mean :0 B : 362
## Con Pashe : 2 0023_01: 1 3rd Qu.:0 C : 355
## (Other) :4173 0027_01: 1 Max. :0 (Other): 346
## NA's : 94 (Other):4271 NA's : 100
## num side
## : 100 : 100
## 4 : 21 P:2084
## 31 : 18 S:2093
## 197 : 16
## 294 : 16
## 228 : 14
## (Other):4092## PassengerId HomePlanet CryoSleep Cabin
## 0001_01: 1 Earth :4602 Mode :logical G/734/S: 8
## 0002_01: 1 Europa:2131 FALSE:5439 B/11/S : 7
## 0003_01: 1 Mars :1759 TRUE :3037 B/201/P: 7
## 0003_02: 1 NA's : 201 NA's :217 B/82/S : 7
## 0004_01: 1 C/137/S: 7
## 0005_01: 1 (Other):8458
## (Other):8687 NA's : 199
## Destination Age VIP RoomService
## 55 Cancri e :1800 Min. : 0.00 Mode :logical Min. : 0.0
## PSO J318.5-22: 796 1st Qu.:19.00 FALSE:8291 1st Qu.: 0.0
## TRAPPIST-1e :5915 Median :27.00 TRUE :199 Median : 0.0
## NA's : 182 Mean :28.83 NA's :203 Mean : 224.7
## 3rd Qu.:38.00 3rd Qu.: 47.0
## Max. :79.00 Max. :14327.0
## NA's :179 NA's :181
## FoodCourt ShoppingMall Spa VRDeck
## Min. : 0.0 Min. : 0.0 Min. : 0.0 Min. : 0.0
## 1st Qu.: 0.0 1st Qu.: 0.0 1st Qu.: 0.0 1st Qu.: 0.0
## Median : 0.0 Median : 0.0 Median : 0.0 Median : 0.0
## Mean : 458.1 Mean : 173.7 Mean : 311.1 Mean : 304.9
## 3rd Qu.: 76.0 3rd Qu.: 27.0 3rd Qu.: 59.0 3rd Qu.: 46.0
## Max. :29813.0 Max. :23492.0 Max. :22408.0 Max. :24133.0
## NA's :183 NA's :208 NA's :183 NA's :188
## Name Transported group pp withgroup
## Alraium Disivering: 2 Mode :logical 0001_01: 1 :8693 Min. :0
## Ankalik Nateansive: 2 FALSE:4315 0002_01: 1 1st Qu.:0
## Anton Woody : 2 TRUE :4378 0003_01: 1 Median :0
## Apix Wala : 2 0003_02: 1 Mean :0
## Asch Stradick : 2 0004_01: 1 3rd Qu.:0
## (Other) :8483 0005_01: 1 Max. :0
## NA's : 200 (Other):8687
## deck num side
## F :2794 : 199 : 199
## G :2559 82 : 28 P:4206
## E : 876 19 : 22 S:4288
## B : 779 86 : 22
## C : 747 176 : 21
## (Other): 739 56 : 21
## NA's : 199 (Other):8380train$num <- droplevels(train$num)
test$num <- droplevels(test$num)
train$side <- droplevels(train$side)
test$side <- droplevels(test$side)## PassengerId HomePlanet CryoSleep Cabin
## 0013_01: 1 Earth :2263 Mode :logical G/160/P: 8
## 0018_01: 1 Europa:1002 FALSE:2640 B/31/P : 7
## 0019_01: 1 Mars : 925 TRUE :1544 D/273/S: 7
## 0021_01: 1 NA's : 87 NA's :93 E/228/S: 7
## 0023_01: 1 G/748/S: 7
## 0027_01: 1 (Other):4141
## (Other):4271 NA's : 100
## Destination Age VIP RoomService
## 55 Cancri e : 841 Min. : 0.00 Mode :logical Min. : 0.0
## PSO J318.5-22: 388 1st Qu.:19.00 FALSE:4110 1st Qu.: 0.0
## TRAPPIST-1e :2956 Median :26.00 TRUE :74 Median : 0.0
## NA's : 92 Mean :28.66 NA's :93 Mean : 219.3
## 3rd Qu.:37.00 3rd Qu.: 53.0
## Max. :79.00 Max. :11567.0
## NA's :91 NA's :82
## FoodCourt ShoppingMall Spa VRDeck
## Min. : 0.0 Min. : 0.0 Min. : 0.0 Min. : 0.0
## 1st Qu.: 0.0 1st Qu.: 0.0 1st Qu.: 0.0 1st Qu.: 0.0
## Median : 0.0 Median : 0.0 Median : 0.0 Median : 0.0
## Mean : 439.5 Mean : 177.3 Mean : 303.1 Mean : 310.7
## 3rd Qu.: 78.0 3rd Qu.: 33.0 3rd Qu.: 50.0 3rd Qu.: 36.0
## Max. :25273.0 Max. :8292.0 Max. :19844.0 Max. :22272.0
## NA's :106 NA's :98 NA's :101 NA's :80
## Name group pp withgroup deck
## Berta Barnolderg: 2 0013_01: 1 :4277 Min. :0 F :1445
## Chrey Colte : 2 0018_01: 1 1st Qu.:0 G :1222
## Cints Erle : 2 0019_01: 1 Median :0 E : 447
## Cocors Cola : 2 0021_01: 1 Mean :0 B : 362
## Con Pashe : 2 0023_01: 1 3rd Qu.:0 C : 355
## (Other) :4173 0027_01: 1 Max. :0 (Other): 346
## NA's : 94 (Other):4271 NA's : 100
## num side
## : 100 : 100
## 4 : 21 P:2084
## 31 : 18 S:2093
## 197 : 16
## 294 : 16
## 228 : 14
## (Other):4092## PassengerId HomePlanet CryoSleep Cabin
## 0001_01: 1 Earth :4602 Mode :logical G/734/S: 8
## 0002_01: 1 Europa:2131 FALSE:5439 B/11/S : 7
## 0003_01: 1 Mars :1759 TRUE :3037 B/201/P: 7
## 0003_02: 1 NA's : 201 NA's :217 B/82/S : 7
## 0004_01: 1 C/137/S: 7
## 0005_01: 1 (Other):8458
## (Other):8687 NA's : 199
## Destination Age VIP RoomService
## 55 Cancri e :1800 Min. : 0.00 Mode :logical Min. : 0.0
## PSO J318.5-22: 796 1st Qu.:19.00 FALSE:8291 1st Qu.: 0.0
## TRAPPIST-1e :5915 Median :27.00 TRUE :199 Median : 0.0
## NA's : 182 Mean :28.83 NA's :203 Mean : 224.7
## 3rd Qu.:38.00 3rd Qu.: 47.0
## Max. :79.00 Max. :14327.0
## NA's :179 NA's :181
## FoodCourt ShoppingMall Spa VRDeck
## Min. : 0.0 Min. : 0.0 Min. : 0.0 Min. : 0.0
## 1st Qu.: 0.0 1st Qu.: 0.0 1st Qu.: 0.0 1st Qu.: 0.0
## Median : 0.0 Median : 0.0 Median : 0.0 Median : 0.0
## Mean : 458.1 Mean : 173.7 Mean : 311.1 Mean : 304.9
## 3rd Qu.: 76.0 3rd Qu.: 27.0 3rd Qu.: 59.0 3rd Qu.: 46.0
## Max. :29813.0 Max. :23492.0 Max. :22408.0 Max. :24133.0
## NA's :183 NA's :208 NA's :183 NA's :188
## Name Transported group pp withgroup
## Alraium Disivering: 2 Mode :logical 0001_01: 1 :8693 Min. :0
## Ankalik Nateansive: 2 FALSE:4315 0002_01: 1 1st Qu.:0
## Anton Woody : 2 TRUE :4378 0003_01: 1 Median :0
## Apix Wala : 2 0003_02: 1 Mean :0
## Asch Stradick : 2 0004_01: 1 3rd Qu.:0
## (Other) :8483 0005_01: 1 Max. :0
## NA's : 200 (Other):8687
## deck num side
## F :2794 : 199 : 199
## G :2559 82 : 28 P:4206
## E : 876 19 : 22 S:4288
## B : 779 86 : 22
## C : 747 176 : 21
## (Other): 739 56 : 21
## NA's : 199 (Other):8380##
## Attaching package: 'zoo'## The following objects are masked from 'package:base':
##
## as.Date, as.Date.numericmost_frequent_hp <- train %>%
filter(!is.na(HomePlanet)) %>%
group_by(Destination, HomePlanet) %>%
summarize(count = n()) %>%
arrange(Destination, desc(count)) %>%
slice(1) %>%
ungroup()## `summarise()` has grouped output by 'Destination'. You can override using the
## `.groups` argument.## # A tibble: 4 × 3
## Destination HomePlanet count
## <fct> <fct> <int>
## 1 55 Cancri e Europa 886
## 2 PSO J318.5-22 Earth 712
## 3 TRAPPIST-1e Earth 3101
## 4 <NA> Earth 99train$HomePlanet <- as.character(train$HomePlanet)
train$Destination <- as.character(train$Destination)train <- train %>%
mutate(HomePlanet= ifelse(is.na(HomePlanet) & Destination == "55 Cancri e","Europa",
ifelse(is.na(HomePlanet), "Earth", HomePlanet)))test <- test %>%
mutate(HomePlanet= ifelse(is.na(HomePlanet) & Destination == "55 Cancri e","Europa",
ifelse(is.na(HomePlanet), "Earth", HomePlanet)))## # A tibble: 20 × 8
## variable type na na_pct unique min mean max
## <chr> <chr> <int> <dbl> <int> <dbl> <dbl> <dbl>
## 1 PassengerId fct 0 0 8693 NA NA NA
## 2 HomePlanet chr 4 0 4 NA NA NA
## 3 CryoSleep lgl 217 2.5 3 0 0.36 1
## 4 Cabin fct 199 2.3 6561 NA NA NA
## 5 Destination chr 182 2.1 4 NA NA NA
## 6 Age dbl 179 2.1 81 0 28.8 79
## 7 VIP lgl 203 2.3 3 0 0.02 1
## 8 RoomService dbl 181 2.1 1274 0 225. 14327
## 9 FoodCourt dbl 183 2.1 1508 0 458. 29813
## 10 ShoppingMall dbl 208 2.4 1116 0 174. 23492
## 11 Spa dbl 183 2.1 1328 0 311. 22408
## 12 VRDeck dbl 188 2.2 1307 0 305. 24133
## 13 Name fct 200 2.3 8474 NA NA NA
## 14 Transported lgl 0 0 2 0 0.5 1
## 15 group fct 0 0 8693 NA NA NA
## 16 pp fct 0 0 1 NA NA NA
## 17 withgroup dbl 0 0 1 0 0 0
## 18 deck fct 199 2.3 9 NA NA NA
## 19 num fct 0 0 1818 NA NA NA
## 20 side fct 0 0 3 NA NA NA## # A tibble: 19 × 8
## variable type na na_pct unique min mean max
## <chr> <chr> <int> <dbl> <int> <dbl> <dbl> <dbl>
## 1 PassengerId fct 0 0 4277 NA NA NA
## 2 HomePlanet chr 2 0 4 NA NA NA
## 3 CryoSleep lgl 93 2.2 3 0 0.37 1
## 4 Cabin fct 100 2.3 3266 NA NA NA
## 5 Destination chr 92 2.2 4 NA NA NA
## 6 Age dbl 91 2.1 80 0 28.7 79
## 7 VIP lgl 93 2.2 3 0 0.02 1
## 8 RoomService dbl 82 1.9 843 0 219. 11567
## 9 FoodCourt dbl 106 2.5 903 0 439. 25273
## 10 ShoppingMall dbl 98 2.3 716 0 177. 8292
## 11 Spa dbl 101 2.4 834 0 303. 19844
## 12 VRDeck dbl 80 1.9 797 0 311. 22272
## 13 Name fct 94 2.2 4177 NA NA NA
## 14 group fct 0 0 4277 NA NA NA
## 15 pp fct 0 0 1 NA NA NA
## 16 withgroup dbl 0 0 1 0 0 0
## 17 deck fct 100 2.3 9 NA NA NA
## 18 num fct 0 0 1506 NA NA NA
## 19 side fct 0 0 3 NA NA NAmost_frequent_destinations <- train %>%
filter(!is.na(Destination)) %>%
group_by(HomePlanet, Destination ) %>%
summarize(count = n()) %>%
arrange(HomePlanet, desc(count)) %>%
slice(1) %>%
ungroup()## `summarise()` has grouped output by 'HomePlanet'. You can override using the
## `.groups` argument.## # A tibble: 3 × 3
## HomePlanet Destination count
## <chr> <chr> <int>
## 1 Earth TRAPPIST-1e 3251
## 2 Europa TRAPPIST-1e 1189
## 3 Mars TRAPPIST-1e 1475test$HomePlanet <- as.factor(test$HomePlanet)
test$Destination <- as.factor(test$Destination)
train$HomePlanet <- as.factor(train$HomePlanet)
train$Destination <- as.factor(train$Destination)## PassengerId HomePlanet CryoSleep Cabin
## 0001_01: 1 Earth :4772 Mode :logical G/734/S: 8
## 0002_01: 1 Europa:2162 FALSE:5439 B/11/S : 7
## 0003_01: 1 Mars :1759 TRUE :3037 B/201/P: 7
## 0003_02: 1 NA's :217 B/82/S : 7
## 0004_01: 1 C/137/S: 7
## 0005_01: 1 (Other):8458
## (Other):8687 NA's : 199
## Destination Age VIP RoomService
## 55 Cancri e :1800 Min. : 0.00 Mode :logical Min. : 0.0
## PSO J318.5-22: 796 1st Qu.:19.00 FALSE:8291 1st Qu.: 0.0
## TRAPPIST-1e :6097 Median :27.00 TRUE :199 Median : 0.0
## Mean :28.83 NA's :203 Mean : 224.7
## 3rd Qu.:38.00 3rd Qu.: 47.0
## Max. :79.00 Max. :14327.0
## NA's :179 NA's :181
## FoodCourt ShoppingMall Spa VRDeck
## Min. : 0.0 Min. : 0.0 Min. : 0.0 Min. : 0.0
## 1st Qu.: 0.0 1st Qu.: 0.0 1st Qu.: 0.0 1st Qu.: 0.0
## Median : 0.0 Median : 0.0 Median : 0.0 Median : 0.0
## Mean : 458.1 Mean : 173.7 Mean : 311.1 Mean : 304.9
## 3rd Qu.: 76.0 3rd Qu.: 27.0 3rd Qu.: 59.0 3rd Qu.: 46.0
## Max. :29813.0 Max. :23492.0 Max. :22408.0 Max. :24133.0
## NA's :183 NA's :208 NA's :183 NA's :188
## Name Transported group pp withgroup
## Alraium Disivering: 2 Mode :logical 0001_01: 1 :8693 Min. :0
## Ankalik Nateansive: 2 FALSE:4315 0002_01: 1 1st Qu.:0
## Anton Woody : 2 TRUE :4378 0003_01: 1 Median :0
## Apix Wala : 2 0003_02: 1 Mean :0
## Asch Stradick : 2 0004_01: 1 3rd Qu.:0
## (Other) :8483 0005_01: 1 Max. :0
## NA's : 200 (Other):8687
## deck num side
## F :2794 : 199 : 199
## G :2559 82 : 28 P:4206
## E : 876 19 : 22 S:4288
## B : 779 86 : 22
## C : 747 176 : 21
## (Other): 739 56 : 21
## NA's : 199 (Other):8380train <- train %>%
mutate(RoomService = coalesce(RoomService, 0),
FoodCourt = coalesce(FoodCourt, 0),
ShoppingMall = coalesce(ShoppingMall, 0),
Spa = coalesce(Spa, 0),
VRDeck = coalesce(VRDeck, 0))test <- test %>%
mutate(RoomService = coalesce(RoomService, 0),
FoodCourt = coalesce(FoodCourt, 0),
ShoppingMall = coalesce(ShoppingMall, 0),
Spa = coalesce(Spa, 0),
VRDeck = coalesce(VRDeck, 0))test <- test %>%
group_by(HomePlanet, Destination) %>%
mutate_at(vars(Age), ~replace_na(., mean(., na.rm = TRUE)))train <- train %>%
group_by(HomePlanet, Destination) %>%
mutate_at(vars(Age), ~replace_na(., mean(., na.rm = TRUE)))train$expense <- train$RoomService + train$FoodCourt + train$ShoppingMall + train$Spa + train$VRDeck## # A tibble: 21 × 8
## variable type na na_pct unique min mean max
## <chr> <chr> <int> <dbl> <int> <dbl> <dbl> <dbl>
## 1 PassengerId fct 0 0 8693 NA NA NA
## 2 HomePlanet fct 0 0 3 NA NA NA
## 3 CryoSleep chr 98 1.1 3 NA NA NA
## 4 Cabin fct 199 2.3 6561 NA NA NA
## 5 Destination fct 0 0 3 NA NA NA
## 6 Age dbl 0 0 88 0 28.8 79
## 7 VIP lgl 203 2.3 3 0 0.02 1
## 8 RoomService dbl 0 0 1273 0 220. 14327
## 9 FoodCourt dbl 0 0 1507 0 448. 29813
## 10 ShoppingMall dbl 0 0 1115 0 170. 23492
## # ℹ 11 more rowstrain <- transform(train, CryoSleep = replace(CryoSleep, is.na(CryoSleep) & expense-0 & Age>12,
"TRUE"))test <- transform(train, CryoSleep = replace(CryoSleep, is.na(CryoSleep) & expense-0 & Age>12,
"TRUE"))## # A tibble: 21 × 8
## variable type na na_pct unique min mean max
## <chr> <chr> <int> <dbl> <int> <dbl> <dbl> <dbl>
## 1 PassengerId fct 0 0 8693 NA NA NA
## 2 HomePlanet fct 0 0 3 NA NA NA
## 3 CryoSleep chr 98 1.1 3 NA NA NA
## 4 Cabin fct 199 2.3 6561 NA NA NA
## 5 Destination fct 0 0 3 NA NA NA
## 6 Age dbl 0 0 88 0 28.8 79
## 7 VIP lgl 203 2.3 3 0 0.02 1
## 8 RoomService dbl 0 0 1273 0 220. 14327
## 9 FoodCourt dbl 0 0 1507 0 448. 29813
## 10 ShoppingMall dbl 0 0 1115 0 170. 23492
## # ℹ 11 more rows## # A tibble: 21 × 8
## variable type na na_pct unique min mean max
## <chr> <chr> <int> <dbl> <int> <dbl> <dbl> <dbl>
## 1 PassengerId fct 0 0 8693 NA NA NA
## 2 HomePlanet fct 0 0 3 NA NA NA
## 3 CryoSleep chr 0 0 2 NA NA NA
## 4 Cabin fct 199 2.3 6561 NA NA NA
## 5 Destination fct 0 0 3 NA NA NA
## 6 Age dbl 0 0 88 0 28.8 79
## 7 VIP lgl 203 2.3 3 0 0.02 1
## 8 RoomService dbl 0 0 1273 0 220. 14327
## 9 FoodCourt dbl 0 0 1507 0 448. 29813
## 10 ShoppingMall dbl 0 0 1115 0 170. 23492
## # ℹ 11 more rows## PassengerId HomePlanet CryoSleep Cabin
## 0001_01: 1 Earth :4772 Length:8693 G/734/S: 8
## 0002_01: 1 Europa:2162 Class :character B/11/S : 7
## 0003_01: 1 Mars :1759 Mode :character B/201/P: 7
## 0003_02: 1 B/82/S : 7
## 0004_01: 1 C/137/S: 7
## 0005_01: 1 (Other):8458
## (Other):8687 NA's : 199
## Destination Age VIP RoomService
## 55 Cancri e :1800 Min. : 0.00 Mode :logical Min. : 0
## PSO J318.5-22: 796 1st Qu.:20.00 FALSE:8291 1st Qu.: 0
## TRAPPIST-1e :6097 Median :27.00 TRUE :199 Median : 0
## Mean :28.83 NA's :203 Mean : 220
## 3rd Qu.:37.00 3rd Qu.: 41
## Max. :79.00 Max. :14327
##
## FoodCourt ShoppingMall Spa VRDeck
## Min. : 0.0 Min. : 0.0 Min. : 0.0 Min. : 0.0
## 1st Qu.: 0.0 1st Qu.: 0.0 1st Qu.: 0.0 1st Qu.: 0.0
## Median : 0.0 Median : 0.0 Median : 0.0 Median : 0.0
## Mean : 448.4 Mean : 169.6 Mean : 304.6 Mean : 298.3
## 3rd Qu.: 61.0 3rd Qu.: 22.0 3rd Qu.: 53.0 3rd Qu.: 40.0
## Max. :29813.0 Max. :23492.0 Max. :22408.0 Max. :24133.0
##
## Name Transported group pp withgroup
## Alraium Disivering: 2 Mode :logical 0001_01: 1 :8693 Min. :0
## Ankalik Nateansive: 2 FALSE:4315 0002_01: 1 1st Qu.:0
## Anton Woody : 2 TRUE :4378 0003_01: 1 Median :0
## Apix Wala : 2 0003_02: 1 Mean :0
## Asch Stradick : 2 0004_01: 1 3rd Qu.:0
## (Other) :8483 0005_01: 1 Max. :0
## NA's : 200 (Other):8687
## deck num side expense cryosleep
## F :2794 : 199 : 199 Min. : 0 FALSE:5656
## G :2559 82 : 28 P:4206 1st Qu.: 0 TRUE :3037
## E : 876 19 : 22 S:4288 Median : 716
## B : 779 86 : 22 Mean : 1441
## C : 747 176 : 21 3rd Qu.: 1441
## (Other): 739 56 : 21 Max. :35987
## NA's : 199 (Other):8380## # A tibble: 22 × 8
## variable type na na_pct unique min mean max
## <chr> <chr> <int> <dbl> <int> <dbl> <dbl> <dbl>
## 1 PassengerId fct 0 0 8693 NA NA NA
## 2 HomePlanet fct 0 0 3 NA NA NA
## 3 CryoSleep chr 0 0 2 NA NA NA
## 4 Cabin fct 199 2.3 6561 NA NA NA
## 5 Destination fct 0 0 3 NA NA NA
## 6 Age dbl 0 0 88 0 28.8 79
## 7 VIP lgl 0 0 2 0 0.02 1
## 8 RoomService dbl 0 0 1273 0 220. 14327
## 9 FoodCourt dbl 0 0 1507 0 448. 29813
## 10 ShoppingMall dbl 0 0 1115 0 170. 23492
## # ℹ 12 more rows## PassengerId HomePlanet CryoSleep Cabin
## 0001_01: 1 Earth :4772 Length:8693 G/734/S: 8
## 0002_01: 1 Europa:2162 Class :character B/11/S : 7
## 0003_01: 1 Mars :1759 Mode :character B/201/P: 7
## 0003_02: 1 B/82/S : 7
## 0004_01: 1 C/137/S: 7
## 0005_01: 1 (Other):8458
## (Other):8687 NA's : 199
## Destination Age VIP RoomService
## 55 Cancri e :1800 Min. : 0.00 Mode :logical Min. : 0
## PSO J318.5-22: 796 1st Qu.:20.00 FALSE:8494 1st Qu.: 0
## TRAPPIST-1e :6097 Median :27.00 TRUE :199 Median : 0
## Mean :28.83 Mean : 220
## 3rd Qu.:37.00 3rd Qu.: 41
## Max. :79.00 Max. :14327
##
## FoodCourt ShoppingMall Spa VRDeck
## Min. : 0.0 Min. : 0.0 Min. : 0.0 Min. : 0.0
## 1st Qu.: 0.0 1st Qu.: 0.0 1st Qu.: 0.0 1st Qu.: 0.0
## Median : 0.0 Median : 0.0 Median : 0.0 Median : 0.0
## Mean : 448.4 Mean : 169.6 Mean : 304.6 Mean : 298.3
## 3rd Qu.: 61.0 3rd Qu.: 22.0 3rd Qu.: 53.0 3rd Qu.: 40.0
## Max. :29813.0 Max. :23492.0 Max. :22408.0 Max. :24133.0
##
## Name Transported group pp withgroup
## Alraium Disivering: 2 Mode :logical 0001_01: 1 :8693 Min. :0
## Ankalik Nateansive: 2 FALSE:4315 0002_01: 1 1st Qu.:0
## Anton Woody : 2 TRUE :4378 0003_01: 1 Median :0
## Apix Wala : 2 0003_02: 1 Mean :0
## Asch Stradick : 2 0004_01: 1 3rd Qu.:0
## (Other) :8483 0005_01: 1 Max. :0
## NA's : 200 (Other):8687
## deck num side expense cryosleep
## F :2794 : 199 : 199 Min. : 0 FALSE:5656
## G :2559 82 : 28 P:4206 1st Qu.: 0 TRUE :3037
## E : 876 19 : 22 S:4288 Median : 716
## B : 779 86 : 22 Mean : 1441
## C : 747 176 : 21 3rd Qu.: 1441
## (Other): 739 56 : 21 Max. :35987
## NA's : 199 (Other):8380most_frequent_deck <- train %>%
filter(!is.na(deck)) %>%
group_by (HomePlanet, deck) %>%
summarize(count = n()) %>%
arrange(HomePlanet, desc(count)) %>%
slice (1) %>%
ungroup()## `summarise()` has grouped output by 'HomePlanet'. You can override using the
## `.groups` argument.## # A tibble: 3 × 3
## HomePlanet deck count
## <fct> <fct> <int>
## 1 Earth G 2553
## 2 Europa B 771
## 3 Mars F 1110train <- train %>%
mutate(deck = ifelse(is.na(deck) & HomePlanet == "Earth", "G",
ifelse(is.na(deck) & HomePlanet == "Europa", "B",
ifelse(is.na(deck) & HomePlanet == "Mars", "F", deck))))test <- test %>%
mutate(deck = ifelse(is.na(deck) & HomePlanet == "Earth", "G",
ifelse(is.na(deck) & HomePlanet == "Europa", "B",
ifelse(is.na(deck) & HomePlanet == "Mars", "F", deck))))most_frequent_side <- train %>%
filter(!is.na(deck)) %>%
group_by (HomePlanet, deck) %>%
summarize(count = n()) %>%
arrange(HomePlanet, desc(count)) %>%
slice (1) %>%
ungroup()## `summarise()` has grouped output by 'HomePlanet'. You can override using the
## `.groups` argument.## # A tibble: 3 × 3
## HomePlanet deck count
## <chr> <chr> <int>
## 1 Earth G 2652
## 2 Europa B 834
## 3 Mars F 1147train <- train %>%
mutate(deck = ifelse(is.na(side) & HomePlanet == "Earth", "G",
ifelse(is.na(side) & HomePlanet == "Europa", "B",
ifelse(is.na(side) & HomePlanet == "Mars", "F", deck))))test <- test %>%
mutate(deck = ifelse(is.na(side) & HomePlanet == "Earth", "G",
ifelse(is.na(side) & HomePlanet == "Europa", "B",
ifelse(is.na(side) & HomePlanet == "Mars", "F", deck))))## # A tibble: 22 × 8
## variable type na na_pct unique min mean max
## <chr> <chr> <int> <dbl> <int> <dbl> <dbl> <dbl>
## 1 PassengerId fct 0 0 8693 NA NA NA
## 2 HomePlanet chr 0 0 3 NA NA NA
## 3 CryoSleep chr 0 0 2 NA NA NA
## 4 Cabin fct 199 2.3 6561 NA NA NA
## 5 Destination fct 0 0 3 NA NA NA
## 6 Age dbl 0 0 88 0 28.8 79
## 7 VIP lgl 0 0 2 0 0.02 1
## 8 RoomService dbl 0 0 1273 0 220. 14327
## 9 FoodCourt dbl 0 0 1507 0 448. 29813
## 10 ShoppingMall dbl 0 0 1115 0 170. 23492
## # ℹ 12 more rows## Adding missing grouping variables: `group`## Adding missing grouping variables: `group`## # A tibble: 18 × 8
## variable type na na_pct unique min mean max
## <chr> <chr> <int> <dbl> <int> <dbl> <dbl> <dbl>
## 1 group fct 0 0 8693 NA NA NA
## 2 PassengerId fct 0 0 8693 NA NA NA
## 3 HomePlanet chr 0 0 3 NA NA NA
## 4 CryoSleep chr 0 0 2 NA NA NA
## 5 Destination fct 0 0 3 NA NA NA
## 6 Age dbl 0 0 88 0 28.8 79
## 7 VIP lgl 0 0 2 0 0.02 1
## 8 RoomService dbl 0 0 1273 0 220. 14327
## 9 FoodCourt dbl 0 0 1507 0 448. 29813
## 10 ShoppingMall dbl 0 0 1115 0 170. 23492
## 11 Spa dbl 0 0 1327 0 305. 22408
## 12 VRDeck dbl 0 0 1306 0 298. 24133
## 13 Transported lgl 0 0 2 0 0.5 1
## 14 withgroup dbl 0 0 1 0 0 0
## 15 deck chr 0 0 8 NA NA NA
## 16 side chr 0 0 3 NA NA NA
## 17 expense dbl 0 0 2336 0 1441. 35987
## 18 cryosleep fct 0 0 2 NA NA NA## `mutate_if()` ignored the following grouping variables:
## • Column `group`## `mutate_if()` ignored the following grouping variables:
## • Column `group`## group PassengerId HomePlanet CryoSleep
## 0001_01: 1 Length:8693 Europa:2162 FALSE:5656
## 0002_01: 1 Class :character Earth :4772 TRUE :3037
## 0003_01: 1 Mode :character Mars :1759
## 0003_02: 1
## 0004_01: 1
## 0005_01: 1
## (Other):8687
## Destination Age VIP RoomService
## 55 Cancri e :1800 Min. : 0.00 Mode :logical Min. : 0
## PSO J318.5-22: 796 1st Qu.:20.00 FALSE:8494 1st Qu.: 0
## TRAPPIST-1e :6097 Median :27.00 TRUE :199 Median : 0
## Mean :28.83 Mean : 220
## 3rd Qu.:37.00 3rd Qu.: 41
## Max. :79.00 Max. :14327
##
## FoodCourt ShoppingMall Spa VRDeck
## Min. : 0.0 Min. : 0.0 Min. : 0.0 Min. : 0.0
## 1st Qu.: 0.0 1st Qu.: 0.0 1st Qu.: 0.0 1st Qu.: 0.0
## Median : 0.0 Median : 0.0 Median : 0.0 Median : 0.0
## Mean : 448.4 Mean : 169.6 Mean : 304.6 Mean : 298.3
## 3rd Qu.: 61.0 3rd Qu.: 22.0 3rd Qu.: 53.0 3rd Qu.: 40.0
## Max. :29813.0 Max. :23492.0 Max. :22408.0 Max. :24133.0
##
## Transported withgroup deck side expense
## Mode :logical Min. :0 F :2831 P:4206 Min. : 0
## FALSE:4315 1st Qu.:0 G :2658 S:4288 1st Qu.: 0
## TRUE :4378 Median :0 E : 876 : 199 Median : 716
## Mean :0 B : 842 Mean : 1441
## 3rd Qu.:0 C : 747 3rd Qu.: 1441
## Max. :0 D : 478 Max. :35987
## (Other): 261
## cryosleep
## FALSE:5656
## TRUE :3037
##
##
##
##
## test$HomePlanet <- as.factor(test$HomePlanet)
test$Destination <- as.factor(test$Destination)
test$deck <- as.factor(test$deck)
test$side <- as.factor(test$side)train$HomePlanet <- as.factor(train$HomePlanet)
train$Destination <- as.factor(train$Destination)
train$deck <- as.factor(train$deck)
train$side <- as.factor(train$side)## Warning in cor(D): the standard deviation is zero## corrplot 0.92 loaded
library(DataExplorer)
create_report(train)##
## Call:
## lm(formula = Transported ~ ., data = train[, 2:16])
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.40936 -0.31038 -0.03383 0.29023 1.78012
##
## Coefficients: (2 not defined because of singularities)
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.562e-01 4.909e-02 7.256 4.33e-13 ***
## HomePlanetEuropa 2.057e-01 2.771e-02 7.422 1.26e-13 ***
## HomePlanetMars 9.750e-02 1.456e-02 6.698 2.25e-11 ***
## CryoSleepTRUE 3.817e-01 1.152e-02 33.134 < 2e-16 ***
## DestinationPSO J318.5-22 -4.337e-02 1.807e-02 -2.399 0.0164 *
## DestinationTRAPPIST-1e -4.628e-02 1.133e-02 -4.085 4.45e-05 ***
## Age -2.306e-03 3.141e-04 -7.341 2.31e-13 ***
## VIPTRUE -3.816e-02 2.976e-02 -1.282 0.1998
## RoomService -1.182e-04 7.056e-06 -16.757 < 2e-16 ***
## FoodCourt 4.283e-05 3.057e-06 14.009 < 2e-16 ***
## ShoppingMall 7.870e-05 7.460e-06 10.550 < 2e-16 ***
## Spa -8.640e-05 4.116e-06 -20.993 < 2e-16 ***
## VRDeck -8.271e-05 4.117e-06 -20.090 < 2e-16 ***
## withgroup NA NA NA NA
## deckB 1.215e-01 2.898e-02 4.191 2.80e-05 ***
## deckC 1.518e-01 2.928e-02 5.183 2.23e-07 ***
## deckD 4.223e-02 3.477e-02 1.214 0.2246
## deckE -2.038e-03 3.582e-02 -0.057 0.9546
## deckF 9.154e-02 3.656e-02 2.504 0.0123 *
## deckG 4.482e-02 3.810e-02 1.176 0.2395
## deckT 5.289e-02 1.816e-01 0.291 0.7709
## sideP -2.210e-02 2.955e-02 -0.748 0.4546
## sideS 6.413e-02 2.952e-02 2.172 0.0299 *
## expense NA NA NA NA
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.4015 on 8671 degrees of freedom
## Multiple R-squared: 0.3567, Adjusted R-squared: 0.3552
## F-statistic: 229 on 21 and 8671 DF, p-value: < 2.2e-16library(caTools)
set.seed(123)
split = sample.split(train$Transported, SplitRatio = 0.75)
train_train = subset(train, split == TRUE)
train_test = subset(train, split == FALSE)##
## Call:
## lm(formula = Transported ~ ., data = train_train[, -c(1)])
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.48713 -0.31346 -0.03176 0.29206 1.75532
##
## Coefficients: (3 not defined because of singularities)
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.751e-01 5.686e-02 6.596 4.56e-11 ***
## HomePlanetEuropa 1.886e-01 3.217e-02 5.861 4.82e-09 ***
## HomePlanetMars 8.668e-02 1.675e-02 5.174 2.36e-07 ***
## CryoSleepTRUE 3.776e-01 1.328e-02 28.445 < 2e-16 ***
## DestinationPSO J318.5-22 -5.948e-02 2.081e-02 -2.858 0.004279 **
## DestinationTRAPPIST-1e -4.993e-02 1.308e-02 -3.816 0.000137 ***
## Age -2.442e-03 3.650e-04 -6.690 2.42e-11 ***
## VIPTRUE -1.618e-02 3.385e-02 -0.478 0.632715
## RoomService -1.178e-04 7.789e-06 -15.118 < 2e-16 ***
## FoodCourt 3.890e-05 3.468e-06 11.216 < 2e-16 ***
## ShoppingMall 8.188e-05 8.413e-06 9.733 < 2e-16 ***
## Spa -8.526e-05 4.711e-06 -18.099 < 2e-16 ***
## VRDeck -8.201e-05 4.776e-06 -17.170 < 2e-16 ***
## withgroup NA NA NA NA
## deckB 1.155e-01 3.313e-02 3.485 0.000495 ***
## deckC 1.449e-01 3.345e-02 4.333 1.49e-05 ***
## deckD 2.188e-02 3.961e-02 0.552 0.580706
## deckE -3.674e-02 4.147e-02 -0.886 0.375642
## deckF 7.079e-02 4.208e-02 1.682 0.092551 .
## deckG 2.552e-02 4.388e-02 0.582 0.560908
## deckT 4.365e-02 1.822e-01 0.240 0.810637
## sideP -7.773e-03 3.454e-02 -0.225 0.821954
## sideS 8.234e-02 3.451e-02 2.386 0.017047 *
## expense NA NA NA NA
## cryosleepTRUE NA NA NA NA
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.4014 on 6498 degrees of freedom
## Multiple R-squared: 0.3577, Adjusted R-squared: 0.3556
## F-statistic: 172.3 on 21 and 6498 DF, p-value: < 2.2e-16## reg_transported_tahmin
## transported_gercek 0 1
## 0 905 174
## 1 325 769## [1] 0.7703636## reg_transported_tahmin
## transported_gercek 0 1
## 0 2680 556
## 1 947 2337## [1] 0.7694785## Loading required package: rJava## Loading required package: leapsregresyon_opt <- glmulti(Transported ~ HomePlanet + Cryosleep + Destination + Age + VIP + RoomService + FoodCourt + ShoppingMall + Spa + VRDeck + withgroup + deck + side, + level = 1, crit = bic, data =train)modelglmulti <- lm(Transported ~ 1 + HomePlanet + Destination + deck + side + Cryosleep + Age + RoomService + FoodCourt + ShoppingMall + Spa + VRDeck, data = train )reg_tahmin_glmulti = predict(modelglmulti, newdata = test[, -c(1)])reg_transported_test_tahmin_glmulti <- ifelse(reg_tahmin_glmulti > 0.5, TRUE, FALSE)Transported <- as.character(reg_transported_test_tahmin_glmulti)
PassengerId <- test$PassengerIdTransported<-as.vector(Transported)submission_regrasyon_glmulti <- cbind(PassengerId, Transported)submission_regrasyon_glmulti <- as.data.frame(submission_regrasyon_glmulti)submission_regrasyon_glmulti$Transported <- str_to_title(submission_regrasyon_glmulti$Transpored)write.csv(submission_regrasyon_glmulti, "submission_regrasyon_glmulti.csv", row.names =FALSE, quote=FALSE)modellog <- lm(Transported ~ 1 + HomePlanet + Destination + deck + side + cryosleep +Age + RoomService + FoodCourt + ShoppingMall + Spa + VRDeck, data = train_log)## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred##
## 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) -6.785e-02 4.240e-01 -0.160 0.872875
## HomePlanetEuropa 1.386e+00 2.512e-01 5.519 3.41e-08 ***
## HomePlanetMars 5.227e-01 1.083e-01 4.824 1.41e-06 ***
## CryoSleepTRUE 1.294e+00 9.154e-02 14.139 < 2e-16 ***
## DestinationPSO J318.5-22 -5.017e-01 1.297e-01 -3.869 0.000109 ***
## DestinationTRAPPIST-1e -4.540e-01 9.401e-02 -4.829 1.37e-06 ***
## Age -9.359e-03 2.397e-03 -3.905 9.42e-05 ***
## VIPTRUE -1.870e-01 2.935e-01 -0.637 0.524085
## RoomService -1.731e-03 1.135e-04 -15.246 < 2e-16 ***
## FoodCourt 4.557e-04 4.444e-05 10.255 < 2e-16 ***
## ShoppingMall 5.211e-04 7.524e-05 6.927 4.30e-12 ***
## Spa -1.951e-03 1.161e-04 -16.810 < 2e-16 ***
## VRDeck -1.879e-03 1.157e-04 -16.243 < 2e-16 ***
## withgroup NA NA NA NA
## deckB 1.260e+00 2.916e-01 4.320 1.56e-05 ***
## deckC 2.398e+00 3.301e-01 7.266 3.71e-13 ***
## deckD 5.535e-01 3.191e-01 1.735 0.082774 .
## deckE -7.900e-02 3.255e-01 -0.243 0.808213
## deckF 5.733e-01 3.287e-01 1.744 0.081110 .
## deckG 1.611e-01 3.378e-01 0.477 0.633416
## deckT -2.762e-01 1.816e+00 -0.152 0.879136
## sideP -1.471e-01 2.452e-01 -0.600 0.548453
## sideS 4.591e-01 2.455e-01 1.870 0.061496 .
## expense NA NA NA NA
## cryosleepTRUE 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: 5612.7 on 6498 degrees of freedom
## AIC: 5656.7
##
## Number of Fisher Scoring iterations: 7## 2 3 7 14 16 21
## -1.0391743 -11.0422312 0.8975435 -1.6060140 -0.2496921 -1.7609533## ── 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() ──
## ✖ scales::discard() masks purrr::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$Transported <- as.factor(result$Transported)
result$logistic_transported_tahmin <- as.factor(result$logistic_transported_tahmin)## # A tibble: 1 × 3
## .metric .estimator .estimate
## <chr> <chr> <dbl>
## 1 accuracy binary 0.785## Truth
## Prediction 0 1
## 0 921 310
## 1 158 784## 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## logistic_transported_tahmin
## transported_gercek 0 1
## 0 921 158
## 1 310 784## [1] 0.7846295## Confusion Matrix and Statistics
##
## Reference
## Prediction 0 1
## 0 921 158
## 1 310 784
##
## Accuracy : 0.7846
## 95% CI : (0.7667, 0.8018)
## No Information Rate : 0.5665
## P-Value [Acc > NIR] : < 2.2e-16
##
## Kappa : 0.5697
##
## Mcnemar's Test P-Value : 2.952e-12
##
## Sensitivity : 0.7482
## Specificity : 0.8323
## Pos Pred Value : 0.8536
## Neg Pred Value : 0.7166
## Prevalence : 0.5665
## Detection Rate : 0.4238
## Detection Prevalence : 0.4965
## Balanced Accuracy : 0.7902
##
## 'Positive' Class : 0
## ## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred##
## 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##
## 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 0.6251545 0.1749073 0.1999382
## TRUE 0.4649817 0.3285627 0.2064555
##
## 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 30.01152 13.45216
## TRUE 27.84835 14.87502
##
## 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 0
## TRUE 0 0
##
## deck
## Y A B C D E
## FALSE 0.0296662546 0.0553152040 0.0568603214 0.0655129790 0.1338071693
## TRUE 0.0301461632 0.1419001218 0.1178440926 0.0484165652 0.0672959805
## deck
## Y F G T
## FALSE 0.3634116193 0.2941903585 0.0012360939
## TRUE 0.2834957369 0.3105968331 0.0003045067
##
## side
## Y P S
## FALSE 0.02348578 0.54079110 0.43572311
## TRUE 0.02131547 0.43300853 0.54567600
##
## expense
## Y [,1] [,2]
## FALSE 2053.8702 3224.219
## TRUE 884.6376 2307.962
##
## cryosleep
## Y FALSE TRUE
## FALSE 0.8702101 0.1297899
## TRUE 0.4336175 0.5663825## FALSE. TRUE.
## 1 0.32131320 6.786868e-01
## 2 1.00000000 3.298257e-135
## 3 0.06619047 9.338095e-01
## 4 0.70820285 2.917971e-01
## 5 0.03490270 9.650973e-01
## 6 0.61151399 3.884860e-01## Transported
## 2 1
## 3 0
## 7 1
## 14 0
## 16 0
## 21 0## Transported_pred_nb
## Transported_test_train 0 1
## 0 530 549
## 1 86 1008## [1] 0.4965486## Warning in svm.default(x, y, scale = scale, ..., na.action = na.action):
## Variable(s) 'withgroup' constant. Cannot scale data.## .
## 2 FALSE
## 3 FALSE
## 7 FALSE
## 14 FALSE
## 16 FALSE
## 21 FALSETransported_pred_svm = ifelse(preds$. == TRUE, 1, 0)
cm = table(Transported_test_train, Transported_pred_svm)## Transported_pred_svm
## Transported_test_train 0 1
## 0 897 182
## 1 378 716## [1] 0.4755399## Warning in svm.default(x, y, scale = scale, ..., na.action = na.action):
## Variable(s) 'withgroup' constant. Cannot scale data.Transported <- as.character(Transported_pred_svm)
PassengerId <- test$PassengerId
Transported <- as.vector(Transported)
sample_submision <- cbind(PassengerId, Transported)## Warning in cbind(PassengerId, Transported): number of rows of result is not a
## multiple of vector length (arg 2)sample_submision <- as.data.frame(sample_submision)
write.csv(sample_submision, "sub_svm_csv", row.names = FALSE, quote = FALSE)fit_svm <- svm(Transported ~ ., data = train_train[, -1],
type= 'C-classification',
kernel = 'radial' )## Warning in svm.default(x, y, scale = scale, ..., na.action = na.action):
## Variable(s) 'withgroup' constant. Cannot scale data.Transported_pred_svm = ifelse(preds$.== TRUE, 1,0)
cm = table(Transported_test_train, Transported_pred_svm) ## Transported_pred_svm
## Transported_test_train 0 1
## 0 897 182
## 1 378 716## [1] 0.4755399## Warning in svm.default(x, y, scale = scale, ..., na.action = na.action):
## Variable(s) 'withgroup' constant. Cannot scale data.Transported <- as.character(Transported_pred_svm)
PassengerId <- test$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_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
##
## Attaching package: 'rpart'## The following object is masked from 'package:dials':
##
## prune## 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## 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 0.04981598 1 0.7667864 0.7671015 0.0103212807
## 3 0.02807107 2 0.7169704 0.7173428 0.0090686704
## 4 0.02784643 3 0.6888994 0.7031918 0.0095398822
## 5 0.01770479 4 0.6610529 0.6654236 0.0096767137
## 6 0.01381587 5 0.6433481 0.6488743 0.0100411278
## 7 0.01194576 6 0.6295323 0.6381676 0.0101816720
## 8 0.01164524 7 0.6175865 0.6303299 0.0102037441
## 9 0.01000000 8 0.6059413 0.6235169 0.0102521030
##
## Variable importance
## expense cryosleep CryoSleep FoodCourt Spa VRDeck
## 21 16 16 12 11 10
## deck HomePlanet ShoppingMall Age Destination
## 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)
## 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)
## Surrogate splits:
## CryoSleep < 0.5 to the left, agree=0.932, adj=0.837, (0 split)
## 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)
##
## 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=0.201, (0 split)
## HomePlanet splits as LRL, agree=0.878, adj=0.161, (0 split)
## Spa < 8955.5 to the left, agree=0.856, adj=0.009, (0 split)
## VRDeck < 11692 to the left, agree=0.856, adj=0.009, (0 split)
##
## Node number 3: 2725 observations, complexity param=0.04981598
## mean=0.7886239, MSE=0.1666963
## left son=6 (1449 obs) right son=7 (1276 obs)
## Primary splits:
## deck splits as RRRRLRL-, improve=0.17874760, (0 missing)
## HomePlanet splits as LRR, improve=0.12440710, (0 missing)
## Destination splits as RLL, improve=0.02625136, (0 missing)
## CryoSleep < 0.5 to the left, improve=0.02268236, (0 missing)
## cryosleep < 0.5 to the left, improve=0.02268236, (0 missing)
## Surrogate splits:
## HomePlanet splits as LRR, agree=0.933, adj=0.857, (0 split)
## Age < 24.5 to the left, agree=0.625, adj=0.200, (0 split)
## Destination splits as RLL, agree=0.591, adj=0.126, (0 split)
## VIP < 0.5 to the left, agree=0.538, adj=0.014, (0 split)
## side splits as RLL, agree=0.533, adj=0.002, (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)
## deck splits as LLRLLRRL, improve=0.02812225, (0 missing)
## side splits as LLR, improve=0.02807513, (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: 1449 observations
## mean=0.6266391, MSE=0.2339625
##
## Node number 7: 1276 observations
## mean=0.9725705, MSE=0.02667709
##
## 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=0.02438006, (0 missing)
## Spa < 537.5 to the right, improve=0.01895890, (0 missing)
## RoomService < 400.5 to the right, improve=0.01706521, (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 LLR, improve=0.04323810, (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=0.07491751, (0 missing)
## Spa < 500 to the right, improve=0.07353369, (0 missing)
## deck splits as LLLLRRRL, improve=0.05075444, (0 missing)
## Surrogate splits:
## HomePlanet splits as RLL, agree=0.867, adj=0.667, (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)
## FoodCourt < 907 to the right, agree=0.722, adj=0.304, (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
## .
## 2 0.1504596
## 3 0.1382114
## 7 0.8496503
## 14 0.1504596
## 16 0.4864865
## 21 0.1504596Transported_pred_tree = ifelse(preds$. >0.5, 1, 0)
cm = table(Transported_test_train, Transported_pred_tree)## Transported_pred_tree
## Transported_test_train 0 1
## 0 807 272
## 1 237 857## [1] 0.679471fit_tree <- rpart(Transported ~ ., data = train[, -1])
preds <- predict(fit_tree, newdata = test) %>%
data.frame()Transported <- as.character(Transported_pred_tree)
PassengerId <- test$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_tree.csv", row.names = FALSE, quote = FALSE)## Warning in randomForest.default(m, y, ...): The response has five or fewer
## unique values. Are you sure you want to do regression?## IncNodePurity
## HomePlanet 39.895123
## CryoSleep 80.141134
## Destination 25.772116
## Age 94.120805
## VIP 2.593687
## RoomService 108.064499
## FoodCourt 116.320446
## ShoppingMall 93.053181
## Spa 115.939271
## VRDeck 105.156138
## withgroup 0.000000
## deck 97.745154
## side 26.504009
## expense 250.784767
## cryosleep 84.477854
## .
## 2 0.09394136
## 3 0.13646667
## 7 0.80923810
## 14 0.22278600
## 16 0.72546365
## 21 0.15576667Transported_pred_forest = ifelse(preds$. >0.5, 1, 0)
cm = table(Transported_test_train, Transported_pred_forest)## Transported_pred_forest
## Transported_test_train 0 1
## 0 823 256
## 1 177 917## [1] 0.4965486## Warning in randomForest.default(m, y, ...): The response has five or fewer
## unique values. Are you sure you want to do regression?Transported <- as.character(Transported_pred_tree)
PassengerId <- test$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_forest.csv", row.names = FALSE, quote = FALSE)