LBB Unsupervised Learning
Roji
4/10/2020
Overview
Berikut ini adalah report yang menjelaskan prediksi tingkat survival penumpang, ketika terjadinya peristiwa karamnya Titanic.
library(dplyr)##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionlibrary(rsample)## Loading required package: tidyrlibrary(tm)## Loading required package: NLPlibrary(tidyverse)## -- Attaching packages --------------------------------------------------------------- tidyverse 1.3.0 --## v ggplot2 3.2.1 v purrr 0.3.3
## v tibble 2.1.3 v stringr 1.4.0
## v readr 1.3.1 v forcats 0.4.0## -- Conflicts ------------------------------------------------------------------ tidyverse_conflicts() --
## x ggplot2::annotate() masks NLP::annotate()
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()library(e1071)
library(caret)## Loading required package: lattice##
## Attaching package: 'caret'## The following object is masked from 'package:purrr':
##
## liftData Preparation
- Read Data
titanic<- read.csv("train.csv")
head(titanic)## PassengerId Survived Pclass
## 1 1 0 3
## 2 2 1 1
## 3 3 1 3
## 4 4 1 1
## 5 5 0 3
## 6 6 0 3
## Name Sex Age SibSp Parch
## 1 Braund, Mr. Owen Harris male 22 1 0
## 2 Cumings, Mrs. John Bradley (Florence Briggs Thayer) female 38 1 0
## 3 Heikkinen, Miss. Laina female 26 0 0
## 4 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35 1 0
## 5 Allen, Mr. William Henry male 35 0 0
## 6 Moran, Mr. James male NA 0 0
## Ticket Fare Cabin Embarked
## 1 A/5 21171 7.2500 S
## 2 PC 17599 71.2833 C85 C
## 3 STON/O2. 3101282 7.9250 S
## 4 113803 53.1000 C123 S
## 5 373450 8.0500 S
## 6 330877 8.4583 Qglimpse(titanic)## Observations: 891
## Variables: 12
## $ PassengerId <int> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, ...
## $ Survived <int> 0, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 1, 0, 1, 0...
## $ Pclass <int> 3, 1, 3, 1, 3, 3, 1, 3, 3, 2, 3, 1, 3, 3, 3, 2, 3, 2, 3...
## $ Name <fct> "Braund, Mr. Owen Harris", "Cumings, Mrs. John Bradley ...
## $ Sex <fct> male, female, female, female, male, male, male, male, f...
## $ Age <dbl> 22, 38, 26, 35, 35, NA, 54, 2, 27, 14, 4, 58, 20, 39, 1...
## $ SibSp <int> 1, 1, 0, 1, 0, 0, 0, 3, 0, 1, 1, 0, 0, 1, 0, 0, 4, 0, 1...
## $ Parch <int> 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 1, 0, 0, 5, 0, 0, 1, 0, 0...
## $ Ticket <fct> A/5 21171, PC 17599, STON/O2. 3101282, 113803, 373450, ...
## $ Fare <dbl> 7.2500, 71.2833, 7.9250, 53.1000, 8.0500, 8.4583, 51.86...
## $ Cabin <fct> , C85, , C123, , , E46, , , , G6, C103, , , , , , , , ,...
## $ Embarked <fct> S, C, S, S, S, Q, S, S, S, C, S, S, S, S, S, S, Q, S, S...- Membuang variabel yang tidak perlu, mengubah tipe data salah satunya “Sex” dimana male menjadi 1 dan female menjadi 0 dan seterusnya
titanic_clean<- titanic %>%
mutate(Survived=as.factor(Survived),
Sex=as.numeric(Sex)-1,
Cabin=as.numeric(Cabin)
) %>%
select(-Name,-Ticket,-Embarked,-PassengerId) %>%
na.omit()
glimpse(titanic_clean)## Observations: 714
## Variables: 8
## $ Survived <fct> 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 1, 0, 0, 0, 1, 1...
## $ Pclass <int> 3, 1, 3, 1, 3, 1, 3, 3, 2, 3, 1, 3, 3, 3, 2, 3, 3, 2, 2, 3...
## $ Sex <dbl> 1, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 1, 1, 0...
## $ Age <dbl> 22, 38, 26, 35, 35, 54, 2, 27, 14, 4, 58, 20, 39, 14, 55, ...
## $ SibSp <int> 1, 1, 0, 1, 0, 0, 3, 0, 1, 1, 0, 0, 1, 0, 0, 4, 1, 0, 0, 0...
## $ Parch <int> 0, 0, 0, 0, 0, 0, 1, 2, 0, 1, 0, 0, 5, 0, 0, 1, 0, 0, 0, 0...
## $ Fare <dbl> 7.2500, 71.2833, 7.9250, 53.1000, 8.0500, 51.8625, 21.0750...
## $ Cabin <dbl> 1, 83, 1, 57, 1, 131, 1, 1, 1, 147, 51, 1, 1, 1, 1, 1, 1, ...- Mengetahu proporsi level target
prop.table(table(titanic$Survived))##
## 0 1
## 0.6161616 0.3838384- Melakukan subsampling pada data imbalance
titanic_up <- upSample(x =titanic_clean[,-1],y = titanic_clean$Survived,yname = "Survived")
prop.table(table(titanic_up$Survived))##
## 0 1
## 0.5 0.5Melakukan cross validation (80% : 30%)
- Cross Validasi
library(rsample)
set.seed(100)
splitted <- initial_split(data = titanic_up, prop = 0.80, strata = "Survived")
up_train <- training(splitted)
up_val <- testing(splitted)- Mengecek proporsi kelas target
prop.table(table(up_train$Survived))##
## 0 1
## 0.5 0.5Membuat model random forest menggunakan data train dengan menerapkan k-fold cross validation
- Menentukan trControl
set.seed(417)
ctrl <- trainControl(method = "repeatedcv",number = 9,repeats = 1)- Membuat Model
titanic_forest <- train(Survived ~., data = up_train, method = "rf", trControl= ctrl)Inspect model random forest
library(randomForest)## randomForest 4.6-14## Type rfNews() to see new features/changes/bug fixes.##
## Attaching package: 'randomForest'## The following object is masked from 'package:ggplot2':
##
## margin## The following object is masked from 'package:dplyr':
##
## combine# print output
titanic_forest## Random Forest
##
## 680 samples
## 7 predictor
## 2 classes: '0', '1'
##
## No pre-processing
## Resampling: Cross-Validated (9 fold, repeated 1 times)
## Summary of sample sizes: 605, 605, 604, 604, 604, 604, ...
## Resampling results across tuning parameters:
##
## mtry Accuracy Kappa
## 2 0.8442105 0.6885230
## 4 0.8720663 0.7441567
## 7 0.8617349 0.7235618
##
## Accuracy was used to select the optimal model using the largest value.
## The final value used for the model was mtry = 4.# model final yang digunakan (karena menggunakan k-fold cross validation)
titanic_forest$finalModel##
## Call:
## randomForest(x = x, y = y, mtry = param$mtry)
## Type of random forest: classification
## Number of trees: 500
## No. of variables tried at each split: 4
##
## OOB estimate of error rate: 12.21%
## Confusion matrix:
## 0 1 class.error
## 0 300 40 0.1176471
## 1 43 297 0.1264706# visualize model
plot(titanic_forest)
Melakukan prediksi dengan data validasi
titanic_prediction <- predict(titanic_forest,up_val)Mengevaluasi model random forest
confusionMatrix(
titanic_prediction,
up_val$Survived,
positive = "1"
)## Confusion Matrix and Statistics
##
## Reference
## Prediction 0 1
## 0 72 18
## 1 12 66
##
## Accuracy : 0.8214
## 95% CI : (0.755, 0.8762)
## No Information Rate : 0.5
## P-Value [Acc > NIR] : <2e-16
##
## Kappa : 0.6429
##
## Mcnemar's Test P-Value : 0.3613
##
## Sensitivity : 0.7857
## Specificity : 0.8571
## Pos Pred Value : 0.8462
## Neg Pred Value : 0.8000
## Prevalence : 0.5000
## Detection Rate : 0.3929
## Detection Prevalence : 0.4643
## Balanced Accuracy : 0.8214
##
## 'Positive' Class : 1
## - Discussion: Jika staf safety ingin memperikirakan jumlah sekoci “life boat” yang harus disediakan, maka “recall” adalah nilai yang harus diperhatikan untuk meminimalisir adanya resiko kekurangan “life boat”
Mengetahui faktor yang paling berpengaruh terhadap prediksi menggunakan varimp
varImp(titanic_forest)## rf variable importance
##
## Overall
## Sex 100.000
## Age 88.518
## Fare 80.041
## Cabin 33.420
## Pclass 21.289
## SibSp 8.774
## Parch 0.000- Berdasarkan tabel di atas “Sex” adalah salah satu faktor terbesar yang mempengaruhi tingkat “Survival” orang-orang pada peristiwa Titanic
Prediksi menggunakan data test
- Read Data
titantest<- read.csv("test.csv")
head(titantest)## PassengerId Pclass Name Sex Age
## 1 892 3 Kelly, Mr. James male 34.5
## 2 893 3 Wilkes, Mrs. James (Ellen Needs) female 47.0
## 3 894 2 Myles, Mr. Thomas Francis male 62.0
## 4 895 3 Wirz, Mr. Albert male 27.0
## 5 896 3 Hirvonen, Mrs. Alexander (Helga E Lindqvist) female 22.0
## 6 897 3 Svensson, Mr. Johan Cervin male 14.0
## SibSp Parch Ticket Fare Cabin Embarked
## 1 0 0 330911 7.8292 Q
## 2 1 0 363272 7.0000 S
## 3 0 0 240276 9.6875 Q
## 4 0 0 315154 8.6625 S
## 5 1 1 3101298 12.2875 S
## 6 0 0 7538 9.2250 S- Data Preparation
glimpse(titantest)## Observations: 418
## Variables: 11
## $ PassengerId <int> 892, 893, 894, 895, 896, 897, 898, 899, 900, 901, 902, ...
## $ Pclass <int> 3, 3, 2, 3, 3, 3, 3, 2, 3, 3, 3, 1, 1, 2, 1, 2, 2, 3, 3...
## $ Name <fct> "Kelly, Mr. James", "Wilkes, Mrs. James (Ellen Needs)",...
## $ Sex <fct> male, female, male, male, female, male, female, male, f...
## $ Age <dbl> 34.5, 47.0, 62.0, 27.0, 22.0, 14.0, 30.0, 26.0, 18.0, 2...
## $ SibSp <int> 0, 1, 0, 0, 1, 0, 0, 1, 0, 2, 0, 0, 1, 1, 1, 1, 0, 0, 1...
## $ Parch <int> 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0...
## $ Ticket <fct> 330911, 363272, 240276, 315154, 3101298, 7538, 330972, ...
## $ Fare <dbl> 7.8292, 7.0000, 9.6875, 8.6625, 12.2875, 9.2250, 7.6292...
## $ Cabin <fct> , , , , , , , , , , , , B45, , E31, , , , , , , , , , B...
## $ Embarked <fct> Q, S, Q, S, S, S, Q, S, C, S, S, S, S, S, S, C, Q, C, S...titantest_clean<- titantest %>%
mutate(Sex=as.numeric(Sex)-1,
Cabin=as.numeric(Cabin)
) %>%
select(-Name,-Ticket,-Embarked,-PassengerId) %>%
na.omit()
glimpse(titantest_clean)## Observations: 331
## Variables: 7
## $ Pclass <int> 3, 3, 2, 3, 3, 3, 3, 2, 3, 3, 1, 1, 2, 1, 2, 2, 3, 3, 3, 1, ...
## $ Sex <dbl> 1, 0, 1, 1, 0, 1, 0, 1, 0, 1, 1, 0, 1, 0, 0, 1, 1, 0, 0, 1, ...
## $ Age <dbl> 34.5, 47.0, 62.0, 27.0, 22.0, 14.0, 30.0, 26.0, 18.0, 21.0, ...
## $ SibSp <int> 0, 1, 0, 0, 1, 0, 0, 1, 0, 2, 0, 1, 1, 1, 1, 0, 0, 1, 0, 1, ...
## $ Parch <int> 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...
## $ Fare <dbl> 7.8292, 7.0000, 9.6875, 8.6625, 12.2875, 9.2250, 7.6292, 29....
## $ Cabin <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 14, 1, 62, 1, 1, 1, 1, 1, 1...- Melakukan prediksi
titantest_prediction <- predict(titanic_forest,titantest_clean)
titantest_clean %>%
mutate(Survived_pred=titantest_prediction)## Pclass Sex Age SibSp Parch Fare Cabin Survived_pred
## 1 3 1 34.50 0 0 7.8292 1 0
## 2 3 0 47.00 1 0 7.0000 1 0
## 3 2 1 62.00 0 0 9.6875 1 1
## 4 3 1 27.00 0 0 8.6625 1 1
## 5 3 0 22.00 1 1 12.2875 1 0
## 6 3 1 14.00 0 0 9.2250 1 0
## 7 3 0 30.00 0 0 7.6292 1 1
## 8 2 1 26.00 1 1 29.0000 1 0
## 9 3 0 18.00 0 0 7.2292 1 1
## 10 3 1 21.00 2 0 24.1500 1 0
## 11 1 1 46.00 0 0 26.0000 1 0
## 12 1 0 23.00 1 0 82.2667 14 1
## 13 2 1 63.00 1 0 26.0000 1 0
## 14 1 0 47.00 1 0 61.1750 62 1
## 15 2 0 24.00 1 0 27.7208 1 1
## 16 2 1 35.00 0 0 12.3500 1 0
## 17 3 1 21.00 0 0 7.2250 1 0
## 18 3 0 27.00 1 0 7.9250 1 0
## 19 3 0 45.00 0 0 7.2250 1 1
## 20 1 1 55.00 1 0 59.4000 1 0
## 21 3 1 9.00 0 1 3.1708 1 1
## 22 1 1 21.00 0 1 61.3792 1 0
## 23 1 0 48.00 1 3 262.3750 17 1
## 24 3 1 50.00 1 0 14.5000 1 0
## 25 1 0 22.00 0 1 61.9792 12 1
## 26 3 1 22.50 0 0 7.2250 1 0
## 27 1 1 41.00 0 0 30.5000 4 0
## 28 2 1 50.00 1 0 26.0000 1 0
## 29 2 1 24.00 2 0 31.5000 1 0
## 30 3 0 33.00 1 2 20.5750 1 1
## 31 1 1 30.00 1 0 57.7500 43 0
## 32 3 1 18.50 0 0 7.2292 1 0
## 33 3 0 21.00 0 0 8.6625 1 0
## 34 3 1 25.00 0 0 9.5000 1 0
## 35 3 1 39.00 0 1 13.4167 1 0
## 36 3 1 41.00 0 0 7.8500 1 0
## 37 2 0 30.00 0 0 13.0000 1 1
## 38 1 0 45.00 1 0 52.5542 52 1
## 39 3 1 25.00 0 0 7.9250 1 0
## 40 1 1 45.00 0 0 29.7000 7 1
## 41 1 0 60.00 0 0 76.2917 51 1
## 42 3 0 36.00 0 2 15.9000 1 1
## 43 1 1 24.00 1 0 60.0000 32 1
## 44 2 1 27.00 0 0 15.0333 1 0
## 45 2 0 20.00 2 1 23.0000 1 1
## 46 1 0 28.00 3 2 263.0000 30 1
## 47 3 1 10.00 4 1 29.1250 1 0
## 48 3 1 35.00 0 0 7.8958 1 0
## 49 3 1 25.00 0 0 7.6500 73 0
## 50 1 0 36.00 0 0 262.3750 19 1
## 51 3 1 17.00 0 0 7.8958 1 0
## 52 2 1 32.00 0 0 13.5000 1 0
## 53 3 1 18.00 0 0 7.7500 1 0
## 54 3 0 22.00 0 0 7.7250 1 1
## 55 1 1 13.00 2 2 262.3750 17 1
## 56 3 0 18.00 0 0 7.8792 1 0
## 57 1 1 47.00 0 0 42.4000 1 0
## 58 1 1 31.00 0 0 28.5375 37 1
## 59 1 0 60.00 1 4 263.0000 30 1
## 60 3 0 24.00 0 0 7.7500 1 1
## 61 3 1 21.00 0 0 7.8958 1 0
## 62 3 0 29.00 0 0 7.9250 1 1
## 63 1 1 28.50 0 0 27.7208 61 1
## 64 1 0 35.00 0 0 211.5000 27 1
## 65 1 1 32.50 0 0 211.5000 28 1
## 66 1 0 55.00 2 0 25.7000 23 1
## 67 2 1 30.00 0 0 13.0000 1 0
## 68 3 0 24.00 0 0 7.7500 1 1
## 69 3 1 6.00 1 1 15.2458 1 1
## 70 1 1 67.00 1 0 221.7792 39 0
## 71 1 1 49.00 0 0 26.0000 1 0
## 72 3 0 27.00 0 0 7.8792 1 1
## 73 3 0 18.00 0 0 8.0500 1 1
## 74 2 1 2.00 1 1 23.0000 1 1
## 75 3 0 22.00 1 0 13.9000 1 0
## 76 1 0 27.00 1 2 52.0000 21 1
## 77 1 1 25.00 0 0 26.0000 1 0
## 78 3 1 25.00 0 0 7.7958 1 0
## 79 1 0 76.00 1 0 78.8500 35 1
## 80 3 1 29.00 0 0 7.9250 1 0
## 81 3 0 20.00 0 0 7.8542 1 0
## 82 3 1 33.00 0 0 8.0500 1 0
## 83 1 0 43.00 1 0 55.4417 26 1
## 84 2 1 27.00 1 0 26.0000 1 0
## 85 3 1 26.00 0 0 7.7750 1 0
## 86 3 0 16.00 1 1 8.5167 1 1
## 87 3 1 28.00 0 0 22.5250 1 0
## 88 3 1 21.00 0 0 7.8208 1 0
## 89 2 1 18.50 0 0 13.0000 70 0
## 90 2 1 41.00 0 0 15.0458 1 0
## 91 1 0 36.00 0 0 31.6792 5 1
## 92 3 0 18.50 0 0 7.2833 1 1
## 93 1 0 63.00 1 0 221.7792 39 1
## 94 3 1 18.00 1 0 14.4542 1 0
## 95 3 0 1.00 1 1 16.7000 77 1
## 96 1 1 36.00 0 0 75.2417 40 0
## 97 2 0 29.00 1 0 26.0000 1 1
## 98 2 0 12.00 0 0 15.7500 1 1
## 99 1 0 35.00 1 0 57.7500 31 1
## 100 3 1 28.00 0 0 7.2500 1 0
## 101 3 0 17.00 0 1 16.1000 1 0
## 102 3 1 22.00 0 0 7.7958 1 0
## 103 2 1 42.00 0 0 13.0000 1 0
## 104 3 1 24.00 0 0 8.0500 1 0
## 105 3 1 32.00 0 0 8.0500 1 0
## 106 1 1 53.00 0 0 28.5000 36 1
## 107 3 1 43.00 0 0 7.8958 1 0
## 108 3 1 24.00 0 0 7.8542 1 0
## 109 3 1 26.50 0 0 7.2250 1 0
## 110 2 1 26.00 0 0 13.0000 1 0
## 111 3 0 23.00 0 0 8.0500 1 0
## 112 3 1 40.00 1 6 46.9000 1 0
## 113 3 0 10.00 5 2 46.9000 1 0
## 114 1 0 33.00 0 0 151.5500 1 1
## 115 1 1 61.00 1 3 262.3750 17 0
## 116 2 1 28.00 0 0 26.0000 1 0
## 117 1 1 42.00 0 0 26.5500 1 1
## 118 3 1 31.00 3 0 18.0000 1 0
## 119 3 1 22.00 0 0 8.0500 1 0
## 120 2 1 30.00 1 1 26.0000 1 0
## 121 1 0 23.00 0 1 83.1583 38 1
## 122 3 0 36.00 0 2 12.1833 1 1
## 123 3 1 13.00 4 2 31.3875 1 0
## 124 3 1 24.00 0 0 7.5500 1 0
## 125 1 0 29.00 0 0 221.7792 48 1
## 126 3 0 23.00 0 0 7.8542 1 0
## 127 1 1 42.00 0 0 26.5500 54 1
## 128 3 0 26.00 0 2 13.7750 1 1
## 129 3 1 7.00 1 1 15.2458 1 1
## 130 2 0 26.00 0 0 13.5000 1 1
## 131 2 1 41.00 0 0 13.0000 1 0
## 132 3 0 26.00 1 1 22.0250 1 0
## 133 1 1 48.00 0 0 50.4958 8 1
## 134 3 1 18.00 2 2 34.3750 1 0
## 135 3 0 22.00 0 0 8.9625 1 0
## 136 3 1 27.00 0 0 7.2250 1 1
## 137 3 1 23.00 1 0 13.9000 1 0
## 138 3 1 40.00 1 5 31.3875 1 0
## 139 2 0 15.00 0 2 39.0000 1 1
## 140 2 0 20.00 0 0 36.7500 1 1
## 141 1 1 54.00 1 0 55.4417 26 0
## 142 2 0 36.00 0 3 39.0000 76 1
## 143 1 0 64.00 0 2 83.1583 65 1
## 144 2 1 30.00 0 0 13.0000 1 0
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## 146 1 0 18.00 1 0 53.1000 56 1
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## 152 2 1 34.00 1 0 26.0000 1 0
## 153 3 1 11.50 1 1 14.5000 1 1
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## 155 2 1 8.00 0 2 32.5000 1 1
## 156 3 1 33.00 0 0 7.8542 1 0
## 157 1 1 6.00 0 2 134.5000 63 1
## 158 3 0 18.00 0 0 7.7750 1 0
## 159 2 1 23.00 0 0 10.5000 1 0
## 160 3 1 0.33 0 2 14.4000 1 1
## 161 1 1 47.00 1 0 227.5250 41 0
## 162 2 0 8.00 1 1 26.0000 1 1
## 163 2 1 25.00 0 0 10.5000 1 0
## 164 3 0 35.00 0 0 7.7500 1 1
## 165 2 1 24.00 0 0 10.5000 1 0
## 166 1 0 33.00 0 0 27.7208 2 1
## 167 3 1 25.00 0 0 7.8958 1 0
## 168 3 1 32.00 0 0 22.5250 1 0
## 169 2 1 17.00 0 0 73.5000 1 0
## 170 2 0 60.00 1 0 26.0000 1 1
## 171 3 0 38.00 4 2 7.7750 1 1
## 172 1 1 42.00 0 0 42.5000 9 1
## 173 1 1 57.00 1 1 164.8667 1 0
## 174 1 0 50.00 1 1 211.5000 44 1
## 175 2 0 30.00 1 0 13.8583 1 1
## 176 3 1 21.00 0 0 8.0500 1 0
## 177 2 0 22.00 0 0 10.5000 75 1
## 178 3 1 21.00 0 0 7.7958 1 0
## 179 1 0 53.00 0 0 27.4458 1 1
## 180 3 1 23.00 0 0 7.7958 1 0
## 181 3 1 40.50 0 0 15.1000 1 0
## 182 2 1 36.00 0 0 13.0000 1 0
## 183 2 1 14.00 0 0 65.0000 1 0
## 184 1 0 21.00 0 0 26.5500 1 1
## 185 3 1 21.00 1 0 6.4958 1 0
## 186 1 1 39.00 1 0 71.2833 45 0
## 187 3 1 20.00 0 0 7.8542 1 0
## 188 1 1 64.00 1 0 75.2500 58 0
## 189 3 1 20.00 0 0 7.2250 1 1
## 190 2 0 18.00 1 1 13.0000 1 1
## 191 1 0 48.00 1 0 106.4250 46 1
## 192 1 0 55.00 0 0 27.7208 1 1
## 193 2 0 45.00 0 2 30.0000 1 1
## 194 1 1 45.00 1 1 134.5000 63 0
## 195 1 1 41.00 1 0 51.8625 53 0
## 196 2 0 22.00 0 0 21.0000 1 1
## 197 2 1 42.00 1 1 32.5000 1 0
## 198 2 0 29.00 1 0 26.0000 1 1
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## 200 3 1 20.00 0 0 7.9250 1 0
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## 202 3 1 24.00 0 0 9.3250 1 0
## 203 3 1 32.50 0 0 9.5000 1 0
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## 215 2 0 20.00 1 0 26.0000 1 1
## 216 2 1 28.00 0 0 10.5000 1 0
## 217 2 1 40.00 1 0 26.0000 1 0
## 218 2 1 30.00 1 0 21.0000 1 0
## 219 2 1 22.00 0 0 10.5000 1 0
## 220 3 0 23.00 0 0 8.6625 1 0
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## 226 3 0 30.00 0 0 6.9500 1 1
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## 228 3 1 36.00 0 0 9.5000 1 0
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## 235 3 1 24.00 0 0 8.6625 1 0
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## 237 1 1 30.00 1 2 151.5500 29 1
## 238 3 1 0.83 0 1 9.3500 1 1
## 239 1 1 55.00 1 1 93.5000 20 0
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## 241 3 1 18.00 0 0 8.6625 1 0
## 242 3 1 22.00 0 0 7.2250 1 0
## 243 3 0 37.00 0 0 7.7500 1 1
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## 249 2 1 22.00 2 0 31.5000 1 0
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## 251 3 1 25.00 0 0 7.2292 72 0
## 252 2 1 26.00 0 0 13.0000 74 1
## 253 1 1 33.00 0 0 26.5500 1 1
## 254 1 0 39.00 0 0 211.3375 1 1
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## 257 1 1 46.00 0 0 79.2000 1 0
## 258 2 1 29.00 1 0 26.0000 1 0
## 259 2 1 21.00 0 0 13.0000 1 0
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## 267 2 1 25.00 0 0 31.5000 1 0
## 268 2 1 18.00 0 0 10.5000 1 0
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## 274 2 1 24.00 0 0 13.5000 1 0
## 275 2 0 31.00 0 0 21.0000 1 1
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## 286 2 0 31.00 0 0 21.0000 1 1
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## 310 1 0 51.00 0 1 39.4000 55 1
## 311 3 1 13.00 0 2 20.2500 1 1
## 312 2 1 47.00 0 0 10.5000 1 0
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## 315 3 1 24.00 0 0 7.2500 1 0
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## 325 2 1 23.00 1 0 10.5000 1 0
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## 329 3 0 28.00 0 0 7.7750 1 1
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## 331 3 1 38.50 0 0 7.2500 1 0