EDA dan Data Preprocessing pada Dataset Titanic
Yulia Candra Dewi
1/11/2022
Pendahuluan
Exploratory Data Analysis (EDA) adalah bagian dari proses data science. EDA menjadi sangat penting sebelum melakukan feature engineering dan modeling karena dalam tahap ini kita harus memahami datanya terlebih dahulu.
Exploratory Data Analysis mengacu pada proses kritis dalam melakukan investigasi awal pada data untuk menemukan pola, untuk menemukan anomali, untuk menguji hipotesis dan untuk memeriksa asumsi dengan bantuan statistik ringkasan dan representasi grafis. Dengan melakukan EDA, kita dapat lebih memahami kondisi dataset yang kita miliki.
Import Library
library(heatmaply)## Warning: package 'heatmaply' was built under R version 4.1.2## Loading required package: plotly## Warning: package 'plotly' was built under R version 4.1.2## Loading required package: ggplot2##
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## The github page is: https://github.com/talgalili/heatmaply/
## Please submit your suggestions and bug-reports at: https://github.com/talgalili/heatmaply/issues
## You may ask questions at stackoverflow, use the r and heatmaply tags:
## https://stackoverflow.com/questions/tagged/heatmaply
## ======================library(visdat)## Warning: package 'visdat' was built under R version 4.1.2library(tidyverse)## -- Attaching packages --------------------------------------- tidyverse 1.3.1 --## v tibble 3.1.2 v dplyr 1.0.6
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library(ggplot2)
library(tidyr)
library(readr)
library(tibble)
library(reshape2)##
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## %+%, alphaMengakses Dataset
df <- read.csv("~/RStudio PSDS/titanic.csv", stringsAsFactors=TRUE)
df[df == ""] <- NA
df[0:10,]## 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
## 7 7 0 1
## 8 8 0 3
## 9 9 1 3
## 10 10 1 2
## 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
## 7 McCarthy, Mr. Timothy J male 54 0 0
## 8 Palsson, Master. Gosta Leonard male 2 3 1
## 9 Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg) female 27 0 2
## 10 Nasser, Mrs. Nicholas (Adele Achem) female 14 1 0
## Ticket Fare Cabin Embarked
## 1 A/5 21171 7.2500 <NA> S
## 2 PC 17599 71.2833 C85 C
## 3 STON/O2. 3101282 7.9250 <NA> S
## 4 113803 53.1000 C123 S
## 5 373450 8.0500 <NA> S
## 6 330877 8.4583 <NA> Q
## 7 17463 51.8625 E46 S
## 8 349909 21.0750 <NA> S
## 9 347742 11.1333 <NA> S
## 10 237736 30.0708 <NA> Chead(df)## 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 <NA> S
## 2 PC 17599 71.2833 C85 C
## 3 STON/O2. 3101282 7.9250 <NA> S
## 4 113803 53.1000 C123 S
## 5 373450 8.0500 <NA> S
## 6 330877 8.4583 <NA> QTujuan Analisis Data
Melakukan klasifikasi penumpang yang selamat dan tidak selamat pada kasus tenggelamnya kapal Titanic.
Memahami Dataset
Dataset Titanic dibuat untuk membuat machine learning untuk melakukan klasifikasi biner (Selamat atau Tidak Selamat). Variabel-variabel yang terdapat pada dataset ini adalah sebagai berikut:
- PassengerId = Nomor Id Penumpang
- Survived = Keterangan Selamat (0=Tidak, 1=Ya)
- Pclass = Kelas Tiket (1=Kelas 1, 2=Kelas 2, dst)
- Name = Nama Penumpang
- Sex = Jenis kelamin
- Age = Usia dalam tahun
- SibSp = Jumlah saudara kandung / pasangan di kapal Titanic
- Parch = Jumlah orang tua / anak di kapal Titanic
- Ticket = Nomor Tiket
- Fare = Harga Tiket
- Cabin = Nama Kabin
- Embarked = Pelabuhan Asal (C = Cherbourg, Q = Queenstown, S = Southampton)
Exploratory Data Analysis
Dimensi Data
dim(df)## [1] 891 12Artinya kita memiliki data dengan 12 kolom dan 891 baris.
Variabel Pada Dataset
names(df)## [1] "PassengerId" "Survived" "Pclass" "Name" "Sex"
## [6] "Age" "SibSp" "Parch" "Ticket" "Fare"
## [11] "Cabin" "Embarked"str(df)## 'data.frame': 891 obs. of 12 variables:
## $ PassengerId: int 1 2 3 4 5 6 7 8 9 10 ...
## $ Survived : int 0 1 1 1 0 0 0 0 1 1 ...
## $ Pclass : int 3 1 3 1 3 3 1 3 3 2 ...
## $ Name : Factor w/ 891 levels "Abbing, Mr. Anthony",..: 109 191 358 277 16 559 520 629 417 581 ...
## $ Sex : Factor w/ 2 levels "female","male": 2 1 1 1 2 2 2 2 1 1 ...
## $ Age : num 22 38 26 35 35 NA 54 2 27 14 ...
## $ SibSp : int 1 1 0 1 0 0 0 3 0 1 ...
## $ Parch : int 0 0 0 0 0 0 0 1 2 0 ...
## $ Ticket : Factor w/ 681 levels "110152","110413",..: 524 597 670 50 473 276 86 396 345 133 ...
## $ Fare : num 7.25 71.28 7.92 53.1 8.05 ...
## $ Cabin : Factor w/ 148 levels "","A10","A14",..: NA 83 NA 57 NA NA 131 NA NA NA ...
## $ Embarked : Factor w/ 4 levels "","C","Q","S": 4 2 4 4 4 3 4 4 4 2 ...Kita dapat mengetahui tipe-tipe data masing-masing variabel dan nama-nama variabel dalam dataset.
Mengecek Missing Data
sapply(df, function(x) sum(is.na(x)))## PassengerId Survived Pclass Name Sex Age
## 0 0 0 0 0 177
## SibSp Parch Ticket Fare Cabin Embarked
## 0 0 0 0 687 2vis_miss(df)
Kolom-kolom dengan data kosong adalah: Age, Cabin, Embarked.
Mengecek Outlier
num_cols <- unlist(lapply(df, is.numeric))
df_num <- df[ , num_cols]
boxplot(df_num)
Kolom numeric yang memiliki outlier adalah Age, Sibsp, Parch, dan Fare.
Melihat Korelasi data
plot_correlation(df_num)## Warning: Removed 12 rows containing missing values (geom_text).
Melihat Statistik Data
summary(df)## PassengerId Survived Pclass
## Min. : 1.0 Min. :0.0000 Min. :1.000
## 1st Qu.:223.5 1st Qu.:0.0000 1st Qu.:2.000
## Median :446.0 Median :0.0000 Median :3.000
## Mean :446.0 Mean :0.3838 Mean :2.309
## 3rd Qu.:668.5 3rd Qu.:1.0000 3rd Qu.:3.000
## Max. :891.0 Max. :1.0000 Max. :3.000
##
## Name Sex Age
## Abbing, Mr. Anthony : 1 female:314 Min. : 0.42
## Abbott, Mr. Rossmore Edward : 1 male :577 1st Qu.:20.12
## Abbott, Mrs. Stanton (Rosa Hunt) : 1 Median :28.00
## Abelson, Mr. Samuel : 1 Mean :29.70
## Abelson, Mrs. Samuel (Hannah Wizosky): 1 3rd Qu.:38.00
## Adahl, Mr. Mauritz Nils Martin : 1 Max. :80.00
## (Other) :885 NA's :177
## SibSp Parch Ticket Fare
## Min. :0.000 Min. :0.0000 1601 : 7 Min. : 0.00
## 1st Qu.:0.000 1st Qu.:0.0000 347082 : 7 1st Qu.: 7.91
## Median :0.000 Median :0.0000 CA. 2343: 7 Median : 14.45
## Mean :0.523 Mean :0.3816 3101295 : 6 Mean : 32.20
## 3rd Qu.:1.000 3rd Qu.:0.0000 347088 : 6 3rd Qu.: 31.00
## Max. :8.000 Max. :6.0000 CA 2144 : 6 Max. :512.33
## (Other) :852
## Cabin Embarked
## B96 B98 : 4 : 0
## C23 C25 C27: 4 C :168
## G6 : 4 Q : 77
## C22 C26 : 3 S :644
## D : 3 NA's: 2
## (Other) :186
## NA's :687Plot Distribusi Data Numerik
d <- melt(df_num)## No id variables; using all as measure variablesggplot(d,aes(x = value)) +
facet_wrap(~variable,scales = "free_x") + geom_histogram()## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.## Warning: Removed 177 rows containing non-finite values (stat_bin).
Plot ini untuk melihat apakah variabel-variabel pada dataset berdistribusi normal. Variabel-variabel Age, SibSp, Parch, dan Fare cenderung memiliki skewnes positif. Itu berarti berarti ekor distribusi berada di sebelah kanan nilai terbanyak.
Pairplot
pairs.panels(df_num,
method = "pearson", # correlation method
hist.col = "#00AFBB",
density = TRUE, # show density plots
ellipses = TRUE # show correlation ellipses
)
Plot ini untuk mengetahui plot untuk masing-masing pasangan data numerik.
Data Preprocessing
Data preprocessing ini digunakan guna menyiapkan data untuk diklasifikasi menggunakan metode SVM (Hanya Contoh).
Setelah mengetahui hasil EDA di atas maka untuk hasil yang baik diperlukan beberapa hal sebagai berikut:
- Menghapus kolom teks yang tidak diperlukan: Name dan Ticket
- Mengisi NaN pada kolom Cabin dengan salah satu nama kabin tertentu, mengisi NaN pada kolom Age dengan nilai rata-ratanya
- Melakukan pengkodean (Encoding) pada kolom Sex, Cabin, dan Embarked
- Menghapus outlier
- Karena nilai data ada yang sangat besar dan kecil maka perlu dilakukan normalisasi
Setelah data diperbaiki dilakukan langkah sebagai berikut:
- Memilih variabel independent dan label
- Membagi data menjadi data training dan data testing
[Note] * Ini hanya contoh untuk melakukan pengisian data NaN (Kosong), nama kabin mungkin menjadi penting sehingga tidak segampang itu diganti.
Seleksi Kolom
df = subset(df, select = -c(Name, Ticket) )
head(df)## PassengerId Survived Pclass Sex Age SibSp Parch Fare Cabin Embarked
## 1 1 0 3 male 22 1 0 7.2500 <NA> S
## 2 2 1 1 female 38 1 0 71.2833 C85 C
## 3 3 1 3 female 26 0 0 7.9250 <NA> S
## 4 4 1 1 female 35 1 0 53.1000 C123 S
## 5 5 0 3 male 35 0 0 8.0500 <NA> S
## 6 6 0 3 male NA 0 0 8.4583 <NA> QMengisi NaN
df$Age[is.na(df$Age)] = 0
m<-mean(df$Age)
df$Age[df$Age==0]<-m
df=df %>% replace_na(list(Cabin = "E49"))df=df %>% replace_na(list(Embarked = "C"))
sapply(df, function(x) sum(is.na(x)))## PassengerId Survived Pclass Sex Age SibSp
## 0 0 0 0 0 0
## Parch Fare Cabin Embarked
## 0 0 0 0head(df)## PassengerId Survived Pclass Sex Age SibSp Parch Fare Cabin
## 1 1 0 3 male 22.00000 1 0 7.2500 E49
## 2 2 1 1 female 38.00000 1 0 71.2833 C85
## 3 3 1 3 female 26.00000 0 0 7.9250 E49
## 4 4 1 1 female 35.00000 1 0 53.1000 C123
## 5 5 0 3 male 35.00000 0 0 8.0500 E49
## 6 6 0 3 male 23.79929 0 0 8.4583 E49
## Embarked
## 1 S
## 2 C
## 3 S
## 4 S
## 5 S
## 6 QMelakukan Pengkodean (Encoding)
df$Sex<- unclass(df$Sex)
df$Embarked<- unclass(df$Embarked)
df$Cabin<- unclass(df$Cabin)
head(df)## PassengerId Survived Pclass Sex Age SibSp Parch Fare Cabin Embarked
## 1 1 0 3 2 22.00000 1 0 7.2500 132 4
## 2 2 1 1 1 38.00000 1 0 71.2833 83 2
## 3 3 1 3 1 26.00000 0 0 7.9250 132 4
## 4 4 1 1 1 35.00000 1 0 53.1000 57 4
## 5 5 0 3 2 35.00000 0 0 8.0500 132 4
## 6 6 0 3 2 23.79929 0 0 8.4583 132 3Menghapus Outlier
Rentang interkuartil (IQR) adalah ukuran penyebaran statistik dan dihitung sebagai perbedaan antara persentil ke-75 dan ke-25. Ini diwakili oleh rumus IQR = Q3 - Q1. Baris kode di bawah menghitung dan mencetak kisaran interkuartil untuk setiap variabel dalam dataset.
Teknik ini menggunakan skor IQR yang dihitung sebelumnya untuk menghilangkan pencilan. Aturan praktisnya adalah bahwa segala sesuatu yang tidak berada dalam kisaran (Q1 - 1.5 IQR) dan (Q3 + 1.5 IQR) adalah pencilan, dan dapat dihapus.
Metode ini dipakai karena untuk identifikasi outlier awal digunakan metode boxplot.
is_outlier <- function(x, na.rm = FALSE) {
qs = quantile(x, probs = c(0.25, 0.75), na.rm = na.rm)
lowerq <- qs[1]
upperq <- qs[2]
iqr = upperq - lowerq
extreme.threshold.upper = (iqr * 3) + upperq
extreme.threshold.lower = lowerq - (iqr * 3)
x > extreme.threshold.upper | x < extreme.threshold.lower
}
remove_outliers <- function(df, cols = names(df)) {
for (col in cols) {
cat("Removing outliers in column: ", col, " \n")
df <- df[!is_outlier(df[[col]]),]
}
df
}
vars_of_interest <- c("Age", "SibSp", "Parch", "Fare")
df_filtered <- remove_outliers(df, vars_of_interest)## Removing outliers in column: Age
## Removing outliers in column: SibSp
## Removing outliers in column: Parch
## Removing outliers in column: Fareboxplot(df_filtered)
Outlier berhasil dihapus.
Normalisasi Data
Karena tujuannya adalah klasifikasi menggunakan SVM maka normalisasi data wajib dilakukan.
unit_length <- function(x) {
x / sqrt(sum(x^2))
}
unit_length_df <- as.data.frame(lapply(df, unit_length))
head(unit_length_df)## PassengerId Survived Pclass Sex Age SibSp Parch
## 1 6.506968e-05 0.00000000 0.04093489 0.03905833 0.02344561 0.02746175 0
## 2 1.301394e-04 0.05407381 0.01364496 0.01952916 0.04049697 0.02746175 0
## 3 1.952090e-04 0.05407381 0.04093489 0.01952916 0.02770845 0.00000000 0
## 4 2.602787e-04 0.05407381 0.01364496 0.01952916 0.03729984 0.02746175 0
## 5 3.253484e-04 0.00000000 0.04093489 0.03905833 0.03729984 0.00000000 0
## 6 3.904181e-04 0.00000000 0.04093489 0.03905833 0.02536314 0.00000000 0
## Fare Cabin Embarked
## 1 0.004103278 0.03582844 0.03701641
## 2 0.040344167 0.02252849 0.01850821
## 3 0.004485308 0.03582844 0.03701641
## 4 0.030052976 0.01547137 0.03701641
## 5 0.004556054 0.03582844 0.03701641
## 6 0.004787139 0.03582844 0.02776231Memilih Variable Target & Variabel Independent
y=df$Survived
x=data.matrix(df[-c(2)])
y## [1] 0 1 1 1 0 0 0 0 1 1 1 1 0 0 0 1 0 1 0 1 0 1 1 1 0 1 0 0 1 0 0 1 1 0 0 0 1
## [38] 0 0 1 0 0 0 1 1 0 0 1 0 0 0 0 1 1 0 1 1 0 1 0 0 1 0 0 0 1 1 0 1 0 0 0 0 0
## [75] 1 0 0 0 1 1 0 1 1 0 1 1 0 0 1 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 1 1 0 1 0
## [112] 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 1 1 0 0 0 0 1 0 0 1 0 0 0 0 1 1 0 0 0 1 0
## [149] 0 0 0 1 0 0 0 0 1 0 0 0 0 1 0 0 0 1 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 1
## [186] 0 1 1 0 0 1 0 1 1 1 1 0 0 1 0 0 0 0 0 1 0 0 1 1 1 0 1 0 0 0 1 1 0 1 0 1 0
## [223] 0 0 1 0 1 0 0 0 1 0 0 1 0 0 0 1 0 0 0 1 0 0 0 0 0 1 1 0 0 0 0 0 0 1 1 1 1
## [260] 1 0 1 0 0 0 0 0 1 1 1 0 1 1 0 1 1 0 0 0 1 0 0 0 1 0 0 1 0 1 1 1 1 0 0 0 0
## [297] 0 0 1 1 1 1 0 1 0 1 1 1 0 1 1 1 0 0 0 1 1 0 1 1 0 0 1 1 0 1 0 1 1 1 1 0 0
## [334] 0 1 0 0 1 1 0 1 1 0 0 0 1 1 1 1 0 0 0 0 0 0 0 1 0 1 1 0 0 0 0 0 0 1 1 1 1
## [371] 1 0 0 0 0 1 1 0 0 0 1 1 0 1 0 0 0 1 0 1 1 1 0 1 1 0 0 0 0 1 1 0 0 0 0 0 0
## [408] 1 0 0 0 0 1 0 1 0 1 1 0 0 0 0 0 0 0 0 1 1 0 1 1 1 1 0 0 1 0 1 0 0 1 0 0 1
## [445] 1 1 1 1 1 1 0 0 0 1 0 1 0 1 1 0 1 0 0 0 0 0 0 0 0 1 0 0 1 1 0 0 0 0 0 1 0
## [482] 0 0 1 1 0 1 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 1 1 0 1 1 0 1 1 0 0 1 0
## [519] 1 0 1 0 0 1 0 0 1 0 0 0 1 0 0 1 0 1 0 1 0 1 1 0 0 1 0 0 1 1 0 1 1 0 0 1 1
## [556] 0 1 0 1 1 0 0 0 0 0 0 0 0 0 1 1 1 1 1 0 0 1 1 0 1 1 1 0 0 0 1 0 1 0 0 0 1
## [593] 0 0 0 0 1 0 0 1 1 0 0 0 1 0 0 1 1 1 0 0 1 0 0 1 0 0 1 0 0 1 1 0 0 0 0 1 0
## [630] 0 1 0 1 0 0 1 0 0 0 0 0 1 0 1 1 1 0 1 0 1 0 1 0 1 0 0 0 0 0 0 1 0 0 0 1 0
## [667] 0 0 0 1 1 0 0 1 0 0 0 1 0 1 0 1 0 0 0 0 0 0 0 1 1 1 1 0 0 0 0 1 0 0 1 1 0
## [704] 0 0 0 1 1 1 1 1 0 1 0 0 0 1 1 0 0 1 0 0 0 1 0 1 1 0 0 1 0 0 0 0 0 0 1 0 0
## [741] 1 0 1 0 1 0 0 1 0 0 1 1 0 0 1 1 0 0 0 1 0 0 1 1 0 1 0 0 0 0 0 0 0 0 1 0 0
## [778] 1 0 1 1 1 0 0 0 0 1 0 1 0 0 0 0 0 0 0 1 1 0 0 0 1 1 1 1 0 0 0 0 1 0 0 0 0
## [815] 0 0 0 0 0 0 1 1 0 1 0 0 0 1 1 1 1 1 0 0 0 1 0 0 1 1 0 0 1 0 0 0 0 0 0 1 0
## [852] 0 0 1 0 1 1 1 1 0 0 0 1 0 0 1 1 0 0 1 0 1 0 0 1 1 0 0 0 1 1 0 0 0 0 0 0 1
## [889] 0 1 0x## PassengerId Pclass Sex Age SibSp Parch Fare Cabin Embarked
## 1 1 3 2 22.00000 1 0 7.2500 132 4
## 2 2 1 1 38.00000 1 0 71.2833 83 2
## 3 3 3 1 26.00000 0 0 7.9250 132 4
## 4 4 1 1 35.00000 1 0 53.1000 57 4
## 5 5 3 2 35.00000 0 0 8.0500 132 4
## 6 6 3 2 23.79929 0 0 8.4583 132 3
## 7 7 1 2 54.00000 0 0 51.8625 131 4
## 8 8 3 2 2.00000 3 1 21.0750 132 4
## 9 9 3 1 27.00000 0 2 11.1333 132 4
## 10 10 2 1 14.00000 1 0 30.0708 132 2
## 11 11 3 1 4.00000 1 1 16.7000 147 4
## 12 12 1 1 58.00000 0 0 26.5500 51 4
## 13 13 3 2 20.00000 0 0 8.0500 132 4
## 14 14 3 2 39.00000 1 5 31.2750 132 4
## 15 15 3 1 14.00000 0 0 7.8542 132 4
## 16 16 2 1 55.00000 0 0 16.0000 132 4
## 17 17 3 2 2.00000 4 1 29.1250 132 3
## 18 18 2 2 23.79929 0 0 13.0000 132 4
## 19 19 3 1 31.00000 1 0 18.0000 132 4
## 20 20 3 1 23.79929 0 0 7.2250 132 2
## 21 21 2 2 35.00000 0 0 26.0000 132 4
## 22 22 2 2 34.00000 0 0 13.0000 113 4
## 23 23 3 1 15.00000 0 0 8.0292 132 3
## 24 24 1 2 28.00000 0 0 35.5000 15 4
## 25 25 3 1 8.00000 3 1 21.0750 132 4
## 26 26 3 1 38.00000 1 5 31.3875 132 4
## 27 27 3 2 23.79929 0 0 7.2250 132 2
## 28 28 1 2 19.00000 3 2 263.0000 65 4
## 29 29 3 1 23.79929 0 0 7.8792 132 3
## 30 30 3 2 23.79929 0 0 7.8958 132 4
## 31 31 1 2 40.00000 0 0 27.7208 132 2
## 32 32 1 1 23.79929 1 0 146.5208 43 2
## 33 33 3 1 23.79929 0 0 7.7500 132 3
## 34 34 2 2 66.00000 0 0 10.5000 132 4
## 35 35 1 2 28.00000 1 0 82.1708 132 2
## 36 36 1 2 42.00000 1 0 52.0000 132 4
## 37 37 3 2 23.79929 0 0 7.2292 132 2
## 38 38 3 2 21.00000 0 0 8.0500 132 4
## 39 39 3 1 18.00000 2 0 18.0000 132 4
## 40 40 3 1 14.00000 1 0 11.2417 132 2
## 41 41 3 1 40.00000 1 0 9.4750 132 4
## 42 42 2 1 27.00000 1 0 21.0000 132 4
## 43 43 3 2 23.79929 0 0 7.8958 132 2
## 44 44 2 1 3.00000 1 2 41.5792 132 2
## 45 45 3 1 19.00000 0 0 7.8792 132 3
## 46 46 3 2 23.79929 0 0 8.0500 132 4
## 47 47 3 2 23.79929 1 0 15.5000 132 3
## 48 48 3 1 23.79929 0 0 7.7500 132 3
## 49 49 3 2 23.79929 2 0 21.6792 132 2
## 50 50 3 1 18.00000 1 0 17.8000 132 4
## 51 51 3 2 7.00000 4 1 39.6875 132 4
## 52 52 3 2 21.00000 0 0 7.8000 132 4
## 53 53 1 1 49.00000 1 0 76.7292 103 2
## 54 54 2 1 29.00000 1 0 26.0000 132 4
## 55 55 1 2 65.00000 0 1 61.9792 25 2
## 56 56 1 2 23.79929 0 0 35.5000 73 4
## 57 57 2 1 21.00000 0 0 10.5000 132 4
## 58 58 3 2 28.50000 0 0 7.2292 132 2
## 59 59 2 1 5.00000 1 2 27.7500 132 4
## 60 60 3 2 11.00000 5 2 46.9000 132 4
## 61 61 3 2 22.00000 0 0 7.2292 132 2
## 62 62 1 1 38.00000 0 0 80.0000 23 2
## 63 63 1 2 45.00000 1 0 83.4750 82 4
## 64 64 3 2 4.00000 3 2 27.9000 132 4
## 65 65 1 2 23.79929 0 0 27.7208 132 2
## 66 66 3 2 23.79929 1 1 15.2458 132 2
## 67 67 2 1 29.00000 0 0 10.5000 144 4
## 68 68 3 2 19.00000 0 0 8.1583 132 4
## 69 69 3 1 17.00000 4 2 7.9250 132 4
## 70 70 3 2 26.00000 2 0 8.6625 132 4
## 71 71 2 2 32.00000 0 0 10.5000 132 4
## 72 72 3 1 16.00000 5 2 46.9000 132 4
## 73 73 2 2 21.00000 0 0 73.5000 132 4
## 74 74 3 2 26.00000 1 0 14.4542 132 2
## 75 75 3 2 32.00000 0 0 56.4958 132 4
## 76 76 3 2 25.00000 0 0 7.6500 142 4
## 77 77 3 2 23.79929 0 0 7.8958 132 4
## 78 78 3 2 23.79929 0 0 8.0500 132 4
## 79 79 2 2 0.83000 0 2 29.0000 132 4
## 80 80 3 1 30.00000 0 0 12.4750 132 4
## 81 81 3 2 22.00000 0 0 9.0000 132 4
## 82 82 3 2 29.00000 0 0 9.5000 132 4
## 83 83 3 1 23.79929 0 0 7.7875 132 3
## 84 84 1 2 28.00000 0 0 47.1000 132 4
## 85 85 2 1 17.00000 0 0 10.5000 132 4
## 86 86 3 1 33.00000 3 0 15.8500 132 4
## 87 87 3 2 16.00000 1 3 34.3750 132 4
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## 891 891 3 2 32.00000 0 0 7.7500 132 3Membagi Data Menjadi Data Training dan Data Testing
require(caTools)## Loading required package: caToolsset.seed(123)
sample = sample.split(df,SplitRatio = 0.75)
train1 =subset(df,sample ==TRUE)
test1=subset(df, sample==FALSE)