EDA & Data Preprocessing in R
Milla Oktavia
22/03/2021
Pendahuluan
Bagian ini merupakan bentuk R Markdown tentang EDA dan Data Preprocessing.
Import Library
if(!require(tidyverse)) install.packages("tidyverse")## Loading required package: tidyverse## Warning: package 'tidyverse' was built under R version 3.6.3## -- Attaching packages --------------------------------------- tidyverse 1.3.0 --## v ggplot2 3.3.3 v purrr 0.3.4
## v tibble 3.1.0 v dplyr 1.0.5
## v tidyr 1.1.3 v stringr 1.4.0
## v readr 1.4.0 v forcats 0.5.1## Warning: package 'ggplot2' was built under R version 3.6.3## Warning: package 'tibble' was built under R version 3.6.3## Warning: package 'tidyr' was built under R version 3.6.3## Warning: package 'readr' was built under R version 3.6.3## Warning: package 'purrr' was built under R version 3.6.3## Warning: package 'dplyr' was built under R version 3.6.3## Warning: package 'stringr' was built under R version 3.6.3## Warning: package 'forcats' was built under R version 3.6.3## -- Conflicts ------------------------------------------ tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()if(!require(skimr)) install.packages("skimr")## Loading required package: skimr## Warning: package 'skimr' was built under R version 3.6.3if(!require(DataExplorer)) install.packages("DataExplorer")## Loading required package: DataExplorer## Warning: package 'DataExplorer' was built under R version 3.6.3if(!require(visdat)) install.packages("visdat")## Loading required package: visdat## Warning: package 'visdat' was built under R version 3.6.3library(visdat)
library(tidyverse)
library(skimr)
library(DataExplorer)
library(dplyr)
library(ggplot2)
library(tidyr)
library(readr)
library(tibble)Mengakses Dataset
df <- read.csv("https://raw.githubusercontent.com/millaoktavia/Kelas-Mahir-Pejuang-Data-2.0/main/titanic_modify.csv",sep=';', stringsAsFactors = T)
df[0:5,]## PassengerId Survived Pclass
## 1 1 0 3
## 2 2 1 1
## 3 3 1 3
## 4 4 1 1
## 5 5 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
## Ticket Fare Cabin Embarked
## 1 A/5 21171 7.25 S
## 2 PC 17599 712.833 C85 C
## 3 STON/O2. 3101282 7.925 S
## 4 113803 53.1 C123 S
## 5 373450 8.05 STujuan 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)
##Eksploratory Data Analysis
Dimensi Data
dim(df)## [1] 707 12Artinya kita memiliki data dengan 12 kolom dan 707 baris
Variabel Pada Dataset
names(df)## [1] "PassengerId" "Survived" "Pclass" "Name" "Sex"
## [6] "Age" "SibSp" "Parch" "Ticket" "Fare"
## [11] "Cabin" "Embarked"Digunakan untuk menampilkan nama variable tiap dataset.
df$Fare=as.numeric(df$Fare)Mengubah tipe data fare menjadi numerik.
str(df)## 'data.frame': 707 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/ 707 levels "Abbott, Mrs. Stanton (Rosa Hunt)",..: 86 149 282 221 8 432 401 493 326 449 ...
## $ 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/ 563 levels "110152","110413",..: 426 492 554 41 385 227 72 321 281 110 ...
## $ Fare : num 158 171 166 133 196 209 130 68 13 101 ...
## $ Cabin : Factor w/ 122 levels "","A10","A14",..: 1 70 1 49 1 1 109 1 1 1 ...
## $ 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 0 145
## SibSp Parch Ticket Fare Cabin Embarked
## 0 0 0 0 0 0Berarti di data Age terdapat 145 data hilang
vis_miss(df)
Mengecek Outlier
plot_boxplot(df,by="Age")
Melihat korelasi data
plot_correlation(df)## 3 features with more than 20 categories ignored!
## Name: 707 categories
## Ticket: 563 categories
## Cabin: 122 categories## Warning: Removed 24 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.:177.5 1st Qu.:0.0000 1st Qu.:2.000
## Median :354.0 Median :0.0000 Median :3.000
## Mean :354.0 Mean :0.3876 Mean :2.308
## 3rd Qu.:530.5 3rd Qu.:1.0000 3rd Qu.:3.000
## Max. :707.0 Max. :1.0000 Max. :3.000
##
## Name Sex
## Abbott, Mrs. Stanton (Rosa Hunt) : 1 female:254
## Abelson, Mr. Samuel : 1 male :453
## Adahl, Mr. Mauritz Nils Martin : 1
## Adams, Mr. John : 1
## Ahlin, Mrs. Johan (Johanna Persdotter Larsson): 1
## Albimona, Mr. Nassef Cassem : 1
## (Other) :701
## Age SibSp Parch Ticket
## Min. : 0.75 Min. :0.0000 Min. :0.0000 CA 2144 : 6
## 1st Qu.:21.00 1st Qu.:0.0000 1st Qu.:0.0000 1601 : 5
## Median :28.00 Median :0.0000 Median :0.0000 3101295 : 5
## Mean :30.03 Mean :0.5304 Mean :0.3833 347082 : 5
## 3rd Qu.:39.00 3rd Qu.:1.0000 3rd Qu.:0.0000 347088 : 5
## Max. :80.00 Max. :8.0000 Max. :6.0000 S.O.C. 14879: 5
## NA's :145 (Other) :676
## Fare Cabin Embarked
## Min. : 1.0 :548 : 1
## 1st Qu.: 57.5 C23 C25 C27: 4 C:136
## Median :119.0 G6 : 4 Q: 64
## Mean :115.8 C22 C26 : 3 S:506
## 3rd Qu.:174.0 D : 3
## Max. :227.0 F2 : 3
## (Other) :142Pairplot
plot_scatterplot(df, by="Survived")
##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_1=df[, -c(4,9)]
df_1## PassengerId Survived Pclass Sex Age SibSp Parch Fare Cabin
## 1 1 0 3 male 22.00 1 0 158
## 2 2 1 1 female 38.00 1 0 171 C85
## 3 3 1 3 female 26.00 0 0 166
## 4 4 1 1 female 35.00 1 0 133 C123
## 5 5 0 3 male 35.00 0 0 196
## 6 6 0 3 male NA 0 0 209
## 7 7 0 1 male 54.00 0 0 130 E46
## 8 8 0 3 male 2.00 3 1 68
## 9 9 1 3 female 27.00 0 2 13
## 10 10 1 2 female 14.00 1 0 101
## 11 11 1 3 female 4.00 1 1 51 G6
## 12 12 1 1 female 58.00 0 0 84 C103
## 13 13 0 3 male 20.00 0 0 196
## 14 14 0 3 male 39.00 1 5 104
## 15 15 0 3 female 14.00 0 0 189
## 16 16 1 2 female 55.00 0 0 49
## 17 17 0 3 male 2.00 4 1 97
## 18 18 1 2 male NA 0 0 25
## 19 19 0 3 female 31.00 1 0 54
## 20 20 1 3 female NA 0 0 157
## 21 21 0 2 male 35.00 0 0 82
## 22 22 1 2 male 34.00 0 0 25 D56
## 23 23 1 3 female 15.00 0 0 199
## 24 24 1 1 male 28.00 0 0 114 A6
## 25 25 0 3 female 8.00 3 1 68
## 26 26 1 3 female 38.00 1 5 105
## 27 27 0 3 male NA 0 0 157
## 28 28 0 1 male 19.00 3 2 88 C23 C25 C27
## 29 29 1 3 female NA 0 0 190
## 30 30 0 3 male NA 0 0 192
## 31 31 0 1 male 40.00 0 0 93
## 32 32 1 1 female NA 1 0 4 B78
## 33 33 1 3 female NA 0 0 162
## 34 34 0 2 male 66.00 0 0 7
## 35 35 0 1 male 28.00 1 0 204
## 36 36 0 1 male 42.00 1 0 131
## 37 37 1 3 male NA 0 0 172
## 38 38 0 3 male 21.00 0 0 196
## 39 39 0 3 female 18.00 2 0 54
## 40 40 1 3 female 14.00 1 0 14
## 41 41 0 3 female 40.00 1 0 218
## 42 42 0 2 female 27.00 1 0 67
## 43 43 0 3 male NA 0 0 192
## 44 44 1 2 female 3.00 1 2 121
## 45 45 1 3 female 19.00 0 0 190
## 46 46 0 3 male NA 0 0 196
## 47 47 0 3 male NA 1 0 40
## 48 48 1 3 female NA 0 0 162
## 49 49 0 3 male NA 2 0 70
## 50 50 0 3 female 18.00 1 0 53
## 51 51 0 3 male 7.00 4 1 119
## 52 52 0 3 male 21.00 0 0 164
## 53 53 1 1 female 49.00 1 0 180 D33
## 54 54 1 2 female 29.00 1 0 82
## 55 55 0 1 male 65.00 0 1 146 B30
## 56 56 1 1 male NA 0 0 114 C52
## 57 57 1 2 female 21.00 0 0 7
## 58 58 0 3 male 28.50 0 0 172
## 59 59 1 2 female 5.00 1 2 91
## 60 60 0 3 male 11.00 5 2 123
## 61 61 0 3 male 22.00 0 0 172
## 62 62 1 1 female 38.00 0 0 198 B28
## 63 63 0 1 male 45.00 1 0 205 C83
## 64 64 0 3 male 4.00 3 2 92
## 65 65 0 1 male NA 0 0 93
## 66 66 1 3 male NA 1 1 47
## 67 67 1 2 female 29.00 0 0 7 F33
## 68 68 0 3 male 19.00 0 0 202
## 69 69 1 3 female 17.00 4 2 166
## 70 70 0 3 male 26.00 2 0 212
## 71 71 0 2 male 32.00 0 0 7
## 72 72 0 3 female 16.00 5 2 123
## 73 73 0 2 male 21.00 0 0 174
## 74 74 0 3 male 26.00 1 0 35
## 75 75 1 3 male 32.00 0 0 137
## 76 76 0 3 male 25.00 0 0 160 F G73
## 77 77 0 3 male NA 0 0 192
## 78 78 0 3 male NA 0 0 196
## 79 79 1 2 male 0.83 0 2 96
## 80 80 1 3 female 30.00 0 0 19
## 81 81 0 3 male 22.00 0 0 215
## 82 82 1 3 male 29.00 0 0 219
## 83 83 1 3 female NA 0 0 184
## 84 84 0 1 male 28.00 0 0 124
## 85 85 1 2 female 17.00 0 0 7
## 86 86 1 3 female 33.00 3 0 43
## 87 87 0 3 male 16.00 1 3 110
## 88 88 0 3 male NA 0 0 196
## 89 89 1 1 female 23.00 3 2 88 C23 C25 C27
## 90 90 0 3 male 24.00 0 0 196
## 91 91 0 3 male 29.00 0 0 196
## 92 92 0 3 male 20.00 0 0 189
## 93 93 0 1 male 46.00 1 0 144 E31
## 94 94 0 3 male 26.00 1 2 65
## 95 95 0 3 male 59.00 0 0 158
## 96 96 0 3 male NA 0 0 196
## 97 97 0 1 male 71.00 0 0 112 A5
## 98 98 1 1 male 23.00 0 1 148 D10 D12
## 99 99 1 2 female 34.00 0 1 75
## 100 100 0 2 male 34.00 1 0 82
## 101 101 0 3 female 28.00 0 0 192
## 102 102 0 3 male NA 0 0 192
## 103 103 0 1 male 21.00 0 1 186 D26
## 104 104 0 3 male 33.00 0 0 211
## 105 105 0 3 male 37.00 2 0 166
## 106 106 0 3 male 28.00 0 0 192
## 107 107 1 3 female 21.00 0 0 160
## 108 108 1 3 male NA 0 0 163
## 109 109 0 3 male 38.00 0 0 192
## 110 110 1 3 female NA 1 0 78
## 111 111 0 1 male 47.00 0 0 131 C110
## 112 112 0 3 female 14.50 1 0 35
## 113 113 0 3 male 22.00 0 0 196
## 114 114 0 3 female 20.00 1 0 220
## 115 115 0 3 female 17.00 0 0 36
## 116 116 0 3 male 21.00 0 0 166
## 117 117 0 3 male 70.50 0 0 162
## 118 118 0 2 male 29.00 1 0 67
## 119 119 0 1 male 24.00 0 1 62 B58 B60
## 120 120 0 3 female 2.00 4 2 104
## 121 121 0 2 male 21.00 2 0 174
## 122 122 0 3 male NA 0 0 196
## 123 123 0 2 male 32.50 1 0 101
## 124 124 1 2 female 32.50 0 0 25 E101
## 125 125 0 1 male 54.00 0 1 186 D26
## 126 126 1 3 male 12.00 1 0 14
## 127 127 0 3 male NA 0 0 162
## 128 128 1 3 male 24.00 0 0 170
## 129 129 1 3 female NA 1 1 73 F E69
## 130 130 0 3 male 45.00 0 0 143
## 131 131 0 3 male 33.00 0 0 192
## 132 132 0 3 male 20.00 0 0 155
## 133 133 0 3 female 47.00 1 0 34
## 134 134 1 2 female 29.00 1 0 82
## 135 135 0 2 male 25.00 0 0 25
## 136 136 0 2 male 23.00 0 0 45
## 137 137 1 1 female 19.00 0 2 86 D47
## 138 138 0 1 male 37.00 1 0 133 C123
## 139 139 0 3 male 16.00 0 0 223
## 140 140 0 1 male 24.00 0 0 194 B86
## 141 141 0 3 female NA 0 2 47
## 142 142 1 3 female 22.00 0 0 162
## 143 143 1 3 female 24.00 1 0 43
## 144 144 0 3 male 19.00 0 0 142
## 145 145 0 2 male 18.00 0 0 12
## 146 146 0 2 male 19.00 1 1 115
## 147 147 1 3 male 27.00 0 0 185
## 148 148 0 3 female 9.00 2 2 110
## 149 149 0 2 male 36.50 0 2 82 F2
## 150 150 0 2 male 42.00 0 0 25
## 151 151 0 2 male 51.00 0 0 20
## 152 152 1 1 female 22.00 1 0 151 C2
## 153 153 0 3 male 55.50 0 0 196
## 154 154 0 3 male 40.50 0 2 34
## 155 155 0 3 male NA 0 0 173
## 156 156 0 1 male 51.00 0 1 145
## 157 157 1 3 female 16.00 0 0 182
## 158 158 0 3 male 30.00 0 0 196
## 159 159 0 3 male NA 0 0 212
## 160 160 0 3 male NA 8 2 154
## 161 161 0 3 male 44.00 0 1 50
## 162 162 1 2 female 40.00 0 0 42
## 163 163 0 3 male 26.00 0 0 163
## 164 164 0 3 male 17.00 0 0 212
## 165 165 0 3 male 1.00 4 1 119
## 166 166 1 3 male 9.00 0 2 64
## 167 167 1 1 female NA 0 1 134 E33
## 168 168 0 3 female 45.00 1 4 92
## 169 169 0 1 male NA 0 0 79
## 170 170 0 3 male 28.00 0 0 137
## 171 171 0 1 male 61.00 0 0 109 B19
## 172 172 0 3 male 4.00 4 1 97
## 173 173 1 3 female 1.00 1 1 13
## 174 174 0 3 male 21.00 0 0 166
## 175 175 0 1 male 56.00 0 0 102 A7
## 176 176 0 3 male 18.00 1 1 189
## 177 177 0 3 male NA 3 1 80
## 178 178 0 1 female 50.00 0 0 95 C49
## 179 179 0 2 male 30.00 0 0 25
## 180 180 0 3 male 36.00 0 0 1
## 181 181 0 3 female NA 8 2 154
## 182 182 0 2 male NA 0 0 38
## 183 183 0 3 male 9.00 4 2 105
## 184 184 1 2 male 1.00 2 1 117 F4
## 185 185 1 3 female 4.00 0 2 71
## 186 186 0 1 male NA 0 0 128 A32
## 187 187 1 3 female NA 1 0 40
## 188 188 1 1 male 45.00 0 0 84
## 189 189 0 3 male 40.00 1 1 40
## 190 190 0 3 male 36.00 0 0 192
## 191 191 1 2 female 32.00 0 0 25
## 192 192 0 2 male 19.00 0 0 25
## 193 193 1 3 female 19.00 1 0 189
## 194 194 1 2 male 3.00 1 1 82 F2
## 195 195 1 1 female 44.00 0 0 93 B4
## 196 196 1 1 female 58.00 0 0 4 B80
## 197 197 0 3 male NA 0 0 162
## 198 198 0 3 male 42.00 0 1 207
## 199 199 1 3 female NA 0 0 162
## 200 200 0 2 female 24.00 0 0 25
## 201 201 0 3 male 28.00 0 0 219
## 202 202 0 3 male NA 8 2 154
## 203 203 0 3 male 34.00 0 0 149
## 204 204 0 3 male 45.50 0 0 157
## 205 205 1 3 male 18.00 0 0 196
## 206 206 0 3 female 2.00 0 1 9 G6
## 207 207 0 3 male 32.00 1 0 43
## 208 208 1 3 male 26.00 0 0 56
## 209 209 1 3 female 16.00 0 0 162
## 210 210 1 1 male 40.00 0 0 103 A31
## 211 211 0 3 male 24.00 0 0 155
## 212 212 1 2 female 35.00 0 0 67
## 213 213 0 3 male 22.00 0 0 158
## 214 214 0 2 male 30.00 0 0 25
## 215 215 0 3 male NA 1 0 162
## 216 216 1 1 female 31.00 1 0 15 D36
## 217 217 1 3 female 27.00 0 0 166
## 218 218 0 2 male 42.00 1 0 90
## 219 219 1 1 female 32.00 0 0 179 D15
## 220 220 0 2 male 30.00 0 0 7
## 221 221 1 3 male 16.00 0 0 196
## 222 222 0 2 male 27.00 0 0 25
## 223 223 0 3 male 51.00 0 0 196
## 224 224 0 3 male NA 0 0 192
## 225 225 1 1 male 38.00 1 0 221 C93
## 226 226 0 3 male 22.00 0 0 217
## 227 227 1 2 male 19.00 0 0 7
## 228 228 0 3 male 20.50 0 0 158
## 229 229 0 2 male 18.00 0 0 25
## 230 230 0 3 female NA 3 1 80
## 231 231 1 1 female 35.00 1 0 205 C83
## 232 232 0 3 male 29.00 0 0 163
## 233 233 0 2 male 59.00 0 0 26
## 234 234 1 3 female 5.00 4 2 105
## 235 235 0 2 male 24.00 0 0 7
## 236 236 0 3 female NA 0 0 159
## 237 237 0 2 male 44.00 1 0 82
## 238 238 1 2 female 8.00 0 2 83
## 239 239 0 2 male 19.00 0 0 7
## 240 240 0 2 male 33.00 0 0 17
## 241 241 0 3 female NA 1 0 35
## 242 242 1 3 female NA 1 0 40
## 243 243 0 2 male 29.00 0 0 7
## 244 244 0 3 male 22.00 0 0 156
## 245 245 0 3 male 30.00 0 0 157
## 246 246 0 1 male 44.00 2 0 221 C78
## 247 247 0 3 female 25.00 0 0 163
## 248 248 1 2 female 24.00 0 2 34
## 249 249 1 1 male 37.00 1 1 132 D35
## 250 250 0 2 male 54.00 1 0 82
## 251 251 0 3 male NA 0 0 158
## 252 252 0 3 female 29.00 1 1 9 G6
## 253 253 0 1 male 62.00 0 0 84 C87
## 254 254 0 3 male 30.00 1 0 50
## 255 255 0 3 female 41.00 0 2 66
## 256 256 1 3 female 29.00 0 2 47
## 257 257 1 1 female NA 0 0 194
## 258 258 1 1 female 30.00 0 0 210 B77
## 259 259 1 1 female 35.00 0 0 127
## 260 260 1 2 female 50.00 0 1 82
## 261 261 0 3 male NA 0 0 162
## 262 262 1 3 male 3.00 4 2 105
## 263 263 0 1 male 52.00 1 1 195 E67
## 264 264 0 1 male 40.00 0 0 1 B94
## 265 265 0 3 female NA 0 0 162
## 266 266 0 2 male 36.00 0 0 7
## 267 267 0 3 male 16.00 4 1 119
## 268 268 1 3 male 25.00 1 0 163
## 269 269 1 1 female 58.00 0 1 5 C125
## 270 270 1 1 female 35.00 0 0 3 C99
## 271 271 0 1 male NA 0 0 103
## 272 272 1 3 male 25.00 0 0 1
## 273 273 1 2 female 41.00 0 1 57
## 274 274 0 1 male 37.00 0 1 98 C118
## 275 275 1 3 female NA 0 0 162
## 276 276 1 1 female 63.00 1 0 187 D7
## 277 277 0 3 female 45.00 0 0 162
## 278 278 0 2 male NA 0 0 1
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## 280 280 1 3 female 35.00 1 1 63
## 281 281 0 3 male 65.00 0 0 162
## 282 282 0 3 male 28.00 0 0 189
## 283 283 0 3 male 16.00 0 0 219
## 284 284 1 3 male 19.00 0 0 196
## 285 285 0 1 male NA 0 0 82 A19
## 286 286 0 3 male 33.00 0 0 212
## 287 287 1 3 male 30.00 0 0 219
## 288 288 0 3 male 22.00 0 0 192
## 289 289 1 2 male 42.00 0 0 25
## 290 290 1 3 female 22.00 0 0 162
## 291 291 1 1 female 26.00 0 0 191
## 292 292 1 1 female 19.00 1 0 222 B49
## 293 293 0 2 male 36.00 0 0 22 D
## 294 294 0 3 female 24.00 0 0 197
## 295 295 0 3 male 24.00 0 0 192
## 296 296 0 1 male NA 0 0 93
## 297 297 0 3 male 23.50 0 0 172
## 298 298 0 1 female 2.00 1 2 46 C22 C26
## 299 299 1 1 male NA 0 0 100 C106
## 300 300 1 1 female 50.00 0 1 62 B58 B60
## 301 301 1 3 female NA 0 0 162
## 302 302 1 3 male NA 2 0 76
## 303 303 0 3 male 19.00 0 0 1
## 304 304 1 2 female NA 0 0 18 E101
## 305 305 0 3 male NA 0 0 196
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## 307 307 1 1 female NA 0 0 2
## 308 308 1 1 female 17.00 1 0 11 C65
## 309 309 0 2 male 30.00 1 0 77
## 310 310 1 1 female 30.00 0 0 138 E36
## 311 311 1 1 female 24.00 0 0 206 C54
## 312 312 1 1 female 18.00 2 2 85 B57 B59 B63 B66
## 313 313 0 2 female 26.00 1 1 82
## 314 314 0 3 male 28.00 0 0 192
## 315 315 0 2 male 43.00 1 1 83
## 316 316 1 3 female 26.00 0 0 189
## 317 317 1 2 female 24.00 1 0 82
## 318 318 0 2 male 54.00 0 0 32
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## 320 320 1 1 female 40.00 1 1 29 E34
## 321 321 0 3 male 22.00 0 0 158
## 322 322 0 3 male 27.00 0 0 192
## 323 323 1 2 female 30.00 0 0 18
## 324 324 1 2 female 22.00 1 1 96
## 325 325 0 3 male NA 8 2 154
## 326 326 1 1 female 36.00 0 0 3 C32
## 327 327 0 3 male 61.00 0 0 147
## 328 328 1 2 female 36.00 0 0 25 D
## 329 329 1 3 female 31.00 1 1 64
## 330 330 1 1 female 16.00 0 1 140 B18
## 331 331 1 3 female NA 2 0 76
## 332 332 0 1 male 45.50 0 0 94 C124
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## 335 335 1 1 female NA 1 0 27
## 336 336 0 3 male NA 0 0 192
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## 344 344 0 2 male 25.00 0 0 25
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## 347 347 1 2 female 40.00 0 0 25
## 348 348 1 3 female NA 1 0 50
## 349 349 1 3 male 3.00 1 1 44
## 350 350 0 3 male 42.00 0 0 212
## 351 351 0 3 male 23.00 0 0 216
## 352 352 0 1 male NA 0 0 113 C128
## 353 353 0 3 male 15.00 1 1 172
## 354 354 0 3 male 25.00 1 0 53
## 355 355 0 3 male NA 0 0 157
## 356 356 0 3 male 28.00 0 0 219
## 357 357 1 1 female 22.00 0 1 134 E33
## 358 358 0 2 female 38.00 0 0 25
## 359 359 1 3 female NA 0 0 190
## 360 360 1 3 female NA 0 0 190
## 361 361 0 3 male 40.00 1 4 92
## 362 362 0 2 male 29.00 1 0 93
## 363 363 0 3 female 45.00 0 1 35
## 364 364 0 3 male 35.00 0 0 155
## 365 365 0 3 male NA 1 0 40
## 366 366 0 3 male 30.00 0 0 158
## 367 367 1 1 female 60.00 1 0 177 D37
## 368 368 1 3 female NA 0 0 172
## 369 369 1 3 female NA 0 0 162
## 370 370 1 1 female 24.00 0 0 153 B35
## 371 371 1 1 male 25.00 1 0 136 E50
## 372 372 0 3 male 18.00 1 0 149
## 373 373 0 3 male 19.00 0 0 196
## 374 374 0 1 male 22.00 0 0 3
## 375 375 0 3 female 3.00 3 1 68
## 376 376 1 1 female NA 1 0 204
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## 659 S
## 660 C
## 661 S
## 662 C
## 663 S
## 664 S
## 665 S
## 666 S
## 667 S
## 668 S
## 669 S
## 670 S
## 671 S
## 672 S
## 673 S
## 674 S
## 675 S
## 676 S
## 677 S
## 678 S
## 679 S
## 680 C
## 681 Q
## 682 C
## 683 S
## 684 S
## 685 S
## 686 C
## 687 S
## 688 S
## 689 S
## 690 S
## 691 S
## 692 C
## 693 S
## 694 C
## 695 S
## 696 S
## 697 S
## 698 Q
## 699 C
## 700 S
## 701 C
## 702 S
## 703 C
## 704 Q
## 705 S
## 706 S
## 707 SMengisi NAN
df$Cabin = ifelse(is.na(df$Cabin),
ave(df$Cabin,FUN=function(x) mean(x, na.rm=TRUE)), df$Cabin)
#Di console ketik "view(df)" lalu enterNormalisasi data
unit_length <- function(x) {
x / sqrt(sum(x^2))
}
unit_length_df <- as.data.frame(lapply(df, unit_length))## Warning in Ops.factor(x, 2): '^' not meaningful for factors## Warning in Ops.factor(x, sqrt(sum(x^2))): '/' not meaningful for factors## Warning in Ops.factor(x, 2): '^' not meaningful for factors## Warning in Ops.factor(x, sqrt(sum(x^2))): '/' not meaningful for factors## Warning in Ops.factor(x, 2): '^' not meaningful for factors## Warning in Ops.factor(x, sqrt(sum(x^2))): '/' not meaningful for factors## Warning in Ops.factor(x, 2): '^' not meaningful for factors## Warning in Ops.factor(x, sqrt(sum(x^2))): '/' not meaningful for factorshead(unit_length_df)## PassengerId Survived Pclass Name Sex Age SibSp Parch Ticket
## 1 9.203883e-05 0.00000000 0.04596386 NA NA NA 0.03157545 0 NA
## 2 1.840777e-04 0.06041221 0.01532129 NA NA NA 0.03157545 0 NA
## 3 2.761165e-04 0.06041221 0.04596386 NA NA NA 0.00000000 0 NA
## 4 3.681553e-04 0.06041221 0.01532129 NA NA NA 0.03157545 0 NA
## 5 4.601941e-04 0.00000000 0.04596386 NA NA NA 0.00000000 0 NA
## 6 5.522330e-04 0.00000000 0.04596386 NA NA NA 0.00000000 0 NA
## Fare Cabin Embarked
## 1 0.04443788 0.001067588 NA
## 2 0.04809417 0.074731156 NA
## 3 0.04668790 0.001067588 NA
## 4 0.03740657 0.052311809 NA
## 5 0.05512548 0.001067588 NA
## 6 0.05878176 0.001067588 NANormalisasi Panjang Unit.Normalisasi Panjang Satuan mengubah x menjadi x ’dengan membagi setiap nilai vektor fitur dengan panjang vektor Euclidean.
##This is the end of Notebooks
Say: Alhamdulillah, panjang banget dah :)