Uas Metodologi Penelitian
Yonathan Anggraiwan
December 21, 2020
1 Introduction
Jadi di Ujian Akhir Semester metode penelitian ini, saya meneliti data untuk memprediksikan harga dari suatu kendaraan roda dua atau motor.
2 Persiapan Data
2.1 Packages
library(dplyr) #data manipulation
library(ggplot2) #data visualization
library(DT) #beautify the data table
library(plotly) #make a pie chart
library(Metrics) #find error value
library(tidyverse)
library(yardstick)
library(data.table)
library(paletti)
library(GGally)
library(plotly)
library(rsample) # Initial Split
library(partykit)
library(rpart) # Decision Tree
library(rpart.plot) # Decision Tree
library(caret) # Confussion Matrix
library(randomForest) # random forest
library(devtools)
#devtools::install_github('skinner927/reprtree')
#library(reprtree)
library(e1071) # naive bayes
library(nnet) # multinomial logistic regression
library(gridExtra)
library(grid)
library(knitr)
library(kableExtra)
library(cowplot)
library(formattable)
#COLORS
library(ggthemes)
library(paletti)
# WARNA
mycolorfill = c(
light_blue="#2f4b7c",
smooth_blue ="#4B87CB",
light_purple ="#665191",
dark_pink="#a05195",
light_pink="#d45087",
light_red="#f95d6a",
dark_orange="#ff6347",
semi_orange="#e79658",
orange="#dda15a",
cream="#b59378",
dark_cream="#A57F5F",
choc="#85664B",
dark_choc="#6b5340",
light_orange="#ff7c43"
)
#viz_palette(mycolorfill)
mycolor_fill <- get_scale_fill(get_pal(mycolorfill))
mycolor_color <- get_scale_color(get_pal(mycolorfill))
mycolor_hex <- get_hex(mycolorfill)2.2 Data Preparation
Saya menyiapkan datanya terlebih dahulu, dengan data tersebut yang berbentuk csv.
databike <- read.csv("bike.csv")
data.frame("Total.Data" = dim(databike)[1],
"Total.Variabel" = dim(databike)[2])## Total.Data Total.Variabel
## 1 1061 7Lalu saya cantumkan 10 data teratas yg ada di dataset databike tersebut.
## name selling_price year seller_type
## 1 Royal Enfield Classic 350 175000 2019 Individual
## 2 Honda Dio 45000 2017 Individual
## 3 Royal Enfield Classic Gunmetal Grey 150000 2018 Individual
## 4 Yamaha Fazer FI V 2.0 [2016-2018] 65000 2015 Individual
## 5 Yamaha SZ [2013-2014] 20000 2011 Individual
## 6 Honda CB Twister 18000 2010 Individual
## 7 Honda CB Hornet 160R 78500 2018 Individual
## 8 Royal Enfield Bullet 350 [2007-2011] 180000 2008 Individual
## 9 Hero Honda CBZ extreme 30000 2010 Individual
## 10 Bajaj Discover 125 50000 2016 Individual
## owner km_driven ex_showroom_price
## 1 1st owner 350 NA
## 2 1st owner 5650 NA
## 3 1st owner 12000 148114
## 4 1st owner 23000 89643
## 5 2nd owner 21000 NA
## 6 1st owner 60000 53857
## 7 1st owner 17000 87719
## 8 2nd owner 39000 NA
## 9 1st owner 32000 NA
## 10 1st owner 42000 601222.3 Deskripsi Variabel
Berikut ini deskripsi dari masing-masing variabel dari dataset ini:
~Name : Nama produk dari kendaraan roda dua tersebut.
~Selling Price: Harga jual dari kendaraan roda dua tersebut.
~Year : Tahun pembuatan kendaraan roda dua tersebut.
~Seller Type : Tipe penjual, ada yg dari individu atau dari dealer.
~Owner : Urutan kepemilikan kendaraan tersebut.
~Km Driven : Jumlah kilometer yang sudah ditempuh dari kendaraan tersebut.
~Ex Showroom Price : Harga yg dijual sebelumnya disaat dijual di showroom.
3 Pembersihan Data
3.1 Melihat Struktur Data
Berikut ini adalah struktur datasetnya.
## Rows: 1,061
## Columns: 7
## $ name <chr> "Royal Enfield Classic 350", "Honda Dio", "Royal ...
## $ selling_price <int> 175000, 45000, 150000, 65000, 20000, 18000, 78500...
## $ year <int> 2019, 2017, 2018, 2015, 2011, 2010, 2018, 2008, 2...
## $ seller_type <chr> "Individual", "Individual", "Individual", "Indivi...
## $ owner <chr> "1st owner", "1st owner", "1st owner", "1st owner...
## $ km_driven <int> 350, 5650, 12000, 23000, 21000, 60000, 17000, 390...
## $ ex_showroom_price <int> NA, NA, 148114, 89643, NA, 53857, 87719, NA, NA, ...## name selling_price year seller_type
## Length:1061 Min. : 5000 Min. :1988 Length:1061
## Class :character 1st Qu.: 28000 1st Qu.:2011 Class :character
## Mode :character Median : 45000 Median :2015 Mode :character
## Mean : 59638 Mean :2014
## 3rd Qu.: 70000 3rd Qu.:2017
## Max. :760000 Max. :2020
##
## owner km_driven ex_showroom_price
## Length:1061 Min. : 350 Min. : 30490
## Class :character 1st Qu.: 13500 1st Qu.: 54852
## Mode :character Median : 25000 Median : 72753
## Mean : 34360 Mean : 87959
## 3rd Qu.: 43000 3rd Qu.: 87032
## Max. :880000 Max. :1278000
## NA's :435Berdasarkan summary statistiknya, diperoleh bahwa harga terkecil dari kendaraan bermotor sebesar 5.000 dollar, dengan rata-rata harga sebesar 59.638 dollar, dan harga terbesarnya adalah 760.000 dollar. Diperoleh juga data bahwa tahun pembuatan kendaraan roda dua tersebut dibuat paling awal di tahun 1988, dengan rata-rata pembuatan di tahun 2015 dan yanhg terbaru di tahun 2020, juga ada data jumlah kilometer yang telah ditempuh kendaraan tersebut dimulai dari 350km sampai 880.000km. Terdapat juga nilai harga showroom jika kendaraan bermotor tersebut pernah dijual di showroom sebelumnya.
Berdasarkan struktur data diatas, terdapat variabel-variabel yang tipe datanya perlu disesuaikan lagi berdasar sifat datanya. Lalu sesuai dengan deskripsi variabel yang sudah dijelaskan, maka data tersebut diubah tipe datanya menjadi:
seller_type: di-konversi dari character menjadi factor.
owner: di-konversi dari character menjadi factor.
year: di-konversi dari integer menjadi factor.
databike <- databike %>%
mutate(
seller_type = as.factor(seller_type),
owner = as.factor(owner),
year = as.factor(year))Berikut ini diperlihatkan struktur dataset setelah dilakukan penyesuaian struktur datanya:
## Rows: 1,061
## Columns: 7
## $ name <chr> "Royal Enfield Classic 350", "Honda Dio", "Royal ...
## $ selling_price <int> 175000, 45000, 150000, 65000, 20000, 18000, 78500...
## $ year <fct> 2019, 2017, 2018, 2015, 2011, 2010, 2018, 2008, 2...
## $ seller_type <fct> Individual, Individual, Individual, Individual, I...
## $ owner <fct> 1st owner, 1st owner, 1st owner, 1st owner, 2nd o...
## $ km_driven <int> 350, 5650, 12000, 23000, 21000, 60000, 17000, 390...
## $ ex_showroom_price <int> NA, NA, 148114, 89643, NA, 53857, 87719, NA, NA, ...3.2 Pencarian Data yg Hilang
## name selling_price year seller_type
## 0 0 0 0
## owner km_driven ex_showroom_price
## 0 0 435Terdapat missing value di data ex_showroom_price di dataset tersebut, maka kita harus menghilangkan missing value tersebut memakai coding is.na()
## name selling_price year seller_type
## 0 0 0 0
## owner km_driven ex_showroom_price
## 0 0 03.3 Data Duplikat
Kita mencari dulu apakah ada duplikat data di dataset ini.
## jumlah.seluruh.data jumlah.data.unik
## 1 1061 1055Nah, disini kita dapat melihat bahwa terdapat 6 baris data duplikat di dataset tersebut, maka kita menggunakan function distinct() untuk menghilangkan data duplikatnya
## name selling_price year seller_type
## 1 Royal Enfield Classic 350 175000 2019 Individual
## 2 Honda Dio 45000 2017 Individual
## 3 Royal Enfield Classic Gunmetal Grey 150000 2018 Individual
## 4 Yamaha Fazer FI V 2.0 [2016-2018] 65000 2015 Individual
## 5 Yamaha SZ [2013-2014] 20000 2011 Individual
## 6 Honda CB Twister 18000 2010 Individual
## 7 Honda CB Hornet 160R 78500 2018 Individual
## 8 Royal Enfield Bullet 350 [2007-2011] 180000 2008 Individual
## 9 Hero Honda CBZ extreme 30000 2010 Individual
## 10 Bajaj Discover 125 50000 2016 Individual
## 11 Yamaha FZ16 35000 2015 Individual
## 12 Honda Navi 28000 2016 Individual
## 13 Bajaj Avenger Street 220 80000 2018 Individual
## 14 Yamaha YZF R3 365000 2019 Individual
## 15 Jawa 42 185000 2020 Individual
## 16 Suzuki Access 125 [2007-2016] 25000 2012 Individual
## 17 Hero Honda Glamour 25000 2006 Individual
## 18 Yamaha YZF R15 S 40000 2010 Individual
## 19 Royal Enfield Classic Gunmetal Grey 150000 2018 Individual
## 20 Yamaha FZ25 120000 2018 Individual
## 21 Hero Passion Pro 110 15000 2008 Individual
## 22 Honda Navi [2016-2017] 26000 2016 Individual
## 23 Honda Activa i 32000 2013 Individual
## 24 Jawa Standard 180000 2019 Individual
## 25 Royal Enfield Thunderbird 350 110000 2016 Individual
## 26 Honda Dream Yuga 25000 2012 Individual
## 27 TVS Apache RTR 160 4V 80000 2018 Individual
## 28 Honda Navi [2016-2017] 42000 2017 Individual
## 29 Yamaha Fazer [2009-2016] 40000 2013 Individual
## 30 Hero Honda Splendor NXG 21000 2009 Individual
## 31 Hero Glamour 125 55000 2017 Individual
## 32 Yamaha FZ S [2012-2016] 38000 2013 Individual
## 33 Hero Xtreme Sports 43000 2016 Individual
## 34 Honda X-Blade 65000 2018 Individual
## 35 Honda CB Shine SP 62000 2019 Individual
## 36 Honda Activa 5G 60000 2018 Individual
## 37 Hero Glamour 125 45000 2018 Individual
## 38 Honda CBR-250R 120000 2016 Individual
## 39 Hero Honda Passion PRO [2012] 30000 2011 Individual
## 40 Royal Enfield Thunderbird 350 90000 2015 Individual
## 41 Bajaj Dominar 400 125000 2017 Individual
## 42 Hero Glamour FI 50000 2018 Individual
## 43 KTM 390 Duke 175000 2017 Individual
## 44 Hero Passion XPro 28000 2012 Individual
## 45 Yamaha FZ S V 2.0 85000 2018 Individual
## 46 Hero Achiever 150 29900 2016 Individual
## 47 Bajaj Discover 125 25000 2011 Individual
## 48 Yamaha FZ16 30000 2009 Individual
## 49 Yamaha Saluto 60000 2018 Individual
## 50 Yamaha FZ16 35000 2012 Individual
## 51 Bajaj Discover 100 25000 2010 Individual
## 52 Honda CB Trigger 30000 2015 Individual
## 53 Royal Enfield Electra 5 S 90000 2010 Individual
## 54 Hero Splendor PRO 35000 2015 Individual
## 55 Honda Activa 5G 70000 2019 Individual
## 56 Hero Honda Passion Plus 25000 2006 Individual
## 57 Bajaj Pulsar 150 25000 2010 Individual
## 58 Royal Enfield Bullet 350 90000 2014 Individual
## 59 Bajaj Discover 125 25000 2013 Individual
## 60 Bajaj Pulsar 150 [2001-2011] 42000 2011 Individual
## 61 Hero Splendor PRO 35000 2015 Individual
## 62 Hero Xtreme Sports 50000 2014 Individual
## 63 Honda Activa 3G 30000 2015 Individual
## 64 Yamaha FZ16 48000 2016 Individual
## 65 Honda CB Hornet 160R 65000 2016 Individual
## 66 Royal Enfield Thunderbird 350 85000 2013 Individual
## 67 Hero Hunk 45000 2014 Individual
## 68 Suzuki Let''s 34000 2014 Individual
## 69 Royal Enfield Electra 4 S 90000 2008 Individual
## 70 TVS Scooty Pep Plus 35000 2012 Individual
## 71 Mahindra Mojo XT300 165000 2017 Individual
## 72 TVS Apache RTR 160 35000 2013 Individual
## 73 Bajaj Pulsar AS200 65000 2015 Individual
## 74 Bajaj Pulsar 150 29500 2013 Individual
## 75 Royal Enfield Thunderbird 350X 165000 2019 Individual
## 76 Suzuki Intruder 150 95000 2018 Individual
## 77 Hero Honda Karizma ZMR [2010] 50000 2010 Individual
## 78 Honda Dio 7500 2009 Individual
## 79 Bajaj Xcd 25000 2008 Individual
## 80 Hero Splendor Plus 35000 2014 Individual
## 81 Royal Enfield Thunderbird 350X 160000 2018 Individual
## 82 Honda CB Unicorn 150 50000 2014 Individual
## 83 Honda Activa 5G 62000 2019 Individual
## 84 Honda CB Twister 30000 2013 Individual
## 85 Hero Splendor PRO 28000 2015 Individual
## 86 Hero Honda CD Deluxe 25000 2009 Individual
## 87 Honda Activa i [2016-2017] 30000 2015 Individual
## 88 Royal Enfield Classic Gunmetal Grey 140000 2018 Individual
## 89 TVS Scooty Zest 110 30000 2017 Individual
## 90 Hero CD Deluxe 25000 2014 Individual
## 91 Suzuki Access 125 [2007-2016] 22000 2010 Individual
## 92 Honda Activa [2000-2015] 22989 2012 Individual
## 93 Honda Activa 125 [2016-2017] 42000 2016 Individual
## 94 Suzuki GS150R 30000 2010 Individual
## 95 Bajaj Pulsar 220S 55000 2015 Individual
## 96 Honda Activa 4G 45000 2018 Individual
## 97 Yamaha FZ25 150000 2019 Individual
## 98 Bajaj Pulsar 150 42000 2015 Individual
## 99 Hero Splendor Plus 30000 2015 Individual
## 100 Bajaj Discover 100 24999 2012 Individual
## 101 Royal Enfield Thunderbird 350X 160000 2019 Individual
## 102 Royal Enfield Classic 350 175000 2020 Individual
## 103 Bajaj Pulsar 150 40000 2013 Individual
## 104 Bajaj Pulsar NS160 85000 2018 Individual
## 105 Royal Enfield Classic Desert Storm 150000 2017 Individual
## 106 Suzuki Gixxer SF 80000 2018 Individual
## 107 TVS Apache RTR 200 4V 95000 2018 Individual
## 108 Bajaj V15 42000 2016 Individual
## 109 TVS XL 100 Heavy Duty 40000 2019 Individual
## 110 Yamaha FZ S V 2.0 50000 2015 Individual
## 111 Hero HF Deluxe 40000 2018 Individual
## 112 Aprilia SR 125 75000 2019 Individual
## 113 Yamaha FZ25 120000 2017 Individual
## 114 Hero Honda Glamour 30000 2008 Individual
## 115 Bajaj Discover 125 ST 17500 2013 Individual
## 116 Hero Honda Passion Plus 7500 2004 Individual
## 117 Honda Aviator 57000 2018 Individual
## 118 Vespa SXL 149 80000 2018 Individual
## 119 TVS Scooty Pep Plus 18000 2010 Individual
## 120 Hero Xtreme [2013-2014] 25000 2013 Individual
## 121 Royal Enfield Thunderbird 350 83900 2014 Individual
## 122 UM Renegade Commando 160000 2018 Individual
## 123 Royal Enfield Classic Gunmetal Grey 135000 2018 Individual
## 124 Honda CB Hornet 160R 55000 2017 Individual
## 125 Honda Shine 50000 2017 Individual
## 126 Bajaj Discover 100 20000 2012 Individual
## 127 Hero Honda CBZ extreme 25000 2010 Individual
## 128 Royal Enfield Classic 350 125000 2017 Individual
## 129 Honda Activa 5G 55000 2019 Individual
## 130 Bajaj Avenger Street 150 [2018] 50000 2016 Individual
## 131 Royal Enfield Continental GT 650 299000 2019 Individual
## 132 Honda Activa 3G 60000 2015 Individual
## 133 Honda Activa 3G 40000 2015 Individual
## 134 Bajaj Pulsar 180 15000 2008 Individual
## 135 Harley-Davidson Street Bob 750000 2013 Individual
## 136 Hero Honda CBZ extreme 20000 2007 Individual
## 137 Royal Enfield Classic Gunmetal Grey 190000 2020 Individual
## 138 KTM 390 Duke ABS [2013-2016] 130000 2015 Individual
## 139 Honda CB Hornet 160R 54786 2016 Individual
## 140 KTM 200 Duke 72000 2013 Individual
## 141 Hero Karizma 2014 35000 2013 Individual
## 142 Hero Glamour 125 45000 2017 Individual
## 143 Royal Enfield Classic 350 85000 2013 Individual
## 144 Royal Enfield Classic 500 100000 2013 Individual
## 145 Bajaj Dominar 400 138000 2017 Individual
## 146 Royal Enfield Thunderbird 350X 150000 2018 Individual
## 147 KTM RC 390 180000 2017 Individual
## 148 Royal Enfield Thunderbird 350 100000 2015 Individual
## 149 Hero Hunk 20000 2010 Individual
## 150 KTM 390 Duke 170000 2017 Individual
## 151 Suzuki Gixxer [2014-2018] 60000 2016 Individual
## 152 Royal Enfield Bullet 350 123000 2016 Individual
## 153 Suzuki Access 125 60000 2017 Individual
## 154 TVS Apache RTR 200 4V 100000 2017 Individual
## 155 Hero Xtreme Sports 22000 2014 Individual
## 156 Honda Activa [2000-2015] 30000 2014 Individual
## 157 Bajaj Discover 125 28000 2013 Individual
## 158 Bajaj Pulsar 150 50000 2007 Individual
## 159 Yamaha Saluto RX 40000 2017 Individual
## 160 Hero HF Deluxe 43000 2018 Individual
## 161 Suzuki Access 125 40000 2015 Individual
## 162 Royal Enfield Classic Desert Storm 100000 2012 Individual
## 163 Hero HF Deluxe 30000 2016 Individual
## 164 KTM RC 200 130000 2017 Individual
## 165 Honda CB Hornet 160R 48000 2016 Individual
## 166 Bajaj CT 100 32000 2017 Individual
## 167 Hero Honda Pleasure 23000 2010 Individual
## 168 Bajaj Pulsar 135 LS 20000 2010 Individual
## 169 Royal Enfield Classic 350 125000 2017 Individual
## 170 Honda Navi [2016-2017] 23000 2016 Individual
## 171 Bajaj Discover 125 15000 2011 Individual
## 172 Yamaha SZ RR V 2.0 35000 2015 Individual
## 173 Bajaj Discover 150 25000 2014 Individual
## 174 Bajaj Pulsar 150 80000 2019 Individual
## 175 TVS Flame 15000 2010 Individual
## 176 Vespa VX 125 45000 2013 Individual
## 177 Yamaha SZ X 25000 2012 Individual
## 178 Honda CBF Stunner 35000 2013 Individual
## 179 Bajaj Discover 100 T 25000 2013 Individual
## 180 Royal Enfield Classic 350 170000 2018 Individual
## 181 Honda Activa [2000-2015] 25000 2014 Individual
## 182 Royal Enfield Electra 5 S 60000 2007 Individual
## 183 Bajaj Pulsar 150 41000 2012 Individual
## 184 Honda Shine 35000 2012 Individual
## 185 Honda CB Unicorn 160 75000 2015 Individual
## 186 Honda Activa 3G 47999 2016 Individual
## 187 Honda Shine 36999 2011 Individual
## 188 Bajaj Pulsar 180 25000 2007 Individual
## 189 Suzuki Gixxer SF 60000 2016 Individual
## 190 Honda CB Unicorn 150 50000 2007 Individual
## 191 Yamaha YZF R15 [2011-2018] 65000 2013 Individual
## 192 TVS Sport 12000 2007 Individual
## 193 Bajaj Pulsar AS200 60000 2015 Individual
## 194 Honda Activa [2000-2015] 20000 2010 Individual
## 195 Yamaha FZ16 27000 2011 Individual
## 196 Bajaj Boxer 10000 2000 Individual
## 197 Hero Honda Hunk 56000 2015 Individual
## 198 Hero Honda Hunk 58000 2015 Individual
## 199 Bajaj Avenger Cruise 220 87000 2018 Individual
## 200 Yamaha Ray Z 35000 2015 Individual
## 201 Bajaj Pulsar 180 35000 2012 Individual
## 202 Bajaj Discover 150F 40000 2014 Individual
## 203 Bajaj Pulsar 135 LS 56000 2017 Individual
## 204 Bajaj Pulsar 180 30000 2008 Individual
## 205 Honda Activa 125 [2016-2017] 40000 2017 Individual
## 206 Hero HF Deluxe 60000 2019 Individual
## 207 Hero Karizma 2014 40000 2013 Individual
## 208 Bajaj Platina 100 40000 2018 Individual
## 209 Royal Enfield Bullet 500 130000 2015 Individual
## 210 Bajaj Pulsar AS200 45000 2015 Individual
## 211 Yamaha FZ S [2012-2016] 42000 2013 Individual
## 212 Bajaj Pulsar 220 Fi 25000 2012 Individual
## 213 Bajaj Pulsar 150 15000 2006 Individual
## 214 Bajaj Dominar 400 [2018] 170000 2018 Individual
## 215 Hero Passion X Pro [2016-2017] 38000 2016 Individual
## 216 Yamaha FZ S V 2.0 60000 2015 Individual
## 217 TVS Apache RTR 160 65000 2017 Individual
## 218 Bajaj Avenger [2015] 35000 2011 Individual
## 219 Bajaj Pulsar 150 70000 2019 Individual
## 220 Hero Honda Glamour 15000 2006 Individual
## 221 Hero Splendor iSmart 50000 2017 Individual
## 222 Royal Enfield Classic 350 145000 2017 Individual
## 223 Yamaha FZ16 25000 2008 Individual
## 224 Hero Xtreme Sports 58000 2016 Individual
## 225 Royal Enfield Bullet 350 [2007-2011] 90000 2007 Individual
## 226 Hero Honda Passion 9000 2002 Individual
## 227 Bajaj Discover 150S 40000 2014 Individual
## 228 TVS Scooty Zest 110 28000 2015 Individual
## 229 Bajaj Avenger Street 150 [2018] 50000 2016 Individual
## 230 Yamaha Cygnus Ray ZR 56000 2019 Individual
## 231 Honda Activa [2000-2015] 35000 2014 Individual
## 232 Honda Shine 30000 2010 Individual
## 233 Honda CB Hornet 160R 95000 2019 Individual
## 234 Honda Activa i [2016-2017] 40000 2017 Individual
## 235 Hero Karizma 2014 30000 2014 Individual
## 236 Bajaj Discover 100 T 18000 2013 Individual
## 237 Honda Aviator 14000 2008 Individual
## 238 Hero Splendor Plus 17000 2010 Individual
## 239 Royal Enfield Thunderbird 350 125000 2017 Individual
## 240 Bajaj CT 110 20000 2015 Individual
## 241 Hero Passion PRO i3s 50000 2017 Individual
## 242 KTM RC 390 210000 2017 Individual
## 243 Bajaj Pulsar 220F 70000 2018 Individual
## 244 Hero Maestro Edge 40000 2017 Individual
## 245 Bajaj Xcd 10000 2008 Individual
## 246 Bajaj Pulsar 150 35000 2010 Individual
## 247 TVS Centra 10000 2005 Individual
## 248 Honda Activa 3G 42000 2015 Individual
## 249 Yamaha FZ16 20000 2009 Individual
## 250 Hero Maestro Edge 25000 2017 Individual
## 251 Bajaj Discover 125 25000 2016 Individual
## 252 Royal Enfield Thunderbird 350X 145000 2019 Individual
## 253 Hero Super Splendor 50000 2016 Individual
## 254 TVS Scooty Pep Plus 30000 2011 Individual
## 255 Yamaha FZ V 2.0 60000 2014 Individual
## 256 Hero Honda Splendor Plus 15000 2005 Individual
## 257 Hero CD Deluxe 15000 2014 Individual
## 258 Hero Honda Passion PRO [2012] 25000 2011 Individual
## 259 Hero HF Deluxe i3s 54000 2019 Individual
## 260 Bajaj Xcd 25000 2007 Individual
## 261 Royal Enfield Interceptor 650 260000 2019 Individual
## 262 Honda CBR-250R 100000 2014 Individual
## 263 Bajaj Kristal 15000 2007 Individual
## 264 Hero Honda Splendor Plus 20000 2006 Individual
## 265 Honda Dream Yuga 35000 2014 Individual
## 266 Yamaha FZ S V 2.0 50000 2016 Individual
## 267 Hero Xpulse 200 90000 2019 Individual
## 268 Bajaj V15 40000 2016 Individual
## 269 Bajaj Avenger Street 220 80000 2016 Individual
## 270 Bajaj Discover 135 20000 2010 Individual
## 271 Honda CB Unicorn 150 70000 2017 Individual
## 272 Vespa SXL 149 98000 2019 Individual
## 273 Royal Enfield Machismo 75000 2008 Individual
## 274 Honda CB Shine SP 50000 2016 Individual
## 275 Honda CB Unicorn 150 40000 2014 Individual
## 276 Honda CB Unicorn 150 75000 2013 Individual
## 277 Honda CB Unicorn 150 50000 2013 Individual
## 278 Bajaj Pulsar 150 60000 2014 Individual
## 279 TVS Apache RTR 160 50000 2016 Individual
## 280 Honda Dio 35000 2014 Individual
## 281 Royal Enfield Thunderbird 350 160000 2018 Individual
## 282 Hero Maestro 25000 2015 Individual
## 283 Hero Honda CBZ extreme 42000 2010 Individual
## 284 Bajaj Dominar 400 200000 2019 Individual
## 285 Honda Activa 125 40000 2014 Individual
## 286 Royal Enfield Classic Desert Storm 130000 2015 Individual
## 287 KTM 250 Duke 195000 2019 Individual
## 288 KTM RC 390 270000 2019 Individual
## 289 Kawasaki Ninja 650 [2018-2019] 425000 2017 Individual
## 290 TVS Apache RTR 200 4V 85000 2018 Individual
## 291 Kawasaki Ninja 250R 125000 2010 Individual
## 292 TVS Wego 45000 2018 Individual
## 293 Honda CBR-250R 85000 2014 Individual
## 294 Bajaj Pulsar NS200 85000 2018 Individual
## 295 Suzuki GSX S750 760000 2019 Individual
## 296 Royal Enfield Thunderbird 350 100000 2015 Individual
## 297 Royal Enfield Classic 350 95000 2015 Individual
## 298 Honda CB Shine SP 48000 2015 Individual
## 299 Bajaj Avenger Street 150 [2018] 40000 2016 Individual
## 300 Honda CBF Stunner 37000 2014 Individual
## 301 Bajaj Avenger [2015] 38000 2014 Individual
## 302 Suzuki Gixxer [2014-2018] 55000 2015 Individual
## 303 Suzuki Access 125 45000 2017 Individual
## 304 TVS Scooty Streak 18000 2009 Individual
## 305 Royal Enfield Thunderbird 350X 140000 2018 Individual
## 306 Yamaha Rx 70000 2000 Individual
## 307 Hero Honda Splendor Plus 15000 2007 Individual
## 308 Bajaj Pulsar 125 65000 2019 Individual
## 309 Honda Activa 5G 42000 2018 Individual
## 310 Yamaha Fazer Dlx 58000 2012 Individual
## 311 Harley-Davidson Street 750 330000 2014 Individual
## 312 Hero Xtreme 2014 35000 2013 Individual
## 313 Royal Enfield Classic 350 162000 2018 Individual
## 314 Hero Honda Hunk 28000 2009 Individual
## 315 Royal Enfield Interceptor 650 285000 2019 Individual
## 316 Royal Enfield Classic 350 125000 2017 Individual
## 317 Bajaj Pulsar 150 [2001-2011] 15000 2005 Individual
## 318 Honda Activa [2000-2015] 29500 2013 Individual
## 319 TVS Apache RR310 200000 2019 Individual
## 320 Jawa Standard 160000 2019 Individual
## 321 Hero Honda Splendor PRO 30000 2012 Individual
## 322 Yamaha FZ25 110000 2019 Individual
## 323 Bajaj Pulsar 220F 70000 2016 Individual
## 324 Yamaha Fascino 110 69000 2019 Individual
## 325 Yamaha FZ16 30000 2011 Individual
## 326 Yamaha FZ16 30000 2010 Individual
## 327 TVS Star City Plus 42000 2017 Individual
## 328 Royal Enfield Classic 350 160000 2018 Individual
## 329 Bajaj Pulsar 135 LS 20000 2010 Individual
## 330 Suzuki Access 125 45000 2017 Individual
## 331 Bajaj Pulsar 135 LS 23000 2010 Individual
## 332 UM Renegade Commando 140000 2017 Individual
## 333 Hero Honda Splendor Plus 12000 2005 Individual
## 334 Hero Passion PRO i3s 50000 2017 Individual
## 335 Royal Enfield Classic 350 140000 2018 Individual
## 336 Honda Activa 5G 55000 2019 Individual
## 337 Yamaha Fascino 110 45000 2016 Individual
## 338 Hero Splendor Plus 25000 2012 Individual
## 339 Royal Enfield Thunderbird 350 90000 2014 Individual
## 340 Yamaha YZF R15 V3 120000 2018 Individual
## 341 KTM RC 200 120000 2015 Individual
## 342 TVS Radeon 35000 2017 Individual
## 343 Hero HF Deluxe 35000 2017 Individual
## 344 BMW G310GS 300000 2018 Individual
## 345 Royal Enfield Thunderbird 350 87000 2012 Individual
## 346 Hero Honda Splendor Plus 18000 2010 Individual
## 347 Yamaha FZ S V 2.0 50000 2014 Individual
## 348 Bajaj Avenger Street 220 60000 2016 Individual
## 349 Hero Honda Splendor Plus 15000 2004 Individual
## 350 Bajaj Platina 100 30000 2018 Individual
## 351 Royal Enfield Thunderbird 350X 160000 2018 Individual
## 352 Honda CB Unicorn 150 50000 2016 Individual
## 353 Suzuki Gixxer [2014-2018] 62000 2017 Individual
## 354 Bajaj Platina 100 45000 2019 Individual
## 355 Royal Enfield Thunderbird 350 120000 2017 Individual
## 356 Royal Enfield Thunderbird 500 125000 2014 Individual
## 357 Bajaj Pulsar AS150 50000 2015 Individual
## 358 Bajaj Pulsar 150 30000 2012 Individual
## 359 Hero Pleasure 40000 2014 Individual
## 360 Honda Aviator 41000 2013 Individual
## 361 Royal Enfield Thunderbird 350 120000 2017 Individual
## 362 Hero Ignitor 35000 2014 Individual
## 363 Hero Honda CD100SS 10000 1997 Individual
## 364 TVS Apache RTR 160 35000 2011 Individual
## 365 Hero Achiever 150 38000 2017 Individual
## 366 Vespa LX 125 50000 2015 Individual
## 367 Honda Activa 3G 40000 2015 Individual
## 368 Hero Splendor Plus i3s 70000 2019 Individual
## 369 Royal Enfield Classic 350 140000 2016 Individual
## 370 Hero Maestro 26500 2016 Individual
## 371 Hero Glamour 125 38000 2016 Dealer
## 372 Yamaha FZ16 60000 2016 Individual
## 373 Royal Enfield Himalayan 145000 2017 Individual
## 374 Yamaha FZ S V 2.0 78000 2017 Individual
## 375 Hero Maestro Edge 58000 2018 Individual
## 376 Yamaha FZ25 130000 2017 Individual
## 377 TVS Victor 20000 2001 Individual
## 378 Bajaj Dominar 400 [2018] 150000 2018 Individual
## 379 Bajaj Pulsar 180 32000 2012 Individual
## 380 Bajaj Pulsar 180 50000 2017 Individual
## 381 KTM 200 Duke 120000 2017 Individual
## 382 Bajaj Avenger Street 150 [2018] 60000 2016 Individual
## 383 Bajaj Discover 100 M 20000 2011 Individual
## 384 Royal Enfield Bullet 350 [2007-2011] 65000 2011 Individual
## 385 Yamaha YZF R15 S 70000 2016 Individual
## 386 Yamaha FZ V 2.0 35000 2014 Individual
## 387 Bajaj Avenger Street 220 95000 2018 Individual
## 388 Bajaj Pulsar 150 8000 2005 Individual
## 389 Royal Enfield Thunderbird 350 85000 2010 Individual
## 390 Hero Achiever 150 65000 2016 Individual
## 391 Hero Passion Pro 110 39000 2013 Individual
## 392 Hero Duet 45000 2017 Individual
## 393 Bajaj Avenger Street 150 [2018] 60000 2016 Individual
## 394 Royal Enfield Classic Stealth Black 160000 2018 Individual
## 395 Bajaj Pulsar 150 35000 2011 Individual
## 396 Bajaj Pulsar 135 LS 40000 2018 Individual
## 397 Yamaha Fazer [2009-2016] 40000 2012 Individual
## 398 Honda CB Hornet 160R 56000 2017 Individual
## 399 Honda CB Unicorn Dazzler 22000 2011 Individual
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## 1055 1st owner 92233 75000Tadinya terdapat 6 baris data yg sama di dataset tersebut, maka kita menggunakan function distinct() untuk menghilangkan data duplikatnya. Kerena sudah tidak ada lagi data yang bermasalah, selanjutnya data yang sudah dibersihkan disimpan untuk keperluan proses selanjutnya.
4 Analisis Eksplorasi Data
# corelation between predictors
ggcorr(databike,label = T, size=3, label_size = 3, hjust=0.95,
layout.exp = 3,low = mycolor_hex("dark_cream"), high = mycolor_hex("light_red"))+
labs(
title="Matriks Korelasi untuk setiap Variabel Data Bike"
)+
theme_minimal()+
theme(
plot.title = element_text(hjust = 0.5),
axis.title=element_text(size=8,face="bold"),
axis.text.y=element_blank()
)## Warning in ggcorr(databike, label = T, size = 3, label_size = 3, hjust = 0.95, :
## data in column(s) 'name', 'year', 'seller_type', 'owner' are not numeric and
## were ignored
Dapat dilihat bahwa terhadap korelasi diantara variabel selling_price, km_driven, dan ex_showroom_price. Korelasi positif diantara variabel ex_showroom_price dengan variabel selling_price sebesar 0,6%, memiliki arti jika harga dari suatu kendaraan bermotor roda dua didalam showroom sebelumnya, akan mempengaruhi harga selling_price yang akan dijual, begitu pula dengan variabel km_driven yang memiliki nilai korelasi minus 0,1, yang artinya adalah semakin besar jumlah jarak kilometer yang telah ditempuh suatu kendaraan bermotor roda dua, maka harga yang akan dijual di showroom atau selling_price akan semakin rendah, atau kebalikannya jika jumlah jarak kilometer yang ditempuh sedikit, maka harga jualnya akan semakin meningkat.
4.1 Pencilan Harga Motor
# length(boxplot(house$price)$out)
#house <- house %>% filter(!Prices %in% bx.plot.outlier)
databike <- databike %>%
mutate(
selling_price = as.numeric(selling_price)
)
glimpse(databike)## Rows: 1,055
## Columns: 7
## $ name <chr> "Royal Enfield Classic 350", "Honda Dio", "Royal ...
## $ selling_price <dbl> 175000, 45000, 150000, 65000, 20000, 18000, 78500...
## $ year <fct> 2019, 2017, 2018, 2015, 2011, 2010, 2018, 2008, 2...
## $ seller_type <fct> Individual, Individual, Individual, Individual, I...
## $ owner <fct> 1st owner, 1st owner, 1st owner, 1st owner, 2nd o...
## $ km_driven <int> 350, 5650, 12000, 23000, 21000, 60000, 17000, 390...
## $ ex_showroom_price <dbl> 0, 0, 148114, 89643, 0, 53857, 87719, 0, 0, 60122...ggplot(databike, aes(x=selling_price)) +
geom_histogram(aes(x=selling_price, y=..density..), colour="black", fill="white")+
geom_density(alpha=.2, fill="blue")+
labs(title="Distribusi Harga Tanpa Outlier")+
theme_minimal()+
theme(
plot.title = element_text(hjust = 0.5),
axis.title=element_text(size=9,face="bold") +
mycolor_fill()
)## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Dapat dilihat bahwa terdapat banyak sekali data kendaraan bermotor yang harganya kecil dengan rata-rata seharga 59638 dollar. Jika dilihat terdapat cukup banyak data yang memiliki harga yang jauh lebih tinggi dari data lainnya sehingga menyebabkan distribusi penyebaran harga kendaraan bermotor roda du aini tidak normal atau kita dapat melihat bahwa kurva condong ke kiri dan menurun ke sebelah kanan (bersifat eksponensial). Sehinga saya memutuskan untuk membuang data dengan harga yang lebih dari USD 250 ribu. Saya memutuskan untuk membuang data yg lebih dari USD 250 ribu karena saya sudah melakukan banyak pencobaan, tetapi membuang data yg lebih daru USD 250 ribu memiliki tingkat r-squared yang paling tinggi, dan kurvanya menjadi:
## [1] 760000## [1] 59713.82## [1] 5000data_clean <- databike %>%
filter(databike$selling_price < 250000)
ggplot(data_clean, aes(x=selling_price)) +
geom_histogram(aes(y=..density..), colour="black", fill="white",binwidth = 50000)+
geom_density(alpha=.2, fill="blue")+
scale_x_continuous(name = "Harga")+
labs(title="Distribusi Harga <US 250 ribu")+
theme_minimal()+
theme(
plot.title = element_text(hjust = 0.5),
axis.title=element_text(size=9,face="bold")+
mycolor_fill())
4.2 Pemisahan Data
Untuk membangun model regresi linear yang baik, saya membagi dataset tersebut menjadi data train dan data test dengan proporsi 80:20. Proporsi ini yang sudah sering digunakan dalam proses pemodelan machine learning.
# Train vs Test
set.seed(999)
idx_bike <- sample(x=nrow(data_clean),size=nrow(databike)*0.8)
data_train <- data_clean[idx_bike,]
data_test <- data_clean[-idx_bike,]## name selling_price year seller_type
## 923 Honda CB Twister 25000 2012 Individual
## 61 Hero Xtreme Sports 50000 2014 Individual
## 583 Honda Dream Yuga 47000 2019 Individual
## 361 Hero Glamour 125 38000 2016 Dealer
## 833 Royal Enfield Classic 350 120000 2017 Individual
## 1034 Hero Hunk 20000 2009 Individual
## 291 Honda CB Shine SP 48000 2015 Individual
## 581 Yamaha Fazer [2009-2016] 45000 2014 Individual
## 391 Suzuki Intruder 150 Fi 100000 2018 Individual
## 678 Yamaha FZ S [2012-2016] 40000 2011 Individual
## 574 Bajaj Discover 125 45000 2016 Individual
## 403 Hero Splendor iSmart 40000 2015 Individual
## 491 Bajaj Avenger Street 150 [2018] 80000 2017 Individual
## 82 Honda Activa 5G 62000 2019 Individual
## 577 Honda Shine 30000 2014 Individual
## 890 Hero Super Splendor 30000 2011 Individual
## 460 Royal Enfield Classic Desert Storm 150000 2015 Individual
## 393 Royal Enfield Electra 5 S 85000 2008 Individual
## 671 Honda CB Unicorn 150 30000 2010 Individual
## 181 Honda Shine 35000 2012 Individual
## 375 Yamaha YZF R15 S 70000 2016 Individual
## 549 Honda Activa 3G 35000 2016 Individual
## 441 Yamaha FZ S [2012-2016] 40000 2014 Individual
## 762 Hero Honda Splendor Plus 20000 2005 Individual
## 378 Bajaj Pulsar 150 8000 2005 Individual
## 580 Hero Super Splendor 45000 2012 Individual
## 632 Bajaj Pulsar NS200 85000 2018 Individual
## 249 Royal Enfield Thunderbird 350X 145000 2019 Individual
## 719 Honda CB Unicorn 150 55000 2014 Individual
## 223 Hero Honda Passion 9000 2002 Individual
## 887 Hero Honda Splendor Plus 20000 2010 Individual
## 16 Hero Honda Glamour 25000 2006 Individual
## 541 Yamaha RX135 65000 1998 Individual
## 363 Royal Enfield Himalayan 145000 2017 Individual
## 258 Honda CBR-250R 100000 2014 Individual
## 9 Hero Honda CBZ extreme 30000 2010 Individual
## 353 Hero Honda CD100SS 10000 1997 Individual
## 411 Hero Honda Splendor 10000 2000 Individual
## 754 Yamaha SZ-S 50000 2014 Individual
## 344 Bajaj Platina 100 45000 2019 Individual
## 552 Bajaj Pulsar 150 [2001-2011] 30000 2010 Individual
## 346 Royal Enfield Thunderbird 500 125000 2014 Individual
## 708 Honda CB Unicorn 160 50000 2017 Individual
## 604 Honda Activa [2000-2015] 15000 2006 Individual
## 836 Hero Super Splendor 50000 2017 Individual
## 41 Hero Glamour FI 50000 2018 Individual
## 199 Bajaj Discover 150F 40000 2014 Individual
## 866 Bajaj Discover 100 24000 2014 Individual
## 397 Royal Enfield Thunderbird 350 60000 2006 Individual
## 793 Hero Xpulse 200 98000 2019 Individual
## 386 Bajaj Pulsar 135 LS 40000 2018 Individual
## 461 Hero Honda CD Deluxe 24700 2012 Individual
## 822 Yamaha FZ V 2.0 60000 2016 Individual
## 487 Bajaj Avenger [2015] 65000 2015 Individual
## 343 Suzuki Gixxer [2014-2018] 62000 2017 Individual
## 569 Bajaj Pulsar 135 LS 25000 2011 Individual
## 706 Bajaj Discover 125 26000 2012 Individual
## 45 Hero Achiever 150 29900 2016 Individual
## 670 TVS Apache RTR 160 4V 75000 2019 Individual
## 780 Hero Honda CD Deluxe 25000 2008 Individual
## 205 Bajaj Platina 100 40000 2018 Individual
## 108 TVS XL 100 Heavy Duty 40000 2019 Individual
## 159 Royal Enfield Classic Desert Storm 100000 2012 Individual
## 770 Hero Super Splendor 40000 2013 Individual
## 91 Honda Activa [2000-2015] 22989 2012 Individual
## 785 Honda CB Unicorn Dazzler 25000 2011 Individual
## 172 TVS Flame 15000 2010 Individual
## 669 Bajaj Pulsar 150 25000 2013 Individual
## 467 Hero Honda CD100 15000 1999 Dealer
## 148 Suzuki Gixxer [2014-2018] 60000 2016 Individual
## 807 Bajaj Discover 100 22000 2014 Individual
## 73 Bajaj Pulsar 150 29500 2013 Individual
## 644 Hero Honda Glamour 35000 2012 Individual
## 1013 Hero Passion Pro 40000 2017 Individual
## 809 Royal Enfield Thunderbird 350 100000 2012 Individual
## 404 Bajaj Pulsar 180 38000 2012 Individual
## 989 Yamaha FZ S V 2.0 55000 2017 Individual
## 535 Bajaj Xcd 25000 2007 Individual
## 891 Bajaj Discover 100 21000 2009 Individual
## 1028 TVS Wego 25000 2012 Individual
## 741 Bajaj Platina 100 40000 2018 Individual
## 27 Honda Navi [2016-2017] 42000 2017 Individual
## 296 Suzuki Access 125 45000 2017 Individual
## 987 TVS Apache RTR 160 60000 2014 Individual
## 371 KTM 200 Duke 120000 2017 Individual
## 610 Hero Karizma 2014 45000 2014 Individual
## 70 Mahindra Mojo XT300 165000 2017 Individual
## 79 Hero Splendor Plus 35000 2014 Individual
## 117 Vespa SXL 149 80000 2018 Individual
## 33 Honda X-Blade 65000 2018 Individual
## 29 Hero Honda Splendor NXG 21000 2009 Individual
## 312 Hero Honda Splendor PRO 30000 2012 Individual
## 621 Bajaj CT 100 8000 2005 Individual
## 164 Hero Honda Pleasure 23000 2010 Individual
## 415 Honda Activa 5G 50000 2018 Individual
## 430 Bajaj Discover 150S 35000 2015 Individual
## 146 Hero Hunk 20000 2010 Individual
## 54 Honda Activa 5G 70000 2019 Individual
## 303 Yamaha Fazer Dlx 58000 2012 Individual
## 464 Royal Enfield Classic 350 150000 2017 Individual
## 124 Honda Shine 50000 2017 Individual
## 150 Suzuki Access 125 60000 2017 Individual
## 771 Yamaha FZ S [2012-2016] 45000 2016 Individual
## 225 TVS Scooty Zest 110 28000 2015 Individual
## 437 Bajaj Pulsar 150 20000 2006 Individual
## 352 Hero Ignitor 35000 2014 Individual
## 794 Bajaj Pulsar 150 75000 2017 Individual
## 877 TVS Victor 18000 2004 Individual
## 944 KTM RC200 165000 2019 Individual
## 1023 Hero Splender Plus 30000 2018 Individual
## 994 Bajaj Pulsar NS 200 50000 2014 Individual
## 547 Hero Honda Passion Plus 18000 2007 Individual
## 196 Bajaj Avenger Cruise 220 87000 2018 Individual
## 328 Yamaha Fascino 110 45000 2016 Individual
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## 884 1st owner 30000 118997
## 889 1st owner 10000 52271
## 894 2nd owner 80000 0
## 899 1st owner 15500 59095
## 900 1st owner 28000 50267
## 905 1st owner 50000 42859
## 907 1st owner 70000 0
## 909 1st owner 70000 85011
## 912 1st owner 100000 49412
## 918 1st owner 22000 64589
## 922 2nd owner 65000 0
## 924 1st owner 30000 0
## 933 1st owner 40000 0
## 945 1st owner 1200 160000
## 952 1st owner 6000 178000
## 962 1st owner 5700 126000
## 967 1st owner 40000 175000
## 968 1st owner 1300 95000
## 974 1st owner 3500 95000
## 975 1st owner 500 95000
## 986 1st owner 25000 99000
## 996 1st owner 8000 88000
## 997 1st owner 4300 51000
## 1003 1st owner 23000 82600
## 1017 1st owner 75000 78700
## 1033 2nd owner 25000 57000
## 1041 1st owner 53000 58000
## 1042 1st owner 92233 750005 Pemodelan: Regresi Linear
Modelling menggunakan metode regresi Linear ini dilakukan untuk memprediksi harga dari suatu motor (Prices).
5.1 Prediktor Tunggal
Berdasarkan matriks korelasi sebelumnya, variabel ex_showroom_price memiliki korelasi paling kuat dengan variabel selling_price. Mari kita coba dulu dengan satu variabel atau single predictor.
5.1.1 Linear Regression with single predictor: ex_showroom_price
lm_ex_showroom_price <- lm(selling_price~ex_showroom_price,data_train)
summary(lm_ex_showroom_price)##
## Call:
## lm(formula = selling_price ~ ex_showroom_price, data = data_train)
##
## Residuals:
## Min 1Q Median 3Q Max
## -50357 -25375 -12281 12944 161083
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.392e+04 1.800e+03 24.40 <2e-16 ***
## ex_showroom_price 2.475e-01 2.581e-02 9.59 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 37870 on 842 degrees of freedom
## Multiple R-squared: 0.09847, Adjusted R-squared: 0.0974
## F-statistic: 91.97 on 1 and 842 DF, p-value: < 2.2e-16Kita dapat melihat jika dengan metode single predictor, model ini hanya memiliki nilai Adjusted R-squared: 0.0974 yang berarti hanya berhasil menangkap variansi dari target sebesar 9.74%, berarti metode ini masih sangat kurang bagus, sehinga kita gunakan metode lainnya atau modeling dengan prediktor ganda untuk melihat hasilnya apakah menjadi lebih baik atau tidak.
5.2 Prediktor Ganda
# Multiple Linear Regression
lm_bike_all <- lm(selling_price~., data=data_train)
summary(lm_bike_all)##
## Call:
## lm(formula = selling_price ~ ., data = data_train)
##
## Residuals:
## Min 1Q Median 3Q Max
## -30849 -5347 0 3993 36448
##
## Coefficients: (3 not defined because of singularities)
## Estimate Std. Error t value
## (Intercept) 9.306e+04 1.263e+04 7.365
## nameActiva 4g -2.405e+04 1.336e+04 -1.801
## nameAprilia SR 125 8.512e+03 1.102e+04 0.772
## nameBajaj ct 100 -2.627e+04 1.325e+04 -1.982
## nameBajaj Avenger [2015] 2.776e+04 8.975e+03 3.093
## nameBajaj Avenger 150 2.739e+04 1.326e+04 2.066
## nameBajaj Avenger 150 street 1.252e+04 1.323e+04 0.946
## nameBajaj Avenger 220 dtsi 2.419e+04 1.102e+04 2.194
## nameBajaj Avenger Cruise 220 3.033e+04 1.014e+04 2.989
## nameBajaj Avenger Street 150 [2018] 2.489e+04 9.129e+03 2.726
## nameBajaj Avenger Street 160 1.406e+04 1.117e+04 1.259
## nameBajaj Avenger Street 220 3.316e+04 8.962e+03 3.700
## nameBajaj Boxer -3.046e+03 1.108e+04 -0.275
## nameBajaj CT 100 -1.487e+04 9.100e+03 -1.634
## nameBajaj CT 110 -1.605e+04 1.337e+04 -1.200
## nameBajaj Discover 100 6.157e+02 8.715e+03 0.071
## nameBajaj Discover 100 M 1.952e+03 1.099e+04 0.178
## nameBajaj Discover 100 T -3.119e+03 9.674e+03 -0.322
## nameBajaj Discover 125 1.309e+03 8.384e+03 0.156
## nameBajaj Discover 125 M 9.166e+03 1.102e+04 0.832
## nameBajaj Discover 125 ST -3.638e+02 1.094e+04 -0.033
## nameBajaj Discover 135 4.686e+03 9.392e+03 0.499
## nameBajaj Discover 150 6.864e+03 9.097e+03 0.754
## nameBajaj Discover 150F 1.529e+04 1.334e+04 1.146
## nameBajaj Discover 150S 8.755e+03 1.095e+04 0.799
## nameBajaj Dominar 400 1.000e+05 1.016e+04 9.846
## nameBajaj Dominar 400 [2018] 1.196e+05 1.123e+04 10.652
## nameBajaj Kristal -1.212e+03 1.357e+04 -0.089
## nameBajaj Platina 100 -9.080e+03 9.011e+03 -1.008
## nameBajaj Pulsar 125 -3.322e+03 1.341e+04 -0.248
## nameBajaj Pulsar 135 LS 7.355e+03 8.659e+03 0.849
## nameBajaj Pulsar 150 1.054e+04 8.231e+03 1.281
## nameBajaj Pulsar 150 [2001-2011] 8.571e+03 8.752e+03 0.979
## nameBajaj Pulsar 180 1.974e+04 8.553e+03 2.308
## nameBajaj Pulsar 180F 3.153e+04 1.344e+04 2.346
## nameBajaj Pulsar 220 DTS-i 2.196e+04 1.109e+04 1.980
## nameBajaj Pulsar 220 F 1.961e+04 1.094e+04 1.792
## nameBajaj Pulsar 220 Fi 3.673e+04 1.346e+04 2.728
## nameBajaj Pulsar 220F 3.276e+04 9.143e+03 3.584
## nameBajaj Pulsar 220S 1.915e+04 1.333e+04 1.436
## nameBajaj Pulsar AS200 3.078e+04 9.677e+03 3.181
## nameBajaj Pulsar NS 200 2.309e+04 1.018e+04 2.269
## nameBajaj Pulsar NS160 2.386e+04 1.009e+04 2.366
## nameBajaj Pulsar NS200 2.862e+04 9.695e+03 2.952
## nameBajaj Pulsar RS 200 5.091e+04 1.102e+04 4.622
## nameBajaj Super -7.142e+04 1.541e+04 -4.636
## nameBajaj V12 3.056e+03 1.325e+04 0.231
## nameBajaj V15 1.120e+04 8.799e+03 1.273
## nameBajaj Xcd 1.262e+03 9.792e+03 0.129
## nameBenelli TNT 25 1.274e+05 1.359e+04 9.374
## nameHero CBZ Xtreme 5.865e+03 1.324e+04 0.443
## nameHero Ignitor Disc -8.364e+03 1.339e+04 -0.625
## nameHero Achiever 150 5.993e+03 1.005e+04 0.597
## nameHero CBZ 1.068e+04 1.100e+04 0.971
## nameHero CD Dawn 2.457e+03 1.338e+04 0.184
## nameHero CD Deluxe -1.024e+04 1.013e+04 -1.011
## nameHero Duet -3.123e+03 9.601e+03 -0.325
## nameHero Extreme 2.714e+04 1.100e+04 2.466
## nameHero Glamour -6.185e+03 1.333e+04 -0.464
## nameHero Glamour 125 6.366e+03 8.534e+03 0.746
## nameHero Glamour FI 7.256e+03 1.092e+04 0.664
## nameHero HF Deluxe -1.126e+04 8.933e+03 -1.261
## nameHero HF Deluxe i3s -1.437e+04 1.342e+04 -1.071
## nameHero Honda Achiever -6.511e+03 1.356e+04 -0.480
## nameHero Honda CBZ 1.175e+04 2.287e+04 0.514
## nameHero Honda CBZ extreme 1.257e+04 8.895e+03 1.413
## nameHero Honda CD Deluxe 4.201e+03 8.981e+03 0.468
## nameHero Honda CD100 -7.589e+04 1.651e+04 -4.597
## nameHero Honda CD100SS -7.535e+04 1.599e+04 -4.713
## nameHero Honda Glamour 5.618e+03 9.482e+03 0.592
## nameHero Honda Hunk 1.384e+04 8.996e+03 1.538
## nameHero Honda Karizma ZMR [2010] 2.943e+04 1.136e+04 2.590
## nameHero Honda Passion -6.922e+03 2.036e+04 -0.340
## nameHero Honda Passion Plus 7.917e+02 9.058e+03 0.087
## nameHero Honda Passion Pro 2.081e+03 1.328e+04 0.157
## nameHero Honda Passion PRO [2012] 4.363e+03 9.456e+03 0.461
## nameHero Honda Pleasure 1.884e+03 1.333e+04 0.141
## nameHero Honda Splendor 5.902e+02 1.075e+04 0.055
## nameHero Honda Splendor NXG -1.248e+03 1.373e+04 -0.091
## nameHero Honda Splendor Plus 5.019e+03 8.622e+03 0.582
## nameHero Honda Splendor PRO 4.116e+03 1.343e+04 0.306
## nameHero Honda Street Smart -1.437e+03 1.638e+04 -0.088
## nameHero Hunk 6.269e+03 9.588e+03 0.654
## nameHero Ignitor -1.703e+02 1.010e+04 -0.017
## nameHero Karizma [2003-2014] 2.945e+04 1.336e+04 2.204
## nameHero Karizma 2014 1.071e+04 9.646e+03 1.111
## nameHero Karizma ZMR 3.408e+04 1.012e+04 3.370
## nameHero Maestro -4.557e+03 1.336e+04 -0.341
## nameHero Maestro Edge 9.150e+01 9.522e+03 0.010
## nameHero Passion Pro -3.693e+03 1.094e+04 -0.338
## nameHero Passion Pro 110 2.081e+04 1.145e+04 1.818
## nameHero Passion PRO i3s 8.485e+03 1.004e+04 0.845
## nameHero Passion PRO TR 6.901e+03 1.338e+04 0.516
## nameHero Passion X pro 4.610e+02 1.319e+04 0.035
## nameHero Passion XPro 7.588e+03 1.326e+04 0.572
## nameHero Pleasure 4.367e+03 1.016e+04 0.430
## nameHero Pleasure [2005-2015] 5.953e+03 1.337e+04 0.445
## nameHero Splender iSmart -3.681e+03 1.091e+04 -0.337
## nameHero Splender Plus -1.996e+04 1.315e+04 -1.518
## nameHero Splendor iSmart 8.863e+03 1.100e+04 0.806
## nameHero Splendor NXG -1.367e+03 1.336e+04 -0.102
## nameHero Splendor Plus 3.034e+03 8.764e+03 0.346
## nameHero Splendor Plus i3s 3.850e+03 1.107e+04 0.348
## nameHero Splendor PRO 2.899e+03 9.118e+03 0.318
## nameHero Super Splendor 8.821e+03 8.956e+03 0.985
## nameHero Xpulse 200 3.690e+04 1.022e+04 3.612
## nameHero Xpulse 200T 4.490e+04 1.337e+04 3.359
## nameHero Xtreme [2013-2014] 1.118e+04 1.099e+04 1.018
## nameHero Xtreme 200R 3.311e+04 1.097e+04 3.019
## nameHero Xtreme 2014 1.831e+04 1.106e+04 1.656
## nameHero Xtreme Sports 1.448e+04 1.003e+04 1.444
## nameHonda Activa [2000-2015] 4.298e+03 8.258e+03 0.521
## nameHonda Activa 125 -2.955e+03 9.599e+03 -0.308
## nameHonda Activa 125 [2016-2017] 4.145e+03 9.540e+03 0.434
## nameHonda Activa 3G 7.174e+03 8.518e+03 0.842
## nameHonda Activa 4G -1.190e+04 1.092e+04 -1.089
## nameHonda Activa 5G -7.030e+02 8.541e+03 -0.082
## nameHonda Activa i 1.060e+04 1.345e+04 0.788
## nameHonda Activa i [2016-2017] -1.901e+03 1.008e+04 -0.189
## nameHonda Aviator 8.696e+03 9.383e+03 0.927
## nameHonda CB Hornet 160R 2.620e+04 8.386e+03 3.124
## nameHonda CB Shine -9.817e+02 1.330e+04 -0.074
## nameHonda CB Shine SP 7.373e+03 8.899e+03 0.829
## nameHonda CB Trigger 1.307e+04 9.090e+03 1.438
## nameHonda CB twister -5.971e+03 1.098e+04 -0.544
## nameHonda CB Twister 1.425e+04 9.544e+03 1.493
## nameHonda CB Unicorn -2.777e+03 1.325e+04 -0.210
## nameHonda CB Unicorn 150 2.040e+04 8.531e+03 2.391
## nameHonda CB Unicorn 160 2.767e+04 9.532e+03 2.903
## nameHonda CB Unicorn Dazzler 1.617e+04 1.006e+04 1.608
## nameHonda CBF Stunner 1.074e+04 1.100e+04 0.976
## nameHonda CBR-250R 8.235e+04 1.064e+04 7.740
## nameHonda CBR 150 3.333e+04 1.106e+04 3.012
## nameHonda CBR150 R 6.493e+04 1.111e+04 5.844
## nameHonda Dio -2.250e+03 9.036e+03 -0.249
## nameHonda Dream Neo 9.032e+03 1.099e+04 0.822
## nameHonda Dream Yuga -1.093e+03 8.786e+03 -0.124
## nameHonda Dream Yuga -1.572e+04 1.334e+04 -1.179
## nameHonda Grazia 5.559e+03 1.326e+04 0.419
## nameHonda Karizma 1.604e+04 1.088e+04 1.474
## nameHonda Livo -9.390e+03 1.006e+04 -0.934
## nameHonda Navi [2016-2017] -7.325e+03 1.096e+04 -0.669
## nameHonda Shine 3.464e+03 8.713e+03 0.398
## nameHonda X-Blade 1.067e+04 1.324e+04 0.805
## nameHyosung GT250R 1.237e+05 1.501e+04 8.245
## nameJawa 42 9.340e+04 1.544e+04 6.050
## nameJawa Standard 1.016e+05 1.117e+04 9.096
## nameKawasaki Ninja 250R 1.333e+05 1.465e+04 9.101
## nameKTM 200 Duke 7.102e+04 9.631e+03 7.374
## nameKTM 250 Duke 1.072e+05 1.105e+04 9.702
## nameKTM 390 Duke 1.505e+05 1.328e+04 11.329
## nameKTM 390 Duke 9.247e+04 1.402e+04 6.596
## nameKTM 390 Duke ABS [2013-2016] 1.117e+05 1.369e+04 8.158
## nameKTM RC 200 9.998e+04 9.384e+03 10.655
## nameKTM RC 390 1.327e+05 1.336e+04 9.927
## nameKTM RC200 1.120e+05 1.359e+04 8.238
## nameKTM RC390 1.083e+05 1.394e+04 7.771
## nameMahindra Flyte 1.420e+04 1.483e+04 0.958
## nameMahindra Gusto -1.380e+04 1.332e+04 -1.036
## nameMahindra Mojo XT300 1.037e+05 1.123e+04 9.234
## nameRoyal Enfield Bullet 350 6.789e+04 9.593e+03 7.078
## nameRoyal Enfield Bullet 350 [2007-2011] 7.353e+04 1.363e+04 5.393
## nameRoyal Enfield Bullet 500 1.086e+05 1.355e+04 8.017
## nameRoyal Enfield Classic 350 8.417e+04 8.254e+03 10.198
## nameRoyal Enfield Classic 500 8.135e+04 1.135e+04 7.169
## nameRoyal Enfield Classic Chrome 1.414e+05 1.147e+04 12.327
## nameRoyal Enfield Classic Desert Storm 1.124e+05 1.006e+04 11.172
## nameRoyal Enfield Classic Gunmetal Grey 1.111e+05 9.676e+03 11.479
## nameRoyal Enfield Classic Stealth Black 1.232e+05 1.371e+04 8.984
## nameRoyal Enfield Continental GT [2013 - 2018] 9.527e+04 1.385e+04 6.877
## nameRoyal Enfield Electra 4 S 5.211e+04 1.338e+04 3.895
## nameRoyal Enfield Electra 5 S 6.352e+04 9.759e+03 6.508
## nameRoyal Enfield Electra Twinspark 6.739e+04 1.339e+04 5.034
## nameRoyal Enfield Himalayan 8.680e+04 1.103e+04 7.870
## nameRoyal Enfield Thunder 350 8.589e+04 9.832e+03 8.735
## nameRoyal Enfield Thunder 500 1.004e+05 1.055e+04 9.523
## nameRoyal Enfield Thunderbird 350 6.962e+04 8.506e+03 8.184
## nameRoyal Enfield Thunderbird 350X 9.786e+04 8.928e+03 10.961
## nameRoyal Enfield Thunderbird 500 1.088e+05 1.372e+04 7.930
## nameRoyal Enfield Thunderbird 500X 1.476e+05 1.369e+04 10.782
## nameSuzuki Access 125 3.696e+03 8.961e+03 0.412
## nameSuzuki Access 125 [2007-2016] 7.245e+03 1.008e+04 0.719
## nameSuzuki Gixxer [2014-2018] 2.181e+04 9.609e+03 2.269
## nameSuzuki Gixxer SF 2.116e+04 1.099e+04 1.926
## nameSuzuki GS150R 1.596e+04 1.310e+04 1.219
## nameSuzuki Intruder 150 3.056e+04 1.096e+04 2.788
## nameSuzuki Intruder 150 Fi 5.508e+04 1.330e+04 4.143
## nameSuzuki Let''s 5.498e+03 1.339e+04 0.411
## nameTVS Apache [2006] 3.224e+03 1.361e+04 0.237
## nameTVS Apache RR310 1.316e+05 1.343e+04 9.796
## nameTVS Apache RTR 160 1.414e+04 8.581e+03 1.648
## nameTVS Apache RTR 160 4V 2.293e+04 1.017e+04 2.255
## nameTVS Apache RTR 180 2.887e+04 1.343e+04 2.149
## nameTVS Apache RTR 200 4V 4.046e+04 9.334e+03 4.335
## nameTVS Centra 1.219e+03 1.375e+04 0.089
## nameTVS Excel -8.594e+04 2.227e+04 -3.859
## nameTVS Flame -6.310e+03 1.338e+04 -0.472
## nameTVS Jupiter 1.554e+04 1.327e+04 1.171
## nameTVS Jupyter -3.671e+03 1.331e+04 -0.276
## nameTVS Max DLX 9.708e+03 2.042e+04 0.475
## nameTVS Radeon -6.433e+03 1.328e+04 -0.484
## nameTVS Scooty Pep DLX -1.118e+04 1.339e+04 -0.835
## nameTVS Scooty Pep Plus 4.118e+03 1.019e+04 0.404
## nameTVS Scooty Streak 1.869e+03 1.367e+04 0.137
## nameTVS Scooty Zest 110 -3.674e+03 1.004e+04 -0.366
## nameTVS Spectra -3.599e+03 1.364e+04 -0.264
## nameTVS Sport -1.450e+04 1.006e+04 -1.442
## nameTVS Sport -1.584e+04 1.333e+04 -1.188
## nameTVS Star City -1.357e+03 1.002e+04 -0.135
## nameTVS Star City Plus 3.179e+03 1.014e+04 0.313
## nameTVS Streak 7.455e+01 1.372e+04 0.005
## nameTVS Victor 7.594e+03 1.151e+04 0.660
## nameTVS Victor GLX 8.841e+03 1.383e+04 0.639
## nameTVS Wego -1.489e+03 9.598e+03 -0.155
## nameTVS XL 100 Heavy Duty -2.837e+04 1.342e+04 -2.114
## nameUM Renegade Commando 1.211e+05 1.358e+04 8.917
## nameUM Renegade Mojave 1.173e+05 1.361e+04 8.615
## nameVespa LX 125 2.049e+04 1.337e+04 1.532
## nameVespa SXL 149 2.756e+04 1.101e+04 2.503
## nameYamaha Cygnus Ray ZR -7.369e+03 1.335e+04 -0.552
## nameYamaha Fascino 110 -6.157e+02 1.004e+04 -0.061
## nameYamaha Fazer [2009-2016] 1.948e+04 9.084e+03 2.144
## nameYamaha Fazer 25 8.517e+04 1.028e+04 8.281
## nameYamaha Fazer Dlx 3.228e+04 1.340e+04 2.410
## nameYamaha Fazer FI V 2.0 [2016-2018] 3.669e+04 1.334e+04 2.750
## nameYamaha FZ v 2.0 1.654e+04 1.091e+04 1.516
## nameYamaha FZ 16 3.489e+04 1.330e+04 2.624
## nameYamaha FZ S 1.432e+04 1.341e+04 1.068
## nameYamaha FZ S [2012-2016] 1.970e+04 8.909e+03 2.211
## nameYamaha FZ S V 2.0 2.305e+04 8.616e+03 2.675
## nameYamaha FZ V 2.0 2.379e+04 1.002e+04 2.375
## nameYamaha FZ16 1.220e+04 8.594e+03 1.420
## nameYamaha FZ25 7.671e+04 9.090e+03 8.439
## nameYamaha Gladiator -6.899e+03 1.365e+04 -0.506
## nameYamaha Ray Z 1.789e+04 1.097e+04 1.631
## nameYamaha Rx 6.888e+04 1.726e+04 3.990
## nameYamaha RX135 -1.586e+04 2.307e+04 -0.687
## nameYamaha RXG -6.618e+04 1.537e+04 -4.306
## nameYamaha Saluto 9.617e+03 1.003e+04 0.959
## nameYamaha Saluto RX -2.833e+03 1.326e+04 -0.214
## nameYamaha SZ-S 2.355e+04 1.337e+04 1.762
## nameYamaha SZ [2013-2014] -1.308e+03 1.351e+04 -0.097
## nameYamaha SZ RR V 2.0 5.132e+03 1.329e+04 0.386
## nameYamaha SZ X 1.333e+04 1.095e+04 1.217
## nameYamaha YZF R15 [2011-2018] 4.014e+04 9.703e+03 4.137
## nameYamaha YZF R15 S 3.685e+04 9.350e+03 3.941
## nameYamaha YZF R15 V3 8.986e+04 1.095e+04 8.209
## nameYo Style -2.268e+02 1.342e+04 -0.017
## year1991 -1.386e+04 1.494e+04 -0.928
## year1997 NA NA NA
## year1998 -9.916e+01 1.608e+04 -0.006
## year1999 NA NA NA
## year2000 -7.995e+04 1.249e+04 -6.399
## year2001 -7.398e+04 1.571e+04 -4.709
## year2002 -7.298e+04 1.741e+04 -4.193
## year2004 -7.760e+04 9.950e+03 -7.799
## year2005 -8.235e+04 8.897e+03 -9.256
## year2006 -7.946e+04 8.654e+03 -9.182
## year2007 -7.512e+04 8.501e+03 -8.836
## year2008 -7.053e+04 8.717e+03 -8.092
## year2009 -6.927e+04 8.647e+03 -8.012
## year2010 -7.019e+04 8.312e+03 -8.445
## year2011 -6.804e+04 8.284e+03 -8.213
## year2012 -6.551e+04 8.276e+03 -7.916
## year2013 -6.328e+04 8.269e+03 -7.653
## year2014 -5.871e+04 8.225e+03 -7.137
## year2015 -5.533e+04 8.178e+03 -6.766
## year2016 -4.827e+04 8.149e+03 -5.923
## year2017 -4.424e+04 8.074e+03 -5.479
## year2018 -3.702e+04 8.007e+03 -4.624
## year2019 -2.314e+04 8.224e+03 -2.814
## year2020 NA NA NA
## seller_typeIndividual -1.438e+03 5.780e+03 -0.249
## owner2nd owner -2.070e+03 1.448e+03 -1.430
## owner3rd owner -1.051e+04 5.673e+03 -1.852
## owner4th owner -1.295e+04 1.459e+04 -0.888
## km_driven -9.700e-03 1.121e-02 -0.865
## ex_showroom_price -8.648e-02 2.302e-02 -3.757
## Pr(>|t|)
## (Intercept) 6.25e-13 ***
## nameActiva 4g 0.072239 .
## nameAprilia SR 125 0.440375
## nameBajaj ct 100 0.047946 *
## nameBajaj Avenger [2015] 0.002079 **
## nameBajaj Avenger 150 0.039242 *
## nameBajaj Avenger 150 street 0.344457
## nameBajaj Avenger 220 dtsi 0.028611 *
## nameBajaj Avenger Cruise 220 0.002915 **
## nameBajaj Avenger Street 150 [2018] 0.006601 **
## nameBajaj Avenger Street 160 0.208423
## nameBajaj Avenger Street 220 0.000237 ***
## nameBajaj Boxer 0.783565
## nameBajaj CT 100 0.102783
## nameBajaj CT 110 0.230707
## nameBajaj Discover 100 0.943708
## nameBajaj Discover 100 M 0.859138
## nameBajaj Discover 100 T 0.747290
## nameBajaj Discover 125 0.875944
## nameBajaj Discover 125 M 0.405843
## nameBajaj Discover 125 ST 0.973493
## nameBajaj Discover 135 0.618031
## nameBajaj Discover 150 0.450869
## nameBajaj Discover 150F 0.252313
## nameBajaj Discover 150S 0.424425
## nameBajaj Dominar 400 < 2e-16 ***
## nameBajaj Dominar 400 [2018] < 2e-16 ***
## nameBajaj Kristal 0.928843
## nameBajaj Platina 100 0.314032
## nameBajaj Pulsar 125 0.804406
## nameBajaj Pulsar 135 LS 0.396007
## nameBajaj Pulsar 150 0.200729
## nameBajaj Pulsar 150 [2001-2011] 0.327865
## nameBajaj Pulsar 180 0.021374 *
## nameBajaj Pulsar 180F 0.019294 *
## nameBajaj Pulsar 220 DTS-i 0.048176 *
## nameBajaj Pulsar 220 F 0.073651 .
## nameBajaj Pulsar 220 Fi 0.006570 **
## nameBajaj Pulsar 220F 0.000368 ***
## nameBajaj Pulsar 220S 0.151545
## nameBajaj Pulsar AS200 0.001549 **
## nameBajaj Pulsar NS 200 0.023617 *
## nameBajaj Pulsar NS160 0.018335 *
## nameBajaj Pulsar NS200 0.003290 **
## nameBajaj Pulsar RS 200 4.71e-06 ***
## nameBajaj Super 4.42e-06 ***
## nameBajaj V12 0.817634
## nameBajaj V15 0.203621
## nameBajaj Xcd 0.897506
## nameBenelli TNT 25 < 2e-16 ***
## nameHero CBZ Xtreme 0.657901
## nameHero Ignitor Disc 0.532452
## nameHero Achiever 150 0.551020
## nameHero CBZ 0.332166
## nameHero CD Dawn 0.854336
## nameHero CD Deluxe 0.312439
## nameHero Duet 0.745071
## nameHero Extreme 0.013952 *
## nameHero Glamour 0.642818
## nameHero Glamour 125 0.455963
## nameHero Glamour FI 0.506814
## nameHero HF Deluxe 0.207867
## nameHero HF Deluxe i3s 0.284672
## nameHero Honda Achiever 0.631367
## nameHero Honda CBZ 0.607771
## nameHero Honda CBZ extreme 0.158099
## nameHero Honda CD Deluxe 0.640145
## nameHero Honda CD100 5.30e-06 ***
## nameHero Honda CD100SS 3.07e-06 ***
## nameHero Honda Glamour 0.553758
## nameHero Honda Hunk 0.124585
## nameHero Honda Karizma ZMR [2010] 0.009845 **
## nameHero Honda Passion 0.734053
## nameHero Honda Passion Plus 0.930382
## nameHero Honda Passion Pro 0.875535
## nameHero Honda Passion PRO [2012] 0.644695
## nameHero Honda Pleasure 0.887649
## nameHero Honda Splendor 0.956242
## nameHero Honda Splendor NXG 0.927612
## nameHero Honda Splendor Plus 0.560736
## nameHero Honda Splendor PRO 0.759415
## nameHero Honda Street Smart 0.930131
## nameHero Hunk 0.513462
## nameHero Ignitor 0.986550
## nameHero Karizma [2003-2014] 0.027952 *
## nameHero Karizma 2014 0.267120
## nameHero Karizma ZMR 0.000804 ***
## nameHero Maestro 0.733093
## nameHero Maestro Edge 0.992337
## nameHero Passion Pro 0.735770
## nameHero Passion Pro 110 0.069599 .
## nameHero Passion PRO i3s 0.398311
## nameHero Passion PRO TR 0.606281
## nameHero Passion X pro 0.972127
## nameHero Passion XPro 0.567455
## nameHero Pleasure 0.667323
## nameHero Pleasure [2005-2015] 0.656309
## nameHero Splender iSmart 0.735980
## nameHero Splender Plus 0.129685
## nameHero Splendor iSmart 0.420616
## nameHero Splendor NXG 0.918539
## nameHero Splendor Plus 0.729349
## nameHero Splendor Plus i3s 0.728070
## nameHero Splendor PRO 0.750636
## nameHero Super Splendor 0.325105
## nameHero Xpulse 200 0.000331 ***
## nameHero Xpulse 200T 0.000835 ***
## nameHero Xtreme [2013-2014] 0.309107
## nameHero Xtreme 200R 0.002648 **
## nameHero Xtreme 2014 0.098340 .
## nameHero Xtreme Sports 0.149212
## nameHonda Activa [2000-2015] 0.602900
## nameHonda Activa 125 0.758287
## nameHonda Activa 125 [2016-2017] 0.664104
## nameHonda Activa 3G 0.400043
## nameHonda Activa 4G 0.276492
## nameHonda Activa 5G 0.934423
## nameHonda Activa i 0.431250
## nameHonda Activa i [2016-2017] 0.850412
## nameHonda Aviator 0.354426
## nameHonda CB Hornet 160R 0.001876 **
## nameHonda CB Shine 0.941204
## nameHonda CB Shine SP 0.407703
## nameHonda CB Trigger 0.150938
## nameHonda CB twister 0.586620
## nameHonda CB Twister 0.136035
## nameHonda CB Unicorn 0.834052
## nameHonda CB Unicorn 150 0.017124 *
## nameHonda CB Unicorn 160 0.003842 **
## nameHonda CB Unicorn Dazzler 0.108369
## nameHonda CBF Stunner 0.329284
## nameHonda CBR-250R 4.56e-14 ***
## nameHonda CBR 150 0.002707 **
## nameHonda CBR150 R 8.58e-09 ***
## nameHonda Dio 0.803437
## nameHonda Dream Neo 0.411617
## nameHonda Dream Yuga 0.901081
## nameHonda Dream Yuga 0.238996
## nameHonda Grazia 0.675310
## nameHonda Karizma 0.141114
## nameHonda Livo 0.350797
## nameHonda Navi [2016-2017] 0.503988
## nameHonda Shine 0.691103
## nameHonda X-Blade 0.420924
## nameHyosung GT250R 1.15e-15 ***
## nameJawa 42 2.63e-09 ***
## nameJawa Standard < 2e-16 ***
## nameKawasaki Ninja 250R < 2e-16 ***
## nameKTM 200 Duke 5.86e-13 ***
## nameKTM 250 Duke < 2e-16 ***
## nameKTM 390 Duke < 2e-16 ***
## nameKTM 390 Duke 9.66e-11 ***
## nameKTM 390 Duke ABS [2013-2016] 2.19e-15 ***
## nameKTM RC 200 < 2e-16 ***
## nameKTM RC 390 < 2e-16 ***
## nameKTM RC200 1.21e-15 ***
## nameKTM RC390 3.65e-14 ***
## nameMahindra Flyte 0.338543
## nameMahindra Gusto 0.300820
## nameMahindra Mojo XT300 < 2e-16 ***
## nameRoyal Enfield Bullet 350 4.34e-12 ***
## nameRoyal Enfield Bullet 350 [2007-2011] 1.02e-07 ***
## nameRoyal Enfield Bullet 500 6.17e-15 ***
## nameRoyal Enfield Classic 350 < 2e-16 ***
## nameRoyal Enfield Classic 500 2.36e-12 ***
## nameRoyal Enfield Classic Chrome < 2e-16 ***
## nameRoyal Enfield Classic Desert Storm < 2e-16 ***
## nameRoyal Enfield Classic Gunmetal Grey < 2e-16 ***
## nameRoyal Enfield Classic Stealth Black < 2e-16 ***
## nameRoyal Enfield Continental GT [2013 - 2018] 1.62e-11 ***
## nameRoyal Enfield Electra 4 S 0.000110 ***
## nameRoyal Enfield Electra 5 S 1.67e-10 ***
## nameRoyal Enfield Electra Twinspark 6.47e-07 ***
## nameRoyal Enfield Himalayan 1.80e-14 ***
## nameRoyal Enfield Thunder 350 < 2e-16 ***
## nameRoyal Enfield Thunder 500 < 2e-16 ***
## nameRoyal Enfield Thunderbird 350 1.81e-15 ***
## nameRoyal Enfield Thunderbird 350X < 2e-16 ***
## nameRoyal Enfield Thunderbird 500 1.16e-14 ***
## nameRoyal Enfield Thunderbird 500X < 2e-16 ***
## nameSuzuki Access 125 0.680197
## nameSuzuki Access 125 [2007-2016] 0.472489
## nameSuzuki Gixxer [2014-2018] 0.023619 *
## nameSuzuki Gixxer SF 0.054611 .
## nameSuzuki GS150R 0.223362
## nameSuzuki Intruder 150 0.005483 **
## nameSuzuki Intruder 150 Fi 3.95e-05 ***
## nameSuzuki Let''s 0.681436
## nameTVS Apache [2006] 0.812795
## nameTVS Apache RR310 < 2e-16 ***
## nameTVS Apache RTR 160 0.099946 .
## nameTVS Apache RTR 160 4V 0.024510 *
## nameTVS Apache RTR 180 0.032034 *
## nameTVS Apache RTR 200 4V 1.72e-05 ***
## nameTVS Centra 0.929389
## nameTVS Excel 0.000127 ***
## nameTVS Flame 0.637399
## nameTVS Jupiter 0.242026
## nameTVS Jupyter 0.782762
## nameTVS Max DLX 0.634758
## nameTVS Radeon 0.628257
## nameTVS Scooty Pep DLX 0.404055
## nameTVS Scooty Pep Plus 0.686215
## nameTVS Scooty Streak 0.891357
## nameTVS Scooty Zest 110 0.714395
## nameTVS Spectra 0.792001
## nameTVS Sport 0.149905
## nameTVS Sport 0.235297
## nameTVS Star City 0.892360
## nameTVS Star City Plus 0.754135
## nameTVS Streak 0.995666
## nameTVS Victor 0.509625
## nameTVS Victor GLX 0.523066
## nameTVS Wego 0.876765
## nameTVS XL 100 Heavy Duty 0.034938 *
## nameUM Renegade Commando < 2e-16 ***
## nameUM Renegade Mojave < 2e-16 ***
## nameVespa LX 125 0.126014
## nameVespa SXL 149 0.012604 *
## nameYamaha Cygnus Ray ZR 0.581085
## nameYamaha Fascino 110 0.951106
## nameYamaha Fazer [2009-2016] 0.032456 *
## nameYamaha Fazer 25 8.77e-16 ***
## nameYamaha Fazer Dlx 0.016276 *
## nameYamaha Fazer FI V 2.0 [2016-2018] 0.006152 **
## nameYamaha FZ v 2.0 0.129986
## nameYamaha FZ 16 0.008935 **
## nameYamaha FZ S 0.286104
## nameYamaha FZ S [2012-2016] 0.027423 *
## nameYamaha FZ S V 2.0 0.007686 **
## nameYamaha FZ V 2.0 0.017862 *
## nameYamaha FZ16 0.156212
## nameYamaha FZ25 2.67e-16 ***
## nameYamaha Gladiator 0.613343
## nameYamaha Ray Z 0.103475
## nameYamaha Rx 7.46e-05 ***
## nameYamaha RX135 0.492211
## nameYamaha RXG 1.96e-05 ***
## nameYamaha Saluto 0.338217
## nameYamaha Saluto RX 0.830905
## nameYamaha SZ-S 0.078591 .
## nameYamaha SZ [2013-2014] 0.922908
## nameYamaha SZ RR V 2.0 0.699545
## nameYamaha SZ X 0.224234
## nameYamaha YZF R15 [2011-2018] 4.06e-05 ***
## nameYamaha YZF R15 S 9.13e-05 ***
## nameYamaha YZF R15 V3 1.50e-15 ***
## nameYo Style 0.986519
## year1991 0.353817
## year1997 NA
## year1998 0.995081
## year1999 NA
## year2000 3.26e-10 ***
## year2001 3.13e-06 ***
## year2002 3.19e-05 ***
## year2004 3.00e-14 ***
## year2005 < 2e-16 ***
## year2006 < 2e-16 ***
## year2007 < 2e-16 ***
## year2008 3.57e-15 ***
## year2009 6.44e-15 ***
## year2010 2.54e-16 ***
## year2011 1.46e-15 ***
## year2012 1.29e-14 ***
## year2013 8.46e-14 ***
## year2014 2.91e-12 ***
## year2015 3.29e-11 ***
## year2016 5.47e-09 ***
## year2017 6.42e-08 ***
## year2018 4.67e-06 ***
## year2019 0.005063 **
## year2020 NA
## seller_typeIndividual 0.803643
## owner2nd owner 0.153292
## owner3rd owner 0.064556 .
## owner4th owner 0.375092
## km_driven 0.387340
## ex_showroom_price 0.000190 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 10560 on 569 degrees of freedom
## Multiple R-squared: 0.9526, Adjusted R-squared: 0.9298
## F-statistic: 41.73 on 274 and 569 DF, p-value: < 2.2e-16Kita dapat melihat banyak informasi yg berubah, selain itu, jika dilihat nilai Adjusted R-squared yang meloncat tajam sebesar 0.9298 , hal ini menandakan bahwa model lm_bike_all dapat memprediksi sebesar 92.98% dan datanya sangat berkorelasi. Angka yang sangat bagus! Sekarang, mari kita selesaikan penelitian ini.
5.3 Evaluasi Model
Formula yang disarankan dengan prediktor berganda adalah:
lm_bike_model <- lm(selling_price ~ ex_showroom_price + km_driven, data=data_train) #Linear model selling price dipengaruhi oleh ex_showroom_price dan km_driven
lm_bike_model##
## Call:
## lm(formula = selling_price ~ ex_showroom_price + km_driven, data = data_train)
##
## Coefficients:
## (Intercept) ex_showroom_price km_driven
## 50676.5659 0.2320 -0.1759Data intercept adalah konstanta, dan data ex_showroom_price serta km_driven adalah koefisiennya.
5.4 Pemeriksaan Asumsi
Asumsi pada regresi linear berganda antara lain: Data interval atau rasio, uji linearitas, uji normalitas, uji non-outlier, uji non-multi kolinearitas, dan uji non-auto korelasi. Penulis melakukan pemeriksaan asumsi linearitas, normalitas residual, uji shapiro, uji normalitas Anderson Darling, uji heteroskedastisitas, dan uji multikolinieritas.
5.4.1 Linearitas
linearity <- data.frame(residual = lm_bike_model$residuals, fitted = data_train$selling_price)
linearity %>%
ggplot(aes(fitted, residual)) +
geom_point() +
geom_smooth(method = lm) +
geom_hline(aes(yintercept = 0)) +
theme_minimal()+
theme(
plot.title = element_text(hjust = 0.5),
axis.title=element_text(size=9,face="bold")+
mycolor_color())## `geom_smooth()` using formula 'y ~ x'
## residual fitted
## Min. :-50180 Min. : 5000
## 1st Qu.:-24682 1st Qu.: 28000
## Median :-12111 Median : 42000
## Mean : 0 Mean : 55820
## 3rd Qu.: 11786 3rd Qu.: 70000
## Max. :155643 Max. :205000Berdasarkan pengujian linearity terlihat bahwa model yang dibuat belum berhasil menangkap variansi data dengan baik, terlihat error atau jarak data dari mean sedikit jauh.
5.4.3 Shapiro Test
##
## Shapiro-Wilk normality test
##
## data: lm_bike_model$residuals[0:5000]
## W = 0.82782, p-value < 2.2e-165.4.4 Anderson Darling Normality Test
##
## Anderson-Darling normality test
##
## data: lm_bike_model$residuals
## A = 47.269, p-value < 2.2e-16Hipotesis: - H0: Residual berdistribusi normal - H1: Residual tidak berdistribusi normal
Kesimpulan dari kedua pengujian: Karena nilai p-value: 0.00000000000000022 < 0.05, maka keputusannya adalah terima H1, dengan kesimpulan residual dari model tidak berdistribusi normal.
5.4.5 Heteroskedastisitas
##
## studentized Breusch-Pagan test
##
## data: lm_bike_model
## BP = 7.1622, df = 2, p-value = 0.02784Hipotesis:
- H0: Data residual Homogen (tidak membentuk sebuah pola)
- H1: Data residual heteros (membentuk sebuah pola)
Kesimpulan Karena nilai p-value: 0.00000000000000022 < 0.05 maka keputusannya adalah terima H1, dengan kesimpulan residual dari model bersifat heteros.
5.5 Prediksi : Data Testing
Berikut data hasil prediksi Price terhadap data test. selling_price : Harga aslinya predict.fit : Hasil prediksi fit value (Batas Maksimum) predict.lower : Hasil prediksi prekiraan harga terendah (Batas kiri atau terkecil) predict.upper : Hasil prediksi perkiraan harga tertinggi (Batas kanan atau terbesar)
predict_price <- predict(
lm_bike_model,
data_test,
interval = "confidence",
level = 0.95
)
bike_predict <- cbind(data_test,predict_price)
bike_predict %>%
select(
selling_price, predict.fit=fit, predict.lower=lwr, predict.upper=upr, km_driven, ex_showroom_price)## selling_price predict.fit predict.lower predict.upper km_driven
## 1 175000 50614.989 46772.08 54457.898 350
## 3 150000 82926.501 77395.14 88457.861 12000
## 8 180000 43815.126 40393.23 47237.017 39000
## 12 28000 59879.967 57162.39 62597.545 10000
## 20 15000 40120.504 36558.01 43682.995 60000
## 28 40000 64180.351 61112.91 67247.791 35000
## 32 43000 60056.360 57045.66 63067.063 50000
## 37 120000 94795.760 86831.80 102759.724 16000
## 42 175000 49867.268 46109.20 53625.333 4600
## 55 25000 37481.489 33669.34 41293.640 75000
## 63 48000 65418.372 62472.89 68363.851 20000
## 66 45000 61677.584 58752.21 64602.957 41000
## 68 90000 41879.848 38416.97 45342.730 50000
## 78 25000 41176.110 37680.28 44671.939 54000
## 81 50000 62282.454 59490.85 65074.059 32000
## 83 30000 61587.508 58847.68 64327.338 9000
## 107 42000 59743.519 57118.19 62368.846 35000
## 109 50000 66654.885 63571.47 69738.296 20000
## 116 57000 63717.990 60882.50 66553.483 6465
## 122 135000 83964.514 78396.50 89532.526 6100
## 126 25000 43639.191 40216.63 47061.748 40000
## 129 50000 62299.405 59330.18 65268.629 40000
## 132 15000 60721.982 57459.67 63984.293 55000
## 138 35000 66226.119 63098.12 69354.114 26000
## 140 85000 42055.782 38599.66 45511.906 49000
## 147 170000 48213.485 44612.44 51814.526 14000
## 155 50000 41527.979 38049.80 45006.160 52000
## 158 40000 50271.917 46468.95 54074.888 2300
## 162 48000 66730.785 63557.13 69904.442 25000
## 167 23000 57904.370 55298.84 60509.897 17450
## 173 45000 58653.509 55784.57 61522.446 50000
## 191 20000 53541.350 50954.29 56128.408 50000
## 200 56000 59025.458 56408.59 61642.328 37714
## 201 30000 59842.310 56495.18 63189.437 60000
## 209 25000 42935.454 39504.08 46366.824 44000
## 212 38000 60867.400 58259.51 63475.293 20000
## 229 30000 42446.005 39002.73 45889.279 46782
## 230 95000 71239.356 67718.18 74760.532 8000
## 234 14000 52794.834 49844.28 55745.391 66000
## 238 50000 56669.357 54129.63 59209.080 41000
## 239 210000 48565.354 44934.63 52196.073 12000
## 262 50000 63832.171 60674.35 66989.989 40000
## 269 75000 47685.682 44125.17 51246.190 17000
## 272 75000 58411.898 55441.13 61382.670 54000
## 276 35000 46278.207 42801.30 49755.113 25000
## 292 40000 64113.464 61170.02 67056.907 29689
## 309 29500 54421.022 51889.64 56952.402 45000
## 315 69000 63644.554 60734.16 66554.942 1600
## 318 42000 46981.944 43467.79 50496.098 21000
## 320 20000 51585.898 48193.20 54978.595 80000
## 323 140000 85519.407 78519.81 92519.004 40000
## 330 90000 48565.354 44934.63 52196.073 12000
## 335 87000 47073.958 43554.25 50593.662 20477
## 340 30000 44166.994 40744.58 47589.407 37000
## 347 50000 68546.657 65356.15 71737.164 5000
## 349 40000 60184.232 57537.58 62830.884 14000
## 360 26500 56127.279 53616.43 58638.129 40000
## 364 78000 65683.297 62621.02 68745.573 26100
## 365 58000 62283.041 59480.88 65085.201 6000
## 374 65000 39240.832 35607.72 42873.948 65000
## 376 35000 67210.592 64129.57 70291.614 13500
## 381 39000 47157.879 43632.98 50682.780 20000
## 383 60000 61395.630 58385.69 64405.568 45137
## 385 35000 44413.303 40989.06 47837.547 35600
## 390 36000 59752.602 57181.35 62323.852 20400
## 394 45000 57006.402 54505.52 59507.281 36000
## 398 140000 88371.854 81375.24 95368.466 25000
## 400 180000 95259.117 87299.03 103219.200 13125
## 401 42000 66950.058 63836.83 70063.289 19600
## 402 16000 57167.527 54620.50 59714.560 40000
## 405 85000 75747.216 71462.97 80031.458 13000
## 408 120000 46348.581 42868.37 49828.793 24600
## 412 20000 60428.186 57651.45 63204.920 7000
## 414 65000 70857.185 67416.58 74297.793 3500
## 422 70000 48125.518 44531.57 51719.468 14500
## 425 130000 50060.796 46281.56 53840.036 3500
## 433 95000 50359.884 46546.83 54172.935 1800
## 438 195000 88244.632 81962.18 94527.082 2845
## 442 45000 54110.851 50740.11 57481.593 75000
## 443 25000 47157.879 43632.98 50682.780 20000
## 445 10000 51782.007 49028.93 54535.082 60000
## 447 45000 60177.628 57578.07 62777.184 18000
## 458 26000 39240.832 35607.72 42873.948 65000
## 463 12000 34842.473 30680.33 39004.616 90000
## 474 15000 41879.848 38416.97 45342.730 50000
## 475 60000 43639.191 40216.63 47061.748 40000
## 480 55000 43287.323 39861.59 46713.057 42000
## 481 75000 63179.077 60231.82 66126.334 35000
## 485 47000 52854.789 49655.96 56053.619 73000
## 493 16000 40296.438 36746.46 43846.420 59000
## 495 50000 42583.585 39144.13 46023.038 46000
## 509 25000 41879.848 38416.97 45342.730 50000
## 514 35000 57148.005 54659.01 59636.998 29500
## 515 40000 41879.848 38416.97 45342.730 50000
## 522 12000 33083.130 28643.34 37522.916 100000
## 523 35000 59761.398 57189.60 62333.192 20350
## 524 25000 41879.848 38416.97 45342.730 50000
## 525 40000 44166.994 40744.58 47589.407 37000
## 531 40000 46960.656 43447.76 50473.548 21121
## 532 50000 57694.727 55181.07 60208.381 25000
## 537 40000 64416.603 61344.05 67489.157 34000
## 545 37000 47836.106 44264.54 51407.671 16145
## 551 21000 48741.288 45094.95 52387.624 11000
## 562 40000 -9141.118 -23412.31 5130.072 340000
## 565 22000 46278.207 42801.30 49755.113 25000
## 571 68000 48917.222 45254.76 52579.685 10000
## 573 25000 57798.273 55278.78 60317.764 35000
## 575 200000 96643.071 88651.70 104634.439 5500
## 578 30000 57389.001 54898.28 59879.725 30000
## 587 50000 43991.060 40569.22 47412.904 38000
## 589 50000 53893.219 51330.76 56455.681 48000
## 593 30900 62784.382 60040.59 65528.175 25000
## 595 15000 33083.130 28643.34 37522.916 100000
## 603 35000 60147.469 57548.54 62746.394 18000
## 606 69000 66369.768 63319.61 69419.926 20000
## 613 25000 42759.520 39324.41 46194.626 45000
## 614 80000 73532.597 69261.55 77803.643 27000
## 618 40000 52431.902 49322.58 55541.222 71250
## 619 20000 45750.404 42295.28 49205.530 28000
## 622 65000 49549.003 45824.53 53273.474 6409
## 623 20000 41176.110 37680.28 44671.939 54000
## 628 65000 64806.588 61891.56 67721.618 23000
## 635 100000 73379.560 69572.37 77186.746 7000
## 637 25000 55693.315 53183.26 58203.374 28000
## 646 60000 67622.524 64487.68 70757.365 14500
## 656 50000 45496.706 42050.14 48943.277 29442
## 657 75000 66654.885 63571.47 69738.296 20000
## 659 70000 64300.268 61138.28 67462.250 38000
## 667 85009 49357.058 45652.09 53062.023 7500
## 696 25000 55652.563 53165.36 58139.770 38000
## 699 70000 29844.229 19664.26 40024.201 240000
## 701 10000 33083.130 28643.34 37522.916 100000
## 703 25000 60799.705 58145.09 63454.317 14000
## 709 26500 58865.208 56289.57 61440.843 18860
## 713 90000 69022.235 65757.57 72286.900 10500
## 721 50000 59188.426 56571.71 61805.139 37000
## 728 27000 64632.701 61790.59 67474.817 14100
## 736 15000 50589.418 47595.96 53582.872 70000
## 737 45000 58264.793 55206.47 61323.119 57000
## 743 8000 41264.077 37772.88 44755.276 53500
## 748 50000 45750.404 42295.28 49205.530 28000
## 750 69000 48565.354 44934.63 52196.073 12000
## 751 28000 41880.024 38417.15 45342.899 49999
## 758 20000 47861.616 44288.14 51435.096 16000
## 760 99000 44342.929 40919.33 47766.527 36000
## 761 90000 38361.160 34644.54 42077.778 70000
## 769 45000 63109.308 60303.07 65915.544 7000
## 781 120000 -10020.790 -24515.09 4473.511 345000
## 783 30000 51583.654 48833.38 54333.927 60000
## 787 25000 60114.163 57544.58 62683.744 25000
## 789 30000 58782.534 56256.46 61308.609 25000
## 791 35000 45750.404 42295.28 49205.530 28000
## 795 55000 65907.641 62909.57 68905.712 20000
## 798 24000 47685.682 44125.17 51246.190 17000
## 806 45000 37481.489 33669.34 41293.640 75000
## 808 30000 59032.349 56468.60 61596.095 20000
## 815 40000 47861.616 44288.14 51435.096 16000
## 821 60000 49972.828 46203.28 53742.374 4000
## 834 25000 62833.194 59714.25 65952.139 43000
## 835 30000 56342.503 53425.13 59259.877 8000
## 838 50000 65120.341 62050.70 68189.979 30000
## 844 130000 48917.222 45254.76 52579.685 10000
## 851 150000 47157.879 43632.98 50682.780 20000
## 852 45000 43639.191 40216.63 47061.748 40000
## 856 30000 63429.263 60660.35 66198.172 12000
## 864 10000 42231.716 38781.75 45681.681 48000
## 867 42000 46278.207 42801.30 49755.113 25000
## 869 60000 48741.288 45094.95 52387.624 11000
## 878 35000 48565.354 44934.63 52196.073 12000
## 880 30000 55596.881 53021.69 58172.068 46070
## 884 80000 73004.794 68732.93 77276.658 30000
## 889 40000 61043.635 58326.90 63760.373 10000
## 894 40000 36601.817 32682.98 40520.653 80000
## 899 42000 61659.104 58995.46 64322.745 15500
## 900 18000 57411.906 54916.25 59907.565 28000
## 905 23000 51822.759 49233.48 54412.044 50000
## 907 25000 38361.160 34644.54 42077.778 70000
## 909 50000 58082.966 54527.54 61638.389 70000
## 912 50000 44546.280 40632.36 48460.195 100000
## 918 45000 61790.091 59136.97 64443.211 22000
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## 933 90000 43639.191 40216.63 47061.748 40000
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## 952 120000 90915.397 84034.37 97796.428 6000
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## 967 90000 84237.655 77491.51 90983.801 40000
## 968 90000 72487.017 68856.35 76117.685 1300
## 974 75000 72099.962 68504.84 75695.080 3500
## 975 72000 72627.765 68983.56 76271.968 500
## 986 60000 69245.338 65720.52 72770.159 25000
## 996 50000 69684.318 66358.85 73009.790 8000
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## 1003 45000 65792.550 62768.21 68816.891 23000
## 1017 38000 55739.198 52219.26 59259.138 75000
## 1033 20000 59501.706 56955.93 62047.478 25000
## 1041 12000 54807.535 52125.22 57489.851 53000
## 1042 10000 51848.953 47901.03 55796.881 92233
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