Predict Classification 1
Saputra Wijaya
Updated: April 09, 2019
1 . Background
1.1 . Introduction
Hello, My name is Saputra Wijaya, Saya adalah seorang mahasiswa Computer Science yang sangat tertarik di bidang Data, baik itu data analyst, Data Visualization, khususnya Data Scientis. Hari ini saya akan menjelaskan sedikit tentang Classification. Semoga kalian menikmati !
1.2 . Libraries and Setup
Baik pertama kita akan membahas terlebih dahulu mengenai library atau setup apa yang akan digunakan untuk project ini :
knitr::opts_chunk$set(cache=TRUE)
options(scipen = 9999)
rm(list=ls())
library(gtools)
library(gmodels)## Warning: package 'gmodels' was built under R version 3.5.3library(ggplot2)
library(class)
library(caret)## Warning: package 'caret' was built under R version 3.5.3## Loading required package: latticelibrary(e1071)## Warning: package 'e1071' was built under R version 3.5.3##
## Attaching package: 'e1071'## The following object is masked from 'package:gtools':
##
## permutationslibrary(grid)2 . Let’s Play
Hari ini kita akan menggunakan dataset berasal dari “wholesale.csv” Data ini berisi bebrapa data hasil penjualan yang ada. Berikut ini adalah hasil detail nya :
wholesale <- read.csv("data_input/wholesale.csv", header=TRUE)
str(wholesale)## 'data.frame': 440 obs. of 8 variables:
## $ Channel : int 2 2 2 1 2 2 2 2 1 2 ...
## $ Region : int 3 3 3 3 3 3 3 3 3 3 ...
## $ Fresh : int 12669 7057 6353 13265 22615 9413 12126 7579 5963 6006 ...
## $ Milk : int 9656 9810 8808 1196 5410 8259 3199 4956 3648 11093 ...
## $ Grocery : int 7561 9568 7684 4221 7198 5126 6975 9426 6192 18881 ...
## $ Frozen : int 214 1762 2405 6404 3915 666 480 1669 425 1159 ...
## $ Detergents_Paper: int 2674 3293 3516 507 1777 1795 3140 3321 1716 7425 ...
## $ Delicassen : int 1338 1776 7844 1788 5185 1451 545 2566 750 2098 ...wholesale <- wholesale[,-2]
colSums(is.na(wholesale))## Channel Fresh Milk Grocery
## 0 0 0 0
## Frozen Detergents_Paper Delicassen
## 0 0 0Selanjutnya maka kita akan menentukan sebuah model yang akan digunakan untuk data kita, Model ini akan terdiri dari 2 hal yaitu berdasarkan penjualan dari Region nya dan berdasarkan channel yang ada. Kemudian kedua-dua nya menggunakan Binomial
Reg <- as.factor(wholesale$Region)
Chan <- as.factor(wholesale$Channel)
#model <- glm(Reg ~ 1 , data=wholesale, family ="binomial")
#summary(model)
model_channel <- glm(Chan ~ 1, data = wholesale, family = "binomial")
summary(model_channel)##
## Call:
## glm(formula = Chan ~ 1, family = "binomial", data = wholesale)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.8828 -0.8828 -0.8828 1.5040 1.5040
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.7413 0.1020 -7.269 0.000000000000361 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 553.44 on 439 degrees of freedom
## Residual deviance: 553.44 on 439 degrees of freedom
## AIC: 555.44
##
## Number of Fisher Scoring iterations: 4Namun dari yang kita miliki, kita akan memilih channel sebagai parameter yang kita gunakan untuk prediksi, Menurut saya Channel cukup mudah di pahami karena hanya terdiri dari 2 faktor
table(wholesale$Channel)##
## 1 2
## 298 142Setelah kita memilih Channel maka langkah selanjutnya adalah kita menghitung Odds beradasarkan kesemaptan prediksi Channel 1 lebih banyak keluar atau tidak
chan_1 = 298/(298+142)
chan_2 = 142/(298+142)
odds.chan1 <- chan_1/chan_2
odds.chan1## [1] 2.098592log(odds.chan1)## [1] 0.7412664prediction_channel_1 <- exp(log(odds.chan1))/(1+(exp(log(odds.chan1))))
prediction_channel_1 ## [1] 0.6772727Dari hasil analaisa kita diatas kita dapat melihat Prediction yang dihasilkan untuk mengeluarkan Channel 1 adalah sebesar 0.67 atau 67% kemungkinan besar Channel 1 akan lebih sering muncul ketimbang Channel 2
Untuk memudahkan visualization kita langkah selanjutnya adalah menggunakan Historgram agar data kita dapat lebih mudah untuk dilihat
hist(wholesale$Channel, breaks = 20)
abline(v = median(wholesale$Channel), col="blue", lwd = 3)
Langkah selanjutnya adalah kita menentukan data mana yang akan kita menentukan besar data yang akan kita gunakan sebagai Train dan Test. Berikut ini adaklah coding nya :
set.seed(400)
intrain <- sample(nrow(wholesale), nrow(wholesale)*0.8)
wholesale.train <- wholesale[intrain, ]
wholesale.test <- wholesale[-intrain, ]
head(wholesale.test)## Channel Fresh Milk Grocery Frozen Detergents_Paper Delicassen
## 1 2 12669 9656 7561 214 2674 1338
## 7 2 12126 3199 6975 480 3140 545
## 12 2 13146 1124 4523 1420 549 497
## 13 2 31714 12319 11757 287 3881 2931
## 18 1 5876 6157 2933 839 370 4478
## 19 2 18601 6327 10099 2205 2767 3181wholesale.test[1:8, ]## Channel Fresh Milk Grocery Frozen Detergents_Paper Delicassen
## 1 2 12669 9656 7561 214 2674 1338
## 7 2 12126 3199 6975 480 3140 545
## 12 2 13146 1124 4523 1420 549 497
## 13 2 31714 12319 11757 287 3881 2931
## 18 1 5876 6157 2933 839 370 4478
## 19 2 18601 6327 10099 2205 2767 3181
## 25 2 22647 9776 13792 2915 4482 5778
## 26 2 16165 4230 7595 201 4003 573 . Let’s Predict
Langkah selanjutnya adalah kita akan melakukan prediksi dengan menggunakan data wholesale seluruhnya untuk memprediksi suatu kelas yang akan keluar berdasarkan label yang ada
set.seed(10)
wholesale.c <- wholesale[sample(nrow(wholesale)), ]
wholesale_train <- wholesale.c[1:352, 1:6]
wholesale_test <- wholesale.c[353:440, 1:6]
wholesale_test_label<- wholesale.c[353:440,7]
wholesale_train_label <- wholesale.c[1:352,7]
wholesale_pred <- knn(train = wholesale_train, test=wholesale_test, cl=wholesale_train_label, k=21)
table("Predicted" = wholesale_pred, "Actual" = wholesale_test_label)## Actual
## Predicted 3 11 27 37 46 63 64 65 70 110 127 204 211 229 244 285 291 295
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## Actual
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## Actual
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## 1902 0 0
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## 2005 0 0
## 2017 0 0
## 2080 0 0
## 2100 0 0
## 2124 0 0
## 2125 0 0
## 2137 0 0
## 2158 0 0
## 2162 0 0
## 2165 0 0
## 2168 0 0
## 2204 0 0
## 2213 0 0
## 2235 0 0
## 2253 0 0
## 2341 0 0
## 2342 0 0
## 2346 0 0
## 2379 0 0
## 2388 0 0
## 2398 0 0
## 2410 0 0
## 2435 0 0
## 2498 0 0
## 2503 0 0
## 2563 0 0
## 2564 0 0
## 2566 0 0
## 2602 0 0
## 2616 0 0
## 2631 0 0
## 2698 0 0
## 2708 0 0
## 2749 0 0
## 2784 0 0
## 2794 0 0
## 2802 0 0
## 2893 0 0
## 2931 0 0
## 2944 0 0
## 3029 0 0
## 3095 0 0
## 3105 0 0
## 3178 0 0
## 3181 0 0
## 3182 0 0
## 3265 0 0
## 3271 0 0
## 3321 0 0
## 3486 0 0
## 3628 0 0
## 3637 0 0
## 4100 0 0
## 4334 0 0
## 4365 0 0
## 4430 0 0
## 4478 0 0
## 4829 0 0
## 4985 0 0
## 5120 0 0
## 5121 0 0
## 5137 0 0
## 5185 0 0
## 5206 0 0
## 5778 0 0
## 5864 0 0
## 6250 0 0
## 6372 0 0
## 6854 0 0
## 14472 0 0
## 47943 0 0summary(wholesale_pred)## 156 290 5206 379 610 686 3 553 776
## 4 4 4 3 3 3 2 2 2
## 860 1080 1874 4334 5185 22 59 143 224
## 2 2 2 2 2 1 1 1 1
## 254 323 340 375 378 529 615 648 689
## 1 1 1 1 1 1 1 1 1
## 749 786 790 819 1083 1163 1266 1328 1378
## 1 1 1 1 1 1 1 1 1
## 1404 1409 1440 1451 1468 1498 1522 1534 1550
## 1 1 1 1 1 1 1 1 1
## 1561 1603 1621 1647 1653 1682 1755 1776 1894
## 1 1 1 1 1 1 1 1 1
## 2158 2213 2346 2379 2503 2564 2784 3029 3182
## 1 1 1 1 1 1 1 1 1
## 5121 6250 7 8 18 36 46 51 52
## 1 1 0 0 0 0 0 0 0
## 56 57 62 72 84 117 120 128 130
## 0 0 0 0 0 0 0 0 0
## 132 139 142 144 167 172 179 181 184
## 0 0 0 0 0 0 0 0 0
## 195 201 203 216 217 222 225 230 238
## 0 0 0 0 0 0 0 0 0
## (Other)
## 04 . Summary
Jadi, Kesimpulannya adalah data yang kita memiliki data yang cukup bersih dan dapat kita lakukan analisa data dengan menggunakan metode KNN. Dari hasil wholesale, kita dapat simpulkan bahwa Data yang kita miliki beradasarkan prediksi channel masih sangat rendah dan kita dapat ambil kesimpulan bahwa channel bukanlah sebuah parameter yang bagus untuk dijadikan indikator classification, selain itu dengan metode KNN maka penggunaan Indicator yang masih sangat luas itu hasilnya kurang bagus dan model kita dirasa kurang tepat untuk dijadikan prediksi.
Saya harap kedepannyas aya dapat melakukan analisa data wholesale.csv dengan memasukkan indicator yang berbeda lagi sehingga dapat dijadikan tolak ukur classification yang baik