Hallo guys,
Berikut ini saya lampirkan tugas kalian untuk pertemuan 10 pada Matakuliah Ekonometrika. Dikumpulkan pada tanggal tertera. Silahkan untuk mengikuti instruksi berikut:
- Mendefenisikan Permasalahan
- Data Preparation
- Exploratory Data Analysis (EDA)
- Data Preprocessing
- Model Building (Perbandingan 5 model)
- Model Evaluation (dengan menggunakan ANOVA, RMSE, Rsquared)
- Conclusion
Catatan: Kerjakan proses tersebut dengan menggunakan Rmarkdwon dan kumpulkan link Rpubsnya disini!
Mendefenisikan Permasalahan
Suatu hari, Xander ingin memprediksi harga dari suatu rumah dengan beberapa fasilitas yang memumpuni seperti:
ID - ID Date - Tanggal Price - Harga Bedrooms - Jumalh Kamar Tidur Bathrooms - Jumlah Kamar Mandi Sqft Living - Luas Bangunan (in foot) Sqft Lot - Luas Tanah (in foot) Floors - Jumlah Lantai Waterfront - Bersebelahan dengan Air seperti danau. View - Gazing/ Pemandangan Condition - KondisiRumah Grade - Grade Bangunan Sqft Above - Luas Bangunan Di Atas Tanah Sqft Basement - Luas Basement Year Built - Tahun Dibangun Year Renovated - Tahun Direnovasi terakhir Zipcode - Kode Pos Latitude - Lintang Longitude - Bujur Sqft Living15 - Luas Ruang Hunian pada 2015 (dalam kaki persegi) Sqft Lot15 - Luas Lahan pada 2015 (dalam kaki persegi).
house_data Preparation
## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr 1.1.4 ✔ readr 2.1.5
## ✔ forcats 1.0.0 ✔ stringr 1.5.1
## ✔ ggplot2 3.5.0 ✔ tibble 3.2.1
## ✔ lubridate 1.9.3 ✔ tidyr 1.3.1
## ✔ purrr 1.0.2
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errorsExploratory Data Analysis (EDA)
summary(house)## id date price bedrooms
## Min. :1.000e+06 Length:21613 Min. : 75000 Min. : 0.000
## 1st Qu.:2.123e+09 Class :character 1st Qu.: 321950 1st Qu.: 3.000
## Median :3.905e+09 Mode :character Median : 450000 Median : 3.000
## Mean :4.580e+09 Mean : 540088 Mean : 3.371
## 3rd Qu.:7.309e+09 3rd Qu.: 645000 3rd Qu.: 4.000
## Max. :9.900e+09 Max. :7700000 Max. :33.000
## bathrooms sqft_living sqft_lot floors
## Min. :0.000 Min. : 290 Min. : 520 Min. :1.000
## 1st Qu.:1.750 1st Qu.: 1427 1st Qu.: 5040 1st Qu.:1.000
## Median :2.250 Median : 1910 Median : 7618 Median :1.500
## Mean :2.115 Mean : 2080 Mean : 15107 Mean :1.494
## 3rd Qu.:2.500 3rd Qu.: 2550 3rd Qu.: 10688 3rd Qu.:2.000
## Max. :8.000 Max. :13540 Max. :1651359 Max. :3.500
## waterfront view condition grade
## Min. :0.000000 Min. :0.0000 Min. :1.000 Min. : 1.000
## 1st Qu.:0.000000 1st Qu.:0.0000 1st Qu.:3.000 1st Qu.: 7.000
## Median :0.000000 Median :0.0000 Median :3.000 Median : 7.000
## Mean :0.007542 Mean :0.2343 Mean :3.409 Mean : 7.657
## 3rd Qu.:0.000000 3rd Qu.:0.0000 3rd Qu.:4.000 3rd Qu.: 8.000
## Max. :1.000000 Max. :4.0000 Max. :5.000 Max. :13.000
## sqft_above sqft_basement yr_built yr_renovated
## Min. : 290 Min. : 0.0 Min. :1900 Min. : 0.0
## 1st Qu.:1190 1st Qu.: 0.0 1st Qu.:1951 1st Qu.: 0.0
## Median :1560 Median : 0.0 Median :1975 Median : 0.0
## Mean :1788 Mean : 291.5 Mean :1971 Mean : 84.4
## 3rd Qu.:2210 3rd Qu.: 560.0 3rd Qu.:1997 3rd Qu.: 0.0
## Max. :9410 Max. :4820.0 Max. :2015 Max. :2015.0
## zipcode lat long sqft_living15
## Min. :98001 Min. :47.16 Min. :-122.5 Min. : 399
## 1st Qu.:98033 1st Qu.:47.47 1st Qu.:-122.3 1st Qu.:1490
## Median :98065 Median :47.57 Median :-122.2 Median :1840
## Mean :98078 Mean :47.56 Mean :-122.2 Mean :1987
## 3rd Qu.:98118 3rd Qu.:47.68 3rd Qu.:-122.1 3rd Qu.:2360
## Max. :98199 Max. :47.78 Max. :-121.3 Max. :6210
## sqft_lot15
## Min. : 651
## 1st Qu.: 5100
## Median : 7620
## Mean : 12768
## 3rd Qu.: 10083
## Max. :871200# Distribusi Histogram
house %>%
keep(is.numeric) %>%
gather(key = "variables", value = "values") %>%
ggplot(aes(x = values)) +
geom_histogram(bins = 30, fill = "blue", color = "black") +
facet_wrap(~variables, scales = "free_x")
# Megecek NA
colSums(is.na(house))## id date price bedrooms bathrooms
## 0 0 0 0 0
## sqft_living sqft_lot floors waterfront view
## 0 0 0 0 0
## condition grade sqft_above sqft_basement yr_built
## 0 0 0 0 0
## yr_renovated zipcode lat long sqft_living15
## 0 0 0 0 0
## sqft_lot15
## 0Catatan:
Berikut adalah penjelasan singkat untuk setiap variabel penting , sebagai berikut:
Harga (Price)
summary(house$price)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 75000 321950 450000 540088 645000 7700000Harga mulai dari 75.000 USD Sampai 7.700.000 USD Dengan Mean senilai 540.088 USD
Kamar Tidur (Bedrooms)
summary (house$bedrooms)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.000 3.000 3.000 3.371 4.000 33.000Tersedia dari 0 sampai 33 Kamar dengan Mean sekitar 3-4 Kamar.
Kamar Mandi (Bathrooms)
summary (house$bathrooms)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.000 1.750 2.250 2.115 2.500 8.000Mulai dari 0-8 kamar Mandi dengan rata-rata memiliki 2-3 Kamar Mandi.
Luas Bangunan
summary (house$sqft_living)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 290 1427 1910 2080 2550 13540Luas Bangunan dimuali dari 290 sqft sampai 13.540 sqft dengan Mean sebesar 2.080 SQFT.
Luas Tanah (Sqft Lot)
summary (house$sqft_lot)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 520 5040 7618 15107 10688 1651359Luas Tanah Dimulai dari 520 SQFT sampai 1.651.359 SQFT (Yallah Luas Sekali) dengan Mean Luas Tanah 15.107 SQFT.
Lantai (Floors)
summary (house$floors)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.000 1.000 1.500 1.494 2.000 3.500Rumah yang dijual memiliki 1 sampai 3,5 Lantai dengan Rata-rata kebanyakan memiliki 1-2 Lantai.
Waterfront
library(dplyr)
house %>% count(waterfront)Sebanyak 21430 Rumah tidak memiliki view Danau,hanya sekitar 163 yang memilikinya.
View
library(dplyr)
house %>% count(view)Kebanyakan rumah memili view yang jelek (0) dibandingkan dengan Kategori 1-4.
Kondisi (Condition)
library(dplyr)
house %>% count(condition)Diketahui bahwa Rata-rata Bangunan kebanyakan berstatus Okay sampai Sangat Bagus jika mengacu bahwa Variabel Kondisi emnggunakan Skala Likert.
Kualitas Konstruksi (Grade)
library(dplyr)
house %>% count(grade)Diketahui bahwa Rata-rata Bangunan kebanyakan berstatus Okay sampai Sangat Bagus jika mengacu bahwa Variabel Kondisi emnggunakan Skala Likert.
Luas Bangunan Utama (Sqft Above)
summary(house$sqft_above)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 290 1190 1560 1788 2210 9410Luas Bangunan dari 290-9410 SQFT Dengan Mean 1.788 SQFT.
Luas Basement (Sqft Basement)
summary(house$sqft_basement)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0 0.0 0.0 291.5 560.0 4820.0Keterangan: Luas basement dari 0 (Tidak ada) hingga 4.820 kaki persegi. Degan Mean 291.5 SQFT
Tahun Dibangun (Year Built)
library(dplyr)
house %>% count(yr_built)Keterangan: Tahun properti dibangun berkisar dari tahun 1900 hingga 2015.
Tahun Direnovasi (Year Renovated)
library(dplyr)
house %>% count(yr_renovated)Kebanyakan Rumah Tidak Pernah Di Renovasi.
Kode Pos (Zipcode)
library(dplyr)
house %>% count(zipcode)ZipCode menunjukkan Banyaknya Rumah DIjual di Daerah Kode Pos TErsedebut.
Luas Hunian di 2015 (Sqft Living15)
summary(house$sqft_living15)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 399 1490 1840 1987 2360 6210Keterangan: Menunjukkan luas hunian seperti yang tercatat pada tahun 2015, dari 399 hingga 6210 kaki persegi. Dengan Mean 1987
Luas Tanah di 2015 (Sqft Lot15)
summary(house$sqft_lot15)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 651 5100 7620 12768 10083 871200Keterangan: Menunjukkan luas tanah seperti yang tercatat pada tahun 2015, dari 651 hingga 871,200 kaki persegi. Dengan Mean 12.768
Data Preprocessing
mengecek dan menangani nilai yang hilang - dan mengisi nilai yang hilang dengan median
house <- house %>%
mutate_if(is.numeric, ~ifelse(is.na(.), median(., na.rm = TRUE), .))Mengubah data house menjadi Distribusi Normal - Contoh menggunakan penskalaan min-max
house <- house %>%
mutate_if(is.numeric, ~(. - min(.)) / (max(.) - min(.)))Model Building (Perbandingan 5 model)
library(nnet)
library(rpart)
library(class)
library(stats)Menyiapkan house untuk model
house <- na.omit(house) # menghilangkan baris yang NAMembagi house menjadi house latihan dan house uji
set.seed(123)
indices <- sample(1:nrow(house), size = 0.75 * nrow(house))
train_house <- house[indices, ]
test_house <- house[-indices, ]Model Linear Regresi (Linear Model)
linmod <- lm(price ~ .-id -zipcode , train_house)Decision Tree
model_tree <- rpart(price ~ .-id, train_house)Linear Model
summary(linmod)##
## Call:
## lm(formula = price ~ . - id - zipcode, data = train_house)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.16595 -0.01282 -0.00112 0.01004 0.56798
##
## Coefficients: (1 not defined because of singularities)
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -8.824e-02 4.335e-03 -20.355 < 2e-16 ***
## date20140503T000000 2.547e-02 1.557e-02 1.637 0.101745
## date20140504T000000 -9.066e-03 1.361e-02 -0.666 0.505314
## date20140505T000000 1.423e-03 4.994e-03 0.285 0.775644
## date20140506T000000 -2.352e-04 5.144e-03 -0.046 0.963535
## date20140507T000000 2.021e-03 4.873e-03 0.415 0.678328
## date20140508T000000 7.719e-03 5.028e-03 1.535 0.124798
## date20140509T000000 -1.937e-03 5.065e-03 -0.382 0.702177
## date20140510T000000 3.887e-03 1.229e-02 0.316 0.751843
## date20140511T000000 1.825e-02 2.642e-02 0.691 0.489740
## date20140512T000000 2.324e-03 4.978e-03 0.467 0.640625
## date20140513T000000 5.667e-03 5.028e-03 1.127 0.259772
## date20140514T000000 2.998e-03 5.012e-03 0.598 0.549694
## date20140515T000000 -2.729e-03 5.046e-03 -0.541 0.588645
## date20140516T000000 -3.214e-04 5.103e-03 -0.063 0.949787
## date20140517T000000 6.173e-03 2.642e-02 0.234 0.815290
## date20140518T000000 3.392e-03 1.229e-02 0.276 0.782548
## date20140519T000000 4.398e-04 5.104e-03 0.086 0.931322
## date20140520T000000 1.422e-03 4.753e-03 0.299 0.764833
## date20140521T000000 -6.432e-04 4.917e-03 -0.131 0.895919
## date20140522T000000 2.119e-03 4.994e-03 0.424 0.671268
## date20140523T000000 3.026e-04 5.029e-03 0.060 0.952011
## date20140524T000000 7.339e-03 9.097e-03 0.807 0.419833
## date20140525T000000 -1.525e-02 1.557e-02 -0.980 0.327340
## date20140526T000000 3.406e-03 1.132e-02 0.301 0.763588
## date20140527T000000 -1.932e-03 4.811e-03 -0.402 0.688054
## date20140528T000000 1.626e-03 4.684e-03 0.347 0.728540
## date20140529T000000 3.314e-03 5.104e-03 0.649 0.516237
## date20140530T000000 2.184e-04 5.368e-03 0.041 0.967551
## date20140531T000000 1.117e-04 1.229e-02 0.009 0.992752
## date20140601T000000 8.902e-03 1.132e-02 0.786 0.431817
## date20140602T000000 4.937e-03 5.188e-03 0.952 0.341364
## date20140603T000000 9.641e-04 4.766e-03 0.202 0.839682
## date20140604T000000 7.797e-03 4.860e-03 1.604 0.108708
## date20140605T000000 3.055e-03 4.930e-03 0.620 0.535505
## date20140606T000000 2.597e-03 5.525e-03 0.470 0.638286
## date20140607T000000 -1.106e-02 1.361e-02 -0.813 0.416341
## date20140608T000000 6.364e-03 1.059e-02 0.601 0.547801
## date20140609T000000 2.348e-03 4.886e-03 0.480 0.630887
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## date20140611T000000 7.356e-03 4.995e-03 1.473 0.140830
## date20140612T000000 3.888e-03 5.012e-03 0.776 0.437973
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## date20140614T000000 4.786e-04 1.361e-02 0.035 0.971950
## date20140615T000000 1.880e-02 1.556e-02 1.208 0.227089
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## date20140628T000000 4.820e-03 9.092e-03 0.530 0.596029
## date20140629T000000 -1.025e-03 1.132e-02 -0.091 0.927865
## date20140630T000000 5.514e-03 5.104e-03 1.080 0.280029
## date20140701T000000 8.681e-03 4.723e-03 1.838 0.066042 .
## date20140702T000000 1.067e-02 4.978e-03 2.144 0.032084 *
## date20140703T000000 8.740e-03 5.237e-03 1.669 0.095141 .
## date20140704T000000 1.831e-02 1.887e-02 0.970 0.331971
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## date20141011T000000 2.664e-01 2.647e-02 10.065 < 2e-16 ***
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## date20150227T000000 9.852e-03 5.639e-03 1.747 0.080646 .
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## date20150304T000000 8.264e-03 4.836e-03 1.709 0.087525 .
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## date20150310T000000 9.809e-03 5.259e-03 1.865 0.062176 .
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## date20150313T000000 1.193e-02 5.167e-03 2.309 0.020968 *
## date20150314T000000 4.353e-03 1.361e-02 0.320 0.749082
## date20150315T000000 9.503e-03 1.556e-02 0.611 0.541489
## date20150316T000000 1.003e-02 5.083e-03 1.974 0.048418 *
## date20150317T000000 6.809e-03 4.874e-03 1.397 0.162457
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## date20150401T000000 8.304e-03 5.010e-03 1.657 0.097473 .
## date20150402T000000 9.513e-03 4.743e-03 2.006 0.044886 *
## date20150403T000000 8.503e-03 5.104e-03 1.666 0.095748 .
## date20150404T000000 1.838e-02 1.556e-02 1.181 0.237668
## date20150405T000000 1.288e-02 1.133e-02 1.137 0.255442
## date20150406T000000 8.493e-03 5.028e-03 1.689 0.091200 .
## date20150407T000000 1.266e-02 4.733e-03 2.675 0.007475 **
## date20150408T000000 1.405e-02 4.702e-03 2.987 0.002822 **
## date20150409T000000 3.541e-03 4.931e-03 0.718 0.472731
## date20150410T000000 1.280e-02 5.123e-03 2.498 0.012494 *
## date20150411T000000 6.617e-03 8.445e-03 0.784 0.433273
## date20150412T000000 -9.350e-03 9.501e-03 -0.984 0.325114
## date20150413T000000 1.163e-02 4.962e-03 2.343 0.019135 *
## date20150414T000000 5.623e-03 4.656e-03 1.207 0.227259
## date20150415T000000 1.075e-02 4.994e-03 2.152 0.031412 *
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## date20150420T000000 2.924e-03 5.047e-03 0.579 0.562308
## date20150421T000000 1.053e-02 4.683e-03 2.249 0.024529 *
## date20150422T000000 7.753e-03 4.665e-03 1.662 0.096571 .
## date20150423T000000 7.859e-03 4.744e-03 1.657 0.097628 .
## date20150424T000000 7.623e-03 4.764e-03 1.600 0.109577
## date20150425T000000 1.643e-02 8.440e-03 1.947 0.051591 .
## date20150426T000000 -4.596e-03 1.059e-02 -0.434 0.664182
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## date20150428T000000 1.010e-02 4.666e-03 2.164 0.030475 *
## date20150429T000000 1.078e-02 4.639e-03 2.325 0.020098 *
## date20150430T000000 8.875e-03 5.047e-03 1.758 0.078694 .
## date20150501T000000 1.090e-02 5.144e-03 2.120 0.034035 *
## date20150502T000000 5.388e-03 1.133e-02 0.476 0.634339
## date20150503T000000 5.113e-03 9.994e-03 0.512 0.608935
## date20150504T000000 6.713e-03 4.874e-03 1.377 0.168417
## date20150505T000000 9.775e-03 4.835e-03 2.022 0.043208 *
## date20150506T000000 1.313e-02 4.961e-03 2.646 0.008147 **
## date20150507T000000 1.234e-02 5.104e-03 2.417 0.015674 *
## date20150508T000000 1.284e-02 5.601e-03 2.292 0.021917 *
## date20150509T000000 1.865e-02 1.888e-02 0.988 0.323164
## date20150510T000000 -9.897e-03 1.887e-02 -0.524 0.600031
## date20150511T000000 1.557e-02 6.152e-03 2.530 0.011404 *
## date20150512T000000 1.006e-02 5.723e-03 1.758 0.078750 .
## date20150513T000000 5.354e-03 6.633e-03 0.807 0.419554
## date20150514T000000 9.634e-03 9.092e-03 1.060 0.289338
## date20150524T000000 1.386e-02 2.642e-02 0.525 0.599880
## date20150527T000000 3.785e-02 2.644e-02 1.432 0.152271
## bedrooms -1.379e-01 9.468e-03 -14.560 < 2e-16 ***
## bathrooms 4.704e-02 3.980e-03 11.820 < 2e-16 ***
## sqft_living 2.444e-01 8.879e-03 27.531 < 2e-16 ***
## sqft_lot 2.762e-02 1.253e-02 2.204 0.027520 *
## floors 2.070e-04 1.371e-03 0.151 0.879996
## waterfront 7.414e-02 2.591e-03 28.613 < 2e-16 ***
## view 2.474e-02 1.285e-03 19.259 < 2e-16 ***
## condition 1.817e-02 1.423e-03 12.769 < 2e-16 ***
## grade 1.579e-01 3.930e-03 40.190 < 2e-16 ***
## sqft_above 3.725e-02 6.065e-03 6.142 8.35e-10 ***
## sqft_basement NA NA NA NA
## yr_built -3.776e-02 1.255e-03 -30.089 < 2e-16 ***
## yr_renovated 5.558e-03 1.122e-03 4.955 7.30e-07 ***
## lat 4.527e-02 9.870e-04 45.870 < 2e-16 ***
## long -1.757e-02 2.181e-03 -8.056 8.41e-16 ***
## sqft_living15 2.041e-02 3.035e-03 6.726 1.81e-11 ***
## sqft_lot15 -4.554e-02 9.934e-03 -4.585 4.58e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02615 on 15827 degrees of freedom
## Multiple R-squared: 0.7083, Adjusted R-squared: 0.7013
## F-statistic: 100.9 on 381 and 15827 DF, p-value: < 2.2e-16Model Linear menggunakan Semua Variabel sebagai Prediktor menghasilkan Model Linear dengan nilai R-SQuared sebesar 69,91% dengan kebanyakan variabel memiliki hubungan yang signifikan dengan Harga.
Decision Tree
model_tree## n= 16209
##
## node), split, n, deviance, yval
## * denotes terminal node
##
## 1) root 16209 37.0945000 0.06095083
## 2) grade< 0.625 13033 8.6908710 0.04764066
## 4) lat< 0.6087341 5479 1.3503030 0.03154457 *
## 5) lat>=0.6087341 7554 4.8914500 0.05931534
## 10) sqft_living< 0.1271698 4847 1.6937420 0.05053339 *
## 11) sqft_living>=0.1271698 2707 2.1545680 0.07503978 *
## 3) grade>=0.625 3176 16.6197900 0.11557030
## 6) sqft_living< 0.2939623 2739 6.0694770 0.10085710
## 12) lat< 0.5932122 597 0.4461721 0.06380461 *
## 13) lat>=0.5932122 2142 4.5752560 0.11118410
## 26) long>=0.2761628 1207 0.8365521 0.09567946 *
## 27) long< 0.2761628 935 3.0739860 0.13119910
## 54) sqft_living< 0.2071698 549 0.9026689 0.10712130 *
## 55) sqft_living>=0.2071698 386 1.4003590 0.16544450
## 110) date=20140505T000000,20140506T000000,20140507T000000,20140508T000000,20140512T000000,20140513T000000,20140514T000000,20140515T000000,20140516T000000,20140519T000000,20140520T000000,20140521T000000,20140522T000000,20140523T000000,20140528T000000,20140530T000000,20140605T000000,20140609T000000,20140610T000000,20140616T000000,20140618T000000,20140619T000000,20140622T000000,20140623T000000,20140625T000000,20140626T000000,20140627T000000,20140628T000000,20140630T000000,20140702T000000,20140703T000000,20140706T000000,20140708T000000,20140709T000000,20140710T000000,20140711T000000,20140714T000000,20140715T000000,20140718T000000,20140720T000000,20140722T000000,20140723T000000,20140725T000000,20140729T000000,20140730T000000,20140802T000000,20140805T000000,20140807T000000,20140808T000000,20140811T000000,20140813T000000,20140814T000000,20140815T000000,20140827T000000,20140828T000000,20140904T000000,20140908T000000,20140909T000000,20140910T000000,20140911T000000,20140915T000000,20140922T000000,20140923T000000,20140925T000000,20140929T000000,20141002T000000,20141006T000000,20141009T000000,20141010T000000,20141013T000000,20141014T000000,20141015T000000,20141020T000000,20141021T000000,20141022T000000,20141023T000000,20141024T000000,20141029T000000,20141031T000000,20141104T000000,20141105T000000,20141106T000000,20141110T000000,20141111T000000,20141112T000000,20141113T000000,20141114T000000,20141117T000000,20141120T000000,20141121T000000,20141124T000000,20141125T000000,20141204T000000,20141208T000000,20141211T000000,20141212T000000,20141215T000000,20141216T000000,20141224T000000,20141231T000000,20150102T000000,20150108T000000,20150109T000000,20150112T000000,20150113T000000,20150114T000000,20150121T000000,20150202T000000,20150203T000000,20150209T000000,20150223T000000,20150224T000000,20150225T000000,20150304T000000,20150305T000000,20150306T000000,20150316T000000,20150320T000000,20150321T000000,20150323T000000,20150324T000000,20150325T000000,20150326T000000,20150331T000000,20150401T000000,20150402T000000,20150403T000000,20150408T000000,20150409T000000,20150412T000000,20150414T000000,20150415T000000,20150420T000000,20150422T000000,20150423T000000,20150426T000000,20150427T000000,20150430T000000,20150502T000000,20150508T000000,20150512T000000 274 0.4931260 0.14445640 *
## 111) date=20140527T000000,20140604T000000,20140612T000000,20140613T000000,20140617T000000,20140620T000000,20140624T000000,20140701T000000,20140721T000000,20140731T000000,20140806T000000,20140819T000000,20140821T000000,20140825T000000,20140826T000000,20140902T000000,20140916T000000,20140924T000000,20140926T000000,20141001T000000,20141003T000000,20141008T000000,20141017T000000,20141018T000000,20141101T000000,20141119T000000,20141126T000000,20141201T000000,20141205T000000,20141209T000000,20141210T000000,20141223T000000,20150106T000000,20150107T000000,20150115T000000,20150116T000000,20150128T000000,20150130T000000,20150204T000000,20150212T000000,20150217T000000,20150218T000000,20150221T000000,20150226T000000,20150227T000000,20150302T000000,20150303T000000,20150310T000000,20150313T000000,20150317T000000,20150318T000000,20150330T000000,20150407T000000,20150410T000000,20150421T000000,20150424T000000,20150428T000000,20150429T000000,20150501T000000,20150505T000000,20150507T000000 112 0.4912577 0.21679050 *
## 7) sqft_living>=0.2939623 437 6.2410500 0.20778850
## 14) date=20140505T000000,20140509T000000,20140512T000000,20140514T000000,20140515T000000,20140516T000000,20140520T000000,20140521T000000,20140522T000000,20140523T000000,20140524T000000,20140527T000000,20140528T000000,20140529T000000,20140530T000000,20140602T000000,20140603T000000,20140604T000000,20140605T000000,20140606T000000,20140609T000000,20140610T000000,20140612T000000,20140613T000000,20140616T000000,20140617T000000,20140618T000000,20140619T000000,20140620T000000,20140623T000000,20140624T000000,20140625T000000,20140626T000000,20140627T000000,20140630T000000,20140701T000000,20140703T000000,20140708T000000,20140709T000000,20140710T000000,20140711T000000,20140714T000000,20140715T000000,20140717T000000,20140718T000000,20140721T000000,20140722T000000,20140723T000000,20140724T000000,20140725T000000,20140728T000000,20140730T000000,20140731T000000,20140804T000000,20140805T000000,20140806T000000,20140807T000000,20140808T000000,20140811T000000,20140814T000000,20140818T000000,20140820T000000,20140822T000000,20140825T000000,20140827T000000,20140902T000000,20140903T000000,20140905T000000,20140909T000000,20140910T000000,20140911T000000,20140912T000000,20140915T000000,20140918T000000,20140924T000000,20140927T000000,20140929T000000,20141001T000000,20141003T000000,20141006T000000,20141007T000000,20141009T000000,20141010T000000,20141014T000000,20141015T000000,20141016T000000,20141017T000000,20141021T000000,20141022T000000,20141024T000000,20141027T000000,20141103T000000,20141106T000000,20141107T000000,20141110T000000,20141112T000000,20141114T000000,20141120T000000,20141121T000000,20141125T000000,20141126T000000,20141201T000000,20141202T000000,20141203T000000,20141209T000000,20141211T000000,20141212T000000,20141213T000000,20141215T000000,20141216T000000,20141217T000000,20141222T000000,20141223T000000,20141224T000000,20141226T000000,20150102T000000,20150112T000000,20150113T000000,20150114T000000,20150115T000000,20150123T000000,20150126T000000,20150127T000000,20150128T000000,20150202T000000,20150203T000000,20150206T000000,20150209T000000,20150210T000000,20150211T000000,20150213T000000,20150219T000000,20150222T000000,20150224T000000,20150303T000000,20150305T000000,20150312T000000,20150317T000000,20150318T000000,20150324T000000,20150325T000000,20150326T000000,20150327T000000,20150328T000000,20150330T000000,20150331T000000,20150401T000000,20150406T000000,20150407T000000,20150409T000000,20150410T000000,20150415T000000,20150422T000000,20150423T000000,20150424T000000,20150427T000000,20150428T000000,20150429T000000,20150430T000000,20150504T000000,20150505T000000 346 2.1726180 0.17543970
## 28) date=20140505T000000,20140512T000000,20140514T000000,20140515T000000,20140520T000000,20140521T000000,20140523T000000,20140527T000000,20140528T000000,20140530T000000,20140602T000000,20140603T000000,20140604T000000,20140606T000000,20140616T000000,20140627T000000,20140715T000000,20140717T000000,20140721T000000,20140722T000000,20140723T000000,20140730T000000,20140731T000000,20140804T000000,20140805T000000,20140806T000000,20140807T000000,20140808T000000,20140814T000000,20140818T000000,20140822T000000,20140825T000000,20140827T000000,20140903T000000,20140909T000000,20140910T000000,20140924T000000,20141006T000000,20141015T000000,20141017T000000,20141024T000000,20141027T000000,20141103T000000,20141107T000000,20141110T000000,20141112T000000,20141125T000000,20141202T000000,20141211T000000,20141215T000000,20141217T000000,20141222T000000,20141223T000000,20141224T000000,20141226T000000,20150114T000000,20150115T000000,20150126T000000,20150128T000000,20150202T000000,20150211T000000,20150213T000000,20150219T000000,20150222T000000,20150224T000000,20150303T000000,20150312T000000,20150317T000000,20150318T000000,20150327T000000,20150328T000000,20150330T000000,20150331T000000,20150406T000000,20150423T000000,20150424T000000,20150427T000000,20150429T000000,20150430T000000 151 0.3235347 0.13555710 *
## 29) date=20140509T000000,20140516T000000,20140522T000000,20140524T000000,20140529T000000,20140605T000000,20140609T000000,20140610T000000,20140612T000000,20140613T000000,20140617T000000,20140618T000000,20140619T000000,20140620T000000,20140623T000000,20140624T000000,20140625T000000,20140626T000000,20140630T000000,20140701T000000,20140703T000000,20140708T000000,20140709T000000,20140710T000000,20140711T000000,20140714T000000,20140718T000000,20140724T000000,20140725T000000,20140728T000000,20140811T000000,20140820T000000,20140902T000000,20140905T000000,20140911T000000,20140912T000000,20140915T000000,20140918T000000,20140927T000000,20140929T000000,20141001T000000,20141003T000000,20141007T000000,20141009T000000,20141010T000000,20141014T000000,20141016T000000,20141021T000000,20141022T000000,20141106T000000,20141114T000000,20141120T000000,20141121T000000,20141126T000000,20141201T000000,20141203T000000,20141209T000000,20141212T000000,20141213T000000,20141216T000000,20150102T000000,20150112T000000,20150113T000000,20150123T000000,20150127T000000,20150203T000000,20150206T000000,20150209T000000,20150210T000000,20150305T000000,20150324T000000,20150325T000000,20150326T000000,20150401T000000,20150407T000000,20150409T000000,20150410T000000,20150415T000000,20150422T000000,20150428T000000,20150504T000000,20150505T000000 195 1.4229120 0.20632310 *
## 15) date=20140507T000000,20140513T000000,20140519T000000,20140608T000000,20140611T000000,20140702T000000,20140729T000000,20140801T000000,20140812T000000,20140815T000000,20140826T000000,20140828T000000,20140829T000000,20140904T000000,20140917T000000,20140919T000000,20140923T000000,20140926T000000,20141011T000000,20141013T000000,20141020T000000,20141029T000000,20141030T000000,20141118T000000,20141124T000000,20141205T000000,20141208T000000,20141219T000000,20141230T000000,20150108T000000,20150109T000000,20150130T000000,20150212T000000,20150218T000000,20150225T000000,20150304T000000,20150402T000000,20150408T000000,20150413T000000,20150417T000000,20150420T000000,20150421T000000,20150501T000000,20150506T000000,20150507T000000,20150508T000000,20150511T000000 91 2.3297000 0.33078500
## 30) sqft_living< 0.5249057 80 0.8172966 0.29550680 *
## 31) sqft_living>=0.5249057 11 0.6887365 0.58735380 *node, split, n, deviance, yval: Menunjukkan struktur pohon keputusan dengan split berdasarkan variabel, jumlah sampel pada node (16209), standard deviance (ukuran ketidakseragaman house di node), dan nilai prediksi (yval) untuk node tersebut. Misalnya, node yang membagi house berdasarkan grade < 0.609 memiliki 13905 sampel dengan deviasi 37,09 dan ketepatan prediksi 0.0609.
weights: Jumlah bobot/neuron pada model, yaitu 211. serta 19-10-1 network: Menunjukkan anatomi jaringan dengan 19 input variable, 10 neuron di hidden layer, dan 1 outputvariable.
Model Evaluation
actual <- test_house$price predictions_lm <- predict(linmod, test_house) predictions_tree <- predict(model_tree, test_house) predictions_nnet <- predict(model_nnet, test_house, type = “raw”)
calc_rmse <- function(predictions, actual) { sqrt(mean((predictions - actual)^2)) }
calc_r_squared <- function(predictions, actual) { 1 - sum((predictions - actual)^2) / sum((actual - mean(actual))^2) }
Calculate RMSE
rmse_lm <- calc_rmse(predictions_lm, actual) rmse_tree <- calc_rmse(predictions_tree, actual) rmse_nnet <- calc_rmse(predictions_nnet, actual)
Calculate R-squared
r_squared_lm <- calc_r_squared(predictions_lm, actual) r_squared_tree <- calc_r_squared(predictions_tree, actual) r_squared_nnet <- calc_r_squared(predictions_nnet, actual)
Print results
cat(“RMSE for Linear Model:”, rmse_lm, “”)
cat(“RMSE for Tree Model:”, rmse_tree, “”)
cat(“RMSE for Neural Network:”, rmse_nnet, “”)
cat(“R-squared for Linear Model:”, r_squared_lm, “”)
cat(“R-squared for Tree Model:”, r_squared_tree, “”)
cat(“R-squared for Neural Network:”, r_squared_nnet, “”)
R-Squared Nilai R-Squared :
Linear Model = 68,8%. Mampu menjelaskan hampir cukup signifikan variasi dalam house. Ini menandakan bahwa model linear cukup efektif dalam memprediksi house price yang digunakan.
Decison Tree Model = 66.87%. Decision Tree Juga mampu menjelaskan dengan baik variabel Price dengan menggunakan prediktor lain (KEcuali ID) . Dibandingkan dengan model linear, model pohon keputusan menunjukkan efektivitas yang sama dalam menjelaskan house price yang ada.
Neural Network: -1.037e-05. Model ini performanya sangat buruk. Nilai Negatif menjelskan bahwa Neural Network sangat gagal dalam melakukan Preidction.
Semua Model Disimpulkan memiliki Nilai Signifikansi yang sama, padahal seharusnya NEural Network tidak sepowerful itu dfalam data ini.
- Conclusion Berdasarkan R-squared beserta RMSE dari tiga model yang diuji,
- Linear Model = 68,8%. Mampu menjelaskan hampir cukup signifikan variasi dalam house. Ini menandakan bahwa model linear cukup efektif dalam memprediksi house price yang digunakan. dengan RMSE atau error paling rendsah. yaitu 2,7%
Decison Tree Model = 66.87%. Decision Tree Juga mampu menjelaskan dengan baik variabel Price dengan menggunakan prediktor lain (KEcuali ID) . Dibandingkan dengan model linear, model pohon keputusan menunjukkan efektivitas yang sama dalam menjelaskan house price yang ada. Dengan Error Rate 2,82%
Neural Network: -1.037e-05. Model ini performanya sangat buruk. Nilai Negatif menjelskan bahwa Neural Network sangat gagal dalam melakukan Preidction.