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Department : Business Statistics
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Jl. CBD Barat Kav, RT.1, Curug Sangereng, Kelapa Dua, Tangerang, Banten 15810.
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Optimasi penting dalam banyak bidang, termasuk dalam ilmu data. Di bidang manufaktur, di mana setiap keputusan sangat penting untuk proses dan keuntungan organisasi, optimasi sering digunakan, mulai dari jumlah setiap produk yang dihasilkan, bagaimana unit dijadwalkan untuk produksi, mendapatkan parameter proses terbaik atau optimal, serta perutean. penentuan seperti masalah salesman keliling. Dalam ilmu data, kami akrab dengan penyetelan model, di mana kami menyetel model kami untuk meningkatkan kinerja model. Algoritma optimasi dapat membantu kita untuk mendapatkan performa model yang lebih baik. Algoritma Genetika (GA) merupakan salah satu algoritma optimasi yang banyak digunakan.
Artikel ini mencoba menjelaskan mekanisme di balik salah satu algoritma yang paling efektif untuk masalah optimasi: Algoritma Genetika (GA). Ada banyak algoritma yang khusus dibuat untuk optimasi, seperti Particle Swarm Optimization (PSO), Simulated Annealing (SA), dan Tabu Search. Namun, di banyak bidang, GA dapat mencapai tujuan lebih baik daripada metode tersebut. Ada berbagai turunan dan variasi implementasi GA, mungkin yang paling terkenal adalah implementasi GA dalam masalah optimasi multiobjective, di mana kita ingin mengoptimalkan dua tujuan secara bersamaan, yang disebut Multi Objective Evolutionary Algorithm (MOEA). Kita akan melihat bagaimana GA umum bekerja dan di mana itu dapat diterapkan, baik pada masalah bisnis maupun di bidang ilmu data.
Tujuan pembelajaran: * Mempelajari pentingnya optimasi dalam bisnis * Mempelajari tentang fungsi biaya atau fungsi tujuan dan varianel keputusan. * Mempelajari hubungan antara pembelajaran mesin dan pengoptimalan. * Mempelajari prinsip dan konsep Algoritma Genetika. * Mempelajari implementasi GA dalam kasus bisnis dan bidang ilmu data.
Untuk menggunakan GA, kita dapat install package GA menggunakan fungsi install.packages()
Berikut ini adalah paket-paket yang akan digunakan di seluruh artikel.
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Katakanlah, kita memiliki 2 baris produk, dengan produk A memiliki keuntungan sebesar USD 50 sedangkan produk B memiliki keuntungan sebesar USD 30. Kita ingin memaksimalkan keuntungan kita, tetapi kita memiliki sumber daya yang terbatas. Kami hanya memiliki 80 jam kerja selama seminggu, dengan produk A memakan waktu sekitar 10 jam dalam pembuatan sedangkan produk B adalah 4 jam dalam pembuatan. Kedua produk berbagi bahan yang sama dan kita hanya memiliki 100 meter kain dengan masing-masing produk A membutuhkan 2 meter kain sedangkan produk B membutuhkan 4 meter kain. Bagaimana kita memutuskan berapa jumlah produk A dan B terbaik yang akan diproduksi? Begitulah masalah optimasi (dalam hal ini, mengoptimalkan keuntungan).
Bagaimana jika kita memutuskan untuk memilih secara acak jumlah setiap produk yang akan diproduksi. Kita mungkin tidak punya cukup waktu untuk menyelesaikannya. Kita mungkin tidak memiliki cukup bahan, atau sebaliknya bisa terjadi: kita mungkin memiliki bahan cadangan.
Kasus lain, katakanlah kita ingin mengirimkan produk kita ke beberapa gudang. Bagaimana kita memutuskan gudang mana yang harus kita kunjungi terlebih dahulu atau bagaimana kita memutuskan rute apa yang harus kita ambil untuk meminimalkan waktu pengiriman?
Optimalisasi penting terutama jika kita memiliki sumber daya yang terbatas. Lebih penting lagi, optimasi memperhatikan keuntungan dan kerugian: memilih rute pengiriman yang salah akan mengakibatkan keterlambatan pengiriman, yang dapat menimbulkan biaya penalti atau merusak produk kita di sepanjang jalan. Itulah mengapa pengoptimalan sangat penting untuk bisnis.
Setiap masalah optimasi selalu memiliki 2 elemen: fungsi tujuan dan variabel keputusan. Fungsi objektif berarti bagaimana kita merumuskan tujuan kita, seperti bagaimana kita mengukur keuntungan untuk memaksimalkannya, atau bagaimana kita mengukur waktu pengiriman untuk meminimalkannya. Di dalam fungsi tujuan terdapat variabel keputusan, apa yang harus menjadi keputusan kita yang dapat menghasilkan keuntungan maksimal? Kami ingin memaksimalkan tujuan kita dengan memilih variabel keputusan yang tepat. Pada contoh sebelumnya, tujuan untuk memaksimalkan laba termasuk ke dalam fungsi tujuan, sedangkan jumlah produk A dan produk B yang akan diproduksi termasuk dalam variabel keputusan.
Beberapa tujuan mungkin memiliki kendala, seperti jumlah jam kerja seminggu hanya 80 jam atau jumlah bahan yang tersedia hanya 100 meter. Kendala itu penting, karena solusi apa pun yang tidak memenuhi kendala adalah solusi yang tidak layak.
Machine learning dan optimasi tidak dapat dipisahkan, keduanya saling berkaitan, meskipun tujuannya berbeda. Pembelajaran mesin berkaitan dengan prediksi atau klasifikasi berdasarkan beberapa prediktor, sedangkan optimasi berkaitan dengan menemukan nilai objektif terbaik berdasarkan pilihan variabel keputusan. Namun, mekanisme di balik sebagian besar model pembelajaran mesin adalah pengoptimalan. Sebagai contoh, metode gradient descent dari model pelatihan Neural Network adalah metode optimasi, yang tujuannya adalah untuk menemukan titik terendah dan konvergen. Contoh lain adalah pemasangan regresi linier, yang meminimalkan jumlah kesalahan kuadrat (dengan demikian, nama metode Kuadrat Terkecil).
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“Algoritma genetika (GA) adalah algoritma pencarian stokastik yang terinspirasi oleh prinsip-prinsip dasar evolusi biologis dan seleksi alam. GA mensimulasikan evolusi organisme hidup, di mana individu yang paling kuat mendominasi yang lebih lemah, dengan meniru mekanisme evolusi biologis, seperti seleksi, persilangan, dan mutasi.” - Luca Scrucca
Algoritma Genetika adalah algoritma optimasi yang menggunakan konsep evolusi melalui seleksi alam. Evolusi melalui seleksi alam, sebagaimana dikemukakan oleh Darwin, adalah mekanisme berapa banyak jenis makhluk hidup yang beradaptasi dengan lingkungannya untuk bertahan hidup, melalui 2 prinsip utama: seleksi alam dan mutasi acak. Algoritma genetika (GA) meminjam konsep dan menggunakannya untuk mendapatkan solusi optimal dengan memilih solusi terbaik atau paling cocok di samping mutasi yang jarang dan acak. Aliran umum dari algoritma ditunjukkan di bawah ini:
\(Genetic Algorithm\)
Ada 3 elemen utama GA: Populasi, Kromosom, dan Gen.
Populasi adalah sekelompok individu atau solusi, terutama disebut kromosom. Sebuah kromosom terdiri dari urutan gen, dengan masing-masing atau beberapa gen (tergantung pada pengkodeannya) mewakili variabel keputusan tunggal yang akan dipasang pada fungsi tujuan.
\(Population, Chromosome, and Gene\)
Gen dapat direpresentasikan atau dikodekan dengan berbagai cara, termasuk:
Pengkodean biner berarti bahwa gen kita dikodekan ke dalam bilangan biner, ini adalah bentuk pengkodean GA yang paling awal dan paling umum.
*Real-Valued Encoding
Pengkodean bernilai nyata berarti bahwa gen kita dikodekan dalam floating point atau angka desimal. Encoding bernilai nyata dapat digunakan untuk mengoptimalkan parameter proses, karena terdiri dari bilangan floating point dan tidak cocok untuk optimasi berbasis integer.
Pengkodean permutasi berarti bahwa gen kita dikodekan ke dalam urutan nomor, setiap nomor secara unik ditugaskan untuk gen sehingga tidak ada 2 gen yang akan memiliki nomor atau nilai yang sama. Pengkodean ini sering digunakan dalam masalah penjadwalan atau perutean, di mana setiap permutasi mewakili satu lokasi atau titik unik.
Seleksi parent dilakukan untuk memilih dua solusi yang akan digunakan untuk crossover untuk membuat solusi baru yang disebut solusi anak. Ada beberapa metode untuk memilih orang tua:
Metode ini memasangkan kromosom individu teratas dalam populasi dengan individu setelahnya sampai sejumlah individu terpenuhi untuk digunakan dalam proses persilangan. Dengan kata lain, metode ini mengawinkan solusi pada baris ganjil dengan solusi pada baris genap. Metodenya sangat sederhana meskipun tidak memodelkan seleksi alam secara akurat.
Orang tua dipilih secara acak dengan kesempatan yang sama untuk dipilih.
Orang tua akan dipilih secara acak berdasarkan nilai fitness. Nilai fitness yang lebih tinggi berarti kesempatan yang lebih tinggi untuk dipilih.
Seleksi turnamen dilakukan dengan menyeleksi sekumpulan kromosom dari populasi dan membuat kriteria seleksi tertentu. Pemilihan turnamen efektif untuk GA yang memiliki populasi besar karena tidak perlu mengurutkan individu di dalam populasi.
Crossover adalah proses perkawinan antara dua individu yang dipilih, proses tersebut mewakili bagaimana gen ditransfer ke keturunannya. Ada beberapa metode crossover:
Crossover satu titik hanya menggunakan satu titik acak sebagai penanda dimana seharusnya crossover terjadi.
Crossover dua titik menggunakan dua titik sebagai penanda dimana setiap kromosom harus berpisah dan bergabung dengan kromosom lainnya.
Crossover tiga titik menggunakan tiga titik sebagai penanda dimana setiap kromosom harus berpisah dan bergabung dengan kromosom lainnya.
Mutasi berarti perubahan pada satu atau beberapa gen di dalam kromosom. Seperti halnya di alam, mutasi jarang terjadi, sehingga kemungkinannya kecil untuk terjadi, biasanya ditetapkan pada 0,01. Ada beberapa contoh mutasi:
Dua gen yang berdekatan ditukar secara acak.
Dua gen secara acak bertukar posisi.
Pergeseran mutasi dilakukan dengan memposisikan ulang gen secara acak dan menggeser gen berikut setelah posisinya.
Mutasi terbalik dilakukan dengan cara membalik barisan antara dua titik acak.
Algoritma Genetika dapat diterapkan dalam berbagai permasalahan bisnis. Karena algoritme adalah tentang pengoptimalan, selama ada fungsi tujuan/kebugaran untuk dioptimalkan, GA dapat diterapkan. Beberapa contoh termasuk penjadwalan produksi, masalah knapsack (berapa banyak/berapa banyak barang yang dapat kami muat di tas atau truk kami untuk memaksimalkan ruang atau beban berat), masalah travelling salesman, dll. GA juga dapat diterapkan di bidang ilmu data, khususnya ketika kami melakukan penyetelan hyper-parameter untuk mengoptimalkan kinerja model.
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Di sini kami akan mengilustrasikan bagaimana GA dapat bekerja menggunakan paket GA baik pada masalah bisnis maupun pada penyetelan hyper-parameter machine learning.
Misalkan kita memiliki berbagai unit mobil yang akan diproduksi di pabrik kita. Ada 7 jenis warna produk yang berbeda. Jika unit berikut memiliki warna yang berbeda dari mobil sebelumnya, maka akan ada biaya changeover (biaya yang timbul karena perubahan jenis produk), seperti biaya material yang dibutuhkan untuk membersihkan peralatan dari warna cat sebelumnya. Ada banyak cara untuk mengoptimalkan urutan warna, tetapi kami akan menggunakan yang paling sederhana di sini. Kami ingin meminimalkan jumlah setup, membuat jumlah pergantian warna atau pergantian terjadi lebih sedikit, sehingga biaya pergantian juga dapat diminimalkan.
Pertama kita impor datanya. Saya sudah menyiapkan data dummy yang bisa Anda akses di sini.
## X color
## 1 1 White
## 2 2 White
## 3 3 White
## 4 4 Gray
## 5 5 Silver
## 6 6 Black
## 7 7 Silver
## 8 8 White
## 9 9 Orange
## 10 10 Silver
## 11 11 Gray
## 12 12 Black
## 13 13 Silver
## 14 14 Solid White
## 15 15 Black
## 16 16 White
## 17 17 Silver
## 18 18 Gray
## 19 19 Orange
## 20 20 White
## 21 21 Black
## 22 22 Solid White
## 23 23 White
## 24 24 Gray
## 25 25 Silver
## 26 26 Silver
## 27 27 White
## 28 28 Silver
The objective function will be:
\[ S = 1 + \Sigma_{k=2}^{DT} \ S_k\]
Kami akan menerjemahkan fungsi tujuan menjadi fungsi R dari min_setup. Algoritma dalam paket GA dijalankan dalam konteks maksimisasi, jadi untuk menyelesaikan masalah minimasi, kita akan membuat nilai fitness kita menjadi negatif.
min_setup <- function(x){
df <- df_car[x, ]
print(df)
S <- df %>%
mutate(color_next = lead(color),
setup = if_else(color == color_next, 0, 1)) %>%
head(nrow(df)-1) %>%
pull("setup") %>%
sum() + 1
S <- -S
return(S)
}Sekarang kita jalankan algoritmanya. Karena masalahnya adalah masalah penjadwalan, kami akan mengkodekan solusi kami dengan pengkodean permutasi. Kami akan menghentikan algoritma jika jumlah iterasi telah mencapai 1000 iterasi atau jika dalam 100 iterasi tidak ada peningkatan nilai fitness yang optimal.
PENTING
Pemilihan ukuran populasi dan jumlah iterasi akan sangat mempengaruhi kinerja GA. Jika ukuran populasi terlalu kecil, akan ada kumpulan gen yang terbatas dalam populasi kita sehingga solusi baru tidak akan terlalu berbeda jauh dari generasi sebelumnya. Jika jumlah iterasi terlalu sedikit, tidak akan ada cukup waktu bagi algoritma untuk menemukan solusi optimal baru. Paket GA dapat dijalankan dengan komputasi paralel.
Parameter algoritma:
type: Jenis pengkodean gen, di sini kita menggunakan “permutasi”kebugaran: Fungsi kebugaran yang akan dioptimalkanlebih rendah: Nilai terendah dari pengkodean permutasi, kami akan mengkodekan permutasi dari 1 ke jumlah mobil yang tersediaatas: Nilai tertinggi dari pengkodean permutasiseed: Nilai seed acak untuk mendapatkan hasil yang dapat direproduksielitism : Jumlah solusi terbaik yang akan bertahan di setiap iterasi, default adalah 5% teratas dari populasi, di sini kami menetapkannya menjadi 50 solusi yang akan bertahanmaxiter: Jumlah iterasi maksimal untuk menjalankan algoritmapopSize: Jumlah solusi dalam suatu populasirun: Jumlah iterasi sebelum algoritma berhenti jika tidak ada perbaikan pada nilai fitness optimalparalel: Apakah kita akan menggunakan komputasi paralel? Dapat berupa nilai logika atau jumlah inti yang digunakanParameter lainnya, seperti probabilitas crossover dan mutasi diserahkan ke pengaturan default.
tic()
gann <- ga(type = "permutation", fitness = min_setup, lower = 1, upper = nrow(df_car),
seed = 123, elitism = 50, maxiter = 300, popSize = 300, run = 30, parallel = parallel::detectCores())
summary(gann)## -- Genetic Algorithm -------------------
##
## GA settings:
## Type = permutation
## Population size = 300
## Number of generations = 300
## Elitism = 50
## Crossover probability = 0.8
## Mutation probability = 0.1
##
## GA results:
## Iterations = 91
## Fitness function value = -6
## Solutions =
## x1 x2 x3 x4 x5 x6 x7 x8 x9 x10 ... x27 x28
## [1,] 24 4 11 18 16 20 1 8 3 23 14 22
## [2,] 4 24 11 18 3 16 20 1 8 23 14 22
## [3,] 4 11 24 18 16 20 1 8 3 23 14 22
## [4,] 4 11 24 18 20 1 8 3 23 16 14 22
## [5,] 3 16 20 1 2 23 8 27 9 19 11 18
## [6,] 4 24 11 18 3 16 20 1 8 23 22 14
## [7,] 3 16 20 1 2 23 8 27 9 19 11 18
## [8,] 14 22 4 11 24 18 3 16 20 1 15 21
## [9,] 4 11 24 18 3 16 20 1 8 23 14 22
## [10,] 3 16 20 1 2 23 8 27 9 19 11 18
## ...
## [18,] 11 24 4 18 3 16 20 1 8 23 14 22
## [19,] 4 11 24 18 16 20 1 8 27 3 14 22
## 123.77 sec elapsed
Kita dapat memplot perkembangan algoritma dengan menggunakan fungsi plot() pada objek GA.
Solusi terbaik atau optimal diperoleh setidaknya pada iterasi ke-67 dan tidak mengalami perbaikan sejak saat itu, sehingga algoritma berhenti setelah 30 iterasi pada iterasi ke-96.
Output Solution adalah solusi yang menghasilkan nilai fitness yang optimal. Kita dapat memilih salah satunya karena akan menghasilkan nilai fitness yang sama. Mari kita periksa salah satunya.
## X color
## 24 24 Gray
## 4 4 Gray
## 11 11 Gray
## 18 18 Gray
## 16 16 White
## 20 20 White
## 1 1 White
## 8 8 White
## 3 3 White
## 23 23 White
## 27 27 White
## 2 2 White
## 7 7 Silver
## 28 28 Silver
## 5 5 Silver
## 17 17 Silver
## 26 26 Silver
## 25 25 Silver
## 13 13 Silver
## 10 10 Silver
## 12 12 Black
## 6 6 Black
## 21 21 Black
## 15 15 Black
## 9 9 Orange
## 19 19 Orange
## 14 14 Solid White
## 22 22 Solid White
## [1] -6
Jika ingin melihat semua nilai fitness dari populasi terakhir, gunakan bisa menggunakan atribut fitness dari objek GA. Kami akan meringkas nilai fitness untuk mendapatkan distribusinya.
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -22.00 -16.00 -13.00 -12.43 -8.00 -6.00
Solusi terburuk dalam populasi memiliki nilai fitness 25, sedangkan solusi optimal memiliki nilai fitness 7. Dari seluruh 300 kromosom atau solusi yang dihasilkan, median nilai fitness adalah 15 sedangkan meannya adalah 14,29.
Mari kita menempatkan urutan optimal untuk data kita. Data frame yang dihasilkan akan menjadi jadwal produksi kita. Karena ada banyak solusi, mari kita lihat dua solusi optimal pertama.
Solusi pertama:
## X color
## 24 24 Gray
## 4 4 Gray
## 11 11 Gray
## 18 18 Gray
## 16 16 White
## 20 20 White
## 1 1 White
## 8 8 White
## 3 3 White
## 23 23 White
## 27 27 White
## 2 2 White
## 7 7 Silver
## 28 28 Silver
## 5 5 Silver
## 17 17 Silver
## 26 26 Silver
## 25 25 Silver
## 13 13 Silver
## 10 10 Silver
## 12 12 Black
## 6 6 Black
## 21 21 Black
## 15 15 Black
## 9 9 Orange
## 19 19 Orange
## 14 14 Solid White
## 22 22 Solid White
Solusi Kedua:
## X color
## 4 4 Gray
## 24 24 Gray
## 11 11 Gray
## 18 18 Gray
## 3 3 White
## 16 16 White
## 20 20 White
## 1 1 White
## 8 8 White
## 23 23 White
## 27 27 White
## 2 2 White
## 7 7 Silver
## 28 28 Silver
## 5 5 Silver
## 17 17 Silver
## 26 26 Silver
## 25 25 Silver
## 13 13 Silver
## 10 10 Silver
## 12 12 Black
## 6 6 Black
## 21 21 Black
## 15 15 Black
## 9 9 Orange
## 19 19 Orange
## 14 14 Solid White
## 22 22 Solid White
Meskipun solusi pertama dan solusi kedua memiliki urutan yang berbeda, fungsi fitness atau jumlah setup adalah sama, oleh karena itu keduanya merupakan solusi optimal dan keputusan untuk memilih mana yang harus digunakan sebenarnya diserahkan kepada pengambil keputusan. , dalam hal ini, staf kontrol produksi.
Misalkan kita memiliki beberapa item untuk dikirim ke pusat distribusi. Namun, truk kami hanya dapat memuat hingga total 10 ton atau 10.000 kg, jadi kami harus memilih barang mana yang akan dikirim dan memaksimalkan berat yang dimuat. Anda dapat langsung menyelesaikan masalah ini menggunakan Metode Knapsack, tetapi Anda juga dapat menggunakan Algoritma Genetika. Di sini kita akan melihat bagaimana GA menangani masalah yang dibatasi (berat tidak boleh melebihi 10 ton atau 10.000 kg).
df_item <- data.frame(item = c("Tires", "Bumper", "Engine", "Chasis", "Seat"), freq = c(80,
50, 70, 50, 70), weight = c(7, 17, 158, 100, 30))
df_item## item freq weight
## 1 Tires 80 7
## 2 Bumper 50 17
## 3 Engine 70 158
## 4 Chasis 50 100
## 5 Seat 70 30
Setiap item memiliki jumlah dan berat masing-masing.s
Mari kita buat dataset baru dengan satu observasi yang sesuai dengan hanya 1 item.
## item weight
## 1 Tires 7
## 1.1 Tires 7
## 1.2 Tires 7
## 1.3 Tires 7
## 1.4 Tires 7
## 1.5 Tires 7
## 1.6 Tires 7
## 1.7 Tires 7
## 1.8 Tires 7
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## 1.11 Tires 7
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## 1.73 Tires 7
## 1.74 Tires 7
## 1.75 Tires 7
## 1.76 Tires 7
## 1.77 Tires 7
## 1.78 Tires 7
## 1.79 Tires 7
## 2 Bumper 17
## 2.1 Bumper 17
## 2.2 Bumper 17
## 2.3 Bumper 17
## 2.4 Bumper 17
## 2.5 Bumper 17
## 2.6 Bumper 17
## 2.7 Bumper 17
## 2.8 Bumper 17
## 2.9 Bumper 17
## 2.10 Bumper 17
## 2.11 Bumper 17
## 2.12 Bumper 17
## 2.13 Bumper 17
## 2.14 Bumper 17
## 2.15 Bumper 17
## 2.16 Bumper 17
## 2.17 Bumper 17
## 2.18 Bumper 17
## 2.19 Bumper 17
## 2.20 Bumper 17
## 2.21 Bumper 17
## 2.22 Bumper 17
## 2.23 Bumper 17
## 2.24 Bumper 17
## 2.25 Bumper 17
## 2.26 Bumper 17
## 2.27 Bumper 17
## 2.28 Bumper 17
## 2.29 Bumper 17
## 2.30 Bumper 17
## 2.31 Bumper 17
## 2.32 Bumper 17
## 2.33 Bumper 17
## 2.34 Bumper 17
## 2.35 Bumper 17
## 2.36 Bumper 17
## 2.37 Bumper 17
## 2.38 Bumper 17
## 2.39 Bumper 17
## 2.40 Bumper 17
## 2.41 Bumper 17
## 2.42 Bumper 17
## 2.43 Bumper 17
## 2.44 Bumper 17
## 2.45 Bumper 17
## 2.46 Bumper 17
## 2.47 Bumper 17
## 2.48 Bumper 17
## 2.49 Bumper 17
## 3 Engine 158
## 3.1 Engine 158
## 3.2 Engine 158
## 3.3 Engine 158
## 3.4 Engine 158
## 3.5 Engine 158
## 3.6 Engine 158
## 3.7 Engine 158
## 3.8 Engine 158
## 3.9 Engine 158
## 3.10 Engine 158
## 3.11 Engine 158
## 3.12 Engine 158
## 3.13 Engine 158
## 3.14 Engine 158
## 3.15 Engine 158
## 3.16 Engine 158
## 3.17 Engine 158
## 3.18 Engine 158
## 3.19 Engine 158
## 3.20 Engine 158
## 3.21 Engine 158
## 3.22 Engine 158
## 3.23 Engine 158
## 3.24 Engine 158
## 3.25 Engine 158
## 3.26 Engine 158
## 3.27 Engine 158
## 3.28 Engine 158
## 3.29 Engine 158
## 3.30 Engine 158
## 3.31 Engine 158
## 3.32 Engine 158
## 3.33 Engine 158
## 3.34 Engine 158
## 3.35 Engine 158
## 3.36 Engine 158
## 3.37 Engine 158
## 3.38 Engine 158
## 3.39 Engine 158
## 3.40 Engine 158
## 3.41 Engine 158
## 3.42 Engine 158
## 3.43 Engine 158
## 3.44 Engine 158
## 3.45 Engine 158
## 3.46 Engine 158
## 3.47 Engine 158
## 3.48 Engine 158
## 3.49 Engine 158
## 3.50 Engine 158
## 3.51 Engine 158
## 3.52 Engine 158
## 3.53 Engine 158
## 3.54 Engine 158
## 3.55 Engine 158
## 3.56 Engine 158
## 3.57 Engine 158
## 3.58 Engine 158
## 3.59 Engine 158
## 3.60 Engine 158
## 3.61 Engine 158
## 3.62 Engine 158
## 3.63 Engine 158
## 3.64 Engine 158
## 3.65 Engine 158
## 3.66 Engine 158
## 3.67 Engine 158
## 3.68 Engine 158
## 3.69 Engine 158
## 4 Chasis 100
## 4.1 Chasis 100
## 4.2 Chasis 100
## 4.3 Chasis 100
## 4.4 Chasis 100
## 4.5 Chasis 100
## 4.6 Chasis 100
## 4.7 Chasis 100
## 4.8 Chasis 100
## 4.9 Chasis 100
## 4.10 Chasis 100
## 4.11 Chasis 100
## 4.12 Chasis 100
## 4.13 Chasis 100
## 4.14 Chasis 100
## 4.15 Chasis 100
## 4.16 Chasis 100
## 4.17 Chasis 100
## 4.18 Chasis 100
## 4.19 Chasis 100
## 4.20 Chasis 100
## 4.21 Chasis 100
## 4.22 Chasis 100
## 4.23 Chasis 100
## 4.24 Chasis 100
## 4.25 Chasis 100
## 4.26 Chasis 100
## 4.27 Chasis 100
## 4.28 Chasis 100
## 4.29 Chasis 100
## 4.30 Chasis 100
## 4.31 Chasis 100
## 4.32 Chasis 100
## 4.33 Chasis 100
## 4.34 Chasis 100
## 4.35 Chasis 100
## 4.36 Chasis 100
## 4.37 Chasis 100
## 4.38 Chasis 100
## 4.39 Chasis 100
## 4.40 Chasis 100
## 4.41 Chasis 100
## 4.42 Chasis 100
## 4.43 Chasis 100
## 4.44 Chasis 100
## 4.45 Chasis 100
## 4.46 Chasis 100
## 4.47 Chasis 100
## 4.48 Chasis 100
## 4.49 Chasis 100
## 5 Seat 30
## 5.1 Seat 30
## 5.2 Seat 30
## 5.3 Seat 30
## 5.4 Seat 30
## 5.5 Seat 30
## 5.6 Seat 30
## 5.7 Seat 30
## 5.8 Seat 30
## 5.9 Seat 30
## 5.10 Seat 30
## 5.11 Seat 30
## 5.12 Seat 30
## 5.13 Seat 30
## 5.14 Seat 30
## 5.15 Seat 30
## 5.16 Seat 30
## 5.17 Seat 30
## 5.18 Seat 30
## 5.19 Seat 30
## 5.20 Seat 30
## 5.21 Seat 30
## 5.22 Seat 30
## 5.23 Seat 30
## 5.24 Seat 30
## 5.25 Seat 30
## 5.26 Seat 30
## 5.27 Seat 30
## 5.28 Seat 30
## 5.29 Seat 30
## 5.30 Seat 30
## 5.31 Seat 30
## 5.32 Seat 30
## 5.33 Seat 30
## 5.34 Seat 30
## 5.35 Seat 30
## 5.36 Seat 30
## 5.37 Seat 30
## 5.38 Seat 30
## 5.39 Seat 30
## 5.40 Seat 30
## 5.41 Seat 30
## 5.42 Seat 30
## 5.43 Seat 30
## 5.44 Seat 30
## 5.45 Seat 30
## 5.46 Seat 30
## 5.47 Seat 30
## 5.48 Seat 30
## 5.49 Seat 30
## 5.50 Seat 30
## 5.51 Seat 30
## 5.52 Seat 30
## 5.53 Seat 30
## 5.54 Seat 30
## 5.55 Seat 30
## 5.56 Seat 30
## 5.57 Seat 30
## 5.58 Seat 30
## 5.59 Seat 30
## 5.60 Seat 30
## 5.61 Seat 30
## 5.62 Seat 30
## 5.63 Seat 30
## 5.64 Seat 30
## 5.65 Seat 30
## 5.66 Seat 30
## 5.67 Seat 30
## 5.68 Seat 30
## 5.69 Seat 30
The objective function will be: \[ Total Weight= \Sigma_{i=1}^{k} \ W_k*n_k\]
Batasan:
\[ Total Weight <= 10000 \]
weightlimit <- 10000
evalFunc <- function(x) {
df <- df_item_long[x == 1, ]
total_weight <- sum(df$weight)
total_weight <- if_else(total_weight > weightlimit, 0, total_weight)
return(total_weight)
}Mari kita jalankan algoritmanya. Kami akan menggunakan pengkodean biner karena variabel keputusan bernilai integer. Setiap bit mewakili nilai logis tunggal apakah item tersebut dipilih.
tic()
gann2 <- ga(type = "binary", fitness = evalFunc, popSize = 100, maxiter = 100, run = 20,
nBits = nrow(df_item_long), seed = 123)
summary(gann2)## -- Genetic Algorithm -------------------
##
## GA settings:
## Type = binary
## Population size = 100
## Number of generations = 100
## Elitism = 5
## Crossover probability = 0.8
## Mutation probability = 0.1
##
## GA results:
## Iterations = 22
## Fitness function value = 10000
## Solutions =
## x1 x2 x3 x4 x5 x6 x7 x8 x9 x10 ... x319 x320
## [1,] 1 1 0 1 1 1 1 0 1 1 0 1
## [2,] 1 1 0 1 1 1 1 0 1 1 1 1
## [3,] 0 1 1 1 1 0 0 1 1 0 0 0
## [4,] 1 1 0 1 1 1 1 0 1 1 1 1
## [5,] 1 1 0 1 1 1 1 0 1 1 1 0
## 1.51 sec elapsed
Mari pilih salah satu solusi sebagai pilihan optimal.
df_sol <- df_item_long[gann2@solution[1, ] == 1, ]
df_sol <- df_sol %>% group_by(item, weight) %>% summarise(freq = n()) %>% mutate(total_weigth = freq *
weight)## `summarise()` has grouped output by 'item'. You can override using the `.groups` argument.
## # A tibble: 5 x 4
## # Groups: item [5]
## item weight freq total_weigth
## <chr> <dbl> <int> <dbl>
## 1 Bumper 17 21 357
## 2 Chasis 100 32 3200
## 3 Engine 158 33 5214
## 4 Seat 30 30 900
## 5 Tires 7 47 329
Total berat:
## [1] 10000
Nilai fitness yang optimal sama dengan constrainnya, sehingga kita dapat memuat kendaraan secara penuh sesuai dengan kapasitas maksimumnya.
Penerapan GA di berbagai bidang masih banyak, terutama dalam proses manufaktur dan engineering. Selama ada fungsi fitness dan variabel keputusan, GA dapat diterapkan terhadap masalah tersebut.
Sekarang kami akan mencoba menerapkan algoritme untuk mengoptimalkan model pembelajaran mesin kami. Kami akan menggunakan dataset attrition.
attrition <- read.csv("data_input/attrition.csv")
attrition <- attrition %>% mutate(job_involvement = as.factor(job_involvement), education = as.factor(education),
environment_satisfaction = as.factor(environment_satisfaction), performance_rating = as.factor(performance_rating),
relationship_satisfaction = as.factor(relationship_satisfaction), work_life_balance = as.factor(work_life_balance))
attrition## attrition age business_travel daily_rate department
## 1 yes 41 travel_rarely 1102 sales
## 2 no 49 travel_frequently 279 research_development
## 3 yes 37 travel_rarely 1373 research_development
## 4 no 33 travel_frequently 1392 research_development
## 5 no 27 travel_rarely 591 research_development
## 6 no 32 travel_frequently 1005 research_development
## 7 no 59 travel_rarely 1324 research_development
## 8 no 30 travel_rarely 1358 research_development
## 9 no 38 travel_frequently 216 research_development
## 10 no 36 travel_rarely 1299 research_development
## 11 no 35 travel_rarely 809 research_development
## 12 no 29 travel_rarely 153 research_development
## 13 no 31 travel_rarely 670 research_development
## 14 no 34 travel_rarely 1346 research_development
## 15 yes 28 travel_rarely 103 research_development
## 16 no 29 travel_rarely 1389 research_development
## 17 no 32 travel_rarely 334 research_development
## 18 no 22 non_travel 1123 research_development
## 19 no 53 travel_rarely 1219 sales
## 20 no 38 travel_rarely 371 research_development
## 21 no 24 non_travel 673 research_development
## 22 yes 36 travel_rarely 1218 sales
## 23 no 34 travel_rarely 419 research_development
## 24 no 21 travel_rarely 391 research_development
## 25 yes 34 travel_rarely 699 research_development
## 26 no 53 travel_rarely 1282 research_development
## 27 yes 32 travel_frequently 1125 research_development
## 28 no 42 travel_rarely 691 sales
## 29 no 44 travel_rarely 477 research_development
## 30 no 46 travel_rarely 705 sales
## 31 no 33 travel_rarely 924 research_development
## 32 no 44 travel_rarely 1459 research_development
## 33 no 30 travel_rarely 125 research_development
## 34 yes 39 travel_rarely 895 sales
## 35 yes 24 travel_rarely 813 research_development
## 36 no 43 travel_rarely 1273 research_development
## 37 yes 50 travel_rarely 869 sales
## 38 no 35 travel_rarely 890 sales
## 39 no 36 travel_rarely 852 research_development
## 40 no 33 travel_frequently 1141 sales
## 41 no 35 travel_rarely 464 research_development
## 42 no 27 travel_rarely 1240 research_development
## 43 yes 26 travel_rarely 1357 research_development
## 44 no 27 travel_frequently 994 sales
## 45 no 30 travel_frequently 721 research_development
## 46 yes 41 travel_rarely 1360 research_development
## 47 no 34 non_travel 1065 sales
## 48 no 37 travel_rarely 408 research_development
## 49 no 46 travel_frequently 1211 sales
## 50 no 35 travel_rarely 1229 research_development
## 51 yes 48 travel_rarely 626 research_development
## 52 yes 28 travel_rarely 1434 research_development
## 53 no 44 travel_rarely 1488 sales
## 54 no 35 non_travel 1097 research_development
## 55 no 26 travel_rarely 1443 sales
## 56 no 33 travel_frequently 515 research_development
## 57 no 35 travel_frequently 853 sales
## 58 no 35 travel_rarely 1142 research_development
## 59 no 31 travel_rarely 655 research_development
## 60 no 37 travel_rarely 1115 research_development
## 61 no 32 travel_rarely 427 research_development
## 62 no 38 travel_frequently 653 research_development
## 63 no 50 travel_rarely 989 research_development
## 64 no 59 travel_rarely 1435 sales
## 65 no 36 travel_rarely 1223 research_development
## 66 no 55 travel_rarely 836 research_development
## 67 no 36 travel_frequently 1195 research_development
## 68 no 45 travel_rarely 1339 research_development
## 69 no 35 travel_frequently 664 research_development
## 70 yes 36 travel_rarely 318 research_development
## 71 no 59 travel_frequently 1225 sales
## 72 no 29 travel_rarely 1328 research_development
## 73 no 31 travel_rarely 1082 research_development
## 74 no 32 travel_rarely 548 research_development
## 75 no 36 travel_rarely 132 research_development
## 76 no 31 travel_rarely 746 research_development
## 77 no 35 travel_rarely 776 sales
## 78 no 45 travel_rarely 193 research_development
## 79 no 37 travel_rarely 397 research_development
## 80 no 46 travel_rarely 945 human_resources
## 81 no 30 travel_rarely 852 research_development
## 82 no 35 travel_rarely 1214 research_development
## 83 no 55 travel_rarely 111 sales
## 84 no 38 non_travel 573 research_development
## 85 no 34 travel_rarely 1153 research_development
## 86 no 56 travel_rarely 1400 research_development
## 87 no 23 travel_rarely 541 sales
## 88 no 51 travel_rarely 432 research_development
## 89 no 30 travel_rarely 288 research_development
## 90 yes 46 travel_rarely 669 sales
## 91 no 40 travel_frequently 530 research_development
## 92 no 51 travel_rarely 632 sales
## 93 no 30 travel_rarely 1334 sales
## 94 no 46 travel_frequently 638 research_development
## 95 no 32 travel_rarely 1093 sales
## 96 no 54 travel_rarely 1217 research_development
## 97 no 24 travel_rarely 1353 sales
## 98 no 28 non_travel 120 sales
## 99 no 58 travel_rarely 682 sales
## 100 no 44 non_travel 489 research_development
## 101 yes 37 travel_rarely 807 human_resources
## 102 no 32 travel_rarely 827 research_development
## 103 yes 20 travel_frequently 871 research_development
## 104 no 34 travel_rarely 665 research_development
## 105 no 37 non_travel 1040 research_development
## 106 no 59 non_travel 1420 human_resources
## 107 no 50 travel_frequently 1115 research_development
## 108 yes 25 travel_rarely 240 sales
## 109 no 25 travel_rarely 1280 research_development
## 110 no 22 travel_rarely 534 research_development
## 111 no 51 travel_frequently 1456 research_development
## 112 yes 34 travel_frequently 658 research_development
## 113 no 54 non_travel 142 human_resources
## 114 no 24 travel_rarely 1127 research_development
## 115 no 34 travel_rarely 1031 research_development
## 116 no 37 travel_rarely 1189 sales
## 117 no 34 travel_rarely 1354 research_development
## 118 no 36 travel_frequently 1467 sales
## 119 no 36 travel_rarely 922 research_development
## 120 no 43 travel_frequently 394 sales
## 121 no 30 travel_frequently 1312 research_development
## 122 no 33 non_travel 750 sales
## 123 yes 56 travel_rarely 441 research_development
## 124 no 51 travel_rarely 684 research_development
## 125 yes 31 travel_rarely 249 sales
## 126 no 26 travel_rarely 841 research_development
## 127 yes 58 travel_rarely 147 research_development
## 128 yes 19 travel_rarely 528 sales
## 129 no 22 travel_rarely 594 research_development
## 130 no 49 travel_rarely 470 research_development
## 131 no 43 travel_frequently 957 research_development
## 132 no 50 travel_frequently 809 sales
## 133 yes 31 travel_rarely 542 sales
## 134 no 41 travel_rarely 802 sales
## 135 no 26 travel_rarely 1355 human_resources
## 136 no 36 travel_rarely 216 research_development
## 137 yes 51 travel_frequently 1150 research_development
## 138 no 39 travel_rarely 1329 sales
## 139 no 25 travel_rarely 959 sales
## 140 no 30 travel_rarely 1240 human_resources
## 141 yes 32 travel_rarely 1033 research_development
## 142 no 45 travel_rarely 1316 research_development
## 143 no 38 travel_rarely 364 research_development
## 144 no 30 travel_rarely 438 research_development
## 145 no 32 travel_frequently 689 sales
## 146 no 30 travel_rarely 201 research_development
## 147 no 30 travel_rarely 1427 research_development
## 148 no 41 travel_frequently 857 research_development
## 149 no 41 travel_rarely 933 research_development
## 150 no 19 travel_rarely 1181 research_development
## 151 no 40 travel_frequently 1395 research_development
## 152 no 35 travel_rarely 662 sales
## 153 no 53 travel_rarely 1436 sales
## 154 no 45 travel_rarely 194 research_development
## 155 no 32 travel_frequently 967 sales
## 156 no 29 non_travel 1496 research_development
## 157 no 51 travel_rarely 1169 research_development
## 158 no 58 travel_rarely 1145 research_development
## 159 no 40 travel_rarely 630 sales
## 160 no 34 travel_frequently 303 sales
## 161 no 22 travel_rarely 1256 research_development
## 162 no 27 non_travel 691 research_development
## 163 no 28 travel_rarely 440 research_development
## 164 no 57 travel_rarely 334 research_development
## 165 no 27 non_travel 1450 research_development
## 166 no 50 travel_rarely 1452 research_development
## 167 no 41 travel_rarely 465 research_development
## 168 no 30 travel_rarely 1339 sales
## 169 no 38 travel_rarely 702 sales
## 170 no 32 travel_rarely 120 research_development
## 171 no 27 travel_rarely 1157 research_development
## 172 yes 19 travel_frequently 602 sales
## 173 no 36 travel_frequently 1480 research_development
## 174 no 30 non_travel 111 research_development
## 175 no 45 travel_rarely 1268 sales
## 176 no 56 travel_rarely 713 research_development
## 177 no 33 travel_rarely 134 research_development
## 178 yes 19 travel_rarely 303 research_development
## 179 no 46 travel_rarely 526 sales
## 180 no 38 travel_rarely 1380 research_development
## 181 no 31 travel_rarely 140 research_development
## 182 no 34 travel_rarely 629 research_development
## 183 yes 41 travel_rarely 1356 sales
## 184 no 50 travel_rarely 328 research_development
## 185 no 53 travel_rarely 1084 research_development
## 186 no 33 travel_rarely 931 research_development
## 187 no 40 travel_rarely 989 research_development
## 188 no 55 travel_rarely 692 research_development
## 189 no 34 travel_frequently 1069 research_development
## 190 no 51 travel_rarely 313 research_development
## 191 no 52 travel_rarely 699 research_development
## 192 no 27 travel_rarely 894 research_development
## 193 yes 35 travel_rarely 556 research_development
## 194 no 43 non_travel 1344 research_development
## 195 no 45 non_travel 1195 research_development
## 196 no 37 travel_rarely 290 research_development
## 197 no 35 travel_frequently 138 research_development
## 198 no 42 non_travel 926 research_development
## 199 no 38 travel_rarely 1261 research_development
## 200 no 38 travel_rarely 1084 research_development
## 201 no 27 travel_frequently 472 research_development
## 202 no 49 non_travel 1002 research_development
## 203 no 34 travel_frequently 878 research_development
## 204 no 40 travel_rarely 905 research_development
## 205 yes 38 travel_rarely 1180 research_development
## 206 yes 29 travel_rarely 121 sales
## 207 no 22 travel_rarely 1136 research_development
## 208 no 36 travel_frequently 635 research_development
## 209 no 40 non_travel 1151 research_development
## 210 no 46 travel_rarely 644 research_development
## 211 yes 32 travel_rarely 1045 sales
## 212 no 30 non_travel 829 research_development
## 213 no 27 travel_frequently 1242 sales
## 214 no 51 travel_rarely 1469 research_development
## 215 yes 30 travel_rarely 1005 research_development
## 216 no 41 travel_rarely 896 sales
## 217 yes 30 travel_frequently 334 sales
## 218 yes 29 travel_rarely 992 research_development
## 219 no 45 non_travel 1052 sales
## 220 no 54 travel_rarely 1147 sales
## 221 no 36 travel_rarely 1396 research_development
## 222 no 33 travel_rarely 147 research_development
## 223 no 37 travel_frequently 663 research_development
## 224 no 38 travel_rarely 119 sales
## 225 no 31 non_travel 979 research_development
## 226 no 59 travel_rarely 142 research_development
## 227 no 37 travel_frequently 319 sales
## 228 no 29 travel_frequently 1413 sales
## 229 no 35 travel_frequently 944 sales
## 230 yes 29 travel_rarely 896 research_development
## 231 no 52 travel_rarely 1323 research_development
## 232 no 42 travel_rarely 532 research_development
## 233 no 59 travel_rarely 818 human_resources
## 234 no 50 travel_rarely 854 sales
## 235 yes 33 travel_rarely 813 research_development
## 236 no 43 travel_rarely 1034 sales
## 237 yes 33 travel_rarely 465 research_development
## 238 no 52 non_travel 771 sales
## 239 no 32 travel_rarely 1401 sales
## 240 yes 32 travel_rarely 515 research_development
## 241 no 39 travel_rarely 1431 research_development
## 242 no 32 non_travel 976 sales
## 243 no 41 travel_rarely 1411 research_development
## 244 no 40 travel_rarely 1300 research_development
## 245 no 45 travel_rarely 252 research_development
## 246 no 31 travel_frequently 1327 research_development
## 247 no 33 travel_rarely 832 research_development
## 248 no 34 travel_rarely 470 research_development
## 249 no 37 travel_rarely 1017 research_development
## 250 no 45 travel_frequently 1199 research_development
## 251 yes 37 travel_frequently 504 research_development
## 252 no 39 travel_frequently 505 research_development
## 253 no 29 travel_rarely 665 research_development
## 254 no 42 travel_rarely 916 research_development
## 255 no 29 travel_rarely 1247 sales
## 256 no 25 travel_rarely 685 research_development
## 257 no 42 travel_rarely 269 research_development
## 258 no 40 travel_rarely 1416 research_development
## 259 no 51 travel_rarely 833 research_development
## 260 yes 31 travel_frequently 307 research_development
## 261 no 32 travel_frequently 1311 research_development
## 262 no 38 non_travel 1327 sales
## 263 no 32 travel_rarely 128 research_development
## 264 no 46 travel_rarely 488 sales
## 265 yes 28 travel_rarely 529 research_development
## 266 no 29 travel_rarely 1210 sales
## 267 no 31 travel_rarely 1463 research_development
## 268 no 25 non_travel 675 research_development
## 269 no 45 travel_rarely 1385 research_development
## 270 no 36 travel_rarely 1403 research_development
## 271 no 55 travel_rarely 452 research_development
## 272 yes 47 non_travel 666 research_development
## 273 no 28 travel_rarely 1158 research_development
## 274 no 37 travel_rarely 228 sales
## 275 no 21 travel_rarely 996 research_development
## 276 no 37 non_travel 728 research_development
## 277 no 35 travel_rarely 1315 research_development
## 278 no 38 travel_rarely 322 sales
## 279 no 26 travel_frequently 1479 research_development
## 280 no 50 travel_rarely 797 research_development
## 281 no 53 travel_rarely 1070 research_development
## 282 no 42 travel_rarely 635 sales
## 283 no 29 travel_frequently 442 sales
## 284 no 55 travel_rarely 147 research_development
## 285 no 26 travel_frequently 496 research_development
## 286 no 37 travel_rarely 1372 research_development
## 287 yes 44 travel_frequently 920 research_development
## 288 no 38 travel_rarely 688 research_development
## 289 yes 26 travel_rarely 1449 research_development
## 290 no 28 travel_rarely 1117 research_development
## 291 no 49 travel_frequently 636 research_development
## 292 no 36 travel_rarely 506 research_development
## 293 no 31 travel_frequently 444 sales
## 294 yes 26 travel_rarely 950 sales
## 295 no 37 travel_frequently 889 research_development
## 296 no 42 travel_frequently 555 sales
## 297 yes 18 travel_rarely 230 research_development
## 298 no 35 travel_rarely 1232 sales
## 299 no 36 travel_frequently 566 research_development
## 300 no 51 travel_rarely 1302 research_development
## 301 no 41 travel_rarely 334 sales
## 302 no 18 travel_rarely 812 sales
## 303 no 28 travel_rarely 1476 research_development
## 304 no 31 travel_rarely 218 sales
## 305 no 39 travel_rarely 1132 research_development
## 306 no 36 non_travel 1105 research_development
## 307 no 32 travel_rarely 906 sales
## 308 no 38 travel_rarely 849 research_development
## 309 no 58 non_travel 390 research_development
## 310 no 31 travel_rarely 691 research_development
## 311 no 31 travel_rarely 106 human_resources
## 312 no 45 travel_frequently 1249 research_development
## 313 no 31 travel_rarely 192 research_development
## 314 no 33 travel_frequently 553 research_development
## 315 no 39 travel_rarely 117 research_development
## 316 no 43 travel_frequently 185 research_development
## 317 no 49 travel_rarely 1091 research_development
## 318 yes 52 travel_rarely 723 research_development
## 319 no 27 travel_rarely 1220 research_development
## 320 no 32 travel_rarely 588 sales
## 321 no 27 travel_rarely 1377 sales
## 322 no 31 travel_rarely 691 sales
## 323 no 32 travel_rarely 1018 research_development
## 324 yes 28 travel_rarely 1157 research_development
## 325 no 30 travel_rarely 1275 research_development
## 326 no 31 travel_frequently 798 research_development
## 327 no 39 travel_frequently 672 research_development
## 328 yes 39 travel_rarely 1162 sales
## 329 no 33 travel_frequently 508 sales
## 330 no 47 travel_rarely 1482 research_development
## 331 no 43 travel_frequently 559 research_development
## 332 no 27 non_travel 210 sales
## 333 no 54 travel_frequently 928 research_development
## 334 no 43 travel_rarely 1001 research_development
## 335 no 45 travel_rarely 549 research_development
## 336 no 40 travel_rarely 1124 sales
## 337 yes 29 travel_rarely 318 research_development
## 338 no 29 travel_rarely 738 research_development
## 339 no 30 travel_rarely 570 sales
## 340 no 27 travel_rarely 1130 sales
## 341 no 37 travel_rarely 1192 research_development
## 342 no 38 travel_rarely 343 research_development
## 343 no 31 travel_rarely 1232 research_development
## 344 no 29 travel_rarely 144 sales
## 345 no 35 travel_rarely 1296 research_development
## 346 no 23 travel_rarely 1309 research_development
## 347 no 41 travel_rarely 483 research_development
## 348 no 47 travel_frequently 1309 sales
## 349 no 42 travel_rarely 810 research_development
## 350 no 29 non_travel 746 sales
## 351 no 42 travel_rarely 544 human_resources
## 352 no 32 travel_rarely 1062 research_development
## 353 no 48 travel_rarely 530 sales
## 354 no 37 travel_rarely 1319 research_development
## 355 no 30 non_travel 641 sales
## 356 no 26 travel_rarely 933 sales
## 357 no 42 travel_rarely 1332 research_development
## 358 yes 21 travel_frequently 756 sales
## 359 no 36 non_travel 845 sales
## 360 no 36 travel_frequently 541 sales
## 361 no 57 travel_rarely 593 research_development
## 362 no 40 travel_rarely 1171 research_development
## 363 no 21 non_travel 895 sales
## 364 yes 33 travel_rarely 350 sales
## 365 no 37 travel_rarely 921 research_development
## 366 no 46 non_travel 1144 research_development
## 367 yes 41 travel_frequently 143 sales
## 368 no 50 travel_rarely 1046 research_development
## 369 yes 40 travel_rarely 575 sales
## 370 no 31 travel_rarely 408 research_development
## 371 yes 21 travel_rarely 156 sales
## 372 no 29 travel_rarely 1283 research_development
## 373 no 35 travel_rarely 755 research_development
## 374 no 27 travel_rarely 1469 research_development
## 375 no 28 travel_rarely 304 sales
## 376 no 49 travel_rarely 1261 research_development
## 377 no 51 travel_rarely 1178 sales
## 378 no 36 travel_rarely 329 research_development
## 379 yes 34 non_travel 1362 sales
## 380 no 55 travel_rarely 1311 research_development
## 381 no 24 travel_rarely 1371 sales
## 382 no 30 travel_rarely 202 sales
## 383 yes 26 travel_frequently 575 research_development
## 384 no 22 travel_rarely 253 research_development
## 385 no 36 travel_rarely 164 sales
## 386 yes 30 travel_frequently 464 research_development
## 387 no 37 travel_rarely 1107 research_development
## 388 no 40 travel_rarely 759 sales
## 389 no 42 travel_rarely 201 research_development
## 390 no 37 travel_rarely 1305 research_development
## 391 no 43 travel_rarely 982 research_development
## 392 no 40 travel_rarely 555 research_development
## 393 no 54 travel_rarely 821 research_development
## 394 no 34 non_travel 1381 sales
## 395 no 31 travel_rarely 480 research_development
## 396 no 43 travel_frequently 313 research_development
## 397 no 43 travel_rarely 1473 research_development
## 398 no 25 travel_rarely 891 sales
## 399 no 37 non_travel 1063 research_development
## 400 no 31 travel_rarely 329 research_development
## 401 no 39 travel_frequently 1218 research_development
## 402 no 56 travel_frequently 906 sales
## 403 no 30 travel_rarely 1082 sales
## 404 no 41 travel_rarely 645 sales
## 405 no 28 travel_rarely 1300 research_development
## 406 yes 25 travel_rarely 688 research_development
## 407 no 52 travel_rarely 319 research_development
## 408 no 45 travel_rarely 192 research_development
## 409 no 52 travel_rarely 1490 research_development
## 410 no 42 travel_frequently 532 research_development
## 411 no 30 travel_rarely 317 research_development
## 412 no 60 travel_rarely 422 research_development
## 413 no 46 travel_rarely 1485 research_development
## 414 no 42 travel_frequently 1368 research_development
## 415 yes 24 travel_rarely 1448 sales
## 416 yes 34 travel_frequently 296 sales
## 417 no 38 travel_frequently 1490 research_development
## 418 no 40 travel_rarely 1398 sales
## 419 no 26 travel_rarely 1349 research_development
## 420 no 30 non_travel 1400 research_development
## 421 no 29 travel_rarely 986 research_development
## 422 yes 29 travel_rarely 408 research_development
## 423 yes 19 travel_rarely 489 human_resources
## 424 no 30 non_travel 1398 sales
## 425 no 57 travel_rarely 210 sales
## 426 no 50 travel_rarely 1099 research_development
## 427 no 30 non_travel 1116 research_development
## 428 no 60 travel_frequently 1499 sales
## 429 no 47 travel_rarely 983 research_development
## 430 no 46 travel_rarely 1009 research_development
## 431 no 35 travel_rarely 144 research_development
## 432 no 54 travel_rarely 548 research_development
## 433 no 34 travel_rarely 1303 research_development
## 434 no 46 travel_rarely 1125 sales
## 435 no 31 travel_rarely 1274 research_development
## 436 yes 33 travel_rarely 1277 research_development
## 437 yes 33 travel_rarely 587 research_development
## 438 no 30 travel_rarely 413 sales
## 439 no 35 travel_rarely 1276 research_development
## 440 yes 31 travel_frequently 534 research_development
## 441 yes 34 travel_frequently 988 human_resources
## 442 no 42 travel_frequently 1474 research_development
## 443 no 36 non_travel 635 sales
## 444 yes 22 travel_frequently 1368 research_development
## 445 no 48 travel_rarely 163 sales
## 446 no 55 travel_rarely 1117 sales
## 447 no 41 non_travel 267 sales
## 448 no 35 travel_rarely 619 sales
## 449 no 40 travel_rarely 302 research_development
## 450 no 39 travel_frequently 443 research_development
## 451 no 31 travel_rarely 828 sales
## 452 no 42 travel_rarely 319 research_development
## 453 no 45 travel_rarely 561 sales
## 454 yes 26 travel_frequently 426 human_resources
## 455 no 29 travel_rarely 232 research_development
## 456 no 33 travel_rarely 922 research_development
## 457 no 31 travel_rarely 688 sales
## 458 yes 18 travel_frequently 1306 sales
## 459 no 40 non_travel 1094 sales
## 460 no 41 non_travel 509 research_development
## 461 no 26 travel_rarely 775 sales
## 462 no 35 travel_rarely 195 sales
## 463 no 34 travel_rarely 258 sales
## 464 yes 26 travel_rarely 471 research_development
## 465 no 37 travel_rarely 799 research_development
## 466 no 46 travel_frequently 1034 research_development
## 467 no 41 travel_rarely 1276 sales
## 468 no 37 non_travel 142 sales
## 469 no 52 travel_rarely 956 research_development
## 470 yes 32 non_travel 1474 sales
## 471 no 24 travel_frequently 535 sales
## 472 no 38 travel_rarely 1495 research_development
## 473 no 37 travel_rarely 446 research_development
## 474 no 49 travel_rarely 1245 research_development
## 475 no 24 travel_rarely 691 research_development
## 476 no 26 travel_rarely 703 sales
## 477 no 24 travel_rarely 823 research_development
## 478 no 50 travel_frequently 1246 human_resources
## 479 no 25 travel_rarely 622 sales
## 480 yes 24 travel_frequently 1287 research_development
## 481 yes 30 travel_frequently 448 sales
## 482 no 34 travel_rarely 254 research_development
## 483 yes 31 travel_rarely 1365 sales
## 484 no 35 travel_rarely 538 research_development
## 485 no 31 travel_rarely 525 sales
## 486 no 27 travel_rarely 798 research_development
## 487 no 37 travel_rarely 558 sales
## 488 no 20 travel_rarely 959 research_development
## 489 no 42 travel_rarely 622 research_development
## 490 no 43 travel_rarely 782 research_development
## 491 no 38 travel_rarely 362 research_development
## 492 no 43 travel_frequently 1001 research_development
## 493 no 48 travel_rarely 1236 research_development
## 494 no 44 travel_rarely 1112 human_resources
## 495 no 34 travel_rarely 204 sales
## 496 yes 27 travel_rarely 1420 sales
## 497 no 21 travel_rarely 1343 sales
## 498 no 44 travel_rarely 1315 research_development
## 499 no 22 travel_rarely 604 research_development
## 500 no 33 travel_rarely 1216 sales
## 501 no 32 travel_rarely 646 research_development
## 502 no 30 travel_frequently 160 research_development
## 503 no 53 travel_rarely 238 sales
## 504 no 34 travel_rarely 1397 research_development
## 505 yes 45 travel_frequently 306 sales
## 506 no 26 travel_rarely 991 research_development
## 507 no 37 travel_rarely 482 research_development
## 508 no 29 travel_rarely 1176 sales
## 509 no 35 travel_rarely 1017 research_development
## 510 no 33 travel_frequently 1296 research_development
## 511 no 54 travel_rarely 397 human_resources
## 512 no 36 travel_rarely 913 research_development
## 513 no 27 travel_rarely 1115 research_development
## 514 yes 20 travel_rarely 1362 research_development
## 515 yes 33 travel_frequently 1076 research_development
## 516 no 35 non_travel 727 research_development
## 517 no 23 travel_rarely 885 research_development
## 518 no 25 travel_rarely 810 sales
## 519 no 38 travel_rarely 243 sales
## 520 no 29 travel_frequently 806 research_development
## 521 no 48 travel_rarely 817 sales
## 522 no 27 travel_frequently 1410 sales
## 523 no 37 travel_rarely 1225 research_development
## 524 no 50 travel_rarely 1207 research_development
## 525 no 34 travel_rarely 1442 research_development
## 526 yes 24 travel_rarely 693 sales
## 527 no 39 travel_rarely 408 research_development
## 528 no 32 travel_rarely 929 sales
## 529 yes 50 travel_frequently 562 sales
## 530 no 38 travel_rarely 827 research_development
## 531 no 27 travel_rarely 608 research_development
## 532 no 32 travel_rarely 1018 research_development
## 533 no 47 travel_rarely 703 sales
## 534 no 40 travel_frequently 580 sales
## 535 no 53 travel_rarely 970 research_development
## 536 no 41 travel_rarely 427 human_resources
## 537 no 60 travel_rarely 1179 sales
## 538 no 27 travel_frequently 294 research_development
## 539 no 41 travel_rarely 314 human_resources
## 540 no 50 travel_rarely 316 sales
## 541 yes 28 travel_rarely 654 research_development
## 542 no 36 non_travel 427 research_development
## 543 no 38 travel_rarely 168 research_development
## 544 no 44 non_travel 381 research_development
## 545 no 47 travel_frequently 217 sales
## 546 no 30 travel_rarely 501 sales
## 547 no 29 travel_rarely 1396 sales
## 548 yes 42 travel_frequently 933 research_development
## 549 no 43 travel_frequently 775 sales
## 550 no 34 travel_rarely 970 research_development
## 551 no 23 travel_rarely 650 research_development
## 552 no 39 travel_rarely 141 human_resources
## 553 no 56 travel_rarely 832 research_development
## 554 no 40 travel_rarely 804 research_development
## 555 no 27 travel_rarely 975 research_development
## 556 no 29 travel_rarely 1090 sales
## 557 no 53 travel_rarely 346 research_development
## 558 no 35 non_travel 1225 research_development
## 559 no 32 travel_frequently 430 research_development
## 560 no 38 travel_rarely 268 research_development
## 561 no 34 travel_rarely 167 research_development
## 562 no 52 travel_rarely 621 sales
## 563 yes 33 travel_rarely 527 research_development
## 564 no 25 travel_rarely 883 sales
## 565 no 45 travel_rarely 954 sales
## 566 no 23 travel_rarely 310 research_development
## 567 yes 47 travel_frequently 719 sales
## 568 no 34 travel_rarely 304 sales
## 569 yes 55 travel_rarely 725 research_development
## 570 no 36 non_travel 1434 sales
## 571 no 52 non_travel 715 research_development
## 572 no 26 travel_frequently 575 research_development
## 573 no 29 travel_rarely 657 research_development
## 574 yes 26 travel_rarely 1146 sales
## 575 no 34 travel_rarely 182 research_development
## 576 no 54 travel_rarely 376 research_development
## 577 no 27 travel_frequently 829 sales
## 578 no 37 travel_rarely 571 research_development
## 579 no 38 travel_frequently 240 research_development
## 580 no 34 travel_rarely 121 research_development
## 581 no 35 travel_rarely 384 sales
## 582 no 30 travel_rarely 921 research_development
## 583 no 40 travel_frequently 791 research_development
## 584 no 34 travel_rarely 1111 sales
## 585 no 42 travel_frequently 570 research_development
## 586 yes 23 travel_rarely 1243 research_development
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## 588 no 52 travel_rarely 1325 research_development
## 589 no 50 travel_rarely 691 research_development
## 590 yes 29 travel_rarely 805 research_development
## 591 no 33 travel_rarely 213 research_development
## 592 yes 33 travel_rarely 118 sales
## 593 no 47 travel_rarely 202 research_development
## 594 no 36 travel_rarely 676 research_development
## 595 no 29 travel_rarely 1252 research_development
## 596 yes 58 travel_rarely 286 research_development
## 597 no 35 travel_rarely 1258 research_development
## 598 no 42 travel_rarely 932 research_development
## 599 yes 28 travel_rarely 890 research_development
## 600 no 36 travel_rarely 1041 human_resources
## 601 no 32 travel_rarely 859 research_development
## 602 no 40 travel_frequently 720 research_development
## 603 no 30 travel_rarely 946 research_development
## 604 no 45 travel_rarely 252 research_development
## 605 no 42 travel_rarely 933 research_development
## 606 no 38 travel_frequently 471 research_development
## 607 no 34 travel_frequently 702 research_development
## 608 yes 49 travel_rarely 1184 sales
## 609 yes 55 travel_rarely 436 sales
## 610 no 43 travel_rarely 589 research_development
## 611 no 27 travel_rarely 269 research_development
## 612 no 35 travel_rarely 950 research_development
## 613 no 28 travel_rarely 760 sales
## 614 no 34 travel_rarely 829 human_resources
## 615 yes 26 travel_frequently 887 research_development
## 616 no 27 non_travel 443 research_development
## 617 no 51 travel_rarely 1318 sales
## 618 no 44 travel_rarely 625 research_development
## 619 no 25 travel_rarely 180 research_development
## 620 no 33 travel_rarely 586 sales
## 621 no 35 travel_rarely 1343 research_development
## 622 no 36 travel_rarely 928 sales
## 623 no 32 travel_rarely 117 sales
## 624 no 30 travel_frequently 1012 research_development
## 625 no 53 travel_rarely 661 sales
## 626 no 45 travel_rarely 930 sales
## 627 no 32 travel_rarely 638 research_development
## 628 no 52 travel_frequently 890 research_development
## 629 no 37 travel_rarely 342 sales
## 630 no 28 travel_rarely 1169 human_resources
## 631 no 22 travel_rarely 1230 research_development
## 632 no 44 travel_rarely 986 research_development
## 633 no 42 travel_frequently 1271 research_development
## 634 no 36 travel_rarely 1278 human_resources
## 635 no 25 travel_rarely 141 sales
## 636 no 35 travel_rarely 607 research_development
## 637 yes 35 travel_frequently 130 research_development
## 638 no 32 non_travel 300 research_development
## 639 no 25 travel_rarely 583 sales
## 640 no 49 travel_rarely 1418 research_development
## 641 no 24 non_travel 1269 research_development
## 642 no 32 travel_frequently 379 sales
## 643 no 38 travel_rarely 395 sales
## 644 no 42 travel_rarely 1265 research_development
## 645 no 31 travel_rarely 1222 research_development
## 646 yes 29 travel_rarely 341 sales
## 647 no 53 travel_rarely 868 sales
## 648 no 35 travel_rarely 672 research_development
## 649 no 37 travel_frequently 1231 sales
## 650 no 53 travel_rarely 102 research_development
## 651 no 43 travel_frequently 422 research_development
## 652 no 47 travel_rarely 249 sales
## 653 no 37 non_travel 1252 sales
## 654 no 50 non_travel 881 research_development
## 655 no 39 travel_rarely 1383 human_resources
## 656 no 33 travel_rarely 1075 human_resources
## 657 yes 32 travel_rarely 374 research_development
## 658 no 29 travel_rarely 1086 research_development
## 659 no 44 travel_rarely 661 research_development
## 660 no 28 travel_rarely 821 sales
## 661 yes 58 travel_frequently 781 research_development
## 662 no 43 travel_rarely 177 research_development
## 663 yes 20 travel_rarely 500 sales
## 664 yes 21 travel_rarely 1427 research_development
## 665 no 36 travel_rarely 1425 research_development
## 666 no 47 travel_rarely 1454 sales
## 667 yes 22 travel_rarely 617 research_development
## 668 yes 41 travel_rarely 1085 research_development
## 669 no 28 travel_rarely 995 research_development
## 670 yes 39 travel_rarely 1122 research_development
## 671 no 27 travel_rarely 618 research_development
## 672 no 34 travel_rarely 546 research_development
## 673 no 42 travel_rarely 462 sales
## 674 no 33 travel_rarely 1198 research_development
## 675 no 58 travel_rarely 1272 research_development
## 676 no 31 travel_rarely 154 sales
## 677 no 35 travel_rarely 1137 research_development
## 678 no 49 travel_rarely 527 research_development
## 679 no 48 travel_rarely 1469 research_development
## 680 no 31 non_travel 1188 sales
## 681 no 36 travel_rarely 188 research_development
## 682 no 38 travel_rarely 1333 research_development
## 683 no 32 non_travel 1184 research_development
## 684 yes 25 travel_rarely 867 sales
## 685 no 40 travel_rarely 658 sales
## 686 no 26 travel_frequently 1283 sales
## 687 no 41 travel_rarely 263 research_development
## 688 no 36 travel_rarely 938 research_development
## 689 yes 19 travel_rarely 419 sales
## 690 yes 20 travel_rarely 129 research_development
## 691 no 31 travel_rarely 616 research_development
## 692 no 40 travel_frequently 1469 research_development
## 693 no 32 travel_rarely 498 research_development
## 694 yes 36 travel_rarely 530 sales
## 695 no 33 travel_rarely 1069 research_development
## 696 yes 37 travel_rarely 625 sales
## 697 no 45 non_travel 805 research_development
## 698 no 29 travel_frequently 1404 sales
## 699 no 35 travel_rarely 1219 sales
## 700 no 52 travel_rarely 1053 research_development
## 701 yes 58 travel_rarely 289 research_development
## 702 no 53 travel_rarely 1376 sales
## 703 no 30 travel_rarely 231 sales
## 704 no 38 non_travel 152 sales
## 705 no 35 travel_rarely 882 sales
## 706 no 39 travel_rarely 903 sales
## 707 yes 40 non_travel 1479 sales
## 708 no 47 travel_frequently 1379 research_development
## 709 no 36 non_travel 1229 sales
## 710 yes 31 non_travel 335 research_development
## 711 no 33 non_travel 722 sales
## 712 yes 29 travel_rarely 906 research_development
## 713 no 33 travel_rarely 461 research_development
## 714 no 45 travel_rarely 974 research_development
## 715 no 50 travel_rarely 1126 research_development
## 716 no 33 travel_frequently 827 research_development
## 717 no 41 travel_frequently 840 research_development
## 718 no 27 travel_rarely 1134 research_development
## 719 no 45 non_travel 248 research_development
## 720 no 47 travel_rarely 955 sales
## 721 yes 30 travel_rarely 138 research_development
## 722 no 50 travel_rarely 939 research_development
## 723 no 38 travel_frequently 1391 research_development
## 724 no 46 travel_rarely 566 research_development
## 725 no 24 travel_rarely 1206 research_development
## 726 yes 35 travel_rarely 622 research_development
## 727 no 31 travel_frequently 853 research_development
## 728 no 18 non_travel 287 research_development
## 729 no 54 travel_rarely 1441 research_development
## 730 no 35 travel_rarely 583 research_development
## 731 no 30 travel_rarely 153 research_development
## 732 yes 20 travel_rarely 1097 research_development
## 733 yes 30 travel_frequently 109 research_development
## 734 no 26 travel_rarely 1066 research_development
## 735 no 22 travel_rarely 217 research_development
## 736 no 48 travel_rarely 277 research_development
## 737 no 48 travel_rarely 1355 research_development
## 738 no 41 travel_rarely 549 research_development
## 739 no 39 travel_rarely 466 research_development
## 740 no 27 travel_rarely 1055 research_development
## 741 no 35 travel_rarely 802 research_development
## 742 no 42 travel_rarely 265 sales
## 743 no 50 travel_rarely 804 research_development
## 744 no 59 travel_rarely 715 research_development
## 745 yes 37 travel_rarely 1141 research_development
## 746 no 55 travel_frequently 135 research_development
## 747 no 41 non_travel 247 research_development
## 748 no 38 travel_rarely 1035 sales
## 749 yes 26 non_travel 265 sales
## 750 yes 52 travel_rarely 266 sales
## 751 no 44 travel_rarely 1448 sales
## 752 no 50 non_travel 145 sales
## 753 yes 36 travel_rarely 885 research_development
## 754 no 39 travel_frequently 945 research_development
## 755 no 33 non_travel 1038 sales
## 756 no 45 travel_rarely 1234 sales
## 757 no 32 non_travel 1109 research_development
## 758 no 34 travel_rarely 216 sales
## 759 no 59 travel_rarely 1089 sales
## 760 no 45 travel_rarely 788 human_resources
## 761 no 53 travel_frequently 124 sales
## 762 yes 36 travel_rarely 660 research_development
## 763 yes 26 travel_frequently 342 research_development
## 764 no 34 travel_rarely 1333 sales
## 765 no 28 travel_rarely 1144 sales
## 766 no 38 travel_frequently 1186 research_development
## 767 no 50 travel_rarely 1464 research_development
## 768 no 37 travel_rarely 124 research_development
## 769 no 40 travel_rarely 300 sales
## 770 no 26 travel_frequently 921 research_development
## 771 no 46 travel_rarely 430 research_development
## 772 no 54 travel_rarely 1082 sales
## 773 no 56 travel_frequently 1240 research_development
## 774 no 36 travel_rarely 796 research_development
## 775 no 55 non_travel 444 research_development
## 776 no 43 travel_rarely 415 sales
## 777 yes 20 travel_frequently 769 sales
## 778 yes 21 travel_rarely 1334 research_development
## 779 no 46 travel_rarely 1003 research_development
## 780 yes 51 travel_rarely 1323 research_development
## 781 yes 28 non_travel 1366 research_development
## 782 no 26 travel_rarely 192 research_development
## 783 no 30 travel_rarely 1176 research_development
## 784 no 41 travel_rarely 509 research_development
## 785 no 38 travel_rarely 330 research_development
## 786 no 40 travel_rarely 1492 research_development
## 787 no 27 non_travel 1277 research_development
## 788 no 55 travel_frequently 1091 research_development
## 789 no 28 travel_rarely 857 research_development
## 790 yes 44 travel_rarely 1376 human_resources
## 791 no 33 travel_rarely 654 research_development
## 792 yes 35 travel_rarely 1204 sales
## 793 yes 33 travel_frequently 827 research_development
## 794 no 28 travel_rarely 895 research_development
## 795 no 34 travel_frequently 618 research_development
## 796 no 37 travel_rarely 309 sales
## 797 yes 25 travel_rarely 1219 research_development
## 798 yes 26 travel_rarely 1330 research_development
## 799 yes 33 travel_rarely 1017 research_development
## 800 no 42 travel_rarely 469 research_development
## 801 yes 28 travel_frequently 1009 research_development
## 802 yes 50 travel_frequently 959 sales
## 803 no 33 travel_frequently 970 sales
## 804 no 34 non_travel 697 research_development
## 805 no 48 non_travel 1262 research_development
## 806 no 45 non_travel 1050 sales
## 807 no 52 travel_rarely 994 research_development
## 808 no 38 travel_rarely 770 sales
## 809 no 29 travel_rarely 1107 research_development
## 810 no 28 travel_rarely 950 research_development
## 811 no 46 travel_rarely 406 sales
## 812 no 38 travel_rarely 130 sales
## 813 no 43 travel_frequently 1082 research_development
## 814 yes 39 travel_frequently 203 research_development
## 815 no 40 travel_rarely 1308 research_development
## 816 no 21 travel_rarely 984 research_development
## 817 no 39 non_travel 439 research_development
## 818 no 36 non_travel 217 research_development
## 819 no 31 travel_frequently 793 sales
## 820 no 28 travel_rarely 1451 research_development
## 821 no 35 travel_frequently 1182 sales
## 822 no 49 travel_rarely 174 sales
## 823 no 34 travel_frequently 1003 research_development
## 824 no 29 travel_frequently 490 research_development
## 825 no 42 travel_rarely 188 research_development
## 826 no 29 travel_rarely 718 research_development
## 827 no 38 travel_rarely 433 human_resources
## 828 no 28 travel_frequently 773 research_development
## 829 yes 18 non_travel 247 research_development
## 830 yes 33 travel_rarely 603 sales
## 831 no 41 travel_rarely 167 research_development
## 832 yes 31 travel_frequently 874 research_development
## 833 no 37 travel_rarely 367 research_development
## 834 no 27 travel_rarely 199 research_development
## 835 no 34 travel_rarely 1400 sales
## 836 no 35 travel_rarely 528 human_resources
## 837 yes 29 travel_rarely 408 sales
## 838 no 40 travel_frequently 593 research_development
## 839 yes 42 travel_frequently 481 sales
## 840 no 42 travel_rarely 647 sales
## 841 no 35 travel_rarely 982 research_development
## 842 no 24 travel_rarely 477 research_development
## 843 yes 28 travel_rarely 1485 research_development
## 844 no 26 travel_rarely 1384 research_development
## 845 no 30 travel_rarely 852 sales
## 846 no 40 travel_frequently 902 research_development
## 847 no 35 travel_rarely 819 research_development
## 848 no 34 travel_frequently 669 research_development
## 849 no 35 travel_frequently 636 research_development
## 850 yes 43 travel_rarely 1372 sales
## 851 no 32 non_travel 862 sales
## 852 no 56 travel_rarely 718 research_development
## 853 no 29 travel_rarely 1401 research_development
## 854 no 19 travel_rarely 645 research_development
## 855 no 45 travel_rarely 1457 research_development
## 856 no 37 travel_rarely 977 research_development
## 857 no 20 travel_rarely 805 research_development
## 858 yes 44 travel_rarely 1097 research_development
## 859 no 53 travel_rarely 1223 research_development
## 860 no 29 travel_rarely 942 research_development
## 861 yes 22 travel_frequently 1256 research_development
## 862 no 46 travel_rarely 1402 sales
## 863 no 44 non_travel 111 research_development
## 864 no 33 travel_rarely 147 human_resources
## 865 yes 41 non_travel 906 research_development
## 866 no 30 travel_rarely 1329 sales
## 867 no 40 travel_frequently 1184 sales
## 868 no 50 travel_frequently 1421 research_development
## 869 no 28 travel_rarely 1179 research_development
## 870 no 46 travel_rarely 1450 research_development
## 871 no 35 travel_rarely 1361 sales
## 872 yes 24 travel_rarely 984 research_development
## 873 no 33 travel_frequently 1146 sales
## 874 no 36 travel_rarely 917 research_development
## 875 no 30 travel_rarely 853 research_development
## 876 no 44 travel_rarely 200 research_development
## 877 no 20 travel_rarely 654 sales
## 878 no 46 travel_rarely 150 research_development
## 879 no 42 non_travel 179 human_resources
## 880 no 60 travel_rarely 696 sales
## 881 no 32 travel_frequently 116 research_development
## 882 no 32 travel_frequently 1316 research_development
## 883 no 36 travel_rarely 363 research_development
## 884 no 33 travel_rarely 117 research_development
## 885 no 40 travel_rarely 107 sales
## 886 no 25 travel_rarely 1356 sales
## 887 no 30 travel_rarely 1465 research_development
## 888 no 42 travel_frequently 458 research_development
## 889 no 35 non_travel 1212 sales
## 890 no 27 travel_rarely 1103 research_development
## 891 no 54 travel_frequently 966 research_development
## 892 no 44 travel_rarely 1117 research_development
## 893 yes 19 non_travel 504 research_development
## 894 no 29 travel_rarely 1010 research_development
## 895 no 54 travel_rarely 685 research_development
## 896 no 31 travel_rarely 1332 research_development
## 897 no 31 travel_rarely 1062 research_development
## 898 no 59 travel_rarely 326 sales
## 899 no 43 travel_rarely 920 research_development
## 900 no 49 travel_rarely 1098 research_development
## 901 no 36 travel_frequently 469 research_development
## 902 no 48 travel_rarely 969 research_development
## 903 no 27 travel_rarely 1167 research_development
## 904 no 29 travel_rarely 1329 research_development
## 905 no 48 travel_rarely 715 research_development
## 906 no 29 travel_rarely 694 research_development
## 907 no 34 travel_rarely 1320 research_development
## 908 no 44 travel_rarely 1099 sales
## 909 no 33 travel_rarely 536 sales
## 910 no 19 travel_rarely 265 research_development
## 911 no 23 travel_rarely 373 research_development
## 912 yes 25 travel_frequently 599 sales
## 913 no 26 travel_rarely 583 research_development
## 914 yes 45 travel_rarely 1449 sales
## 915 no 55 non_travel 177 research_development
## 916 yes 21 travel_frequently 251 research_development
## 917 no 46 travel_rarely 168 sales
## 918 no 34 travel_rarely 131 sales
## 919 no 51 travel_frequently 237 sales
## 920 no 59 travel_rarely 1429 research_development
## 921 no 34 travel_frequently 135 research_development
## 922 no 28 travel_frequently 791 research_development
## 923 no 44 travel_rarely 1199 research_development
## 924 no 34 travel_frequently 648 human_resources
## 925 no 35 travel_rarely 735 research_development
## 926 no 42 travel_rarely 603 research_development
## 927 no 43 travel_rarely 531 sales
## 928 no 36 travel_rarely 429 research_development
## 929 yes 44 travel_rarely 621 research_development
## 930 no 28 travel_frequently 193 research_development
## 931 no 51 travel_frequently 968 research_development
## 932 no 30 non_travel 879 research_development
## 933 yes 29 travel_rarely 806 research_development
## 934 no 28 travel_rarely 640 research_development
## 935 no 25 travel_rarely 266 research_development
## 936 no 32 travel_rarely 604 sales
## 937 no 45 travel_frequently 364 research_development
## 938 no 39 travel_rarely 412 research_development
## 939 no 58 travel_rarely 848 research_development
## 940 yes 32 travel_rarely 1089 research_development
## 941 yes 39 travel_rarely 360 research_development
## 942 no 30 travel_rarely 1138 research_development
## 943 no 36 travel_rarely 325 research_development
## 944 no 46 travel_rarely 991 human_resources
## 945 no 28 non_travel 1476 research_development
## 946 no 50 travel_rarely 1322 research_development
## 947 yes 40 travel_rarely 299 sales
## 948 yes 52 travel_rarely 1030 sales
## 949 no 30 travel_rarely 634 research_development
## 950 no 39 travel_rarely 524 research_development
## 951 no 31 non_travel 587 sales
## 952 no 41 non_travel 256 sales
## 953 yes 31 travel_frequently 1060 sales
## 954 yes 44 travel_rarely 935 research_development
## 955 no 42 non_travel 495 research_development
## 956 no 55 travel_rarely 282 research_development
## 957 no 56 travel_rarely 206 human_resources
## 958 no 40 non_travel 458 research_development
## 959 no 34 travel_rarely 943 research_development
## 960 no 40 travel_rarely 523 research_development
## 961 no 41 travel_frequently 1018 sales
## 962 no 35 travel_frequently 482 research_development
## 963 no 51 travel_rarely 770 human_resources
## 964 no 38 travel_rarely 1009 sales
## 965 no 34 travel_rarely 507 sales
## 966 no 25 travel_rarely 882 research_development
## 967 yes 58 travel_rarely 601 research_development
## 968 no 40 travel_rarely 329 research_development
## 969 no 36 travel_frequently 607 sales
## 970 no 48 travel_rarely 855 research_development
## 971 no 27 travel_rarely 1291 sales
## 972 no 51 travel_rarely 1405 research_development
## 973 no 18 non_travel 1124 research_development
## 974 no 35 travel_rarely 817 research_development
## 975 no 27 travel_frequently 793 sales
## 976 yes 55 travel_rarely 267 sales
## 977 no 56 travel_rarely 1369 research_development
## 978 no 34 non_travel 999 research_development
## 979 no 40 travel_rarely 1202 research_development
## 980 no 34 travel_rarely 285 research_development
## 981 yes 31 travel_frequently 703 sales
## 982 yes 35 travel_frequently 662 sales
## 983 no 38 travel_frequently 693 research_development
## 984 no 34 travel_rarely 404 research_development
## 985 no 28 travel_rarely 736 sales
## 986 yes 31 travel_rarely 330 research_development
## 987 no 39 travel_rarely 1498 sales
## 988 no 51 travel_frequently 541 sales
## 989 no 41 travel_frequently 1200 research_development
## 990 no 37 travel_rarely 1439 research_development
## 991 no 33 travel_frequently 1111 sales
## 992 no 32 travel_rarely 499 sales
## 993 no 39 non_travel 1485 research_development
## 994 no 25 travel_rarely 1372 sales
## 995 no 52 travel_frequently 322 research_development
## 996 no 43 travel_rarely 930 research_development
## 997 no 27 travel_rarely 205 sales
## 998 yes 27 travel_rarely 135 research_development
## 999 no 26 travel_rarely 683 research_development
## 1000 no 42 travel_rarely 1147 human_resources
## 1001 no 52 travel_rarely 258 research_development
## 1002 no 37 travel_rarely 1462 research_development
## 1003 no 35 travel_frequently 200 research_development
## 1004 no 25 travel_rarely 949 research_development
## 1005 no 26 travel_rarely 652 research_development
## 1006 no 29 travel_rarely 332 human_resources
## 1007 yes 49 travel_frequently 1475 research_development
## 1008 yes 29 travel_frequently 337 research_development
## 1009 no 54 travel_rarely 971 research_development
## 1010 no 58 travel_rarely 1055 research_development
## 1011 no 55 travel_rarely 1136 research_development
## 1012 no 36 travel_rarely 1174 sales
## 1013 yes 31 travel_frequently 667 sales
## 1014 no 30 travel_rarely 855 sales
## 1015 no 31 travel_rarely 182 research_development
## 1016 no 34 travel_frequently 560 research_development
## 1017 yes 31 travel_rarely 202 research_development
## 1018 no 27 travel_rarely 1377 research_development
## 1019 no 36 travel_rarely 172 research_development
## 1020 no 36 travel_rarely 329 sales
## 1021 no 47 travel_rarely 465 research_development
## 1022 yes 25 travel_rarely 383 sales
## 1023 no 37 non_travel 1413 research_development
## 1024 no 56 travel_rarely 1255 research_development
## 1025 no 47 travel_rarely 359 research_development
## 1026 no 24 travel_rarely 1476 sales
## 1027 no 32 travel_rarely 601 sales
## 1028 no 34 travel_rarely 401 research_development
## 1029 no 41 travel_rarely 1283 research_development
## 1030 no 40 non_travel 663 research_development
## 1031 no 31 travel_rarely 326 sales
## 1032 yes 46 travel_rarely 377 sales
## 1033 yes 39 non_travel 592 research_development
## 1034 yes 31 travel_frequently 1445 research_development
## 1035 no 45 travel_rarely 1038 research_development
## 1036 no 31 travel_rarely 1398 human_resources
## 1037 yes 31 travel_frequently 523 research_development
## 1038 no 45 travel_rarely 1448 research_development
## 1039 no 48 travel_rarely 1221 sales
## 1040 yes 34 travel_rarely 1107 human_resources
## 1041 no 40 non_travel 218 research_development
## 1042 no 28 travel_rarely 866 sales
## 1043 no 44 non_travel 981 research_development
## 1044 no 53 travel_rarely 447 research_development
## 1045 no 49 travel_rarely 1495 research_development
## 1046 no 40 travel_rarely 896 research_development
## 1047 no 44 travel_rarely 1467 research_development
## 1048 no 33 travel_frequently 430 sales
## 1049 no 34 travel_rarely 1326 sales
## 1050 no 30 travel_rarely 1358 sales
## 1051 no 42 travel_frequently 748 research_development
## 1052 no 44 travel_frequently 383 sales
## 1053 no 30 non_travel 990 research_development
## 1054 no 57 travel_rarely 405 research_development
## 1055 no 49 travel_rarely 1490 research_development
## 1056 no 34 travel_frequently 829 research_development
## 1057 yes 28 travel_frequently 1496 sales
## 1058 yes 29 travel_frequently 115 sales
## 1059 yes 34 travel_rarely 790 sales
## 1060 no 35 travel_rarely 660 sales
## 1061 yes 24 travel_frequently 381 research_development
## 1062 no 24 non_travel 830 sales
## 1063 no 44 travel_frequently 1193 research_development
## 1064 no 29 travel_rarely 1246 sales
## 1065 no 30 travel_rarely 330 human_resources
## 1066 no 55 travel_rarely 1229 research_development
## 1067 no 33 travel_rarely 1099 research_development
## 1068 no 47 travel_rarely 571 sales
## 1069 yes 28 travel_frequently 289 research_development
## 1070 no 28 travel_rarely 1423 research_development
## 1071 no 28 travel_frequently 467 sales
## 1072 no 49 travel_rarely 271 research_development
## 1073 no 29 travel_frequently 410 research_development
## 1074 no 28 travel_rarely 1083 research_development
## 1075 no 33 travel_rarely 516 research_development
## 1076 no 32 travel_rarely 495 research_development
## 1077 no 54 travel_frequently 1050 research_development
## 1078 yes 29 travel_rarely 224 research_development
## 1079 no 44 travel_rarely 136 research_development
## 1080 no 39 travel_rarely 1089 research_development
## 1081 no 46 travel_rarely 228 sales
## 1082 no 35 travel_rarely 1029 research_development
## 1083 no 23 travel_rarely 507 research_development
## 1084 yes 40 travel_rarely 676 research_development
## 1085 no 34 travel_rarely 971 sales
## 1086 yes 31 travel_frequently 561 research_development
## 1087 no 50 travel_frequently 333 research_development
## 1088 no 34 travel_rarely 1440 sales
## 1089 no 42 travel_rarely 1210 research_development
## 1090 no 37 travel_rarely 674 research_development
## 1091 no 29 travel_rarely 441 research_development
## 1092 no 33 travel_rarely 575 research_development
## 1093 no 45 travel_rarely 950 research_development
## 1094 no 42 travel_frequently 288 research_development
## 1095 no 40 travel_rarely 1342 sales
## 1096 no 33 travel_rarely 589 research_development
## 1097 no 40 travel_rarely 898 human_resources
## 1098 no 24 travel_rarely 350 research_development
## 1099 no 40 non_travel 1142 research_development
## 1100 no 45 travel_rarely 538 research_development
## 1101 no 35 travel_rarely 1402 sales
## 1102 no 32 travel_rarely 824 research_development
## 1103 no 36 travel_rarely 1157 sales
## 1104 no 48 travel_rarely 492 sales
## 1105 no 29 travel_rarely 598 research_development
## 1106 no 33 travel_rarely 1242 sales
## 1107 yes 30 travel_rarely 740 sales
## 1108 no 38 travel_frequently 888 human_resources
## 1109 no 35 travel_rarely 992 research_development
## 1110 no 30 travel_rarely 1288 sales
## 1111 yes 35 travel_rarely 104 research_development
## 1112 yes 53 travel_rarely 607 research_development
## 1113 yes 38 travel_rarely 903 research_development
## 1114 no 32 non_travel 1200 research_development
## 1115 no 48 travel_rarely 1108 research_development
## 1116 no 34 travel_rarely 479 research_development
## 1117 no 55 travel_rarely 685 sales
## 1118 no 34 travel_rarely 1351 research_development
## 1119 no 26 travel_rarely 474 research_development
## 1120 no 38 travel_rarely 1245 sales
## 1121 no 38 travel_rarely 437 sales
## 1122 no 36 travel_rarely 884 sales
## 1123 no 29 travel_rarely 1370 research_development
## 1124 no 35 travel_rarely 670 research_development
## 1125 no 39 travel_rarely 1462 sales
## 1126 no 29 travel_frequently 995 research_development
## 1127 no 50 travel_rarely 264 sales
## 1128 no 23 travel_rarely 977 research_development
## 1129 no 36 travel_frequently 1302 research_development
## 1130 no 42 travel_rarely 1059 research_development
## 1131 no 35 travel_rarely 750 research_development
## 1132 no 34 travel_frequently 653 research_development
## 1133 no 40 travel_rarely 118 sales
## 1134 no 43 travel_rarely 990 research_development
## 1135 no 35 travel_rarely 1349 research_development
## 1136 no 46 travel_rarely 563 sales
## 1137 yes 28 travel_rarely 329 research_development
## 1138 no 22 non_travel 457 research_development
## 1139 no 50 travel_frequently 1234 research_development
## 1140 no 32 travel_rarely 634 research_development
## 1141 no 44 travel_rarely 1313 research_development
## 1142 no 30 travel_rarely 241 research_development
## 1143 no 45 travel_rarely 1015 research_development
## 1144 no 45 non_travel 336 sales
## 1145 no 31 travel_frequently 715 sales
## 1146 no 36 travel_rarely 559 research_development
## 1147 no 34 travel_frequently 426 research_development
## 1148 no 49 travel_rarely 722 research_development
## 1149 no 39 travel_rarely 1387 research_development
## 1150 no 27 travel_rarely 1302 research_development
## 1151 no 35 travel_rarely 819 research_development
## 1152 no 28 travel_rarely 580 research_development
## 1153 no 21 travel_rarely 546 research_development
## 1154 yes 18 travel_frequently 544 sales
## 1155 no 47 travel_rarely 1176 human_resources
## 1156 no 39 travel_rarely 170 research_development
## 1157 no 40 travel_rarely 884 research_development
## 1158 no 35 non_travel 208 research_development
## 1159 no 37 travel_rarely 671 research_development
## 1160 no 39 travel_frequently 711 research_development
## 1161 no 45 travel_rarely 1329 research_development
## 1162 no 38 travel_rarely 397 research_development
## 1163 yes 35 travel_rarely 737 sales
## 1164 no 37 travel_rarely 1470 research_development
## 1165 no 40 travel_rarely 448 research_development
## 1166 no 44 travel_frequently 602 human_resources
## 1167 no 48 travel_frequently 365 research_development
## 1168 yes 35 travel_rarely 763 sales
## 1169 no 24 travel_frequently 567 research_development
## 1170 no 27 travel_rarely 486 research_development
## 1171 no 27 travel_frequently 591 research_development
## 1172 yes 40 travel_rarely 1329 research_development
## 1173 no 29 travel_rarely 469 sales
## 1174 no 36 travel_rarely 711 research_development
## 1175 no 25 travel_frequently 772 research_development
## 1176 no 39 travel_rarely 492 research_development
## 1177 no 49 travel_rarely 301 research_development
## 1178 no 50 travel_rarely 813 research_development
## 1179 no 20 travel_rarely 1141 sales
## 1180 no 34 travel_rarely 1130 research_development
## 1181 no 36 travel_rarely 311 research_development
## 1182 no 49 travel_rarely 465 research_development
## 1183 no 36 non_travel 894 research_development
## 1184 no 36 travel_rarely 1040 research_development
## 1185 no 54 travel_rarely 584 research_development
## 1186 no 43 travel_rarely 1291 research_development
## 1187 yes 35 travel_frequently 880 sales
## 1188 no 38 travel_frequently 1189 research_development
## 1189 no 29 travel_rarely 991 sales
## 1190 no 33 travel_rarely 392 sales
## 1191 no 32 travel_rarely 977 research_development
## 1192 no 31 travel_rarely 1112 sales
## 1193 no 49 travel_rarely 464 research_development
## 1194 no 38 travel_frequently 148 research_development
## 1195 no 47 travel_rarely 1225 sales
## 1196 no 49 travel_rarely 809 research_development
## 1197 no 41 travel_rarely 1206 sales
## 1198 no 20 travel_rarely 727 sales
## 1199 no 33 non_travel 530 sales
## 1200 no 36 travel_rarely 1351 research_development
## 1201 no 44 travel_rarely 528 human_resources
## 1202 yes 23 travel_rarely 1320 research_development
## 1203 no 38 travel_rarely 1495 research_development
## 1204 no 53 travel_rarely 1395 research_development
## 1205 yes 48 travel_frequently 708 sales
## 1206 yes 32 travel_rarely 1259 research_development
## 1207 no 26 non_travel 786 research_development
## 1208 no 55 travel_rarely 1441 research_development
## 1209 no 34 travel_rarely 1157 research_development
## 1210 no 60 travel_rarely 370 research_development
## 1211 no 33 travel_rarely 267 research_development
## 1212 no 37 travel_frequently 1278 sales
## 1213 no 34 travel_rarely 678 research_development
## 1214 yes 23 travel_rarely 427 sales
## 1215 no 44 travel_rarely 921 research_development
## 1216 no 35 travel_frequently 146 research_development
## 1217 no 43 travel_rarely 1179 sales
## 1218 no 24 travel_rarely 581 research_development
## 1219 no 41 travel_rarely 918 sales
## 1220 no 29 travel_rarely 1082 research_development
## 1221 no 36 travel_rarely 530 sales
## 1222 no 45 non_travel 1238 research_development
## 1223 yes 24 travel_rarely 240 human_resources
## 1224 yes 47 travel_frequently 1093 sales
## 1225 no 26 travel_rarely 390 research_development
## 1226 no 45 travel_rarely 1005 research_development
## 1227 no 32 travel_frequently 585 research_development
## 1228 no 31 travel_rarely 741 research_development
## 1229 no 41 non_travel 552 human_resources
## 1230 no 40 travel_rarely 369 research_development
## 1231 no 24 travel_rarely 506 research_development
## 1232 no 46 travel_rarely 717 research_development
## 1233 no 35 travel_rarely 1370 research_development
## 1234 no 30 travel_rarely 793 research_development
## 1235 no 47 non_travel 543 sales
## 1236 no 46 travel_rarely 1277 sales
## 1237 yes 36 travel_rarely 1456 sales
## 1238 yes 32 travel_rarely 964 sales
## 1239 no 23 travel_rarely 160 research_development
## 1240 no 31 travel_frequently 163 research_development
## 1241 no 39 non_travel 792 research_development
## 1242 no 32 travel_rarely 371 sales
## 1243 no 40 travel_rarely 611 sales
## 1244 no 45 travel_rarely 176 human_resources
## 1245 no 30 travel_frequently 1312 research_development
## 1246 no 24 travel_frequently 897 human_resources
## 1247 yes 30 travel_frequently 600 human_resources
## 1248 no 31 travel_rarely 1003 sales
## 1249 no 27 travel_rarely 1054 research_development
## 1250 yes 29 travel_rarely 428 sales
## 1251 no 29 travel_frequently 461 research_development
## 1252 no 30 travel_rarely 979 sales
## 1253 no 34 travel_rarely 181 research_development
## 1254 no 33 non_travel 1283 sales
## 1255 no 49 travel_rarely 1313 sales
## 1256 yes 33 travel_rarely 211 sales
## 1257 no 38 travel_frequently 594 research_development
## 1258 yes 31 travel_rarely 1079 sales
## 1259 no 29 travel_rarely 590 research_development
## 1260 no 30 travel_rarely 305 research_development
## 1261 no 32 non_travel 953 research_development
## 1262 no 38 travel_rarely 833 research_development
## 1263 yes 43 travel_frequently 807 research_development
## 1264 no 42 travel_rarely 855 research_development
## 1265 no 55 travel_rarely 478 research_development
## 1266 no 33 non_travel 775 research_development
## 1267 no 41 travel_rarely 548 research_development
## 1268 no 34 non_travel 1375 sales
## 1269 no 53 non_travel 661 research_development
## 1270 no 43 travel_rarely 244 human_resources
## 1271 no 34 travel_rarely 511 sales
## 1272 yes 21 travel_rarely 337 sales
## 1273 no 38 travel_rarely 1153 research_development
## 1274 yes 22 travel_rarely 1294 research_development
## 1275 no 31 travel_rarely 196 sales
## 1276 no 51 travel_rarely 942 research_development
## 1277 no 37 travel_rarely 589 sales
## 1278 no 46 travel_rarely 734 research_development
## 1279 no 36 travel_rarely 1383 research_development
## 1280 yes 44 travel_frequently 429 research_development
## 1281 no 37 travel_rarely 1239 human_resources
## 1282 yes 35 travel_rarely 303 sales
## 1283 no 33 travel_rarely 867 research_development
## 1284 no 28 travel_rarely 1181 research_development
## 1285 no 39 travel_rarely 1253 research_development
## 1286 no 46 non_travel 849 sales
## 1287 no 40 travel_rarely 616 research_development
## 1288 no 42 travel_rarely 1128 research_development
## 1289 no 35 non_travel 1180 research_development
## 1290 no 38 non_travel 1336 human_resources
## 1291 yes 34 travel_frequently 234 research_development
## 1292 yes 37 travel_rarely 370 research_development
## 1293 no 39 travel_frequently 766 sales
## 1294 no 43 non_travel 343 research_development
## 1295 no 41 travel_rarely 447 research_development
## 1296 no 41 travel_rarely 796 sales
## 1297 no 30 travel_rarely 1092 research_development
## 1298 yes 26 travel_rarely 920 human_resources
## 1299 yes 46 travel_rarely 261 research_development
## 1300 no 40 travel_rarely 1194 research_development
## 1301 no 34 travel_rarely 810 sales
## 1302 no 58 non_travel 350 sales
## 1303 no 35 travel_rarely 185 research_development
## 1304 no 47 travel_rarely 1001 research_development
## 1305 no 40 travel_rarely 750 research_development
## 1306 no 54 travel_rarely 431 research_development
## 1307 no 31 travel_frequently 1125 sales
## 1308 no 28 travel_rarely 1217 research_development
## 1309 no 38 travel_rarely 723 sales
## 1310 no 26 travel_rarely 572 sales
## 1311 no 58 travel_frequently 1216 research_development
## 1312 no 18 non_travel 1431 research_development
## 1313 yes 31 travel_rarely 359 human_resources
## 1314 yes 29 travel_rarely 350 human_resources
## 1315 no 45 non_travel 589 sales
## 1316 no 36 travel_rarely 430 research_development
## 1317 no 43 travel_frequently 1422 sales
## 1318 no 27 travel_frequently 1297 research_development
## 1319 no 29 travel_frequently 574 research_development
## 1320 no 32 travel_frequently 1318 sales
## 1321 no 42 non_travel 355 research_development
## 1322 no 47 travel_rarely 207 research_development
## 1323 no 46 travel_rarely 706 research_development
## 1324 no 28 non_travel 280 human_resources
## 1325 no 29 travel_rarely 726 research_development
## 1326 no 42 travel_rarely 1142 research_development
## 1327 yes 32 travel_rarely 414 sales
## 1328 no 46 travel_rarely 1319 sales
## 1329 no 27 travel_rarely 728 sales
## 1330 no 29 travel_rarely 352 human_resources
## 1331 no 43 travel_rarely 823 research_development
## 1332 no 48 travel_rarely 1224 research_development
## 1333 yes 29 travel_frequently 459 research_development
## 1334 yes 46 travel_rarely 1254 sales
## 1335 no 27 travel_frequently 1131 research_development
## 1336 no 39 travel_rarely 835 research_development
## 1337 no 55 travel_rarely 836 research_development
## 1338 no 28 travel_rarely 1172 sales
## 1339 yes 30 travel_rarely 945 sales
## 1340 yes 22 travel_rarely 391 research_development
## 1341 no 36 travel_rarely 1266 sales
## 1342 no 31 travel_rarely 311 research_development
## 1343 no 34 travel_rarely 1480 sales
## 1344 no 29 travel_rarely 592 research_development
## 1345 no 37 travel_rarely 783 research_development
## 1346 no 35 travel_rarely 219 research_development
## 1347 no 45 travel_rarely 556 research_development
## 1348 no 36 travel_frequently 1213 human_resources
## 1349 no 40 travel_rarely 1137 research_development
## 1350 no 26 travel_rarely 482 research_development
## 1351 no 27 travel_rarely 511 sales
## 1352 no 48 travel_frequently 117 research_development
## 1353 no 44 travel_rarely 170 research_development
## 1354 yes 34 non_travel 967 research_development
## 1355 yes 56 travel_rarely 1162 research_development
## 1356 no 36 travel_rarely 335 sales
## 1357 no 41 travel_rarely 337 sales
## 1358 no 42 travel_rarely 1396 research_development
## 1359 no 31 travel_rarely 1079 sales
## 1360 no 34 travel_rarely 735 sales
## 1361 no 31 travel_rarely 471 research_development
## 1362 no 26 travel_frequently 1096 research_development
## 1363 no 45 travel_frequently 1297 research_development
## 1364 no 33 travel_rarely 217 sales
## 1365 no 28 travel_frequently 783 sales
## 1366 yes 29 travel_frequently 746 sales
## 1367 no 39 non_travel 1251 sales
## 1368 no 27 travel_rarely 1354 research_development
## 1369 no 34 travel_frequently 735 research_development
## 1370 yes 28 travel_rarely 1475 sales
## 1371 no 47 non_travel 1169 research_development
## 1372 no 56 travel_rarely 1443 sales
## 1373 no 39 travel_rarely 867 research_development
## 1374 no 38 travel_frequently 1394 research_development
## 1375 no 58 travel_rarely 605 sales
## 1376 yes 32 travel_frequently 238 research_development
## 1377 no 38 travel_rarely 1206 research_development
## 1378 no 49 travel_frequently 1064 research_development
## 1379 no 42 travel_rarely 419 sales
## 1380 yes 27 travel_frequently 1337 human_resources
## 1381 no 35 travel_rarely 682 sales
## 1382 no 28 non_travel 1103 research_development
## 1383 no 31 non_travel 976 research_development
## 1384 no 36 non_travel 1351 research_development
## 1385 no 34 travel_rarely 937 sales
## 1386 no 34 travel_rarely 1239 sales
## 1387 no 26 travel_rarely 157 research_development
## 1388 no 29 travel_rarely 136 research_development
## 1389 no 32 non_travel 1146 research_development
## 1390 no 31 travel_frequently 1125 research_development
## 1391 yes 28 travel_rarely 1404 research_development
## 1392 no 38 travel_rarely 1404 sales
## 1393 no 35 travel_rarely 1224 sales
## 1394 no 27 travel_rarely 954 sales
## 1395 no 32 travel_rarely 1373 research_development
## 1396 yes 31 travel_frequently 754 sales
## 1397 yes 53 travel_rarely 1168 sales
## 1398 no 54 travel_rarely 155 research_development
## 1399 no 33 travel_frequently 1303 research_development
## 1400 no 43 travel_rarely 574 research_development
## 1401 no 38 travel_frequently 1444 human_resources
## 1402 no 55 travel_rarely 189 human_resources
## 1403 no 31 travel_rarely 1276 research_development
## 1404 no 39 travel_rarely 119 sales
## 1405 no 42 non_travel 335 research_development
## 1406 no 31 non_travel 697 research_development
## 1407 no 54 travel_rarely 157 research_development
## 1408 no 24 travel_rarely 771 research_development
## 1409 no 23 travel_rarely 571 research_development
## 1410 no 40 travel_frequently 692 research_development
## 1411 no 40 travel_rarely 444 sales
## 1412 no 25 travel_rarely 309 human_resources
## 1413 no 30 travel_rarely 911 research_development
## 1414 no 25 travel_rarely 977 research_development
## 1415 no 47 travel_rarely 1180 research_development
## 1416 no 33 non_travel 1313 research_development
## 1417 no 38 travel_rarely 1321 sales
## 1418 no 31 travel_rarely 1154 sales
## 1419 no 38 travel_frequently 508 research_development
## 1420 no 42 travel_rarely 557 research_development
## 1421 no 41 travel_rarely 642 research_development
## 1422 no 47 non_travel 1162 research_development
## 1423 no 35 travel_rarely 1490 research_development
## 1424 no 22 travel_rarely 581 research_development
## 1425 no 35 travel_rarely 1395 research_development
## 1426 no 33 travel_rarely 501 research_development
## 1427 no 32 travel_rarely 267 research_development
## 1428 no 40 travel_rarely 543 research_development
## 1429 no 32 travel_rarely 234 sales
## 1430 no 39 travel_rarely 116 research_development
## 1431 no 38 travel_rarely 201 research_development
## 1432 no 32 travel_rarely 801 sales
## 1433 no 37 travel_rarely 161 research_development
## 1434 no 25 travel_rarely 1382 sales
## 1435 no 52 non_travel 585 sales
## 1436 no 44 travel_rarely 1037 research_development
## 1437 no 21 travel_rarely 501 sales
## 1438 no 39 non_travel 105 research_development
## 1439 yes 23 travel_frequently 638 sales
## 1440 no 36 travel_rarely 557 sales
## 1441 no 36 travel_frequently 688 research_development
## 1442 no 56 non_travel 667 research_development
## 1443 yes 29 travel_rarely 1092 research_development
## 1444 no 42 travel_rarely 300 research_development
## 1445 yes 56 travel_rarely 310 research_development
## 1446 no 41 travel_rarely 582 research_development
## 1447 no 34 travel_rarely 704 sales
## 1448 no 36 non_travel 301 sales
## 1449 no 41 travel_rarely 930 sales
## 1450 no 32 travel_rarely 529 research_development
## 1451 no 35 travel_rarely 1146 human_resources
## 1452 no 38 travel_rarely 345 sales
## 1453 yes 50 travel_frequently 878 sales
## 1454 no 36 travel_rarely 1120 sales
## 1455 no 45 travel_rarely 374 sales
## 1456 no 40 travel_rarely 1322 research_development
## 1457 no 35 travel_frequently 1199 research_development
## 1458 no 40 travel_rarely 1194 research_development
## 1459 no 35 travel_rarely 287 research_development
## 1460 no 29 travel_rarely 1378 research_development
## 1461 no 29 travel_rarely 468 research_development
## 1462 yes 50 travel_rarely 410 sales
## 1463 no 39 travel_rarely 722 sales
## 1464 no 31 non_travel 325 research_development
## 1465 no 26 travel_rarely 1167 sales
## 1466 no 36 travel_frequently 884 research_development
## 1467 no 39 travel_rarely 613 research_development
## 1468 no 27 travel_rarely 155 research_development
## 1469 no 49 travel_frequently 1023 sales
## 1470 no 34 travel_rarely 628 research_development
## distance_from_home education education_field employee_count
## 1 1 2 life_sciences 1
## 2 8 1 life_sciences 1
## 3 2 2 other 1
## 4 3 4 life_sciences 1
## 5 2 1 medical 1
## 6 2 2 life_sciences 1
## 7 3 3 medical 1
## 8 24 1 life_sciences 1
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## 1256 3 3 sales_executive 1
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## 1292 3 2 manufacturing_director 1
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## 1 single 5993 19479 8 y
## 2 married 5130 24907 1 y
## 3 single 2090 2396 6 y
## 4 married 2909 23159 1 y
## 5 married 3468 16632 9 y
## 6 single 3068 11864 0 y
## 7 married 2670 9964 4 y
## 8 divorced 2693 13335 1 y
## 9 single 9526 8787 0 y
## 10 married 5237 16577 6 y
## 11 married 2426 16479 0 y
## 12 single 4193 12682 0 y
## 13 divorced 2911 15170 1 y
## 14 divorced 2661 8758 0 y
## 15 single 2028 12947 5 y
## 16 divorced 9980 10195 1 y
## 17 divorced 3298 15053 0 y
## 18 divorced 2935 7324 1 y
## 19 married 15427 22021 2 y
## 20 single 3944 4306 5 y
## 21 divorced 4011 8232 0 y
## 22 single 3407 6986 7 y
## 23 single 11994 21293 0 y
## 24 single 1232 19281 1 y
## 25 single 2960 17102 2 y
## 26 divorced 19094 10735 4 y
## 27 single 3919 4681 1 y
## 28 married 6825 21173 0 y
## 29 married 10248 2094 3 y
## 30 single 18947 22822 3 y
## 31 single 2496 6670 4 y
## 32 married 6465 19121 2 y
## 33 single 2206 16117 1 y
## 34 married 2086 3335 3 y
## 35 married 2293 3020 2 y
## 36 divorced 2645 21923 1 y
## 37 married 2683 3810 1 y
## 38 married 2014 9687 1 y
## 39 married 3419 13072 9 y
## 40 married 5376 3193 2 y
## 41 divorced 1951 10910 1 y
## 42 divorced 2341 19715 1 y
## 43 single 2293 10558 1 y
## 44 single 8726 2975 1 y
## 45 single 4011 10781 1 y
## 46 married 19545 16280 1 y
## 47 single 4568 10034 0 y
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## 50 married 2269 4892 1 y
## 51 single 5381 19294 9 y
## 52 single 3441 11179 1 y
## 53 divorced 5454 4009 5 y
## 54 married 9884 8302 2 y
## 55 married 4157 21436 7 y
## 56 single 13458 15146 1 y
## 57 married 9069 11031 1 y
## 58 married 4014 16002 3 y
## 59 divorced 5915 9528 3 y
## 60 divorced 5993 2689 1 y
## 61 married 6162 10877 1 y
## 62 single 2406 5456 1 y
## 63 divorced 18740 16701 5 y
## 64 single 7637 2354 7 y
## 65 divorced 10096 8202 1 y
## 66 divorced 14756 19730 2 y
## 67 single 6499 22656 1 y
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## 70 married 3388 21777 0 y
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## 72 married 2703 4956 0 y
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## 76 single 4424 20682 1 y
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## 79 single 13664 25258 4 y
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## 82 single 2859 26278 1 y
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## 121 divorced 2613 22310 1 y
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## 201 married 4298 9679 5 y
## 202 divorced 6804 23793 1 y
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## 225 married 4345 4381 0 y
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## 230 single 2389 14961 1 y
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## 238 single 19068 21030 1 y
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## 240 single 3730 9571 0 y
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## 242 married 4465 12069 0 y
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## 245 married 19202 15970 0 y
## 246 divorced 13675 13523 9 y
## 247 married 2911 14776 1 y
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## 252 single 10938 6420 0 y
## 253 single 2340 22673 1 y
## 254 single 6545 23016 3 y
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## 338 single 3983 7621 0 y
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## 342 divorced 11510 15682 0 y
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## 345 single 8095 18264 0 y
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## 354 divorced 5974 17001 4 y
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## 370 single 2657 7551 0 y
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## 373 single 6540 19394 9 y
## 374 divorced 3816 17881 1 y
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## 376 single 10965 12066 8 y
## 377 married 4936 14862 4 y
## 378 married 2543 11868 4 y
## 379 single 5304 4652 8 y
## 380 single 16659 23258 2 y
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## 383 single 3102 6582 0 y
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## 386 single 2285 3427 9 y
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## 389 divorced 2576 20490 3 y
## 390 single 4197 21123 2 y
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## 393 married 19406 8509 4 y
## 394 married 6538 12740 9 y
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## 397 divorced 4522 2227 4 y
## 398 single 4487 12090 1 y
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## 400 married 2218 16193 1 y
## 401 divorced 19197 8213 1 y
## 402 married 13212 18256 9 y
## 403 single 6577 19558 0 y
## 404 married 8392 19566 1 y
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## 1108 married 6077 14814 3 y
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## 1111 divorced 2074 26619 1 y
## 1112 married 10169 14618 0 y
## 1113 married 4855 7653 4 y
## 1114 married 4087 25174 4 y
## 1115 married 2367 16530 8 y
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## 1118 married 5484 13008 9 y
## 1119 married 2061 11133 1 y
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## 1123 single 4723 16213 1 y
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## 1128 married 2073 12826 2 y
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## 1132 married 5063 15332 1 y
## 1133 married 4639 11262 1 y
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## 1138 married 2814 10293 1 y
## 1139 married 11245 20689 2 y
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## 1149 married 5377 3835 2 y
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## 1152 divorced 4877 20460 0 y
## 1153 single 3117 26009 1 y
## 1154 single 1569 18420 1 y
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## 1156 divorced 3069 10302 0 y
## 1157 married 10435 25800 1 y
## 1158 married 4148 12250 1 y
## 1159 married 5768 26493 3 y
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## 1163 married 10306 21530 9 y
## 1164 married 3936 9953 1 y
## 1165 single 7945 19948 6 y
## 1166 married 5743 10503 4 y
## 1167 married 15202 5602 2 y
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## 1170 married 3517 22490 7 y
## 1171 single 2580 6297 2 y
## 1172 single 2166 3339 3 y
## 1173 single 5869 23413 9 y
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## 1176 married 5295 7693 4 y
## 1177 married 16413 3498 3 y
## 1178 divorced 13269 21981 5 y
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## 1180 divorced 5433 19332 1 y
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## 1187 single 4581 10414 3 y
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## 1194 single 2440 23826 1 y
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## 1196 single 15379 22384 4 y
## 1197 single 7082 11591 3 y
## 1198 single 2728 21082 1 y
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## 1201 divorced 3195 4167 4 y
## 1202 single 3989 20586 1 y
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## 1204 married 7005 3458 3 y
## 1205 married 2655 11740 2 y
## 1206 single 1393 24852 1 y
## 1207 single 2570 11925 1 y
## 1208 divorced 3537 23737 5 y
## 1209 married 3986 11912 1 y
## 1210 divorced 10883 20467 3 y
## 1211 married 2028 13637 1 y
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## 1213 married 2929 20338 1 y
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## 1215 married 7879 14810 1 y
## 1216 single 4930 13970 0 y
## 1217 married 7847 6069 1 y
## 1218 married 4401 17616 1 y
## 1219 single 9241 15869 1 y
## 1220 married 2974 25412 9 y
## 1221 single 4502 7439 3 y
## 1222 married 10748 3395 3 y
## 1223 married 1555 11585 1 y
## 1224 married 12936 24164 7 y
## 1225 married 2305 6217 1 y
## 1226 single 16704 17119 1 y
## 1227 married 3433 17360 6 y
## 1228 married 3477 18103 1 y
## 1229 married 6430 20794 6 y
## 1230 married 6516 5041 2 y
## 1231 divorced 3907 3622 1 y
## 1232 single 5562 9697 6 y
## 1233 married 6883 5151 2 y
## 1234 married 2862 3811 1 y
## 1235 married 4978 3536 7 y
## 1236 divorced 10368 5596 4 y
## 1237 divorced 6134 8658 5 y
## 1238 single 6735 12147 6 y
## 1239 single 3295 12862 1 y
## 1240 single 5238 6670 2 y
## 1241 married 6472 8989 1 y
## 1242 married 9610 3840 3 y
## 1243 single 19833 4349 1 y
## 1244 married 9756 6595 4 y
## 1245 single 4968 26427 0 y
## 1246 married 2145 2097 0 y
## 1247 divorced 2180 9732 6 y
## 1248 married 8346 20943 1 y
## 1249 single 3445 6152 1 y
## 1250 single 2760 14630 1 y
## 1251 single 6294 23060 8 y
## 1252 divorced 7140 3088 2 y
## 1253 married 2932 5586 0 y
## 1254 single 5147 10697 8 y
## 1255 single 4507 8191 3 y
## 1256 single 8564 10092 2 y
## 1257 married 2468 15963 4 y
## 1258 married 8161 19002 2 y
## 1259 divorced 2109 10007 1 y
## 1260 married 5294 9128 3 y
## 1261 single 2718 17674 2 y
## 1262 married 5811 24539 3 y
## 1263 married 2437 15587 9 y
## 1264 divorced 2766 8952 8 y
## 1265 married 19038 19805 8 y
## 1266 divorced 3055 6194 5 y
## 1267 divorced 2289 20520 1 y
## 1268 divorced 4001 12313 1 y
## 1269 married 12965 22308 4 y
## 1270 single 3539 5033 0 y
## 1271 single 6029 25353 5 y
## 1272 single 2679 4567 1 y
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## 1274 married 2398 15999 1 y
## 1275 married 5468 13402 1 y
## 1276 married 13116 22984 2 y
## 1277 married 4189 8800 1 y
## 1278 divorced 19328 14218 7 y
## 1279 married 8321 25949 7 y
## 1280 divorced 2342 11092 1 y
## 1281 divorced 4071 12832 2 y
## 1282 single 5813 13492 1 y
## 1283 married 3143 6076 6 y
## 1284 married 2044 5531 1 y
## 1285 single 13464 7914 7 y
## 1286 single 7991 25166 8 y
## 1287 married 3377 25605 4 y
## 1288 married 5538 5696 5 y
## 1289 divorced 5762 24442 2 y
## 1290 divorced 2592 7129 5 y
## 1291 married 5346 6208 4 y
## 1292 single 4213 4992 1 y
## 1293 divorced 4127 19188 2 y
## 1294 single 2438 24978 4 y
## 1295 single 6870 15530 3 y
## 1296 divorced 10447 26458 0 y
## 1297 single 9667 2739 9 y
## 1298 married 2148 6889 0 y
## 1299 married 8926 10842 4 y
## 1300 divorced 6513 9060 4 y
## 1301 married 6799 22128 1 y
## 1302 divorced 16291 22577 4 y
## 1303 married 2705 9696 0 y
## 1304 divorced 10333 19271 8 y
## 1305 divorced 4448 10748 2 y
## 1306 married 6854 15696 4 y
## 1307 married 9637 8277 2 y
## 1308 married 3591 12719 1 y
## 1309 married 5405 4244 2 y
## 1310 single 4684 9125 1 y
## 1311 married 15787 21624 2 y
## 1312 single 1514 8018 1 y
## 1313 married 2956 21495 0 y
## 1314 divorced 2335 3157 4 y
## 1315 married 5154 19665 4 y
## 1316 married 6962 19573 4 y
## 1317 married 5675 19246 1 y
## 1318 single 2379 19826 0 y
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## 1320 single 4648 26075 8 y
## 1321 married 2936 6161 3 y
## 1322 single 2105 5411 4 y
## 1323 divorced 8578 19989 3 y
## 1324 divorced 2706 10494 1 y
## 1325 divorced 6384 21143 8 y
## 1326 single 3968 13624 4 y
## 1327 single 9907 26186 7 y
## 1328 divorced 13225 7739 2 y
## 1329 married 3540 7018 1 y
## 1330 married 2804 15434 1 y
## 1331 married 19392 22539 7 y
## 1332 married 19665 13583 4 y
## 1333 single 2439 14753 1 y
## 1334 married 7314 14011 5 y
## 1335 married 4774 23844 0 y
## 1336 divorced 3902 5141 8 y
## 1337 married 2662 7975 8 y
## 1338 married 2856 3692 1 y
## 1339 single 1081 16019 1 y
## 1340 single 2472 26092 1 y
## 1341 married 5673 6060 1 y
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## 1343 married 9713 24444 2 y
## 1344 single 2062 19384 3 y
## 1345 married 4284 13588 5 y
## 1346 married 4788 25388 0 y
## 1347 married 5906 23888 0 y
## 1348 single 3886 4223 1 y
## 1349 divorced 16823 18991 2 y
## 1350 married 2933 14908 1 y
## 1351 single 6500 26997 0 y
## 1352 divorced 17174 2437 3 y
## 1353 married 5033 9364 2 y
## 1354 married 2307 14460 1 y
## 1355 single 2587 10261 1 y
## 1356 married 5507 16822 2 y
## 1357 married 4393 26841 5 y
## 1358 married 13348 14842 9 y
## 1359 divorced 6583 20115 2 y
## 1360 married 8103 16495 3 y
## 1361 divorced 3978 16031 8 y
## 1362 married 2544 7102 0 y
## 1363 single 5399 14511 4 y
## 1364 single 5487 10410 1 y
## 1365 married 6834 19255 1 y
## 1366 single 1091 10642 1 y
## 1367 married 5736 3987 6 y
## 1368 married 2226 6073 1 y
## 1369 married 5747 26496 1 y
## 1370 single 9854 23352 3 y
## 1371 married 5467 2125 8 y
## 1372 married 5380 20328 4 y
## 1373 married 5151 12315 1 y
## 1374 divorced 2133 18115 1 y
## 1375 married 17875 11761 4 y
## 1376 single 2432 15318 3 y
## 1377 divorced 4771 14293 2 y
## 1378 married 19161 13738 3 y
## 1379 divorced 5087 2900 3 y
## 1380 married 2863 19555 1 y
## 1381 married 5561 15975 0 y
## 1382 single 2144 2122 1 y
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## 1388 married 5373 6225 0 y
## 1389 divorced 6667 16542 5 y
## 1390 married 5003 5771 1 y
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## 1392 single 2858 11473 4 y
## 1393 married 5204 13586 1 y
## 1394 single 4105 5099 1 y
## 1395 single 9679 10138 8 y
## 1396 married 5617 21075 1 y
## 1397 single 10448 5843 6 y
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## 1400 married 7510 16873 1 y
## 1401 married 2991 5224 0 y
## 1402 married 19636 25811 4 y
## 1403 divorced 1129 17536 1 y
## 1404 single 13341 25098 0 y
## 1405 single 4332 14811 1 y
## 1406 married 11031 26862 4 y
## 1407 single 4440 25198 6 y
## 1408 single 4617 14120 1 y
## 1409 single 2647 13672 1 y
## 1410 married 6323 26849 1 y
## 1411 married 5677 4258 3 y
## 1412 married 2187 19655 4 y
## 1413 married 3748 4077 1 y
## 1414 divorced 3977 7298 6 y
## 1415 single 8633 13084 2 y
## 1416 divorced 2008 20439 1 y
## 1417 married 4440 7636 0 y
## 1418 married 3067 6393 0 y
## 1419 married 5321 14284 2 y
## 1420 divorced 5410 11189 6 y
## 1421 married 2782 21412 3 y
## 1422 married 11957 17231 0 y
## 1423 married 2660 20232 7 y
## 1424 single 3375 17624 0 y
## 1425 single 5098 18698 1 y
## 1426 married 4878 21653 0 y
## 1427 single 2837 15919 1 y
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## 1429 married 2269 18024 0 y
## 1430 single 4108 5340 7 y
## 1431 married 13206 3376 3 y
## 1432 married 10422 24032 1 y
## 1433 married 13744 15471 1 y
## 1434 divorced 4907 13684 0 y
## 1435 divorced 3482 19788 2 y
## 1436 single 2436 13422 6 y
## 1437 single 2380 25479 1 y
## 1438 single 19431 15302 2 y
## 1439 married 1790 26956 1 y
## 1440 married 7644 12695 0 y
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## 1442 divorced 6306 26236 1 y
## 1443 married 4787 26124 9 y
## 1444 married 18880 17312 5 y
## 1445 married 2339 3666 8 y
## 1446 married 13570 5640 0 y
## 1447 married 6712 8978 1 y
## 1448 divorced 5406 10436 1 y
## 1449 divorced 8938 12227 2 y
## 1450 single 2439 11288 1 y
## 1451 single 8837 16642 1 y
## 1452 married 5343 5982 1 y
## 1453 divorced 6728 14255 7 y
## 1454 married 6652 14369 4 y
## 1455 single 4850 23333 8 y
## 1456 single 2809 2725 2 y
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## 1459 married 2977 8952 1 y
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## 1461 single 3785 8489 1 y
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## 1463 married 12031 8828 0 y
## 1464 single 9936 3787 0 y
## 1465 single 2966 21378 0 y
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## 1468 married 6142 5174 1 y
## 1469 married 5390 13243 2 y
## 1470 married 4404 10228 2 y
## over_time percent_salary_hike performance_rating relationship_satisfaction
## 1 yes 11 3 1
## 2 no 23 4 4
## 3 yes 15 3 2
## 4 yes 11 3 3
## 5 no 12 3 4
## 6 no 13 3 3
## 7 yes 20 4 1
## 8 no 22 4 2
## 9 no 21 4 2
## 10 no 13 3 2
## 11 no 13 3 3
## 12 yes 12 3 4
## 13 no 17 3 4
## 14 no 11 3 3
## 15 yes 14 3 2
## 16 no 11 3 3
## 17 yes 12 3 4
## 18 yes 13 3 2
## 19 no 16 3 3
## 20 yes 11 3 3
## 21 no 18 3 4
## 22 no 23 4 2
## 23 no 11 3 3
## 24 no 14 3 4
## 25 no 11 3 3
## 26 no 11 3 4
## 27 yes 22 4 2
## 28 no 11 3 4
## 29 no 14 3 4
## 30 no 12 3 4
## 31 no 11 3 4
## 32 yes 13 3 4
## 33 no 13 3 1
## 34 no 14 3 3
## 35 yes 16 3 1
## 36 no 12 3 4
## 37 yes 14 3 3
## 38 no 13 3 1
## 39 yes 14 3 4
## 40 no 19 3 1
## 41 no 12 3 3
## 42 no 13 3 4
## 43 no 12 3 3
## 44 no 15 3 4
## 45 no 23 4 4
## 46 no 12 3 4
## 47 no 20 4 3
## 48 no 21 4 1
## 49 yes 21 4 3
## 50 no 19 3 4
## 51 yes 13 3 4
## 52 yes 13 3 3
## 53 yes 21 4 3
## 54 yes 13 3 3
## 55 yes 19 3 3
## 56 yes 12 3 3
## 57 no 22 4 4
## 58 yes 15 3 3
## 59 no 22 4 4
## 60 no 18 3 3
## 61 yes 22 4 2
## 62 no 11 3 4
## 63 yes 12 3 4
## 64 no 11 3 4
## 65 no 13 3 2
## 66 yes 14 3 3
## 67 no 13 3 3
## 68 no 17 3 3
## 69 no 13 3 4
## 70 yes 17 3 1
## 71 no 11 3 4
## 72 no 23 4 4
## 73 no 17 3 2
## 74 no 17 3 2
## 75 no 12 3 2
## 76 no 23 4 4
## 77 no 14 3 2
## 78 yes 14 3 2
## 79 no 13 3 1
## 80 yes 22 4 4
## 81 yes 12 3 3
## 82 no 18 3 1
## 83 no 14 3 4
## 84 yes 12 3 4
## 85 no 15 3 3
## 86 no 11 3 1
## 87 no 13 3 3
## 88 no 23 4 2
## 89 no 19 3 1
## 90 no 16 3 4
## 91 no 22 4 4
## 92 yes 22 4 4
## 93 yes 12 3 2
## 94 yes 13 3 3
## 95 no 16 3 1
## 96 no 12 3 1
## 97 no 21 4 1
## 98 no 15 3 2
## 99 no 13 3 3
## 100 no 12 3 3
## 101 yes 22 4 4
## 102 no 13 3 4
## 103 yes 18 3 2
## 104 no 14 3 3
## 105 no 14 3 4
## 106 no 21 4 4
## 107 yes 19 3 1
## 108 yes 11 3 4
## 109 no 11 3 3
## 110 no 15 3 3
## 111 no 20 4 3
## 112 yes 24 4 4
## 113 yes 12 3 3
## 114 no 12 3 3
## 115 no 15 3 3
## 116 no 12 3 1
## 117 no 12 3 4
## 118 no 14 3 3
## 119 no 22 4 1
## 120 yes 12 3 4
## 121 no 25 4 3
## 122 no 13 3 1
## 123 yes 18 3 1
## 124 no 13 3 3
## 125 yes 18 3 2
## 126 no 19 3 3
## 127 no 12 3 4
## 128 yes 19 3 4
## 129 no 14 3 3
## 130 no 14 3 3
## 131 no 12 3 4
## 132 no 11 3 4
## 133 yes 11 3 3
## 134 yes 13 3 3
## 135 no 23 4 4
## 136 no 20 4 4
## 137 no 15 3 4
## 138 no 14 3 3
## 139 no 18 3 4
## 140 yes 19 3 4
## 141 no 22 4 1
## 142 no 13 3 2
## 143 yes 20 4 2
## 144 no 14 3 3
## 145 no 17 3 4
## 146 no 14 3 4
## 147 no 13 3 4
## 148 no 13 3 2
## 149 no 21 4 4
## 150 no 14 3 4
## 151 no 11 3 1
## 152 no 13 3 1
## 153 yes 20 4 4
## 154 no 18 3 3
## 155 no 14 3 4
## 156 no 13 3 1
## 157 no 17 3 3
## 158 yes 20 4 2
## 159 no 11 3 1
## 160 no 18 3 4
## 161 no 24 4 1
## 162 no 18 3 4
## 163 no 11 3 3
## 164 yes 16 3 2
## 165 yes 15 3 4
## 166 no 15 3 2
## 167 no 12 3 1
## 168 no 12 3 3
## 169 no 22 4 3
## 170 no 20 4 1
## 171 yes 16 3 4
## 172 no 21 4 1
## 173 no 12 3 3
## 174 no 11 3 3
## 175 yes 11 3 1
## 176 yes 18 3 3
## 177 no 14 3 1
## 178 no 22 4 3
## 179 no 25 4 3
## 180 no 12 3 3
## 181 yes 23 4 3
## 182 no 15 3 4
## 183 yes 22 4 4
## 184 no 15 3 4
## 185 no 11 3 3
## 186 no 13 3 4
## 187 no 14 3 2
## 188 no 11 3 4
## 189 no 17 3 3
## 190 no 18 3 3
## 191 no 14 3 1
## 192 no 16 3 4
## 193 yes 13 3 1
## 194 no 14 3 4
## 195 no 23 4 4
## 196 yes 12 3 1
## 197 no 11 3 4
## 198 no 16 3 2
## 199 no 14 3 2
## 200 no 18 3 1
## 201 no 19 3 3
## 202 yes 15 3 1
## 203 yes 17 3 4
## 204 yes 15 3 3
## 205 yes 19 3 2
## 206 no 22 4 4
## 207 yes 16 3 1
## 208 no 13 3 1
## 209 no 14 3 4
## 210 no 16 3 3
## 211 no 11 3 3
## 212 no 22 4 3
## 213 no 14 3 4
## 214 no 16 3 4
## 215 yes 11 3 3
## 216 yes 18 3 3
## 217 no 15 3 3
## 218 no 14 3 4
## 219 no 12 3 4
## 220 no 14 3 4
## 221 no 16 3 4
## 222 no 21 4 4
## 223 yes 14 3 3
## 224 no 12 3 3
## 225 no 12 3 4
## 226 no 17 3 1
## 227 no 17 3 3
## 228 no 14 3 4
## 229 no 14 3 1
## 230 yes 13 3 3
## 231 no 15 3 2
## 232 no 11 3 4
## 233 no 17 3 4
## 234 no 11 3 3
## 235 yes 13 3 3
## 236 yes 22 4 3
## 237 no 20 4 1
## 238 yes 18 3 4
## 239 no 11 3 1
## 240 yes 14 3 4
## 241 no 14 3 3
## 242 no 18 3 1
## 243 no 16 3 1
## 244 no 17 3 1
## 245 no 11 3 3
## 246 no 12 3 1
## 247 no 13 3 3
## 248 no 13 3 2
## 249 no 14 3 1
## 250 no 17 3 4
## 251 no 11 3 2
## 252 no 25 4 4
## 253 no 19 3 1
## 254 yes 13 3 3
## 255 no 14 3 4
## 256 no 12 3 4
## 257 yes 11 3 3
## 258 no 19 3 4
## 259 no 11 3 2
## 260 no 11 3 2
## 261 no 20 4 3
## 262 no 18 3 4
## 263 no 13 3 4
## 264 yes 12 3 2
## 265 no 11 3 3
## 266 no 19 3 2
## 267 no 21 4 2
## 268 no 12 3 4
## 269 yes 14 3 2
## 270 no 11 3 3
## 271 yes 14 3 3
## 272 yes 12 3 4
## 273 no 23 4 4
## 274 no 14 3 2
## 275 no 17 3 1
## 276 yes 18 3 1
## 277 no 18 3 2
## 278 yes 24 4 3
## 279 no 20 4 1
## 280 no 14 3 1
## 281 yes 16 3 4
## 282 no 25 4 3
## 283 no 18 3 1
## 284 yes 19 3 4
## 285 yes 13 3 3
## 286 no 12 3 2
## 287 yes 22 4 4
## 288 no 14 3 2
## 289 yes 13 3 4
## 290 no 21 4 4
## 291 yes 11 3 4
## 292 no 12 3 4
## 293 no 11 3 3
## 294 yes 12 3 2
## 295 yes 12 3 3
## 296 no 14 3 4
## 297 no 13 3 3
## 298 no 15 3 3
## 299 no 18 3 4
## 300 no 18 3 4
## 301 no 19 3 2
## 302 no 12 3 1
## 303 no 19 3 3
## 304 no 11 3 2
## 305 no 17 3 1
## 306 no 15 3 3
## 307 no 14 3 3
## 308 no 17 3 3
## 309 yes 13 3 4
## 310 yes 12 3 3
## 311 no 12 3 4
## 312 no 18 3 1
## 313 yes 18 3 2
## 314 no 11 3 2
## 315 yes 14 3 4
## 316 no 19 3 1
## 317 yes 12 3 4
## 318 no 15 3 1
## 319 yes 12 3 2
## 320 yes 11 3 4
## 321 yes 11 3 1
## 322 no 12 3 4
## 323 no 16 3 3
## 324 yes 13 3 4
## 325 no 13 3 4
## 326 no 24 4 1
## 327 no 15 3 1
## 328 yes 18 3 1
## 329 no 14 3 3
## 330 no 11 3 2
## 331 no 11 3 2
## 332 yes 13 3 4
## 333 no 12 3 4
## 334 no 16 3 1
## 335 no 14 3 1
## 336 yes 22 4 3
## 337 yes 11 3 4
## 338 no 17 3 3
## 339 no 13 3 3
## 340 no 18 3 1
## 341 no 16 3 3
## 342 yes 14 3 2
## 343 yes 14 3 3
## 344 yes 14 3 1
## 345 no 13 3 4
## 346 no 12 3 3
## 347 yes 15 3 4
## 348 no 19 3 1
## 349 no 14 3 2
## 350 no 14 3 1
## 351 yes 11 3 3
## 352 no 13 3 3
## 353 no 21 4 2
## 354 yes 13 3 1
## 355 yes 12 3 2
## 356 no 17 3 2
## 357 no 23 4 2
## 358 yes 11 3 3
## 359 no 15 3 2
## 360 no 11 3 1
## 361 no 11 3 3
## 362 yes 13 3 3
## 363 no 24 4 3
## 364 yes 13 3 2
## 365 no 20 4 2
## 366 no 14 3 3
## 367 no 18 3 3
## 368 no 15 3 4
## 369 yes 12 3 1
## 370 yes 16 3 4
## 371 no 15 3 4
## 372 no 16 3 4
## 373 no 19 3 3
## 374 no 11 3 2
## 375 no 16 3 4
## 376 no 24 4 3
## 377 no 11 3 3
## 378 no 13 3 2
## 379 yes 13 3 2
## 380 yes 13 3 3
## 381 yes 12 3 4
## 382 no 18 3 1
## 383 no 22 4 3
## 384 no 13 3 4
## 385 no 13 3 2
## 386 yes 23 4 3
## 387 no 12 3 3
## 388 no 12 3 3
## 389 no 16 3 2
## 390 yes 12 3 4
## 391 no 11 3 3
## 392 no 22 4 2
## 393 no 11 3 3
## 394 no 15 3 1
## 395 no 12 3 2
## 396 no 20 4 1
## 397 yes 14 3 4
## 398 yes 11 3 2
## 399 yes 15 3 1
## 400 no 12 3 3
## 401 yes 14 3 3
## 402 no 11 3 4
## 403 no 11 3 2
## 404 no 16 3 3
## 405 no 12 3 4
## 406 no 13 3 3
## 407 yes 14 3 3
## 408 no 21 4 4
## 409 no 13 3 4
## 410 no 11 3 2
## 411 no 20 4 3
## 412 no 11 3 4
## 413 no 14 3 3
## 414 no 11 3 4
## 415 yes 16 3 2
## 416 no 16 3 4
## 417 yes 23 4 3
## 418 no 14 3 4
## 419 no 22 4 2
## 420 no 15 3 3
## 421 no 18 3 3
## 422 no 16 3 2
## 423 no 12 3 3
## 424 no 11 3 3
## 425 no 12 3 3
## 426 no 15 3 2
## 427 no 14 3 3
## 428 no 19 3 4
## 429 no 13 3 3
## 430 no 13 3 3
## 431 no 15 3 3
## 432 no 11 3 3
## 433 no 12 3 3
## 434 yes 19 3 3
## 435 no 25 4 4
## 436 yes 12 3 4
## 437 no 13 3 1
## 438 no 14 3 1
## 439 yes 12 3 3
## 440 no 12 3 1
## 441 yes 15 3 3
## 442 no 17 3 4
## 443 no 14 3 4
## 444 no 16 3 3
## 445 no 14 3 1
## 446 no 23 4 4
## 447 no 14 3 4
## 448 no 11 3 3
## 449 no 15 3 3
## 450 no 11 3 1
## 451 yes 13 3 3
## 452 yes 21 4 4
## 453 no 19 3 2
## 454 yes 11 3 2
## 455 no 12 3 2
## 456 no 19 3 3
## 457 no 21 4 3
## 458 yes 14 3 4
## 459 no 15 3 3
## 460 yes 17 3 1
## 461 no 12 3 1
## 462 no 16 3 4
## 463 no 12 3 4
## 464 yes 18 3 2
## 465 no 17 3 4
## 466 no 11 3 4
## 467 no 16 3 2
## 468 no 13 3 4
## 469 yes 12 3 2
## 470 no 12 3 4
## 471 no 13 3 3
## 472 no 19 3 3
## 473 no 12 3 2
## 474 yes 17 3 3
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## 1397 yes 13 3 2
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## 1400 no 17 3 2
## 1401 yes 11 3 2
## 1402 yes 18 3 1
## 1403 yes 11 3 3
## 1404 no 12 3 1
## 1405 no 12 3 4
## 1406 no 20 4 3
## 1407 yes 19 3 4
## 1408 no 12 3 2
## 1409 no 13 3 3
## 1410 no 11 3 1
## 1411 no 14 3 3
## 1412 no 14 3 3
## 1413 no 13 3 3
## 1414 yes 19 3 3
## 1415 no 23 4 2
## 1416 no 12 3 3
## 1417 no 15 3 1
## 1418 no 19 3 3
## 1419 no 11 3 4
## 1420 yes 17 3 3
## 1421 no 22 4 1
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## 1423 yes 11 3 3
## 1424 no 12 3 4
## 1425 no 19 3 2
## 1426 yes 13 3 1
## 1427 no 13 3 3
## 1428 no 19 3 3
## 1429 no 14 3 2
## 1430 no 13 3 1
## 1431 no 12 3 1
## 1432 no 19 3 3
## 1433 yes 25 4 1
## 1434 yes 22 4 2
## 1435 no 15 3 2
## 1436 yes 12 3 3
## 1437 yes 11 3 4
## 1438 no 13 3 3
## 1439 no 19 3 1
## 1440 no 19 3 3
## 1441 no 13 3 2
## 1442 no 21 4 1
## 1443 yes 14 3 2
## 1444 no 11 3 1
## 1445 no 11 3 4
## 1446 no 23 4 3
## 1447 no 21 4 4
## 1448 no 24 4 1
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## 1450 no 14 3 4
## 1451 yes 16 3 3
## 1452 no 11 3 3
## 1453 no 12 3 4
## 1454 no 13 3 1
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## 1456 no 14 3 4
## 1457 yes 14 3 4
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## 1461 no 14 3 2
## 1462 yes 13 3 2
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## 1465 no 18 3 4
## 1466 no 17 3 3
## 1467 no 15 3 1
## 1468 yes 20 4 2
## 1469 no 14 3 4
## 1470 no 12 3 1
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Kami membagi data menjadi training set dan testing dataset, dengan 80% data akan digunakan sebagai training set.
## <Analysis/Assess/Total>
## <1175/295/1470>
Kami akan melakukan preprocess data dengan langkah berikut:
over_18 karena hanya memiliki 1 level faktorrec <- recipe(attrition ~ ., data = training(intrain)) %>%
themis::step_upsample(attrition) %>%
step_scale(all_numeric()) %>%
step_nzv(all_numeric()) %>%
step_pca(all_numeric(), threshold = 0.9) %>%
step_rm(over_18) %>%
prep()## Registered S3 methods overwritten by 'themis':
## method from
## bake.step_downsample recipes
## bake.step_upsample recipes
## prep.step_downsample recipes
## prep.step_upsample recipes
## tidy.step_downsample recipes
## tidy.step_upsample recipes
## tunable.step_downsample recipes
## tunable.step_upsample recipes
## # A tibble: 1,972 x 18
## business_travel department education education_field environment_sat~ gender
## <fct> <fct> <fct> <fct> <fct> <fct>
## 1 travel_frequent~ research_~ 4 life_sciences 4 female
## 2 travel_rarely research_~ 1 medical 1 male
## 3 travel_rarely research_~ 3 medical 3 female
## 4 travel_rarely research_~ 1 life_sciences 4 male
## 5 travel_rarely research_~ 3 medical 3 male
## 6 travel_rarely research_~ 3 medical 1 male
## 7 travel_rarely research_~ 2 life_sciences 4 female
## 8 travel_rarely research_~ 1 life_sciences 1 male
## 9 travel_rarely research_~ 4 life_sciences 2 female
## 10 travel_rarely research_~ 2 life_sciences 1 male
## # ... with 1,962 more rows, and 12 more variables: job_involvement <fct>,
## # job_role <fct>, marital_status <fct>, over_time <fct>,
## # performance_rating <fct>, relationship_satisfaction <fct>,
## # work_life_balance <fct>, attrition <fct>, PC1 <dbl>, PC2 <dbl>, PC3 <dbl>,
## # PC4 <dbl>
Kami akan melatih model menggunakan hutan acak. Kami akan mengoptimalkan nilai mtry (jumlah node di setiap pohon) dan nilai min_n (jumlah minimum anggota di setiap pohon untuk dipecah) dengan memaksimalkan akurasi model pada kumpulan data pengujian. Karena jumlah mtry dan min_n adalah bilangan bulat, lebih baik kita menggunakan encoding biner daripada encoding real-valued.
Kami akan mengatur rentang mtry dari 1 hingga 32. Kami akan membatasi rentang min_n dari 1 hingga 128. Mari kita periksa berapa bit yang kita perlukan untuk menutupi angka-angka itu.
## [1] 32
## [1] 128
Jadi, kita membutuhkan setidaknya 12 bit biner. Jika nilai konversi bit untuk mtry atau min_n adalah 0, kami mengubahnya menjadi 1.
Kami akan menulis model sebagai fungsi kebugaran. Kami akan memaksimalkan akurasi model kami pada dataset pengujian.
fit_function <- function(x){
a <- binary2decimal(x[1:5])
b <- binary2decimal(x[6:12])
if (a == 0) {
a <- 1
}
if (b == 0) {
b <- 1
}
if (a > 17) {
a <- 17
}
#define model spec
model_spec <- rand_forest(
mode = "classification",
mtry = a,
min_n = b,
trees = 500)
#define model engine
model_spec <- set_engine(model_spec,
engine = "ranger",
seed = 123,
num.threads = parallel::detectCores(),
importance = "impurity")
#model fitting
set.seed(123)
model <- fit_xy(
object = model_spec,
x = select(data_train, -attrition),
y = select(data_train, attrition)
)
pred_test <- predict(model, new_data = data_test %>% select(-attrition))
acc <- accuracy_vec(truth = data_test$attrition, estimate = pred_test$.pred_class)
return(acc)
}IMPORTANT Karena beberapa model memerlukan banyak waktu untuk dilatih, Anda mungkin perlu ekstra hati-hati dalam memilih ukuran populasi dan jumlah iterasi. Jika Anda tidak memiliki masalah untuk menjalankan GA dalam jangka waktu yang lama, Anda dapat memilih ukuran populasi dan jumlah iterasi yang lebih besar. Di sini saya akan mengatur ukuran populasi menjadi hanya 100 dan jumlah maksimum iterasi adalah 100. Anda dapat menggunakan parallel = TRUE jika Anda ingin komputasi yang lebih cepat dengan komputasi paralel.
Kami akan mencoba menggunakan pemilihan turnamen alih-alih pemilihan peringkat linier default.
tic()
ga_rf <- ga(type = "binary", fitness = fit_function, nBits = 12, seed = 123,
popSize = 100, maxiter = 100, run = 10, parallel = T,
selection = gabin_tourSelection)
summary(ga_rf)## -- Genetic Algorithm -------------------
##
## GA settings:
## Type = binary
## Population size = 100
## Number of generations = 100
## Elitism = 5
## Crossover probability = 0.8
## Mutation probability = 0.1
##
## GA results:
## Iterations = 13
## Fitness function value = 0.8576271
## Solution =
## x1 x2 x3 x4 x5 x6 x7 x8 x9 x10 x11 x12
## [1,] 0 0 1 0 0 0 0 0 1 1 0 1
## 1331.06 sec elapsed
Mari kita plot perkembangan GA
Mari kita periksa setiap variabel keputusan dan akurasi yang digunakan sebagai nilai fitness.
# Transform decision variables into data frame
ga_rf_pop <- as.data.frame(ga_rf@population)
# Convert decision variables into mtry and min_n
ga_mtry <- apply(ga_rf_pop[ , 1:5], 1, binary2decimal)
ga_min_n <- apply(ga_rf_pop[ , 6:12], 1, binary2decimal)
# Show the list of solutions in the final population
data.frame(mtry = ga_mtry,
min_n = ga_min_n,
accuracy = ga_rf@fitness) %>%
mutate(mtry = ifelse(mtry == 0, 1, mtry),
min_n = ifelse(min_n == 0, 1, min_n),
mtry = ifelse(mtry > 17, 17, mtry)) %>%
distinct() %>%
arrange(desc(accuracy))## mtry min_n accuracy
## 1 4 13 0.8576271
## 2 6 14 0.8508475
## 3 4 14 0.8508475
## 4 4 10 0.8508475
## 5 2 14 0.8508475
## 6 4 9 0.8440678
## 7 4 29 0.8305085
## 8 17 13 0.8203390
## 9 4 45 0.8101695
Anda mungkin ingin membandingkan waktu komputasi GA dengan waktu yang diperlukan untuk validasi silang K-fold berulang menggunakan paket caret konvensional. Kami akan menggunakan k = 5 dan 10 pengulangan.
tic()
set.seed(123)
ctrl <- trainControl(method="repeatedcv", number=5, repeats=10)
attrition_forest <- train(attrition ~ ., data=data_train, method="rf", trControl = ctrl)
toc()## 653.66 sec elapsed
## Random Forest
##
## 1972 samples
## 17 predictor
## 2 classes: 'no', 'yes'
##
## No pre-processing
## Resampling: Cross-Validated (5 fold, repeated 10 times)
## Summary of sample sizes: 1578, 1577, 1577, 1578, 1578, 1577, ...
## Resampling results across tuning parameters:
##
## mtry Accuracy Kappa
## 2 0.9513710 0.9027417
## 22 0.9646545 0.9293085
## 42 0.9578604 0.9157203
##
## Accuracy was used to select the optimal model using the largest value.
## The final value used for the model was mtry = 22.
Mari kita periksa keakuratan model hutan acak dengan caret
pred_rf <- predict(attrition_forest, newdata = data_test)
accuracy_vec(truth = data_test$attrition, estimate = pred_rf)## [1] 0.820339
Performa model oleh GA lebih baik daripada yang dioptimalkan dengan validasi silang K-fold sederhana, meskipun GA memerlukan lebih banyak waktu untuk menghitung. Pertukaran waktu komputasi vs kinerja ini harus dipertimbangkan.
Masalah optimasi adalah salah satu dari banyak perhatian dalam bisnis, yang mungkin berusaha untuk memaksimalkan keuntungan dan meminimalkan kerugian. Pembelajaran mesin menerapkan pengoptimalan pada metode model pelatihan mereka untuk mendapatkan hasil terbaik. GA merupakan algoritma optimasi yang dapat diterapkan di berbagai bidang, termasuk data science. Jika seorang ilmuwan data dapat memanfaatkan manfaat GA, ia bisa mendapatkan model yang lebih optimal dengan kinerja yang lebih baik. Kelemahan GA adalah membutuhkan waktu untuk menghitung karena termasuk dalam kelompok algoritma pencarian, di mana ia mengeksplorasi solusi yang mungkin secara efisien dibandingkan dengan pencarian acak.
Penelitian dalam algoritma evolusioner masih berkembang dalam beberapa tahun terakhir, beberapa membahas skema crossover dan mutasi terbaik, sementara yang lain meningkatkan metode. GA juga dapat diterapkan untuk optimasi multi-tujuan, di mana dua atau lebih tujuan yang saling bertentangan perlu dioptimalkan. Beberapa metode seperti NSGA-II atau SPEA2 merupakan metode GA populer yang dapat digunakan untuk melakukan optimasi multi-objektif. Banyak algoritma evolusioner multi-tujuan atau bahkan banyak-tujuan baru (yang memiliki lebih dari 3 tujuan) diturunkan dari dua metode ini1. Salah satu aplikasi yang paling menarik adalah di sektor manufaktur, terutama untuk masalah penjadwalan, di mana masalah optimasi kombinatorial yang kompleks perlu dipecahkan karena memiliki dampak yang besar terhadap produktivitas dan biaya2.
Chand, Shelvin and Markus Wagner. 2015. Evolutionary many-objective optimization: A quick-start guide. Surveys in Operations Research and Management Science, 20(2), 35-42.↩︎
Gen, M., & Lin, L. 2013. Multiobjective evolutionary algorithm for manufacturing scheduling problems: state-of-the-art survey. Journal of Intelligent Manufacturing, 25(5), 849–866.↩︎