From b4c2257308f4db1afb09a24f4f58ee8f980c8128 Mon Sep 17 00:00:00 2001
From: 100gle <569590461@qq.com>
Date: Thu, 28 Jul 2022 22:01:43 +0800
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---
 data/spaceship-titanic/README.md              |     20 +
 data/spaceship-titanic/data.csv               |   8694 +
 .../mock_choosing_watermelon.py               |     41 +
 ...ock_ml_model_for_choosing_watermelon.ipynb |    507 +
 projects/machine-learning/sklearn_base.ipynb  |   1537 +
 .../spaceship_titanic_prediction.ipynb        | 147017 +++++++++++++++
 6 files changed, 157816 insertions(+)
 create mode 100644 data/spaceship-titanic/README.md
 create mode 100755 data/spaceship-titanic/data.csv
 create mode 100644 projects/machine-learning/mock_choosing_watermelon.py
 create mode 100644 projects/machine-learning/mock_ml_model_for_choosing_watermelon.ipynb
 create mode 100644 projects/machine-learning/sklearn_base.ipynb
 create mode 100644 projects/machine-learning/spaceship_titanic_prediction.ipynb

diff --git a/data/spaceship-titanic/README.md b/data/spaceship-titanic/README.md
new file mode 100644
index 0000000..0b8f512
--- /dev/null
+++ b/data/spaceship-titanic/README.md
@@ -0,0 +1,20 @@
+# 数据说明
+
+项目介绍及数据来源见 Kaggle：[Spaceship Titanic](https://www.kaggle.com/competitions/spaceship-titanic/overview)。
+
+数据详情：
+
+- `data.csv` 训练集样本量约为 8700 人；
+- 数据字段详情：
+  - 数据字段共有 14 个；
+  - 字段详情：
+    - `PassengerId`：每个乘客的唯一标识。其形式为 `gggg_pp`，其中 `gggg` 表示乘客一同乘坐的团体，`pp` 是他们在团体中的编号。团体中的个人通常是家庭成员，**但这也不是绝对的**；
+    - `HomePlanet`：乘客所离开的星球，通常是他们永久居住的星球；
+    - `CryoSleep`：表示乘客是否选择在航行期间进入深度睡眠，处于深度睡眠状态的乘客被限制在他们坐在的船舱内；
+    - `Cabin`：乘客所住的船舱及座位号，采取 `甲板/编号/侧面` 的形式编号，其中侧面的 P 代表左舷（船身左半边），S 代表右舷（船身右半边）；
+    - `Destination`：乘客此次航行的目的地；
+    - `Age`：乘客的年龄；
+    - `VIP`：乘客是否为 VIP；
+    - `RoomService`，`FoodCourt`，`ShoppingMall`，`Spa`，`VRDeck`：即乘客在太空船的众多豪华设施项中的**花销金额**；
+    - `Name`：乘客的名字和姓氏；
+    - `Transported`：乘客是否被运送到另一个空间，**这也是训练集的参考答案以及需要在测试集中预测的结果**。
diff --git a/data/spaceship-titanic/data.csv b/data/spaceship-titanic/data.csv
new file mode 100755
index 0000000..cbfd74d
--- /dev/null
+++ b/data/spaceship-titanic/data.csv
@@ -0,0 +1,8694 @@
+PassengerId,HomePlanet,CryoSleep,Cabin,Destination,Age,VIP,RoomService,FoodCourt,ShoppingMall,Spa,VRDeck,Name,Transported
+0001_01,Europa,False,B/0/P,TRAPPIST-1e,39.0,False,0.0,0.0,0.0,0.0,0.0,Maham Ofracculy,False
+0002_01,Earth,False,F/0/S,TRAPPIST-1e,24.0,False,109.0,9.0,25.0,549.0,44.0,Juanna Vines,True
+0003_01,Europa,False,A/0/S,TRAPPIST-1e,58.0,True,43.0,3576.0,0.0,6715.0,49.0,Altark Susent,False
+0003_02,Europa,False,A/0/S,TRAPPIST-1e,33.0,False,0.0,1283.0,371.0,3329.0,193.0,Solam Susent,False
+0004_01,Earth,False,F/1/S,TRAPPIST-1e,16.0,False,303.0,70.0,151.0,565.0,2.0,Willy Santantines,True
+0005_01,Earth,False,F/0/P,PSO J318.5-22,44.0,False,0.0,483.0,0.0,291.0,0.0,Sandie Hinetthews,True
+0006_01,Earth,False,F/2/S,TRAPPIST-1e,26.0,False,42.0,1539.0,3.0,0.0,0.0,Billex Jacostaffey,True
+0006_02,Earth,True,G/0/S,TRAPPIST-1e,28.0,False,0.0,0.0,0.0,0.0,,Candra Jacostaffey,True
+0007_01,Earth,False,F/3/S,TRAPPIST-1e,35.0,False,0.0,785.0,17.0,216.0,0.0,Andona Beston,True
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+0008_02,Europa,True,B/1/P,TRAPPIST-1e,34.0,False,0.0,0.0,,0.0,0.0,Altardr Flatic,True
+0008_03,Europa,False,B/1/P,55 Cancri e,45.0,False,39.0,7295.0,589.0,110.0,124.0,Wezena Flatic,True
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+0016_01,Mars,True,F/5/P,TRAPPIST-1e,45.0,False,0.0,0.0,0.0,0.0,0.0,Alus Upead,True
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+0024_01,Europa,True,C/2/S,TRAPPIST-1e,62.0,False,0.0,0.0,,0.0,0.0,Penton Fullided,True
+0025_01,Earth,False,F/6/S,TRAPPIST-1e,15.0,False,0.0,225.0,0.0,998.0,0.0,Karard Brookenson,False
+0026_01,Europa,False,C/0/P,55 Cancri e,34.0,False,22.0,6073.0,0.0,1438.0,328.0,Anyoni Unconary,False
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+0031_01,Mars,False,F/9/P,TRAPPIST-1e,47.0,False,214.0,0.0,1411.0,0.0,1229.0,Coobix Datie,True
+0031_02,Mars,False,F/9/P,TRAPPIST-1e,2.0,False,0.0,0.0,0.0,0.0,0.0,Cinets Datie,True
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+0034_01,Europa,True,D/1/S,55 Cancri e,28.0,False,0.0,0.0,0.0,0.0,0.0,Ziba Oingwhedly,True
+0035_01,Mars,False,D/1/P,55 Cancri e,23.0,False,784.0,964.0,0.0,951.0,0.0,Luse Butte,False
+0036_01,Earth,False,F/8/S,55 Cancri e,15.0,,0.0,492.0,48.0,20.0,401.0,Marina Leodger,False
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+0039_01,Earth,True,G/1/P,55 Cancri e,30.0,False,0.0,0.0,,0.0,0.0,Jorgie Batthewitt,False
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+0044_03,Earth,True,G/3/P,PSO J318.5-22,21.0,False,0.0,0.0,0.0,0.0,0.0,Cassa Coopelandez,True
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+3715_04,Earth,True,G/602/P,TRAPPIST-1e,16.0,False,0.0,0.0,0.0,0.0,0.0,Hen Hubbarton,False
+3716_01,Earth,False,G/603/P,TRAPPIST-1e,18.0,False,18.0,2.0,1769.0,1.0,2.0,Fredy Batestonley,True
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+3737_01,Earth,False,G/614/S,TRAPPIST-1e,48.0,False,0.0,0.0,632.0,250.0,0.0,Ryany Bucklandez,False
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+3747_03,Europa,,B/118/P,TRAPPIST-1e,53.0,False,0.0,3377.0,0.0,193.0,5563.0,Dsch Brakebarne,False
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+3761_01,,False,E/222/P,55 Cancri e,45.0,False,824.0,2196.0,385.0,19.0,1113.0,Nuscham Stingive,True
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+3772_02,Earth,False,F/777/P,TRAPPIST-1e,20.0,False,0.0,,422.0,0.0,0.0,Tany Castron,True
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+3790_01,,True,G/620/P,TRAPPIST-1e,13.0,False,0.0,,0.0,,0.0,Trick Meyersones,True
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+3821_01,Earth,False,,PSO J318.5-22,,False,0.0,2.0,0.0,0.0,867.0,Violan Mcphernard,False
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+3850_02,Europa,True,C/137/S,55 Cancri e,19.0,False,0.0,0.0,0.0,0.0,0.0,Astula Unteckery,True
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+3850_05,Europa,True,C/137/S,TRAPPIST-1e,15.0,False,0.0,0.0,0.0,0.0,0.0,Thallah Unteckery,True
+3850_06,Europa,False,C/137/S,TRAPPIST-1e,31.0,False,0.0,1565.0,0.0,97.0,1678.0,Sabiark Unteckery,True
+3850_07,Europa,True,C/137/S,55 Cancri e,25.0,False,0.0,0.0,0.0,0.0,0.0,Azharga Chuble,True
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+4720_01,Earth,False,F/887/S,TRAPPIST-1e,21.0,False,0.0,681.0,51.0,131.0,1.0,Maya Lardy,True
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+4723_02,Earth,False,F/888/S,PSO J318.5-22,24.0,False,154.0,7.0,241.0,213.0,453.0,Gabrin Savaraldez,False
+4725_01,Earth,False,G/767/P,55 Cancri e,27.0,False,866.0,0.0,0.0,0.0,23.0,Leony Georgasey,False
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+4726_02,Europa,False,C/187/S,55 Cancri e,26.0,False,0.0,3452.0,0.0,4.0,1960.0,Korneb Praugnox,True
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+4738_01,Mars,False,F/893/S,TRAPPIST-1e,30.0,False,531.0,807.0,35.0,200.0,0.0,Suls Pread,False
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+4741_02,Mars,False,F/961/P,55 Cancri e,0.0,,0.0,0.0,0.0,0.0,0.0,Chal Bau,True
+4741_03,Mars,,F/961/P,TRAPPIST-1e,6.0,False,0.0,0.0,0.0,0.0,0.0,Oslos Bau,True
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+4822_03,,True,F/981/P,TRAPPIST-1e,22.0,False,0.0,0.0,0.0,0.0,0.0,Miten Hotte,True
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+4827_01,Earth,True,G/789/S,PSO J318.5-22,30.0,,0.0,0.0,0.0,0.0,0.0,Wandy Deckerson,True
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+4832_01,Earth,False,E/301/P,,37.0,False,542.0,440.0,1.0,0.0,0.0,Erman Estron,False
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+4840_01,,True,F/915/S,TRAPPIST-1e,36.0,False,0.0,0.0,,0.0,0.0,,True
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+6414_01,Earth,False,G/1040/S,TRAPPIST-1e,31.0,False,294.0,480.0,31.0,3.0,0.0,Troyra Kellson,True
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+6416_01,Earth,False,,TRAPPIST-1e,21.0,False,365.0,52.0,0.0,0.0,288.0,Karard Deckerson,True
+6418_01,Earth,False,F/1329/P,TRAPPIST-1e,27.0,False,93.0,0.0,0.0,0.0,772.0,Gerly Beachez,False
+6419_01,Earth,,E/428/S,TRAPPIST-1e,31.0,False,,0.0,873.0,0.0,0.0,Estina Holcompson,True
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+6422_02,Mars,True,F/1330/P,TRAPPIST-1e,11.0,False,0.0,0.0,0.0,0.0,0.0,Oats Panie,True
+6422_03,Mars,False,F/1330/P,TRAPPIST-1e,23.0,False,1233.0,0.0,527.0,0.0,0.0,Tote Peake,False
+6422_04,Mars,False,E/408/P,TRAPPIST-1e,34.0,False,801.0,0.0,246.0,1.0,0.0,Unchre Panie,False
+6424_01,Earth,False,F/1331/P,TRAPPIST-1e,20.0,False,,0.0,151.0,508.0,0.0,Stelly Hardley,False
+6428_01,Europa,True,C/198/P,55 Cancri e,20.0,False,,0.0,0.0,0.0,0.0,Nesch Pumbody,True
+6428_02,Europa,True,C/198/P,TRAPPIST-1e,16.0,False,0.0,0.0,0.0,0.0,0.0,Alphah Pumbody,True
+6430_01,Earth,True,G/1037/P,TRAPPIST-1e,18.0,False,0.0,0.0,0.0,0.0,0.0,Rancy Byerton,False
+6431_01,Earth,False,F/1334/P,55 Cancri e,20.0,False,24.0,56.0,1.0,,468.0,Melara Kramosley,False
+6431_02,Earth,True,G/1038/P,PSO J318.5-22,20.0,False,0.0,0.0,0.0,0.0,0.0,Timyra Kramosley,True
+6431_03,Earth,False,G/1038/P,55 Cancri e,27.0,False,30.0,0.0,763.0,0.0,57.0,Adriet Kramosley,False
+6432_01,Europa,False,D/202/P,55 Cancri e,55.0,False,114.0,222.0,0.0,2575.0,2919.0,Asch Stradick,False
+6433_01,Mars,False,D/193/S,55 Cancri e,53.0,False,83.0,0.0,1590.0,60.0,0.0,Permit Prité,True
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+6434_01,Earth,True,G/1039/P,PSO J318.5-22,55.0,False,0.0,0.0,0.0,0.0,0.0,,True
+6436_01,Earth,False,G/1043/S,TRAPPIST-1e,24.0,False,552.0,1.0,0.0,231.0,0.0,Hene Barks,True
+6437_01,Earth,False,F/1228/S,55 Cancri e,24.0,False,14.0,0.0,1.0,528.0,109.0,Loree Drakeekss,True
+6439_01,Earth,True,G/1044/S,PSO J318.5-22,49.0,False,0.0,0.0,0.0,0.0,0.0,Judya Brightez,True
+6442_01,Earth,False,G/1045/S,TRAPPIST-1e,17.0,False,0.0,0.0,0.0,1806.0,0.0,Camiet Gainebergan,False
+6443_01,Earth,False,G/1046/S,TRAPPIST-1e,39.0,False,0.0,0.0,710.0,56.0,1.0,Rence Patrichane,True
+6443_02,Earth,True,G/1046/S,55 Cancri e,5.0,False,0.0,0.0,0.0,0.0,0.0,Shanie Cartyernan,True
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+6443_05,Earth,False,F/1229/S,TRAPPIST-1e,16.0,False,45.0,0.0,622.0,0.0,564.0,Kimena Cartyernan,False
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+6443_07,Earth,True,G/1046/S,TRAPPIST-1e,5.0,False,0.0,0.0,0.0,0.0,0.0,Everly Cochrisons,True
+6444_01,Earth,,G/1041/P,TRAPPIST-1e,19.0,False,0.0,0.0,0.0,0.0,0.0,Sandie Domington,False
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+6446_03,Earth,True,G/1047/S,TRAPPIST-1e,1.0,False,0.0,0.0,0.0,0.0,0.0,Willip Yatters,True
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+6450_02,Europa,True,C/200/P,55 Cancri e,42.0,False,0.0,0.0,0.0,0.0,0.0,Altaira Unative,True
+6451_01,Mars,False,,,37.0,False,610.0,,410.0,148.0,14.0,Carkes Panad,False
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+6452_02,Earth,False,G/1044/P,TRAPPIST-1e,12.0,False,,0.0,0.0,0.0,0.0,Iree Woodwardy,True
+6452_03,Earth,True,G/1044/P,TRAPPIST-1e,2.0,False,0.0,0.0,0.0,0.0,0.0,Racyle Woodwardy,True
+6453_01,Earth,False,G/1045/P,PSO J318.5-22,28.0,False,0.0,7.0,17.0,0.0,1724.0,Bel Harmontry,False
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+6455_01,Europa,False,D/203/P,TRAPPIST-1e,26.0,False,0.0,3818.0,0.0,1877.0,136.0,Alrak Unative,False
+6456_01,Earth,,E/410/P,TRAPPIST-1e,36.0,False,32.0,3.0,657.0,58.0,41.0,Miken Gainney,False
+6457_01,Earth,False,F/1337/P,55 Cancri e,28.0,False,,728.0,0.0,0.0,82.0,Veroy Morrows,True
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+6459_01,Earth,True,G/1047/P,TRAPPIST-1e,14.0,False,0.0,0.0,0.0,0.0,0.0,Danie Olshaffey,False
+6461_01,Earth,False,F/1232/S,TRAPPIST-1e,28.0,False,0.0,602.0,0.0,115.0,0.0,Henrie Moodmandez,True
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+6463_01,Earth,False,F/1234/S,TRAPPIST-1e,24.0,False,1256.0,2.0,8.0,1530.0,0.0,Guadae Jacobbinson,False
+6464_01,Earth,False,F/1338/P,PSO J318.5-22,18.0,False,22.0,0.0,20.0,596.0,5.0,Arllia Ellahan,False
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+6468_04,Mars,False,D/204/P,TRAPPIST-1e,32.0,True,1179.0,0.0,4.0,0.0,0.0,Unchal Crité,False
+6470_01,Earth,False,E/432/S,TRAPPIST-1e,19.0,False,14.0,614.0,0.0,160.0,0.0,Sterry Chardst,False
+6471_01,Earth,False,G/1050/S,TRAPPIST-1e,12.0,False,0.0,0.0,0.0,0.0,0.0,Adria Cardners,True
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+6472_02,Europa,True,B/250/S,55 Cancri e,13.0,False,0.0,0.0,0.0,0.0,0.0,Cellah Steary,True
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+6477_01,Europa,False,D/196/S,55 Cancri e,23.0,False,0.0,44.0,0.0,3452.0,83.0,Tabih Witeronfus,False
+6477_02,Europa,False,D/196/S,55 Cancri e,17.0,False,0.0,311.0,6.0,4236.0,557.0,Ranan Witeronfus,False
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+6481_01,Earth,False,G/1052/S,TRAPPIST-1e,22.0,False,89.0,0.0,795.0,0.0,3.0,Jayne Bulliamoses,True
+6482_01,Mars,False,F/1237/S,55 Cancri e,26.0,False,414.0,239.0,681.0,352.0,0.0,Chtios Letie,False
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+6485_01,Earth,False,F/1238/S,55 Cancri e,78.0,False,0.0,213.0,202.0,288.0,0.0,Regory Haydenzier,True
+6485_02,Earth,False,G/1053/S,TRAPPIST-1e,24.0,False,0.0,0.0,53.0,652.0,0.0,Benry Haydenzier,True
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+6489_01,Earth,True,G/1049/P,TRAPPIST-1e,16.0,False,0.0,0.0,0.0,0.0,0.0,Elsane Balley,False
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+6490_02,Europa,False,C/201/P,55 Cancri e,29.0,False,0.0,889.0,0.0,4921.0,290.0,Phecca Ineded,False
+6490_03,Europa,False,C/201/P,55 Cancri e,27.0,False,0.0,6924.0,849.0,149.0,1166.0,Tabiton Ightive,True
+6490_04,,False,C/201/P,TRAPPIST-1e,42.0,False,0.0,1972.0,0.0,108.0,5.0,Marga Ineded,True
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+6493_01,Earth,True,G/1050/P,PSO J318.5-22,18.0,False,0.0,0.0,0.0,0.0,0.0,Terald Dillips,True
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+6502_01,Earth,False,G/1058/S,TRAPPIST-1e,6.0,,0.0,0.0,0.0,0.0,0.0,Thelix Mcdanield,True
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+6508_01,Earth,False,F/1242/S,PSO J318.5-22,32.0,False,397.0,0.0,7.0,182.0,5.0,Tonyn Braymon,False
+6509_01,Earth,False,F/1243/S,PSO J318.5-22,23.0,False,601.0,0.0,113.0,0.0,39.0,Belix Adavisons,True
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+6511_03,Earth,True,G/1055/P,55 Cancri e,0.0,False,0.0,0.0,0.0,0.0,0.0,Thelix Gainney,True
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+6522_01,Earth,False,F/1245/S,55 Cancri e,20.0,False,0.0,0.0,715.0,2.0,0.0,Breney Garden,True
+6523_01,Earth,True,F/1246/S,PSO J318.5-22,20.0,False,0.0,0.0,0.0,0.0,0.0,Derine Bardina,True
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+6525_02,Earth,True,G/1061/S,PSO J318.5-22,32.0,False,0.0,0.0,0.0,0.0,0.0,Mistie Cardners,True
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+6548_01,Earth,False,G/1065/S,TRAPPIST-1e,38.0,False,395.0,1035.0,0.0,0.0,4.0,Stanya Kellyons,True
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+6549_02,Mars,False,D/198/S,TRAPPIST-1e,61.0,,1143.0,0.0,580.0,0.0,0.0,Llat Flate,False
+6552_01,,False,G/1061/P,TRAPPIST-1e,20.0,False,0.0,564.0,25.0,158.0,62.0,Fayey Webstenson,False
+6553_01,Earth,False,F/1360/P,PSO J318.5-22,39.0,False,0.0,0.0,0.0,0.0,744.0,Bel Phillis,False
+6556_01,Mars,True,F/1362/P,TRAPPIST-1e,29.0,False,0.0,0.0,0.0,0.0,0.0,Nes Gine,True
+6556_02,Mars,True,F/1362/P,55 Cancri e,68.0,False,0.0,0.0,0.0,0.0,0.0,Morms Gine,True
+6557_01,Europa,False,E/417/P,55 Cancri e,38.0,False,0.0,201.0,0.0,1308.0,4085.0,Batares Airdring,False
+6558_01,Earth,False,F/1363/P,TRAPPIST-1e,31.0,False,3.0,0.0,0.0,747.0,43.0,Philda Cleminglas,True
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+6734_01,Europa,False,C/249/S,55 Cancri e,47.0,False,0.0,2699.0,0.0,671.0,8.0,Pleion Sparsaxle,True
+6734_02,Europa,True,C/249/S,55 Cancri e,14.0,False,0.0,0.0,0.0,0.0,0.0,Shail Sparsaxle,True
+6734_03,Europa,True,C/249/S,55 Cancri e,37.0,True,0.0,0.0,0.0,0.0,0.0,Alhaino Sparsaxle,True
+6734_04,Europa,True,C/249/S,55 Cancri e,33.0,False,0.0,0.0,0.0,0.0,0.0,Wein Sparsaxle,True
+6736_01,Mars,False,D/203/S,,33.0,False,1373.0,0.0,23.0,920.0,6.0,Frunk Erry,True
+6739_01,Earth,False,G/1097/S,TRAPPIST-1e,44.0,False,0.0,714.0,174.0,0.0,0.0,Ninaha Stantiagord,True
+6741_01,Earth,False,G/1098/S,TRAPPIST-1e,37.0,False,0.0,1190.0,4.0,455.0,71.0,Heald Reyersendry,True
+6741_02,Earth,,F/1286/S,PSO J318.5-22,18.0,,103.0,0.0,181.0,1611.0,22.0,Jeandy Reyersendry,False
+6742_01,Earth,False,F/1287/S,TRAPPIST-1e,33.0,False,1288.0,2.0,0.0,0.0,1.0,Elanie Talls,True
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+6744_02,Mars,False,F/1399/P,TRAPPIST-1e,38.0,False,1717.0,16.0,39.0,0.0,48.0,Ane Nan,False
+6744_03,Mars,False,D/212/P,55 Cancri e,16.0,False,1337.0,0.0,2614.0,0.0,0.0,Crick Nan,False
+6744_04,Mars,True,E/431/P,TRAPPIST-1e,58.0,False,0.0,,0.0,0.0,0.0,Frus Nan,False
+6744_05,Mars,False,E/431/P,TRAPPIST-1e,41.0,False,1203.0,756.0,140.0,0.0,0.0,Row Nan,False
+6745_01,Earth,False,F/1400/P,PSO J318.5-22,28.0,False,545.0,0.0,501.0,0.0,0.0,Jimmyn Mcinton,False
+6746_01,Earth,True,G/1092/P,TRAPPIST-1e,28.0,False,0.0,0.0,0.0,0.0,0.0,Luison Mcinton,True
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+6748_03,Mars,False,F/1289/S,TRAPPIST-1e,25.0,False,1610.0,29.0,6.0,0.0,0.0,Gut Scake,False
+6748_04,Mars,False,D/205/S,TRAPPIST-1e,34.0,False,470.0,0.0,,45.0,539.0,Moss Scake,False
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+6756_02,Earth,False,G/1095/P,PSO J318.5-22,46.0,False,0.0,0.0,743.0,4.0,6.0,Monah Pittson,True
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+6760_01,Mars,False,F/1404/P,PSO J318.5-22,30.0,False,51.0,0.0,1373.0,0.0,5.0,Shex Pri,True
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+6762_01,Earth,False,F/1293/S,55 Cancri e,54.0,False,0.0,704.0,0.0,0.0,0.0,Monard Jenkinnedy,False
+6763_01,Earth,False,G/1097/P,TRAPPIST-1e,20.0,False,0.0,97.0,725.0,26.0,104.0,Bonyam Berges,True
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+6766_01,Earth,False,G/1098/P,PSO J318.5-22,16.0,False,399.0,0.0,3.0,0.0,137.0,Viviay Nieldson,False
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+6832_01,Mars,True,F/1421/P,TRAPPIST-1e,33.0,False,0.0,0.0,0.0,0.0,0.0,Quid Wala,True
+6833_01,Earth,False,E/452/S,,23.0,False,7.0,0.0,0.0,716.0,800.0,Sta Mccarveymon,True
+6834_01,Earth,True,G/1111/S,TRAPPIST-1e,23.0,False,0.0,0.0,0.0,0.0,,Sonald Coolez,True
+6837_01,Mars,True,E/435/P,TRAPPIST-1e,43.0,False,0.0,0.0,0.0,0.0,0.0,Cireal Carté,True
+6839_01,Earth,False,F/1307/S,TRAPPIST-1e,36.0,False,1912.0,0.0,0.0,2.0,0.0,Ellena Fryan,False
+6840_01,Mars,True,F/1422/P,TRAPPIST-1e,26.0,False,0.0,0.0,0.0,0.0,0.0,Clow Sake,True
+6841_01,Mars,True,E/454/S,55 Cancri e,29.0,False,,0.0,0.0,0.0,0.0,Tekris Meate,True
+6843_01,Europa,True,B/260/S,TRAPPIST-1e,21.0,False,0.0,,0.0,0.0,0.0,Acceleb Rimplighty,True
+6843_02,Europa,True,B/260/S,55 Cancri e,30.0,False,0.0,0.0,0.0,0.0,0.0,Saxinon Rimplighty,True
+6844_01,Mars,False,E/455/S,55 Cancri e,23.0,False,71.0,0.0,1376.0,0.0,890.0,Chones Brin,False
+6848_01,Europa,True,D/214/P,TRAPPIST-1e,13.0,False,0.0,0.0,0.0,0.0,0.0,Marinon Ambleetive,True
+6850_01,Earth,False,F/1424/P,TRAPPIST-1e,22.0,,735.0,138.0,,0.0,0.0,Adamy Wolferton,False
+6850_02,Earth,True,G/1113/P,PSO J318.5-22,25.0,False,0.0,0.0,0.0,0.0,0.0,Weslie Wolferton,False
+6850_03,Earth,False,E/436/P,TRAPPIST-1e,52.0,False,2.0,46.0,800.0,0.0,773.0,Brancy Wolferton,False
+6851_01,Earth,False,E/456/S,TRAPPIST-1e,22.0,False,0.0,752.0,18.0,0.0,18.0,Glorey Bookerson,True
+6853_01,Europa,False,C/218/P,55 Cancri e,31.0,False,0.0,1604.0,4058.0,4094.0,124.0,Menkaa Cathome,False
+6854_01,Mars,False,E/437/P,TRAPPIST-1e,23.0,False,3211.0,4.0,243.0,0.0,0.0,Graw Bate,False
+6857_01,Earth,False,F/1310/S,PSO J318.5-22,35.0,False,22.0,0.0,1179.0,0.0,33.0,Jenn Jenson,False
+6858_01,Europa,True,B/229/P,TRAPPIST-1e,26.0,False,0.0,0.0,0.0,0.0,0.0,Asteron Spandisket,True
+6858_02,Europa,True,B/229/P,TRAPPIST-1e,14.0,False,0.0,0.0,0.0,0.0,0.0,Kabiak Spandisket,True
+6858_03,Europa,False,B/229/P,55 Cancri e,39.0,False,0.0,60.0,0.0,310.0,4674.0,Zinopus Spandisket,False
+6858_04,Europa,False,B/229/P,,8.0,False,0.0,0.0,0.0,0.0,0.0,Taura Spandisket,True
+6859_01,Earth,True,G/1113/S,PSO J318.5-22,29.0,False,0.0,0.0,0.0,0.0,0.0,Blancy Harry,True
+6861_01,Earth,True,G/1114/S,PSO J318.5-22,15.0,False,0.0,0.0,0.0,0.0,0.0,Iney Lambucketty,True
+6862_01,Earth,True,G/1115/S,TRAPPIST-1e,48.0,False,0.0,0.0,0.0,0.0,0.0,Diandi Mcfaddennon,True
+6862_02,Earth,False,G/1115/S,TRAPPIST-1e,9.0,False,0.0,0.0,0.0,0.0,0.0,Dianie Mcfaddennon,False
+6863_01,Earth,False,F/1311/S,TRAPPIST-1e,25.0,False,0.0,10.0,909.0,0.0,17.0,Franda Patrichane,False
+6864_01,Europa,True,A/71/P,TRAPPIST-1e,13.0,False,0.0,0.0,0.0,0.0,0.0,Aske Scoudelle,True
+6864_02,Europa,True,A/71/P,55 Cancri e,25.0,,0.0,0.0,0.0,0.0,0.0,Phamark Scoudelle,True
+6865_01,Europa,False,D/208/S,TRAPPIST-1e,27.0,,69.0,2878.0,0.0,4232.0,3798.0,Thabih Peducting,False
+6865_02,Europa,True,D/208/S,TRAPPIST-1e,32.0,False,0.0,0.0,0.0,0.0,0.0,Errim Peducting,True
+6866_01,Europa,False,C/253/S,55 Cancri e,20.0,False,0.0,17958.0,99.0,14.0,57.0,Gork Hetforhaft,True
+6866_02,Europa,False,C/253/S,55 Cancri e,40.0,False,6103.0,7570.0,860.0,13117.0,192.0,Dscha Hetforhaft,False
+6868_01,Earth,False,F/1427/P,TRAPPIST-1e,18.0,False,0.0,18.0,73.0,2134.0,25.0,Jerek Brookenson,False
+6869_01,Earth,False,F/1428/P,TRAPPIST-1e,21.0,False,0.0,0.0,901.0,3.0,0.0,Megany Philloway,False
+6871_01,Earth,False,G/1116/S,TRAPPIST-1e,38.0,False,190.0,1.0,616.0,0.0,,Faithy Rickson,True
+6873_01,Europa,False,A/72/P,55 Cancri e,62.0,False,362.0,1049.0,46.0,104.0,829.0,Mesarix Hervel,True
+6879_01,Earth,True,G/1119/S,TRAPPIST-1e,23.0,False,0.0,0.0,0.0,0.0,0.0,Joyces Englence,True
+6880_01,Mars,False,F/1315/S,TRAPPIST-1e,48.0,True,2191.0,0.0,172.0,1.0,0.0,Dunch Mepie,False
+6880_02,Mars,True,E/457/S,TRAPPIST-1e,27.0,False,0.0,0.0,0.0,0.0,0.0,Gacke Mepie,False
+6882_01,Mars,True,F/1430/P,55 Cancri e,32.0,False,0.0,0.0,0.0,0.0,0.0,Rail Dake,True
+6882_02,Mars,False,E/438/P,TRAPPIST-1e,53.0,False,0.0,0.0,0.0,0.0,0.0,Giamon Dake,False
+6883_01,Earth,False,G/1120/S,TRAPPIST-1e,26.0,False,0.0,0.0,1.0,0.0,398.0,Evaned Wolferguson,True
+6885_01,Mars,True,F/1316/S,TRAPPIST-1e,46.0,False,,0.0,0.0,0.0,0.0,Whee Vloaf,True
+6887_01,Earth,False,G/1122/S,TRAPPIST-1e,32.0,False,0.0,326.0,31.0,5.0,545.0,Beathy Patrichane,False
+6889_01,Earth,False,F/1317/S,,40.0,False,862.0,0.0,0.0,0.0,59.0,Sallie Santuez,False
+6891_01,Mars,False,D/215/P,TRAPPIST-1e,27.0,False,725.0,0.0,1660.0,0.0,0.0,Treynx Casty,True
+6892_01,Earth,,F/1318/S,TRAPPIST-1e,28.0,False,0.0,669.0,0.0,1.0,2.0,Cellex Merkins,True
+6893_01,Europa,True,B/261/S,TRAPPIST-1e,29.0,False,0.0,0.0,0.0,0.0,0.0,Winork Dreamspric,True
+6894_01,Mars,True,F/1319/S,TRAPPIST-1e,37.0,False,0.0,0.0,0.0,0.0,0.0,,True
+6894_02,Mars,False,F/1319/S,TRAPPIST-1e,0.0,False,0.0,0.0,0.0,0.0,0.0,Otlios Hacle,True
+6894_03,Mars,False,F/1319/S,TRAPPIST-1e,12.0,,0.0,0.0,0.0,0.0,0.0,Risps Hacle,True
+6895_01,Europa,False,C/220/P,TRAPPIST-1e,25.0,False,0.0,4612.0,0.0,2585.0,1919.0,Ainon Conachets,False
+6896_01,Mars,False,E/459/S,TRAPPIST-1e,,False,473.0,0.0,575.0,0.0,0.0,Stote Khake,False
+6896_02,Mars,False,F/1320/S,TRAPPIST-1e,3.0,False,0.0,0.0,0.0,0.0,0.0,Goats Khake,True
+6896_03,Mars,False,F/1320/S,TRAPPIST-1e,9.0,False,0.0,0.0,0.0,0.0,0.0,Dies Khake,True
+6898_01,Europa,False,,55 Cancri e,27.0,False,146.0,3786.0,490.0,0.0,18.0,Gemmark Bootious,True
+6899_01,Europa,False,A/73/P,TRAPPIST-1e,59.0,False,0.0,0.0,0.0,0.0,0.0,Anyonon Coweboded,True
+6899_02,Europa,,A/73/P,TRAPPIST-1e,24.0,False,0.0,0.0,0.0,0.0,0.0,Ruchban Coweboded,False
+6900_01,Earth,True,G/1123/S,TRAPPIST-1e,16.0,False,0.0,0.0,0.0,0.0,0.0,Kyley Hendrys,True
+6901_01,Mars,False,F/1321/S,TRAPPIST-1e,16.0,False,501.0,974.0,1164.0,0.0,0.0,Anch Opie,False
+6902_01,Europa,True,D/217/P,TRAPPIST-1e,46.0,False,0.0,0.0,0.0,0.0,0.0,Strali Idalpus,True
+6903_01,Earth,False,F/1431/P,PSO J318.5-22,25.0,False,0.0,1428.0,0.0,1483.0,0.0,Gabrin Woodwinez,False
+6905_01,Earth,False,F/1322/S,TRAPPIST-1e,39.0,False,130.0,741.0,0.0,0.0,0.0,Vana Acobson,False
+6905_02,Earth,False,E/460/S,TRAPPIST-1e,15.0,False,0.0,0.0,0.0,938.0,44.0,Lilasa Acobson,False
+6905_03,Earth,False,F/1322/S,55 Cancri e,16.0,False,43.0,3.0,750.0,0.0,0.0,Gaylah Acobson,True
+6906_01,Mars,False,F/1432/P,TRAPPIST-1e,35.0,False,1719.0,4389.0,1015.0,0.0,14.0,Elix Pelte,True
+6906_02,Mars,False,E/440/P,55 Cancri e,43.0,False,528.0,13.0,1583.0,11.0,0.0,Apies Bun,False
+6906_03,Mars,True,F/1432/P,TRAPPIST-1e,7.0,False,0.0,0.0,0.0,0.0,0.0,Sesh Pelte,True
+6907_01,Earth,True,G/1117/P,TRAPPIST-1e,29.0,False,0.0,0.0,0.0,0.0,0.0,Louisa Mcmahoney,True
+6908_01,Europa,False,C/254/S,TRAPPIST-1e,60.0,False,0.0,960.0,46.0,854.0,17074.0,Sheleon Swinvul,False
+6910_01,Earth,False,G/1124/S,TRAPPIST-1e,49.0,False,173.0,0.0,18.0,580.0,1.0,Carry Johnsondez,False
+6911_01,Mars,False,F/1323/S,TRAPPIST-1e,50.0,False,118.0,0.0,1041.0,0.0,0.0,Powhex Sutte,True
+6912_01,Europa,False,C/221/P,TRAPPIST-1e,40.0,False,0.0,120.0,0.0,2814.0,318.0,Weino Slomed,False
+6913_01,Mars,True,F/1324/S,TRAPPIST-1e,49.0,False,0.0,0.0,0.0,0.0,0.0,Slus Hacle,True
+6913_02,Mars,False,E/461/S,,18.0,False,1988.0,0.0,95.0,0.0,0.0,Cakers Hacle,False
+6915_01,Mars,True,F/1325/S,TRAPPIST-1e,29.0,False,0.0,0.0,0.0,0.0,0.0,Chrets Conca,True
+6917_01,Mars,False,F/1433/P,TRAPPIST-1e,56.0,True,995.0,0.0,177.0,0.0,739.0,Proos Horte,False
+6918_01,Earth,True,G/1126/S,PSO J318.5-22,29.0,False,0.0,0.0,0.0,0.0,0.0,Neilda Barris,True
+6920_01,Earth,False,E/462/S,TRAPPIST-1e,29.0,False,0.0,10.0,0.0,5.0,975.0,Verek Wolferguson,False
+6921_01,Europa,False,C/255/S,TRAPPIST-1e,22.0,False,0.0,1914.0,2090.0,100.0,0.0,Sadrix Stersetery,True
+6921_02,Europa,True,C/255/S,55 Cancri e,24.0,False,0.0,0.0,0.0,,0.0,Eleron Fordulgaug,True
+6921_03,Europa,False,C/255/S,TRAPPIST-1e,23.0,False,69.0,6101.0,0.0,0.0,214.0,Grum Stersetery,True
+6921_04,Europa,False,C/255/S,TRAPPIST-1e,28.0,True,0.0,14076.0,0.0,928.0,372.0,Dsch Stersetery,True
+6921_05,Europa,True,C/255/S,55 Cancri e,19.0,False,0.0,0.0,0.0,0.0,0.0,Arrain Fordulgaug,True
+6921_06,Europa,False,C/255/S,TRAPPIST-1e,32.0,False,0.0,89.0,0.0,8330.0,1.0,Azhah Stersetery,False
+6924_01,Earth,False,G/1127/S,TRAPPIST-1e,37.0,False,0.0,0.0,0.0,0.0,0.0,Lilasa Solon,True
+6925_01,Earth,False,G/1120/P,TRAPPIST-1e,26.0,False,0.0,0.0,677.0,1.0,108.0,Juanie Hayerson,False
+6926_01,,False,F/1435/P,TRAPPIST-1e,43.0,False,1944.0,17.0,657.0,0.0,0.0,Smals Birt,True
+6928_01,Earth,False,E/463/S,TRAPPIST-1e,25.0,False,439.0,0.0,0.0,557.0,0.0,Dont Bookerson,False
+6928_02,Earth,False,G/1128/S,TRAPPIST-1e,11.0,False,0.0,0.0,0.0,0.0,0.0,Jodye Bookerson,False
+6929_01,Earth,False,G/1122/P,TRAPPIST-1e,4.0,False,0.0,0.0,0.0,0.0,0.0,Nelly Llovers,True
+6929_02,Earth,True,G/1122/P,TRAPPIST-1e,32.0,False,0.0,0.0,0.0,0.0,0.0,Natha Llovers,True
+6929_03,Earth,False,E/441/P,TRAPPIST-1e,15.0,False,9.0,776.0,4.0,0.0,2.0,Tricki Llovers,True
+6930_01,Earth,True,G/1129/S,TRAPPIST-1e,17.0,False,0.0,0.0,0.0,0.0,0.0,Letta Howayery,True
+6931_01,Europa,True,C/222/P,55 Cancri e,37.0,False,0.0,0.0,0.0,0.0,0.0,Spurin Amonsmane,True
+6932_01,Earth,False,G/1130/S,TRAPPIST-1e,20.0,False,559.0,0.0,83.0,112.0,0.0,Shaele Ewiseston,True
+6934_01,Mars,False,F/1436/P,TRAPPIST-1e,47.0,False,160.0,0.0,1232.0,36.0,0.0,Cats Nutty,True
+6934_02,Mars,False,E/443/P,55 Cancri e,16.0,False,1228.0,0.0,450.0,446.0,,Bleark Nutty,False
+6937_01,Earth,True,G/1131/S,TRAPPIST-1e,8.0,False,0.0,0.0,0.0,0.0,0.0,Robyna Petton,True
+6939_01,Earth,False,G/1124/P,TRAPPIST-1e,22.0,False,245.0,0.0,0.0,661.0,1.0,Blanie Bears,False
+6940_01,Mars,False,F/1326/S,TRAPPIST-1e,39.0,False,9.0,0.0,687.0,0.0,0.0,Goats Steen,True
+6943_01,Europa,True,B/262/S,55 Cancri e,57.0,False,0.0,0.0,0.0,0.0,0.0,Mirakis Dynatic,True
+6944_01,Europa,True,C/223/P,TRAPPIST-1e,29.0,False,0.0,0.0,0.0,0.0,0.0,Alzird Gented,True
+6945_01,Europa,True,B/263/S,55 Cancri e,,False,0.0,0.0,0.0,0.0,0.0,Schird Sophaft,True
+6946_01,Earth,False,F/1439/P,TRAPPIST-1e,70.0,False,28.0,0.0,0.0,672.0,104.0,Samany Buckson,False
+6947_01,Europa,True,B/264/S,TRAPPIST-1e,13.0,False,0.0,0.0,0.0,0.0,0.0,Becruxa Coudered,True
+6947_02,Europa,False,B/264/S,TRAPPIST-1e,49.0,False,0.0,1757.0,0.0,797.0,567.0,Cellum Obnoble,True
+6949_01,Earth,False,E/464/S,55 Cancri e,25.0,False,0.0,12.0,851.0,19.0,103.0,Lolas Dillestron,False
+6950_01,Europa,True,B/265/S,55 Cancri e,19.0,False,0.0,0.0,0.0,0.0,0.0,Alurops Pringry,True
+6950_02,Europa,True,B/265/S,55 Cancri e,15.0,False,0.0,0.0,0.0,0.0,0.0,Medib Pringry,True
+6951_01,Europa,False,D/219/P,TRAPPIST-1e,37.0,False,26.0,2898.0,9.0,0.0,40.0,Vegas Lendly,True
+6951_02,Europa,True,D/219/P,TRAPPIST-1e,,False,0.0,0.0,0.0,0.0,0.0,Mares Beling,True
+6954_01,Mars,False,E/445/P,TRAPPIST-1e,28.0,,1998.0,0.0,13.0,87.0,0.0,Blug Flie,False
+6954_02,,False,E/445/P,TRAPPIST-1e,22.0,False,969.0,47.0,23.0,134.0,0.0,Weet Flie,False
+6957_01,Europa,False,B/266/S,TRAPPIST-1e,26.0,False,135.0,2080.0,7.0,34.0,84.0,Minoton Sloweber,True
+6960_01,Earth,False,G/1135/S,PSO J318.5-22,25.0,False,60.0,0.0,0.0,0.0,777.0,Allyn Bonnondry,False
+6962_01,Earth,False,G/1136/S,TRAPPIST-1e,19.0,False,0.0,0.0,0.0,804.0,5.0,Dona Gibbsonton,False
+6963_01,Mars,False,F/1328/S,TRAPPIST-1e,20.0,False,3839.0,57.0,1967.0,4.0,2.0,Soyos Boste,False
+6964_01,Earth,False,F/1444/P,55 Cancri e,35.0,False,0.0,71.0,631.0,0.0,0.0,Lilasa Thonyderson,False
+6966_01,Earth,False,F/1446/P,TRAPPIST-1e,37.0,False,951.0,0.0,0.0,80.0,0.0,Leneva Freevenson,False
+6967_01,Europa,False,C/256/S,55 Cancri e,37.0,False,0.0,9965.0,0.0,5697.0,676.0,Winon Oilpfulle,False
+6968_01,Earth,False,F/1447/P,TRAPPIST-1e,13.0,False,0.0,114.0,682.0,0.0,7.0,Dandra Yanton,False
+6968_02,Earth,False,F/1447/P,PSO J318.5-22,20.0,False,0.0,,0.0,0.0,1318.0,Randa Yanton,False
+6970_01,Mars,False,,TRAPPIST-1e,65.0,,3358.0,1.0,12.0,0.0,0.0,Ret Gin,False
+6970_02,Mars,False,E/465/S,,29.0,False,456.0,230.0,363.0,0.0,0.0,Coakey Pread,False
+6971_01,Europa,False,C/225/P,TRAPPIST-1e,0.0,False,0.0,0.0,0.0,0.0,,Alcoran Distured,True
+6971_02,Europa,False,C/225/P,TRAPPIST-1e,4.0,False,0.0,0.0,0.0,0.0,0.0,Chard Distured,True
+6971_03,Europa,True,C/225/P,TRAPPIST-1e,19.0,False,0.0,0.0,0.0,0.0,0.0,Alkaida Distured,True
+6971_04,Europa,True,C/225/P,55 Cancri e,17.0,False,0.0,0.0,0.0,0.0,0.0,Asteron Distured,True
+6971_05,Europa,False,C/225/P,55 Cancri e,28.0,False,0.0,4173.0,0.0,33.0,420.0,Schird Pretive,True
+6971_06,Europa,False,C/225/P,55 Cancri e,28.0,False,0.0,160.0,0.0,1538.0,1426.0,Terfak Distured,False
+6972_01,,False,G/1126/P,TRAPPIST-1e,21.0,False,0.0,0.0,0.0,43.0,667.0,Terina Jenson,False
+6973_01,Earth,False,E/466/S,PSO J318.5-22,45.0,False,1.0,15.0,44.0,0.0,1541.0,Hary Owerson,False
+6976_01,Earth,False,G/1127/P,TRAPPIST-1e,21.0,False,27.0,,782.0,0.0,0.0,Annya Talls,True
+6977_01,Earth,True,G/1137/S,PSO J318.5-22,20.0,False,0.0,0.0,0.0,0.0,0.0,Rankie Burker,True
+6977_02,Earth,False,G/1137/S,TRAPPIST-1e,38.0,False,0.0,806.0,3.0,0.0,0.0,Jeret Burker,True
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+8381_01,Earth,False,E/550/S,TRAPPIST-1e,25.0,False,0.0,0.0,0.0,788.0,3.0,Line Vinozarks,False
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+8383_03,Earth,False,G/1368/P,TRAPPIST-1e,3.0,False,0.0,0.0,0.0,0.0,0.0,Garry Gibbsonton,True
+8383_04,Earth,True,G/1368/P,TRAPPIST-1e,3.0,False,0.0,0.0,0.0,0.0,0.0,Weslie Gibbsonton,True
+8383_05,Earth,False,G/1368/P,TRAPPIST-1e,8.0,,0.0,0.0,0.0,0.0,0.0,Gwenda Gibbsonton,False
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+8383_07,Earth,False,G/1368/P,TRAPPIST-1e,11.0,False,0.0,0.0,0.0,0.0,0.0,Elica Gibbsonton,False
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+8386_01,Earth,False,G/1369/P,55 Cancri e,27.0,False,0.0,1283.0,0.0,2256.0,0.0,Ferry Chavezalez,False
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+8390_01,Earth,True,G/1370/P,PSO J318.5-22,14.0,False,0.0,0.0,0.0,0.0,0.0,Arla Sextones,True
+8392_01,Earth,False,F/1610/S,PSO J318.5-22,24.0,False,86.0,669.0,1.0,0.0,0.0,Therly Brightez,False
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+8394_01,Europa,False,B/276/P,55 Cancri e,33.0,True,9.0,1365.0,0.0,5577.0,1483.0,Rasalm Foller,False
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+8401_01,Europa,True,C/315/S,55 Cancri e,31.0,False,0.0,0.0,0.0,0.0,0.0,Ariton Susent,True
+8403_01,Europa,True,C/316/S,55 Cancri e,35.0,False,0.0,0.0,0.0,0.0,0.0,Antitak Goortedant,True
+8404_01,Earth,True,G/1346/S,TRAPPIST-1e,48.0,False,0.0,0.0,0.0,,0.0,Leria Hurles,True
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+8429_01,Earth,True,G/1351/S,55 Cancri e,23.0,False,0.0,0.0,0.0,0.0,0.0,Briney Donson,True
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+8431_02,Europa,,B/277/P,55 Cancri e,26.0,False,1.0,4680.0,25.0,564.0,13.0,Zinoces Myling,False
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+8444_01,Earth,False,F/1733/P,TRAPPIST-1e,48.0,False,0.0,1570.0,1.0,0.0,2.0,Robyna Monsoney,False
+8447_01,Earth,False,F/1734/P,TRAPPIST-1e,25.0,False,1.0,0.0,1.0,14.0,1491.0,Erine Joyneidez,False
+8448_01,Europa,True,B/278/P,55 Cancri e,46.0,False,0.0,0.0,0.0,0.0,,Phernar Haftical,True
+8449_01,Europa,False,C/317/S,55 Cancri e,32.0,False,0.0,2154.0,0.0,9655.0,859.0,Arrain Purellliss,False
+8449_02,Europa,False,C/317/S,TRAPPIST-1e,50.0,False,0.0,3070.0,0.0,44.0,562.0,Sarino Purellliss,True
+8451_01,Mars,False,F/1625/S,55 Cancri e,31.0,False,1997.0,0.0,0.0,0.0,0.0,Ale Start,False
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+8452_01,Europa,False,C/279/P,55 Cancri e,52.0,True,0.0,831.0,1536.0,0.0,7953.0,Alraia Voliat,False
+8452_02,Europa,True,C/279/P,TRAPPIST-1e,34.0,False,0.0,0.0,0.0,0.0,0.0,Jabbab Voliat,True
+8452_03,Europa,True,C/279/P,55 Cancri e,40.0,False,0.0,0.0,0.0,0.0,0.0,Atrino Voliat,True
+8452_04,Europa,False,C/279/P,TRAPPIST-1e,36.0,False,0.0,1055.0,18.0,1669.0,49.0,Muonon Voliat,True
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+8458_02,Europa,True,C/319/S,55 Cancri e,24.0,False,0.0,0.0,0.0,0.0,0.0,Canon Sempreate,True
+8458_03,Europa,True,C/319/S,55 Cancri e,19.0,False,0.0,0.0,0.0,0.0,0.0,Etamino Sempreate,True
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+8469_01,Earth,True,G/1356/S,55 Cancri e,23.0,False,0.0,0.0,0.0,0.0,0.0,Benja Cames,True
+8470_01,Europa,,C/281/P,55 Cancri e,31.0,True,8243.0,280.0,0.0,3220.0,2285.0,Brafirk Aloubtled,False
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+8474_01,Earth,,F/1632/S,55 Cancri e,21.0,False,39.0,53.0,94.0,33.0,518.0,Eriky Weaves,False
+8475_01,Europa,False,B/331/S,55 Cancri e,49.0,False,0.0,2808.0,1083.0,110.0,1132.0,Sadares Aindlylid,False
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+8482_01,Earth,True,G/1358/S,55 Cancri e,47.0,False,0.0,0.0,0.0,0.0,0.0,Lynnie Thonyderson,False
+8483_01,Europa,True,D/253/S,TRAPPIST-1e,29.0,True,0.0,0.0,0.0,0.0,0.0,Axinon Clutcheed,True
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+8487_01,Europa,,C/282/P,55 Cancri e,32.0,True,65.0,,0.0,88.0,9811.0,Kajamak Boderbint,False
+8489_01,Earth,True,G/1383/P,PSO J318.5-22,22.0,False,,0.0,0.0,0.0,0.0,Elicey Danielps,True
+8491_01,Mars,False,F/1635/S,55 Cancri e,35.0,False,319.0,0.0,6331.0,8.0,0.0,Murs Panie,True
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+8503_03,Europa,True,B/279/P,55 Cancri e,18.0,False,0.0,0.0,0.0,0.0,0.0,Tabdhib Coweboded,True
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+8504_02,Earth,True,G/1385/P,55 Cancri e,22.0,False,0.0,0.0,0.0,0.0,0.0,Kencey Moranthons,True
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+8508_03,Mars,False,F/1747/P,TRAPPIST-1e,0.0,False,0.0,0.0,0.0,0.0,0.0,Bonutz Bake,True
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+8512_01,Earth,False,F/1637/S,55 Cancri e,48.0,False,0.0,717.0,0.0,0.0,10.0,Verly Flyncharlan,True
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+8530_01,Mars,False,F/1640/S,TRAPPIST-1e,,False,2502.0,0.0,44.0,23.0,29.0,Sher Shpie,False
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+8536_01,Europa,False,E/559/S,55 Cancri e,28.0,False,0.0,101.0,0.0,0.0,12143.0,Mesatik Plattering,False
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+8537_02,Mars,False,F/1643/S,TRAPPIST-1e,39.0,False,5327.0,0.0,290.0,0.0,0.0,Cireal Nutte,False
+8537_03,Mars,True,F/1643/S,TRAPPIST-1e,16.0,,0.0,0.0,0.0,0.0,0.0,Tonch Nutte,True
+8539_01,Earth,False,G/1372/S,TRAPPIST-1e,20.0,False,59.0,29.0,27.0,,0.0,Tance Simsey,False
+8540_01,Earth,False,F/1645/S,TRAPPIST-1e,24.0,False,686.0,0.0,13.0,0.0,0.0,Dory Sextones,False
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+8824_01,Earth,False,F/1819/P,TRAPPIST-1e,29.0,False,128.0,0.0,130.0,132.0,473.0,Ruthy Whiters,False
+8825_01,Earth,True,G/1422/S,55 Cancri e,21.0,False,0.0,0.0,0.0,0.0,0.0,Iree Halexandez,True
+8826_01,Europa,False,C/290/P,55 Cancri e,13.0,False,1212.0,4485.0,0.0,4016.0,57.0,Marin Hednigic,False
+8826_02,Europa,False,C/290/P,55 Cancri e,25.0,False,1369.0,310.0,3.0,4502.0,377.0,Nusakab Hednigic,False
+8826_03,Europa,False,C/290/P,TRAPPIST-1e,46.0,False,1384.0,229.0,0.0,1572.0,1.0,Aston Hednigic,False
+8827_01,Earth,False,F/1820/P,55 Cancri e,37.0,False,88.0,0.0,719.0,88.0,0.0,Rickie Mclarseney,True
+8828_01,Earth,False,F/1711/S,55 Cancri e,19.0,,0.0,6.0,0.0,0.0,1179.0,Charly Robersondez,True
+8830_01,Mars,False,E/578/S,TRAPPIST-1e,29.0,False,230.0,0.0,1404.0,464.0,0.0,Rankes Vloaf,False
+8830_02,Mars,True,F/1712/S,TRAPPIST-1e,21.0,False,0.0,0.0,0.0,0.0,0.0,Pon Vloaf,True
+8831_01,Mars,True,F/1713/S,TRAPPIST-1e,13.0,False,0.0,0.0,0.0,0.0,0.0,Yaphex Beake,True
+8833_01,Earth,True,G/1424/S,55 Cancri e,15.0,False,0.0,0.0,0.0,0.0,0.0,Jennya Vanglis,True
+8834_01,Earth,False,F/1715/S,TRAPPIST-1e,42.0,False,4.0,0.0,0.0,0.0,792.0,Milda Howayery,False
+8835_01,Earth,True,G/1425/S,TRAPPIST-1e,,False,0.0,0.0,0.0,0.0,0.0,Shalle Bartines,False
+8836_01,Earth,False,F/1821/P,TRAPPIST-1e,26.0,False,903.0,0.0,182.0,0.0,27.0,Harlos Mcinton,False
+8836_02,Earth,True,G/1430/P,TRAPPIST-1e,19.0,False,0.0,0.0,0.0,0.0,0.0,Jeron Mcinton,False
+8838_01,Earth,False,G/1431/P,TRAPPIST-1e,18.0,,0.0,181.0,628.0,0.0,0.0,Briam Chanash,True
+8840_01,Earth,False,F/1716/S,55 Cancri e,50.0,False,0.0,433.0,4.0,0.0,2020.0,Naomin Litthews,False
+8841_01,Mars,True,F/1822/P,TRAPPIST-1e,28.0,False,0.0,0.0,0.0,0.0,0.0,Asinch Derry,True
+8842_01,Europa,False,D/267/S,TRAPPIST-1e,24.0,False,3.0,2910.0,0.0,112.0,36.0,Hekaron Amspring,True
+8843_01,Europa,False,C/327/S,TRAPPIST-1e,31.0,False,,927.0,0.0,2760.0,1.0,Zedares Rinicapead,False
+8844_01,Earth,False,E/579/S,55 Cancri e,19.0,False,0.0,0.0,717.0,1.0,0.0,Jacky Wynneyerson,True
+8845_01,Earth,False,F/1823/P,TRAPPIST-1e,20.0,False,735.0,0.0,0.0,41.0,1.0,Jorgie Coolez,False
+8845_02,Earth,False,E/566/P,TRAPPIST-1e,23.0,False,0.0,1204.0,40.0,0.0,375.0,Paulah Coolez,False
+8848_01,,False,F/1824/P,TRAPPIST-1e,19.0,False,1037.0,0.0,40.0,0.0,0.0,Leos Lacke,True
+8849_01,Earth,True,G/1427/S,55 Cancri e,27.0,False,0.0,0.0,,0.0,,Valex Chanash,True
+8850_01,Mars,False,E/580/S,TRAPPIST-1e,16.0,False,1032.0,0.0,267.0,0.0,0.0,Koat Kie,False
+8852_01,Mars,True,D/278/P,TRAPPIST-1e,76.0,False,0.0,0.0,0.0,0.0,0.0,Aark Homin,True
+8852_02,Mars,False,D/278/P,TRAPPIST-1e,41.0,True,22.0,484.0,354.0,0.0,0.0,Toats Homin,False
+8853_01,Earth,False,G/1433/P,PSO J318.5-22,24.0,False,726.0,3488.0,200.0,17.0,275.0,Kimena Ellahan,False
+8854_01,Earth,True,G/1429/S,TRAPPIST-1e,12.0,False,0.0,0.0,0.0,0.0,0.0,Pristy Hammons,False
+8856_01,Earth,True,G/1434/P,PSO J318.5-22,28.0,False,0.0,0.0,0.0,0.0,0.0,Teremy Mckinsond,True
+8858_01,Europa,False,B/285/P,TRAPPIST-1e,36.0,True,0.0,0.0,0.0,0.0,0.0,Medib Ricled,False
+8860_01,Mars,True,F/1825/P,TRAPPIST-1e,20.0,False,0.0,0.0,0.0,0.0,0.0,Turs Mity,True
+8860_02,,True,F/1825/P,TRAPPIST-1e,7.0,False,0.0,0.0,0.0,0.0,0.0,Guls Vloaf,True
+8860_03,Mars,True,F/1825/P,TRAPPIST-1e,21.0,False,0.0,0.0,0.0,0.0,0.0,Mulk Vloaf,True
+8860_04,Mars,False,E/567/P,TRAPPIST-1e,33.0,,1054.0,0.0,222.0,22.0,0.0,Appers Vloaf,False
+8861_01,Europa,False,C/291/P,TRAPPIST-1e,32.0,False,0.0,2583.0,3.0,500.0,129.0,Arkard Secened,True
+8862_01,Europa,False,C/329/S,55 Cancri e,34.0,False,0.0,3126.0,0.0,112.0,15.0,Pharmus Myling,True
+8862_02,Europa,False,C/329/S,PSO J318.5-22,38.0,True,0.0,2191.0,863.0,207.0,6262.0,Menkin Cluted,False
+8862_03,Europa,True,C/329/S,TRAPPIST-1e,,False,0.0,0.0,0.0,0.0,0.0,Alchib Myling,True
+8862_04,Europa,True,C/329/S,55 Cancri e,28.0,False,0.0,0.0,0.0,0.0,0.0,Alramix Myling,True
+8862_05,Europa,True,C/329/S,TRAPPIST-1e,25.0,False,0.0,0.0,0.0,0.0,0.0,Prope Myling,True
+8863_01,Earth,False,E/582/S,55 Cancri e,24.0,False,0.0,784.0,5.0,0.0,1.0,Dona Greeves,True
+8864_01,Mars,True,F/1717/S,TRAPPIST-1e,18.0,False,0.0,0.0,0.0,0.0,0.0,Tench Pert,True
+8865_01,Earth,False,F/1826/P,TRAPPIST-1e,24.0,False,121.0,10.0,341.0,307.0,47.0,Bettie Sancockett,False
+8867_01,Europa,True,B/286/P,55 Cancri e,30.0,False,0.0,0.0,0.0,0.0,0.0,Sollum Ambleetive,True
+8867_02,Europa,True,B/286/P,55 Cancri e,38.0,False,0.0,0.0,0.0,0.0,0.0,Alrairk Ambleetive,True
+8868_01,Earth,False,G/1430/S,TRAPPIST-1e,39.0,False,0.0,798.0,0.0,56.0,0.0,Chesty Loverthyo,True
+8869_01,Earth,True,G/1431/S,TRAPPIST-1e,31.0,False,0.0,0.0,0.0,0.0,0.0,Melica Starkers,True
+8871_01,Earth,False,G/1432/S,TRAPPIST-1e,33.0,False,0.0,116.0,412.0,379.0,0.0,Nelly Benney,True
+8871_02,Earth,True,G/1432/S,PSO J318.5-22,15.0,False,,0.0,,0.0,0.0,Nadie Benney,True
+8872_01,Mars,True,F/1828/P,TRAPPIST-1e,23.0,False,0.0,0.0,0.0,0.0,0.0,Rinnab Surry,True
+8874_01,Earth,True,G/1436/P,TRAPPIST-1e,24.0,,0.0,0.0,0.0,0.0,0.0,Cline Popelacruz,False
+8876_01,Europa,False,B/287/P,55 Cancri e,44.0,False,0.0,71.0,7148.0,427.0,394.0,Mulkes Suarging,True
+8878_01,Earth,False,E/568/P,TRAPPIST-1e,22.0,False,0.0,0.0,0.0,0.0,906.0,Tracek Rames,False
+8880_01,Mars,True,F/1829/P,TRAPPIST-1e,28.0,False,0.0,0.0,0.0,0.0,0.0,Croo Cure,True
+8881_01,Earth,False,F/1719/S,55 Cancri e,34.0,False,0.0,0.0,717.0,0.0,0.0,Timmy Garnettiz,True
+8883_01,,False,G/1438/P,TRAPPIST-1e,8.0,False,0.0,0.0,0.0,0.0,0.0,Rence Monroellynn,False
+8884_01,Earth,False,F/1830/P,TRAPPIST-1e,26.0,False,1471.0,0.0,7.0,5.0,0.0,Jenne Mckins,True
+8885_01,Mars,False,D/269/S,TRAPPIST-1e,29.0,False,876.0,1.0,54.0,125.0,0.0,Squars Anake,False
+8886_01,Earth,False,G/1433/S,55 Cancri e,58.0,False,0.0,0.0,620.0,0.0,0.0,Jerrye Mclarseney,False
+8886_02,Earth,True,G/1433/S,TRAPPIST-1e,4.0,False,0.0,0.0,0.0,0.0,0.0,Samana Mclarseney,False
+8886_03,Earth,False,G/1433/S,PSO J318.5-22,1.0,False,0.0,0.0,0.0,0.0,0.0,Ale Mclarseney,True
+8889_01,,False,G/1439/P,55 Cancri e,17.0,False,0.0,0.0,615.0,0.0,0.0,Erman Russelloyd,False
+8890_01,Earth,False,G/1434/S,PSO J318.5-22,27.0,False,0.0,0.0,0.0,1606.0,3675.0,Coreen Tersimpson,False
+8891_01,Earth,False,F/1831/P,TRAPPIST-1e,24.0,False,1186.0,0.0,381.0,3.0,173.0,Jimmyn Pooley,True
+8891_02,Earth,False,F/1831/P,TRAPPIST-1e,16.0,False,0.0,1332.0,0.0,0.0,0.0,Isa Pooley,True
+8892_01,Earth,False,F/1832/P,TRAPPIST-1e,28.0,False,0.0,16.0,496.0,318.0,0.0,Shanya Ricklaney,False
+8894_01,Mars,True,F/1721/S,TRAPPIST-1e,23.0,False,0.0,0.0,0.0,0.0,0.0,Ars Shpie,True
+8896_01,Mars,True,F/1722/S,55 Cancri e,33.0,False,0.0,0.0,0.0,0.0,0.0,Gone Pri,True
+8897_01,Earth,True,G/1440/P,PSO J318.5-22,24.0,False,0.0,0.0,0.0,0.0,0.0,Courta Hoppers,True
+8899_01,Earth,False,G/1435/S,TRAPPIST-1e,6.0,False,0.0,0.0,0.0,0.0,0.0,Helle Bookson,True
+8900_01,Earth,False,G/1441/P,TRAPPIST-1e,19.0,False,23.0,659.0,0.0,15.0,113.0,Stal Landolphrey,False
+8901_01,Europa,True,B/341/S,TRAPPIST-1e,31.0,False,0.0,0.0,0.0,0.0,0.0,Alphah Cousel,True
+8902_01,Earth,False,G/1436/S,TRAPPIST-1e,19.0,False,16.0,1739.0,9.0,5.0,8.0,Enry Goodson,True
+8903_01,Earth,False,G/1437/S,TRAPPIST-1e,18.0,False,0.0,0.0,0.0,0.0,810.0,Darrie Herreras,False
+8904_01,Earth,False,G/1442/P,TRAPPIST-1e,35.0,False,522.0,0.0,1.0,324.0,60.0,Troya Welles,False
+8905_01,Mars,False,E/570/P,TRAPPIST-1e,32.0,True,0.0,0.0,0.0,0.0,0.0,Flakes Dutte,True
+8906_01,Earth,False,F/1723/S,55 Cancri e,19.0,False,720.0,2.0,,,0.0,Rena Gainney,False
+8907_01,Earth,True,G/1438/S,PSO J318.5-22,24.0,False,0.0,0.0,0.0,0.0,0.0,Karay Booters,True
+8910_01,Earth,False,E/585/S,TRAPPIST-1e,33.0,False,0.0,3.0,0.0,782.0,7.0,Therta Crayeson,False
+8918_01,Earth,False,G/1448/P,TRAPPIST-1e,20.0,False,0.0,1.0,32.0,755.0,21.0,Brada Hayder,False
+8919_01,Europa,False,C/331/S,TRAPPIST-1e,27.0,False,0.0,2481.0,0.0,3657.0,93.0,Zedark Crantiale,False
+8920_01,Europa,False,D/280/P,TRAPPIST-1e,26.0,False,0.0,70.0,0.0,41.0,,Tach Openduave,False
+8922_01,Earth,True,G/1449/P,TRAPPIST-1e,28.0,False,0.0,0.0,0.0,0.0,0.0,Walley Pager,True
+8923_01,Earth,False,G/1450/P,TRAPPIST-1e,19.0,False,0.0,0.0,0.0,0.0,0.0,Lilley Fuentryoung,True
+8926_01,Earth,False,E/571/P,TRAPPIST-1e,24.0,False,0.0,,3.0,80.0,0.0,Paulah Pooles,True
+8928_01,Europa,True,C/333/S,55 Cancri e,52.0,False,0.0,0.0,0.0,,0.0,Meisam Hoodjuded,True
+8930_01,Earth,False,F/1725/S,PSO J318.5-22,23.0,False,0.0,471.0,120.0,13.0,6.0,Alvira Cleachrand,True
+8932_01,Earth,False,F/1727/S,TRAPPIST-1e,23.0,False,0.0,0.0,0.0,912.0,41.0,Bretty Blanglison,False
+8937_01,Europa,True,E/574/P,TRAPPIST-1e,25.0,False,0.0,0.0,0.0,0.0,0.0,Zosmark Unaasor,True
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+8938_02,Mars,True,F/1729/S,TRAPPIST-1e,35.0,False,0.0,0.0,0.0,0.0,0.0,Jumars Must,True
+8939_01,Earth,True,G/1441/S,PSO J318.5-22,24.0,False,0.0,0.0,0.0,0.0,0.0,Jillie Sweett,True
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+8941_03,Mars,False,E/575/P,TRAPPIST-1e,14.0,False,1737.0,0.0,336.0,0.0,0.0,Tifet Perie,False
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+8944_01,Earth,True,G/1442/S,PSO J318.5-22,0.0,False,,0.0,0.0,0.0,0.0,Calvia Hobbinson,True
+8944_02,Earth,,G/1442/S,PSO J318.5-22,17.0,False,0.0,0.0,0.0,0.0,0.0,Evan Hobbinson,True
+8944_03,Earth,False,G/1442/S,TRAPPIST-1e,18.0,False,327.0,170.0,0.0,0.0,130.0,Darley Hobbinson,False
+8945_01,Europa,False,D/270/S,TRAPPIST-1e,43.0,False,,1632.0,346.0,957.0,128.0,Maham Cralinal,False
+8945_02,Europa,False,D/270/S,TRAPPIST-1e,21.0,,3036.0,32.0,0.0,2.0,416.0,Zinonon Cralinal,False
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+8948_01,Europa,False,B/342/S,TRAPPIST-1e,57.0,,0.0,13.0,0.0,52.0,11325.0,Sterfik Hetforhaft,False
+8948_02,Europa,False,B/342/S,55 Cancri e,18.0,False,0.0,4715.0,0.0,36.0,995.0,Hadirah Hetforhaft,True
+8954_01,Earth,False,G/1452/P,PSO J318.5-22,73.0,False,1806.0,0.0,47.0,0.0,0.0,Ianne Torrez,False
+8955_01,Earth,False,F/1837/P,TRAPPIST-1e,31.0,False,0.0,1524.0,0.0,0.0,0.0,Felice Trankliney,False
+8956_01,Earth,False,F/1838/P,TRAPPIST-1e,25.0,False,0.0,0.0,0.0,6.0,1989.0,Mela Bonnondry,False
+8956_02,Earth,True,G/1453/P,,20.0,False,0.0,0.0,0.0,0.0,0.0,Erina Bonnondry,True
+8956_03,Earth,False,G/1453/P,PSO J318.5-22,24.0,False,0.0,0.0,40.0,0.0,575.0,Sallyl Bonnondry,False
+8956_04,Earth,False,G/1453/P,TRAPPIST-1e,,False,194.0,1.0,10.0,629.0,0.0,Krisa Bonnondry,False
+8956_05,Earth,True,G/1453/P,TRAPPIST-1e,21.0,False,0.0,0.0,0.0,0.0,0.0,Deandy Bonnondry,False
+8956_06,Earth,False,F/1838/P,PSO J318.5-22,22.0,False,0.0,29.0,165.0,404.0,0.0,Valex Bonnondry,False
+8956_07,Earth,False,F/1838/P,55 Cancri e,39.0,False,0.0,0.0,1291.0,224.0,0.0,Lolas Bonnondry,False
+8956_08,Earth,False,F/1838/P,TRAPPIST-1e,26.0,False,0.0,0.0,0.0,0.0,741.0,Harvin Bonnondry,False
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+8961_01,Earth,True,G/1455/P,TRAPPIST-1e,1.0,False,,0.0,0.0,0.0,0.0,Mariel Blancoy,True
+8961_02,Earth,False,G/1455/P,TRAPPIST-1e,7.0,False,0.0,0.0,0.0,0.0,0.0,Chael Blancoy,False
+8961_03,Earth,False,G/1455/P,TRAPPIST-1e,4.0,False,0.0,0.0,0.0,0.0,0.0,Chery Wilderssen,False
+8961_04,,False,F/1839/P,TRAPPIST-1e,21.0,False,0.0,6.0,0.0,593.0,0.0,Dandy Blancoy,False
+8961_05,Earth,True,G/1455/P,TRAPPIST-1e,12.0,False,0.0,0.0,0.0,0.0,0.0,Rena Blancoy,False
+8964_01,Mars,False,F/1734/S,TRAPPIST-1e,28.0,False,1708.0,,65.0,1.0,0.0,Chones Grean,False
+8964_02,Mars,False,F/1734/S,TRAPPIST-1e,0.0,False,0.0,0.0,0.0,0.0,0.0,Tornes Grean,True
+8964_03,Mars,True,F/1734/S,TRAPPIST-1e,18.0,False,0.0,0.0,0.0,0.0,0.0,Hamus Grean,True
+8966_01,Mars,False,F/1735/S,TRAPPIST-1e,52.0,False,1231.0,2.0,8.0,0.0,0.0,Dow Bush,False
+8967_01,Earth,False,G/1445/S,TRAPPIST-1e,18.0,False,13.0,822.0,27.0,0.0,46.0,Maryn Mayson,False
+8968_01,Europa,True,E/588/S,TRAPPIST-1e,51.0,False,0.0,0.0,0.0,0.0,0.0,Phamark Watorivedy,True
+8969_01,Europa,False,C/299/P,55 Cancri e,21.0,False,0.0,846.0,4407.0,0.0,2491.0,Achiram Turtertive,True
+8970_01,Europa,False,C/300/P,55 Cancri e,28.0,False,,3751.0,0.0,5025.0,11741.0,Batomam Clugeammy,False
+8972_01,Mars,True,F/1736/S,TRAPPIST-1e,34.0,False,0.0,,0.0,0.0,0.0,Frunch Spore,True
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+9252_02,Europa,True,B/301/P,55 Cancri e,19.0,False,0.0,0.0,0.0,0.0,0.0,Muonon Stranbeate,True
+9253_01,Earth,True,G/1493/S,PSO J318.5-22,16.0,False,0.0,0.0,0.0,0.0,0.0,Jimmyn Thomason,True
+9254_01,Earth,True,G/1502/P,PSO J318.5-22,61.0,False,0.0,0.0,0.0,0.0,0.0,Kenny Mallerez,False
+9256_01,Earth,True,G/1494/S,TRAPPIST-1e,28.0,False,0.0,0.0,0.0,0.0,0.0,Darley Franton,True
+9257_01,,False,F/1892/P,TRAPPIST-1e,13.0,False,39.0,0.0,1085.0,24.0,0.0,Ties Apple,False
+9259_01,Earth,,F/1893/P,TRAPPIST-1e,44.0,False,1030.0,1015.0,0.0,11.0,,Annah Gilleyons,True
+9261_01,Earth,True,G/1504/P,PSO J318.5-22,22.0,False,0.0,0.0,0.0,0.0,0.0,Therta Casentry,False
+9264_01,Europa,True,E/606/S,55 Cancri e,30.0,False,0.0,0.0,0.0,0.0,0.0,Giedir Sionerorly,True
+9267_01,Europa,True,E/607/S,TRAPPIST-1e,21.0,False,0.0,0.0,0.0,0.0,0.0,Arraid Opshaft,True
+9267_02,Europa,True,E/607/S,TRAPPIST-1e,20.0,False,0.0,0.0,0.0,0.0,0.0,Sabi Opshaft,True
+9268_01,Earth,True,G/1505/P,TRAPPIST-1e,31.0,False,0.0,0.0,0.0,0.0,0.0,Agnesa Baldson,True
+9270_01,Earth,True,G/1497/S,55 Cancri e,33.0,False,0.0,0.0,0.0,0.0,0.0,Lan Mckinsond,True
+9272_01,Earth,False,G/1507/P,TRAPPIST-1e,26.0,False,240.0,242.0,510.0,0.0,0.0,Ireene Simson,True
+9272_02,Earth,False,F/1894/P,TRAPPIST-1e,21.0,False,86.0,3.0,149.0,208.0,329.0,Gordo Simson,False
+9274_01,,True,G/1508/P,TRAPPIST-1e,23.0,False,0.0,0.0,0.0,0.0,0.0,Chelsa Bullisey,True
+9275_01,Europa,False,A/97/P,TRAPPIST-1e,0.0,False,0.0,0.0,0.0,0.0,0.0,Polaton Conable,True
+9275_02,Europa,False,A/97/P,TRAPPIST-1e,32.0,False,1.0,1146.0,0.0,50.0,34.0,Diram Conable,False
+9275_03,Europa,,A/97/P,TRAPPIST-1e,30.0,False,0.0,3208.0,0.0,2.0,330.0,Atlasym Conable,True
+9276_01,Europa,False,A/98/P,55 Cancri e,41.0,True,0.0,6819.0,0.0,1643.0,74.0,Gravior Noxnuther,False
+9278_01,Earth,True,G/1499/S,PSO J318.5-22,18.0,False,0.0,0.0,0.0,0.0,0.0,Kurta Mondalley,False
+9279_01,Earth,False,G/1500/S,TRAPPIST-1e,26.0,False,0.0,0.0,1872.0,1.0,0.0,Fayey Connon,True
+9280_01,Europa,False,E/608/S,55 Cancri e,32.0,False,0.0,1049.0,0.0,353.0,3235.0,Celeon Hontichre,False
+9280_02,Europa,False,E/608/S,TRAPPIST-1e,44.0,False,126.0,4688.0,0.0,0.0,12.0,Propsh Hontichre,True
diff --git a/projects/machine-learning/mock_choosing_watermelon.py b/projects/machine-learning/mock_choosing_watermelon.py
new file mode 100644
index 0000000..55665e4
--- /dev/null
+++ b/projects/machine-learning/mock_choosing_watermelon.py
@@ -0,0 +1,41 @@
+import unittest
+
+
+def get_answer_by_feature(feature, answer):
+    if feature in answer:
+        return True
+    return False
+
+
+def is_better_watermelon(appearance, pattern, spot_range, echo):
+    features = [
+        get_answer_by_feature(appearance, {"round", "oval"}),
+        get_answer_by_feature(pattern, {"clear", "bright"}),
+        get_answer_by_feature(spot_range, {"middle", "big"}),
+        get_answer_by_feature(echo, {"clear", "pleasant"}),
+    ]
+
+    if features.count(True) >= 3:
+        return True
+    return False
+
+
+class TestChooseBetterWatermelon(unittest.TestCase):
+
+    samples = [
+        dict(appearance="broken", pattern="clear", spot_range="big", echo="depressing"),
+        dict(appearance="round", pattern="clear", spot_range="big", echo="pleasant"),
+        dict(appearance="oval", pattern="bright", spot_range="middle", echo="clear"),
+        dict(
+            appearance="round", pattern="blured", spot_range="small", echo="depressing"
+        ),
+    ]
+
+    def test_is_better_watermelon(self):
+        for sample in self.samples:
+            with self.subTest(**sample):
+                self.assertTrue(is_better_watermelon(**sample))
+
+
+if __name__ == "__main__":
+    unittest.main(verbosity=2)
diff --git a/projects/machine-learning/mock_ml_model_for_choosing_watermelon.ipynb b/projects/machine-learning/mock_ml_model_for_choosing_watermelon.ipynb
new file mode 100644
index 0000000..aa7410f
--- /dev/null
+++ b/projects/machine-learning/mock_ml_model_for_choosing_watermelon.ipynb
@@ -0,0 +1,507 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "from sklearn.linear_model import LogisticRegression\n",
+    "from sklearn.preprocessing import LabelEncoder"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>appearance</th>\n",
+       "      <th>pattern</th>\n",
+       "      <th>spot_range</th>\n",
+       "      <th>echo</th>\n",
+       "      <th>is_good</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>broken</td>\n",
+       "      <td>clear</td>\n",
+       "      <td>big</td>\n",
+       "      <td>depressing</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>round</td>\n",
+       "      <td>clear</td>\n",
+       "      <td>big</td>\n",
+       "      <td>pleasant</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>oval</td>\n",
+       "      <td>bright</td>\n",
+       "      <td>middle</td>\n",
+       "      <td>clear</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>round</td>\n",
+       "      <td>blured</td>\n",
+       "      <td>small</td>\n",
+       "      <td>depressing</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  appearance pattern spot_range        echo  is_good\n",
+       "0     broken   clear        big  depressing        0\n",
+       "1      round   clear        big    pleasant        1\n",
+       "2       oval  bright     middle       clear        1\n",
+       "3      round  blured      small  depressing        0"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data = [\n",
+    "    dict(\n",
+    "        appearance=\"broken\",\n",
+    "        pattern=\"clear\",\n",
+    "        spot_range=\"big\",\n",
+    "        echo=\"depressing\",\n",
+    "        is_good=0,\n",
+    "    ),\n",
+    "    dict(\n",
+    "        appearance=\"round\",\n",
+    "        pattern=\"clear\",\n",
+    "        spot_range=\"big\",\n",
+    "        echo=\"pleasant\",\n",
+    "        is_good=1,\n",
+    "    ),\n",
+    "    dict(\n",
+    "        appearance=\"oval\",\n",
+    "        pattern=\"bright\",\n",
+    "        spot_range=\"middle\",\n",
+    "        echo=\"clear\",\n",
+    "        is_good=1,\n",
+    "    ),\n",
+    "    dict(\n",
+    "        appearance=\"round\",\n",
+    "        pattern=\"blured\",\n",
+    "        spot_range=\"small\",\n",
+    "        echo=\"depressing\",\n",
+    "        is_good=0,\n",
+    "    ),\n",
+    "]\n",
+    "df = pd.DataFrame(data)\n",
+    "df.head()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'C': 1.0,\n",
+       " 'class_weight': None,\n",
+       " 'dual': False,\n",
+       " 'fit_intercept': True,\n",
+       " 'intercept_scaling': 1,\n",
+       " 'l1_ratio': None,\n",
+       " 'max_iter': 100,\n",
+       " 'multi_class': 'auto',\n",
+       " 'n_jobs': None,\n",
+       " 'penalty': 'l2',\n",
+       " 'random_state': 0,\n",
+       " 'solver': 'lbfgs',\n",
+       " 'tol': 0.0001,\n",
+       " 'verbose': 0,\n",
+       " 'warm_start': False}"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "encoder = LabelEncoder()\n",
+    "model = LogisticRegression(random_state=0)\n",
+    "\n",
+    "X = df.drop([\"is_good\"], axis=1).apply(encoder.fit_transform)\n",
+    "y = df[\"is_good\"].values\n",
+    "\n",
+    "estimator = model.fit(X, y)\n",
+    "estimator.get_params()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>appearance</th>\n",
+       "      <th>pattern</th>\n",
+       "      <th>spot_range</th>\n",
+       "      <th>echo</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   appearance  pattern  spot_range  echo\n",
+       "0           0        0           2     0\n",
+       "1           1        1           1     1\n",
+       "2           2        1           0     1"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "unknown_data = [\n",
+    "    dict(\n",
+    "        appearance=\"broken\",\n",
+    "        pattern=\"blured\",\n",
+    "        spot_range=\"small\",\n",
+    "        echo=\"clear\",\n",
+    "    ),\n",
+    "    dict(\n",
+    "        appearance=\"oval\",\n",
+    "        pattern=\"clear\",\n",
+    "        spot_range=\"middle\",\n",
+    "        echo=\"depressing\",\n",
+    "    ),\n",
+    "    dict(\n",
+    "        appearance=\"round\",\n",
+    "        pattern=\"clear\",\n",
+    "        spot_range=\"big\",\n",
+    "        echo=\"depressing\",\n",
+    "    ),\n",
+    "]\n",
+    "records = pd.DataFrame(unknown_data)\n",
+    "target = records.apply(encoder.fit_transform)\n",
+    "target.head()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/Bobot/pyenvs/pandas-startup/lib/python3.9/site-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but LogisticRegression was fitted with feature names\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>appearance</th>\n",
+       "      <th>pattern</th>\n",
+       "      <th>spot_range</th>\n",
+       "      <th>echo</th>\n",
+       "      <th>is_good</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>broken</td>\n",
+       "      <td>blured</td>\n",
+       "      <td>small</td>\n",
+       "      <td>clear</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>oval</td>\n",
+       "      <td>clear</td>\n",
+       "      <td>middle</td>\n",
+       "      <td>depressing</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>round</td>\n",
+       "      <td>clear</td>\n",
+       "      <td>big</td>\n",
+       "      <td>depressing</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  appearance pattern spot_range        echo  is_good\n",
+       "0     broken  blured      small       clear        0\n",
+       "1       oval   clear     middle  depressing        0\n",
+       "2      round   clear        big  depressing        1"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\n",
+    "records[\"is_good\"] = estimator.predict(target.values)\n",
+    "records.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/Bobot/pyenvs/pandas-startup/lib/python3.9/site-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but LogisticRegression was fitted with feature names\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>appearance</th>\n",
+       "      <th>pattern</th>\n",
+       "      <th>spot_range</th>\n",
+       "      <th>echo</th>\n",
+       "      <th>is_good</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>broken</td>\n",
+       "      <td>blured</td>\n",
+       "      <td>small</td>\n",
+       "      <td>clear</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>oval</td>\n",
+       "      <td>clear</td>\n",
+       "      <td>middle</td>\n",
+       "      <td>depressing</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>round</td>\n",
+       "      <td>clear</td>\n",
+       "      <td>big</td>\n",
+       "      <td>depressing</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  appearance pattern spot_range        echo  is_good\n",
+       "0     broken  blured      small       clear        0\n",
+       "1       oval   clear     middle  depressing        0\n",
+       "2      round   clear        big  depressing        1"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "records[\"is_good\"] = estimator.predict(target.values)\n",
+    "records.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/Bobot/pyenvs/pandas-startup/lib/python3.9/site-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but LogisticRegression was fitted with feature names\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "array([[0.74707445, 0.25292555],\n",
+       "       [0.56146879, 0.43853121],\n",
+       "       [0.38539327, 0.61460673]])"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "estimator.predict_proba(target.values)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "interpreter": {
+   "hash": "13977d4cc82dee5f9d9535ceb495bd0ab12a43c33c664e5f0d53c24cf634b67f"
+  },
+  "kernelspec": {
+   "display_name": "Python 3.9.0 ('pandas-startup')",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.0"
+  },
+  "orig_nbformat": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/projects/machine-learning/sklearn_base.ipynb b/projects/machine-learning/sklearn_base.ipynb
new file mode 100644
index 0000000..c3ab0f6
--- /dev/null
+++ b/projects/machine-learning/sklearn_base.ipynb
@@ -0,0 +1,1537 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "outputs": [],
+   "source": [],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "### Processing and transform features"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "#### Encode categorical features"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "   id  gender\n0   1    male\n1   2  female",
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>id</th>\n      <th>gender</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n      <td>male</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2</td>\n      <td>female</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\n",
+    "import pandas as pd\n",
+    "from sklearn.preprocessing import LabelEncoder\n",
+    "\n",
+    "data = pd.DataFrame(dict(\n",
+    "    id=[1, 2],\n",
+    "    gender=[\"male\", \"female\"],\n",
+    "))\n",
+    "data.head()"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "array(['female', 'male'], dtype=object)"
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\n",
+    "target = data[\"gender\"]\n",
+    "\n",
+    "encoder = LabelEncoder()\n",
+    "encoder.fit(target)\n",
+    "encoder.classes_"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "array([1, 0])"
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "encoder.transform(target)"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "(array([0, 1]), Index(['male', 'female'], dtype='object'))"
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pd.factorize(data[\"gender\"])"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "   id  is_female  is_male\n0   1          0        1\n1   2          1        0",
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>id</th>\n      <th>is_female</th>\n      <th>is_male</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pd.get_dummies(data, prefix=\"is\")"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[['male']\n",
+      " ['female']]\n",
+      "[array(['female', 'male'], dtype=object)]\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.preprocessing import OneHotEncoder\n",
+    "\n",
+    "array = target.values.reshape(-1, 1)\n",
+    "print(array)\n",
+    "\n",
+    "encoder = OneHotEncoder(sparse=False)\n",
+    "encoder.fit(array)\n",
+    "print(encoder.categories_)"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "array([[0., 1.],\n       [1., 0.]])"
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "encoder.transform(array)"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "#### Scaling numerical features"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "raw data mean: [-0.25  0.5   0.5 ]\n",
+      "raw data std: [1.47901995 0.5        1.11803399]\n"
+     ]
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "from sklearn.preprocessing import StandardScaler\n",
+    "\n",
+    "data = np.array([\n",
+    "    [0, 0, 1],\n",
+    "    [-1, 1, 0],\n",
+    "    [-2, 0, 2],\n",
+    "    [2, 1, -1],\n",
+    "])\n",
+    "print(f\"raw data mean: {data.mean(axis=0)}\")\n",
+    "print(f\"raw data std: {data.std(axis=0)}\")"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "array([[ 0.16903085, -1.        ,  0.4472136 ],\n       [-0.50709255,  1.        , -0.4472136 ],\n       [-1.18321596, -1.        ,  1.34164079],\n       [ 1.52127766,  1.        , -1.34164079]])"
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "scaler = StandardScaler()\n",
+    "scaler.fit(data)\n",
+    "scaler.transform(data)"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Mean = [0. 0. 0.]\n",
+      "Std = [1. 1. 1.]\n"
+     ]
+    }
+   ],
+   "source": [
+    "scaled = scaler.fit_transform(data)\n",
+    "print(f\"Mean = {scaled.mean(axis=0)}\")\n",
+    "print(f\"Std = {scaled.std(axis=0)}\")"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "array([[0.5       , 0.        , 0.66666667],\n       [0.25      , 1.        , 0.33333333],\n       [0.        , 0.        , 1.        ],\n       [1.        , 1.        , 0.        ]])"
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.preprocessing import MinMaxScaler\n",
+    "\n",
+    "scaler = MinMaxScaler()\n",
+    "scaler.fit_transform(data)"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "array([[ 0.  ,  0.  ,  1.  ],\n       [-0.5 ,  0.5 ,  0.  ],\n       [-0.5 ,  0.  ,  0.5 ],\n       [ 0.5 ,  0.25, -0.25]])"
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.preprocessing import Normalizer\n",
+    "\n",
+    "scaler = Normalizer(norm=\"l1\")\n",
+    "scaler.fit_transform(data)"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "### Split data"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "   sepal.length  sepal.width  petal.length  petal.width variety\n0           5.1          3.5           1.4          0.2  Setosa\n1           4.9          3.0           1.4          0.2  Setosa\n2           4.7          3.2           1.3          0.2  Setosa\n3           4.6          3.1           1.5          0.2  Setosa\n4           5.0          3.6           1.4          0.2  Setosa",
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>sepal.length</th>\n      <th>sepal.width</th>\n      <th>petal.length</th>\n      <th>petal.width</th>\n      <th>variety</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>5.1</td>\n      <td>3.5</td>\n      <td>1.4</td>\n      <td>0.2</td>\n      <td>Setosa</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>4.9</td>\n      <td>3.0</td>\n      <td>1.4</td>\n      <td>0.2</td>\n      <td>Setosa</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>4.7</td>\n      <td>3.2</td>\n      <td>1.3</td>\n      <td>0.2</td>\n      <td>Setosa</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>4.6</td>\n      <td>3.1</td>\n      <td>1.5</td>\n      <td>0.2</td>\n      <td>Setosa</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>5.0</td>\n      <td>3.6</td>\n      <td>1.4</td>\n      <td>0.2</td>\n      <td>Setosa</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\n",
+    "import pathlib\n",
+    "\n",
+    "import pandas as pd\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "\n",
+    "csvfile = pathlib.Path(\"../../data/iris.csv\")\n",
+    "iris = pd.read_csv(csvfile)\n",
+    "# iris[\"variety\"] = pd.factorize(iris[\"variety\"])[0]\n",
+    "iris.head()"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "   sepal.length  sepal.width  petal.length  petal.width\n0           5.1          3.5           1.4          0.2\n1           4.9          3.0           1.4          0.2\n2           4.7          3.2           1.3          0.2\n3           4.6          3.1           1.5          0.2\n4           5.0          3.6           1.4          0.2",
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>sepal.length</th>\n      <th>sepal.width</th>\n      <th>petal.length</th>\n      <th>petal.width</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>5.1</td>\n      <td>3.5</td>\n      <td>1.4</td>\n      <td>0.2</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>4.9</td>\n      <td>3.0</td>\n      <td>1.4</td>\n      <td>0.2</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>4.7</td>\n      <td>3.2</td>\n      <td>1.3</td>\n      <td>0.2</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>4.6</td>\n      <td>3.1</td>\n      <td>1.5</td>\n      <td>0.2</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>5.0</td>\n      <td>3.6</td>\n      <td>1.4</td>\n      <td>0.2</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "X = iris.drop(\"variety\", axis=1)\n",
+    "X.head()"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "outputs": [],
+   "source": [
+    "y = iris[\"variety\"]"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "X_train shape: (105, 4)\n",
+      "X_test shape: (45, 4)\n",
+      "y_train shape: (105,)\n",
+      "y_test shape: (45,)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# train:test = 7:3\n",
+    "\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3)\n",
+    "print(f\"X_train shape: {X_train.shape}\")\n",
+    "print(f\"X_test shape: {X_test.shape}\")\n",
+    "print(f\"y_train shape: {y_train.shape}\")\n",
+    "print(f\"y_test shape: {y_test.shape}\")"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "X_train shape: (120, 4)\n",
+      "X_test shape: (30, 4)\n",
+      "y_train shape: (120,)\n",
+      "y_test shape: (30,)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# train:test = 0.8:0.2\n",
+    "\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, train_size=0.8)\n",
+    "print(f\"X_train shape: {X_train.shape}\")\n",
+    "print(f\"X_test shape: {X_test.shape}\")\n",
+    "print(f\"y_train shape: {y_train.shape}\")\n",
+    "print(f\"y_test shape: {y_test.shape}\")"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "outputs": [],
+   "source": [
+    "X_train, X_test, y_train, y_test = train_test_split(\n",
+    "    X,\n",
+    "    y,\n",
+    "    test_size=0.3,\n",
+    "    random_state=233,\n",
+    ")"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "outputs": [],
+   "source": [
+    "# train, validation, test\n",
+    "\n",
+    "X_train, X_, y_train, y_ = train_test_split(\n",
+    "    X, y, test_size=0.3, random_state=233\n",
+    ")\n",
+    "\n",
+    "X_val, X_test, y_val, y_test = train_test_split(\n",
+    "    X_, y_, test_size=0.5, random_state=233\n",
+    ")"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "X_train shape: (105, 4)\n",
+      "X_val shape: (22, 4)\n",
+      "X_test shape: (23, 4)\n",
+      "y_train shape: (105,)\n",
+      "y_val shape: (22,)\n",
+      "y_test shape: (23,)\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(f\"X_train shape: {X_train.shape}\")\n",
+    "print(f\"X_val shape: {X_val.shape}\")\n",
+    "print(f\"X_test shape: {X_test.shape}\")\n",
+    "print(f\"y_train shape: {y_train.shape}\")\n",
+    "print(f\"y_val shape: {y_val.shape}\")\n",
+    "print(f\"y_test shape: {y_test.shape}\")"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## Grid Search\n"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[ -63.76056336  182.20484712   32.45346758   70.87751298  351.51266014\n",
+      "    -7.01893893   59.89221748   33.89847802   75.65454954   47.92124999]\n",
+      " [   0.69419195    3.35562957    5.69461738 -152.93603272  187.21731051\n",
+      "    -3.10092616   -2.3676087  -236.78609838  -22.06231977 -159.77767906]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.datasets import make_classification\n",
+    "from sklearn.model_selection import GridSearchCV, train_test_split\n",
+    "from sklearn.pipeline import Pipeline\n",
+    "from sklearn.preprocessing import StandardScaler\n",
+    "from sklearn.svm import SVC\n",
+    "\n",
+    "X, y = make_classification(\n",
+    "    n_samples=500,\n",
+    "    n_classes=3,\n",
+    "    n_features=10,\n",
+    "    n_informative=6,\n",
+    "    scale=None,\n",
+    "    random_state=233,\n",
+    ")\n",
+    "\n",
+    "print(X[:2])"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[0 0 1 0 2]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(y[:5])\n"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "outputs": [],
+   "source": [
+    "X_train, X_test, y_train, y_test = train_test_split(\n",
+    "    X, y, test_size=0.25, random_state=233\n",
+    ")"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Fitting 5 folds for each of 72 candidates, totalling 360 fits\n",
+      "[CV] END model__C=1, model__degree=3, model__gamma=0.001, model__kernel=linear; total time=   0.0s\n",
+      "[CV] END model__C=1, model__degree=3, model__gamma=0.001, model__kernel=linear; total time=   0.0s\n",
+      "[CV] END model__C=1, model__degree=3, model__gamma=0.001, model__kernel=linear; total time=   0.0s\n",
+      "[CV] END model__C=1, model__degree=3, model__gamma=0.001, model__kernel=linear; total time=   0.0s\n",
+      "[CV] END model__C=1, model__degree=3, model__gamma=0.001, model__kernel=linear; total time=   0.0s\n",
+      "[CV] END model__C=1, model__degree=3, model__gamma=0.001, model__kernel=sigmoid; total time=   0.0s\n",
+      "[CV] END model__C=1, model__degree=3, model__gamma=0.001, model__kernel=sigmoid; total time=   0.0s\n",
+      "[CV] END model__C=1, model__degree=3, model__gamma=0.001, model__kernel=sigmoid; total time=   0.0s\n",
+      "[CV] END model__C=1, model__degree=3, model__gamma=0.001, model__kernel=sigmoid; total time=   0.0s\n",
+      "[CV] END model__C=1, model__degree=3, model__gamma=0.001, model__kernel=sigmoid; total time=   0.0s\n",
+      "[CV] END model__C=1, model__degree=3, model__gamma=0.001, model__kernel=rbf; total time=   0.0s\n",
+      "[CV] END model__C=1, model__degree=3, model__gamma=0.001, model__kernel=rbf; total time=   0.0s\n",
+      "[CV] END model__C=1, model__degree=3, model__gamma=0.001, model__kernel=rbf; total time=   0.0s\n",
+      "[CV] END model__C=1, model__degree=3, model__gamma=0.001, model__kernel=rbf; total time=   0.0s\n",
+      "[CV] END model__C=1, model__degree=3, model__gamma=0.001, model__kernel=rbf; total time=   0.0s\n",
+      "[CV] END model__C=1, model__degree=3, model__gamma=0.0001, model__kernel=linear; total time=   0.0s\n",
+      "[CV] END model__C=1, model__degree=3, model__gamma=0.0001, model__kernel=linear; total time=   0.0s\n",
+      "[CV] END model__C=1, model__degree=3, model__gamma=0.0001, model__kernel=linear; total time=   0.0s\n",
+      "[CV] END model__C=1, model__degree=3, model__gamma=0.0001, model__kernel=linear; total time=   0.0s\n",
+      "[CV] END model__C=1, model__degree=3, model__gamma=0.0001, model__kernel=linear; total time=   0.0s\n",
+      "[CV] END model__C=1, model__degree=3, model__gamma=0.0001, model__kernel=sigmoid; total time=   0.0s\n",
+      "[CV] END model__C=1, model__degree=3, model__gamma=0.0001, model__kernel=sigmoid; total time=   0.0s\n",
+      "[CV] END model__C=1, model__degree=3, model__gamma=0.0001, model__kernel=sigmoid; total time=   0.0s\n",
+      "[CV] END model__C=1, model__degree=3, model__gamma=0.0001, model__kernel=sigmoid; total time=   0.0s\n",
+      "[CV] END model__C=1, model__degree=3, model__gamma=0.0001, model__kernel=sigmoid; total time=   0.0s\n",
+      "[CV] END model__C=1, model__degree=3, model__gamma=0.0001, model__kernel=rbf; total time=   0.0s\n",
+      "[CV] END model__C=1, model__degree=3, model__gamma=0.0001, model__kernel=rbf; total time=   0.0s\n",
+      "[CV] END model__C=1, model__degree=3, model__gamma=0.0001, model__kernel=rbf; total time=   0.0s\n",
+      "[CV] END model__C=1, model__degree=3, model__gamma=0.0001, model__kernel=rbf; total time=   0.0s\n",
+      "[CV] END model__C=1, model__degree=3, model__gamma=0.0001, model__kernel=rbf; total time=   0.0s\n",
+      "[CV] END model__C=1, model__degree=4, model__gamma=0.001, model__kernel=linear; total time=   0.0s\n",
+      "[CV] END model__C=1, model__degree=4, model__gamma=0.001, model__kernel=linear; total time=   0.0s\n",
+      "[CV] END model__C=1, model__degree=4, model__gamma=0.001, model__kernel=linear; total time=   0.0s\n",
+      "[CV] END model__C=1, model__degree=4, model__gamma=0.001, model__kernel=linear; total time=   0.0s\n",
+      "[CV] END model__C=1, model__degree=4, model__gamma=0.001, model__kernel=linear; total time=   0.0s\n",
+      "[CV] END model__C=1, model__degree=4, model__gamma=0.001, model__kernel=sigmoid; total time=   0.0s\n",
+      "[CV] END model__C=1, model__degree=4, model__gamma=0.001, model__kernel=sigmoid; total time=   0.0s\n",
+      "[CV] END model__C=1, model__degree=4, model__gamma=0.001, model__kernel=sigmoid; total time=   0.0s\n",
+      "[CV] END model__C=1, model__degree=4, model__gamma=0.001, model__kernel=sigmoid; total time=   0.0s\n",
+      "[CV] END model__C=1, model__degree=4, model__gamma=0.001, model__kernel=sigmoid; total time=   0.0s\n",
+      "[CV] END model__C=1, model__degree=4, model__gamma=0.001, model__kernel=rbf; total time=   0.0s\n",
+      "[CV] END model__C=1, model__degree=4, model__gamma=0.001, model__kernel=rbf; total time=   0.0s\n",
+      "[CV] END model__C=1, model__degree=4, model__gamma=0.001, model__kernel=rbf; total time=   0.0s\n",
+      "[CV] END model__C=1, model__degree=4, model__gamma=0.001, model__kernel=rbf; total time=   0.0s\n",
+      "[CV] END model__C=1, model__degree=4, model__gamma=0.001, model__kernel=rbf; total time=   0.0s\n",
+      "[CV] END model__C=1, model__degree=4, model__gamma=0.0001, model__kernel=linear; total time=   0.0s\n",
+      "[CV] END model__C=1, model__degree=4, model__gamma=0.0001, model__kernel=linear; total time=   0.0s\n",
+      "[CV] END model__C=1, model__degree=4, model__gamma=0.0001, model__kernel=linear; total time=   0.0s\n",
+      "[CV] END model__C=1, model__degree=4, model__gamma=0.0001, model__kernel=linear; total time=   0.0s\n",
+      "[CV] END model__C=1, model__degree=4, model__gamma=0.0001, model__kernel=linear; total time=   0.0s\n",
+      "[CV] END model__C=1, model__degree=4, model__gamma=0.0001, model__kernel=sigmoid; total time=   0.0s\n",
+      "[CV] END model__C=1, model__degree=4, model__gamma=0.0001, model__kernel=sigmoid; total time=   0.0s\n",
+      "[CV] END model__C=1, model__degree=4, model__gamma=0.0001, model__kernel=sigmoid; total time=   0.0s\n",
+      "[CV] END model__C=1, model__degree=4, model__gamma=0.0001, model__kernel=sigmoid; total time=   0.0s\n",
+      "[CV] END model__C=1, model__degree=4, model__gamma=0.0001, model__kernel=sigmoid; total time=   0.0s\n",
+      "[CV] END model__C=1, model__degree=4, model__gamma=0.0001, model__kernel=rbf; total time=   0.0s\n",
+      "[CV] END model__C=1, model__degree=4, model__gamma=0.0001, model__kernel=rbf; total time=   0.0s\n",
+      "[CV] END model__C=1, model__degree=4, model__gamma=0.0001, model__kernel=rbf; total time=   0.0s\n",
+      "[CV] END model__C=1, model__degree=4, model__gamma=0.0001, model__kernel=rbf; total time=   0.0s\n",
+      "[CV] END model__C=1, model__degree=4, model__gamma=0.0001, model__kernel=rbf; total time=   0.0s\n",
+      "[CV] END model__C=1, model__degree=5, model__gamma=0.001, model__kernel=linear; total time=   0.0s\n",
+      "[CV] END model__C=1, model__degree=5, model__gamma=0.001, model__kernel=linear; total time=   0.0s\n",
+      "[CV] END model__C=1, model__degree=5, model__gamma=0.001, model__kernel=linear; total time=   0.0s\n",
+      "[CV] END model__C=1, model__degree=5, model__gamma=0.001, model__kernel=linear; total time=   0.0s\n",
+      "[CV] END model__C=1, model__degree=5, model__gamma=0.001, model__kernel=linear; total time=   0.0s\n",
+      "[CV] END model__C=1, model__degree=5, model__gamma=0.001, model__kernel=sigmoid; total time=   0.0s\n",
+      "[CV] END model__C=1, model__degree=5, model__gamma=0.001, model__kernel=sigmoid; total time=   0.0s\n",
+      "[CV] END model__C=1, model__degree=5, model__gamma=0.001, model__kernel=sigmoid; total time=   0.0s\n",
+      "[CV] END model__C=1, model__degree=5, model__gamma=0.001, model__kernel=sigmoid; total time=   0.0s\n",
+      "[CV] END model__C=1, model__degree=5, model__gamma=0.001, model__kernel=sigmoid; total time=   0.0s\n",
+      "[CV] END model__C=1, model__degree=5, model__gamma=0.001, model__kernel=rbf; total time=   0.0s\n",
+      "[CV] END model__C=1, model__degree=5, model__gamma=0.001, model__kernel=rbf; total time=   0.0s\n",
+      "[CV] END model__C=1, model__degree=5, model__gamma=0.001, model__kernel=rbf; total time=   0.0s\n",
+      "[CV] END model__C=1, model__degree=5, model__gamma=0.001, model__kernel=rbf; total time=   0.0s\n",
+      "[CV] END model__C=1, model__degree=5, model__gamma=0.001, model__kernel=rbf; total time=   0.0s\n",
+      "[CV] END model__C=1, model__degree=5, model__gamma=0.0001, model__kernel=linear; total time=   0.0s\n",
+      "[CV] END model__C=1, model__degree=5, model__gamma=0.0001, model__kernel=linear; total time=   0.0s\n",
+      "[CV] END model__C=1, model__degree=5, model__gamma=0.0001, model__kernel=linear; total time=   0.0s\n",
+      "[CV] END model__C=1, model__degree=5, model__gamma=0.0001, model__kernel=linear; total time=   0.0s\n",
+      "[CV] END model__C=1, model__degree=5, model__gamma=0.0001, model__kernel=linear; total time=   0.0s\n",
+      "[CV] END model__C=1, model__degree=5, model__gamma=0.0001, model__kernel=sigmoid; total time=   0.0s\n",
+      "[CV] END model__C=1, model__degree=5, model__gamma=0.0001, model__kernel=sigmoid; total time=   0.0s\n",
+      "[CV] END model__C=1, model__degree=5, model__gamma=0.0001, model__kernel=sigmoid; total time=   0.0s\n",
+      "[CV] END model__C=1, model__degree=5, model__gamma=0.0001, model__kernel=sigmoid; total time=   0.0s\n",
+      "[CV] END model__C=1, model__degree=5, model__gamma=0.0001, model__kernel=sigmoid; total time=   0.0s\n",
+      "[CV] END model__C=1, model__degree=5, model__gamma=0.0001, model__kernel=rbf; total time=   0.0s\n",
+      "[CV] END model__C=1, model__degree=5, model__gamma=0.0001, model__kernel=rbf; total time=   0.0s\n",
+      "[CV] END model__C=1, model__degree=5, model__gamma=0.0001, model__kernel=rbf; total time=   0.0s\n",
+      "[CV] END model__C=1, model__degree=5, model__gamma=0.0001, model__kernel=rbf; total time=   0.0s\n",
+      "[CV] END model__C=1, model__degree=5, model__gamma=0.0001, model__kernel=rbf; total time=   0.0s\n",
+      "[CV] END model__C=10, model__degree=3, model__gamma=0.001, model__kernel=linear; total time=   0.0s\n",
+      "[CV] END model__C=10, model__degree=3, model__gamma=0.001, model__kernel=linear; total time=   0.0s\n",
+      "[CV] END model__C=10, model__degree=3, model__gamma=0.001, model__kernel=linear; total time=   0.0s\n",
+      "[CV] END model__C=10, model__degree=3, model__gamma=0.001, model__kernel=linear; total time=   0.0s\n",
+      "[CV] END model__C=10, model__degree=3, model__gamma=0.001, model__kernel=linear; total time=   0.0s\n",
+      "[CV] END model__C=10, model__degree=3, model__gamma=0.001, model__kernel=sigmoid; total time=   0.0s\n",
+      "[CV] END model__C=10, model__degree=3, model__gamma=0.001, model__kernel=sigmoid; total time=   0.0s\n",
+      "[CV] END model__C=10, model__degree=3, model__gamma=0.001, model__kernel=sigmoid; total time=   0.0s\n",
+      "[CV] END model__C=10, model__degree=3, model__gamma=0.001, model__kernel=sigmoid; total time=   0.0s\n",
+      "[CV] END model__C=10, model__degree=3, model__gamma=0.001, model__kernel=sigmoid; total time=   0.0s\n",
+      "[CV] END model__C=10, model__degree=3, model__gamma=0.001, model__kernel=rbf; total time=   0.0s\n",
+      "[CV] END model__C=10, model__degree=3, model__gamma=0.001, model__kernel=rbf; total time=   0.0s\n",
+      "[CV] END model__C=10, model__degree=3, model__gamma=0.001, model__kernel=rbf; total time=   0.0s\n",
+      "[CV] END model__C=10, model__degree=3, model__gamma=0.001, model__kernel=rbf; total time=   0.0s\n",
+      "[CV] END model__C=10, model__degree=3, model__gamma=0.001, model__kernel=rbf; total time=   0.0s\n",
+      "[CV] END model__C=10, model__degree=3, model__gamma=0.0001, model__kernel=linear; total time=   0.0s\n",
+      "[CV] END model__C=10, model__degree=3, model__gamma=0.0001, model__kernel=linear; total time=   0.0s\n",
+      "[CV] END model__C=10, model__degree=3, model__gamma=0.0001, model__kernel=linear; total time=   0.0s\n",
+      "[CV] END model__C=10, model__degree=3, model__gamma=0.0001, model__kernel=linear; total time=   0.0s\n",
+      "[CV] END model__C=10, model__degree=3, model__gamma=0.0001, model__kernel=linear; total time=   0.0s\n",
+      "[CV] END model__C=10, model__degree=3, model__gamma=0.0001, model__kernel=sigmoid; total time=   0.0s\n",
+      "[CV] END model__C=10, model__degree=3, model__gamma=0.0001, model__kernel=sigmoid; total time=   0.0s\n",
+      "[CV] END model__C=10, model__degree=3, model__gamma=0.0001, model__kernel=sigmoid; total time=   0.0s\n",
+      "[CV] END model__C=10, model__degree=3, model__gamma=0.0001, model__kernel=sigmoid; total time=   0.0s\n",
+      "[CV] END model__C=10, model__degree=3, model__gamma=0.0001, model__kernel=sigmoid; total time=   0.0s\n",
+      "[CV] END model__C=10, model__degree=3, model__gamma=0.0001, model__kernel=rbf; total time=   0.0s\n",
+      "[CV] END model__C=10, model__degree=3, model__gamma=0.0001, model__kernel=rbf; total time=   0.0s\n",
+      "[CV] END model__C=10, model__degree=3, model__gamma=0.0001, model__kernel=rbf; total time=   0.0s\n",
+      "[CV] END model__C=10, model__degree=3, model__gamma=0.0001, model__kernel=rbf; total time=   0.0s\n",
+      "[CV] END model__C=10, model__degree=3, model__gamma=0.0001, model__kernel=rbf; total time=   0.0s\n",
+      "[CV] END model__C=10, model__degree=4, model__gamma=0.001, model__kernel=linear; total time=   0.0s\n",
+      "[CV] END model__C=10, model__degree=4, model__gamma=0.001, model__kernel=linear; total time=   0.0s\n",
+      "[CV] END model__C=10, model__degree=4, model__gamma=0.001, model__kernel=linear; total time=   0.0s\n",
+      "[CV] END model__C=10, model__degree=4, model__gamma=0.001, model__kernel=linear; total time=   0.0s\n",
+      "[CV] END model__C=10, model__degree=4, model__gamma=0.001, model__kernel=linear; total time=   0.0s\n",
+      "[CV] END model__C=10, model__degree=4, model__gamma=0.001, model__kernel=sigmoid; total time=   0.0s\n",
+      "[CV] END model__C=10, model__degree=4, model__gamma=0.001, model__kernel=sigmoid; total time=   0.0s\n",
+      "[CV] END model__C=10, model__degree=4, model__gamma=0.001, model__kernel=sigmoid; total time=   0.0s\n",
+      "[CV] END model__C=10, model__degree=4, model__gamma=0.001, model__kernel=sigmoid; total time=   0.0s\n",
+      "[CV] END model__C=10, model__degree=4, model__gamma=0.001, model__kernel=sigmoid; total time=   0.0s\n",
+      "[CV] END model__C=10, model__degree=4, model__gamma=0.001, model__kernel=rbf; total time=   0.0s\n",
+      "[CV] END model__C=10, model__degree=4, model__gamma=0.001, model__kernel=rbf; total time=   0.0s\n",
+      "[CV] END model__C=10, model__degree=4, model__gamma=0.001, model__kernel=rbf; total time=   0.0s\n",
+      "[CV] END model__C=10, model__degree=4, model__gamma=0.001, model__kernel=rbf; total time=   0.0s\n",
+      "[CV] END model__C=10, model__degree=4, model__gamma=0.001, model__kernel=rbf; total time=   0.0s\n",
+      "[CV] END model__C=10, model__degree=4, model__gamma=0.0001, model__kernel=linear; total time=   0.0s\n",
+      "[CV] END model__C=10, model__degree=4, model__gamma=0.0001, model__kernel=linear; total time=   0.0s\n",
+      "[CV] END model__C=10, model__degree=4, model__gamma=0.0001, model__kernel=linear; total time=   0.0s\n",
+      "[CV] END model__C=10, model__degree=4, model__gamma=0.0001, model__kernel=linear; total time=   0.0s\n",
+      "[CV] END model__C=10, model__degree=4, model__gamma=0.0001, model__kernel=linear; total time=   0.0s\n",
+      "[CV] END model__C=10, model__degree=4, model__gamma=0.0001, model__kernel=sigmoid; total time=   0.0s\n",
+      "[CV] END model__C=10, model__degree=4, model__gamma=0.0001, model__kernel=sigmoid; total time=   0.0s\n",
+      "[CV] END model__C=10, model__degree=4, model__gamma=0.0001, model__kernel=sigmoid; total time=   0.0s\n",
+      "[CV] END model__C=10, model__degree=4, model__gamma=0.0001, model__kernel=sigmoid; total time=   0.0s\n",
+      "[CV] END model__C=10, model__degree=4, model__gamma=0.0001, model__kernel=sigmoid; total time=   0.0s\n",
+      "[CV] END model__C=10, model__degree=4, model__gamma=0.0001, model__kernel=rbf; total time=   0.0s\n",
+      "[CV] END model__C=10, model__degree=4, model__gamma=0.0001, model__kernel=rbf; total time=   0.0s\n",
+      "[CV] END model__C=10, model__degree=4, model__gamma=0.0001, model__kernel=rbf; total time=   0.0s\n",
+      "[CV] END model__C=10, model__degree=4, model__gamma=0.0001, model__kernel=rbf; total time=   0.0s\n",
+      "[CV] END model__C=10, model__degree=4, model__gamma=0.0001, model__kernel=rbf; total time=   0.0s\n",
+      "[CV] END model__C=10, model__degree=5, model__gamma=0.001, model__kernel=linear; total time=   0.0s\n",
+      "[CV] END model__C=10, model__degree=5, model__gamma=0.001, model__kernel=linear; total time=   0.0s\n",
+      "[CV] END model__C=10, model__degree=5, model__gamma=0.001, model__kernel=linear; total time=   0.0s\n",
+      "[CV] END model__C=10, model__degree=5, model__gamma=0.001, model__kernel=linear; total time=   0.0s\n",
+      "[CV] END model__C=10, model__degree=5, model__gamma=0.001, model__kernel=linear; total time=   0.0s\n",
+      "[CV] END model__C=10, model__degree=5, model__gamma=0.001, model__kernel=sigmoid; total time=   0.0s\n",
+      "[CV] END model__C=10, model__degree=5, model__gamma=0.001, model__kernel=sigmoid; total time=   0.0s\n",
+      "[CV] END model__C=10, model__degree=5, model__gamma=0.001, model__kernel=sigmoid; total time=   0.0s\n",
+      "[CV] END model__C=10, model__degree=5, model__gamma=0.001, model__kernel=sigmoid; total time=   0.0s\n",
+      "[CV] END model__C=10, model__degree=5, model__gamma=0.001, model__kernel=sigmoid; total time=   0.0s\n",
+      "[CV] END model__C=10, model__degree=5, model__gamma=0.001, model__kernel=rbf; total time=   0.0s\n",
+      "[CV] END model__C=10, model__degree=5, model__gamma=0.001, model__kernel=rbf; total time=   0.0s\n",
+      "[CV] END model__C=10, model__degree=5, model__gamma=0.001, model__kernel=rbf; total time=   0.0s\n",
+      "[CV] END model__C=10, model__degree=5, model__gamma=0.001, model__kernel=rbf; total time=   0.0s\n",
+      "[CV] END model__C=10, model__degree=5, model__gamma=0.001, model__kernel=rbf; total time=   0.0s\n",
+      "[CV] END model__C=10, model__degree=5, model__gamma=0.0001, model__kernel=linear; total time=   0.0s\n",
+      "[CV] END model__C=10, model__degree=5, model__gamma=0.0001, model__kernel=linear; total time=   0.0s\n",
+      "[CV] END model__C=10, model__degree=5, model__gamma=0.0001, model__kernel=linear; total time=   0.0s\n",
+      "[CV] END model__C=10, model__degree=5, model__gamma=0.0001, model__kernel=linear; total time=   0.0s\n",
+      "[CV] END model__C=10, model__degree=5, model__gamma=0.0001, model__kernel=linear; total time=   0.0s\n",
+      "[CV] END model__C=10, model__degree=5, model__gamma=0.0001, model__kernel=sigmoid; total time=   0.0s\n",
+      "[CV] END model__C=10, model__degree=5, model__gamma=0.0001, model__kernel=sigmoid; total time=   0.0s\n",
+      "[CV] END model__C=10, model__degree=5, model__gamma=0.0001, model__kernel=sigmoid; total time=   0.0s\n",
+      "[CV] END model__C=10, model__degree=5, model__gamma=0.0001, model__kernel=sigmoid; total time=   0.0s\n",
+      "[CV] END model__C=10, model__degree=5, model__gamma=0.0001, model__kernel=sigmoid; total time=   0.0s\n",
+      "[CV] END model__C=10, model__degree=5, model__gamma=0.0001, model__kernel=rbf; total time=   0.0s\n",
+      "[CV] END model__C=10, model__degree=5, model__gamma=0.0001, model__kernel=rbf; total time=   0.0s\n",
+      "[CV] END model__C=10, model__degree=5, model__gamma=0.0001, model__kernel=rbf; total time=   0.0s\n",
+      "[CV] END model__C=10, model__degree=5, model__gamma=0.0001, model__kernel=rbf; total time=   0.0s\n",
+      "[CV] END model__C=10, model__degree=5, model__gamma=0.0001, model__kernel=rbf; total time=   0.0s\n",
+      "[CV] END model__C=100, model__degree=3, model__gamma=0.001, model__kernel=linear; total time=   0.2s\n",
+      "[CV] END model__C=100, model__degree=3, model__gamma=0.001, model__kernel=linear; total time=   0.2s\n",
+      "[CV] END model__C=100, model__degree=3, model__gamma=0.001, model__kernel=linear; total time=   0.3s\n",
+      "[CV] END model__C=100, model__degree=3, model__gamma=0.001, model__kernel=linear; total time=   0.2s\n",
+      "[CV] END model__C=100, model__degree=3, model__gamma=0.001, model__kernel=linear; total time=   0.4s\n",
+      "[CV] END model__C=100, model__degree=3, model__gamma=0.001, model__kernel=sigmoid; total time=   0.0s\n",
+      "[CV] END model__C=100, model__degree=3, model__gamma=0.001, model__kernel=sigmoid; total time=   0.0s\n",
+      "[CV] END model__C=100, model__degree=3, model__gamma=0.001, model__kernel=sigmoid; total time=   0.0s\n",
+      "[CV] END model__C=100, model__degree=3, model__gamma=0.001, model__kernel=sigmoid; total time=   0.0s\n",
+      "[CV] END model__C=100, model__degree=3, model__gamma=0.001, model__kernel=sigmoid; total time=   0.0s\n",
+      "[CV] END model__C=100, model__degree=3, model__gamma=0.001, model__kernel=rbf; total time=   0.0s\n",
+      "[CV] END model__C=100, model__degree=3, model__gamma=0.001, model__kernel=rbf; total time=   0.0s\n",
+      "[CV] END model__C=100, model__degree=3, model__gamma=0.001, model__kernel=rbf; total time=   0.0s\n",
+      "[CV] END model__C=100, model__degree=3, model__gamma=0.001, model__kernel=rbf; total time=   0.0s\n",
+      "[CV] END model__C=100, model__degree=3, model__gamma=0.001, model__kernel=rbf; total time=   0.0s\n",
+      "[CV] END model__C=100, model__degree=3, model__gamma=0.0001, model__kernel=linear; total time=   0.2s\n",
+      "[CV] END model__C=100, model__degree=3, model__gamma=0.0001, model__kernel=linear; total time=   0.2s\n",
+      "[CV] END model__C=100, model__degree=3, model__gamma=0.0001, model__kernel=linear; total time=   0.3s\n",
+      "[CV] END model__C=100, model__degree=3, model__gamma=0.0001, model__kernel=linear; total time=   0.2s\n",
+      "[CV] END model__C=100, model__degree=3, model__gamma=0.0001, model__kernel=linear; total time=   0.4s\n",
+      "[CV] END model__C=100, model__degree=3, model__gamma=0.0001, model__kernel=sigmoid; total time=   0.0s\n",
+      "[CV] END model__C=100, model__degree=3, model__gamma=0.0001, model__kernel=sigmoid; total time=   0.0s\n",
+      "[CV] END model__C=100, model__degree=3, model__gamma=0.0001, model__kernel=sigmoid; total time=   0.0s\n",
+      "[CV] END model__C=100, model__degree=3, model__gamma=0.0001, model__kernel=sigmoid; total time=   0.0s\n",
+      "[CV] END model__C=100, model__degree=3, model__gamma=0.0001, model__kernel=sigmoid; total time=   0.0s\n",
+      "[CV] END model__C=100, model__degree=3, model__gamma=0.0001, model__kernel=rbf; total time=   0.0s\n",
+      "[CV] END model__C=100, model__degree=3, model__gamma=0.0001, model__kernel=rbf; total time=   0.0s\n",
+      "[CV] END model__C=100, model__degree=3, model__gamma=0.0001, model__kernel=rbf; total time=   0.0s\n",
+      "[CV] END model__C=100, model__degree=3, model__gamma=0.0001, model__kernel=rbf; total time=   0.0s\n",
+      "[CV] END model__C=100, model__degree=3, model__gamma=0.0001, model__kernel=rbf; total time=   0.0s\n",
+      "[CV] END model__C=100, model__degree=4, model__gamma=0.001, model__kernel=linear; total time=   0.2s\n",
+      "[CV] END model__C=100, model__degree=4, model__gamma=0.001, model__kernel=linear; total time=   0.2s\n",
+      "[CV] END model__C=100, model__degree=4, model__gamma=0.001, model__kernel=linear; total time=   0.3s\n",
+      "[CV] END model__C=100, model__degree=4, model__gamma=0.001, model__kernel=linear; total time=   0.2s\n",
+      "[CV] END model__C=100, model__degree=4, model__gamma=0.001, model__kernel=linear; total time=   0.4s\n",
+      "[CV] END model__C=100, model__degree=4, model__gamma=0.001, model__kernel=sigmoid; total time=   0.0s\n",
+      "[CV] END model__C=100, model__degree=4, model__gamma=0.001, model__kernel=sigmoid; total time=   0.0s\n",
+      "[CV] END model__C=100, model__degree=4, model__gamma=0.001, model__kernel=sigmoid; total time=   0.0s\n",
+      "[CV] END model__C=100, model__degree=4, model__gamma=0.001, model__kernel=sigmoid; total time=   0.0s\n",
+      "[CV] END model__C=100, model__degree=4, model__gamma=0.001, model__kernel=sigmoid; total time=   0.0s\n",
+      "[CV] END model__C=100, model__degree=4, model__gamma=0.001, model__kernel=rbf; total time=   0.0s\n",
+      "[CV] END model__C=100, model__degree=4, model__gamma=0.001, model__kernel=rbf; total time=   0.0s\n",
+      "[CV] END model__C=100, model__degree=4, model__gamma=0.001, model__kernel=rbf; total time=   0.0s\n",
+      "[CV] END model__C=100, model__degree=4, model__gamma=0.001, model__kernel=rbf; total time=   0.0s\n",
+      "[CV] END model__C=100, model__degree=4, model__gamma=0.001, model__kernel=rbf; total time=   0.0s\n",
+      "[CV] END model__C=100, model__degree=4, model__gamma=0.0001, model__kernel=linear; total time=   0.2s\n",
+      "[CV] END model__C=100, model__degree=4, model__gamma=0.0001, model__kernel=linear; total time=   0.2s\n",
+      "[CV] END model__C=100, model__degree=4, model__gamma=0.0001, model__kernel=linear; total time=   0.3s\n",
+      "[CV] END model__C=100, model__degree=4, model__gamma=0.0001, model__kernel=linear; total time=   0.2s\n",
+      "[CV] END model__C=100, model__degree=4, model__gamma=0.0001, model__kernel=linear; total time=   0.4s\n",
+      "[CV] END model__C=100, model__degree=4, model__gamma=0.0001, model__kernel=sigmoid; total time=   0.0s\n",
+      "[CV] END model__C=100, model__degree=4, model__gamma=0.0001, model__kernel=sigmoid; total time=   0.0s\n",
+      "[CV] END model__C=100, model__degree=4, model__gamma=0.0001, model__kernel=sigmoid; total time=   0.0s\n",
+      "[CV] END model__C=100, model__degree=4, model__gamma=0.0001, model__kernel=sigmoid; total time=   0.0s\n",
+      "[CV] END model__C=100, model__degree=4, model__gamma=0.0001, model__kernel=sigmoid; total time=   0.0s\n",
+      "[CV] END model__C=100, model__degree=4, model__gamma=0.0001, model__kernel=rbf; total time=   0.0s\n",
+      "[CV] END model__C=100, model__degree=4, model__gamma=0.0001, model__kernel=rbf; total time=   0.0s\n",
+      "[CV] END model__C=100, model__degree=4, model__gamma=0.0001, model__kernel=rbf; total time=   0.0s\n",
+      "[CV] END model__C=100, model__degree=4, model__gamma=0.0001, model__kernel=rbf; total time=   0.0s\n",
+      "[CV] END model__C=100, model__degree=4, model__gamma=0.0001, model__kernel=rbf; total time=   0.0s\n",
+      "[CV] END model__C=100, model__degree=5, model__gamma=0.001, model__kernel=linear; total time=   0.2s\n",
+      "[CV] END model__C=100, model__degree=5, model__gamma=0.001, model__kernel=linear; total time=   0.2s\n",
+      "[CV] END model__C=100, model__degree=5, model__gamma=0.001, model__kernel=linear; total time=   0.3s\n",
+      "[CV] END model__C=100, model__degree=5, model__gamma=0.001, model__kernel=linear; total time=   0.2s\n",
+      "[CV] END model__C=100, model__degree=5, model__gamma=0.001, model__kernel=linear; total time=   0.4s\n",
+      "[CV] END model__C=100, model__degree=5, model__gamma=0.001, model__kernel=sigmoid; total time=   0.0s\n",
+      "[CV] END model__C=100, model__degree=5, model__gamma=0.001, model__kernel=sigmoid; total time=   0.0s\n",
+      "[CV] END model__C=100, model__degree=5, model__gamma=0.001, model__kernel=sigmoid; total time=   0.0s\n",
+      "[CV] END model__C=100, model__degree=5, model__gamma=0.001, model__kernel=sigmoid; total time=   0.0s\n",
+      "[CV] END model__C=100, model__degree=5, model__gamma=0.001, model__kernel=sigmoid; total time=   0.0s\n",
+      "[CV] END model__C=100, model__degree=5, model__gamma=0.001, model__kernel=rbf; total time=   0.0s\n",
+      "[CV] END model__C=100, model__degree=5, model__gamma=0.001, model__kernel=rbf; total time=   0.0s\n",
+      "[CV] END model__C=100, model__degree=5, model__gamma=0.001, model__kernel=rbf; total time=   0.0s\n",
+      "[CV] END model__C=100, model__degree=5, model__gamma=0.001, model__kernel=rbf; total time=   0.0s\n",
+      "[CV] END model__C=100, model__degree=5, model__gamma=0.001, model__kernel=rbf; total time=   0.0s\n",
+      "[CV] END model__C=100, model__degree=5, model__gamma=0.0001, model__kernel=linear; total time=   0.2s\n",
+      "[CV] END model__C=100, model__degree=5, model__gamma=0.0001, model__kernel=linear; total time=   0.2s\n",
+      "[CV] END model__C=100, model__degree=5, model__gamma=0.0001, model__kernel=linear; total time=   0.3s\n",
+      "[CV] END model__C=100, model__degree=5, model__gamma=0.0001, model__kernel=linear; total time=   0.2s\n",
+      "[CV] END model__C=100, model__degree=5, model__gamma=0.0001, model__kernel=linear; total time=   0.4s\n",
+      "[CV] END model__C=100, model__degree=5, model__gamma=0.0001, model__kernel=sigmoid; total time=   0.0s\n",
+      "[CV] END model__C=100, model__degree=5, model__gamma=0.0001, model__kernel=sigmoid; total time=   0.0s\n",
+      "[CV] END model__C=100, model__degree=5, model__gamma=0.0001, model__kernel=sigmoid; total time=   0.0s\n",
+      "[CV] END model__C=100, model__degree=5, model__gamma=0.0001, model__kernel=sigmoid; total time=   0.0s\n",
+      "[CV] END model__C=100, model__degree=5, model__gamma=0.0001, model__kernel=sigmoid; total time=   0.0s\n",
+      "[CV] END model__C=100, model__degree=5, model__gamma=0.0001, model__kernel=rbf; total time=   0.0s\n",
+      "[CV] END model__C=100, model__degree=5, model__gamma=0.0001, model__kernel=rbf; total time=   0.0s\n",
+      "[CV] END model__C=100, model__degree=5, model__gamma=0.0001, model__kernel=rbf; total time=   0.0s\n",
+      "[CV] END model__C=100, model__degree=5, model__gamma=0.0001, model__kernel=rbf; total time=   0.0s\n",
+      "[CV] END model__C=100, model__degree=5, model__gamma=0.0001, model__kernel=rbf; total time=   0.0s\n",
+      "[CV] END model__C=1000, model__degree=3, model__gamma=0.001, model__kernel=linear; total time=   1.7s\n",
+      "[CV] END model__C=1000, model__degree=3, model__gamma=0.001, model__kernel=linear; total time=   1.3s\n",
+      "[CV] END model__C=1000, model__degree=3, model__gamma=0.001, model__kernel=linear; total time=   2.3s\n",
+      "[CV] END model__C=1000, model__degree=3, model__gamma=0.001, model__kernel=linear; total time=   1.7s\n",
+      "[CV] END model__C=1000, model__degree=3, model__gamma=0.001, model__kernel=linear; total time=   2.0s\n",
+      "[CV] END model__C=1000, model__degree=3, model__gamma=0.001, model__kernel=sigmoid; total time=   0.0s\n",
+      "[CV] END model__C=1000, model__degree=3, model__gamma=0.001, model__kernel=sigmoid; total time=   0.0s\n",
+      "[CV] END model__C=1000, model__degree=3, model__gamma=0.001, model__kernel=sigmoid; total time=   0.0s\n",
+      "[CV] END model__C=1000, model__degree=3, model__gamma=0.001, model__kernel=sigmoid; total time=   0.0s\n",
+      "[CV] END model__C=1000, model__degree=3, model__gamma=0.001, model__kernel=sigmoid; total time=   0.0s\n",
+      "[CV] END model__C=1000, model__degree=3, model__gamma=0.001, model__kernel=rbf; total time=   0.0s\n",
+      "[CV] END model__C=1000, model__degree=3, model__gamma=0.001, model__kernel=rbf; total time=   0.0s\n",
+      "[CV] END model__C=1000, model__degree=3, model__gamma=0.001, model__kernel=rbf; total time=   0.0s\n",
+      "[CV] END model__C=1000, model__degree=3, model__gamma=0.001, model__kernel=rbf; total time=   0.0s\n",
+      "[CV] END model__C=1000, model__degree=3, model__gamma=0.001, model__kernel=rbf; total time=   0.0s\n",
+      "[CV] END model__C=1000, model__degree=3, model__gamma=0.0001, model__kernel=linear; total time=   1.5s\n",
+      "[CV] END model__C=1000, model__degree=3, model__gamma=0.0001, model__kernel=linear; total time=   1.2s\n",
+      "[CV] END model__C=1000, model__degree=3, model__gamma=0.0001, model__kernel=linear; total time=   2.2s\n",
+      "[CV] END model__C=1000, model__degree=3, model__gamma=0.0001, model__kernel=linear; total time=   2.0s\n",
+      "[CV] END model__C=1000, model__degree=3, model__gamma=0.0001, model__kernel=linear; total time=   2.5s\n",
+      "[CV] END model__C=1000, model__degree=3, model__gamma=0.0001, model__kernel=sigmoid; total time=   0.0s\n",
+      "[CV] END model__C=1000, model__degree=3, model__gamma=0.0001, model__kernel=sigmoid; total time=   0.0s\n",
+      "[CV] END model__C=1000, model__degree=3, model__gamma=0.0001, model__kernel=sigmoid; total time=   0.0s\n",
+      "[CV] END model__C=1000, model__degree=3, model__gamma=0.0001, model__kernel=sigmoid; total time=   0.0s\n",
+      "[CV] END model__C=1000, model__degree=3, model__gamma=0.0001, model__kernel=sigmoid; total time=   0.0s\n",
+      "[CV] END model__C=1000, model__degree=3, model__gamma=0.0001, model__kernel=rbf; total time=   0.0s\n",
+      "[CV] END model__C=1000, model__degree=3, model__gamma=0.0001, model__kernel=rbf; total time=   0.0s\n",
+      "[CV] END model__C=1000, model__degree=3, model__gamma=0.0001, model__kernel=rbf; total time=   0.0s\n",
+      "[CV] END model__C=1000, model__degree=3, model__gamma=0.0001, model__kernel=rbf; total time=   0.0s\n",
+      "[CV] END model__C=1000, model__degree=3, model__gamma=0.0001, model__kernel=rbf; total time=   0.0s\n",
+      "[CV] END model__C=1000, model__degree=4, model__gamma=0.001, model__kernel=linear; total time=   1.7s\n",
+      "[CV] END model__C=1000, model__degree=4, model__gamma=0.001, model__kernel=linear; total time=   1.3s\n",
+      "[CV] END model__C=1000, model__degree=4, model__gamma=0.001, model__kernel=linear; total time=   2.3s\n",
+      "[CV] END model__C=1000, model__degree=4, model__gamma=0.001, model__kernel=linear; total time=   1.8s\n",
+      "[CV] END model__C=1000, model__degree=4, model__gamma=0.001, model__kernel=linear; total time=   2.3s\n",
+      "[CV] END model__C=1000, model__degree=4, model__gamma=0.001, model__kernel=sigmoid; total time=   0.0s\n",
+      "[CV] END model__C=1000, model__degree=4, model__gamma=0.001, model__kernel=sigmoid; total time=   0.0s\n",
+      "[CV] END model__C=1000, model__degree=4, model__gamma=0.001, model__kernel=sigmoid; total time=   0.0s\n",
+      "[CV] END model__C=1000, model__degree=4, model__gamma=0.001, model__kernel=sigmoid; total time=   0.0s\n",
+      "[CV] END model__C=1000, model__degree=4, model__gamma=0.001, model__kernel=sigmoid; total time=   0.0s\n",
+      "[CV] END model__C=1000, model__degree=4, model__gamma=0.001, model__kernel=rbf; total time=   0.0s\n",
+      "[CV] END model__C=1000, model__degree=4, model__gamma=0.001, model__kernel=rbf; total time=   0.0s\n",
+      "[CV] END model__C=1000, model__degree=4, model__gamma=0.001, model__kernel=rbf; total time=   0.0s\n",
+      "[CV] END model__C=1000, model__degree=4, model__gamma=0.001, model__kernel=rbf; total time=   0.0s\n",
+      "[CV] END model__C=1000, model__degree=4, model__gamma=0.001, model__kernel=rbf; total time=   0.0s\n",
+      "[CV] END model__C=1000, model__degree=4, model__gamma=0.0001, model__kernel=linear; total time=   1.7s\n",
+      "[CV] END model__C=1000, model__degree=4, model__gamma=0.0001, model__kernel=linear; total time=   1.3s\n",
+      "[CV] END model__C=1000, model__degree=4, model__gamma=0.0001, model__kernel=linear; total time=   2.2s\n",
+      "[CV] END model__C=1000, model__degree=4, model__gamma=0.0001, model__kernel=linear; total time=   1.7s\n",
+      "[CV] END model__C=1000, model__degree=4, model__gamma=0.0001, model__kernel=linear; total time=   2.1s\n",
+      "[CV] END model__C=1000, model__degree=4, model__gamma=0.0001, model__kernel=sigmoid; total time=   0.0s\n",
+      "[CV] END model__C=1000, model__degree=4, model__gamma=0.0001, model__kernel=sigmoid; total time=   0.0s\n",
+      "[CV] END model__C=1000, model__degree=4, model__gamma=0.0001, model__kernel=sigmoid; total time=   0.0s\n",
+      "[CV] END model__C=1000, model__degree=4, model__gamma=0.0001, model__kernel=sigmoid; total time=   0.0s\n",
+      "[CV] END model__C=1000, model__degree=4, model__gamma=0.0001, model__kernel=sigmoid; total time=   0.0s\n",
+      "[CV] END model__C=1000, model__degree=4, model__gamma=0.0001, model__kernel=rbf; total time=   0.0s\n",
+      "[CV] END model__C=1000, model__degree=4, model__gamma=0.0001, model__kernel=rbf; total time=   0.0s\n",
+      "[CV] END model__C=1000, model__degree=4, model__gamma=0.0001, model__kernel=rbf; total time=   0.0s\n",
+      "[CV] END model__C=1000, model__degree=4, model__gamma=0.0001, model__kernel=rbf; total time=   0.0s\n",
+      "[CV] END model__C=1000, model__degree=4, model__gamma=0.0001, model__kernel=rbf; total time=   0.0s\n",
+      "[CV] END model__C=1000, model__degree=5, model__gamma=0.001, model__kernel=linear; total time=   1.6s\n",
+      "[CV] END model__C=1000, model__degree=5, model__gamma=0.001, model__kernel=linear; total time=   1.3s\n",
+      "[CV] END model__C=1000, model__degree=5, model__gamma=0.001, model__kernel=linear; total time=   2.2s\n",
+      "[CV] END model__C=1000, model__degree=5, model__gamma=0.001, model__kernel=linear; total time=   1.7s\n",
+      "[CV] END model__C=1000, model__degree=5, model__gamma=0.001, model__kernel=linear; total time=   2.0s\n",
+      "[CV] END model__C=1000, model__degree=5, model__gamma=0.001, model__kernel=sigmoid; total time=   0.0s\n",
+      "[CV] END model__C=1000, model__degree=5, model__gamma=0.001, model__kernel=sigmoid; total time=   0.0s\n",
+      "[CV] END model__C=1000, model__degree=5, model__gamma=0.001, model__kernel=sigmoid; total time=   0.0s\n",
+      "[CV] END model__C=1000, model__degree=5, model__gamma=0.001, model__kernel=sigmoid; total time=   0.0s\n",
+      "[CV] END model__C=1000, model__degree=5, model__gamma=0.001, model__kernel=sigmoid; total time=   0.0s\n",
+      "[CV] END model__C=1000, model__degree=5, model__gamma=0.001, model__kernel=rbf; total time=   0.0s\n",
+      "[CV] END model__C=1000, model__degree=5, model__gamma=0.001, model__kernel=rbf; total time=   0.0s\n",
+      "[CV] END model__C=1000, model__degree=5, model__gamma=0.001, model__kernel=rbf; total time=   0.0s\n",
+      "[CV] END model__C=1000, model__degree=5, model__gamma=0.001, model__kernel=rbf; total time=   0.0s\n",
+      "[CV] END model__C=1000, model__degree=5, model__gamma=0.001, model__kernel=rbf; total time=   0.0s\n",
+      "[CV] END model__C=1000, model__degree=5, model__gamma=0.0001, model__kernel=linear; total time=   1.6s\n",
+      "[CV] END model__C=1000, model__degree=5, model__gamma=0.0001, model__kernel=linear; total time=   1.3s\n",
+      "[CV] END model__C=1000, model__degree=5, model__gamma=0.0001, model__kernel=linear; total time=   2.3s\n",
+      "[CV] END model__C=1000, model__degree=5, model__gamma=0.0001, model__kernel=linear; total time=   1.7s\n",
+      "[CV] END model__C=1000, model__degree=5, model__gamma=0.0001, model__kernel=linear; total time=   2.1s\n",
+      "[CV] END model__C=1000, model__degree=5, model__gamma=0.0001, model__kernel=sigmoid; total time=   0.0s\n",
+      "[CV] END model__C=1000, model__degree=5, model__gamma=0.0001, model__kernel=sigmoid; total time=   0.0s\n",
+      "[CV] END model__C=1000, model__degree=5, model__gamma=0.0001, model__kernel=sigmoid; total time=   0.0s\n",
+      "[CV] END model__C=1000, model__degree=5, model__gamma=0.0001, model__kernel=sigmoid; total time=   0.0s\n",
+      "[CV] END model__C=1000, model__degree=5, model__gamma=0.0001, model__kernel=sigmoid; total time=   0.0s\n",
+      "[CV] END model__C=1000, model__degree=5, model__gamma=0.0001, model__kernel=rbf; total time=   0.0s\n",
+      "[CV] END model__C=1000, model__degree=5, model__gamma=0.0001, model__kernel=rbf; total time=   0.0s\n",
+      "[CV] END model__C=1000, model__degree=5, model__gamma=0.0001, model__kernel=rbf; total time=   0.0s\n",
+      "[CV] END model__C=1000, model__degree=5, model__gamma=0.0001, model__kernel=rbf; total time=   0.0s\n",
+      "[CV] END model__C=1000, model__degree=5, model__gamma=0.0001, model__kernel=rbf; total time=   0.0s\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": "GridSearchCV(cv=5,\n             estimator=Pipeline(steps=[('scaler', StandardScaler()),\n                                       ('model', SVC())]),\n             param_grid={'model__C': [1, 10, 100, 1000],\n                         'model__degree': [3, 4, 5],\n                         'model__gamma': [0.001, 0.0001],\n                         'model__kernel': ['linear', 'sigmoid', 'rbf']},\n             verbose=2)"
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\n",
+    "pipe = Pipeline(steps=[(\"scaler\", StandardScaler()), (\"model\", SVC())])\n",
+    "\n",
+    "param_grid = dict(\n",
+    "    model__C=[1, 10, 100, 1000],\n",
+    "    model__kernel=[\"linear\", \"sigmoid\", \"rbf\"],\n",
+    "    model__gamma=[0.001, 0.0001],\n",
+    "    model__degree=[3, 4, 5],\n",
+    ")\n",
+    "\n",
+    "gscv = GridSearchCV(\n",
+    "    pipe,\n",
+    "    param_grid=param_grid,\n",
+    "    cv=5,\n",
+    "    verbose=2,\n",
+    ")\n",
+    "gscv.fit(X_train, y_train)"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "Pipeline(steps=[('scaler', StandardScaler()),\n                ('model', SVC(C=1000, gamma=0.001))])"
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "gscv.best_estimator_"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "0.768"
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "gscv.best_score_"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "{'model__C': 1000,\n 'model__degree': 3,\n 'model__gamma': 0.001,\n 'model__kernel': 'rbf'}"
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "gscv.best_params_"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "Pipeline(steps=[('scaler', StandardScaler()),\n                ('model', SVC(C=1000, gamma=0.001))])"
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pipe.set_params(**gscv.best_params_)"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "Pipeline(steps=[('scaler', StandardScaler()),\n                ('model', SVC(C=1000, gamma=0.001))])"
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pipe.fit(X_train, y_train)"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "0.792"
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pipe.score(X_test, y_test)"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## Pipeline"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "Pipeline(steps=[('scaler', StandardScaler()), ('model', SVC())])"
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.preprocessing import StandardScaler\n",
+    "from sklearn.svm import SVC\n",
+    "from sklearn.pipeline import make_pipeline\n",
+    "\n",
+    "pipe = Pipeline(steps=[(\"scaler\", StandardScaler()), (\"model\", SVC())])\n",
+    "pipe"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "Pipeline(steps=[('standardscaler', StandardScaler()), ('svc', SVC())])"
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pipe = make_pipeline(StandardScaler(), SVC())\n",
+    "pipe"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "    feature1    feature2   feature3    feature4    feature5  feature6  \\\n0 -63.760563  182.204847  32.453468   70.877513  351.512660 -7.018939   \n1   0.694192    3.355630   5.694617 -152.936033  187.217311 -3.100926   \n2  -5.393549  184.011971 -38.685624 -119.911350   75.420933 -3.545307   \n3  -6.519019   39.696925  33.622070 -103.923978   97.519965 -0.220483   \n4 -29.057685   65.604430  72.474747   34.571492  155.318598 -2.501579   \n\n    feature7    feature8    feature9   feature10  y  \n0  59.892217   33.898478   75.654550   47.921250  0  \n1  -2.367609 -236.786098  -22.062320 -159.777679  0  \n2  78.284389   71.452933  -46.133634  -23.066554  1  \n3  28.255805  -90.692126  -51.072717    6.297472  0  \n4 -98.117314  209.305727 -113.667228   24.212523  2  ",
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>feature1</th>\n      <th>feature2</th>\n      <th>feature3</th>\n      <th>feature4</th>\n      <th>feature5</th>\n      <th>feature6</th>\n      <th>feature7</th>\n      <th>feature8</th>\n      <th>feature9</th>\n      <th>feature10</th>\n      <th>y</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>-63.760563</td>\n      <td>182.204847</td>\n      <td>32.453468</td>\n      <td>70.877513</td>\n      <td>351.512660</td>\n      <td>-7.018939</td>\n      <td>59.892217</td>\n      <td>33.898478</td>\n      <td>75.654550</td>\n      <td>47.921250</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>0.694192</td>\n      <td>3.355630</td>\n      <td>5.694617</td>\n      <td>-152.936033</td>\n      <td>187.217311</td>\n      <td>-3.100926</td>\n      <td>-2.367609</td>\n      <td>-236.786098</td>\n      <td>-22.062320</td>\n      <td>-159.777679</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>-5.393549</td>\n      <td>184.011971</td>\n      <td>-38.685624</td>\n      <td>-119.911350</td>\n      <td>75.420933</td>\n      <td>-3.545307</td>\n      <td>78.284389</td>\n      <td>71.452933</td>\n      <td>-46.133634</td>\n      <td>-23.066554</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>-6.519019</td>\n      <td>39.696925</td>\n      <td>33.622070</td>\n      <td>-103.923978</td>\n      <td>97.519965</td>\n      <td>-0.220483</td>\n      <td>28.255805</td>\n      <td>-90.692126</td>\n      <td>-51.072717</td>\n      <td>6.297472</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>-29.057685</td>\n      <td>65.604430</td>\n      <td>72.474747</td>\n      <td>34.571492</td>\n      <td>155.318598</td>\n      <td>-2.501579</td>\n      <td>-98.117314</td>\n      <td>209.305727</td>\n      <td>-113.667228</td>\n      <td>24.212523</td>\n      <td>2</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "from sklearn.datasets import make_classification\n",
+    "\n",
+    "X, y = make_classification(\n",
+    "    n_samples=500,\n",
+    "    n_classes=3,\n",
+    "    n_features=10,\n",
+    "    n_informative=6,\n",
+    "    scale=None,\n",
+    "    random_state=233,\n",
+    ")\n",
+    "\n",
+    "data = pd.DataFrame(\n",
+    "    X,\n",
+    "    columns=[f\"feature{n + 1}\" for n in range(0, 10)],\n",
+    ")\n",
+    "data[\"y\"] = y\n",
+    "data.head()"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "Pipeline(steps=[('preprocessor',\n                 ColumnTransformer(transformers=[('odd_features_with_standardscaler',\n                                                  StandardScaler(),\n                                                  ['feature3', 'feature5',\n                                                   'feature7', 'feature9']),\n                                                 ('even_features_with_mimaxscaler',\n                                                  MinMaxScaler(),\n                                                  ['feature2', 'feature4',\n                                                   'feature6', 'feature8']),\n                                                 ('passthrough_feature',\n                                                  'passthrough',\n                                                  ['feature1', 'feature10'])])),\n                ('model', SVC())])"
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.compose import ColumnTransformer\n",
+    "from sklearn.preprocessing import StandardScaler, MinMaxScaler\n",
+    "\n",
+    "preprocessor = ColumnTransformer(\n",
+    "    transformers=[\n",
+    "        (\"odd_features_with_standardscaler\", StandardScaler(), [f\"feature{n}\" for n in [3, 5, 7, 9]]),\n",
+    "        (\"even_features_with_mimaxscaler\", MinMaxScaler(), [f\"feature{n}\" for n in [2, 4, 6, 8]]),\n",
+    "        (\"passthrough_feature\", \"passthrough\", [\"feature1\", \"feature10\"]),\n",
+    "    ],\n",
+    "    remainder=\"drop\",\n",
+    ")\n",
+    "\n",
+    "\n",
+    "pipe = Pipeline(\n",
+    "    steps=[\n",
+    "        (\"preprocessor\", preprocessor),\n",
+    "        (\"model\", SVC()),\n",
+    "    ]\n",
+    ")\n",
+    "pipe.fit(data, data[\"y\"])"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## Persistence"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "[PosixPath('/var/folders/0t/s0c95rbs6ds7w_b0d471p0kc0000gn/T/tmpeuwitk1w/LinearRegression.pkl')]"
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\n",
+    "import pickle\n",
+    "import pathlib\n",
+    "import tempfile\n",
+    "\n",
+    "from sklearn.linear_model import LinearRegression\n",
+    "\n",
+    "root = pathlib.Path(tempfile.mkdtemp())\n",
+    "fpath = root.joinpath(\"LinearRegression.pkl\")\n",
+    "\n",
+    "regressor = LinearRegression()\n",
+    "\n",
+    "\n",
+    "with open(fpath, \"wb\") as file:\n",
+    "    pickle.dump(regressor, file)\n",
+    "\n",
+    "files = list(root.iterdir())\n",
+    "files"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "{'copy_X': True,\n 'fit_intercept': True,\n 'n_jobs': None,\n 'normalize': 'deprecated',\n 'positive': False}"
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "file = open(fpath, \"rb\")\n",
+    "model = pickle.load(file)\n",
+    "file.close()\n",
+    "\n",
+    "model.get_params()"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[PosixPath('/var/folders/0t/s0c95rbs6ds7w_b0d471p0kc0000gn/T/tmpeuwitk1w/SVC.joblib'), PosixPath('/var/folders/0t/s0c95rbs6ds7w_b0d471p0kc0000gn/T/tmpeuwitk1w/LinearRegression.pkl')]\n"
+     ]
+    }
+   ],
+   "source": [
+    "import joblib\n",
+    "from sklearn.svm import SVC\n",
+    "\n",
+    "classifier = SVC()\n",
+    "fpath = root.joinpath(\"SVC.joblib\")\n",
+    "\n",
+    "joblib.dump(classifier, fpath)\n",
+    "files = list(root.iterdir())\n",
+    "print(files)"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "{'C': 1.0,\n 'break_ties': False,\n 'cache_size': 200,\n 'class_weight': None,\n 'coef0': 0.0,\n 'decision_function_shape': 'ovr',\n 'degree': 3,\n 'gamma': 'scale',\n 'kernel': 'rbf',\n 'max_iter': -1,\n 'probability': False,\n 'random_state': None,\n 'shrinking': True,\n 'tol': 0.001,\n 'verbose': False}"
+     },
+     "execution_count": 38,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model = joblib.load(fpath)\n",
+    "model.get_params()"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "outputs": [],
+   "source": [],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
\ No newline at end of file
diff --git a/projects/machine-learning/spaceship_titanic_prediction.ipynb b/projects/machine-learning/spaceship_titanic_prediction.ipynb
new file mode 100644
index 0000000..6a11ffe
--- /dev/null
+++ b/projects/machine-learning/spaceship_titanic_prediction.ipynb
@@ -0,0 +1,147017 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "# Description\n",
+    "\n",
+    "项目介绍及数据来源见 Kaggle：[Spaceship Titanic](https://www.kaggle.com/competitions/spaceship-titanic/overview)。\n",
+    "\n",
+    "数据详情：\n",
+    "\n",
+    "- `data.csv` 训练集样本量约为 8700 人；\n",
+    "- 数据字段详情：\n",
+    "  - 数据字段共有 14 个；\n",
+    "  - 字段详情：\n",
+    "    - `PassengerId`：每个乘客的唯一标识。其形式为 `gggg_pp`，其中 `gggg` 表示乘客一同乘坐的团体，`pp` 是他们在团体中的编号。团体中的个人通常是家庭成员，**但这也不是绝对的**；\n",
+    "    - `HomePlanet`：乘客所离开的星球，通常是他们永久居住的星球；\n",
+    "    - `CryoSleep`：表示乘客是否选择在航行期间进入深度睡眠，处于深度睡眠状态的乘客被限制在他们坐在的船舱内；\n",
+    "    - `Cabin`：乘客所住的船舱及座位号，采取 `甲板/编号/侧面` 的形式编号，其中侧面的 P 代表左舷（船身左半边），S 代表右舷（船身右半边）；\n",
+    "    - `Destination`：乘客此次航行的目的地；\n",
+    "    - `Age`：乘客的年龄；\n",
+    "    - `VIP`：乘客是否为 VIP；\n",
+    "    - `RoomService`，`FoodCourt`，`ShoppingMall`，`Spa`，`VRDeck`：即乘客在太空船的众多豪华设施项中的**花销金额**；\n",
+    "    - `Name`：乘客的名字和姓氏；\n",
+    "    - `Transported`：乘客是否被运送到另一个空间，**这也是训练集的参考答案以及需要在测试集中预测的结果**。\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    },
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "%matplotlib inline\n",
+    "%config InlineBackend.figure_format = \"svg\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    },
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import itertools\n",
+    "\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import seaborn as sns\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# figure settings\n",
+    "sns.set_theme()\n",
+    "plt.rcParams[\"axes.unicode_minus\"] = False"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "## Utilities Definition"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# use dict to store handled features\n",
+    "features = {}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    },
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "def make_frequency(df, column, as_percent=True, use_total=True, **kwargs):\n",
+    "    \"\"\"\n",
+    "    create the report table about the target column with \n",
+    "    frequency, percentage and total information\n",
+    "    \"\"\"\n",
+    "    \n",
+    "    percent = pd.Series(dtype=\"object\")\n",
+    "    data = []\n",
+    "    opts = dict(dropna=False)\n",
+    "\n",
+    "    \n",
+    "    if kwargs:\n",
+    "        if kwargs.get(\"normalize\"):\n",
+    "            kwargs.pop(\"normalize\")\n",
+    "            \n",
+    "        opts.update(kwargs)\n",
+    "    \n",
+    "    base = df[column].value_counts(**opts).to_frame(name=\"N\")\n",
+    "    data.append(base)\n",
+    "    \n",
+    "    if as_percent:\n",
+    "        opts[\"normalize\"] = True\n",
+    "        percent = df[column].value_counts(**opts).to_frame(name=\"%\")\n",
+    "        data.append(percent)\n",
+    "\n",
+    "    report = pd.concat(data, join=\"inner\", axis=\"columns\")\n",
+    "    \n",
+    "    if use_total:\n",
+    "        total = report.sum().to_frame().transpose().set_index(pd.Index([\"Total\"]))\n",
+    "        report = pd.concat([report, total])\n",
+    "\n",
+    "    if \"%\" in report.columns:\n",
+    "        report[\"%\"] = report[\"%\"].map(lambda v: f\"{v*100:.2f}%\")\n",
+    "    \n",
+    "    report.index.name = column\n",
+    "    return report.astype({\"N\": pd.Int64Dtype()})\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def both_direction_fill(df, na_group, like):\n",
+    "    filled = (\n",
+    "        df.query(f\"Group.isin(@na_group)\")\n",
+    "        .groupby(\"Group\")\n",
+    "        .apply(\n",
+    "            lambda grp: grp.filter(like=f\"{like}\")\n",
+    "            .fillna(method=\"bfill\")\n",
+    "            .fillna(method=\"ffill\")\n",
+    "        )\n",
+    "    )\n",
+    "    return filled"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "# Data Explore"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    },
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>PassengerId</th>\n",
+       "      <th>HomePlanet</th>\n",
+       "      <th>CryoSleep</th>\n",
+       "      <th>Cabin</th>\n",
+       "      <th>Destination</th>\n",
+       "      <th>Age</th>\n",
+       "      <th>VIP</th>\n",
+       "      <th>RoomService</th>\n",
+       "      <th>FoodCourt</th>\n",
+       "      <th>ShoppingMall</th>\n",
+       "      <th>Spa</th>\n",
+       "      <th>VRDeck</th>\n",
+       "      <th>Name</th>\n",
+       "      <th>Transported</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>PassengerId</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0001_01</th>\n",
+       "      <td>0001_01</td>\n",
+       "      <td>Europa</td>\n",
+       "      <td>False</td>\n",
+       "      <td>B/0/P</td>\n",
+       "      <td>TRAPPIST-1e</td>\n",
+       "      <td>39.0</td>\n",
+       "      <td>False</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>Maham Ofracculy</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0002_01</th>\n",
+       "      <td>0002_01</td>\n",
+       "      <td>Earth</td>\n",
+       "      <td>False</td>\n",
+       "      <td>F/0/S</td>\n",
+       "      <td>TRAPPIST-1e</td>\n",
+       "      <td>24.0</td>\n",
+       "      <td>False</td>\n",
+       "      <td>109.0</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>25.0</td>\n",
+       "      <td>549.0</td>\n",
+       "      <td>44.0</td>\n",
+       "      <td>Juanna Vines</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0003_01</th>\n",
+       "      <td>0003_01</td>\n",
+       "      <td>Europa</td>\n",
+       "      <td>False</td>\n",
+       "      <td>A/0/S</td>\n",
+       "      <td>TRAPPIST-1e</td>\n",
+       "      <td>58.0</td>\n",
+       "      <td>True</td>\n",
+       "      <td>43.0</td>\n",
+       "      <td>3576.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>6715.0</td>\n",
+       "      <td>49.0</td>\n",
+       "      <td>Altark Susent</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0003_02</th>\n",
+       "      <td>0003_02</td>\n",
+       "      <td>Europa</td>\n",
+       "      <td>False</td>\n",
+       "      <td>A/0/S</td>\n",
+       "      <td>TRAPPIST-1e</td>\n",
+       "      <td>33.0</td>\n",
+       "      <td>False</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1283.0</td>\n",
+       "      <td>371.0</td>\n",
+       "      <td>3329.0</td>\n",
+       "      <td>193.0</td>\n",
+       "      <td>Solam Susent</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0004_01</th>\n",
+       "      <td>0004_01</td>\n",
+       "      <td>Earth</td>\n",
+       "      <td>False</td>\n",
+       "      <td>F/1/S</td>\n",
+       "      <td>TRAPPIST-1e</td>\n",
+       "      <td>16.0</td>\n",
+       "      <td>False</td>\n",
+       "      <td>303.0</td>\n",
+       "      <td>70.0</td>\n",
+       "      <td>151.0</td>\n",
+       "      <td>565.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>Willy Santantines</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            PassengerId HomePlanet CryoSleep  Cabin  Destination   Age    VIP  \\\n",
+       "PassengerId                                                                     \n",
+       "0001_01         0001_01     Europa     False  B/0/P  TRAPPIST-1e  39.0  False   \n",
+       "0002_01         0002_01      Earth     False  F/0/S  TRAPPIST-1e  24.0  False   \n",
+       "0003_01         0003_01     Europa     False  A/0/S  TRAPPIST-1e  58.0   True   \n",
+       "0003_02         0003_02     Europa     False  A/0/S  TRAPPIST-1e  33.0  False   \n",
+       "0004_01         0004_01      Earth     False  F/1/S  TRAPPIST-1e  16.0  False   \n",
+       "\n",
+       "             RoomService  FoodCourt  ShoppingMall     Spa  VRDeck  \\\n",
+       "PassengerId                                                         \n",
+       "0001_01              0.0        0.0           0.0     0.0     0.0   \n",
+       "0002_01            109.0        9.0          25.0   549.0    44.0   \n",
+       "0003_01             43.0     3576.0           0.0  6715.0    49.0   \n",
+       "0003_02              0.0     1283.0         371.0  3329.0   193.0   \n",
+       "0004_01            303.0       70.0         151.0   565.0     2.0   \n",
+       "\n",
+       "                          Name  Transported  \n",
+       "PassengerId                                  \n",
+       "0001_01        Maham Ofracculy        False  \n",
+       "0002_01           Juanna Vines         True  \n",
+       "0003_01          Altark Susent        False  \n",
+       "0003_02           Solam Susent        False  \n",
+       "0004_01      Willy Santantines         True  "
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data = pd.read_csv(\"../../data/spaceship-titanic/data.csv\").set_index(\"PassengerId\", drop=False)\n",
+    "data.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    },
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "Index: 8693 entries, 0001_01 to 9280_02\n",
+      "Data columns (total 14 columns):\n",
+      " #   Column        Non-Null Count  Dtype  \n",
+      "---  ------        --------------  -----  \n",
+      " 0   PassengerId   8693 non-null   object \n",
+      " 1   HomePlanet    8492 non-null   object \n",
+      " 2   CryoSleep     8476 non-null   object \n",
+      " 3   Cabin         8494 non-null   object \n",
+      " 4   Destination   8511 non-null   object \n",
+      " 5   Age           8514 non-null   float64\n",
+      " 6   VIP           8490 non-null   object \n",
+      " 7   RoomService   8512 non-null   float64\n",
+      " 8   FoodCourt     8510 non-null   float64\n",
+      " 9   ShoppingMall  8485 non-null   float64\n",
+      " 10  Spa           8510 non-null   float64\n",
+      " 11  VRDeck        8505 non-null   float64\n",
+      " 12  Name          8493 non-null   object \n",
+      " 13  Transported   8693 non-null   bool   \n",
+      "dtypes: bool(1), float64(6), object(7)\n",
+      "memory usage: 959.3+ KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "data.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "PassengerId       0\n",
+       "HomePlanet      201\n",
+       "CryoSleep       217\n",
+       "Cabin           199\n",
+       "Destination     182\n",
+       "Age             179\n",
+       "VIP             203\n",
+       "RoomService     181\n",
+       "FoodCourt       183\n",
+       "ShoppingMall    208\n",
+       "Spa             183\n",
+       "VRDeck          188\n",
+       "Name            200\n",
+       "Transported       0\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "## Categorical Fields"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "### HomePlanet counter"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    },
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>N</th>\n",
+       "      <th>%</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>HomePlanet</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Earth</th>\n",
+       "      <td>4602</td>\n",
+       "      <td>52.94%</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Europa</th>\n",
+       "      <td>2131</td>\n",
+       "      <td>24.51%</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Mars</th>\n",
+       "      <td>1759</td>\n",
+       "      <td>20.23%</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>NaN</th>\n",
+       "      <td>201</td>\n",
+       "      <td>2.31%</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Total</th>\n",
+       "      <td>8693</td>\n",
+       "      <td>100.00%</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "               N        %\n",
+       "HomePlanet               \n",
+       "Earth       4602   52.94%\n",
+       "Europa      2131   24.51%\n",
+       "Mars        1759   20.23%\n",
+       "NaN          201    2.31%\n",
+       "Total       8693  100.00%"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "make_frequency(data, \"HomePlanet\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "### CryoSleep counter"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    },
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>N</th>\n",
+       "      <th>%</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>CryoSleep</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>False</th>\n",
+       "      <td>5439</td>\n",
+       "      <td>62.57%</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>True</th>\n",
+       "      <td>3037</td>\n",
+       "      <td>34.94%</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>NaN</th>\n",
+       "      <td>217</td>\n",
+       "      <td>2.50%</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Total</th>\n",
+       "      <td>8693</td>\n",
+       "      <td>100.00%</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "              N        %\n",
+       "CryoSleep               \n",
+       "False      5439   62.57%\n",
+       "True       3037   34.94%\n",
+       "NaN         217    2.50%\n",
+       "Total      8693  100.00%"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "make_frequency(data, \"CryoSleep\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "### Cabin counter"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    },
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>N</th>\n",
+       "      <th>%</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Cabin</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>NaN</th>\n",
+       "      <td>199</td>\n",
+       "      <td>2.29%</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>G/734/S</th>\n",
+       "      <td>8</td>\n",
+       "      <td>0.09%</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>C/137/S</th>\n",
+       "      <td>7</td>\n",
+       "      <td>0.08%</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>B/201/P</th>\n",
+       "      <td>7</td>\n",
+       "      <td>0.08%</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>G/109/P</th>\n",
+       "      <td>7</td>\n",
+       "      <td>0.08%</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>E/231/S</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0.01%</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>G/545/S</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0.01%</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>G/543/S</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0.01%</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>C/178/S</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0.01%</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Total</th>\n",
+       "      <td>8693</td>\n",
+       "      <td>100.00%</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>6562 rows × 2 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            N        %\n",
+       "Cabin                 \n",
+       "NaN       199    2.29%\n",
+       "G/734/S     8    0.09%\n",
+       "C/137/S     7    0.08%\n",
+       "B/201/P     7    0.08%\n",
+       "G/109/P     7    0.08%\n",
+       "...       ...      ...\n",
+       "E/231/S     1    0.01%\n",
+       "G/545/S     1    0.01%\n",
+       "G/543/S     1    0.01%\n",
+       "C/178/S     1    0.01%\n",
+       "Total    8693  100.00%\n",
+       "\n",
+       "[6562 rows x 2 columns]"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "make_frequency(data, \"Cabin\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "### Destination counter"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    },
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>N</th>\n",
+       "      <th>%</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Destination</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>TRAPPIST-1e</th>\n",
+       "      <td>5915</td>\n",
+       "      <td>68.04%</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>55 Cancri e</th>\n",
+       "      <td>1800</td>\n",
+       "      <td>20.71%</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>PSO J318.5-22</th>\n",
+       "      <td>796</td>\n",
+       "      <td>9.16%</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>NaN</th>\n",
+       "      <td>182</td>\n",
+       "      <td>2.09%</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Total</th>\n",
+       "      <td>8693</td>\n",
+       "      <td>100.00%</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                  N        %\n",
+       "Destination                 \n",
+       "TRAPPIST-1e    5915   68.04%\n",
+       "55 Cancri e    1800   20.71%\n",
+       "PSO J318.5-22   796    9.16%\n",
+       "NaN             182    2.09%\n",
+       "Total          8693  100.00%"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "make_frequency(data, \"Destination\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "### VIP counter"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    },
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>N</th>\n",
+       "      <th>%</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>VIP</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>False</th>\n",
+       "      <td>8291</td>\n",
+       "      <td>95.38%</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>NaN</th>\n",
+       "      <td>203</td>\n",
+       "      <td>2.34%</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>True</th>\n",
+       "      <td>199</td>\n",
+       "      <td>2.29%</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Total</th>\n",
+       "      <td>8693</td>\n",
+       "      <td>100.00%</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          N        %\n",
+       "VIP                 \n",
+       "False  8291   95.38%\n",
+       "NaN     203    2.34%\n",
+       "True    199    2.29%\n",
+       "Total  8693  100.00%"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "make_frequency(data, \"VIP\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "### Transported counter"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    },
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>N</th>\n",
+       "      <th>%</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Transported</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>True</th>\n",
+       "      <td>4378</td>\n",
+       "      <td>50.36%</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>False</th>\n",
+       "      <td>4315</td>\n",
+       "      <td>49.64%</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Total</th>\n",
+       "      <td>8693</td>\n",
+       "      <td>100.00%</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                N        %\n",
+       "Transported               \n",
+       "True         4378   50.36%\n",
+       "False        4315   49.64%\n",
+       "Total        8693  100.00%"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "make_frequency(data, \"Transported\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Drop name"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**improved: can guess the gender from name**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "PassengerId\n",
+       "0001_01      Maham Ofracculy\n",
+       "0002_01         Juanna Vines\n",
+       "0003_01        Altark Susent\n",
+       "0003_02         Solam Susent\n",
+       "0004_01    Willy Santantines\n",
+       "Name: Name, dtype: object"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data[\"Name\"].head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "## Numerical Fields"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    },
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Age</th>\n",
+       "      <th>RoomService</th>\n",
+       "      <th>FoodCourt</th>\n",
+       "      <th>ShoppingMall</th>\n",
+       "      <th>Spa</th>\n",
+       "      <th>VRDeck</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>PassengerId</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0001_01</th>\n",
+       "      <td>39.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0002_01</th>\n",
+       "      <td>24.0</td>\n",
+       "      <td>109.0</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>25.0</td>\n",
+       "      <td>549.0</td>\n",
+       "      <td>44.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0003_01</th>\n",
+       "      <td>58.0</td>\n",
+       "      <td>43.0</td>\n",
+       "      <td>3576.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>6715.0</td>\n",
+       "      <td>49.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0003_02</th>\n",
+       "      <td>33.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1283.0</td>\n",
+       "      <td>371.0</td>\n",
+       "      <td>3329.0</td>\n",
+       "      <td>193.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0004_01</th>\n",
+       "      <td>16.0</td>\n",
+       "      <td>303.0</td>\n",
+       "      <td>70.0</td>\n",
+       "      <td>151.0</td>\n",
+       "      <td>565.0</td>\n",
+       "      <td>2.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "              Age  RoomService  FoodCourt  ShoppingMall     Spa  VRDeck\n",
+       "PassengerId                                                            \n",
+       "0001_01      39.0          0.0        0.0           0.0     0.0     0.0\n",
+       "0002_01      24.0        109.0        9.0          25.0   549.0    44.0\n",
+       "0003_01      58.0         43.0     3576.0           0.0  6715.0    49.0\n",
+       "0003_02      33.0          0.0     1283.0         371.0  3329.0   193.0\n",
+       "0004_01      16.0        303.0       70.0         151.0   565.0     2.0"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "numeric = data.select_dtypes(include=\"number\")\n",
+    "numeric.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    },
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Age</th>\n",
+       "      <th>RoomService</th>\n",
+       "      <th>FoodCourt</th>\n",
+       "      <th>ShoppingMall</th>\n",
+       "      <th>Spa</th>\n",
+       "      <th>VRDeck</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>8514.000000</td>\n",
+       "      <td>8512.000000</td>\n",
+       "      <td>8510.000000</td>\n",
+       "      <td>8485.000000</td>\n",
+       "      <td>8510.000000</td>\n",
+       "      <td>8505.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>28.827930</td>\n",
+       "      <td>224.687617</td>\n",
+       "      <td>458.077203</td>\n",
+       "      <td>173.729169</td>\n",
+       "      <td>311.138778</td>\n",
+       "      <td>304.854791</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>14.489021</td>\n",
+       "      <td>666.717663</td>\n",
+       "      <td>1611.489240</td>\n",
+       "      <td>604.696458</td>\n",
+       "      <td>1136.705535</td>\n",
+       "      <td>1145.717189</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>19.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>27.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>38.000000</td>\n",
+       "      <td>47.000000</td>\n",
+       "      <td>76.000000</td>\n",
+       "      <td>27.000000</td>\n",
+       "      <td>59.000000</td>\n",
+       "      <td>46.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>79.000000</td>\n",
+       "      <td>14327.000000</td>\n",
+       "      <td>29813.000000</td>\n",
+       "      <td>23492.000000</td>\n",
+       "      <td>22408.000000</td>\n",
+       "      <td>24133.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "               Age   RoomService     FoodCourt  ShoppingMall           Spa  \\\n",
+       "count  8514.000000   8512.000000   8510.000000   8485.000000   8510.000000   \n",
+       "mean     28.827930    224.687617    458.077203    173.729169    311.138778   \n",
+       "std      14.489021    666.717663   1611.489240    604.696458   1136.705535   \n",
+       "min       0.000000      0.000000      0.000000      0.000000      0.000000   \n",
+       "25%      19.000000      0.000000      0.000000      0.000000      0.000000   \n",
+       "50%      27.000000      0.000000      0.000000      0.000000      0.000000   \n",
+       "75%      38.000000     47.000000     76.000000     27.000000     59.000000   \n",
+       "max      79.000000  14327.000000  29813.000000  23492.000000  22408.000000   \n",
+       "\n",
+       "             VRDeck  \n",
+       "count   8505.000000  \n",
+       "mean     304.854791  \n",
+       "std     1145.717189  \n",
+       "min        0.000000  \n",
+       "25%        0.000000  \n",
+       "50%        0.000000  \n",
+       "75%       46.000000  \n",
+       "max    24133.000000  "
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "numeric.describe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
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+    "jupyter": {
+     "outputs_hidden": false
+    },
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [
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+    "fig, axes = plt.subplots(nrows=2, ncols=3, figsize=(13, 8))\n",
+    "\n",
+    "\n",
+    "for column, ax in zip(numeric.columns, itertools.chain(*axes)):\n",
+    "    df = numeric[column]\n",
+    "    if column != \"Age\":\n",
+    "        df = df[df>0].transform(np.log10)\n",
+    "    df.plot.hist(ax=ax, title=f\"{column}\")\n",
+    "\n",
+    "plt.tight_layout()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "services = (\n",
+    "    numeric.drop(\"Age\", axis=1)\n",
+    "    .assign(TotalServiceFee=lambda r: r.sum(axis=1))\n",
+    "    .fillna(0)\n",
+    "    .add(1)\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "services = services.transform(np.log10).join(data[\"Transported\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
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+       "             RoomService  FoodCourt  ShoppingMall       Spa    VRDeck  \\\n",
+       "PassengerId                                                             \n",
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+       "\n",
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+     "output_type": "display_data"
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+   ],
+   "source": [
+    "g = sns.PairGrid(data=services.drop(\"TotalServiceFee\", axis=1).sample(frac=0.05), hue=\"Transported\")\n",
+    "g.map_upper(sns.regplot)\n",
+    "g.map_diag(sns.kdeplot)\n",
+    "g.map_lower(sns.kdeplot, fill=True)\n",
+    "g.add_legend()"
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+  },
+  {
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+   "execution_count": 23,
+   "metadata": {},
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+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ax = sns.kdeplot(data=services, x=\"TotalServiceFee\", hue=\"Transported\", fill=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    },
+    "tags": []
+   },
+   "source": [
+    "# Feature Engineering"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "## Data type convert"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    },
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "Index: 8693 entries, 0001_01 to 9280_02\n",
+      "Data columns (total 14 columns):\n",
+      " #   Column        Non-Null Count  Dtype  \n",
+      "---  ------        --------------  -----  \n",
+      " 0   PassengerId   8693 non-null   string \n",
+      " 1   HomePlanet    8492 non-null   string \n",
+      " 2   CryoSleep     8476 non-null   string \n",
+      " 3   Cabin         8494 non-null   string \n",
+      " 4   Destination   8511 non-null   string \n",
+      " 5   Age           8514 non-null   Int64  \n",
+      " 6   VIP           8490 non-null   string \n",
+      " 7   RoomService   8512 non-null   float64\n",
+      " 8   FoodCourt     8510 non-null   float64\n",
+      " 9   ShoppingMall  8485 non-null   float64\n",
+      " 10  Spa           8510 non-null   float64\n",
+      " 11  VRDeck        8505 non-null   float64\n",
+      " 12  Name          8493 non-null   string \n",
+      " 13  Transported   8693 non-null   string \n",
+      "dtypes: Int64(1), float64(5), string(8)\n",
+      "memory usage: 1.3+ MB\n"
+     ]
+    }
+   ],
+   "source": [
+    "types = dict(\n",
+    "    # convert object, boolean to StringDtype\n",
+    "    PassengerId=\"string\",\n",
+    "    HomePlanet=\"string\",\n",
+    "    Cabin=\"string\",\n",
+    "    Destination=\"string\",\n",
+    "    Name=\"string\",\n",
+    "    CryoSleep=\"string\",\n",
+    "    VIP=\"string\",\n",
+    "    Transported=\"string\",\n",
+    "    \n",
+    "    # convert Age to Int64Dytype to avoid\n",
+    "    # being float type because of NA.\n",
+    "    Age=pd.Int64Dtype(),\n",
+    "\n",
+    ")\n",
+    "\n",
+    "data = data.astype(types)\n",
+    "data.info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "## PassengerId Group Extraction\n",
+    "\n",
+    "- Is single group or not\n",
+    "- Merge raw data by `PassengerId`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    },
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
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+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0004_01</td>\n",
+       "      <td>0004</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  PassengerId Group\n",
+       "0     0001_01  0001\n",
+       "1     0002_01  0002\n",
+       "2     0003_01  0003\n",
+       "3     0003_02  0003\n",
+       "4     0004_01  0004"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "passenger_group = (\n",
+    "    data[\"PassengerId\"]\n",
+    "    .str.extract(\"(?P<Group>\\d{4})_\", expand=True)\n",
+    "    .reset_index()\n",
+    ")\n",
+    "passenger_group.head(5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    },
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:ylabel='Frequency'>"
+      ]
+     },
+     "execution_count": 26,
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+    "passenger_group.value_counts(\"Group\").plot.hist()"
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+       "      <td>77.29%</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>False</th>\n",
+       "      <td>1412</td>\n",
+       "      <td>22.71%</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Total</th>\n",
+       "      <td>6217</td>\n",
+       "      <td>100.00%</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                  N        %\n",
+       "IsSingleGroup               \n",
+       "True           4805   77.29%\n",
+       "False          1412   22.71%\n",
+       "Total          6217  100.00%"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "make_frequency(is_single_group, \"IsSingleGroup\", as_percent=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    },
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Group</th>\n",
+       "      <th>IsSingleGroup</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>PassengerId</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0001_01</th>\n",
+       "      <td>0001</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0002_01</th>\n",
+       "      <td>0002</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0003_01</th>\n",
+       "      <td>0003</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0003_02</th>\n",
+       "      <td>0003</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0004_01</th>\n",
+       "      <td>0004</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            Group  IsSingleGroup\n",
+       "PassengerId                     \n",
+       "0001_01      0001           True\n",
+       "0002_01      0002           True\n",
+       "0003_01      0003          False\n",
+       "0003_02      0003          False\n",
+       "0004_01      0004           True"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "passenger_group = (\n",
+    "    passenger_group\n",
+    "    .merge(is_single_group, on=\"Group\", how=\"left\")\n",
+    "    .set_index(\"PassengerId\")\n",
+    ")\n",
+    "passenger_group.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    },
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "features[\"passenger_group\"] = passenger_group.astype(\"string\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "## Split Cabin Code"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    },
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>CabinDesk</th>\n",
+       "      <th>CabinNumber</th>\n",
+       "      <th>CabinSide</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>PassengerId</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0001_01</th>\n",
+       "      <td>B</td>\n",
+       "      <td>0</td>\n",
+       "      <td>P</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0002_01</th>\n",
+       "      <td>F</td>\n",
+       "      <td>0</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0003_01</th>\n",
+       "      <td>A</td>\n",
+       "      <td>0</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0003_02</th>\n",
+       "      <td>A</td>\n",
+       "      <td>0</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0004_01</th>\n",
+       "      <td>F</td>\n",
+       "      <td>1</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            CabinDesk CabinNumber CabinSide\n",
+       "PassengerId                                \n",
+       "0001_01             B           0         P\n",
+       "0002_01             F           0         S\n",
+       "0003_01             A           0         S\n",
+       "0003_02             A           0         S\n",
+       "0004_01             F           1         S"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cabin = data[\"Cabin\"].str.split(r\"/\", expand=True).rename(columns={0: \"CabinDesk\", 1: \"CabinNumber\", 2: \"CabinSide\"})\n",
+    "cabin.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    },
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>N</th>\n",
+       "      <th>%</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>CabinDesk</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>F</th>\n",
+       "      <td>2794</td>\n",
+       "      <td>32.14%</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>G</th>\n",
+       "      <td>2559</td>\n",
+       "      <td>29.44%</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>E</th>\n",
+       "      <td>876</td>\n",
+       "      <td>10.08%</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>B</th>\n",
+       "      <td>779</td>\n",
+       "      <td>8.96%</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>C</th>\n",
+       "      <td>747</td>\n",
+       "      <td>8.59%</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>D</th>\n",
+       "      <td>478</td>\n",
+       "      <td>5.50%</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>A</th>\n",
+       "      <td>256</td>\n",
+       "      <td>2.94%</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>&lt;NA&gt;</th>\n",
+       "      <td>199</td>\n",
+       "      <td>2.29%</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>T</th>\n",
+       "      <td>5</td>\n",
+       "      <td>0.06%</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Total</th>\n",
+       "      <td>8693</td>\n",
+       "      <td>100.00%</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "              N        %\n",
+       "CabinDesk               \n",
+       "F          2794   32.14%\n",
+       "G          2559   29.44%\n",
+       "E           876   10.08%\n",
+       "B           779    8.96%\n",
+       "C           747    8.59%\n",
+       "D           478    5.50%\n",
+       "A           256    2.94%\n",
+       "<NA>        199    2.29%\n",
+       "T             5    0.06%\n",
+       "Total      8693  100.00%"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "make_frequency(cabin, \"CabinDesk\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    },
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>N</th>\n",
+       "      <th>%</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>CabinNumber</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>&lt;NA&gt;</th>\n",
+       "      <td>199</td>\n",
+       "      <td>2.29%</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>82</th>\n",
+       "      <td>28</td>\n",
+       "      <td>0.32%</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>86</th>\n",
+       "      <td>22</td>\n",
+       "      <td>0.25%</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>22</td>\n",
+       "      <td>0.25%</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>56</th>\n",
+       "      <td>21</td>\n",
+       "      <td>0.24%</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1797</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0.01%</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1754</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0.01%</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1723</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0.01%</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1894</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0.01%</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Total</th>\n",
+       "      <td>8693</td>\n",
+       "      <td>100.00%</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1819 rows × 2 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                N        %\n",
+       "CabinNumber               \n",
+       "<NA>          199    2.29%\n",
+       "82             28    0.32%\n",
+       "86             22    0.25%\n",
+       "19             22    0.25%\n",
+       "56             21    0.24%\n",
+       "...           ...      ...\n",
+       "1797            1    0.01%\n",
+       "1754            1    0.01%\n",
+       "1723            1    0.01%\n",
+       "1894            1    0.01%\n",
+       "Total        8693  100.00%\n",
+       "\n",
+       "[1819 rows x 2 columns]"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "make_frequency(cabin, \"CabinNumber\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    },
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "cabin[\"IsEvenCabinNumber\"] = cabin[\"CabinNumber\"].astype(pd.Int64Dtype()).map(lambda v: v%2==0).astype(\"string\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>N</th>\n",
+       "      <th>%</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>IsEvenCabinNumber</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>False</th>\n",
+       "      <td>4285</td>\n",
+       "      <td>49.29%</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>True</th>\n",
+       "      <td>4209</td>\n",
+       "      <td>48.42%</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>&lt;NA&gt;</th>\n",
+       "      <td>199</td>\n",
+       "      <td>2.29%</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Total</th>\n",
+       "      <td>8693</td>\n",
+       "      <td>100.00%</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                      N        %\n",
+       "IsEvenCabinNumber               \n",
+       "False              4285   49.29%\n",
+       "True               4209   48.42%\n",
+       "<NA>                199    2.29%\n",
+       "Total              8693  100.00%"
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "make_frequency(cabin, \"IsEvenCabinNumber\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cabin = cabin.drop(\"CabinNumber\", axis=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    },
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>N</th>\n",
+       "      <th>%</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>CabinSide</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>S</th>\n",
+       "      <td>4288</td>\n",
+       "      <td>49.33%</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>P</th>\n",
+       "      <td>4206</td>\n",
+       "      <td>48.38%</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>&lt;NA&gt;</th>\n",
+       "      <td>199</td>\n",
+       "      <td>2.29%</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Total</th>\n",
+       "      <td>8693</td>\n",
+       "      <td>100.00%</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "              N        %\n",
+       "CabinSide               \n",
+       "S          4288   49.33%\n",
+       "P          4206   48.38%\n",
+       "<NA>        199    2.29%\n",
+       "Total      8693  100.00%"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "make_frequency(cabin, \"CabinSide\", as_percent=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "features[\"cabin\"] = cabin"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>PassengerId</th>\n",
+       "      <th>HomePlanet</th>\n",
+       "      <th>CryoSleep</th>\n",
+       "      <th>Cabin</th>\n",
+       "      <th>Destination</th>\n",
+       "      <th>Age</th>\n",
+       "      <th>VIP</th>\n",
+       "      <th>RoomService</th>\n",
+       "      <th>FoodCourt</th>\n",
+       "      <th>ShoppingMall</th>\n",
+       "      <th>Spa</th>\n",
+       "      <th>VRDeck</th>\n",
+       "      <th>Name</th>\n",
+       "      <th>Transported</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>PassengerId</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0001_01</th>\n",
+       "      <td>0001_01</td>\n",
+       "      <td>Europa</td>\n",
+       "      <td>False</td>\n",
+       "      <td>B/0/P</td>\n",
+       "      <td>TRAPPIST-1e</td>\n",
+       "      <td>39</td>\n",
+       "      <td>False</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>Maham Ofracculy</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0002_01</th>\n",
+       "      <td>0002_01</td>\n",
+       "      <td>Earth</td>\n",
+       "      <td>False</td>\n",
+       "      <td>F/0/S</td>\n",
+       "      <td>TRAPPIST-1e</td>\n",
+       "      <td>24</td>\n",
+       "      <td>False</td>\n",
+       "      <td>109.0</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>25.0</td>\n",
+       "      <td>549.0</td>\n",
+       "      <td>44.0</td>\n",
+       "      <td>Juanna Vines</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0003_01</th>\n",
+       "      <td>0003_01</td>\n",
+       "      <td>Europa</td>\n",
+       "      <td>False</td>\n",
+       "      <td>A/0/S</td>\n",
+       "      <td>TRAPPIST-1e</td>\n",
+       "      <td>58</td>\n",
+       "      <td>True</td>\n",
+       "      <td>43.0</td>\n",
+       "      <td>3576.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>6715.0</td>\n",
+       "      <td>49.0</td>\n",
+       "      <td>Altark Susent</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0003_02</th>\n",
+       "      <td>0003_02</td>\n",
+       "      <td>Europa</td>\n",
+       "      <td>False</td>\n",
+       "      <td>A/0/S</td>\n",
+       "      <td>TRAPPIST-1e</td>\n",
+       "      <td>33</td>\n",
+       "      <td>False</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1283.0</td>\n",
+       "      <td>371.0</td>\n",
+       "      <td>3329.0</td>\n",
+       "      <td>193.0</td>\n",
+       "      <td>Solam Susent</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0004_01</th>\n",
+       "      <td>0004_01</td>\n",
+       "      <td>Earth</td>\n",
+       "      <td>False</td>\n",
+       "      <td>F/1/S</td>\n",
+       "      <td>TRAPPIST-1e</td>\n",
+       "      <td>16</td>\n",
+       "      <td>False</td>\n",
+       "      <td>303.0</td>\n",
+       "      <td>70.0</td>\n",
+       "      <td>151.0</td>\n",
+       "      <td>565.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>Willy Santantines</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            PassengerId HomePlanet CryoSleep  Cabin  Destination  Age    VIP  \\\n",
+       "PassengerId                                                                    \n",
+       "0001_01         0001_01     Europa     False  B/0/P  TRAPPIST-1e   39  False   \n",
+       "0002_01         0002_01      Earth     False  F/0/S  TRAPPIST-1e   24  False   \n",
+       "0003_01         0003_01     Europa     False  A/0/S  TRAPPIST-1e   58   True   \n",
+       "0003_02         0003_02     Europa     False  A/0/S  TRAPPIST-1e   33  False   \n",
+       "0004_01         0004_01      Earth     False  F/1/S  TRAPPIST-1e   16  False   \n",
+       "\n",
+       "             RoomService  FoodCourt  ShoppingMall     Spa  VRDeck  \\\n",
+       "PassengerId                                                         \n",
+       "0001_01              0.0        0.0           0.0     0.0     0.0   \n",
+       "0002_01            109.0        9.0          25.0   549.0    44.0   \n",
+       "0003_01             43.0     3576.0           0.0  6715.0    49.0   \n",
+       "0003_02              0.0     1283.0         371.0  3329.0   193.0   \n",
+       "0004_01            303.0       70.0         151.0   565.0     2.0   \n",
+       "\n",
+       "                          Name Transported  \n",
+       "PassengerId                                 \n",
+       "0001_01        Maham Ofracculy       False  \n",
+       "0002_01           Juanna Vines        True  \n",
+       "0003_01          Altark Susent       False  \n",
+       "0003_02           Solam Susent       False  \n",
+       "0004_01      Willy Santantines        True  "
+      ]
+     },
+     "execution_count": 39,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Merge all data together"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>HomePlanet</th>\n",
+       "      <th>CryoSleep</th>\n",
+       "      <th>Destination</th>\n",
+       "      <th>Age</th>\n",
+       "      <th>VIP</th>\n",
+       "      <th>RoomService</th>\n",
+       "      <th>FoodCourt</th>\n",
+       "      <th>ShoppingMall</th>\n",
+       "      <th>Spa</th>\n",
+       "      <th>VRDeck</th>\n",
+       "      <th>Transported</th>\n",
+       "      <th>Group</th>\n",
+       "      <th>IsSingleGroup</th>\n",
+       "      <th>CabinDesk</th>\n",
+       "      <th>CabinSide</th>\n",
+       "      <th>IsEvenCabinNumber</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>PassengerId</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0001_01</th>\n",
+       "      <td>Europa</td>\n",
+       "      <td>False</td>\n",
+       "      <td>TRAPPIST-1e</td>\n",
+       "      <td>39</td>\n",
+       "      <td>False</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>False</td>\n",
+       "      <td>0001</td>\n",
+       "      <td>True</td>\n",
+       "      <td>B</td>\n",
+       "      <td>P</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0002_01</th>\n",
+       "      <td>Earth</td>\n",
+       "      <td>False</td>\n",
+       "      <td>TRAPPIST-1e</td>\n",
+       "      <td>24</td>\n",
+       "      <td>False</td>\n",
+       "      <td>109.0</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>25.0</td>\n",
+       "      <td>549.0</td>\n",
+       "      <td>44.0</td>\n",
+       "      <td>True</td>\n",
+       "      <td>0002</td>\n",
+       "      <td>True</td>\n",
+       "      <td>F</td>\n",
+       "      <td>S</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0003_01</th>\n",
+       "      <td>Europa</td>\n",
+       "      <td>False</td>\n",
+       "      <td>TRAPPIST-1e</td>\n",
+       "      <td>58</td>\n",
+       "      <td>True</td>\n",
+       "      <td>43.0</td>\n",
+       "      <td>3576.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>6715.0</td>\n",
+       "      <td>49.0</td>\n",
+       "      <td>False</td>\n",
+       "      <td>0003</td>\n",
+       "      <td>False</td>\n",
+       "      <td>A</td>\n",
+       "      <td>S</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0003_02</th>\n",
+       "      <td>Europa</td>\n",
+       "      <td>False</td>\n",
+       "      <td>TRAPPIST-1e</td>\n",
+       "      <td>33</td>\n",
+       "      <td>False</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1283.0</td>\n",
+       "      <td>371.0</td>\n",
+       "      <td>3329.0</td>\n",
+       "      <td>193.0</td>\n",
+       "      <td>False</td>\n",
+       "      <td>0003</td>\n",
+       "      <td>False</td>\n",
+       "      <td>A</td>\n",
+       "      <td>S</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0004_01</th>\n",
+       "      <td>Earth</td>\n",
+       "      <td>False</td>\n",
+       "      <td>TRAPPIST-1e</td>\n",
+       "      <td>16</td>\n",
+       "      <td>False</td>\n",
+       "      <td>303.0</td>\n",
+       "      <td>70.0</td>\n",
+       "      <td>151.0</td>\n",
+       "      <td>565.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>True</td>\n",
+       "      <td>0004</td>\n",
+       "      <td>True</td>\n",
+       "      <td>F</td>\n",
+       "      <td>S</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            HomePlanet CryoSleep  Destination  Age    VIP  RoomService  \\\n",
+       "PassengerId                                                              \n",
+       "0001_01         Europa     False  TRAPPIST-1e   39  False          0.0   \n",
+       "0002_01          Earth     False  TRAPPIST-1e   24  False        109.0   \n",
+       "0003_01         Europa     False  TRAPPIST-1e   58   True         43.0   \n",
+       "0003_02         Europa     False  TRAPPIST-1e   33  False          0.0   \n",
+       "0004_01          Earth     False  TRAPPIST-1e   16  False        303.0   \n",
+       "\n",
+       "             FoodCourt  ShoppingMall     Spa  VRDeck Transported Group  \\\n",
+       "PassengerId                                                              \n",
+       "0001_01            0.0           0.0     0.0     0.0       False  0001   \n",
+       "0002_01            9.0          25.0   549.0    44.0        True  0002   \n",
+       "0003_01         3576.0           0.0  6715.0    49.0       False  0003   \n",
+       "0003_02         1283.0         371.0  3329.0   193.0       False  0003   \n",
+       "0004_01           70.0         151.0   565.0     2.0        True  0004   \n",
+       "\n",
+       "            IsSingleGroup CabinDesk CabinSide IsEvenCabinNumber  \n",
+       "PassengerId                                                      \n",
+       "0001_01              True         B         P              True  \n",
+       "0002_01              True         F         S              True  \n",
+       "0003_01             False         A         S              True  \n",
+       "0003_02             False         A         S              True  \n",
+       "0004_01              True         F         S             False  "
+      ]
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dropped = [\n",
+    "    \"PassengerId\",\n",
+    "    \"Cabin\", \n",
+    "    \"Name\", \n",
+    "]\n",
+    "\n",
+    "data = pd.concat([data, *features.values()], axis=\"columns\").drop(dropped, axis=1)\n",
+    "data.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "Index: 8693 entries, 0001_01 to 9280_02\n",
+      "Data columns (total 16 columns):\n",
+      " #   Column             Non-Null Count  Dtype  \n",
+      "---  ------             --------------  -----  \n",
+      " 0   HomePlanet         8492 non-null   string \n",
+      " 1   CryoSleep          8476 non-null   string \n",
+      " 2   Destination        8511 non-null   string \n",
+      " 3   Age                8514 non-null   Int64  \n",
+      " 4   VIP                8490 non-null   string \n",
+      " 5   RoomService        8512 non-null   float64\n",
+      " 6   FoodCourt          8510 non-null   float64\n",
+      " 7   ShoppingMall       8485 non-null   float64\n",
+      " 8   Spa                8510 non-null   float64\n",
+      " 9   VRDeck             8505 non-null   float64\n",
+      " 10  Transported        8693 non-null   string \n",
+      " 11  Group              8693 non-null   string \n",
+      " 12  IsSingleGroup      8693 non-null   string \n",
+      " 13  CabinDesk          8494 non-null   string \n",
+      " 14  CabinSide          8494 non-null   string \n",
+      " 15  IsEvenCabinNumber  8494 non-null   string \n",
+      "dtypes: Int64(1), float64(5), string(10)\n",
+      "memory usage: 1.4+ MB\n"
+     ]
+    }
+   ],
+   "source": [
+    "data.info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Filling homeplanet records by passenager group"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "PassengerId\n",
+       "0064_02    0064\n",
+       "0119_01    0119\n",
+       "0444_02    0444\n",
+       "0445_02    0445\n",
+       "0470_02    0470\n",
+       "           ... \n",
+       "8938_01    8938\n",
+       "8961_04    8961\n",
+       "9030_02    9030\n",
+       "9072_01    9072\n",
+       "9194_01    9194\n",
+       "Name: Group, Length: 92, dtype: string"
+      ]
+     },
+     "execution_count": 42,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "homeplanet_na_group = data.groupby(\"Group\").filter(lambda grp: len(grp)>=2).query(\"HomePlanet.isna()\")[\"Group\"]\n",
+    "homeplanet_na_group"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>HomePlanet</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>PassengerId</th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0064_01</th>\n",
+       "      <td>Mars</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0064_02</th>\n",
+       "      <td>Mars</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0119_01</th>\n",
+       "      <td>Europa</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0119_02</th>\n",
+       "      <td>Europa</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0444_01</th>\n",
+       "      <td>Mars</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0444_02</th>\n",
+       "      <td>Mars</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0445_01</th>\n",
+       "      <td>Earth</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0445_02</th>\n",
+       "      <td>Earth</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0470_01</th>\n",
+       "      <td>Mars</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0470_02</th>\n",
+       "      <td>Mars</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            HomePlanet\n",
+       "PassengerId           \n",
+       "0064_01           Mars\n",
+       "0064_02           Mars\n",
+       "0119_01         Europa\n",
+       "0119_02         Europa\n",
+       "0444_01           Mars\n",
+       "0444_02           Mars\n",
+       "0445_01          Earth\n",
+       "0445_02          Earth\n",
+       "0470_01           Mars\n",
+       "0470_02           Mars"
+      ]
+     },
+     "execution_count": 43,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "filled_homeplanet = both_direction_fill(data, homeplanet_na_group, like=\"Planet\")\n",
+    "filled_homeplanet.head(10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data.loc[filled_homeplanet.index, filled_homeplanet.columns] = filled_homeplanet"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>HomePlanet</th>\n",
+       "      <th>CryoSleep</th>\n",
+       "      <th>Destination</th>\n",
+       "      <th>Age</th>\n",
+       "      <th>VIP</th>\n",
+       "      <th>RoomService</th>\n",
+       "      <th>FoodCourt</th>\n",
+       "      <th>ShoppingMall</th>\n",
+       "      <th>Spa</th>\n",
+       "      <th>VRDeck</th>\n",
+       "      <th>Transported</th>\n",
+       "      <th>Group</th>\n",
+       "      <th>IsSingleGroup</th>\n",
+       "      <th>CabinDesk</th>\n",
+       "      <th>CabinSide</th>\n",
+       "      <th>IsEvenCabinNumber</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>PassengerId</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0119_01</th>\n",
+       "      <td>Europa</td>\n",
+       "      <td>False</td>\n",
+       "      <td>TRAPPIST-1e</td>\n",
+       "      <td>39</td>\n",
+       "      <td>False</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2344.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>65.0</td>\n",
+       "      <td>6898.0</td>\n",
+       "      <td>False</td>\n",
+       "      <td>0119</td>\n",
+       "      <td>False</td>\n",
+       "      <td>A</td>\n",
+       "      <td>P</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0119_02</th>\n",
+       "      <td>Europa</td>\n",
+       "      <td>True</td>\n",
+       "      <td>TRAPPIST-1e</td>\n",
+       "      <td>13</td>\n",
+       "      <td>False</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>True</td>\n",
+       "      <td>0119</td>\n",
+       "      <td>False</td>\n",
+       "      <td>A</td>\n",
+       "      <td>P</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            HomePlanet CryoSleep  Destination  Age    VIP  RoomService  \\\n",
+       "PassengerId                                                              \n",
+       "0119_01         Europa     False  TRAPPIST-1e   39  False          0.0   \n",
+       "0119_02         Europa      True  TRAPPIST-1e   13  False          0.0   \n",
+       "\n",
+       "             FoodCourt  ShoppingMall   Spa  VRDeck Transported Group  \\\n",
+       "PassengerId                                                            \n",
+       "0119_01         2344.0           0.0  65.0  6898.0       False  0119   \n",
+       "0119_02            0.0           0.0   0.0     0.0        True  0119   \n",
+       "\n",
+       "            IsSingleGroup CabinDesk CabinSide IsEvenCabinNumber  \n",
+       "PassengerId                                                      \n",
+       "0119_01             False         A         P              True  \n",
+       "0119_02             False         A         P              True  "
+      ]
+     },
+     "execution_count": 45,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.query(\"Group=='0119'\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Filling cabin records by passenager group"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cabin_na_group = data.groupby(\"Group\").filter(lambda grp: len(grp)>=2).query(\"IsEvenCabinNumber.isna()\")[\"Group\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>CabinDesk</th>\n",
+       "      <th>CabinSide</th>\n",
+       "      <th>IsEvenCabinNumber</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>PassengerId</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0110_01</th>\n",
+       "      <td>B</td>\n",
+       "      <td>P</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0110_02</th>\n",
+       "      <td>B</td>\n",
+       "      <td>P</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0110_03</th>\n",
+       "      <td>B</td>\n",
+       "      <td>P</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0110_04</th>\n",
+       "      <td>B</td>\n",
+       "      <td>P</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0244_01</th>\n",
+       "      <td>F</td>\n",
+       "      <td>S</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9081_04</th>\n",
+       "      <td>G</td>\n",
+       "      <td>P</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9081_05</th>\n",
+       "      <td>F</td>\n",
+       "      <td>P</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9081_06</th>\n",
+       "      <td>F</td>\n",
+       "      <td>P</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9081_07</th>\n",
+       "      <td>G</td>\n",
+       "      <td>P</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9081_08</th>\n",
+       "      <td>G</td>\n",
+       "      <td>P</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>347 rows × 3 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            CabinDesk CabinSide IsEvenCabinNumber\n",
+       "PassengerId                                      \n",
+       "0110_01             B         P             False\n",
+       "0110_02             B         P             False\n",
+       "0110_03             B         P             False\n",
+       "0110_04             B         P             False\n",
+       "0244_01             F         S             False\n",
+       "...               ...       ...               ...\n",
+       "9081_04             G         P              True\n",
+       "9081_05             F         P              True\n",
+       "9081_06             F         P              True\n",
+       "9081_07             G         P              True\n",
+       "9081_08             G         P              True\n",
+       "\n",
+       "[347 rows x 3 columns]"
+      ]
+     },
+     "execution_count": 47,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "filled_cabin = both_direction_fill(data, cabin_na_group, like=\"Cabin\")\n",
+    "filled_cabin"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data.loc[filled_cabin.index, filled_cabin.columns] = filled_cabin"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Tidy data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "codes = (\n",
+    "    data.drop([\"Group\"], axis=1)\n",
+    "    .select_dtypes(exclude=\"number\")\n",
+    "    .fillna(\"Unknown\")\n",
+    ")\n",
+    "data.loc[:, codes.columns] = codes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data = data.dropna(subset=[\"Age\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "service_cols = [\"RoomService\", \"FoodCourt\", \"ShoppingMall\", \"Spa\", \"VRDeck\"]\n",
+    "data.loc[:, service_cols] = data[service_cols].fillna(1).add(1).transform(np.log10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "HomePlanet           0\n",
+       "CryoSleep            0\n",
+       "Destination          0\n",
+       "Age                  0\n",
+       "VIP                  0\n",
+       "RoomService          0\n",
+       "FoodCourt            0\n",
+       "ShoppingMall         0\n",
+       "Spa                  0\n",
+       "VRDeck               0\n",
+       "Transported          0\n",
+       "Group                0\n",
+       "IsSingleGroup        0\n",
+       "CabinDesk            0\n",
+       "CabinSide            0\n",
+       "IsEvenCabinNumber    0\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 52,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.isna().sum()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Work with Scikit-learn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.compose import ColumnTransformer\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.preprocessing import StandardScaler, OrdinalEncoder\n",
+    "from sklearn.pipeline import Pipeline\n",
+    "\n",
+    "X = data.drop([\"Group\", \"Transported\"], axis=1).reset_index(drop=True)\n",
+    "y = data[\"Transported\"].map({\"False\":0, \"True\": 1})\n",
+    "\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=233)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "categorical_cols = X.select_dtypes(exclude=\"number\").columns\n",
+    "numeric_cols = X.select_dtypes(include=\"number\").columns\n",
+    "\n",
+    "transformer = ColumnTransformer(\n",
+    "    transformers=[\n",
+    "        (\"categorical_feature\", OrdinalEncoder(), categorical_cols),\n",
+    "        (\"numeric_feature\", StandardScaler(), numeric_cols),\n",
+    "    ]\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Training and Build Classification Model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Fitting 10 folds for each of 18 candidates, totalling 180 fits\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "GridSearchCV(cv=10,\n",
+       "             estimator=Pipeline(steps=[('preprocessor',\n",
+       "                                        ColumnTransformer(transformers=[('categorical_feature',\n",
+       "                                                                         OrdinalEncoder(),\n",
+       "                                                                         Index(['HomePlanet', 'CryoSleep', 'Destination', 'VIP', 'IsSingleGroup',\n",
+       "       'CabinDesk', 'CabinSide', 'IsEvenCabinNumber'],\n",
+       "      dtype='object')),\n",
+       "                                                                        ('numeric_feature',\n",
+       "                                                                         StandardScaler(),\n",
+       "                                                                         Index(['Age', 'RoomService', 'FoodCourt', 'ShoppingMall', 'Spa', 'VRDeck'], dtype='object'))])),\n",
+       "                                       ('model', RandomForestClassifier())]),\n",
+       "             n_jobs=14,\n",
+       "             param_grid={'model__criterion': ['gini', 'entropy'],\n",
+       "                         'model__max_depth': [3, 5, 7],\n",
+       "                         'model__n_estimators': [50, 100, 150]},\n",
+       "             verbose=1)"
+      ]
+     },
+     "execution_count": 55,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import os\n",
+    "\n",
+    "from sklearn.ensemble import RandomForestClassifier\n",
+    "from sklearn.model_selection import GridSearchCV\n",
+    "from sklearn.pipeline import Pipeline\n",
+    "\n",
+    "pipe = Pipeline(\n",
+    "    steps=[\n",
+    "        (\"preprocessor\", transformer),\n",
+    "        (\"model\", RandomForestClassifier()),\n",
+    "    ]\n",
+    ")\n",
+    "\n",
+    "\n",
+    "param_grid=dict(\n",
+    "    model__n_estimators=[50, 100, 150],\n",
+    "    model__criterion=[\"gini\", \"entropy\"],\n",
+    "    model__max_depth=[3, 5, 7],\n",
+    "    \n",
+    ")\n",
+    "\n",
+    "gscv = GridSearchCV(\n",
+    "    estimator=pipe,\n",
+    "    param_grid=param_grid,\n",
+    "    cv=10,\n",
+    "    verbose=1,\n",
+    "    n_jobs=os.cpu_count()-2,\n",
+    ")\n",
+    "\n",
+    "gscv.fit(X_train, y_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.799177921315326"
+      ]
+     },
+     "execution_count": 56,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "gscv.score(X_test, y_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "                precision    recall  f1-score   support\n",
+      "\n",
+      "No transported       0.82      0.77      0.79       856\n",
+      "   Transported       0.78      0.83      0.80       847\n",
+      "\n",
+      "      accuracy                           0.80      1703\n",
+      "     macro avg       0.80      0.80      0.80      1703\n",
+      "  weighted avg       0.80      0.80      0.80      1703\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.metrics import classification_report\n",
+    "\n",
+    "y_pred = gscv.predict(X_test)\n",
+    "\n",
+    "print(classification_report(y_test,y_pred,  target_names=[\"No transported\", \"Transported\"]))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/svg+xml": [
+       "<?xml version=\"1.0\" encoding=\"utf-8\" standalone=\"no\"?>\n",
+       "<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n",
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+       " <metadata>\n",
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+       " </metadata>\n",
+       " <defs>\n",
+       "  <style type=\"text/css\">*{stroke-linejoin: round; stroke-linecap: butt}</style>\n",
+       " </defs>\n",
+       " <g id=\"figure_1\">\n",
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+       "     </g>\n",
+       "    </g>\n",
+       "   </g>\n",
+       "  </g>\n",
+       " </g>\n",
+       " <defs>\n",
+       "  <clipPath id=\"p3c3193446d\">\n",
+       "   <rect x=\"47.25125\" y=\"7.2\" width=\"334.8\" height=\"217.44\"/>\n",
+       "  </clipPath>\n",
+       " </defs>\n",
+       "</svg>\n"
+      ],
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from sklearn.metrics import RocCurveDisplay\n",
+    "\n",
+    "RocCurveDisplay.from_estimator(gscv, X_test, y_test).plot()\n",
+    "\n",
+    "plt.savefig('05-model-roc.png', dpi=300)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'model__criterion': 'gini', 'model__max_depth': 7, 'model__n_estimators': 100}"
+      ]
+     },
+     "execution_count": 59,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "gscv.best_params_"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Pipeline(steps=[('preprocessor',\n",
+       "                 ColumnTransformer(transformers=[('categorical_feature',\n",
+       "                                                  OrdinalEncoder(),\n",
+       "                                                  Index(['HomePlanet', 'CryoSleep', 'Destination', 'VIP', 'IsSingleGroup',\n",
+       "       'CabinDesk', 'CabinSide', 'IsEvenCabinNumber'],\n",
+       "      dtype='object')),\n",
+       "                                                 ('numeric_feature',\n",
+       "                                                  StandardScaler(),\n",
+       "                                                  Index(['Age', 'RoomService', 'FoodCourt', 'ShoppingMall', 'Spa', 'VRDeck'], dtype='object'))])),\n",
+       "                ('model', RandomForestClassifier(max_depth=7))])"
+      ]
+     },
+     "execution_count": 60,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pipe.set_params(**gscv.best_params_)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['spaceship-titanic.model']"
+      ]
+     },
+     "execution_count": 61,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import joblib\n",
+    "\n",
+    "joblib.dump(pipe, \"spaceship-titanic.model\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.0"
+  },
+  "toc-autonumbering": true
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}