Machine Learning Projects (Classic)/spaceship-titanic-end-to-end.ipynb

{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.10.12","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"none","dataSources":[{"sourceId":34377,"databundleVersionId":3220602,"sourceType":"competition"}],"dockerImageVersionId":30635,"isInternetEnabled":true,"language":"python","sourceType":"notebook","isGpuEnabled":false}},"nbformat_minor":4,"nbformat":4,"cells":[{"source":"<a href=\"https://www.kaggle.com/code/kelixirr/spaceship-titanic-end-to-end?scriptVersionId=158933252\" target=\"_blank\"><img align=\"left\" alt=\"Kaggle\" title=\"Open in Kaggle\" src=\"https://kaggle.com/static/images/open-in-kaggle.svg\"></a>","metadata":{},"cell_type":"markdown"},{"cell_type":"markdown","source":"## Spaceship Titanic \n**Predict which passengers are transported to an alternate dimension**\n\nWelcome to the year 2912, where your data science skills are needed to solve a cosmic mystery. We've received a transmission from four lightyears away and things aren't looking good.\n\nThe Spaceship Titanic was an interstellar passenger liner launched a month ago. With almost 13,000 passengers on board, the vessel set out on its maiden voyage transporting emigrants from our solar system to three newly habitable exoplanets orbiting nearby stars.\n\nWhile rounding Alpha Centauri en route to its first destination—the torrid 55 Cancri E—the unwary Spaceship Titanic collided with a spacetime anomaly hidden within a dust cloud. Sadly, it met a similar fate as its namesake from 1000 years before. Though the ship stayed intact, almost half of the passengers were transported to an alternate dimension!\n\nTo help rescue crews and retrieve the lost passengers, you are challenged to predict which passengers were transported by the anomaly using records recovered from the spaceship’s damaged computer system.\n\nHelp save them and change history!\n\nIn this competition your task is to predict whether a passenger was transported to an alternate dimension during the Spaceship Titanic's collision with the spacetime anomaly. To help you make these predictions, you're given a set of personal records recovered from the ship's damaged computer system.\n\nFile and Data Field Descriptions\n\ntrain.csv - Personal records for about two-thirds (~8700) of the passengers, to be used as training data.\n\n1. PassengerId - A unique Id for each passenger. Each Id takes the form gggg_pp where gggg indicates a group the passenger is travelling with and pp is their number within the group. People in a group are often family members, but not always.\n1. HomePlanet - The planet the passenger departed from, typically their planet of permanent residence.\n1. CryoSleep - Indicates whether the passenger elected to be put into suspended animation for the duration of the voyage. Passengers in cryosleep are confined to their cabins.\n1. Cabin - The cabin number where the passenger is staying. Takes the form deck/num/side, where side can be either P for Port or S for Starboard.\n1. Destination - The planet the passenger will be debarking to.\n1. Age - The age of the passenger.\n1. VIP - Whether the passenger has paid for special VIP service during the voyage.\n1. RoomService, FoodCourt, ShoppingMall, Spa, VRDeck - Amount the passenger has billed at each of the Spaceship Titanic's many luxury amenities.\n1. Name - The first and last names of the passenger.\n1. Transported - Whether the passenger was transported to another dimension. This is the target, the column you are trying to predict.\n\ntest.csv - Personal records for the remaining one-third (~4300) of the passengers, to be used as test data. Your task is to predict the value of Transported for the passengers in this set.\n\nsample_submission.csv - A submission file in the correct format.\nPassengerId - Id for each passenger in the test set.\nTransported - The target. For each passenger, predict either True or False.","metadata":{}},{"cell_type":"markdown","source":"## Analysing Our Training Data","metadata":{}},{"cell_type":"code","source":"import numpy as np\nimport matplotlib.pyplot as plt\nimport pandas as pd\n\ndata = pd.read_csv(\"/kaggle/input/spaceship-titanic/train.csv\")","metadata":{"execution":{"iopub.status.busy":"2024-01-14T10:35:54.01993Z","iopub.execute_input":"2024-01-14T10:35:54.020317Z","iopub.status.idle":"2024-01-14T10:35:54.486302Z","shell.execute_reply.started":"2024-01-14T10:35:54.020283Z","shell.execute_reply":"2024-01-14T10:35:54.485408Z"},"trusted":true},"execution_count":2,"outputs":[]},{"cell_type":"code","source":"data.head()","metadata":{"execution":{"iopub.status.busy":"2024-01-14T10:35:56.714858Z","iopub.execute_input":"2024-01-14T10:35:56.715271Z","iopub.status.idle":"2024-01-14T10:35:56.751641Z","shell.execute_reply.started":"2024-01-14T10:35:56.71524Z","shell.execute_reply":"2024-01-14T10:35:56.750717Z"},"trusted":true},"execution_count":3,"outputs":[{"execution_count":3,"output_type":"execute_result","data":{"text/plain":"  PassengerId HomePlanet CryoSleep  Cabin  Destination   Age    VIP  \\\n0     0001_01     Europa     False  B/0/P  TRAPPIST-1e  39.0  False   \n1     0002_01      Earth     False  F/0/S  TRAPPIST-1e  24.0  False   \n2     0003_01     Europa     False  A/0/S  TRAPPIST-1e  58.0   True   \n3     0003_02     Europa     False  A/0/S  TRAPPIST-1e  33.0  False   \n4     0004_01      Earth     False  F/1/S  TRAPPIST-1e  16.0  False   \n\n   RoomService  FoodCourt  ShoppingMall     Spa  VRDeck               Name  \\\n0          0.0        0.0           0.0     0.0     0.0    Maham Ofracculy   \n1        109.0        9.0          25.0   549.0    44.0       Juanna Vines   \n2         43.0     3576.0           0.0  6715.0    49.0      Altark Susent   \n3          0.0     1283.0         371.0  3329.0   193.0       Solam Susent   \n4        303.0       70.0         151.0   565.0     2.0  Willy Santantines   \n\n   Transported  \n0        False  \n1         True  \n2        False  \n3        False  \n4         True  ","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  </thead>\n  <tbody>\n    <tr>\n      <th>0</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>1</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>2</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>3</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>4</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>"},"metadata":{}}]},{"cell_type":"code","source":"data.dtypes","metadata":{"execution":{"iopub.status.busy":"2024-01-14T10:35:59.195245Z","iopub.execute_input":"2024-01-14T10:35:59.195761Z","iopub.status.idle":"2024-01-14T10:35:59.205421Z","shell.execute_reply.started":"2024-01-14T10:35:59.195716Z","shell.execute_reply":"2024-01-14T10:35:59.204168Z"},"trusted":true},"execution_count":4,"outputs":[{"execution_count":4,"output_type":"execute_result","data":{"text/plain":"PassengerId      object\nHomePlanet       object\nCryoSleep        object\nCabin            object\nDestination      object\nAge             float64\nVIP              object\nRoomService     float64\nFoodCourt       float64\nShoppingMall    float64\nSpa             float64\nVRDeck          float64\nName             object\nTransported        bool\ndtype: object"},"metadata":{}}]},{"cell_type":"code","source":"data.shape","metadata":{"execution":{"iopub.status.busy":"2024-01-14T10:36:03.73484Z","iopub.execute_input":"2024-01-14T10:36:03.735273Z","iopub.status.idle":"2024-01-14T10:36:03.741578Z","shell.execute_reply.started":"2024-01-14T10:36:03.735238Z","shell.execute_reply":"2024-01-14T10:36:03.740763Z"},"trusted":true},"execution_count":5,"outputs":[{"execution_count":5,"output_type":"execute_result","data":{"text/plain":"(8693, 14)"},"metadata":{}}]},{"cell_type":"code","source":"data.isna().sum()","metadata":{"execution":{"iopub.status.busy":"2024-01-14T10:36:06.124575Z","iopub.execute_input":"2024-01-14T10:36:06.125044Z","iopub.status.idle":"2024-01-14T10:36:06.143309Z","shell.execute_reply.started":"2024-01-14T10:36:06.125005Z","shell.execute_reply":"2024-01-14T10:36:06.142109Z"},"trusted":true},"execution_count":6,"outputs":[{"execution_count":6,"output_type":"execute_result","data":{"text/plain":"PassengerId       0\nHomePlanet      201\nCryoSleep       217\nCabin           199\nDestination     182\nAge             179\nVIP             203\nRoomService     181\nFoodCourt       183\nShoppingMall    208\nSpa             183\nVRDeck          188\nName            200\nTransported       0\ndtype: int64"},"metadata":{}}]},{"cell_type":"code","source":"data.describe()","metadata":{"execution":{"iopub.status.busy":"2024-01-14T10:36:09.053784Z","iopub.execute_input":"2024-01-14T10:36:09.054184Z","iopub.status.idle":"2024-01-14T10:36:09.089916Z","shell.execute_reply.started":"2024-01-14T10:36:09.05415Z","shell.execute_reply":"2024-01-14T10:36:09.088868Z"},"trusted":true},"execution_count":7,"outputs":[{"execution_count":7,"output_type":"execute_result","data":{"text/plain":"               Age   RoomService     FoodCourt  ShoppingMall           Spa  \\\ncount  8514.000000   8512.000000   8510.000000   8485.000000   8510.000000   \nmean     28.827930    224.687617    458.077203    173.729169    311.138778   \nstd      14.489021    666.717663   1611.489240    604.696458   1136.705535   \nmin       0.000000      0.000000      0.000000      0.000000      0.000000   \n25%      19.000000      0.000000      0.000000      0.000000      0.000000   \n50%      27.000000      0.000000      0.000000      0.000000      0.000000   \n75%      38.000000     47.000000     76.000000     27.000000     59.000000   \nmax      79.000000  14327.000000  29813.000000  23492.000000  22408.000000   \n\n             VRDeck  \ncount   8505.000000  \nmean     304.854791  \nstd     1145.717189  \nmin        0.000000  \n25%        0.000000  \n50%        0.000000  \n75%       46.000000  \nmax    24133.000000  ","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>"},"metadata":{}}]},{"cell_type":"code","source":"data.info()","metadata":{"execution":{"iopub.status.busy":"2024-01-14T10:36:11.964667Z","iopub.execute_input":"2024-01-14T10:36:11.965054Z","iopub.status.idle":"2024-01-14T10:36:11.999225Z","shell.execute_reply.started":"2024-01-14T10:36:11.965023Z","shell.execute_reply":"2024-01-14T10:36:11.998053Z"},"trusted":true},"execution_count":8,"outputs":[{"name":"stdout","text":"<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 8693 entries, 0 to 8692\nData 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   \ndtypes: bool(1), float64(6), object(7)\nmemory usage: 891.5+ KB\n","output_type":"stream"}]},{"cell_type":"code","source":"data.duplicated().sum()","metadata":{"execution":{"iopub.status.busy":"2024-01-14T10:36:14.20402Z","iopub.execute_input":"2024-01-14T10:36:14.204411Z","iopub.status.idle":"2024-01-14T10:36:14.233632Z","shell.execute_reply.started":"2024-01-14T10:36:14.204381Z","shell.execute_reply":"2024-01-14T10:36:14.232509Z"},"trusted":true},"execution_count":9,"outputs":[{"execution_count":9,"output_type":"execute_result","data":{"text/plain":"0"},"metadata":{}}]},{"cell_type":"code","source":"data.tail()","metadata":{"execution":{"iopub.status.busy":"2024-01-14T10:36:16.810242Z","iopub.execute_input":"2024-01-14T10:36:16.810638Z","iopub.status.idle":"2024-01-14T10:36:16.833881Z","shell.execute_reply.started":"2024-01-14T10:36:16.810607Z","shell.execute_reply":"2024-01-14T10:36:16.832632Z"},"trusted":true},"execution_count":10,"outputs":[{"execution_count":10,"output_type":"execute_result","data":{"text/plain":"     PassengerId HomePlanet CryoSleep     Cabin    Destination   Age    VIP  \\\n8688     9276_01     Europa     False    A/98/P    55 Cancri e  41.0   True   \n8689     9278_01      Earth      True  G/1499/S  PSO J318.5-22  18.0  False   \n8690     9279_01      Earth     False  G/1500/S    TRAPPIST-1e  26.0  False   \n8691     9280_01     Europa     False   E/608/S    55 Cancri e  32.0  False   \n8692     9280_02     Europa     False   E/608/S    TRAPPIST-1e  44.0  False   \n\n      RoomService  FoodCourt  ShoppingMall     Spa  VRDeck               Name  \\\n8688          0.0     6819.0           0.0  1643.0    74.0  Gravior Noxnuther   \n8689          0.0        0.0           0.0     0.0     0.0    Kurta Mondalley   \n8690          0.0        0.0        1872.0     1.0     0.0       Fayey Connon   \n8691          0.0     1049.0           0.0   353.0  3235.0   Celeon Hontichre   \n8692        126.0     4688.0           0.0     0.0    12.0   Propsh Hontichre   \n\n      Transported  \n8688        False  \n8689        False  \n8690         True  \n8691        False  \n8692         True  ","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  </thead>\n  <tbody>\n    <tr>\n      <th>8688</th>\n      <td>9276_01</td>\n      <td>Europa</td>\n      <td>False</td>\n      <td>A/98/P</td>\n      <td>55 Cancri e</td>\n      <td>41.0</td>\n      <td>True</td>\n      <td>0.0</td>\n      <td>6819.0</td>\n      <td>0.0</td>\n      <td>1643.0</td>\n      <td>74.0</td>\n      <td>Gravior Noxnuther</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>8689</th>\n      <td>9278_01</td>\n      <td>Earth</td>\n      <td>True</td>\n      <td>G/1499/S</td>\n      <td>PSO J318.5-22</td>\n      <td>18.0</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>Kurta Mondalley</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>8690</th>\n      <td>9279_01</td>\n      <td>Earth</td>\n      <td>False</td>\n      <td>G/1500/S</td>\n      <td>TRAPPIST-1e</td>\n      <td>26.0</td>\n      <td>False</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1872.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>Fayey Connon</td>\n      <td>True</td>\n    </tr>\n    <tr>\n      <th>8691</th>\n      <td>9280_01</td>\n      <td>Europa</td>\n      <td>False</td>\n      <td>E/608/S</td>\n      <td>55 Cancri e</td>\n      <td>32.0</td>\n      <td>False</td>\n      <td>0.0</td>\n      <td>1049.0</td>\n      <td>0.0</td>\n      <td>353.0</td>\n      <td>3235.0</td>\n      <td>Celeon Hontichre</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>8692</th>\n      <td>9280_02</td>\n      <td>Europa</td>\n      <td>False</td>\n      <td>E/608/S</td>\n      <td>TRAPPIST-1e</td>\n      <td>44.0</td>\n      <td>False</td>\n      <td>126.0</td>\n      <td>4688.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>12.0</td>\n      <td>Propsh Hontichre</td>\n      <td>True</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"data.hist(bins = 50, figsize = (20, 10))\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2024-01-14T10:36:19.445168Z","iopub.execute_input":"2024-01-14T10:36:19.446168Z","iopub.status.idle":"2024-01-14T10:36:21.230829Z","shell.execute_reply.started":"2024-01-14T10:36:19.446128Z","shell.execute_reply":"2024-01-14T10:36:21.229948Z"},"trusted":true},"execution_count":11,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 2000x1000 with 6 Axes>","image/png":"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"},"metadata":{}}]},{"cell_type":"code","source":"corr = data.corrwith(data[\"Transported\"], numeric_only = True)\ncorr.sort_values(ascending = True)","metadata":{"execution":{"iopub.status.busy":"2024-01-14T10:36:24.437865Z","iopub.execute_input":"2024-01-14T10:36:24.43825Z","iopub.status.idle":"2024-01-14T10:36:24.45681Z","shell.execute_reply.started":"2024-01-14T10:36:24.43822Z","shell.execute_reply":"2024-01-14T10:36:24.455557Z"},"trusted":true},"execution_count":12,"outputs":[{"execution_count":12,"output_type":"execute_result","data":{"text/plain":"RoomService    -0.244611\nSpa            -0.221131\nVRDeck         -0.207075\nAge            -0.075026\nShoppingMall    0.010141\nFoodCourt       0.046566\nTransported     1.000000\ndtype: float64"},"metadata":{}}]},{"cell_type":"markdown","source":"## Data Cleaning And Preparation Without NaN values for HistGradientBoostingClassifier\n It handles NaN values internally so we don't need to do anything. ","metadata":{}},{"cell_type":"code","source":"df = data[:]\ndf.head()","metadata":{"execution":{"iopub.status.busy":"2024-01-14T12:41:11.70199Z","iopub.execute_input":"2024-01-14T12:41:11.702419Z","iopub.status.idle":"2024-01-14T12:41:11.725468Z","shell.execute_reply.started":"2024-01-14T12:41:11.702385Z","shell.execute_reply":"2024-01-14T12:41:11.724666Z"},"trusted":true},"execution_count":189,"outputs":[{"execution_count":189,"output_type":"execute_result","data":{"text/plain":"  PassengerId HomePlanet CryoSleep  Cabin  Destination   Age    VIP  \\\n0     0001_01     Europa     False  B/0/P  TRAPPIST-1e  39.0  False   \n1     0002_01      Earth     False  F/0/S  TRAPPIST-1e  24.0  False   \n2     0003_01     Europa     False  A/0/S  TRAPPIST-1e  58.0   True   \n3     0003_02     Europa     False  A/0/S  TRAPPIST-1e  33.0  False   \n4     0004_01      Earth     False  F/1/S  TRAPPIST-1e  16.0  False   \n\n   RoomService  FoodCourt  ShoppingMall     Spa  VRDeck               Name  \\\n0          0.0        0.0           0.0     0.0     0.0    Maham Ofracculy   \n1        109.0        9.0          25.0   549.0    44.0       Juanna Vines   \n2         43.0     3576.0           0.0  6715.0    49.0      Altark Susent   \n3          0.0     1283.0         371.0  3329.0   193.0       Solam Susent   \n4        303.0       70.0         151.0   565.0     2.0  Willy Santantines   \n\n   Transported  \n0        False  \n1         True  \n2        False  \n3        False  \n4         True  ","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  </thead>\n  <tbody>\n    <tr>\n      <th>0</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>1</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>2</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>3</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>4</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>"},"metadata":{}}]},{"cell_type":"code","source":"X = df.drop([\"Transported\", 'PassengerId', 'Cabin', 'Name'], axis = 1)\nX.head()","metadata":{"execution":{"iopub.status.busy":"2024-01-14T12:41:14.239883Z","iopub.execute_input":"2024-01-14T12:41:14.240315Z","iopub.status.idle":"2024-01-14T12:41:14.26404Z","shell.execute_reply.started":"2024-01-14T12:41:14.240278Z","shell.execute_reply":"2024-01-14T12:41:14.262737Z"},"trusted":true},"execution_count":190,"outputs":[{"execution_count":190,"output_type":"execute_result","data":{"text/plain":"  HomePlanet CryoSleep  Destination   Age    VIP  RoomService  FoodCourt  \\\n0     Europa     False  TRAPPIST-1e  39.0  False          0.0        0.0   \n1      Earth     False  TRAPPIST-1e  24.0  False        109.0        9.0   \n2     Europa     False  TRAPPIST-1e  58.0   True         43.0     3576.0   \n3     Europa     False  TRAPPIST-1e  33.0  False          0.0     1283.0   \n4      Earth     False  TRAPPIST-1e  16.0  False        303.0       70.0   \n\n   ShoppingMall     Spa  VRDeck  \n0           0.0     0.0     0.0  \n1          25.0   549.0    44.0  \n2           0.0  6715.0    49.0  \n3         371.0  3329.0   193.0  \n4         151.0   565.0     2.0  ","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>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    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Europa</td>\n      <td>False</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    </tr>\n    <tr>\n      <th>1</th>\n      <td>Earth</td>\n      <td>False</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    </tr>\n    <tr>\n      <th>2</th>\n      <td>Europa</td>\n      <td>False</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    </tr>\n    <tr>\n      <th>3</th>\n      <td>Europa</td>\n      <td>False</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    </tr>\n    <tr>\n      <th>4</th>\n      <td>Earth</td>\n      <td>False</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    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"y = df[\"Transported\"]\ny.head()","metadata":{"execution":{"iopub.status.busy":"2024-01-14T12:41:16.470738Z","iopub.execute_input":"2024-01-14T12:41:16.471953Z","iopub.status.idle":"2024-01-14T12:41:16.482563Z","shell.execute_reply.started":"2024-01-14T12:41:16.471895Z","shell.execute_reply":"2024-01-14T12:41:16.481566Z"},"trusted":true},"execution_count":191,"outputs":[{"execution_count":191,"output_type":"execute_result","data":{"text/plain":"0    False\n1     True\n2    False\n3    False\n4     True\nName: Transported, dtype: bool"},"metadata":{}}]},{"cell_type":"code","source":"X.shape","metadata":{"execution":{"iopub.status.busy":"2024-01-14T12:41:18.988194Z","iopub.execute_input":"2024-01-14T12:41:18.988649Z","iopub.status.idle":"2024-01-14T12:41:18.995136Z","shell.execute_reply.started":"2024-01-14T12:41:18.988614Z","shell.execute_reply":"2024-01-14T12:41:18.994249Z"},"trusted":true},"execution_count":192,"outputs":[{"execution_count":192,"output_type":"execute_result","data":{"text/plain":"(8693, 10)"},"metadata":{}}]},{"cell_type":"code","source":"X.dtypes","metadata":{"execution":{"iopub.status.busy":"2024-01-14T12:41:21.133259Z","iopub.execute_input":"2024-01-14T12:41:21.133729Z","iopub.status.idle":"2024-01-14T12:41:21.142567Z","shell.execute_reply.started":"2024-01-14T12:41:21.133693Z","shell.execute_reply":"2024-01-14T12:41:21.141384Z"},"trusted":true},"execution_count":193,"outputs":[{"execution_count":193,"output_type":"execute_result","data":{"text/plain":"HomePlanet       object\nCryoSleep        object\nDestination      object\nAge             float64\nVIP              object\nRoomService     float64\nFoodCourt       float64\nShoppingMall    float64\nSpa             float64\nVRDeck          float64\ndtype: object"},"metadata":{}}]},{"cell_type":"code","source":"X = pd.get_dummies(X, columns = ['HomePlanet', 'CryoSleep', \"Destination\", 'VIP'])\nX.shape","metadata":{"execution":{"iopub.status.busy":"2024-01-14T12:41:24.820244Z","iopub.execute_input":"2024-01-14T12:41:24.821559Z","iopub.status.idle":"2024-01-14T12:41:24.842989Z","shell.execute_reply.started":"2024-01-14T12:41:24.821514Z","shell.execute_reply":"2024-01-14T12:41:24.842109Z"},"trusted":true},"execution_count":194,"outputs":[{"execution_count":194,"output_type":"execute_result","data":{"text/plain":"(8693, 16)"},"metadata":{}}]},{"cell_type":"code","source":"from sklearn.ensemble import HistGradientBoostingClassifier\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.metrics import accuracy_score\n\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 42)\n\nprint(X_train.shape)\nprint(X_test.shape)\nprint(y_train.shape)\nprint(y_test.shape)","metadata":{"execution":{"iopub.status.busy":"2024-01-14T12:44:23.195662Z","iopub.execute_input":"2024-01-14T12:44:23.196073Z","iopub.status.idle":"2024-01-14T12:44:23.206813Z","shell.execute_reply.started":"2024-01-14T12:44:23.196041Z","shell.execute_reply":"2024-01-14T12:44:23.205811Z"},"trusted":true},"execution_count":197,"outputs":[{"name":"stdout","text":"(6954, 16)\n(1739, 16)\n(6954,)\n(1739,)\n","output_type":"stream"}]},{"cell_type":"code","source":"clf = HistGradientBoostingClassifier().fit(X_train, y_train)","metadata":{"execution":{"iopub.status.busy":"2024-01-14T12:44:32.682548Z","iopub.execute_input":"2024-01-14T12:44:32.682985Z","iopub.status.idle":"2024-01-14T12:44:33.325523Z","shell.execute_reply.started":"2024-01-14T12:44:32.68295Z","shell.execute_reply":"2024-01-14T12:44:33.324541Z"},"trusted":true},"execution_count":198,"outputs":[]},{"cell_type":"code","source":"y_pred = clf.predict(X_test)","metadata":{"execution":{"iopub.status.busy":"2024-01-14T12:44:35.543021Z","iopub.execute_input":"2024-01-14T12:44:35.543467Z","iopub.status.idle":"2024-01-14T12:44:35.565413Z","shell.execute_reply.started":"2024-01-14T12:44:35.543429Z","shell.execute_reply":"2024-01-14T12:44:35.564409Z"},"trusted":true},"execution_count":199,"outputs":[]},{"cell_type":"code","source":"accuracy_score(y_test, y_pred)","metadata":{"execution":{"iopub.status.busy":"2024-01-14T12:44:38.836872Z","iopub.execute_input":"2024-01-14T12:44:38.837915Z","iopub.status.idle":"2024-01-14T12:44:38.846093Z","shell.execute_reply.started":"2024-01-14T12:44:38.837871Z","shell.execute_reply":"2024-01-14T12:44:38.844745Z"},"trusted":true},"execution_count":200,"outputs":[{"execution_count":200,"output_type":"execute_result","data":{"text/plain":"0.7837837837837838"},"metadata":{}}]},{"cell_type":"code","source":"from sklearn.metrics import confusion_matrix\nfrom sklearn.metrics import classification_report\n\nprint(confusion_matrix(y_test, y_pred))\nprint(\"\\n\")\nprint(classification_report(y_test, y_pred))","metadata":{"execution":{"iopub.status.busy":"2024-01-14T12:44:50.683446Z","iopub.execute_input":"2024-01-14T12:44:50.683989Z","iopub.status.idle":"2024-01-14T12:44:50.705971Z","shell.execute_reply.started":"2024-01-14T12:44:50.683953Z","shell.execute_reply":"2024-01-14T12:44:50.705055Z"},"trusted":true},"execution_count":201,"outputs":[{"name":"stdout","text":"[[642 219]\n [157 721]]\n\n\n              precision    recall  f1-score   support\n\n       False       0.80      0.75      0.77       861\n        True       0.77      0.82      0.79       878\n\n    accuracy                           0.78      1739\n   macro avg       0.79      0.78      0.78      1739\nweighted avg       0.79      0.78      0.78      1739\n\n","output_type":"stream"}]},{"cell_type":"markdown","source":"## Data Preprocessing Data For Other Models\nWe will not preprocess the data for other models because they don't handle null values like the previous model","metadata":{}},{"cell_type":"code","source":"new_df = data[:]\nnew_df.head()","metadata":{"execution":{"iopub.status.busy":"2024-01-14T12:45:02.007044Z","iopub.execute_input":"2024-01-14T12:45:02.007615Z","iopub.status.idle":"2024-01-14T12:45:02.04005Z","shell.execute_reply.started":"2024-01-14T12:45:02.007565Z","shell.execute_reply":"2024-01-14T12:45:02.038896Z"},"trusted":true},"execution_count":202,"outputs":[{"execution_count":202,"output_type":"execute_result","data":{"text/plain":"  PassengerId HomePlanet CryoSleep  Cabin  Destination   Age    VIP  \\\n0     0001_01     Europa     False  B/0/P  TRAPPIST-1e  39.0  False   \n1     0002_01      Earth     False  F/0/S  TRAPPIST-1e  24.0  False   \n2     0003_01     Europa     False  A/0/S  TRAPPIST-1e  58.0   True   \n3     0003_02     Europa     False  A/0/S  TRAPPIST-1e  33.0  False   \n4     0004_01      Earth     False  F/1/S  TRAPPIST-1e  16.0  False   \n\n   RoomService  FoodCourt  ShoppingMall     Spa  VRDeck               Name  \\\n0          0.0        0.0           0.0     0.0     0.0    Maham Ofracculy   \n1        109.0        9.0          25.0   549.0    44.0       Juanna Vines   \n2         43.0     3576.0           0.0  6715.0    49.0      Altark Susent   \n3          0.0     1283.0         371.0  3329.0   193.0       Solam Susent   \n4        303.0       70.0         151.0   565.0     2.0  Willy Santantines   \n\n   Transported  \n0        False  \n1         True  \n2        False  \n3        False  \n4         True  ","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  </thead>\n  <tbody>\n    <tr>\n      <th>0</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>1</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>2</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>3</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>4</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>"},"metadata":{}}]},{"cell_type":"code","source":"X_2 = new_df.drop([\"Transported\", \"PassengerId\", \"Name\", \"Cabin\"], axis = 1)\nX_2.head()","metadata":{"execution":{"iopub.status.busy":"2024-01-14T12:45:04.621819Z","iopub.execute_input":"2024-01-14T12:45:04.622222Z","iopub.status.idle":"2024-01-14T12:45:04.643936Z","shell.execute_reply.started":"2024-01-14T12:45:04.622191Z","shell.execute_reply":"2024-01-14T12:45:04.642749Z"},"trusted":true},"execution_count":203,"outputs":[{"execution_count":203,"output_type":"execute_result","data":{"text/plain":"  HomePlanet CryoSleep  Destination   Age    VIP  RoomService  FoodCourt  \\\n0     Europa     False  TRAPPIST-1e  39.0  False          0.0        0.0   \n1      Earth     False  TRAPPIST-1e  24.0  False        109.0        9.0   \n2     Europa     False  TRAPPIST-1e  58.0   True         43.0     3576.0   \n3     Europa     False  TRAPPIST-1e  33.0  False          0.0     1283.0   \n4      Earth     False  TRAPPIST-1e  16.0  False        303.0       70.0   \n\n   ShoppingMall     Spa  VRDeck  \n0           0.0     0.0     0.0  \n1          25.0   549.0    44.0  \n2           0.0  6715.0    49.0  \n3         371.0  3329.0   193.0  \n4         151.0   565.0     2.0  ","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>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    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Europa</td>\n      <td>False</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    </tr>\n    <tr>\n      <th>1</th>\n      <td>Earth</td>\n      <td>False</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    </tr>\n    <tr>\n      <th>2</th>\n      <td>Europa</td>\n      <td>False</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    </tr>\n    <tr>\n      <th>3</th>\n      <td>Europa</td>\n      <td>False</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    </tr>\n    <tr>\n      <th>4</th>\n      <td>Earth</td>\n      <td>False</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    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"y_2 = df[\"Transported\"]\ny_2.head()","metadata":{"execution":{"iopub.status.busy":"2024-01-14T12:45:06.792065Z","iopub.execute_input":"2024-01-14T12:45:06.792649Z","iopub.status.idle":"2024-01-14T12:45:06.801577Z","shell.execute_reply.started":"2024-01-14T12:45:06.792598Z","shell.execute_reply":"2024-01-14T12:45:06.80014Z"},"trusted":true},"execution_count":204,"outputs":[{"execution_count":204,"output_type":"execute_result","data":{"text/plain":"0    False\n1     True\n2    False\n3    False\n4     True\nName: Transported, dtype: bool"},"metadata":{}}]},{"cell_type":"code","source":"X_2.isna().sum()","metadata":{"execution":{"iopub.status.busy":"2024-01-14T12:45:08.962168Z","iopub.execute_input":"2024-01-14T12:45:08.962578Z","iopub.status.idle":"2024-01-14T12:45:08.976179Z","shell.execute_reply.started":"2024-01-14T12:45:08.962544Z","shell.execute_reply":"2024-01-14T12:45:08.974937Z"},"trusted":true},"execution_count":205,"outputs":[{"execution_count":205,"output_type":"execute_result","data":{"text/plain":"HomePlanet      201\nCryoSleep       217\nDestination     182\nAge             179\nVIP             203\nRoomService     181\nFoodCourt       183\nShoppingMall    208\nSpa             183\nVRDeck          188\ndtype: int64"},"metadata":{}}]},{"cell_type":"code","source":"X_2.dtypes","metadata":{"execution":{"iopub.status.busy":"2024-01-14T12:45:12.340007Z","iopub.execute_input":"2024-01-14T12:45:12.340463Z","iopub.status.idle":"2024-01-14T12:45:12.349273Z","shell.execute_reply.started":"2024-01-14T12:45:12.340425Z","shell.execute_reply":"2024-01-14T12:45:12.348074Z"},"trusted":true},"execution_count":206,"outputs":[{"execution_count":206,"output_type":"execute_result","data":{"text/plain":"HomePlanet       object\nCryoSleep        object\nDestination      object\nAge             float64\nVIP              object\nRoomService     float64\nFoodCourt       float64\nShoppingMall    float64\nSpa             float64\nVRDeck          float64\ndtype: object"},"metadata":{}}]},{"cell_type":"code","source":"X_2.fillna({\"Age\": X_2[\"Age\"].median(),\n            \"RoomService\": X_2[\"RoomService\"].median(),\n            \"FoodCourt\": X_2[\"FoodCourt\"].median(),\n            \"ShoppingMall\": X_2[\"FoodCourt\"].median(),\n            \"Spa\": X_2[\"Spa\"].median(),\n            \"VRDeck\": X_2[\"VRDeck\"].median()}, inplace=True)\nX_2.isna().sum()","metadata":{"execution":{"iopub.status.busy":"2024-01-14T12:45:16.26924Z","iopub.execute_input":"2024-01-14T12:45:16.269659Z","iopub.status.idle":"2024-01-14T12:45:16.292521Z","shell.execute_reply.started":"2024-01-14T12:45:16.269626Z","shell.execute_reply":"2024-01-14T12:45:16.291353Z"},"trusted":true},"execution_count":207,"outputs":[{"execution_count":207,"output_type":"execute_result","data":{"text/plain":"HomePlanet      201\nCryoSleep       217\nDestination     182\nAge               0\nVIP             203\nRoomService       0\nFoodCourt         0\nShoppingMall      0\nSpa               0\nVRDeck            0\ndtype: int64"},"metadata":{}}]},{"cell_type":"code","source":"X_2.fillna({\n    \"HomePlanet\": X_2[\"HomePlanet\"].mode()[0],\n    \"CryoSleep\": X_2[\"CryoSleep\"].mode()[0],\n    \"Destination\": X_2[\"Destination\"].mode()[0],\n    \"VIP\": X_2[\"VIP\"].mode()[0]\n}, inplace = True)\n\nX_2.isna().sum()","metadata":{"execution":{"iopub.status.busy":"2024-01-14T12:45:18.862266Z","iopub.execute_input":"2024-01-14T12:45:18.862769Z","iopub.status.idle":"2024-01-14T12:45:18.892615Z","shell.execute_reply.started":"2024-01-14T12:45:18.86273Z","shell.execute_reply":"2024-01-14T12:45:18.891372Z"},"trusted":true},"execution_count":208,"outputs":[{"execution_count":208,"output_type":"execute_result","data":{"text/plain":"HomePlanet      0\nCryoSleep       0\nDestination     0\nAge             0\nVIP             0\nRoomService     0\nFoodCourt       0\nShoppingMall    0\nSpa             0\nVRDeck          0\ndtype: int64"},"metadata":{}}]},{"cell_type":"code","source":"y_2 = df[\"Transported\"]\ny_2.head()","metadata":{"execution":{"iopub.status.busy":"2024-01-14T12:45:21.190106Z","iopub.execute_input":"2024-01-14T12:45:21.190573Z","iopub.status.idle":"2024-01-14T12:45:21.199434Z","shell.execute_reply.started":"2024-01-14T12:45:21.190536Z","shell.execute_reply":"2024-01-14T12:45:21.198232Z"},"trusted":true},"execution_count":209,"outputs":[{"execution_count":209,"output_type":"execute_result","data":{"text/plain":"0    False\n1     True\n2    False\n3    False\n4     True\nName: Transported, dtype: bool"},"metadata":{}}]},{"cell_type":"code","source":"# preprocessing main test_data at the same time. \ntest_data = pd.read_csv(\"/kaggle/input/spaceship-titanic/test.csv\")\ntest_data = test_data.drop([\"PassengerId\", \"Name\", \"Cabin\"], axis = 1)\ntest_data.fillna({\"Age\": test_data[\"Age\"].median(),\n            \"RoomService\": test_data[\"RoomService\"].median(),\n            \"FoodCourt\": test_data[\"FoodCourt\"].median(),\n            \"ShoppingMall\": test_data[\"FoodCourt\"].median(),\n            \"Spa\": test_data[\"Spa\"].median(),\n            \"VRDeck\": test_data[\"VRDeck\"].median(),\n            \"HomePlanet\": test_data[\"HomePlanet\"].mode()[0],\n            \"CryoSleep\": test_data[\"CryoSleep\"].mode()[0],\n            \"Destination\": test_data[\"Destination\"].mode()[0],\n            \"VIP\": test_data[\"VIP\"].mode()[0]}, inplace=True)\ntest_data.head()","metadata":{"execution":{"iopub.status.busy":"2024-01-14T12:45:24.497807Z","iopub.execute_input":"2024-01-14T12:45:24.498742Z","iopub.status.idle":"2024-01-14T12:45:24.566143Z","shell.execute_reply.started":"2024-01-14T12:45:24.498688Z","shell.execute_reply":"2024-01-14T12:45:24.564867Z"},"trusted":true},"execution_count":210,"outputs":[{"execution_count":210,"output_type":"execute_result","data":{"text/plain":"  HomePlanet  CryoSleep  Destination   Age    VIP  RoomService  FoodCourt  \\\n0      Earth       True  TRAPPIST-1e  27.0  False          0.0        0.0   \n1      Earth      False  TRAPPIST-1e  19.0  False          0.0        9.0   \n2     Europa       True  55 Cancri e  31.0  False          0.0        0.0   \n3     Europa      False  TRAPPIST-1e  38.0  False          0.0     6652.0   \n4      Earth      False  TRAPPIST-1e  20.0  False         10.0        0.0   \n\n   ShoppingMall     Spa  VRDeck  \n0           0.0     0.0     0.0  \n1           0.0  2823.0     0.0  \n2           0.0     0.0     0.0  \n3           0.0   181.0   585.0  \n4         635.0     0.0     0.0  ","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>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    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Earth</td>\n      <td>True</td>\n      <td>TRAPPIST-1e</td>\n      <td>27.0</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    </tr>\n    <tr>\n      <th>1</th>\n      <td>Earth</td>\n      <td>False</td>\n      <td>TRAPPIST-1e</td>\n      <td>19.0</td>\n      <td>False</td>\n      <td>0.0</td>\n      <td>9.0</td>\n      <td>0.0</td>\n      <td>2823.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Europa</td>\n      <td>True</td>\n      <td>55 Cancri e</td>\n      <td>31.0</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    </tr>\n    <tr>\n      <th>3</th>\n      <td>Europa</td>\n      <td>False</td>\n      <td>TRAPPIST-1e</td>\n      <td>38.0</td>\n      <td>False</td>\n      <td>0.0</td>\n      <td>6652.0</td>\n      <td>0.0</td>\n      <td>181.0</td>\n      <td>585.0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Earth</td>\n      <td>False</td>\n      <td>TRAPPIST-1e</td>\n      <td>20.0</td>\n      <td>False</td>\n      <td>10.0</td>\n      <td>0.0</td>\n      <td>635.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"test_data.isna().sum()","metadata":{"execution":{"iopub.status.busy":"2024-01-14T12:45:28.979322Z","iopub.execute_input":"2024-01-14T12:45:28.979779Z","iopub.status.idle":"2024-01-14T12:45:28.989991Z","shell.execute_reply.started":"2024-01-14T12:45:28.97974Z","shell.execute_reply":"2024-01-14T12:45:28.988805Z"},"trusted":true},"execution_count":211,"outputs":[{"execution_count":211,"output_type":"execute_result","data":{"text/plain":"HomePlanet      0\nCryoSleep       0\nDestination     0\nAge             0\nVIP             0\nRoomService     0\nFoodCourt       0\nShoppingMall    0\nSpa             0\nVRDeck          0\ndtype: int64"},"metadata":{}}]},{"cell_type":"code","source":"test_data.dtypes","metadata":{"execution":{"iopub.status.busy":"2024-01-14T12:45:31.057216Z","iopub.execute_input":"2024-01-14T12:45:31.057656Z","iopub.status.idle":"2024-01-14T12:45:31.067023Z","shell.execute_reply.started":"2024-01-14T12:45:31.05762Z","shell.execute_reply":"2024-01-14T12:45:31.065809Z"},"trusted":true},"execution_count":212,"outputs":[{"execution_count":212,"output_type":"execute_result","data":{"text/plain":"HomePlanet       object\nCryoSleep          bool\nDestination      object\nAge             float64\nVIP                bool\nRoomService     float64\nFoodCourt       float64\nShoppingMall    float64\nSpa             float64\nVRDeck          float64\ndtype: object"},"metadata":{}}]},{"cell_type":"code","source":"X_2.dtypes","metadata":{"execution":{"iopub.status.busy":"2024-01-14T12:45:33.459181Z","iopub.execute_input":"2024-01-14T12:45:33.45993Z","iopub.status.idle":"2024-01-14T12:45:33.470184Z","shell.execute_reply.started":"2024-01-14T12:45:33.459886Z","shell.execute_reply":"2024-01-14T12:45:33.468747Z"},"trusted":true},"execution_count":213,"outputs":[{"execution_count":213,"output_type":"execute_result","data":{"text/plain":"HomePlanet       object\nCryoSleep          bool\nDestination      object\nAge             float64\nVIP                bool\nRoomService     float64\nFoodCourt       float64\nShoppingMall    float64\nSpa             float64\nVRDeck          float64\ndtype: object"},"metadata":{}}]},{"cell_type":"code","source":"print(test_data.shape)\nprint(X_2.shape)","metadata":{"execution":{"iopub.status.busy":"2024-01-14T12:45:36.020028Z","iopub.execute_input":"2024-01-14T12:45:36.02044Z","iopub.status.idle":"2024-01-14T12:45:36.026202Z","shell.execute_reply.started":"2024-01-14T12:45:36.020409Z","shell.execute_reply":"2024-01-14T12:45:36.025045Z"},"trusted":true},"execution_count":214,"outputs":[{"name":"stdout","text":"(4277, 10)\n(8693, 10)\n","output_type":"stream"}]},{"cell_type":"code","source":"from sklearn.preprocessing import OneHotEncoder\nfrom sklearn.compose import ColumnTransformer\n\ncolumns = [\"HomePlanet\", \"Destination\"]\n\ncolum_trans = ColumnTransformer( \n    transformers = [(\"cat\", OneHotEncoder(), columns)], \n    remainder = \"passthrough\")\n\nc_transformer = colum_trans.fit(X_2)\nX_2 = c_transformer.transform(X_2)\ntest_data = c_transformer.transform(test_data)","metadata":{"execution":{"iopub.status.busy":"2024-01-14T12:45:39.539978Z","iopub.execute_input":"2024-01-14T12:45:39.540793Z","iopub.status.idle":"2024-01-14T12:45:39.59173Z","shell.execute_reply.started":"2024-01-14T12:45:39.540752Z","shell.execute_reply":"2024-01-14T12:45:39.590507Z"},"trusted":true},"execution_count":215,"outputs":[]},{"cell_type":"code","source":"X_2.shape","metadata":{"execution":{"iopub.status.busy":"2024-01-14T12:45:42.969227Z","iopub.execute_input":"2024-01-14T12:45:42.969662Z","iopub.status.idle":"2024-01-14T12:45:42.976812Z","shell.execute_reply.started":"2024-01-14T12:45:42.969627Z","shell.execute_reply":"2024-01-14T12:45:42.97573Z"},"trusted":true},"execution_count":216,"outputs":[{"execution_count":216,"output_type":"execute_result","data":{"text/plain":"(8693, 14)"},"metadata":{}}]},{"cell_type":"code","source":"test_data.shape","metadata":{"execution":{"iopub.status.busy":"2024-01-14T12:45:45.297469Z","iopub.execute_input":"2024-01-14T12:45:45.298103Z","iopub.status.idle":"2024-01-14T12:45:45.305411Z","shell.execute_reply.started":"2024-01-14T12:45:45.298058Z","shell.execute_reply":"2024-01-14T12:45:45.304247Z"},"trusted":true},"execution_count":217,"outputs":[{"execution_count":217,"output_type":"execute_result","data":{"text/plain":"(4277, 14)"},"metadata":{}}]},{"cell_type":"code","source":"# dividing training set into training and test set. Don't touch the original test set\nX2_train, X2_test, y2_train, y2_test = train_test_split(X_2, y_2, test_size = 0.2, random_state = 42)\n\nprint(X2_train.shape)\nprint(X2_test.shape)\nprint(y2_train.shape)\nprint(y2_test.shape)","metadata":{"execution":{"iopub.status.busy":"2024-01-14T12:45:48.552816Z","iopub.execute_input":"2024-01-14T12:45:48.554218Z","iopub.status.idle":"2024-01-14T12:45:48.567905Z","shell.execute_reply.started":"2024-01-14T12:45:48.554168Z","shell.execute_reply":"2024-01-14T12:45:48.566865Z"},"trusted":true},"execution_count":218,"outputs":[{"name":"stdout","text":"(6954, 14)\n(1739, 14)\n(6954,)\n(1739,)\n","output_type":"stream"}]},{"cell_type":"markdown","source":"## Gradient Boosting Classifier","metadata":{}},{"cell_type":"code","source":"from sklearn.ensemble import GradientBoostingClassifier\n\nclf2 = GradientBoostingClassifier().fit(X2_train, y2_train)\ny2_pred = clf2.predict(X2_test)\nprint(confusion_matrix(y2_test, y2_pred))\nprint(\"\\n\")\nprint(accuracy_score(y2_test, y2_pred))","metadata":{"execution":{"iopub.status.busy":"2024-01-14T12:45:51.38598Z","iopub.execute_input":"2024-01-14T12:45:51.386411Z","iopub.status.idle":"2024-01-14T12:45:52.280438Z","shell.execute_reply.started":"2024-01-14T12:45:51.386368Z","shell.execute_reply":"2024-01-14T12:45:52.27927Z"},"trusted":true},"execution_count":219,"outputs":[{"name":"stdout","text":"[[622 239]\n [142 736]]\n\n\n0.7809085681426107\n","output_type":"stream"}]},{"cell_type":"code","source":"clf3 = GradientBoostingClassifier(n_estimators = 300, subsample = 0.5).fit(X2_train, y2_train)\ny2_pred = clf3.predict(X2_test)\nprint(confusion_matrix(y2_test, y2_pred))\nprint(\"\\n\")\nprint(accuracy_score(y2_test, y2_pred))","metadata":{"execution":{"iopub.status.busy":"2024-01-14T12:45:57.778844Z","iopub.execute_input":"2024-01-14T12:45:57.779255Z","iopub.status.idle":"2024-01-14T12:45:59.789791Z","shell.execute_reply.started":"2024-01-14T12:45:57.779221Z","shell.execute_reply":"2024-01-14T12:45:59.788738Z"},"trusted":true},"execution_count":220,"outputs":[{"name":"stdout","text":"[[638 223]\n [155 723]]\n\n\n0.7826336975273146\n","output_type":"stream"}]},{"cell_type":"code","source":"# performing grid search\nfrom sklearn.model_selection import GridSearchCV\n\nparam_grid = {\n    'n_estimators': [50, 100, 150, 300],\n    'learning_rate': [0.01, 0.1, 0.2],\n    'max_depth': [3, 4, 5],\n    'min_samples_split': [2, 3, 4],\n    'min_samples_leaf': [1, 2, 3]\n}\n\n\nclf_grad = GradientBoostingClassifier(random_state=42)\n\ngrid_search = GridSearchCV(\n    estimator=clf_grad,\n    param_grid=param_grid,\n    scoring='accuracy',  \n    cv=3, \n    n_jobs=-1 \n)\n\n\ngrid_search.fit(X2_train, y2_train)\nprint(\"Best Parameters:\", grid_search.best_params_)","metadata":{"execution":{"iopub.status.busy":"2024-01-14T12:49:34.260204Z","iopub.execute_input":"2024-01-14T12:49:34.260645Z","iopub.status.idle":"2024-01-14T12:57:12.264105Z","shell.execute_reply.started":"2024-01-14T12:49:34.260612Z","shell.execute_reply":"2024-01-14T12:57:12.262447Z"},"trusted":true},"execution_count":222,"outputs":[{"name":"stdout","text":"Best Parameters: {'learning_rate': 0.1, 'max_depth': 4, 'min_samples_leaf': 1, 'min_samples_split': 4, 'n_estimators': 50}\n","output_type":"stream"}]},{"cell_type":"code","source":"best_gbc = grid_search.best_estimator_\ny_pred = best_gbc.predict(X2_test)\naccuracy_score(y2_test, y2_pred)","metadata":{"execution":{"iopub.status.busy":"2024-01-14T12:58:29.491511Z","iopub.execute_input":"2024-01-14T12:58:29.492001Z","iopub.status.idle":"2024-01-14T12:58:29.50901Z","shell.execute_reply.started":"2024-01-14T12:58:29.491957Z","shell.execute_reply":"2024-01-14T12:58:29.507724Z"},"trusted":true},"execution_count":223,"outputs":[{"execution_count":223,"output_type":"execute_result","data":{"text/plain":"0.7826336975273146"},"metadata":{}}]},{"cell_type":"markdown","source":"## Random Forest Classifier","metadata":{}},{"cell_type":"code","source":"from sklearn.ensemble import RandomForestClassifier\n\nclf4 = RandomForestClassifier().fit(X2_train, y2_train)\ny2_pred = clf4.predict(X2_test)\nprint(confusion_matrix(y2_test, y2_pred))\nprint(\"\\n\")\nprint(accuracy_score(y2_test, y2_pred))","metadata":{"execution":{"iopub.status.busy":"2024-01-14T12:58:41.68954Z","iopub.execute_input":"2024-01-14T12:58:41.690328Z","iopub.status.idle":"2024-01-14T12:58:42.681931Z","shell.execute_reply.started":"2024-01-14T12:58:41.690283Z","shell.execute_reply":"2024-01-14T12:58:42.68071Z"},"trusted":true},"execution_count":224,"outputs":[{"name":"stdout","text":"[[658 203]\n [176 702]]\n\n\n0.78205865439908\n","output_type":"stream"}]},{"cell_type":"code","source":"clf5 = RandomForestClassifier(n_estimators= 50).fit(X2_train, y2_train)\ny2_pred = clf5.predict(X2_test)\nprint(confusion_matrix(y2_test, y2_pred))\nprint(\"\\n\")\nprint(accuracy_score(y2_test, y2_pred))","metadata":{"execution":{"iopub.status.busy":"2024-01-14T12:58:48.378514Z","iopub.execute_input":"2024-01-14T12:58:48.378931Z","iopub.status.idle":"2024-01-14T12:58:48.881284Z","shell.execute_reply.started":"2024-01-14T12:58:48.378896Z","shell.execute_reply":"2024-01-14T12:58:48.880139Z"},"trusted":true},"execution_count":225,"outputs":[{"name":"stdout","text":"[[662 199]\n [188 690]]\n\n\n0.777458309373203\n","output_type":"stream"}]},{"cell_type":"code","source":"param_grid = {\n    'n_estimators': [50, 100, 150],\n    'max_depth': [None, 5, 10, 15],\n    'min_samples_split': [2, 5, 10],\n    'min_samples_leaf': [1, 2, 4]\n}\n\nrandom_forest = RandomForestClassifier(random_state=42)\n\ngrid_search = GridSearchCV(\n    estimator=random_forest,\n    param_grid=param_grid,\n    scoring='accuracy', \n    cv=3, \n    n_jobs=-1 \n)\n\ngrid_search.fit(X2_train, y2_train)\nprint(\"Best Parameters:\", grid_search.best_params_)","metadata":{"execution":{"iopub.status.busy":"2024-01-14T12:58:52.868167Z","iopub.execute_input":"2024-01-14T12:58:52.868579Z","iopub.status.idle":"2024-01-14T13:00:03.510215Z","shell.execute_reply.started":"2024-01-14T12:58:52.868546Z","shell.execute_reply":"2024-01-14T13:00:03.509065Z"},"trusted":true},"execution_count":226,"outputs":[{"name":"stdout","text":"Best Parameters: {'max_depth': 10, 'min_samples_leaf': 1, 'min_samples_split': 10, 'n_estimators': 150}\n","output_type":"stream"}]},{"cell_type":"code","source":"best_rf = grid_search.best_estimator_\ny_pred = best_rf.predict(X2_test)\naccuracy_score(y2_test, y2_pred)","metadata":{"execution":{"iopub.status.busy":"2024-01-14T13:00:46.500898Z","iopub.execute_input":"2024-01-14T13:00:46.501332Z","iopub.status.idle":"2024-01-14T13:00:46.565302Z","shell.execute_reply.started":"2024-01-14T13:00:46.501295Z","shell.execute_reply":"2024-01-14T13:00:46.564353Z"},"trusted":true},"execution_count":227,"outputs":[{"execution_count":227,"output_type":"execute_result","data":{"text/plain":"0.777458309373203"},"metadata":{}}]},{"cell_type":"markdown","source":"## Ada Boost Classifier","metadata":{}},{"cell_type":"code","source":"from sklearn.ensemble import AdaBoostClassifier\nclf6 = AdaBoostClassifier().fit(X2_train, y2_train)\ny2_pred = clf6.predict(X2_test)\nprint(confusion_matrix(y2_test, y2_pred))\nprint(\"\\n\")\nprint(accuracy_score(y2_test, y2_pred))","metadata":{"execution":{"iopub.status.busy":"2024-01-14T13:01:12.938307Z","iopub.execute_input":"2024-01-14T13:01:12.938742Z","iopub.status.idle":"2024-01-14T13:01:14.226582Z","shell.execute_reply.started":"2024-01-14T13:01:12.938708Z","shell.execute_reply":"2024-01-14T13:01:14.225418Z"},"trusted":true},"execution_count":228,"outputs":[{"name":"stdout","text":"[[632 229]\n [158 720]]\n\n\n0.777458309373203\n","output_type":"stream"}]},{"cell_type":"code","source":"param_grid = {\n    'n_estimators': [50, 100, 150],\n    'learning_rate': [0.01, 0.1, 0.2],\n}\n\nadaboost = AdaBoostClassifier(random_state=42)\n\ngrid_search = GridSearchCV(\n    estimator=adaboost,\n    param_grid=param_grid,\n    scoring='accuracy', \n    cv=3,  \n    n_jobs=-1 \n)\n\ngrid_search.fit(X2_train, y2_train)","metadata":{"execution":{"iopub.status.busy":"2024-01-14T13:01:43.37617Z","iopub.execute_input":"2024-01-14T13:01:43.376911Z","iopub.status.idle":"2024-01-14T13:02:04.764792Z","shell.execute_reply.started":"2024-01-14T13:01:43.376869Z","shell.execute_reply":"2024-01-14T13:02:04.763446Z"},"trusted":true},"execution_count":229,"outputs":[{"execution_count":229,"output_type":"execute_result","data":{"text/plain":"GridSearchCV(cv=3, estimator=AdaBoostClassifier(random_state=42), n_jobs=-1,\n             param_grid={'learning_rate': [0.01, 0.1, 0.2],\n                         'n_estimators': [50, 100, 150]},\n             scoring='accuracy')","text/html":"<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=3, estimator=AdaBoostClassifier(random_state=42), n_jobs=-1,\n             param_grid={&#x27;learning_rate&#x27;: [0.01, 0.1, 0.2],\n                         &#x27;n_estimators&#x27;: [50, 100, 150]},\n             scoring=&#x27;accuracy&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" ><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=3, estimator=AdaBoostClassifier(random_state=42), n_jobs=-1,\n             param_grid={&#x27;learning_rate&#x27;: [0.01, 0.1, 0.2],\n                         &#x27;n_estimators&#x27;: [50, 100, 150]},\n             scoring=&#x27;accuracy&#x27;)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" ><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: AdaBoostClassifier</label><div class=\"sk-toggleable__content\"><pre>AdaBoostClassifier(random_state=42)</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-8\" type=\"checkbox\" ><label for=\"sk-estimator-id-8\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">AdaBoostClassifier</label><div class=\"sk-toggleable__content\"><pre>AdaBoostClassifier(random_state=42)</pre></div></div></div></div></div></div></div></div></div></div>"},"metadata":{}}]},{"cell_type":"code","source":"best_ada = grid_search.best_estimator_\ny_pred = best_ada.predict(X2_test)\naccuracy_score(y2_test, y2_pred)","metadata":{"execution":{"iopub.status.busy":"2024-01-14T13:02:04.767572Z","iopub.execute_input":"2024-01-14T13:02:04.768481Z","iopub.status.idle":"2024-01-14T13:02:05.113355Z","shell.execute_reply.started":"2024-01-14T13:02:04.768444Z","shell.execute_reply":"2024-01-14T13:02:05.112195Z"},"trusted":true},"execution_count":230,"outputs":[{"execution_count":230,"output_type":"execute_result","data":{"text/plain":"0.777458309373203"},"metadata":{}}]},{"cell_type":"markdown","source":"## SGD Classifier","metadata":{}},{"cell_type":"code","source":"from sklearn.linear_model import SGDClassifier\n\nparam_grid = {\n    'loss': ['hinge', 'log_loss', 'modified_huber', 'squared_hinge'],\n    'penalty': ['l1', 'l2', 'elasticnet'],\n    'alpha': [0.0001, 0.001, 0.01],\n    'max_iter': [1000, 2000, 3000],\n    'tol': [1e-3, 1e-4, 1e-5]\n}\n\nsgd_classifier = SGDClassifier(random_state=42)\n\ngrid_search = GridSearchCV(\n    estimator=sgd_classifier,\n    param_grid=param_grid,\n    scoring='accuracy',\n    cv=3,\n    n_jobs=-1\n)\n\ngrid_search.fit(X2_train, y2_train)","metadata":{"execution":{"iopub.status.busy":"2024-01-14T13:06:55.90353Z","iopub.execute_input":"2024-01-14T13:06:55.904524Z","iopub.status.idle":"2024-01-14T13:07:18.103601Z","shell.execute_reply.started":"2024-01-14T13:06:55.904477Z","shell.execute_reply":"2024-01-14T13:07:18.101484Z"},"trusted":true},"execution_count":232,"outputs":[{"execution_count":232,"output_type":"execute_result","data":{"text/plain":"GridSearchCV(cv=3, estimator=SGDClassifier(random_state=42), n_jobs=-1,\n             param_grid={'alpha': [0.0001, 0.001, 0.01],\n                         'loss': ['hinge', 'log_loss', 'modified_huber',\n                                  'squared_hinge'],\n                         'max_iter': [1000, 2000, 3000],\n                         'penalty': ['l1', 'l2', 'elasticnet'],\n                         'tol': [0.001, 0.0001, 1e-05]},\n             scoring='accuracy')","text/html":"<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=3, estimator=SGDClassifier(random_state=42), n_jobs=-1,\n             param_grid={&#x27;alpha&#x27;: [0.0001, 0.001, 0.01],\n                         &#x27;loss&#x27;: [&#x27;hinge&#x27;, &#x27;log_loss&#x27;, &#x27;modified_huber&#x27;,\n                                  &#x27;squared_hinge&#x27;],\n                         &#x27;max_iter&#x27;: [1000, 2000, 3000],\n                         &#x27;penalty&#x27;: [&#x27;l1&#x27;, &#x27;l2&#x27;, &#x27;elasticnet&#x27;],\n                         &#x27;tol&#x27;: [0.001, 0.0001, 1e-05]},\n             scoring=&#x27;accuracy&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-9\" type=\"checkbox\" ><label for=\"sk-estimator-id-9\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=3, estimator=SGDClassifier(random_state=42), n_jobs=-1,\n             param_grid={&#x27;alpha&#x27;: [0.0001, 0.001, 0.01],\n                         &#x27;loss&#x27;: [&#x27;hinge&#x27;, &#x27;log_loss&#x27;, &#x27;modified_huber&#x27;,\n                                  &#x27;squared_hinge&#x27;],\n                         &#x27;max_iter&#x27;: [1000, 2000, 3000],\n                         &#x27;penalty&#x27;: [&#x27;l1&#x27;, &#x27;l2&#x27;, &#x27;elasticnet&#x27;],\n                         &#x27;tol&#x27;: [0.001, 0.0001, 1e-05]},\n             scoring=&#x27;accuracy&#x27;)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-10\" type=\"checkbox\" ><label for=\"sk-estimator-id-10\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: SGDClassifier</label><div class=\"sk-toggleable__content\"><pre>SGDClassifier(random_state=42)</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-11\" type=\"checkbox\" ><label for=\"sk-estimator-id-11\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SGDClassifier</label><div class=\"sk-toggleable__content\"><pre>SGDClassifier(random_state=42)</pre></div></div></div></div></div></div></div></div></div></div>"},"metadata":{}}]},{"cell_type":"code","source":"best_sgdc = grid_search.best_estimator_\ny_pred = best_sgdc.predict(X2_test)\naccuracy_score(y2_test, y2_pred)","metadata":{"execution":{"iopub.status.busy":"2024-01-14T13:07:26.573531Z","iopub.execute_input":"2024-01-14T13:07:26.573969Z","iopub.status.idle":"2024-01-14T13:07:26.603874Z","shell.execute_reply.started":"2024-01-14T13:07:26.57393Z","shell.execute_reply":"2024-01-14T13:07:26.602338Z"},"trusted":true},"execution_count":233,"outputs":[{"execution_count":233,"output_type":"execute_result","data":{"text/plain":"0.777458309373203"},"metadata":{}}]},{"cell_type":"code","source":"y_pred = best_gbc.predict(test_data)\ny_pred","metadata":{"execution":{"iopub.status.busy":"2024-01-14T13:08:44.955529Z","iopub.execute_input":"2024-01-14T13:08:44.955961Z","iopub.status.idle":"2024-01-14T13:08:44.979695Z","shell.execute_reply.started":"2024-01-14T13:08:44.955927Z","shell.execute_reply":"2024-01-14T13:08:44.977428Z"},"trusted":true},"execution_count":234,"outputs":[{"execution_count":234,"output_type":"execute_result","data":{"text/plain":"array([ True, False,  True, ...,  True,  True,  True])"},"metadata":{}}]},{"cell_type":"code","source":"submission = pd.read_csv(\"/kaggle/input/spaceship-titanic/test.csv\")\nsubmission","metadata":{"execution":{"iopub.status.busy":"2024-01-14T13:15:43.100082Z","iopub.execute_input":"2024-01-14T13:15:43.100516Z","iopub.status.idle":"2024-01-14T13:15:43.153096Z","shell.execute_reply.started":"2024-01-14T13:15:43.100482Z","shell.execute_reply":"2024-01-14T13:15:43.151978Z"},"trusted":true},"execution_count":239,"outputs":[{"execution_count":239,"output_type":"execute_result","data":{"text/plain":"     PassengerId HomePlanet CryoSleep     Cabin    Destination   Age    VIP  \\\n0        0013_01      Earth      True     G/3/S    TRAPPIST-1e  27.0  False   \n1        0018_01      Earth     False     F/4/S    TRAPPIST-1e  19.0  False   \n2        0019_01     Europa      True     C/0/S    55 Cancri e  31.0  False   \n3        0021_01     Europa     False     C/1/S    TRAPPIST-1e  38.0  False   \n4        0023_01      Earth     False     F/5/S    TRAPPIST-1e  20.0  False   \n...          ...        ...       ...       ...            ...   ...    ...   \n4272     9266_02      Earth      True  G/1496/S    TRAPPIST-1e  34.0  False   \n4273     9269_01      Earth     False       NaN    TRAPPIST-1e  42.0  False   \n4274     9271_01       Mars      True   D/296/P    55 Cancri e   NaN  False   \n4275     9273_01     Europa     False   D/297/P            NaN   NaN  False   \n4276     9277_01      Earth      True  G/1498/S  PSO J318.5-22  43.0  False   \n\n      RoomService  FoodCourt  ShoppingMall     Spa  VRDeck              Name  \n0             0.0        0.0           0.0     0.0     0.0   Nelly Carsoning  \n1             0.0        9.0           0.0  2823.0     0.0    Lerome Peckers  \n2             0.0        0.0           0.0     0.0     0.0   Sabih Unhearfus  \n3             0.0     6652.0           0.0   181.0   585.0  Meratz Caltilter  \n4            10.0        0.0         635.0     0.0     0.0   Brence Harperez  \n...           ...        ...           ...     ...     ...               ...  \n4272          0.0        0.0           0.0     0.0     0.0       Jeron Peter  \n4273          0.0      847.0          17.0    10.0   144.0     Matty Scheron  \n4274          0.0        0.0           0.0     0.0     0.0       Jayrin Pore  \n4275          0.0     2680.0           0.0     0.0   523.0    Kitakan Conale  \n4276          0.0        0.0           0.0     0.0     0.0  Lilace Leonzaley  \n\n[4277 rows x 13 columns]","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    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0013_01</td>\n      <td>Earth</td>\n      <td>True</td>\n      <td>G/3/S</td>\n      <td>TRAPPIST-1e</td>\n      <td>27.0</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>Nelly Carsoning</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>0018_01</td>\n      <td>Earth</td>\n      <td>False</td>\n      <td>F/4/S</td>\n      <td>TRAPPIST-1e</td>\n      <td>19.0</td>\n      <td>False</td>\n      <td>0.0</td>\n      <td>9.0</td>\n      <td>0.0</td>\n      <td>2823.0</td>\n      <td>0.0</td>\n      <td>Lerome Peckers</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>0019_01</td>\n      <td>Europa</td>\n      <td>True</td>\n      <td>C/0/S</td>\n      <td>55 Cancri e</td>\n      <td>31.0</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>Sabih Unhearfus</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>0021_01</td>\n      <td>Europa</td>\n      <td>False</td>\n      <td>C/1/S</td>\n      <td>TRAPPIST-1e</td>\n      <td>38.0</td>\n      <td>False</td>\n      <td>0.0</td>\n      <td>6652.0</td>\n      <td>0.0</td>\n      <td>181.0</td>\n      <td>585.0</td>\n      <td>Meratz Caltilter</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>0023_01</td>\n      <td>Earth</td>\n      <td>False</td>\n      <td>F/5/S</td>\n      <td>TRAPPIST-1e</td>\n      <td>20.0</td>\n      <td>False</td>\n      <td>10.0</td>\n      <td>0.0</td>\n      <td>635.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>Brence Harperez</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>4272</th>\n      <td>9266_02</td>\n      <td>Earth</td>\n      <td>True</td>\n      <td>G/1496/S</td>\n      <td>TRAPPIST-1e</td>\n      <td>34.0</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>Jeron Peter</td>\n    </tr>\n    <tr>\n      <th>4273</th>\n      <td>9269_01</td>\n      <td>Earth</td>\n      <td>False</td>\n      <td>NaN</td>\n      <td>TRAPPIST-1e</td>\n      <td>42.0</td>\n      <td>False</td>\n      <td>0.0</td>\n      <td>847.0</td>\n      <td>17.0</td>\n      <td>10.0</td>\n      <td>144.0</td>\n      <td>Matty Scheron</td>\n    </tr>\n    <tr>\n      <th>4274</th>\n      <td>9271_01</td>\n      <td>Mars</td>\n      <td>True</td>\n      <td>D/296/P</td>\n      <td>55 Cancri e</td>\n      <td>NaN</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>Jayrin Pore</td>\n    </tr>\n    <tr>\n      <th>4275</th>\n      <td>9273_01</td>\n      <td>Europa</td>\n      <td>False</td>\n      <td>D/297/P</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>False</td>\n      <td>0.0</td>\n      <td>2680.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>523.0</td>\n      <td>Kitakan Conale</td>\n    </tr>\n    <tr>\n      <th>4276</th>\n      <td>9277_01</td>\n      <td>Earth</td>\n      <td>True</td>\n      <td>G/1498/S</td>\n      <td>PSO J318.5-22</td>\n      <td>43.0</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>Lilace Leonzaley</td>\n    </tr>\n  </tbody>\n</table>\n<p>4277 rows × 13 columns</p>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"final_result = pd.DataFrame({\n    \n    \"PassengerId\": submission[\"PassengerId\"],\n    \"Transported\": y_pred\n})\n\nfinal_result.head()","metadata":{"execution":{"iopub.status.busy":"2024-01-14T13:15:56.766093Z","iopub.execute_input":"2024-01-14T13:15:56.766503Z","iopub.status.idle":"2024-01-14T13:15:56.778381Z","shell.execute_reply.started":"2024-01-14T13:15:56.766473Z","shell.execute_reply":"2024-01-14T13:15:56.777284Z"},"trusted":true},"execution_count":240,"outputs":[{"execution_count":240,"output_type":"execute_result","data":{"text/plain":"  PassengerId  Transported\n0     0013_01         True\n1     0018_01        False\n2     0019_01         True\n3     0021_01         True\n4     0023_01         True","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>Transported</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0013_01</td>\n      <td>True</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>0018_01</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>0019_01</td>\n      <td>True</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>0021_01</td>\n      <td>True</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>0023_01</td>\n      <td>True</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"final_result.to_csv(\"submission.csv\", index = False)","metadata":{"execution":{"iopub.status.busy":"2024-01-14T13:51:03.28497Z","iopub.execute_input":"2024-01-14T13:51:03.285432Z","iopub.status.idle":"2024-01-14T13:51:03.30391Z","shell.execute_reply.started":"2024-01-14T13:51:03.285395Z","shell.execute_reply":"2024-01-14T13:51:03.302648Z"},"trusted":true},"execution_count":249,"outputs":[]},{"cell_type":"markdown","source":"If you have read it till the end, do note one thing. The accuracy could be achieve further by doing feature engineering on one important column that I have removed due to one hot encoding issue. It was happening because of the high number of unqiue values in both train and main test set. One hot encoder won't work due to that, additional using label encoder is not a good idea here due to high cardinality and many models may assume ordinal relationship which does not seem to be the case. After removing Cabin column, my accuracy dropped from 86 to 78 on X_test but I do believe including Cabin column in some way may increase the performance.\n\n**Please upvote if you found this helpful**","metadata":{}}]}