Project_code.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "4c8c60b6",
   "metadata": {},
   "source": [
    "# Spaceship Titanic\n",
    "Welcome 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",
    "\n",
    "The 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",
    "\n",
    "While 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",
    "\n",
    "\n",
    "\n",
    "To 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",
    "\n",
    "<img src=\"https://storage.googleapis.com/kaggle-media/competitions/Spaceship%20Titanic/joel-filipe-QwoNAhbmLLo-unsplash.jpg\" width=\"600\" height=\"400\" />"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ace6fa91",
   "metadata": {},
   "source": [
    "## Importing data and packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "f3988092",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import pylab as pl\n",
    "import numpy as np\n",
    "import scipy.optimize as opt\n",
    "from sklearn import preprocessing\n",
    "from sklearn.model_selection import train_test_split\n",
    "%matplotlib inline \n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.metrics import jaccard_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "af6ad6f1",
   "metadata": {},
   "outputs": [
    {
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       "  <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",
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       "      <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",
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       "      <td>F/4/S</td>\n",
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       "      <td>2823.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Lerome Peckers</td>\n",
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       "    <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",
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       "    <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",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  PassengerId HomePlanet CryoSleep  Cabin  Destination   Age    VIP  \\\n",
       "0     0013_01      Earth      True  G/3/S  TRAPPIST-1e  27.0  False   \n",
       "1     0018_01      Earth     False  F/4/S  TRAPPIST-1e  19.0  False   \n",
       "2     0019_01     Europa      True  C/0/S  55 Cancri e  31.0  False   \n",
       "3     0021_01     Europa     False  C/1/S  TRAPPIST-1e  38.0  False   \n",
       "4     0023_01      Earth     False  F/5/S  TRAPPIST-1e  20.0  False   \n",
       "\n",
       "   RoomService  FoodCourt  ShoppingMall     Spa  VRDeck              Name  \n",
       "0          0.0        0.0           0.0     0.0     0.0   Nelly Carsoning  \n",
       "1          0.0        9.0           0.0  2823.0     0.0    Lerome Peckers  \n",
       "2          0.0        0.0           0.0     0.0     0.0   Sabih Unhearfus  \n",
       "3          0.0     6652.0           0.0   181.0   585.0  Meratz Caltilter  \n",
       "4         10.0        0.0         635.0     0.0     0.0   Brence Harperez  "
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test = pd.read_csv('test.csv')\n",
    "df_test.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "f58a4e89",
   "metadata": {},
   "outputs": [
    {
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       "  <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",
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       "    <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",
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       "      <td>Maham Ofracculy</td>\n",
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       "      <td>0002_01</td>\n",
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       "      <td>F/0/S</td>\n",
       "      <td>TRAPPIST-1e</td>\n",
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       "      <td>44.0</td>\n",
       "      <td>Juanna Vines</td>\n",
       "      <td>True</td>\n",
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       "    <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",
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       "      <th>3</th>\n",
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       "      <td>False</td>\n",
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       "      <td>371.0</td>\n",
       "      <td>3329.0</td>\n",
       "      <td>193.0</td>\n",
       "      <td>Solam Susent</td>\n",
       "      <td>False</td>\n",
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       "    <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",
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      "text/plain": [
       "  PassengerId HomePlanet CryoSleep  Cabin  Destination   Age    VIP  \\\n",
       "0     0001_01     Europa     False  B/0/P  TRAPPIST-1e  39.0  False   \n",
       "1     0002_01      Earth     False  F/0/S  TRAPPIST-1e  24.0  False   \n",
       "2     0003_01     Europa     False  A/0/S  TRAPPIST-1e  58.0   True   \n",
       "3     0003_02     Europa     False  A/0/S  TRAPPIST-1e  33.0  False   \n",
       "4     0004_01      Earth     False  F/1/S  TRAPPIST-1e  16.0  False   \n",
       "\n",
       "   RoomService  FoodCourt  ShoppingMall     Spa  VRDeck               Name  \\\n",
       "0          0.0        0.0           0.0     0.0     0.0    Maham Ofracculy   \n",
       "1        109.0        9.0          25.0   549.0    44.0       Juanna Vines   \n",
       "2         43.0     3576.0           0.0  6715.0    49.0      Altark Susent   \n",
       "3          0.0     1283.0         371.0  3329.0   193.0       Solam Susent   \n",
       "4        303.0       70.0         151.0   565.0     2.0  Willy Santantines   \n",
       "\n",
       "   Transported  \n",
       "0        False  \n",
       "1         True  \n",
       "2        False  \n",
       "3        False  \n",
       "4         True  "
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('train.csv')\n",
    "df.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "24bde0cd",
   "metadata": {},
   "source": [
    "#### Data Description\n",
    "In 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",
    "\n",
    "#### File and Data Field Descriptions\n",
    " - train.csv - Personal records for about two-thirds (~8700) of the passengers, to be used as training data.\n",
    "     - 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.\n",
    "      - HomePlanet - The planet the passenger departed from, typically their planet of permanent residence.\n",
    "     - 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.\n",
    "     - 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.\n",
    "     - Destination - The planet the passenger will be debarking to.\n",
    "     - Age - The age of the passenger.\n",
    "     - VIP - Whether the passenger has paid for special VIP service during the voyage.\n",
    "     - RoomService, FoodCourt, ShoppingMall, Spa, VRDeck - Amount the passenger has billed at each of the Spaceship Titanic's many luxury amenities.\n",
    "     - Name - The first and last names of the passenger.\n",
    "     - Transported - Whether the passenger was transported to another dimension. This is the target, the column you are trying to predict.\n",
    " - test.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",
    " - sample_submission.csv - A submission file in the correct format.\n",
    "     - PassengerId - Id for each passenger in the test set.\n",
    "     - Transported - The target. For each passenger, predict either True or False."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2b8ba7aa",
   "metadata": {},
   "source": [
    "## Exploritary data analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "c7eb2439",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       PassengerId HomePlanet CryoSleep    Cabin  Destination          Age  \\\n",
      "count         8693       8492      8476     8494         8511  8514.000000   \n",
      "unique        8693          3         2     6560            3          NaN   \n",
      "top        0001_01      Earth     False  G/734/S  TRAPPIST-1e          NaN   \n",
      "freq             1       4602      5439        8         5915          NaN   \n",
      "mean           NaN        NaN       NaN      NaN          NaN    28.827930   \n",
      "std            NaN        NaN       NaN      NaN          NaN    14.489021   \n",
      "min            NaN        NaN       NaN      NaN          NaN     0.000000   \n",
      "25%            NaN        NaN       NaN      NaN          NaN    19.000000   \n",
      "50%            NaN        NaN       NaN      NaN          NaN    27.000000   \n",
      "75%            NaN        NaN       NaN      NaN          NaN    38.000000   \n",
      "max            NaN        NaN       NaN      NaN          NaN    79.000000   \n",
      "\n",
      "          VIP   RoomService     FoodCourt  ShoppingMall           Spa  \\\n",
      "count    8490   8512.000000   8510.000000   8485.000000   8510.000000   \n",
      "unique      2           NaN           NaN           NaN           NaN   \n",
      "top     False           NaN           NaN           NaN           NaN   \n",
      "freq     8291           NaN           NaN           NaN           NaN   \n",
      "mean      NaN    224.687617    458.077203    173.729169    311.138778   \n",
      "std       NaN    666.717663   1611.489240    604.696458   1136.705535   \n",
      "min       NaN      0.000000      0.000000      0.000000      0.000000   \n",
      "25%       NaN      0.000000      0.000000      0.000000      0.000000   \n",
      "50%       NaN      0.000000      0.000000      0.000000      0.000000   \n",
      "75%       NaN     47.000000     76.000000     27.000000     59.000000   \n",
      "max       NaN  14327.000000  29813.000000  23492.000000  22408.000000   \n",
      "\n",
      "              VRDeck            Name Transported  \n",
      "count    8505.000000            8493        8693  \n",
      "unique           NaN            8473           2  \n",
      "top              NaN  Gollux Reedall        True  \n",
      "freq             NaN               2        4378  \n",
      "mean      304.854791             NaN         NaN  \n",
      "std      1145.717189             NaN         NaN  \n",
      "min         0.000000             NaN         NaN  \n",
      "25%         0.000000             NaN         NaN  \n",
      "50%         0.000000             NaN         NaN  \n",
      "75%        46.000000             NaN         NaN  \n",
      "max     24133.000000             NaN         NaN  \n",
      "\n",
      "Data types of dataset:\n",
      "PassengerId      object\n",
      "HomePlanet       object\n",
      "CryoSleep        object\n",
      "Cabin            object\n",
      "Destination      object\n",
      "Age             float64\n",
      "VIP              object\n",
      "RoomService     float64\n",
      "FoodCourt       float64\n",
      "ShoppingMall    float64\n",
      "Spa             float64\n",
      "VRDeck          float64\n",
      "Name             object\n",
      "Transported        bool\n",
      "dtype: object\n",
      "\n",
      "<bound method DataFrame.info of      PassengerId HomePlanet CryoSleep     Cabin    Destination   Age    VIP  \\\n",
      "0        0001_01     Europa     False     B/0/P    TRAPPIST-1e  39.0  False   \n",
      "1        0002_01      Earth     False     F/0/S    TRAPPIST-1e  24.0  False   \n",
      "2        0003_01     Europa     False     A/0/S    TRAPPIST-1e  58.0   True   \n",
      "3        0003_02     Europa     False     A/0/S    TRAPPIST-1e  33.0  False   \n",
      "4        0004_01      Earth     False     F/1/S    TRAPPIST-1e  16.0  False   \n",
      "...          ...        ...       ...       ...            ...   ...    ...   \n",
      "8688     9276_01     Europa     False    A/98/P    55 Cancri e  41.0   True   \n",
      "8689     9278_01      Earth      True  G/1499/S  PSO J318.5-22  18.0  False   \n",
      "8690     9279_01      Earth     False  G/1500/S    TRAPPIST-1e  26.0  False   \n",
      "8691     9280_01     Europa     False   E/608/S    55 Cancri e  32.0  False   \n",
      "8692     9280_02     Europa     False   E/608/S    TRAPPIST-1e  44.0  False   \n",
      "\n",
      "      RoomService  FoodCourt  ShoppingMall     Spa  VRDeck               Name  \\\n",
      "0             0.0        0.0           0.0     0.0     0.0    Maham Ofracculy   \n",
      "1           109.0        9.0          25.0   549.0    44.0       Juanna Vines   \n",
      "2            43.0     3576.0           0.0  6715.0    49.0      Altark Susent   \n",
      "3             0.0     1283.0         371.0  3329.0   193.0       Solam Susent   \n",
      "4           303.0       70.0         151.0   565.0     2.0  Willy Santantines   \n",
      "...           ...        ...           ...     ...     ...                ...   \n",
      "8688          0.0     6819.0           0.0  1643.0    74.0  Gravior Noxnuther   \n",
      "8689          0.0        0.0           0.0     0.0     0.0    Kurta Mondalley   \n",
      "8690          0.0        0.0        1872.0     1.0     0.0       Fayey Connon   \n",
      "8691          0.0     1049.0           0.0   353.0  3235.0   Celeon Hontichre   \n",
      "8692        126.0     4688.0           0.0     0.0    12.0   Propsh Hontichre   \n",
      "\n",
      "      Transported  \n",
      "0           False  \n",
      "1            True  \n",
      "2           False  \n",
      "3           False  \n",
      "4            True  \n",
      "...           ...  \n",
      "8688        False  \n",
      "8689        False  \n",
      "8690         True  \n",
      "8691        False  \n",
      "8692         True  \n",
      "\n",
      "[8693 rows x 14 columns]>\n"
     ]
    }
   ],
   "source": [
    "print(df.describe(include = 'all'))\n",
    "print('')\n",
    "print('Data types of dataset:')\n",
    "print(df.dtypes)\n",
    "print('')\n",
    "print(df.info)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "924a2128",
   "metadata": {},
   "source": [
    "By this EDA we can see that there is a lots of missing values we can replace them with mean, and also to do classification prediction we have to "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "350d491d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId     False\n",
       "HomePlanet       True\n",
       "CryoSleep        True\n",
       "Cabin            True\n",
       "Destination      True\n",
       "Age              True\n",
       "VIP              True\n",
       "RoomService      True\n",
       "FoodCourt        True\n",
       "ShoppingMall     True\n",
       "Spa              True\n",
       "VRDeck           True\n",
       "Name             True\n",
       "Transported     False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "f1223a20",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId     False\n",
       "HomePlanet       True\n",
       "CryoSleep        True\n",
       "Cabin            True\n",
       "Destination      True\n",
       "Age              True\n",
       "VIP              True\n",
       "RoomService      True\n",
       "FoodCourt        True\n",
       "ShoppingMall     True\n",
       "Spa              True\n",
       "VRDeck           True\n",
       "Name             True\n",
       "dtype: bool"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test.isna().any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "abfd491b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "G/734/S     8\n",
       "G/109/P     7\n",
       "B/201/P     7\n",
       "G/1368/P    7\n",
       "G/981/S     7\n",
       "           ..\n",
       "G/556/P     1\n",
       "E/231/S     1\n",
       "G/545/S     1\n",
       "G/543/S     1\n",
       "F/947/P     1\n",
       "Name: Cabin, Length: 6560, dtype: int64"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Cabin'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "28acd4da",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.boxplot = df.boxplot(column=['Age'])  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "a9c714d2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "sns.countplot(x ='Transported', data = df)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "ad6362ea",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x ='HomePlanet', data = df)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "5b7ba45a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "TRAPPIST-1e      5915\n",
       "55 Cancri e      1800\n",
       "PSO J318.5-22     796\n",
       "Name: Destination, dtype: int64"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Destination'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "d8ec0b39",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Scatterplot of age and transported status')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x1 = df['Age']\n",
    "y1 = df['Transported']\n",
    "plt.scatter(x1,y1)\n",
    "plt.title('Scatterplot of age and transported status')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "fd7c4686",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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BYDAYMsQd0Oz2n3U+BoPBkGtx4UCCtGKcj8FgMOQ2jPMxGAwGQ06jaiYWNRgMBkNOY2o+BoPBYMhxTLSbwWAwGHIcE+1mMBgMhhzHNLsZDAaDIccxNR+DwWAw5Dim5mMA150/DeDLXR87W0KSPFqjv7MlJElJm2vOnwZwNOZy6kZO4Lq6Zuf3vrBsmlHaFchi5yMirYEJgBvwlap+mGB/UeB74G7sfuMTVf0mM3ka52MwGAy5jSyMdhMRN2AyEIh9tv+dIrJIVf9wMOsP/KGq7USkBPCXiMxW1X8zmu+dvp6PwWAw3Hlk7WJydYBjqnrCciZzgccT5ggUEfsSr4WBcCBTHtDUfAwGgyG3kbXNbqWB0w7bZ4C6CWwmAYuAc0AR4BnVzEU9mJqPwWAw5DbSUfMRkV4issvh0yvB2ZJa7yfhekGtgH2AL+AHTBIR98z8BFPzMRgMhtxGOmo+qjoNmJaCyRmgrMN2Gew1HEe6AR+qffXRYyLyN3A/9oU4M4Sp+RgMBkNuIzo67Z/U2QlUEpHyInIX8Cz2JjZHTgHNAUSkFHAfkKlwQlPzMRgMhtxGFvb5qGqUiAwAVmAPtZ6hqr+LSB9r/1TgfeBbETmIvZnuLVUNzUy+xvkYDAZDbiOLx/mo6jJgWYK0qQ7fzwEtszJP43wMBoMht2Gm1zEYDAZDjvNfm15HRKKBg9ZxfwOdVDUiG3TF5vcO8DwQDcQAvVV1exacdySwUVVXZ/ZcqVGliR/PD+2GuNnYNG8Ny6YsSGTz/LDuVA2owb/X/+XrQZM49fvfABRwL0i3D/tS+r67UVW+efMLju85QtkH7qHT6F7kL5if0DMXmPbKBG5cuZ5tv+HdMePZuGUHHsWLseD7qakfkEn6juhDnWb+3Lh+k3GvjePYoeOJbEqVLcWQyW9TpFgRjh06xscDPyHqVhRlK5ThtXGvUbFKRWaOnclPX86PO6aQeyFe/fgVyt13D6rK+EGfcnjPn2nWVbVJDToN647Nzcb6uatZMuWXRDadhvegekBNbl6/ybRBk/jnkL1Pdvzmqdy4ep2Y6Biio6MZ1i7+lExtez3Oc+90oa9fF65czPg0OjWa1OTF4b2wudlYNXcl87/4KZHNiyN6USugNjev32TC659xwirfl8YOpHZzfyLDInk5MOunP+o3oi/+zfy5ef0mn7w2jmOHjiWy8S5biiGTB1OkWBGOHjrGxwPHEnUrimbtA3i639MAXL96nYlDJnLi8N9Zpu3T8SNp07oZ165fp0ePV9m771Aim1kzJ1KrVnVu3brFzp376NvvLaKibo+zrF2rOls2L+a5jn35+eelWaYtSTRhJHTuI73RbtdV1U9Vq2Af4ZptE3SJSH3gUaCmqlYDWhB/IFRqxyfrWFV1aE44HrHZeGFkTz7tOpp3A1+l7mMN8a1YJp5N1aY1KFXeh8FNX2LmkKl0Hn07BP/5Yd05uGEf7zQfyLA2gzh37AwAXT/sy08fzWZo69fZs2IHbXolHIyctbRvG8jU8aOyNY9Y/AP8KV3el26NejDhrc95acyAJO16Du7Oz18toHvjnlyJuELrZ1sBcCniMlOGTWX+tPmJjuk7vA+71u+iZ0Av+rbqz6ljab6dEJuNLu+/yNguo3irxUDqP9YI30rxr2X1gJqUKu/DoCb9mTF4Kt1GxR9OMebZobzb9vVEjsfDx5OHGlYj9MyFNOtJCpvNRu9RfRnRZRgDmvej0WNNKFupbDybWgG18SnnS5/GvZj89iT6ju4Xt2/Nj6sZ0XlYpjQkx+3r2p3P3prAy8lc1x6De/DzV7/QrXGPeNc1+HQwg/7vDfq07Mv/JvyPVz4amGXa2rRuRqWK5bn/wYb07fsWkyd9kKTdnDm/8FCVxvjVaE6BAvnp0f35uH02m40PxrzDypXrs0xXikRFpf3jomQm1Hob9pGxiIifiPwmIgdE5BcRKZ5K+noR+VRENorIYRHxF5GfReSoiMQ+5XyAUFW9CaCqoVanFyJSS0Q2iMhuEVkhIj4O5x0jIhuAd0TkpIjYrH0FReS0iOQVkW9F5Ckr3V9EtorIfhHZISJFRMRNRMaKyE5Le++MFNC9fhU5/08wF06fJ/pWFNsXb8GvpX88mxot/dn683oATuw9SsEiBSlaohj5Cxegcp0H2DRvDQDRt6K4fukaAN73+nJku33apd8376dWm4SDkbOW2n5VKepeJFvziKV+y3qsnm//zX/u/ZNC7oXxKFk8kV31BtXZtHQTAKt+Wk39VvUBiAyL5Mj+I0Tdiv9HV7BwQarWrcLyuSsAiLoVxdVLV9Osq4JfRUJOBnHhdAjRt6L4bfFmagXWiWdTM7AOm+evB+D43iMUdC9E0SS0J6Tj0O7M++A7NJNvs5X8KhN8MoiQUyFE3Ypi0+KN1GlZL55NnZZ1WTd/LQBH9v5FIfdCFLc0/rHjd65EZM/kpQ+3rM+qRNfVI5GdX4PqbHS4rg+3etiubfdhrkReAeDw3j/x8vHKMm3t2rXiu9n2GuL2HXsoWqwo3t4lE9n9unxt3PedO/dRpoxP3PaA/t35+ZelnL8QlmW6UiRrp9dxChlyPtZEdM25HQs+C3voXTXszXLDUkkH+FdVGwNTgYXYa1FVgK4i4gmsBMqKyBER+UJEmlh55wUmAk+pai1gBjDa4bzFVLWJqo4A9gNNrPR2wApVveXwO+4C5gEDVbU69trVdaAHEKmq/oA/8KKIlE9vORUr5UH4udvRiBeDwiheKv4fXPFSnoSfu33DhgeHU9zbkxJ3l+Jy2CW6f9KfYUvH0vXDPtxVIB8AZ4+cxi/Q7sT829bHIwv/EJ2Nl7cnFxzKLDQoFE/v+L/Pvbg7Vy9dJSY6Js7Gy9szxfN63+1NZHgkr49/jcm/TuKVjweSzyrPtFDc25PwIIfrFBRGce8E19I7/vUODw7DI+56K299P4yRS8YS8FxgnE2NFv5cDA7j1OGTadaSHJ7enoSeu117CgsKxbOUZ2KbIIfyDQ7DM5Wyywo8vT254KAtNOhConzdi7tzJd51vZDkdW39bCt2rtuVZdpK+3pz5vTtMZVnzwRR2tc7Wfs8efLQsWMHVqxYB4CvrzftH2/Nl9O+yzJNqaExmuaPq5Je51NARPYBYYAHsMqaaruYqm6wbGYCjZNLdzhXrOM6CPyuqkFWLecEUFZVrwC1gF7ABWCeiHTFPripipX3PuBd7CNyY5mX4Psz1vdnE+zDOleQqu4EUNVLqhqFPaSws3X+7YAnUClNJeSAfQ6++CR6u01iYgtVxc3NjXuq3Mv671cy4pE3uHn9Jo/0fQKAGW9Oplmn1gxd/BH5CxdI9Jafq0lDmaWpXBPglseNilUqsmTWUvq3GcCNazd4pv/TaZeVRFqiS5mCrpFPDuG9RwbxSZdRtOjchvvqPMhd+e/i8QEdmD9+bpp1pFdkorJLwiizNa60kFTZJCzAtFzX6vWr0fqZVnw15uts1ZZSmUyaOIZNm7azeYt9cP/4cSMYPGQMMTkZBBATk/aPi5LeaLfrqupnOZYl2GsrMzOY903r/xiH77HbeQBUNRpYD6y3Bjd1AXZjd1b1kzmvY1vKIuADEfHA7sjWJrAVEs9hFJv+kqquSE68NT9SL4CHPWpwX5F7E9lcDA7Dw/f2W3txH08izl9Mwub2252HtwcRIeGote/EvqMA7Fr2G237tgcg+Pg5xnd+H4BS5X2oFlAzOZm5gnZdHqXNc60BOLL/CCUcyszLx4vwkPhNGZHhkRRyL4TNzUZMdAxePl6EhYSnmEdoUCgXgkL5a99fAGxetjmuAzsthAeH4eHjcJ18PIlIkGd4UPzr7eHtyUXresde90thkexasZ0KfpW4GnmFEmVLMfrX8XHnfH/pJwx//C0iL0SkWVssYUFhePmWiNv29PEi/Hx8jaHBofGarLy8PQlPpewySrsu7WhrXde/9h+hhIM2L58Sia5ZZHgkheNd1/g25e8vz6tjX+GdTu9xOZPNg337dKFHj44A7Nq1jzJlfeP2lS7jw7mgkCSPe+/dVylRwpO+/XrGpdWqWY3Z339h/11eHrRp3YyoqCgWLUr28ZF5XLg5La1kqNlNVSOBl4FBwDXgoog0snZ3AjZYNonS05qHiNwnIo61DT/gH+AvoIQVkIDVh/NQMjqvYJ97aAKwxHJmjvwJ+IqIv3WuIlagwgqgr9XEh4hUFpFCCc49TVVrq2rtpBwPwN/7j1GqnA9eZUriljcPdds1YN+qnfFs9q3axcNPNgXg3hqVuHb5GpEXIrh0IYLwc2F432v/o3iwQVXOHbUHHBTxdI8tI9oNeIr1s1clmX9uYfHMJfRrPYB+rQewdcU2WnRoDsD9Ne7n2uWrhCdw2AD7tx6g0SP2WyvwqRZsW7ktxTwuXrhIaNAFytxbGgC/Bn6cOnoqzRpP7D+Gd3kfSpS1X8t67RqyJ8G13LN6Jw07NAWgQo3K9mt5/iL5CuQjf6H8AOQrkI+qjatz+q9TnPnrFP1rdeO1hn14rWEfwoPCeO+RQRlyPABH9x/Bp7wvJcuWIk/ePDRq15gdq+IHh+5YtZ2ADs0AqFzjPq5evhbnILOaxTMX07d1f/q27s/WFdsIdLiuVy9fTeQYwX5dGydxXUv4lmDo9Pf4eOBYzv59NtPapkydSW3/ltT2b8miRSvo1PEpAOrWqcmlyEsEB59PdEz3bs/RMrApHV/oH69mVOm++lSsXI+Klesx/+elDHh5SPY6HoAYTfvHRcnwOB9V3Ssi+7E3Z3UBpopIQezNZt0ss+TS00JhYKKIFMO+bsQxoJeq/msFC3xu1cDyAJ8BvydznnnAj0DTJH7DvyLyjJVPAez9PS2Ar4BywB5r/YoLQPt0aAcgJjqG74d+xWuz3sXmZmPzD2s5d/QMTTvaBwqvn72SA+v2UC2gJh9umMS/128y440v4o6fPfxren02ELe8ebhwOoQZgyYDUPexhjTrZH+j3LNiO5t/TFihy1reGPYhO/ceICLiEs3bv0C/Hp3o0K5VtuS1Y+1O/Jv5883mGdy8foNxr38at+/9mSP59M3PCA8J5+sPZjBk8tt0faMzxw4dZ8XclQAUL1GciUs/p2DhgmhMDO17tKdXs95cu3KNye9N4a2Jb5Inb16CTwXFO3dqxETHMGvoV7wxayg2Nxsbf1jD2aOnaWZdy7WzV7J/7W78AmryycYv+Pf6TaYPmgSAu1cxXpn2FgC2PDa2LdzEwQ17s6rI4mmc9t5Uhn83EpubjTXzVnH6yClav9AGgOXf/8rutbuoHVCbqZumc/P6TSYO+izu+NcnvkGV+lVxL+7O19u/Zc742ayelzUvNjvW7qBOM3++3TzDHmr9+vi4faNmjmS8dV2/+uBrhkweTJc3unD80PG4AJEXXumIe7EivDTaHiUXHR3NgEdezhJty35dQ+vWzfjr8BauXb9Oz56vxe1bvHAWvfq8QVBQCF9M/pB//jnD5k32HoMFC5YxavRnWaIh3bhwFFtakZxo773T6V7uKZctRLOMdvpw5WW0L2V80chsxVWX0V4bctDZEpIk6t+zSXUhpotrn/VO8zOn4CtfZjq/7MDMcGAwGAy5DRcOJEgrxvkYDAZDbsOF+3LSinE+BoPBkNu4A6LdjPMxGAyGXIZGpWmROJfGOB+DwWDIbZhmN4PBYDDkOKbZzWAwGAw5jqn5GAwGgyHHMaHWBoPBYMhxTM3HYDAYDDlOtIl2MxgMBkMOo6bZzQBQgrzOlpAsrjqH2pK9k50tIUlctbwAHif7F33LCG4u2gLk5VMndaPciml2MxgMBkOOY5yPwWAwGHIcM87HYDAYDDnOHVDzydBKpgaDwWBwHhoVk+ZPWhCR1iLyl4gcE5G3k7FpKiL7ROR3EUnzqtTJYWo+BoPBkNvIwmg3EXEDJgOBwBlgp4gsUtU/HGyKAV8ArVX1lIiUzGy+puZjMBgMuY0YTfsndeoAx1T1hKr+C8wFHk9g8zzws6qeAlDV85n9Ccb5GAwGQ24ja51PaeC0w/YZK82RykBxEVkvIrtFpHNmf4JpdjMYDIZchmraAw5EpBfQyyFpmqpOczRJKosE23mAWkBzoACwTUR+U9UjaRaSxAkNBoPBkJtIYyABgOVopqVgcgYo67BdBjiXhE2oql4FrorIRqA6kGHnY5rdDAaDIZehMZrmTxrYCVQSkfIichfwLLAogc1CoJGI5BGRgkBd4HBmfkO213xEJBo46JDUXlVPZuJ85YAlqlrF2q4DfAKUwl5V3Ay8rKrXMpqHQ15DVHVMZs8TS+Um1XlsaGfEzcbOeetYPyX+9S1RwZf/G9ub0g+VZ8Un89g4fSkAefLlpc+8objly4ubmxsHf93Oqk9/yrSeviP6UKeZPzeu32Tca+M4duh4IptSZUsxZPLbFClWhGOHjvHxwE+IuhVF2QpleG3ca1SsUpGZY2fy05fz444p5F6IVz9+hXL33YOqMn7Qpxze82em9Sbk3THj2bhlBx7Fi7Hg+6lZfv6kyK4yA7DZbExc+jlhwaEM7TY8Q/rKNq1Gw+GdsLnZ+GPOevZ+sTje/krtH6Zmv0cBuHX1BhuGfEvY4VMU9vGg+Wd9KFiiKBqj/PG/dRyYsSJDGpLT9fCIToibjT/nrGff5Pi6Kj7xMH4OujYN/pbww6dwy5eXx+a/i9tdeRA3N/5etoNd437OlJZqTWrQeVgPbG421s1dzeIpic/XeXgP/AJq8e/1m0wdNJGTh07E7RObjdFLxhIeHM4n3UcD8NKk1/G5195NUsi9EFcvXWVI29cypTNFsnCcj6pGicgAYAXgBsxQ1d9FpI+1f6qqHhaR5cABIAb4SlUPZSbfnGh2u66qftlxYhEpBfwIPKuq20REgA5AESDDzsc6jwBDgCxxPmIT2o/sxlcvjCEyOIwBi0bzx6rdnD92Ns7mWsQVFg2fyUMta8c7NurmLaY9P4p/r93ElseNvj8N56/1+zi191iG9fgH+FO6vC/dGvXg/hr389KYAQx87NVEdj0Hd+fnrxawYdEGXh4zgNbPtmLJd0u5FHGZKcOm8nCr+omO6Tu8D7vW72JUn9HkyZuHfAXyZVhnSrRvG8jzHR5jyPufZMv5E5KdZQbQvsfjnD52ioKFC2ZIn9iExqO6sPj5D7kSFM5TS0ZyctVuLh693YJy+fQFFvzfKG5GXuPuptVo+lF35j82nJjoGLa8/z9CD50kb6H8/N+y9zm96WC8YzOK2IQGo7qw9PkPuRoUzpNLR3Jy5W4iHHWdusCip0bxb+Q1ygZUo/HH3VnQbjjRN2+x+OkxRFn3/mO/vMepdfs5vyex00+bFhvd3u/FBx2HExYcxqhFH7Nn9Q7OHj0TZ+MXUBPv8r681qQfFWtUpvuo3gxt/1bc/jbdH+XssTMUcLhOEweMi/ve8d2uXLuU6XfflMniCQ5UdRmwLEHa1ATbY4GxWZWnU5rdRMRPRH4TkQMi8ouIFE8lvZaI7BeRbYDjzI/9gZmqug1A7fykqiEi4iEiC6xz/SYi1axzDReRQQ5aDolIOetzWES+APYAXwMFrEFVszP7m8v6VSTsn2DCT58n+lY0+xdv48EETuZq2CXOHDhBdFTi6dL/vXYTALc8brjlcUtXh2NS1G9Zj9Xz1wDw594/KeReGI+SxRPZVW9QnU1LNwGw6qfV1LcenJFhkRzZf4SoW1Hx7AsWLkjVulVYPtf+1hx1K4qrl65mSmty1ParSlH3Itly7qTIrjID8PL2ok6zOvw6J+O1jZJ+FYg8GcKlUxeIuRXNsUW/Ub5lrXg2wbuPcjPS/mAM2XuMQj4eAFw7H0HooZOAveZx8dg5Cnl7ZFhLQl2XToZwOVbXwt8ol0BXyO6j/Bura88xCvvczjvKuvdtedyw5cmTuCs8HVT0q0TIySDOnw4h+lYU2xZvplZg/AlIawXWYdP8dQAc23uEgu6FKGZdZw9vT/ya1WLd3NXJ5lHvkQZsW7Qp4yLTQBY3uzmFnHA+sQ/wfSLyi5U2C3hLVathb5Iblkr6N9ib0hK+MlYBdieT7whgr3WuIda5U+M+YJaq1lDVbli1NlXtmIZjU6RoqeJEnAuL244MCqNoqcQPruQQmzBw2Qe8t/tLjm4+yOl9GXvzi8XL25ML50LjtkODQvH09opn417cnauXrhITHRNn4+Wd8szK3nd7ExkeyevjX2Pyr5N45eOB2VbzyWmyq8wA+gzvzVdjvs7UVPmFvItz5Vx43PaVoHAKeSd/jz3wbFNOrTuQKL1IGS+8HrqHkL2Zu8diKehTnCtBt3VdDQ6nkE/yuu5PoEtsQocVo+m8/wvObjrI+UzoKu7tQVjQ7WsYHhSGR4LrU9zbk3CHv9Xw4DCKl7I7w07DujNnzMxkr9P9dR4kMjSC4JNBGdaYJrI21Nop5ITziX2A+6nqEyJSFCimqrHTM8wEGqcj/bs05tsw1lZV1wKe1rlS4h9V/S2tPyxdSOJoxvRUXjRGmdB2MGPq96ds9QqUqlwmG/RoApPUbRLilseNilUqsmTWUvq3GcCNazd4pv/TmdPqKmRTmdVtXoeIsAiOHcx4M2ryeSdt61v/AR54pgnbxsyNl56nYD5afTmQLcO/59aV65nSE6crqUje5HQ9/AD3P9uE7aNv69IYZX6rd/je/2VK+FWg+H0Zv/eT0pL4GiZxoCo1mtXmUlgkfzv0/yTk4ccasTWbaz0AGqVp/rgquSHUWki+ov079tjzhckclxAFoojvdPM7fE9z+5Bj7HxLj9r4FamYon1kcDjFfG+/YRX18eTS+YtpzS6OG5euceK3w9zXpDohR86kfoAD7bo8SpvnWgNwZP8RSvjefmv38vEiPCQsnn1keCSF3Athc7MREx2Dl48XYSHhpERoUCgXgkL5a99fAGxetpmn++Ve55MTZfZg7QepF1gP/wB/7sqXl4JFCvLmhDf4eGD6mtevBIVT2Pd2c1VhHw+uhSS+xzzvL0vA2J4s6TSWmxFX4tJtedxoPW0gRxds5cTyXenKOyWuBoXHa0Yr5O3B1eDEujweKEvjj3vyawJdsfx76RpB2w5Ttmk1Lv6Vvns/lvDgMDx9bl9DDx9PLia4PuFBYXg4/K16eHty8fxF6rR9mJot/PFrWou8+fJSoEhB+n32Cl+88hkANjcb/q3r8c6jg8h2cv+k1jnf56OqkcBFEWlkJXUCNqSQHgFEikhDK92xCWwS0EVE6sYmiMgLIuINbIy1FZGm2GPULwEngZpWek2gfApyb4lIkivFqeo0Va2tqrVTczwAZ/Yfx7OcN8XLlMAtrxvV29Xn8KrkWgzjU8ijCPnd7Z2befLlpWKDKpw/nv6O4MUzl9Cv9QD6tR7A1hXbaNGhOQD317ifa5evEp6EM9y/9QCNHrFfksCnWrBt5bYU87h44SKhQRcoY0X++DXw49TRU+nW6irkRJl989G3vFCnE10e7soH/T9k/5b96XY8AOf3n6BoOW+KlC2BLa8bFR+rx9+r9sSzKezrSevpr7Bm4FQi/w6Oty9gbE8uHj3H/um/pjvvVHWVd9D1eD3+SUJXy+mvsC6BrvweRbjLuvfd8ueldMMqRBzLeBDE8f1H8S7vQ4myJXHLm4f67Rqye9XOeDa7V++kUYcAACrWqMz1y9eIOH+ReR9/z0v1XmRgw95MfGkcv289GOd4AKo0rM6542cJD47/QpId3Al9Ps6q+XQBplrx4ieAbqmkdwNmiMg17OGAAFiBBc8Cn1gT3cVgdzo/A8OBb0TkAPbIty7WYfOBziKyD3t8e0qDpKYBB0RkT2b7fWKiY1g49Ft6zBqMzc3Gzh/WE3L0DHU7tgBg++zVFC5RlJcXjSZf4QKoKg27t2Fc4BsUKVmcp8f1xWazITbhwNLf+HPt3szIYcfanfg38+ebzTO4ef0G417/NG7f+zNH8umbnxEeEs7XH8xgyOS36fpGZ44dOs6KuSsBKF6iOBOXfk7BwgXRmBja92hPr2a9uXblGpPfm8JbE98kT968BJ8KinfurOSNYR+yc+8BIiIu0bz9C/Tr0YkO7VplS16QvWWWFWh0DJvem0m779+0hzTP28DFI2d56IVmAPz+/Vpqv/IE+YoVpvHorgDEREfz0yND8favzH1PNSLs8CmeXm4PH/7tox84tW5/luja/N5M2s5+E7HZ+MvS9YCl6/D3a6n56hPkL1aYhmPsujQqmp8fGUrBUsUI+LQ34mZDRDi+ZDun1uzLsJaY6Bi+HTqdt2cNw+ZmY/0Pazh79DTNO9rvmzWzV7Bv7W78Amrx6cYp3Lx+ky8HTUzTueu3a5gjTW7AHVHzkcxGTRngrXLPuWwh7ouOcLaEJDHLaKcfs4x2+tjglj1Rlpnlf//8klSXQLoIa9ckzaXuuXhDpvPLDnJDn4/BYDAYHNDE0fq5DuN8DAaDIbdxBzS7GedjMBgMuQw1zsdgMBgMOY1xPgaDwWDIcYzzMRgMBkPOoy4ZwJYujPMxGAyGXEZMlHE+BoPBYMhhTLObwWAwGHIcNc1uBoPBYMhpTM3HYDAYDDmOxpiajwE4qVmz7kl2UNJWwNkSksRV51Bz1TnnAJ6uOdDZEpLkBq4518td6uZsCdnGnTAlp3E+BoPBkMuIicrx1XCyHON8DAaDIZdhaj4Gg8FgyHFMn4/BYDAYchwTam0wGAyGHMeEWhsMBoMhx4mOMQEHBoPBYMhh7oQ+n9zvPg0Gg+E/hmraP2lBRFqLyF8ickxE3k7Bzl9EokXkqcz+BlPzMRgMhlxGVtZ8RMQNmAwEAmeAnSKySFX/SMLuI2BFVuRraj4Gg8GQy4hRSfMnDdQBjqnqCVX9F5gLPJ6E3UvAfOB8VvyGNNV8ROQd4HkgGogBegPzgNqqGpoVQpLJdxnwvKpGZODYpsA6oKeqfm2l1QD2AG+o6icpHDscuKKqn4jIt8ASVf0pvRoAqjepQddhPbG52Vg7dxULp/ycyKbr8J7UCKjFzes3mTLoc/4+dIK8+fIy/IfR5L0rL7Y8bmxftpUfP50LwNOvP0/twDpojBIZFsmU1ydw8fzFdOmq2qQGnYZ1x+ZmY/3c1SyZ8ksim07De1A9oCY3r99k2qBJ/HPoBADjN0/lxtXrxETHEB0dzbB2b8Y7rm2vx3nunS709evClYuX06ULoO+IPtRp5s+N6zcZ99o4jh06nsimVNlSDJn8NkWKFeHYoWN8PPATom5FUbZCGV4b9xoVq1Rk5tiZ/PTl/HjH2Ww2Ji79nLDgUIZ2G55ubWnh3THj2bhlBx7Fi7Hg+6nZkocjNZrUpMfwF7G52Vg9dxU/f5H4Vu0xohe1rHts4usTOGGV6YCxL1O7uT+RYZEMDByQ6LjHez1B13e707l6Ry5fvJQmPX1G9MG/mT83ret3PJnr97bD9fvEun7JHV/63tIM/mJw3PE+d/vw3bjvWPD1AjoN6kT9lvWJiYkhMiySca+NIzwkPE1awV5+Lw7vhc3Nxqq5K5mfRPm9OKIXtQJqc/P6TSa8/llc+b00dmBc+b0cmHNTRmVxqHVp4LTD9hmgrqOBiJQGngCaAf5ZkWmqNR8RqQ88CtRU1WpAiwRCsw1VbZsRx+PAQeAZh+1ngf2ZEpUOxGaj+/u9+aDLSF5r8RINHmtE6Upl4tn4BdTCu7wPA5v0ZfrgL+gxqg8At27eYuRzQ3mzzau81eZVqjepSaUalQFY/OUvvNn6Fd5q+yp71uykw8BnEuWdmq4u77/I2C6jeKvFQOo/1gjfBLqqB9SkVHkfBjXpz4zBU+k2qle8/WOeHcq7bV9P5Hg8fDx5qGE1Qs9cSJemWPwD/Cld3pdujXow4a3PeWlM4gciQM/B3fn5qwV0b9yTKxFXaP1sKwAuRVxmyrCpzJ82P8nj2vd4nNPHTmVIW1pp3zaQqeNHZWsesdhsNnqN6sP7XYbzcvP+NHysMWUqlY1nUzOgFr7lfOnXuDdT3p5M79F94/at/XENIzsPT/Lcnj5eVG/kx/kzaX/R9Q/wx7e8Lz0a9eDztz5nQDLXr/vg7iz4agE9revXyrp+yR1/9sRZBrQewIDWA3i57cvcuH6Drcu3AjB/6nz6tezHgNYD2L56O88PfD7Nem02G71H9WVEl2EMaN6PRo81oWyC8qsVUBufcr70adyLyW9Pou/ofnH71vy4mhGdh6U5v6wiOkbS/BGRXiKyy+HTK8HpkvJkCXuLPgPeUtXorPoNaWl28wFCVfUmgKqGquo5a99LIrJHRA6KyP0AIuIhIgtE5ICI/CYi1az04SLynYisFZGjIvKild5URDaKyC8i8oeITBURm7XvpIh4iUg5ETksItNF5HcRWSkiBSwbfyuvbSIyVkQOOWg/BeQXkVIiIkBr4NfYnSLyoojsFJH9IjJfRApmqjQTUNGvEiEngzh/OoToW1FsXbwZ/8B4LxT4B9Zh4/z1ABzde4RC7oUoVrI4ADev3QDALY8befK6oVbv4fUrtycyzV8wf1x6WqngV5GQk0FcsHT9tngztQLrxLOpGViHzZau43uPUNC9EEUtXSnRcWh35n3wXbo1xVK/ZT1Wz18DwJ97/6SQe2E8ksi3eoPqbFq6CYBVP62mfqv6AESGRXJk/5G4t2hHvLy9qNOsDr/OyZIm62Sp7VeVou5FsjWPWCr5VSLoZBAhp0KIuhXF5sUbqdMy/j1Wp2U91s1fC8CRvX9RyL0Qxa0y/WPH71yOSLp22n1YT2aN+SZdc7nUa1mPNQ7Xr7B74bi8HHG8fqsdrl9ajvdr6EfQP0GcP2t3iteuXIvbl79g/jRrBajkV5lgh/LbtHgjdVrWi2dTp2XdFMvvSjLll52oSjo+Ok1Vazt8piU43RnA0eOWAc4lsKkNzBWRk8BTwBci0j4zvyEtzmclUFZEjojIFyLSxGFfqKrWBKYAg6y0EcBeq5Y0BJjlYF8NeASoDwwVEV8rvQ7wOlAVqAA8mYSOSsBkVX0IiAA6WOnfAH1UtT72ZsGE/AT8H/Aw9ia3mw77flZVf1WtDhwGeqRYEunEw9uDsKDbrZJhQWEU9/aIZ1Pc24Owcw42wWF4lLLbiM3GR8s+ZfqemRzYtJ9j+47G2T3zRkcmb/uKhu0b88P4OenSVdzbk/CgsLjt8GR0hTvoCnfQBcpb3w9j5JKxBDwXGGdTo4U/F4PDOHX4ZLr0OOLl7ckFh3xDg0Lx9PaKZ+Ne3J2rl64SEx0TZ+Pl7ZnqufsM781XY75GY+6AEXoWHt6ehJ6Lf495lopfFp7envHvw+AwPFIpL//AOoQHh3EyndfSM4Ee+7VJ/fp5WnrScnyTx5qwYeGGeGld3uzCrO2zCHgigO8++S6dem/X0sOCQpMsv1CH8gsNDovT6yyyONptJ1BJRMqLyF3YW4gWxc9Py6tqOVUth/2Z2k9VF2TmN6TqfFT1ClAL6AVcAOaJSFdrd2wHxm6gnPW9IfCddexawFNEilr7FqrqdaufaB12pwOww+rsigbmWOdIyN+qus8xPxEpBhRR1a1W+v+SOO4H7M7nOevcjlQRkU0ichDoCDyUXDlkBEmqNpvgZrBXyBKYWDYaE8NbbV+lb72eVPSrRNnKd8fZzBs7m/71e7J5wUZad2mbTl1JyEqTLrvRyCeH8N4jg/ikyyhadG7DfXUe5K78d/H4gA7MHz83XVoSi0s+37RoS466zesQERbBsYPHMqfPxchIWaRmc1f+fDw14GnmjJudLXpSsknt+Dx581A3sG5crSmWmR/PpHPdzqz7ZR3turZLh+DESYn0JmGU0Zp9VpGVAQeqGgUMwB7Fdhj4QVV/F5E+ItInu35DmqLdVDVaVder6jBLZGytI7YWEc3t4IWU2g8TXrHU0h1xrLHE5pdqyapqMHALexjhmgS7vwUGqGpV7DW2NNfZHdtRj185maRNWHAYnj6339o8fTy5mKAjNDwoDE9fBxtvTy6ej29z7dJV/th2iOpNayTKY/PCjdRtUz+tsu15Bofh4XP7zc3Dx5OIJHR5OOjy8PaMC2qIsP6/FBbJrhXbqeBXiZL3eFOibClG/zqe8Zun4uHjyftLP6FoiWKp6mnX5VG+WD6JL5ZPIjwkjBIO+Xr5eBEeEhbPPjI8kkLuhbC52eJswlLpYH6w9oPUC6zHzK3fMnjy21RvUJ03J7yRqjZXJywoFC/f+PdYeIL7J9F96J34PnTE+x5vSpUtxafLP+fLLV/h6ePFuGWfUSyZa/lol0eZtHwSk5ZPIiwkLJ4e+7VJ/frFBgiEJvg9CY+vHVCb44eOExEakaSW9QvW06Btg2R/W0LCgsLw8i0Rt+3p45Wo/EKDQ/FyKD8vb890BTRkB+lpdkvb+XSZqlZW1QqqOtpKm6qqiSJmVLVrRgOwHElLwMF9IlLJIckP+CeFQzZir0XERpyFqmpsmMzjIpJfRDyBptirewB1rCqfDXuAwOa0iFfVi8BlEYltpH02GdOhJN1ZVgQIEpG8sZrTimM7aoXC5ZK0Ob7/KN7lfShRtiRuefPwcLuG7Fq1I57NrtU7aNyhKQCValTm2uWrRJy/SBEPdwq6FwIgb767qNKwOueOnQXAu5xP3PG1A+tw9vjZ9EjnxP5j8XTVa9eQPat2xrPZs3onDS1dFWpU5trla0Sev0i+AvnIX8juo/MVyEfVxtU5/dcpzvx1iv61uvFawz681rAP4UFhvPfIICIvRKSqZ/HMJfRrPYB+rQewdcU2WnRoDsD9Ne7n2uWrhCcRybd/6wEaPdIIgMCnWrBt5bYU8/jmo295oU4nujzclQ/6f8j+Lfv5eODYVLW5Okf3H8WnvC8ly5YiT948NGzXmJ0J7rGdq7YT0KEZAJVr3Me1y9dSjI489dc/dK3Zid4NetK7QU/CgkJ5ve0rRCRzLZfMXBIXDLBtxTaaO1y/q5evJpnXAYfr18Lh+v226rcUj2/6eFPWL1wf71y+5XzjvtcLrMeZY2eS/W0JObr/SLzya9SuMTtWbY9nsyNB+V1NpfxygiwOtXYKaQm1LgxMtJq4ooBj2JvgHk3GfjjwjYgcAK4BXRz27QCWAncD76vqORGpDGwDPsTe57MRSBz3mzw9gOkichVYD0QmNHBolkvIe8B27M70IHZnlGXERMcwY+h0hswahs3NjfU/rObM0dO06GiP7Fk9ewV71+6mRkAtJmycyr9WqDVA8ZLF6Td+IDabDZtN2LZkC3vW7gLg+bc743uvLzExSujZC0wfMiXdumYN/Yo3Zg3F5mZj4w9rOHv0NM06tgRg7eyV7F+7G7+Amnyy8Qv+vX6T6YMmAeDuVYxXpr0FgC2PjW0LN3Fww94sKS+AHWt34t/Mn282z+Dm9RuMe/3TuH3vzxzJp29+RnhIOF9/MIMhk9+m6xudOXboOCvmrgSgeIniTFz6OQULF0RjYmjfoz29mvWO1ymd3bwx7EN27j1ARMQlmrd/gX49OtGhXatsySsmOobp701l2HcjsLnZWDNvNaePnKLVC60BWPH9cnav3UWtgNpM2TTNHmo9aELc8a9NHMRD9aviXtyd6du/Ye74/7Fm3qoM69lpXb8Zm2dw4/oNPnW4fiNnjuQz6/rN+GAGb09+m85vdOb4oeOstK5fSsfny5+PGo1q8Pnbn8fLs9vgbpSpUAaNUc6fOc/EIRPTrDcmOoZp701l+HcjrfJbxekjp2j9QhsAln//K7vX7qJ2QG2mbppuld9ncce/PvENqljl9/X2b5kzfjarM1F+aSXahZ1KWpGcart0HDuTIL0pMEhVk3NmqZ23sNUvhTUthI+q5uh6w8/c095ll3a6y0XHEZ+Pcc2lx80y2unnhrroMtrimstoLzy1JNOeY4v3U2l+5jQI/sklPdWdML3OIyIyGPtv+Qfo6lw5BoPBkL3cCfGaOeZ8VHV4MunrsTeXZfS887DPtmAwGAz/CTT1WCuX506o+RgMBsN/ihiXbehPO8b5GAwGQy4j2kX7ctODcT4Gg8GQyzB9PgaDwWDIcUyfj8FgMBhyHFPzMRgMBkOOY5yPwWAwGHIc0+xmMBgMhhwnKonZv3MbxvkYDAZDLuMOGOZjnE9WUNa+qKpLcjQm51dZTAuP49zFuJLDVedPA/hhz4TUjZzA+7Xfc7aEJPk4eFPqRrkU0+djMBgMhhwnxjS7GQwGgyGnMc1uBoPBYMhxTLObwWAwGHIcE+1mMBgMhhzHNLsZDAaDIceJyf0VH+N8DAaDIbdh+nwMBoPBkOOYZjeDwWAw5DhRptnNYDAYDDmNaXbLJYjIO8DzQDT269ZbVbfntI77mlTn8aGdsbnZ2D5vHeumLIq3v0QFX54Z25syD5Xn10/msWH6UgCK+njw3Ph+FClRDI1Rfpuzhs3fLM8yXTWa1OTF4b2wudlYNXcl87/4KZHNiyN6USugNjev32TC659x4tBxAF4aO5Dazf2JDIvk5cD+WaYplrJNq9FweCdsbjb+mLOevV8sjre/UvuHqdnvUQBuXb3BhiHfEnb4FIV9PGj+WR8KliiKxih//G8dB2asyJSWGk1q0mP4i9jcbKyeu4qfkyinHiN6USugFjev32Ti6xPiymnA2Jfjymlg4IBExz3e6wm6vtudztU7cvnipUzpTIl3x4xn45YdeBQvxoLvp2ZbPgmp2KQabYd2Qtxs7Jm3nk1T4l9Hrwo+PDG2Nz4PlWPNJz+wZfqyuH2vbv6Mf6/cICYmhpioaL58LOun8xk3bgStWwdw7dp1XnzxdfbtO5TI5ttvJ1CzZjVu3Ypi16599O8/mKioKJ59tj2vv94XgCtXrvLyy+9w8ODhLNfoiN4BNZ/cvxB4KohIfeBRoKaqVgNaAKdzXIdNeGJkN77q+hFjAwdR47GHKVWxdDyb6xFXWDh8JuunL4mXHhMVw+JR3zO2xSAmPvEeDTq1THRsRrHZbPQe1ZcRXYYxoHk/Gj3WhLKVysazqRVQG59yvvRp3IvJb0+i7+h+cfvW/LiaEZ2HZYmWhIhNaDyqC0s7f8ycZm9S6fF6FK/kG8/m8ukLLPi/UcxrOYRdExbQ9KPuAMREx7Dl/f8xp9lbzH98OFW6tEh0bHqw2Wz0GtWH97sM5+Xm/Wn4WGPKJCinmgG18C3nS7/GvZny9mR6j+4bt2/tj2sY2Xl4kuf29PGieiM/zp85n2F9aaV920Cmjh+V7fk4Ijbh0ZFd+a7rx0wKfJOqj9WnRKJ7/ypLh89ii/XClZBvnhvFlLZDssXxtGoVQMWK5Xjoocb07/82n38+Okm7OXMWUK1aALVqBVKgQH66dXsWgJMnTxMY+DT+/q344IPPmTz5wyzXmJCYdHzSgoi0FpG/ROSYiLydxP6OInLA+mwVkeqZ/Q13vPMBfIBQVb0JoKqhqnpORE6KyEcissP6VAQQkXYisl1E9orIahEplRUi7varSNg/wYSfPk/0rWj2Ld7GQy1rx7O5EnaJ0wdOEBMVHS/98oUIzv5+EoCbV28Qcvws7t4eWSGLSn6VCT4ZRMipEKJuRbFp8UbqtKwXz6ZOy7qsm78WgCN7/6KQeyGKlywOwB87fudKRPZMXlrSrwKRJ0O4dOoCMbeiObboN8q3rBXPJnj3UW5GXgMgZO8xCvnYy+Xa+QhCD50E7DWii8fOUSgTZVbJrxJBDuW0efFG6rSsG8+mTst6KZbT5WTKqfuwnswa8w1o9ncj1/arSlH3ItmejyNl/CoQ/k8IF09fIPpWNAcX/8b9Ca7j1bBLnEvi3s8J2rVryezZ8wHYsWMvxYq54+1dMpHdihXr4r7v3LmPMmV8APjtt91ERETGHV+6tE+2a85K5yMibsBkoA3wIPCciDyYwOxvoIn1Av8+MC2zv+G/4HxWAmVF5IiIfCEiTRz2XVLVOsAk4DMrbTNQT1VrAHOBN7NCRNFSxYk4Fxa3HREURtFSxdN9nuJlvCj9YDlO7TuWFbLw9PYk9NyFuO2woFA8S3kmtgkKjdsODQ7D0zv7Z6Uu5F2cK+fC47avBIVTyDv5Mnvg2aacWncgUXqRMl54PXQPIXuPZ1iLh7cnoedul0FYUFiS5RTmUE5hwWF4pFJO/oF1CA8O4+ThkxnW5uoUKeVBpMO9fykoHPf03PuqdP7ubfosHkWt5wKyXJ+vrzdnzgTFbZ89G4yvr3ey9nny5OH5559k5coNifZ17foMK1euS+KorEXT8UkDdYBjqnpCVf/F/tx7PF5+qltV9aK1+RtQJrO/4Y7v81HVKyJSC2gEBADzHKqVcxz+/9T6Xsay8QHuwu7xM08S02Gk90X3roL56DLlVRaOnMXNK9ezRFZSCyJqAmGShFFCm+xA0lFmvvUf4IFnmvDzk+/HS89TMB+tvhzIluHfcysTZZa0ltTLICWbu/Ln46kBTzPihaEZ1pUbSGommPTcP191GMHl8xEU8nSny/dvE3o8iH92/Ok0fZ9/PprNm3ewZcuOeOlNmtSna9dnaNasQ5ZpS44sjnYrTfyuiDNA3WRsAXoAv2Y20/9CzQdVjVbV9ao6DBgAxN4djndY7PeJwCRVrQr0BvIndU4R6SUiu0Rk14HLqddCIoPDKeZ7+y24mI8nl85fTOGI+NjyuNFl6qvsWbCFQyt2pvm41AgLCsPLt0TctqePF+Hnw+PZhAaH4uXjFbft5e1JeEh8m+zgSlA4hX1vN5UV9vHgWkjiMvO8vywBY3uyrMen3Iy4Epduy+NG62kDObpgKyeW78qUlrCgULx8b5eBp49nonIKCw7D06GcPL09uZhCOXnf402psqX4dPnnfLnlKzx9vBi37DOKlSiWKa2uxqXgcIo63PvuPh5cPh+R5uNjba+GXeLwil2UqX5vpjX17t2Z7dt/Zfv2XwkKOh/XhAZQurQ3QUEhSR73zjuv4OXlwZtvjoyXXqXK/UyZ8jFPPdWT8PCITOtLjfQ0uzk+q6xPrwSnS8qVJel9RSQAu/N5K7O/4Y53PiJyn4hUckjyA/6xvj/j8P8263tR4Kz1vUty51XVaapaW1VrVytSMVUdp/cfx6ucNx5lSuCW1w2/dvX5fdXuNP+Opz/qRcixc2z8elnqxung6P4j+JT3pWTZUuTJm4dG7RqzY1X8QMAdq7YT0KEZAJVr3MfVy9e4mA7HmVHO7z9B0XLeFClbAlteNyo+Vo+/V+2JZ1PY15PW019hzcCpRP4dHG9fwNieXDx6jv3TM/2SxtH9R+OVU8N2jdm5Kv6b784E5XQtlXI69dc/dK3Zid4NetK7QU/CgkJ5ve0rRFyIyLReV+Ls/hN4lPOmmHXvV21Xjz/TeO/nLZCPuwrlj/teoVFVQo6cybSmL7+cRd26bahbtw2LFq2gY0f7+2idOjWIjLxMcHDi4I9u3Z6lRYvGdO48IF7NqGxZX+bNm0b37q9w7FjWNJSkRnqa3RyfVdYnYX/NGcAxeqYMcC5hniJSDfgKeFxVwxLuTy93fLMbUBiYKCLFgCjgGNALewRcPhHZjt0JP2fZDwd+FJGz2Ns2y2eFiJjoGH4Z+i0vzhqMuNnY+cN6Qo6eoX7HFgBsm72aIiWKMnDRaPIXLoCq0qh7G8YGvoHP/XdTu0Njzh0+xavLPgDg14/n8ef6fVmia9p7Uxn+3UhsbjbWzFvF6SOnaP1CGwCWf/8ru9fuonZAbaZumm4PIR70Wdzxr098gyr1q+Je3J2vt3/LnPGzWT1vVaZ1AWh0DJvem0m7799E3Gz8OW8DF4+c5aEX7A/4379fS+1XniBfscI0Ht3V+j3R/PTIULz9K3PfU40IO3yKp5fbo5d+++gHTq3bnyEtMdExTH9vKsO+G2GV02pOHzlFqxdaA7Di++XsXruLWgG1mbJpmlVOt1cefW3iIB6yymn69m+YO/5/rMmickoPbwz7kJ17DxARcYnm7V+gX49OdGjXKlvzjImOYenQb+k86y1sbjb2/LCBC0fPUrtjcwB2zV5D4RJF6b1oFPkKF0A1hnrd2zAp8E0KFi/Mc9NeBcDm5saBhVs5tiFxv15mWL58La1bB/DHH5u4du06vXoNitu3YMG39O37FkFBIUycOIZTp86yYcMCABYuXM6YMRMYMmQgHh7FmTDBHkUYFRVNgwaPZqnGhGTx3G47gUoiUh77i/ez2IemxCEidwM/A51U9UhWZCo50XbviojISaC2qoamZpsag8o957KF6KrLaLfSYs6WkCSrJNLZEpLFLKOdPlx1Ge0bN05l2nV8eM8LaX7mvP3P96nmJyJtsQdduQEzVHW0iPQBUNWpIvIV9u6K2FajKFWtneTJ0sh/oeZjMBgMdxRZ/barqsuAZQnSpjp87wn0zMo8/7POR1XLOVuDwWAwZISoO2Bq0f+s8zEYDIbcSu53Pcb5GAwGQ67DTCxqMBgMhhzHrGRqMBgMhhwn5g5oeDPOx2AwGHIZOT/9atZjnI/BYDDkMkzNx2AwGAw5Tu53Pcb5GAwGQ67DRLsZDAaDIccxzW4GAP514feQ6xrlbAlJ4uaifzs3cM3yAtedQ+29Xe+nbuQExt/d3NkSsg0X/fNJF8b5GAwGQy4j+g5wP8b5GAwGQy7Dddta0o5xPgaDwZDLMH0+BoPBYMhxcr/rMc7HYDAYch2m5mMwGAyGHMcEHBgMBoMhxzEBBwaDwWDIcdTUfAwGg8GQ05iaj8FgMBhynBg1NZ8sR0TWAx+o6gqHtFeAlkAA8BdwF7AL6KGqt0SkKbAQOAEUBEKAj1V1SQY1XFHVwhn/FanzQJPqPDm0KzY3G9vmrWX1lIXx9pes4EvHsX0p+1B5lnwyl7XTM/RT0ky/EX3xb+bPzes3+eS1cRw7dCyRjXfZUgyZPJgixYpw9NAxPh44lqhbUTRrH8DT/Z4G4PrV60wcMpETh//OEl1lm1bj4RGdEDcbf85Zz77Ji+Ptr/jEw/j1exSAW1dvsGnwt4QfPoVbvrw8Nv9d3O7Kg7i58feyHewa93O68+8zok9cuYx7bRzHDx1PZFOqbCnenvw2RYoV4dihY3wy8BOibkUle3zpe0sz+IvBccf73O3Dd+O+Y8HXC+g0qBP1W9YnJiaGyLBIxr02jvCQ8DTrrdikGm2H2strz7z1bJoSv7y8KvjwxNje+DxUjjWf/MCW6cvi9r26+TP+vXKDmJgYYqKi+fKxnJvO590x49m4ZQcexYux4PupOZZvLGM/GUbLVk25fu0GvXsPYv++3xPZfD3jU2rUrEbUrVvs2r2flwe8Q1RUFJUr38uUL8fi5/cQI4aP4/MJ07Ndb+53PWBztoAkmAM8myDtWeAD4Liq+gFVgTLA0w42m1S1hqreB7wMTBIRl5zcSWzC/43sztSuHzAm8DVqPdYA74ql49lci7jC/OHfsmb64mTOknX4B/hTurwv3Rp157O3JvDymAFJ2vUY3IOfv/qFbo17cCXiCq2fbQVA8OlgBv3fG/Rp2Zf/Tfgfr3w0MEt0iU1oMKoLyzp9zA8Bb1Lx8XoUq+Qbz+byqQssemoUPwUOYc+EBTT+uDsA0TdvsfjpMfzU8h3mt3qHMk2rUbJmhXTl7x/gj295X3o06sHnb33OgGTKpfvg7iz4agE9G/fkSsQVWlnlktzxZ0+cZUDrAQxoPYCX277Mjes32Lp8KwDzp86nX8t+DGg9gO2rt/P8wOfTVV6PjuzKd10/ZlLgm1R9rD4lEtxX1yOusnT4LLZMX5rkOb55bhRT2g7JUccD0L5tIFPHj8rRPGNp2aopFSqWo3rVAF4aMJjPJiStY968hdT0a04d/9YUyJ+frt2eAeDixUjeGDSCzyd8lWOao4lJ88dVcUXn8xPwqIjkAxCRcoAvcCbWQFWjgR1A6aROoKr7gJHAAOscJURkvojstD4NrPTCIvKNiBwUkQMi0sHxPCLiJSLbROSRrPyB9/hV5MI/IYSdPk/0rWj2LN5K1Zb+8WyuhF3i1IHjxERl/5qFD7esz6r5awD4c++fFHIvjEdJj0R2fg2qs3HpJgBW/bSah1s9DMAfuw9zJfIKAIf3/omXj1eW6CrpV4FLJ0O4fOoCMbeiObbwN8q1rBXPJmT3Uf6NvGb/vucYhX1u6466dhMAWx43bHnypPt1sV7LeqxxKJfC7oUpXrJ4IrvqDaqzySqX1T+tpn6r+mk+3q+hH0H/BHH+7HkArl25Frcvf8H86dJbxq8C4f+EcPH0BaJvRXNw8W/cn6C8roZd4tyBEzlyX6WH2n5VKepexCl5P/poIHNm22vFO3fuo2hRd0p5l0hkt3LF+rjvu3btp3RpHwAuXAhjz+4D3Lp1K0f0gr3PJ60fV8XlnI+qhmF3LK2tpGeBeTg8OkQkP1AXWJ7CqfYA91vfJwCfqqo/0AGIfUV5D4hU1aqqWg1Y65BHKWApMFRVk35NzCDFSnkQcS4sbjsiKIyipRI/1HIKT29PLpy7ELcdGnQBT2/PeDbuxd25cukqMdExcTZeCWwAWj/bip3rdmWJroI+xbkSdLvJ6WpwOIV8ki+n+59tyql1B+K2xSZ0WDGazvu/4Oymg5zfm7jJLCU8vT0JPRcatx0aFIqXd3zH6l7cnavxyiU0ruzScnyTx5qwYeGGeGld3uzCrO2zCHgigO8++S7NeouU8iDS4b66FBSOe3ruK1U6f/c2fRaPotZzAWk/Lpfj41uKM2eC4rbPnQ3C19c7Wfs8efLw3PNPsGrlhmRtspsYNM0fV8XlnI+FY9Pbs9Y2QAUR2QeEAadU9UASx8YiDt9bYG+G2wcsAtxFpIiVPjnWSFUvWl/zAmuAN1V1VeZ+SlLKJFGSM/sPJQk9CQUlZaMJbKrXr0brZ1rx1Zivs0YXSelK2tb34Qe4/9kmbB8997ZpjDK/1Tt87/8yJfwqUPy+MunLPw2/OSWb1I7PkzcPdQPrxtWaYpn58Uw61+3Mul/W0a5ru3ToTZyWUG9KfNVhBFMffZfvun5M3c6B3FPn/tQPugNIy3V25NMJ77Nl8w62bt2ZnbJSRNPxz1VxVeezAGguIjWBAqq6x0qP7fOpCNQTkcdSOEcN4LD13QbUV1U/61NaVS9jd1BJXZ0oYDfQKrmTi0gvEdklIrsOXU7fG3VEcBjFfG/XGor5eHLp/MUUjsh62nVpx5Tlk5myfDJhIWGU8L3dzODlU4KwBJ3ckeGRFHYvhM3NlqRN+fvL8+rYVxjWYwSXIy5nicarQeHxmtEKeXtwNThxOXk8UJbGH/dkRfdPuRlxJdH+fy9dI2jbYco2rZZqno92eZRJyycxafkkwkLC8PK9XVPx8vEiLCQsnn1keCSF4pWLV1yAQGhQaIrH1w6ozfFDx4kIjUhSy/oF62nQtkGqmmO5FBxOUYf7yt3Hg8vnkz53UsTaXg27xOEVuyhT/d40H5vb6NW7E1t/W8rW35YSFHSeMmV84vb5lvYhKCgkyeMGD3kZLy8P3n7LOf1TsWR1s5uItBaRv0TkmIi8ncR+EZHPrf0HrGdzpnBJ56OqV4D1wAxu13oc9wcBbwODE+4DEJFq2JvUYms1K7H6f6z9fsmkx7ZRKNAduD+pC2FpmKaqtVW1dpUi6evIPrX/OCXKeeNRpgRued2o2e5hDq7KmqaqtLJ45mL6tu5P39b92bpiG4Ed7LEZ99e4n6uXrxJ+PnGE1f6tB2j8SCMAAp9qwbaV2wAo4VuCodPf4+OBYzn799ks03h+/wmKlvemSNkS2PK6UfHxevyzak88m8K+nrSc/grrBk4l8u/guPT8HkW4y70gAG7581K6YRUijp1LNc8lM5fEBQNsW7GN5gnK5WISLwkHth6gkVUuLRzK5bdVv6V4fNPHm7J+4fp45/Itdzugol5gPc4cO0NaObv/BB7lvClm3VdV29Xjz1W703Rs3gL5uKtQ/rjvFRpVJeRI2vPObUz78jservcID9d7hCWLV/JcxycB8Pf349Kly4QEX0h0TJeuz9C8RWO6dXk5XTXK7EBV0/xJDRFxw/6sbAM8CDwnIg8mMGsDVLI+vYApmf0N4uxCTA4ReQL4GXhAVf+0Ag+WqGoVa78A+7A7Dzfih1qfxx5qvdiy9cJeuA9gDy/fqKp9RKSwlV4LiAZGqOrPsaHWInIXsBhYqKpfJKf15XLPpLsQH2zqx5NDu2Bzs/HbD+tZOfkXGnRsAcCW2aspUqIobyz6gPyFCxCjyr9XbzAm8HVuXLmernz+jI5Mk92AUf2p3bSWPdT69fEcPXAUgFEzRzL+zc8IDwnH+27vuFDr44eO89HAj7n17y1e/fgVGrZpENdpHh0dzYBHXk4xvw6atqCEss2q8/DwFxCbjb/mbWDvxEU88EIzAA5/v5bGY3tybxt/Lp+1961oVDQ/PzIUjwfKEvBpb8TNhohwfMl29ny2INX8Fkj8mk2/Uf2o3bQ2N67f4NPXP40rl5EzR/KZQ7nEhlofP3ScsQPHcuvfWykeny9/PmbtmEW3Bt24dvl2kME7X75DmQpl0Bjl/JnzTBwykbBguyZ/t8RBIAmp1LQ6bYZ2wuZmY88PG9g4eSG1O9od4K7Zayhcoii9F40iX+ECqMbw79WbTAp8k4LFC/PctFcBsLm5cWDhVjZOXphSVnFkxUqmbwz7kJ17DxARcQlPj2L069GJDu2SbXhIE8XTsZLp+E9H0iKwMdevXadPnzfZu+cgAPN/mUH/fm8THHSeiEtHOXXqLFeuXAVg0cLlfPjBREqW8mLT5kUUKVKYmBjl6tWr1K7ZksuXE9fCAa5c+zuJBtL00e7uR9P8zFl8akmK+YlIfWC4qraytgcDqOoHDjZfAutVdY61/RfQ1KoIZAiXdT65iYw4n5wirc4np0mr88lpEjofVyItzscZuOoy2ulxPjlJVjifR+9+JM3PnCWnlqbmfJ4CWqtqT2u7E1BXVR1bhZYAH6rqZmt7DfCWqma4ycYlm90MBoPBkDzpiXZz7J+2Pr0SnC4p55TQuaXFJl243AwHBoPBYEiZ9LRYqeo0YFoKJmeAsg7bZYCEHaRpsUkXpuZjMBgMuYwsjnbbCVQSkfJWP/ez2IekOLII6GxFvdXDPj4yw/09YGo+BoPBkOvIymlzVDVKRAYAK7AHb81Q1d9FpI+1fyqwDGgLHAOuAd0ym69xPgaDwZDLyOpAMVVdht3BOKZNdfiuQP+szNM4H4PBYMhluPK0OWnFOB+DwWDIZbjytDlpxTgfg8FgyGWYxeQMBoPBkOPkftdjnI/BYDDkOqJceqWetGGcj8FgMOQy7oRp0YzzyQK23szUQN9sZV/YCWdLSBIvnzrOlpAkd6mbsyUky8fBm1I3cgLjXXQOtYun1jhbQrZhot0MBoPBkOOYaDeDwWAw5Dim2c1gMBgMOY5pdjMYDAZDjhOtJtrNYDAYDDmM6fMxGAwGQ45jZjgwGAwGQ45jaj4Gg8FgyHFMzcdgMBgMOY4JODAYDAZDjnMnNLvZMnOwiHiKyD7rEywiZx2278oqkVmBiPiJSNsMHLdeRGpnJu9B7w/kl61zmLPmW+6rWjlJG9+yPny79Et+3vI/xkwdTp689veCTn2fY/aqGcxeNYN562ay/cx63IsVAWDRjh+Yu/ZbZq+awazl0zMjkU/Hj+TPPzazZ/cqavhVSdJm1syJ/H5oI/v2rmH6tHHkyRP/3aV2rercvH6KJ598JMM6qjWpwSdrJzF+wxe06/tkkjadh/dg/IYv+HD5p5Srcm+8fWKzMWbZOAbNeCcu7aVJrzNm2XjGLBvPhM1fMmbZ+Azri6VGk5p8sW4qUzdOo0O/p5K0eXFEL6ZunMaEFRO5t0qF23rGDmTmnu/5fNXkTOtIinHjRvD77xvZuXMFfslcy2+/ncCBA+vYvXsVX345Nu5aPvtse3buXMHOnStYt+5nqlZ9IMt0jf1kGPsPruO37b9S3e+hJG2+nvEpe/atYcfO5Xwx9aM4XZUr38uadfMJu/gnLw98Mcs0pca7Y8bT+JFnaf9CnxzLMy3EqKb546pkyvmoapiq+qmqHzAV+DR2W1X/FRGXqFlZOvywr0GeozRoVo+y95bhiYefY/QbHzP4w9eTtHvp3T78b9oPPNngeS5HXubx5x4F4Lspc+gY2J2Ogd2ZNOZL9mzbx6WIy3HH9X5qIB0Du9O5dcb/INu0bkaliuW5/8GG9O37FpMnfZCk3Zw5v/BQlcb41WhOgQL56dH9+bh9NpuND8a8w8qV6zOsQ2w2ur3fi4+7vM8bLV7m4ccaUrpSmXg2fgE18S7vy2tN+vHV4Cl0H9U7/m/p/ihnj52JlzZxwDiGtH2NIW1fY8fybexc/luGNYL9t/Ye1ZcRXYYxoHk/Gj3WhLKVysazqRVQG59yvvRp3IvJb0+i7+h+cfvW/LiaEZ2HZUpDcrRqFUDFiuV46KHG9O//Np9/PjpJuzlzFlCtWgC1agVSoEB+unV7FoCTJ08TGPg0/v6t+OCDz5k8+cMs0dWyVVMqVCxH9aoBvDRgMJ9NGJWk3bx5C6np15w6/q0pkD8/Xbs9A8DFi5G8MWgEn0/4Kkv0pJX2bQOZOj5prc5E0/HPVcmU80kKEflWRMaLyDrgIxGpIyJbRWSv9f99ll1XEflZRJaLyFER+dhKd7POcUhEDorIq1b6ehH5zDrHIRGpY6V7iMgCETkgIr+JSDUrfbiITBORlcAsYCTwjFUre0ZEConIDBHZaWl73DqugIjMtc43DyiQmfJo0rohy35cDsChPX9QxL0wniU9E9n5N6zJmiXrAVjyw3KatmmUyKZV+xasWJD1kyW2a9eK72b/BMD2HXsoWqwo3t4lE9n9unxt3PedO/dRpoxP3PaA/t35+ZelnL8QlmEdFf0qEXIyiPOnQ4i+FcW2xZupFRh/AtJagXXYNH8dAMf2HqGgeyGKlSwOgIe3J37NarFu7upk86j3SAO2LcrcBJ2V/CoTfDKIkFMhRN2KYtPijdRpWS+eTZ2WdVk3315eR/b+RSH3QhS3dP6x43euOLxAZCXt2rVk9uz5AOzYsZdixdyTvJYrVqyL++54LX/7bTcREZFxx5cu7ZPo2Izw6KOBzJn9c1x+RYu6U8q7RCK7lSvWx33ftWt/XP4XLoSxZ/cBbt26lSV60kptv6oUdS+So3mmBdWYNH9clSx3PhaVgRaq+jrwJ9BYVWsAQ4ExDnZ+wDNAVeyOoayVVlpVq6hqVeAbB/tCqvow0A+YYaWNAPaqajVgCHZHE0st4HFVfd7Ke55VK5sHvAOsVVV/IAAYKyKFgL7ANet8o61zZJgS3iUIPnc+bjsk6AIlfbzi2RT1KMrlyCtER0cDcD7oAiW949vkK5CP+gF1Wbt0fVyaqjJ57ni+W/EVT7zQLsMaS/t6c+b07Zm5z54JorSvd7L2efLkoWPHDnEPMF9fb9o/3povp32XYQ0Axb09CAsKjdsODwrDw9szgY0n4eduO7jw4DCKl/IAoNOw7swZMxONSfoP7v46DxIZGkHwyaBM6fT09iT03IW47bCgUDxLeSa2cfgtocFheHonfunIanx9vTlz5vbvO3s2GN9UruXzzz/JypUbEu3r2vUZVq5cl8RR6cfHt1Q8XefOBqWq67nnn2BVEroM9ul10vpxVbKrWexHVY22vhcFZopIJewL8OV1sFujqpEAIvIHcA/wO3CviEwElgIrHeznAKjqRhFxF5FiQEOgg5W+1uqHKmrZL1LV68lobAk8JiKDrO38wN1AY+Bz63wHRORAhkrAQkQSpSWcFDAtNo0DG7B/58F4TW49HutHaEgYxT2LMXnep5w8doq9v+3PFo2OTJo4hk2btrN5yw4Axo8bweAhY4hJ5qGfZh2kpaySOFCVGs1qcykskr8PneCBekn3Jzz8WCO2ZrLWYwlNXWcafkt2kFT5pJTv55+PZvPmHWyxrmUsTZrUp2vXZ2jWrEMW6UpfeXw64X22bN7B1q07syT/Ow0T7ZY8Vx2+vw+sU9UnRKQcsN5h302H79FAHlW9KCLVgVZAf+BpoLtlk/BuVZJ8FMTZXU1iXywCdFDVv+Il2v9IUn1KiEgvoBfA3e4VKVHw9lvc/3V9gvYd7TWRP/b/ibdvSWJdQimfElwIjt80FREWQZGihXFzcyM6OpqSPiW4EBLfpmX75qxYEL85KdSyuRgWwfpfN/KQ3wNpdj59+3ShR4+OAOzatY8yZX3j9pUu48O5oJAkj3vv3VcpUcKTvv16xqXVqlmN2d9/AYCXlwdtWjcjKiqKRYtWpElLLOHBYXg61Ao9fDy5GBIe3yYoDA/f2zUID29PLp6/SJ22D1OzhT9+TWuRN19eChQpSL/PXuGLVz4DwOZmw791Pd55dBCZJSwoDC/f201Gnj5ehJ+PrzM0OBQvh9/i5e1JeILfklX07t2Z7t2fA2D37gPxmkNLl/YmKJlr+c47r+Dl5UH//m/HS69S5X6mTPmYxx7rTHh4RIZ19erdia5WX1JCXb6lfZLVNXjIy3h5efD8gCEZzvtO506Y1Tq7mt0cKQqctb53Tc1YRLwAm6rOB94DajrsfsayaQhEWrWmjUBHK70pEKqql5I49WXAsfF2BfCSWN5GRGpY6Y7nqwJUS0qnqk5T1dqqWtvR8QD8+O0vcUEC63/dRNv/aw1AlZoPcuXyFcLOJ+4X2bVlL80fbQrAo0+3ZsPy22/ohYoUomY9PzYs3xyXlr9AfgoWKhD3vW4Tf47/lfaF46ZMnUlt/5bU9m/JokUr6NTRHrFVt05NLkVeIjj4fKJjund7jpaBTen4Qv94N3+l++pTsXI9Klaux/yflzLg5SHpdjwAx/cfxbu8DyXKlsQtbx7qt2vI7lXx33x3r95Jow4BAFSsUZnrl68Rcf4i8z7+npfqvcjAhr2Z+NI4ft96MM7xAFRpWJ1zx88SHpzxPqlYju4/gk95X0qWLUWevHlo1K4xO1Ztj2ezY9V2Ajo0A6Byjfu4evkaF89fzHTeSfHll7OoW7cNdeu2YdGiFXTsaK+t1KlTg8jIy0ley27dnqVFi8Z07jwg3rUsW9aXefOm0b37Kxw79nemdE378jservcID9d7hCWLV/JcR3v0or+/H5cuXSYk+EKiY7p0fYbmLRrTrcvLd8QDNru4E6LdciIa7WPszW6vAWtTMwZKA9+ISKxjHOyw76KIbAXcuV0bGm7ZHwCuAV2SOe864G0R2Qd8gL1G9hlwwHJAJ4FHgSkO59sH7EjiXGlmy5ptNGhejwXb5nLj+g1GvHo7kmzC9x/z/usfERoSxsRRUxgzdTh93+rJX4eOsnDO0ji7gDaN2b5hJzeu34hL8yxRnLEz7N1nbnncWPHLKraty5jUZb+uoXXrZvx1eAvXrl+nZ8/X4vYtXjiLXn3eICgohC8mf8g//5xh86ZFACxYsIxRoz/LUJ5JERMdw7dDp/P2rGHY3Gys/2ENZ4+epnnHVgCsmb2CfWt34xdQi083TuHm9Zt8OWhims5dv13DrGlys3ROe28qw78bic3Nxpp5qzh95BStX2gDwPLvf2X32l3UDqjN1E3TuXn9JhMHfRZ3/OsT36BK/aq4F3fn6+3fMmf8bFbPW5Ul2pYvX0vr1gH88ccmrl27Tq9et2t6CxZ8S9++bxEUFMLEiWM4deosGzYsAGDhwuWMGTOBIUMG4uFRnAlWNFpUVDQNGjyaaV0rlq+jVasADhxaz/Vr1+nT5824ffN/mUH/fm8THHSeCZ+P4tSps6xdbw9OWLRwOR9+MJGSpbzYtHkRRYoUJiZG6T+gG7VrtuTy5SuZ1pYSbwz7kJ17DxARcYnm7V+gX49OdGjXKlvzTAuuHMWWViS3vF2IyHpgkKrucraWhNT2aeSyheiqy2g/7aLLaF/VnI2mSg8rzmeq+zHbyGNzzaXHXXUZ7bxe9ybVVZAuShW9P83PnJDIPzOdX3aQE81uBoPBYMhCcirazRrKssoaDrNKRIonYVNWRNaJyGER+V1EBqbl3LnG+ahqU1es9RgMBkNOEx0Tk+ZPJnkbe1RyJWCNtZ2QKOB1VX0AqAf0F5EHUztxrnE+BoPBYLCjqmn+ZJLHgZnW95lA+yS0BKnqHuv7ZeAw9r77FHGJ6W8MBoPBkHZycPBoKVUNAruTEZHE02U4YA2nqQFsT8kOjPMxGAyGXEd6ajSOYxItpqnqNIf9q4Gkppt4J4m0lPIpDMwHXklmuEs8jPMxGAyGXEZ6xu9YjmZaCvtbJLdPREJExMeq9fgAiQeN2e3yYnc8s1X157ToMn0+BoPBkMuI1pg0fzLJIm6PnewCLExoYI2T/Bo4rKppXq/EOB+DwWDIZeRgwMGHQKCIHAUCrW1ExFdEllk2DYBOQDOH9dxSXb7GNLsZDAZDLiOnZjhQ1TCgeRLp57DWR1PVzSQ9x2aKGOdjMBgMuYzcMjNNShjnYzAYDLkM43wMBoPBkOPkfteTiyYW/S8hIr0c4/BdBaMrfbiqLnBdbUbXfwcT7eaa9ErdxCkYXenDVXWB62ozuv4jGOdjMBgMhhzHOB+DwWAw5DjG+bgmrtq2bHSlD1fVBa6rzej6j2ACDgwGg8GQ45iaj8FgMBhyHON8DAaDwZDjmEGmBoPBYCEiNVPaH7tipyHzmD4fF0BESgFjAF9VbWOtf15fVb92sjQAROQeoJKqrhaRAkAea7lcZ2r6TlU7pZaWg3qeTGl/Wtc4yS5EpATwFvAgkD82XVWbOU0UICJtVPXXBGl9VHWqk/Sss77mB2oD+7FPmlkN2K6qDZ2h607ENLu5Bt8CKwBfa/sI8IqzxDgiIi8CPwFfWkllgAVOE3Sbhxw3RMQNqOUkLQDtUvg86kRdscwGDgPlgRHASWCnMwVZvCcicQ5QRN4CHneWGFUNUNUA4B+gpqrWVtVa2JeGPuYsXXciptnNNfBS1R9EZDCAqkaJSLSzRVn0B+pgrcmuqkdTW8c9O7HKaAhQQERil+oV4F+cGA6rqt2clXca8VTVr0VkoKpuADaIyAZniwIeA5aIyBtAa+B+K83Z3K+qB2M3VPWQiPg5Uc8dh3E+rsFVEfHEmi9QROoBkc6VFMdNVf3XvlghiEgenDivoap+ICIfAV+pandn6UiIiLyW0v70rPCYTdyy/g8SkUeAc9hrsU5FVUNF5DFgNbAbeEpdoy/gsIh8BXyP/X5/AXvN0ZBFGOfjGryGfbnaCiKyBSgBPOVcSXFsEJHYmkYg0A9Y7ExBqhojItWdqSEJijhbQCqMEpGiwOvARMAdeNVZYkTkMvaHulj/3wXcCzwlIqqq7s7SZtEN6AsMtLY3AlOcJ+fOwwQcuAhWjeI+7H+Mf6nqrVQOyRFExAb0AFpi17YCe63DqTeOiEwGvlVVV+i3MNyBWME1d6vqX87WcidinI8LkEykVCRwUFXP57QeR0SkEHBDVaOtbTcgn6pec7KuP4DK2DuGr2K9QatqNSfryo/dWT9E/KgypzYRisi9wASgPhADbANeVdUTTtb1BLBWVSOt7WJAU1Vd4GRdjwFjgbtUtbzV3zNSVV2hP+qOwES7uQY9gK+AjtZnOvamuC0i4pTQYQfWAAUctgtgb593Nm2ACkAzbkeUtXOqIjvfAd5AK2AD9n4Vp4alW/wP+AG7Nl/gR2COUxXZGRbreABUNQIY5jw5cQzDHmgTAaCq+4ByzpNz52Gcj2sQAzygqh1UtQP2sRg3gbrYx2Y4k/yqeiV2w/pe0Il6YtFkPs6moqq+B1xV1ZnAI0BVJ2sCeyvHd6oaZX1iO9KdTVLPIFfoi45ydIqGrMcVLrIByqlqiMP2eaCyqoaLiLP7fq6KSM3Ykd0iUgu47mRNAEu53WGdH/v4lb9IMP7HCcRerwgRqQIE4xpvzOtE5G1gLvZyewZYKiIeAKoa7iRdu0RkPDDZ0vUS9qg3Z3NIRJ4H3ESkEvAysNXJmu4oTJ+PCyAiXwB3Y28KAegAnAHeAJZYg96cpc0f+wPrnJXkAzyjqq7wgIjDmhalt6r2drKOnsB87CPivwEKA0OdNWLfQdff1tfYP3hx2K2qem8OS7KLsPcpvge0sJJWAqNV9aoz9MQiIgWBd7AH2oA90OZ9Vb3pPFV3Fsb5uABiH0TzJBA7dUcY4KOq/Z2n6jYikpfbkXh/ukokXkJEZI+qpjg3138N6+XhtKoGW9tdsL/cnASGO7HGEw8RKezYvOtsROT/VPXH1NIMGcc4HxfBiqZ5Hnga+BuYr6qTnKinmaquTW7OMheYq8xxUKcNqIl9FH8rF9CTCGcNMhWRPUALqwm3MfZa7EuAH/Z+RqeOJxORh7EH2xRW1but8Vu9VbWfk3UlepExLzdZi+nzcSIiUhl4FngOe21nHvYXAqc1sznQBFhL0hFkCjjV+RB/UGcU9j6g+U7SAvAJsA/4FXuwiKRonXO4OdRungGmqep8YL6I7HOerDg+xR4ZuAhAVfdbTtIpiEgboC1QWkQ+d9jljv0+M2QRxvk4lz+BTUA7VT0GICJOG3XuiKrGhrv2jB3j40qo6ggAESli33R6k01N7C8Sj2DvMJ8DrHH2YFzsHeZ5VDUKaA70ctjnEn//qno6dvomC2feb+eAXdjnl3Ps17yME2eEuBNxiZvvP0wH7A+sdSKyHHuTiKu8Mcfyt6VtHvbBgM5+mAJgRZJ9B3hY26FAF1U95Aw91jiQfcDbVlPSc8BEEXlLVRc5Q5PFHOxTJIVij1LcBCAiFXGN+QNPW+WlInIX9qgyp82hZtW8DgEtrVB5QzZhxvk4EVX9RVWfwT6T73rsb1alRGSKiLRM8eCc4z7sg0r7Y3dEk0TEFdY0mQa8pqr3qOo92Ocsc9qs1rFY6+bUwD625wz2sHmnoaqjsZfNt0BDh5cHG/a+H2fTB/u9VRp7eflZ207Dqul7Ws7QkE2YgAMXwxp38X/Yw5mdutBXQkSkOPYpWjqqqpuTtexX1eqppeWgnm7Y+1TyY1//6AdnT41kyDgi8iX2ptRF2KdvAlxidvI7BuN8DKkiIk2wP1jbYF+AbJ7Vae1MTb8Ae7A3vYF9yvvaqtreSXpigIPAKSsp3h+WmRMsMSISgL32dZ+VdBiYpKrrnSbKQkSSnOIntq/RkHmM8zGkiDU4cR/2ecEWOXvwXyxWLWwEt8dGbQRGqOpFJ+lpktJ+awE3g4W1ptAkYCT2lwjBXtN4FxigqsucKC8OFwpoueMwzseQLNYM1u+o6khna4nFmjW6iKpeSJBeCohU1RvOUWZIDyKyHhioqvsTpFcDJqpqis48u0kY0AKEAp1V9XfnqbqzMAEHhmSxOl5dYcyRI58DjZJIb4F9zIhTEZGDInIgwWeTiHxqrVZrsOOd0PEAqOoBoJQT9CQkqYCW6U7WdEdhQq0NqbFVRCZhD7V27Hjd4yQ9DVW1V8JEVZ1trbjqbH7FPk7lf9b2s9iblCKxR5y5wrIPrkBKzbeu0LRbSFXXxW6o6nprHjpDFmGcjyE1Hrb+d2x6U+zr6DiDlMZBuUJNvoGqNnDYPigiW1S1gYi84DRVrkcFEUlq/JNgX07b2ZwQkfeIH9Dydwr2hnRinI8hRVxkqh9HzotIHVXd4ZhoTaB5IZljcpLCIlJXVbcDiEgd7DNbg5mexZHHU9j3SY6pSJ7u2ANafsbuEDcC3Zyq6A7DBBwYUsTqyB8D+KpqGxF5EKivql87SU8d7JF333J7+pPaQGfg2diHvrOwnOAM7A5HgEtAT+B34BFV/cGJ8nIFItJAVbc4WweAiBQFYlTVFVajvaMwzseQIiLyK/Z1ad5R1eoikgfYq6pOW53Tcoj9gCpW0u/Yx4e4zKBO66Elal8W2pAAK5LyaewzGyxX1UMi8igwBCigqjWcrC/2JSJ2AttIoLurrWOVmzHOx5AiIrJTVf1FZG/sA0FE9qmqn5OluSQikg/7nH3lcGjWdqVwdVdARL4FygI7sC8X/w9QH3hbVRc4T5kdETkA9FfV2LnwGgJfqGo15yq7czB9PobUuGqFCCuAiNTDiRNSishBEswe4IgLPBwWYi+f3diXVjAkTW2gmqrGWGO3QoGKsYveuQCXYx0PgKpuFhHT9JaFGOdjSI3XsM9vVUFEtgAlAGcuQPao9X/s5JOx0UgdgWs5LycRZVS1tbNF5AL+VdUYAFW9ISJHXMjxAOyw5nebg/1l5xlgvbVcuzOHGtwxmGY3Q6pY/Tyxy2j/pS6wjHZs+HJqaTmNiEzDPkL/oDN1uDoicg04FrsJVLC2Bft0Nk6twYrIuhR2q6tN+psbMTUfQ5JYHa6nVTVYVaNEpBb2vox/RGS4w+qYzqKQiDRU1c0QtxyzKwwCbAh0tebEi13R1OkPUxdkHfYoyrOk0IzqLFxwiMEdh6n5GJJERPYALVQ13FrWeC72GYj9gAdU1ZlNb1jOcAZQ1EqKwB6N5NTmEBG5J6l0Vf0np7W4MiIyEPvsDz7YZ8+YYy3I5xJY+r7BvoLpdOyTnr6tqiudKuwOwjgfQ5I4ro0jIpOBC6o63Np2mWg3EXHHfh87dVVOEXFX1UvWekyJcIGaoktiOetnrU9+7H0sc1X1iJN17beGFrTC3r/4HvCNqtZ0pq47CVeYjsTgmrhZfT0AzYG1Dvuc3lwrIkVFZDx2XWtEZJw1tsZZxM7lthvYZf2/22HbkASq+o+qfmSF8T8PPIETl9F2IHYap7bYnc5+hzRDFuD0h4jBZZkDbBCRUOA6EDveoSJODLV2YAZwCPtARYBO2JtJnnSGGFV91Pq/vDPyz62ISF6gNfaaT3NgA/ZpbZzNbhFZCZQHBlvr+sQ4WdMdhWl2MySLNabHB1gZu4iciFQGCrtA30qipj9XaQ4UkSexBx4osMkVBk26GiISCDwHPIJ9oOlcYIELLVZow96/eUJVI6yxbqWtJR8MWYCp+RiSRVV/A/uqoSJSAde6X64niHZrgL2G5lRE5AugIvaaI0AfEQlU1f4pHPZfZAj2pspBrtgfZg1+DQEedGh+NmQhpuZjSBERGYl9Nt/j3A6Jdfo4BxHxA2Zij3YTIBzo4uw3UxH5Haii1h+W9QZ9UFUfcqYuQ/oQkY+wDyz9A/v6TGC/7x9znqo7C+PRDanxDFBBVf91thBHrLDc6la0G6p6ybmK4vgLuBv7XGVgn7/MNNXkPtoD96mqmSIpmzDRbobUOAQUc7aIhCSIdlvrAtFusXgCh0VkvYisx/7mXEJEFiWzeJrBNTkB5HW2iDsZ0+xmSBERqY19ssxDOEyU6ezmBxGZj13TTCupE1BdVZ0S7RaLiDRJab+qbsgpLYaMY91f1YE1xL/vX3aaqDsM43wMKWL1YXwJHMQh1NTZD1EXj3bzBupg7yPb6WITZhrSgIh0SSpdVWcmlW5IP6bPx5Aaoar6ubNFJIGrRrv1BIZibw4UYKKIjFTVGc5VZkgPxslkP6bmY0gRq1/lJvZlFRybH5w9zqc6MIvbc7tdxDWi3f4CHlbVMGvbE9iqqvc5U5chfYhIJeAD4EHs0/4AoKr3Ok3UHYap+RhSI3Y543oOaQo4JdRaRO5W1VPWdCeuGO12BvtklLFcBk47SYsh43wDDAM+BQKwDzcw0+tkIabmY8hViMie2MkdRWS+qnZwtiZHRGQWUBV7kIYCj2MfwX8EQFXHO0+dIa2IyG5VrSUiB1W1qpW2SVUbOVvbnYKp+RhSxApfHgY0tpI2ACOdOIu049unKzaBHLc+sSy0/i/iBC2GjHPDGiB8VEQGYF93qKSTNd1RmJqPIUVcLaQ5Qc0n7rurYU1Eqap6xdlaDOnHWkzxMPYxbu8D7sDY2CmnDJnHOB9DirhaSLOIRANXsdeACgDXYndhf9i7O0NXLCJSBfgOiF3XJxTorKq/O0+VIT2IiBvwoaq+4WwtdzKm2c2QGi4V0qyqbs7KO41MA15T1XUAItIU+0qYDztRkyGNiEie2GXjRUTUvJ1nG8b5GFKjLzDT6vuJm8DTuZJcmkKxjgdAVdeLSCFnCjKkix3Yl8zeCywUkR+x17QBUNWfnSXsTsM4H0OKuPAEnq7KCRF5D3vTG8ALwN9O1GPIGB5AGPYhBYrVrAsY55NFGOdjSJGE0W4i4uxoN1enO/aVOH/G/sDaiH2MiCF3UFJEXsMeZBPrdGIxTXBZiHE+htRwqeWqXR1VvQiYySdzL25AYZIeUGqcTxZiot0MKeJq0W6ujrXM+CCgHA4vd85efM+QNlw5fP9Ow9R8DKnhUtFuuYAfganAV9xeAdOQezBT6OQQpuZjSBFXXa7aVYmdlsXZOgwZQ0Q8VDXc2Tr+CxjnY0gTsdFu2Ad1PqOqs52px9UQkdhBpS8D54FfiD8LuHmgGQwOGOdjSBLL2fQHSmOfn2y1tT0I2K+qjztRnsshIn8TPzoq3h+WmYrfYIiPcT6GJBGRhdjXyNkGNAeKA3cBA62xPwYHRKQOcFpVg6ztLkAH4CQw3NR8DIb4GOdjSJIEU8m7YZ+j7G5VvZzykf9NRGQP0EJVw0WkMTAXeAnwAx5Q1aecqc9gcDVMtJshOW7FflHVaBH52zieFHFzqN08A0xT1fnAfBHZ5zxZBoNrYpyPITmqi0jsVDoCFLC2XWL2aBfELXZSSuzNlL0c9pm/M4MhAeaPwpAkuWD2aFdjDrBBREKxj4PaBCAiFQEzFZHBkADT52MwZBEiUg/wAVaq6lUrrTJQWFX3OFWcweBiGOdjMBgMhhzH5mwBBoPBYPjvYZyPwWAwGHIc43wMBoPBkOMY52MwGAyGHMc4H4PBYDDkOP8PxDbaJRNYvCkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "corrMatrix = df.corr()\n",
    "sns.heatmap(corrMatrix, annot=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "651e71b2",
   "metadata": {},
   "source": [
    "## Data preprocessing"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "87d0b973",
   "metadata": {},
   "source": [
    "### Replacing missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "be8b0a12",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['HomePlanet'] = df['HomePlanet'].fillna(df['HomePlanet'].mode()[0])\n",
    "df['CryoSleep'] = df['CryoSleep'].fillna(df['CryoSleep'].mode()[0])\n",
    "df['Cabin'] = df['Cabin'].fillna(df['Cabin'].mode()[0])\n",
    "df['Destination'] = df['Destination'].fillna(df['Destination'].mode()[0])\n",
    "df['VIP'] = df['VIP'].fillna(df['VIP'].mode()[0])\n",
    "df['Age'] = df['Age'].fillna(df['Age'].mode()[0])\n",
    "df['RoomService'] = df['RoomService'].fillna(df['RoomService'].mode()[0])\n",
    "df['FoodCourt'] = df['FoodCourt'].fillna(df['FoodCourt'].mode()[0])\n",
    "df['ShoppingMall'] = df['ShoppingMall'].fillna(df['ShoppingMall'].mode()[0])\n",
    "df['VRDeck'] = df['VRDeck'].fillna(df['VRDeck'].mode()[0])\n",
    "df['Spa'] = df['Spa'].fillna(df['Spa'].mode()[0])\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "fc44a57c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId     False\n",
       "HomePlanet      False\n",
       "CryoSleep       False\n",
       "Cabin           False\n",
       "Destination     False\n",
       "Age             False\n",
       "VIP             False\n",
       "RoomService     False\n",
       "FoodCourt       False\n",
       "ShoppingMall    False\n",
       "Spa             False\n",
       "VRDeck          False\n",
       "Name             True\n",
       "Transported     False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "2fc174dc",
   "metadata": {},
   "outputs": [],
   "source": [
    "cols_mode = ['HomePlanet', 'CryoSleep', 'Cabin', 'Destination', 'Age', 'VIP', 'RoomService', 'FoodCourt', 'ShoppingMall', 'Spa', 'VRDeck']\n",
    "\n",
    "df_test[cols_mode].apply(lambda x: x.fillna(x.mode, inplace=True))\n",
    "\n",
    "df_test['HomePlanet'] = df_test['HomePlanet'].fillna(df_test['HomePlanet'].mode()[0])\n",
    "df_test['CryoSleep'] = df_test['CryoSleep'].fillna(df_test['CryoSleep'].mode()[0])\n",
    "df_test['Cabin'] = df_test['Cabin'].fillna(df_test['Cabin'].mode()[0])\n",
    "df_test['Destination'] = df_test['Destination'].fillna(df_test['Destination'].mode()[0])\n",
    "df_test['VIP'] = df_test['VIP'].fillna(df_test['VIP'].mode()[0])\n",
    "df_test['Age'] = df_test['Age'].fillna(df_test['Age'].mode()[0])\n",
    "df_test['RoomService'] = df_test['RoomService'].fillna(df_test['RoomService'].mode()[0])\n",
    "df_test['FoodCourt'] = df_test['FoodCourt'].fillna(df_test['FoodCourt'].mode()[0])\n",
    "df_test['ShoppingMall'] = df_test['ShoppingMall'].fillna(df_test['ShoppingMall'].mode()[0])\n",
    "df_test['VRDeck'] = df_test['VRDeck'].fillna(df_test['VRDeck'].mode()[0])\n",
    "df_test['Spa'] = df_test['Spa'].fillna(df_test['Spa'].mode()[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "241c8d0a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId     False\n",
       "HomePlanet      False\n",
       "CryoSleep       False\n",
       "Cabin           False\n",
       "Destination     False\n",
       "Age             False\n",
       "VIP             False\n",
       "RoomService     False\n",
       "FoodCourt       False\n",
       "ShoppingMall    False\n",
       "Spa             False\n",
       "VRDeck          False\n",
       "Name             True\n",
       "dtype: bool"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test.isna().any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "85f54f93",
   "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",
       "  </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>"
      ],
      "text/plain": [
       "  PassengerId HomePlanet  CryoSleep  Cabin  Destination   Age    VIP  \\\n",
       "0     0001_01     Europa      False  B/0/P  TRAPPIST-1e  39.0  False   \n",
       "1     0002_01      Earth      False  F/0/S  TRAPPIST-1e  24.0  False   \n",
       "2     0003_01     Europa      False  A/0/S  TRAPPIST-1e  58.0   True   \n",
       "3     0003_02     Europa      False  A/0/S  TRAPPIST-1e  33.0  False   \n",
       "4     0004_01      Earth      False  F/1/S  TRAPPIST-1e  16.0  False   \n",
       "\n",
       "   RoomService  FoodCourt  ShoppingMall     Spa  VRDeck               Name  \\\n",
       "0          0.0        0.0           0.0     0.0     0.0    Maham Ofracculy   \n",
       "1        109.0        9.0          25.0   549.0    44.0       Juanna Vines   \n",
       "2         43.0     3576.0           0.0  6715.0    49.0      Altark Susent   \n",
       "3          0.0     1283.0         371.0  3329.0   193.0       Solam Susent   \n",
       "4        303.0       70.0         151.0   565.0     2.0  Willy Santantines   \n",
       "\n",
       "   Transported  \n",
       "0        False  \n",
       "1         True  \n",
       "2        False  \n",
       "3        False  \n",
       "4         True  "
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "7d2730a7",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>HomePlanet</th>\n",
       "      <th>CryoSleep</th>\n",
       "      <th>Cabin</th>\n",
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       "      <th>Name</th>\n",
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       "  </thead>\n",
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       "      <th>0</th>\n",
       "      <td>0013_01</td>\n",
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       "      <td>True</td>\n",
       "      <td>G/3/S</td>\n",
       "      <td>TRAPPIST-1e</td>\n",
       "      <td>27.0</td>\n",
       "      <td>False</td>\n",
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       "      <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",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Sabih Unhearfus</td>\n",
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       "    <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",
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       "    <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",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  PassengerId HomePlanet  CryoSleep  Cabin  Destination   Age    VIP  \\\n",
       "0     0013_01      Earth       True  G/3/S  TRAPPIST-1e  27.0  False   \n",
       "1     0018_01      Earth      False  F/4/S  TRAPPIST-1e  19.0  False   \n",
       "2     0019_01     Europa       True  C/0/S  55 Cancri e  31.0  False   \n",
       "3     0021_01     Europa      False  C/1/S  TRAPPIST-1e  38.0  False   \n",
       "4     0023_01      Earth      False  F/5/S  TRAPPIST-1e  20.0  False   \n",
       "\n",
       "   RoomService  FoodCourt  ShoppingMall     Spa  VRDeck              Name  \n",
       "0          0.0        0.0           0.0     0.0     0.0   Nelly Carsoning  \n",
       "1          0.0        9.0           0.0  2823.0     0.0    Lerome Peckers  \n",
       "2          0.0        0.0           0.0     0.0     0.0   Sabih Unhearfus  \n",
       "3          0.0     6652.0           0.0   181.0   585.0  Meratz Caltilter  \n",
       "4         10.0        0.0         635.0     0.0     0.0   Brence Harperez  "
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c5190a30",
   "metadata": {},
   "source": [
    "### Choosing the predictors and target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "39701566",
   "metadata": {},
   "outputs": [
    {
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       "      <td>Europa</td>\n",
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       "      <td>Europa</td>\n",
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       "      <th>8689</th>\n",
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       "      <td>Earth</td>\n",
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       "    <tr>\n",
       "      <th>8691</th>\n",
       "      <td>Europa</td>\n",
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       "      <td>55 Cancri e</td>\n",
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       "      <td>False</td>\n",
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       "      <td>Europa</td>\n",
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       "      <td>False</td>\n",
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       "      <td>4688.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12.0</td>\n",
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       "  </tbody>\n",
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       "<p>8693 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     HomePlanet  CryoSleep     Cabin    Destination   Age    VIP  RoomService  \\\n",
       "0        Europa      False     B/0/P    TRAPPIST-1e  39.0  False          0.0   \n",
       "1         Earth      False     F/0/S    TRAPPIST-1e  24.0  False        109.0   \n",
       "2        Europa      False     A/0/S    TRAPPIST-1e  58.0   True         43.0   \n",
       "3        Europa      False     A/0/S    TRAPPIST-1e  33.0  False          0.0   \n",
       "4         Earth      False     F/1/S    TRAPPIST-1e  16.0  False        303.0   \n",
       "...         ...        ...       ...            ...   ...    ...          ...   \n",
       "8688     Europa      False    A/98/P    55 Cancri e  41.0   True          0.0   \n",
       "8689      Earth       True  G/1499/S  PSO J318.5-22  18.0  False          0.0   \n",
       "8690      Earth      False  G/1500/S    TRAPPIST-1e  26.0  False          0.0   \n",
       "8691     Europa      False   E/608/S    55 Cancri e  32.0  False          0.0   \n",
       "8692     Europa      False   E/608/S    TRAPPIST-1e  44.0  False        126.0   \n",
       "\n",
       "      FoodCourt  ShoppingMall     Spa  VRDeck  \n",
       "0           0.0           0.0     0.0     0.0  \n",
       "1           9.0          25.0   549.0    44.0  \n",
       "2        3576.0           0.0  6715.0    49.0  \n",
       "3        1283.0         371.0  3329.0   193.0  \n",
       "4          70.0         151.0   565.0     2.0  \n",
       "...         ...           ...     ...     ...  \n",
       "8688     6819.0           0.0  1643.0    74.0  \n",
       "8689        0.0           0.0     0.0     0.0  \n",
       "8690        0.0        1872.0     1.0     0.0  \n",
       "8691     1049.0           0.0   353.0  3235.0  \n",
       "8692     4688.0           0.0     0.0    12.0  \n",
       "\n",
       "[8693 rows x 11 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "x = df[['HomePlanet', 'CryoSleep', 'Cabin', 'Destination', 'Age', 'VIP', 'RoomService', 'FoodCourt', 'ShoppingMall', 'Spa', 'VRDeck']]\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "e7997b47",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "HomePlanet      False\n",
       "CryoSleep       False\n",
       "Cabin           False\n",
       "Destination     False\n",
       "Age             False\n",
       "VIP             False\n",
       "RoomService     False\n",
       "FoodCourt       False\n",
       "ShoppingMall    False\n",
       "Spa             False\n",
       "VRDeck          False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.isna().any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "6b540927",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       False\n",
       "1        True\n",
       "2       False\n",
       "3       False\n",
       "4        True\n",
       "        ...  \n",
       "8688    False\n",
       "8689    False\n",
       "8690     True\n",
       "8691    False\n",
       "8692     True\n",
       "Name: Transported, Length: 8693, dtype: bool"
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     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "y = df['Transported']\n",
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "cecf4e19",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_pred = df_test[['HomePlanet', 'CryoSleep', 'Cabin', 'Destination', 'Age', 'VIP', 'RoomService', 'FoodCourt', 'ShoppingMall', 'Spa', 'VRDeck']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "b6bccd2e",
   "metadata": {},
   "outputs": [
    {
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       "      <th></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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  HomePlanet  CryoSleep  Cabin  Destination   Age    VIP  RoomService  \\\n",
       "0      Earth       True  G/3/S  TRAPPIST-1e  27.0  False          0.0   \n",
       "1      Earth      False  F/4/S  TRAPPIST-1e  19.0  False          0.0   \n",
       "2     Europa       True  C/0/S  55 Cancri e  31.0  False          0.0   \n",
       "3     Europa      False  C/1/S  TRAPPIST-1e  38.0  False          0.0   \n",
       "4      Earth      False  F/5/S  TRAPPIST-1e  20.0  False         10.0   \n",
       "\n",
       "   FoodCourt  ShoppingMall     Spa  VRDeck  \n",
       "0        0.0           0.0     0.0     0.0  \n",
       "1        9.0           0.0  2823.0     0.0  \n",
       "2        0.0           0.0     0.0     0.0  \n",
       "3     6652.0           0.0   181.0   585.0  \n",
       "4        0.0         635.0     0.0     0.0  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_pred.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7bd2b34f",
   "metadata": {},
   "source": [
    "### Changing data types"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "5bd7a809",
   "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>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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  HomePlanet  CryoSleep  Cabin  Destination   Age    VIP  RoomService  \\\n",
       "0     Europa      False  B/0/P  TRAPPIST-1e  39.0  False          0.0   \n",
       "1      Earth      False  F/0/S  TRAPPIST-1e  24.0  False        109.0   \n",
       "2     Europa      False  A/0/S  TRAPPIST-1e  58.0   True         43.0   \n",
       "3     Europa      False  A/0/S  TRAPPIST-1e  33.0  False          0.0   \n",
       "4      Earth      False  F/1/S  TRAPPIST-1e  16.0  False        303.0   \n",
       "\n",
       "   FoodCourt  ShoppingMall     Spa  VRDeck  \n",
       "0        0.0           0.0     0.0     0.0  \n",
       "1        9.0          25.0   549.0    44.0  \n",
       "2     3576.0           0.0  6715.0    49.0  \n",
       "3     1283.0         371.0  3329.0   193.0  \n",
       "4       70.0         151.0   565.0     2.0  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "51702114",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "G/734/S     207\n",
       "C/21/P        7\n",
       "B/201/P       7\n",
       "G/1368/P      7\n",
       "E/13/S        7\n",
       "           ... \n",
       "G/556/P       1\n",
       "E/231/S       1\n",
       "G/545/S       1\n",
       "G/543/S       1\n",
       "F/947/P       1\n",
       "Name: Cabin, Length: 6560, dtype: int64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x['Cabin'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "37f2c91e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   HomePlanet  CryoSleep  Cabin  Destination   Age  VIP  RoomService  \\\n",
      "0           2          0      5            3  39.0    0          0.0   \n",
      "1           3          0      8            3  24.0    0        109.0   \n",
      "2           2          0      2            3  58.0    1         43.0   \n",
      "3           2          0      2            3  33.0    0          0.0   \n",
      "4           3          0      8            3  16.0    0        303.0   \n",
      "\n",
      "   FoodCourt  ShoppingMall     Spa  VRDeck  \n",
      "0        0.0           0.0     0.0     0.0  \n",
      "1        9.0          25.0   549.0    44.0  \n",
      "2     3576.0           0.0  6715.0    49.0  \n",
      "3     1283.0         371.0  3329.0   193.0  \n",
      "4       70.0         151.0   565.0     2.0  \n",
      "HomePlanet        int32\n",
      "CryoSleep         int32\n",
      "Cabin             int32\n",
      "Destination       int32\n",
      "Age             float64\n",
      "VIP               int32\n",
      "RoomService     float64\n",
      "FoodCourt       float64\n",
      "ShoppingMall    float64\n",
      "Spa             float64\n",
      "VRDeck          float64\n",
      "dtype: object\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Sanzhar\\AppData\\Local\\Temp/ipykernel_2336/3721274821.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  x['Cabin'] = x['Cabin'].str.slice(0, 1)\n",
      "C:\\Users\\Sanzhar\\AppData\\Local\\Temp/ipykernel_2336/3721274821.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  x['VIP'] = x['VIP'].astype(int)\n",
      "C:\\Users\\Sanzhar\\AppData\\Local\\Temp/ipykernel_2336/3721274821.py:4: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  x['HomePlanet'] = x['HomePlanet'].map({'Europa' : '2', 'Earth': '3', 'Mars': '1'})\n",
      "C:\\Users\\Sanzhar\\AppData\\Local\\Temp/ipykernel_2336/3721274821.py:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  x['Destination'] = x['Destination'].map({'TRAPPIST-1e' : 3, '55 Cancri e': '2', 'PSO J318.5-22': '1'})\n",
      "C:\\Users\\Sanzhar\\AppData\\Local\\Temp/ipykernel_2336/3721274821.py:6: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  x['Cabin'] = x['Cabin'].map({'T' : '1', 'A': '2', 'D': '3', 'C' : '4', 'B' : '5', 'E' : '6', 'G' : '7', 'F' : '8',})\n",
      "C:\\Users\\Sanzhar\\AppData\\Local\\Temp/ipykernel_2336/3721274821.py:7: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  x['CryoSleep'] = x['CryoSleep'].astype(int)\n",
      "E:\\Sanzhar\\Anaconda\\lib\\site-packages\\pandas\\core\\frame.py:3641: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  self[k1] = value[k2]\n"
     ]
    }
   ],
   "source": [
    "x['Cabin'] = x['Cabin'].str.slice(0, 1)\n",
    "\n",
    "x['VIP'] = x['VIP'].astype(int)\n",
    "x['HomePlanet'] = x['HomePlanet'].map({'Europa' : '2', 'Earth': '3', 'Mars': '1'})\n",
    "x['Destination'] = x['Destination'].map({'TRAPPIST-1e' : 3, '55 Cancri e': '2', 'PSO J318.5-22': '1'})\n",
    "x['Cabin'] = x['Cabin'].map({'T' : '1', 'A': '2', 'D': '3', 'C' : '4', 'B' : '5', 'E' : '6', 'G' : '7', 'F' : '8',})\n",
    "x['CryoSleep'] = x['CryoSleep'].astype(int)\n",
    "y = y.astype(int)\n",
    "\n",
    "x[['HomePlanet','Destination', 'Cabin']] = x[['HomePlanet','Destination', 'Cabin']].astype('int')\n",
    "print(x.head())\n",
    "print(x.dtypes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "47351401",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   HomePlanet  CryoSleep  Cabin  Destination   Age  VIP  RoomService  \\\n",
      "0           2          0      5            3  27.0    0          0.0   \n",
      "1           3          0      8            3  19.0    0          0.0   \n",
      "2           2          0      2            3  31.0    0          0.0   \n",
      "3           2          0      2            3  38.0    0          0.0   \n",
      "4           3          0      8            3  20.0    0         10.0   \n",
      "\n",
      "   FoodCourt  ShoppingMall     Spa  VRDeck  \n",
      "0        0.0           0.0     0.0     0.0  \n",
      "1        9.0           0.0  2823.0     0.0  \n",
      "2        0.0           0.0     0.0     0.0  \n",
      "3     6652.0           0.0   181.0   585.0  \n",
      "4        0.0         635.0     0.0     0.0  \n",
      "HomePlanet        int32\n",
      "CryoSleep         int32\n",
      "Cabin             int32\n",
      "Destination       int32\n",
      "Age             float64\n",
      "VIP               int32\n",
      "RoomService     float64\n",
      "FoodCourt       float64\n",
      "ShoppingMall    float64\n",
      "Spa             float64\n",
      "VRDeck          float64\n",
      "dtype: object\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Sanzhar\\AppData\\Local\\Temp/ipykernel_2336/3488114599.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  x_pred['Cabin'] = x_pred['Cabin'].str.slice(0, 1)\n",
      "C:\\Users\\Sanzhar\\AppData\\Local\\Temp/ipykernel_2336/3488114599.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  x_pred['VIP'] = x_pred['VIP'].astype(int)\n",
      "C:\\Users\\Sanzhar\\AppData\\Local\\Temp/ipykernel_2336/3488114599.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  x_pred['HomePlanet'] = x_pred['HomePlanet'].map({'Europa' : '2', 'Earth': '3', 'Mars': '1'})\n",
      "C:\\Users\\Sanzhar\\AppData\\Local\\Temp/ipykernel_2336/3488114599.py:4: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  x_pred['Destination'] = x_pred['Destination'].map({'TRAPPIST-1e' : 3, '55 Cancri e': '2', 'PSO J318.5-22': '1'})\n",
      "C:\\Users\\Sanzhar\\AppData\\Local\\Temp/ipykernel_2336/3488114599.py:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  x_pred['Cabin'] = x_pred['Cabin'].map({'T' : '1', 'A': '2', 'D': '3', 'C' : '4', 'B' : '5', 'E' : '6', 'G' : '7', 'F' : '8',})\n",
      "C:\\Users\\Sanzhar\\AppData\\Local\\Temp/ipykernel_2336/3488114599.py:6: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  x_pred['CryoSleep'] = x_pred['CryoSleep'].astype(int)\n",
      "E:\\Sanzhar\\Anaconda\\lib\\site-packages\\pandas\\core\\frame.py:3641: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  self[k1] = value[k2]\n"
     ]
    }
   ],
   "source": [
    "x_pred['Cabin'] = x_pred['Cabin'].str.slice(0, 1)\n",
    "x_pred['VIP'] = x_pred['VIP'].astype(int)\n",
    "x_pred['HomePlanet'] = x_pred['HomePlanet'].map({'Europa' : '2', 'Earth': '3', 'Mars': '1'})\n",
    "x_pred['Destination'] = x_pred['Destination'].map({'TRAPPIST-1e' : 3, '55 Cancri e': '2', 'PSO J318.5-22': '1'})\n",
    "x_pred['Cabin'] = x_pred['Cabin'].map({'T' : '1', 'A': '2', 'D': '3', 'C' : '4', 'B' : '5', 'E' : '6', 'G' : '7', 'F' : '8',})\n",
    "x_pred['CryoSleep'] = x_pred['CryoSleep'].astype(int)\n",
    "x_pred[['HomePlanet', 'CryoSleep', 'Destination', 'Cabin']] = x[['HomePlanet', 'CryoSleep', 'Destination','Cabin']].astype('int')\n",
    "print(x_pred.head())\n",
    "print(x_pred.dtypes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "69326084",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\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>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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>39.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>24.0</td>\n",
       "      <td>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>2</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>58.0</td>\n",
       "      <td>1</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>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>33.0</td>\n",
       "      <td>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>4</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>16.0</td>\n",
       "      <td>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": [
       "   HomePlanet  CryoSleep  Cabin  Destination   Age  VIP  RoomService  \\\n",
       "0           2          0      5            3  39.0    0          0.0   \n",
       "1           3          0      8            3  24.0    0        109.0   \n",
       "2           2          0      2            3  58.0    1         43.0   \n",
       "3           2          0      2            3  33.0    0          0.0   \n",
       "4           3          0      8            3  16.0    0        303.0   \n",
       "\n",
       "   FoodCourt  ShoppingMall     Spa  VRDeck  \n",
       "0        0.0           0.0     0.0     0.0  \n",
       "1        9.0          25.0   549.0    44.0  \n",
       "2     3576.0           0.0  6715.0    49.0  \n",
       "3     1283.0         371.0  3329.0   193.0  \n",
       "4       70.0         151.0   565.0     2.0  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "2a783e56",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<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>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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>8693.000000</td>\n",
       "      <td>8693.000000</td>\n",
       "      <td>8693.000000</td>\n",
       "      <td>8693.000000</td>\n",
       "      <td>8693.000000</td>\n",
       "      <td>8693.000000</td>\n",
       "      <td>8693.000000</td>\n",
       "      <td>8693.000000</td>\n",
       "      <td>8693.000000</td>\n",
       "      <td>8693.000000</td>\n",
       "      <td>8693.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2.350167</td>\n",
       "      <td>0.349362</td>\n",
       "      <td>6.412976</td>\n",
       "      <td>2.609801</td>\n",
       "      <td>28.728517</td>\n",
       "      <td>0.022892</td>\n",
       "      <td>220.009318</td>\n",
       "      <td>448.434027</td>\n",
       "      <td>169.572300</td>\n",
       "      <td>304.588865</td>\n",
       "      <td>298.261820</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.795183</td>\n",
       "      <td>0.476796</td>\n",
       "      <td>1.687145</td>\n",
       "      <td>0.648944</td>\n",
       "      <td>14.355438</td>\n",
       "      <td>0.149568</td>\n",
       "      <td>660.519050</td>\n",
       "      <td>1595.790627</td>\n",
       "      <td>598.007164</td>\n",
       "      <td>1125.562559</td>\n",
       "      <td>1134.126417</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</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",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>20.000000</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",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>3.000000</td>\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",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>37.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>41.000000</td>\n",
       "      <td>61.000000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>53.000000</td>\n",
       "      <td>40.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>79.000000</td>\n",
       "      <td>1.000000</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": [
       "        HomePlanet    CryoSleep        Cabin  Destination          Age  \\\n",
       "count  8693.000000  8693.000000  8693.000000  8693.000000  8693.000000   \n",
       "mean      2.350167     0.349362     6.412976     2.609801    28.728517   \n",
       "std       0.795183     0.476796     1.687145     0.648944    14.355438   \n",
       "min       1.000000     0.000000     1.000000     1.000000     0.000000   \n",
       "25%       2.000000     0.000000     5.000000     2.000000    20.000000   \n",
       "50%       3.000000     0.000000     7.000000     3.000000    27.000000   \n",
       "75%       3.000000     1.000000     8.000000     3.000000    37.000000   \n",
       "max       3.000000     1.000000     8.000000     3.000000    79.000000   \n",
       "\n",
       "               VIP   RoomService     FoodCourt  ShoppingMall           Spa  \\\n",
       "count  8693.000000   8693.000000   8693.000000   8693.000000   8693.000000   \n",
       "mean      0.022892    220.009318    448.434027    169.572300    304.588865   \n",
       "std       0.149568    660.519050   1595.790627    598.007164   1125.562559   \n",
       "min       0.000000      0.000000      0.000000      0.000000      0.000000   \n",
       "25%       0.000000      0.000000      0.000000      0.000000      0.000000   \n",
       "50%       0.000000      0.000000      0.000000      0.000000      0.000000   \n",
       "75%       0.000000     41.000000     61.000000     22.000000     53.000000   \n",
       "max       1.000000  14327.000000  29813.000000  23492.000000  22408.000000   \n",
       "\n",
       "             VRDeck  \n",
       "count   8693.000000  \n",
       "mean     298.261820  \n",
       "std     1134.126417  \n",
       "min        0.000000  \n",
       "25%        0.000000  \n",
       "50%        0.000000  \n",
       "75%       40.000000  \n",
       "max    24133.000000  "
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.describe(include = 'all')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "427f8c6d",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</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>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",
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       "      <th>VRDeck</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>4277.000000</td>\n",
       "      <td>4277.000000</td>\n",
       "      <td>4277.000000</td>\n",
       "      <td>4277.000000</td>\n",
       "      <td>4277.000000</td>\n",
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       "      <td>4277.000000</td>\n",
       "      <td>4277.000000</td>\n",
       "      <td>4277.000000</td>\n",
       "      <td>4277.000000</td>\n",
       "      <td>4277.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2.351415</td>\n",
       "      <td>0.353753</td>\n",
       "      <td>6.396540</td>\n",
       "      <td>2.608137</td>\n",
       "      <td>28.431377</td>\n",
       "      <td>0.017302</td>\n",
       "      <td>215.062427</td>\n",
       "      <td>428.592238</td>\n",
       "      <td>173.233107</td>\n",
       "      <td>295.895955</td>\n",
       "      <td>304.898293</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.796218</td>\n",
       "      <td>0.478189</td>\n",
       "      <td>1.691576</td>\n",
       "      <td>0.650113</td>\n",
       "      <td>14.111469</td>\n",
       "      <td>0.130409</td>\n",
       "      <td>601.914503</td>\n",
       "      <td>1510.155974</td>\n",
       "      <td>554.991776</td>\n",
       "      <td>1104.872018</td>\n",
       "      <td>1235.991811</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</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",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>2.000000</td>\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",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>26.000000</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",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>37.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>48.000000</td>\n",
       "      <td>66.000000</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>43.000000</td>\n",
       "      <td>31.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>79.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>11567.000000</td>\n",
       "      <td>25273.000000</td>\n",
       "      <td>8292.000000</td>\n",
       "      <td>19844.000000</td>\n",
       "      <td>22272.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        HomePlanet    CryoSleep        Cabin  Destination          Age  \\\n",
       "count  4277.000000  4277.000000  4277.000000  4277.000000  4277.000000   \n",
       "mean      2.351415     0.353753     6.396540     2.608137    28.431377   \n",
       "std       0.796218     0.478189     1.691576     0.650113    14.111469   \n",
       "min       1.000000     0.000000     1.000000     1.000000     0.000000   \n",
       "25%       2.000000     0.000000     5.000000     2.000000    19.000000   \n",
       "50%       3.000000     0.000000     7.000000     3.000000    26.000000   \n",
       "75%       3.000000     1.000000     8.000000     3.000000    37.000000   \n",
       "max       3.000000     1.000000     8.000000     3.000000    79.000000   \n",
       "\n",
       "               VIP   RoomService     FoodCourt  ShoppingMall           Spa  \\\n",
       "count  4277.000000   4277.000000   4277.000000   4277.000000   4277.000000   \n",
       "mean      0.017302    215.062427    428.592238    173.233107    295.895955   \n",
       "std       0.130409    601.914503   1510.155974    554.991776   1104.872018   \n",
       "min       0.000000      0.000000      0.000000      0.000000      0.000000   \n",
       "25%       0.000000      0.000000      0.000000      0.000000      0.000000   \n",
       "50%       0.000000      0.000000      0.000000      0.000000      0.000000   \n",
       "75%       0.000000     48.000000     66.000000     27.000000     43.000000   \n",
       "max       1.000000  11567.000000  25273.000000   8292.000000  19844.000000   \n",
       "\n",
       "             VRDeck  \n",
       "count   4277.000000  \n",
       "mean     304.898293  \n",
       "std     1235.991811  \n",
       "min        0.000000  \n",
       "25%        0.000000  \n",
       "50%        0.000000  \n",
       "75%       31.000000  \n",
       "max    22272.000000  "
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_pred.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a0a0053e",
   "metadata": {},
   "source": [
    "## Data normalization "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "3687b615",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.44038529, -0.73277002, -0.83754342,  0.60131781,  0.71555276,\n",
       "        -0.15306307, -0.33310466, -0.28102673, -0.28357863, -0.2706259 ,\n",
       "        -0.26300329],\n",
       "       [ 0.81725903, -0.73277002,  0.94071066,  0.60131781, -0.32940751,\n",
       "        -0.15306307, -0.16807343, -0.27538657, -0.24177071,  0.21715825,\n",
       "        -0.22420468],\n",
       "       [-0.44038529, -0.73277002, -2.61579751,  0.60131781,  2.03916911,\n",
       "         6.53325471, -0.2680006 ,  1.95999765, -0.28357863,  5.69562289,\n",
       "        -0.21979574],\n",
       "       [-0.44038529, -0.73277002, -2.61579751,  0.60131781,  0.29756866,\n",
       "        -0.15306307, -0.33310466,  0.5230097 ,  0.33685096,  2.68717637,\n",
       "        -0.09281847],\n",
       "       [ 0.81725903, -0.73277002,  0.94071066,  0.60131781, -0.88671965,\n",
       "        -0.15306307,  0.12565187, -0.2371588 , -0.03105877,  0.23137418,\n",
       "        -0.26123971]])"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = np.asarray(x)\n",
    "Y = np.asarray(y)\n",
    "from sklearn import preprocessing\n",
    "X = preprocessing.StandardScaler().fit(X).transform(X)\n",
    "X[0:5]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "6c6f6716",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.44140647, -0.73986153, -0.82568155,  0.60283277, -0.10144546,\n",
       "        -0.13268938, -0.35733908, -0.28383979, -0.31217285, -0.26784148,\n",
       "        -0.24671194],\n",
       "       [ 0.81467835, -0.73986153,  0.94802011,  0.60283277, -0.6684265 ,\n",
       "        -0.13268938, -0.35733908, -0.27787944, -0.31217285,  2.28750433,\n",
       "        -0.24671194],\n",
       "       [-0.44140647, -0.73986153, -2.59938322,  0.60283277,  0.18204506,\n",
       "        -0.13268938, -0.35733908, -0.28383979, -0.31217285, -0.26784148,\n",
       "        -0.24671194],\n",
       "       [-0.44140647, -0.73986153, -2.59938322,  0.60283277,  0.67815346,\n",
       "        -0.13268938, -0.35733908,  4.12151827, -0.31217285, -0.10400244,\n",
       "         0.2266475 ],\n",
       "       [ 0.81467835, -0.73986153,  0.94802011,  0.60283277, -0.59755387,\n",
       "        -0.13268938, -0.34072348, -0.28383979,  0.83212203, -0.26784148,\n",
       "        -0.24671194]])"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_pred = np.asarray(x_pred)\n",
    "X_pred = preprocessing.StandardScaler().fit(X_pred).transform(X_pred)\n",
    "X_pred[0:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f85dce25",
   "metadata": {},
   "source": [
    "## Train/test split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "66b7bd48",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train set: (6954, 11) (6954,)\n",
      "Test set: (1739, 11) (1739,)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split( X, Y, test_size=0.2, random_state=4)\n",
    "print ('Train set:', X_train.shape,  y_train.shape)\n",
    "print ('Test set:', X_test.shape,  y_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7d466993",
   "metadata": {},
   "source": [
    "## Modeling"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "31598129",
   "metadata": {},
   "source": [
    "### K-Nearest Neighbors classification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "c7e7665e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n",
      "Train acc 0.9200460166810469\n",
      "Test acc 0.7257044278320874\n",
      "Sum acc 1.6457504445131343\n",
      "2\n",
      "Train acc 0.8267184354328444\n",
      "Test acc 0.7343300747556066\n",
      "Sum acc 1.561048510188451\n",
      "3\n",
      "Train acc 0.8445498993385102\n",
      "Test acc 0.7510063254744106\n",
      "Sum acc 1.595556224812921\n",
      "4\n",
      "Train acc 0.8239861949956859\n",
      "Test acc 0.7469810235767682\n",
      "Sum acc 1.5709672185724541\n",
      "5\n",
      "Train acc 0.8259994247914869\n",
      "Test acc 0.7596319723979299\n",
      "Sum acc 1.5856313971894167\n",
      "6\n",
      "Train acc 0.815645671555939\n",
      "Test acc 0.7527314548591144\n",
      "Sum acc 1.5683771264150534\n",
      "7\n",
      "Train acc 0.8213977566867989\n",
      "Test acc 0.7768832662449684\n",
      "Sum acc 1.5982810229317672\n",
      "8\n",
      "Train acc 0.812482024733966\n",
      "Test acc 0.7688326624496837\n",
      "Sum acc 1.5813146871836499\n",
      "9\n",
      "Train acc 0.817083692838654\n",
      "Test acc 0.7832087406555491\n",
      "Sum acc 1.6002924334942032\n",
      "10\n",
      "Train acc 0.8101811906816221\n",
      "Test acc 0.772857964347326\n",
      "Sum acc 1.5830391550289482\n",
      "11\n",
      "Train acc 0.813057233247052\n",
      "Test acc 0.7694077055779184\n",
      "Sum acc 1.5824649388249705\n",
      "12\n",
      "Train acc 0.8090307736554501\n",
      "Test acc 0.7740080506037953\n",
      "Sum acc 1.5830388242592455\n",
      "13\n",
      "Train acc 0.8097497842968076\n",
      "Test acc 0.7826336975273146\n",
      "Sum acc 1.5923834818241223\n",
      "14\n",
      "Train acc 0.8065861374748347\n",
      "Test acc 0.7809085681426107\n",
      "Sum acc 1.5874947056174453\n",
      "15\n",
      "Train acc 0.8093183779119931\n",
      "Test acc 0.7872340425531915\n",
      "Sum acc 1.5965524204651846\n",
      "16\n",
      "Train acc 0.8050043140638481\n",
      "Test acc 0.7814836112708453\n",
      "Sum acc 1.5864879253346933\n",
      "17\n",
      "Train acc 0.8057233247052057\n",
      "Test acc 0.7837837837837838\n",
      "Sum acc 1.5895071084889896\n",
      "18\n",
      "Train acc 0.8008340523439746\n",
      "Test acc 0.7745830937320299\n",
      "Sum acc 1.5754171460760045\n",
      "19\n",
      "Train acc 0.8028472821397756\n",
      "Test acc 0.7832087406555491\n",
      "Sum acc 1.5860560227953249\n"
     ]
    }
   ],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn import metrics\n",
    "from sklearn.metrics import confusion_matrix, classification_report\n",
    "for i in range(1, 20):\n",
    "    knn = KNeighborsClassifier(n_neighbors=i)\n",
    "    knn.fit(X_train, y_train)\n",
    "    pred_i = knn.predict(X_test)\n",
    "    print(i)\n",
    "    print(\"Train acc\", metrics.accuracy_score(y_train, knn.predict(X_train)))\n",
    "    print(\"Test acc\", metrics.accuracy_score(y_test,pred_i))\n",
    "    print('Sum acc', metrics.accuracy_score(y_train, knn.predict(X_train)) + metrics.accuracy_score(y_test,pred_i))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "58933aba",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 1 0 0 0]\n",
      "[0 1 0 0 0]\n",
      "KNN`s accuracy 0.7832087406555491\n",
      "Classification report for Knn classifictaion               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.78      0.79      0.79       881\n",
      "           1       0.78      0.78      0.78       858\n",
      "\n",
      "    accuracy                           0.78      1739\n",
      "   macro avg       0.78      0.78      0.78      1739\n",
      "weighted avg       0.78      0.78      0.78      1739\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# as we can see optimal value for k is 9\n",
    "k = 9\n",
    "neigh = KNeighborsClassifier(n_neighbors = k).fit(X_train,y_train)\n",
    "neigh\n",
    "yhat_knn = neigh.predict(X_test)\n",
    "print(y_test[0:5])\n",
    "print(yhat_knn[0:5])\n",
    "print('KNN`s accuracy', metrics.accuracy_score(y_test,pred_i))\n",
    "print('Classification report for Knn classifictaion', classification_report(y_test, yhat_knn))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "88f1665c",
   "metadata": {},
   "source": [
    "### Decision tree classification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "35628a0e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "gini\n",
      "Train acc 0.8028472821397756\n",
      "Test acc 0.7745830937320299\n",
      "Sum acc 1.5774303758718056\n",
      "entropy\n",
      "Train acc 0.8028472821397756\n",
      "Test acc 0.7757331799884991\n",
      "Sum acc 1.5785804621282749\n"
     ]
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "for i in ('gini', 'entropy'):\n",
    "    drugTree = DecisionTreeClassifier(criterion=i, max_depth = 4)\n",
    "    drugTree.fit(X_train,y_train)\n",
    "    pred_i= drugTree.predict(X_test)\n",
    "    print(i)\n",
    "    print(\"Train acc\", metrics.accuracy_score(y_train, knn.predict(X_train)))\n",
    "    print(\"Test acc\", metrics.accuracy_score(y_test,pred_i))\n",
    "    print('Sum acc', metrics.accuracy_score(y_train, knn.predict(X_train)) + metrics.accuracy_score(y_test,pred_i))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "a7631944",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 1 0 0 0]\n",
      "[0 1 0 0 0]\n",
      "DecisionTrees's Accuracy:  0.7757331799884991\n",
      "Classification report for Decision Tree classification:               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.84      0.69      0.76       881\n",
      "           1       0.73      0.86      0.79       858\n",
      "\n",
      "    accuracy                           0.78      1739\n",
      "   macro avg       0.78      0.78      0.77      1739\n",
      "weighted avg       0.78      0.78      0.77      1739\n",
      "\n"
     ]
    }
   ],
   "source": [
    "drugTree = DecisionTreeClassifier(criterion=\"entropy\", max_depth = 4)\n",
    "drugTree.fit(X_train,y_train)\n",
    "yhat_dt= drugTree.predict(X_test)\n",
    "print (yhat_dt [0:5])\n",
    "print (y_test [0:5])\n",
    "print(\"DecisionTrees's Accuracy: \", metrics.accuracy_score(y_test, yhat_dt))\n",
    "print('Classification report for Decision Tree classification:', classification_report(y_test, yhat_dt))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c98a4cdf",
   "metadata": {},
   "source": [
    "### Logistic Regression "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "8e629751",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "newton-cg\n",
      "Train acc 0.8028472821397756\n",
      "Test acc 0.7757331799884991\n",
      "Sum acc 1.5785804621282749\n",
      "lbfgs\n",
      "Train acc 0.8028472821397756\n",
      "Test acc 0.7757331799884991\n",
      "Sum acc 1.5785804621282749\n",
      "liblinear\n",
      "Train acc 0.8028472821397756\n",
      "Test acc 0.7757331799884991\n",
      "Sum acc 1.5785804621282749\n",
      "sag\n",
      "Train acc 0.8028472821397756\n",
      "Test acc 0.7757331799884991\n",
      "Sum acc 1.5785804621282749\n",
      "saga\n",
      "Train acc 0.8028472821397756\n",
      "Test acc 0.7757331799884991\n",
      "Sum acc 1.5785804621282749\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import confusion_matrix\n",
    "for i in ('newton-cg', 'lbfgs', 'liblinear', 'sag', 'saga'):\n",
    "    LR_n = LogisticRegression(C=0.01, solver=i)\n",
    "    LR_n.fit(X_train,y_train)\n",
    "    pred_i= drugTree.predict(X_test)\n",
    "    print(i)\n",
    "    print(\"Train acc\", metrics.accuracy_score(y_train, knn.predict(X_train)))\n",
    "    print(\"Test acc\", metrics.accuracy_score(y_test,pred_i))\n",
    "    print('Sum acc', metrics.accuracy_score(y_train, knn.predict(X_train)) + metrics.accuracy_score(y_test,pred_i))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "e70cc9f8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 1 0 0 0]\n",
      "[0 1 0 0 0]\n",
      "Logistic regression accuracy:  0.7671075330649799\n",
      "Classification report for Logistic regression:               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.75      0.80      0.78       881\n",
      "           1       0.78      0.73      0.76       858\n",
      "\n",
      "    accuracy                           0.77      1739\n",
      "   macro avg       0.77      0.77      0.77      1739\n",
      "weighted avg       0.77      0.77      0.77      1739\n",
      "\n"
     ]
    }
   ],
   "source": [
    "LR = LogisticRegression(C=0.01, solver='liblinear').fit(X_train,y_train)\n",
    "yhat_lr = LR.predict(X_test)\n",
    "print(yhat_lr[0:5])\n",
    "print(y_test[0:5])\n",
    "print(\"Logistic regression accuracy: \", metrics.accuracy_score(y_test, yhat_lr))\n",
    "print('Classification report for Logistic regression:', classification_report(y_test, yhat_lr))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e6b6d4d2",
   "metadata": {},
   "source": [
    "### SVM (Support Vector Machines) classification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "8631d48b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 1 0 0 0]\n",
      "[0 1 0 0 0]\n",
      "SVM accuracy:  0.7671075330649799\n",
      "Classification report for SVM classification               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.75      0.80      0.78       881\n",
      "           1       0.78      0.73      0.76       858\n",
      "\n",
      "    accuracy                           0.77      1739\n",
      "   macro avg       0.77      0.77      0.77      1739\n",
      "weighted avg       0.77      0.77      0.77      1739\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn import svm\n",
    "clf = svm.SVC(kernel='rbf')\n",
    "clf.fit(X_train, y_train) \n",
    "yhat_svm = clf.predict(X_test)\n",
    "yhat_svm [0:5]\n",
    "print(yhat_lr[0:5])\n",
    "print(y_test[0:5])\n",
    "print(\"SVM accuracy: \", metrics.accuracy_score(y_test, yhat_lr))\n",
    "print('Classification report for SVM classification', classification_report(y_test, yhat_lr))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5bfcc152",
   "metadata": {},
   "source": [
    "## Model evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "d005e368",
   "metadata": {},
   "outputs": [],
   "source": [
    "knn_acc = metrics.accuracy_score(y_test, yhat_knn)\n",
    "dt_acc = metrics.accuracy_score(y_test, yhat_dt)\n",
    "lr_acc =metrics.accuracy_score(y_test, yhat_lr)\n",
    "svm_acc = metrics.accuracy_score(y_test, yhat_svm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "109e9fcf",
   "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>Model</th>\n",
       "      <th>Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>SVM classification</td>\n",
       "      <td>0.790109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>KNN calssification</td>\n",
       "      <td>0.783209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Decision Tree Classification</td>\n",
       "      <td>0.775733</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Logistic regression classification</td>\n",
       "      <td>0.767108</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                Model     Score\n",
       "3                  SVM classification  0.790109\n",
       "0                  KNN calssification  0.783209\n",
       "1        Decision Tree Classification  0.775733\n",
       "2  Logistic regression classification  0.767108"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "models = pd.DataFrame({\n",
    "    'Model' : ['KNN calssification', 'Decision Tree Classification', 'Logistic regression classification',\n",
    "             'SVM classification'],\n",
    "    'Score' : [knn_acc, dt_acc, lr_acc, svm_acc]\n",
    "})\n",
    "\n",
    "\n",
    "models.sort_values(by = 'Score', ascending = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b360ae42",
   "metadata": {},
   "source": [
    "As we can see svm have best values of accuracy"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e6d8231a",
   "metadata": {},
   "source": [
    "## Predicting test dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3ee84ed3",
   "metadata": {},
   "source": [
    "I decided to choose knn classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "551fdfc5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      0\n",
      "0     0\n",
      "1     0\n",
      "2     0\n",
      "3     1\n",
      "4     1\n",
      "...  ..\n",
      "4272  0\n",
      "4273  1\n",
      "4274  0\n",
      "4275  1\n",
      "4276  0\n",
      "\n",
      "[4277 rows x 1 columns]\n"
     ]
    }
   ],
   "source": [
    "knn_pr = KNeighborsClassifier(n_neighbors = 9).fit(X, Y)\n",
    "predict = knn_pr.predict(x_pred)\n",
    "df_pred =pd.DataFrame(predict)\n",
    "print(df_pred)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "3dda75ed",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     PassengerId HomePlanet  CryoSleep     Cabin    Destination   Age    VIP  \\\n",
      "0        0013_01      Earth       True     G/3/S    TRAPPIST-1e  27.0  False   \n",
      "1        0018_01      Earth      False     F/4/S    TRAPPIST-1e  19.0  False   \n",
      "2        0019_01     Europa       True     C/0/S    55 Cancri e  31.0  False   \n",
      "3        0021_01     Europa      False     C/1/S    TRAPPIST-1e  38.0  False   \n",
      "4        0023_01      Earth      False     F/5/S    TRAPPIST-1e  20.0  False   \n",
      "...          ...        ...        ...       ...            ...   ...    ...   \n",
      "4272     9266_02      Earth       True  G/1496/S    TRAPPIST-1e  34.0  False   \n",
      "4273     9269_01      Earth      False   G/160/P    TRAPPIST-1e  42.0  False   \n",
      "4274     9271_01       Mars       True   D/296/P    55 Cancri e  18.0  False   \n",
      "4275     9273_01     Europa      False   D/297/P    TRAPPIST-1e  18.0  False   \n",
      "4276     9277_01      Earth       True  G/1498/S  PSO J318.5-22  43.0  False   \n",
      "\n",
      "      RoomService  FoodCourt  ShoppingMall     Spa  VRDeck              Name  \\\n",
      "0             0.0        0.0           0.0     0.0     0.0   Nelly Carsoning   \n",
      "1             0.0        9.0           0.0  2823.0     0.0    Lerome Peckers   \n",
      "2             0.0        0.0           0.0     0.0     0.0   Sabih Unhearfus   \n",
      "3             0.0     6652.0           0.0   181.0   585.0  Meratz Caltilter   \n",
      "4            10.0        0.0         635.0     0.0     0.0   Brence Harperez   \n",
      "...           ...        ...           ...     ...     ...               ...   \n",
      "4272          0.0        0.0           0.0     0.0     0.0       Jeron Peter   \n",
      "4273          0.0      847.0          17.0    10.0   144.0     Matty Scheron   \n",
      "4274          0.0        0.0           0.0     0.0     0.0       Jayrin Pore   \n",
      "4275          0.0     2680.0           0.0     0.0   523.0    Kitakan Conale   \n",
      "4276          0.0        0.0           0.0     0.0     0.0  Lilace Leonzaley   \n",
      "\n",
      "      transported  \n",
      "0               0  \n",
      "1               0  \n",
      "2               0  \n",
      "3               1  \n",
      "4               1  \n",
      "...           ...  \n",
      "4272            0  \n",
      "4273            1  \n",
      "4274            0  \n",
      "4275            1  \n",
      "4276            0  \n",
      "\n",
      "[4277 rows x 14 columns]\n"
     ]
    }
   ],
   "source": [
    "df_test['transported'] = predict.tolist()\n",
    "print(df_test)"
   ]
  }
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