SpaceShip Competition.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "af2988cd",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "589a6391",
   "metadata": {},
   "outputs": [],
   "source": [
    "data=pd.read_csv(\"C:\\\\Users\\\\aarus\\\\Downloads\\\\spaceship-titanic\\\\train.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "2f500681",
   "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>3798</th>\n",
       "      <td>4055_01</td>\n",
       "      <td>Earth</td>\n",
       "      <td>False</td>\n",
       "      <td>F/836/P</td>\n",
       "      <td>TRAPPIST-1e</td>\n",
       "      <td>21.0</td>\n",
       "      <td>False</td>\n",
       "      <td>3.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>600.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>Milda Mirandry</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId HomePlanet CryoSleep    Cabin  Destination   Age    VIP  \\\n",
       "3798     4055_01      Earth     False  F/836/P  TRAPPIST-1e  21.0  False   \n",
       "\n",
       "      RoomService  FoodCourt  ShoppingMall  Spa  VRDeck            Name  \\\n",
       "3798          3.0       34.0         600.0  0.0     8.0  Milda Mirandry   \n",
       "\n",
       "      Transported  \n",
       "3798        False  "
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.sample()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "38621e80",
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.DataFrame(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "5f8b1032",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "value_counts = df['CryoSleep'].value_counts()\n",
    "plt.pie(value_counts.values, labels=value_counts.index, autopct='%1.1f%%')\n",
    "plt.title('Distribution of Values in the Column')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "2fd0dcb6",
   "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>Age</th>\n",
       "      <th>RoomService</th>\n",
       "      <th>FoodCourt</th>\n",
       "      <th>ShoppingMall</th>\n",
       "      <th>Spa</th>\n",
       "      <th>VRDeck</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>8514.000000</td>\n",
       "      <td>8512.000000</td>\n",
       "      <td>8510.000000</td>\n",
       "      <td>8485.000000</td>\n",
       "      <td>8510.000000</td>\n",
       "      <td>8505.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>28.827930</td>\n",
       "      <td>224.687617</td>\n",
       "      <td>458.077203</td>\n",
       "      <td>173.729169</td>\n",
       "      <td>311.138778</td>\n",
       "      <td>304.854791</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>14.489021</td>\n",
       "      <td>666.717663</td>\n",
       "      <td>1611.489240</td>\n",
       "      <td>604.696458</td>\n",
       "      <td>1136.705535</td>\n",
       "      <td>1145.717189</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>19.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>27.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>38.000000</td>\n",
       "      <td>47.000000</td>\n",
       "      <td>76.000000</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>59.000000</td>\n",
       "      <td>46.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>79.000000</td>\n",
       "      <td>14327.000000</td>\n",
       "      <td>29813.000000</td>\n",
       "      <td>23492.000000</td>\n",
       "      <td>22408.000000</td>\n",
       "      <td>24133.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Age   RoomService     FoodCourt  ShoppingMall           Spa  \\\n",
       "count  8514.000000   8512.000000   8510.000000   8485.000000   8510.000000   \n",
       "mean     28.827930    224.687617    458.077203    173.729169    311.138778   \n",
       "std      14.489021    666.717663   1611.489240    604.696458   1136.705535   \n",
       "min       0.000000      0.000000      0.000000      0.000000      0.000000   \n",
       "25%      19.000000      0.000000      0.000000      0.000000      0.000000   \n",
       "50%      27.000000      0.000000      0.000000      0.000000      0.000000   \n",
       "75%      38.000000     47.000000     76.000000     27.000000     59.000000   \n",
       "max      79.000000  14327.000000  29813.000000  23492.000000  22408.000000   \n",
       "\n",
       "             VRDeck  \n",
       "count   8505.000000  \n",
       "mean     304.854791  \n",
       "std     1145.717189  \n",
       "min        0.000000  \n",
       "25%        0.000000  \n",
       "50%        0.000000  \n",
       "75%       46.000000  \n",
       "max    24133.000000  "
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "2ffbf77b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId       0\n",
       "HomePlanet      201\n",
       "CryoSleep       217\n",
       "Cabin           199\n",
       "Destination     182\n",
       "Age             179\n",
       "VIP             203\n",
       "RoomService     181\n",
       "FoodCourt       183\n",
       "ShoppingMall    208\n",
       "Spa             183\n",
       "VRDeck          188\n",
       "Name            200\n",
       "Transported       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "cebe8534",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3037"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['CryoSleep'].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "ba7f026f",
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.set_option('display.max_rows',100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "d126001a",
   "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",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8688</th>\n",
       "      <td>9276_01</td>\n",
       "      <td>Europa</td>\n",
       "      <td>False</td>\n",
       "      <td>A/98/P</td>\n",
       "      <td>55 Cancri e</td>\n",
       "      <td>41.0</td>\n",
       "      <td>True</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6819.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1643.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>Gravior Noxnuther</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8689</th>\n",
       "      <td>9278_01</td>\n",
       "      <td>Earth</td>\n",
       "      <td>True</td>\n",
       "      <td>G/1499/S</td>\n",
       "      <td>PSO J318.5-22</td>\n",
       "      <td>18.0</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Kurta Mondalley</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8690</th>\n",
       "      <td>9279_01</td>\n",
       "      <td>Earth</td>\n",
       "      <td>False</td>\n",
       "      <td>G/1500/S</td>\n",
       "      <td>TRAPPIST-1e</td>\n",
       "      <td>26.0</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1872.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Fayey Connon</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8691</th>\n",
       "      <td>9280_01</td>\n",
       "      <td>Europa</td>\n",
       "      <td>False</td>\n",
       "      <td>E/608/S</td>\n",
       "      <td>55 Cancri e</td>\n",
       "      <td>32.0</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1049.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>353.0</td>\n",
       "      <td>3235.0</td>\n",
       "      <td>Celeon Hontichre</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8692</th>\n",
       "      <td>9280_02</td>\n",
       "      <td>Europa</td>\n",
       "      <td>False</td>\n",
       "      <td>E/608/S</td>\n",
       "      <td>TRAPPIST-1e</td>\n",
       "      <td>44.0</td>\n",
       "      <td>False</td>\n",
       "      <td>126.0</td>\n",
       "      <td>4688.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>Propsh Hontichre</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8693 rows × 14 columns</p>\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",
       "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]"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "ecf62f89",
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(len(df['CryoSleep'])):\n",
    "    if df['CryoSleep'].iloc[i] == True:\n",
    "        df.at[i, 'FoodCourt'] = 0.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "d46a8aa4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "113"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['FoodCourt'].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "ef3ad104",
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(len(df['ShoppingMall'])):\n",
    "    if df['CryoSleep'].iloc[i] == True:\n",
    "        df.at[i, 'ShoppingMall'] = 0.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "2244ec4d",
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(len(df['RoomService'])):\n",
    "    if df['CryoSleep'].iloc[i] == True:\n",
    "        df.at[i, 'RoomService'] = 0.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "a89c7047",
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(len(df['VIP'])):\n",
    "    if df['CryoSleep'].iloc[i] == True:\n",
    "        df.at[i, 'VIP'] = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "764ee5c8",
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(len(df['Spa'])):\n",
    "    if df['CryoSleep'].iloc[i] == True:\n",
    "        df.at[i, 'Spa'] = 0.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "f3d90b9f",
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(len(df['VRDeck'])):\n",
    "    if df['CryoSleep'].iloc[i] == True:\n",
    "        df.at[i, 'VRDeck'] = 0.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "823ba246",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "sns.countplot(x='Transported', data=df)\n",
    "plt.xlabel('VIP')\n",
    "plt.ylabel('Count')\n",
    "plt.title('Bar Plot')\n",
    "plt.xticks(rotation=45)\n",
    "# Display the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "b1e556e9",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(columns=['HomePlanet','Cabin','Destination','VIP','Name','PassengerId'],inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "08b14970",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "217"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['CryoSleep'].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "0d9347de",
   "metadata": {},
   "outputs": [],
   "source": [
    "column_to_impute = 'CryoSleep'\n",
    "def random_impute(value):\n",
    "    if pd.isnull(value):\n",
    "        return np.random.choice([True, False])\n",
    "    return value\n",
    "\n",
    "df[column_to_impute] = df[column_to_impute].apply(random_impute)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "94530fc4",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Age'].fillna(df['Age'].median(),inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "ead1a4e6",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['RoomService'].fillna(0.0,inplace=True)\n",
    "df['FoodCourt'].fillna(0.0,inplace=True)\n",
    "df['ShoppingMall'].fillna(0.0,inplace=True)\n",
    "df['Spa'].fillna(0.0,inplace=True)\n",
    "df['VRDeck'].fillna(0.0,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "85927aad",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CryoSleep       0\n",
       "Age             0\n",
       "RoomService     0\n",
       "FoodCourt       0\n",
       "ShoppingMall    0\n",
       "Spa             0\n",
       "VRDeck          0\n",
       "Transported     0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "700d8c4f",
   "metadata": {},
   "outputs": [],
   "source": [
    "test_data=pd.read_csv(\"C:\\\\Users\\\\aarus\\\\Downloads\\\\spaceship-titanic\\\\test.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "67c97dec",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_test=pd.DataFrame(test_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "b7991d4a",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_test.drop(columns=['HomePlanet','Cabin','Destination','VIP','Name'],inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "7fa92042",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId       0\n",
       "CryoSleep        93\n",
       "Age              91\n",
       "RoomService      82\n",
       "FoodCourt       106\n",
       "ShoppingMall     98\n",
       "Spa             101\n",
       "VRDeck           80\n",
       "dtype: int64"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "ed072416",
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(len(df_test['CryoSleep'])):\n",
    "    if df_test['CryoSleep'].iloc[i] == True:\n",
    "        df_test.at[i, 'FoodCourt'] = 0.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "97d9d310",
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(len(df_test['ShoppingMall'])):\n",
    "    if df_test['CryoSleep'].iloc[i] == True:\n",
    "        df_test.at[i, 'ShoppingMall'] = 0.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "04602efa",
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(len(df_test['RoomService'])):\n",
    "    if df_test['CryoSleep'].iloc[i] == True:\n",
    "        df_test.at[i, 'RoomService'] = 0.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "8e66ec80",
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(len(df_test['Spa'])):\n",
    "    if df_test['CryoSleep'].iloc[i] == True:\n",
    "        df_test.at[i, 'Spa'] = 0.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "48462153",
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(len(df_test['VRDeck'])):\n",
    "    if df_test['CryoSleep'].iloc[i] == True:\n",
    "        df_test.at[i, 'VRDeck'] = 0.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "83df770e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId      0\n",
       "CryoSleep       93\n",
       "Age             91\n",
       "RoomService     57\n",
       "FoodCourt       67\n",
       "ShoppingMall    63\n",
       "Spa             59\n",
       "VRDeck          51\n",
       "dtype: int64"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "id": "4b94e04c",
   "metadata": {},
   "outputs": [],
   "source": [
    "column_to_impute = 'CryoSleep'\n",
    "def random_impute(value):\n",
    "    if pd.isnull(value):\n",
    "        return np.random.choice([True, False])\n",
    "    return value\n",
    "\n",
    "df_test[column_to_impute] = df_test[column_to_impute].apply(random_impute)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "id": "2b452098",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_test['Age'].fillna(df['Age'].median(),inplace=True)\n",
    "df_test['RoomService'].fillna(0.0,inplace=True)\n",
    "df_test['FoodCourt'].fillna(0.0,inplace=True)\n",
    "df_test['ShoppingMall'].fillna(0.0,inplace=True)\n",
    "df_test['Spa'].fillna(0.0,inplace=True)\n",
    "df_test['VRDeck'].fillna(0.0,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "id": "c57841e9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId     0\n",
       "CryoSleep       0\n",
       "Age             0\n",
       "RoomService     0\n",
       "FoodCourt       0\n",
       "ShoppingMall    0\n",
       "Spa             0\n",
       "VRDeck          0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "27b84124",
   "metadata": {},
   "source": [
    "# MODEL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "id": "50e761fc",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train=df.drop(columns=['Transported'])\n",
    "Y_train=df['Transported']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "id": "a1212433",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\aarus\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\ensemble\\_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
      "  warn(\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "# Create the RandomForestClassifier object with desired hyperparameters\n",
    "rf_classifier = RandomForestClassifier(n_estimators=100, max_depth=10, max_features='auto')\n",
    "\n",
    "# Train the model on your training data (X_train, y_train)\n",
    "rf_classifier.fit(X_train, Y_train)\n",
    "\n",
    "# Make predictions on the test data (X_test)\n",
    "y_pred = rf_classifier.predict(df_test.drop(columns=['PassengerId']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "id": "18b0fc1f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ True, False,  True, ...,  True,  True,  True])"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "id": "681bd64d",
   "metadata": {},
   "outputs": [],
   "source": [
    "submit_df=pd.DataFrame()\n",
    "submit_df['PassengerId']=df_test['PassengerId']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "id": "56795207",
   "metadata": {},
   "outputs": [],
   "source": [
    "submit_df['Transported']=y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "id": "901db722",
   "metadata": {},
   "outputs": [],
   "source": [
    "submit_df.to_csv('SpaceShip Submission 1.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "79090b90",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "id": "4e0b3e27",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.metrics import accuracy_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "id": "4072018e",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = RandomForestClassifier(n_estimators=100, random_state=42)  \n",
    "model.fit(X_train, Y_train)\n",
    "Y_pred = model.predict(df_test.drop(columns=['PassengerId']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "id": "6d7bd7e0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ True, False,  True, ...,  True,  True,  True])"
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "id": "39720bd7",
   "metadata": {},
   "outputs": [],
   "source": [
    "submit_df=pd.DataFrame()\n",
    "submit_df['PassengerId']=df_test['PassengerId']\n",
    "submit_df['Transported']=Y_pred\n",
    "submit_df.to_csv('SpaceShip Submission New RF.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9ed02a36",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "75ad02e7",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f276e539",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cccc0866",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0d5231c6",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cec5ce6a",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "03cf53cd",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5ace0a25",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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