titanic_spaceship.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "721d7fd2",
   "metadata": {},
   "outputs": [],
   "source": [
    "#basic import\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "79720711",
   "metadata": {},
   "outputs": [],
   "source": [
    "#data aqistion\n",
    "train_sp = pd.read_csv(\"train_spaceship.csv\")\n",
    "test_sp = pd.read_csv(\"test_spaceship.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "0ac6941d",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "<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": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_sp.head()\n",
    "#categorical - HomePlanet,CryoSleep,Destination,VIP\n",
    "#target - Transported\n",
    "#numerical - Age,RoomService,FoofCourt,ShoppingMall,Spa,VRDeck\n",
    "#string - PassengerId,Cabin,Name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "4a1e3a77",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 8693 entries, 0 to 8692\n",
      "Data columns (total 14 columns):\n",
      " #   Column        Non-Null Count  Dtype  \n",
      "---  ------        --------------  -----  \n",
      " 0   PassengerId   8693 non-null   object \n",
      " 1   HomePlanet    8492 non-null   object \n",
      " 2   CryoSleep     8476 non-null   object \n",
      " 3   Cabin         8494 non-null   object \n",
      " 4   Destination   8511 non-null   object \n",
      " 5   Age           8514 non-null   float64\n",
      " 6   VIP           8490 non-null   object \n",
      " 7   RoomService   8512 non-null   float64\n",
      " 8   FoodCourt     8510 non-null   float64\n",
      " 9   ShoppingMall  8485 non-null   float64\n",
      " 10  Spa           8510 non-null   float64\n",
      " 11  VRDeck        8505 non-null   float64\n",
      " 12  Name          8493 non-null   object \n",
      " 13  Transported   8693 non-null   bool   \n",
      "dtypes: bool(1), float64(6), object(7)\n",
      "memory usage: 891.5+ KB\n"
     ]
    }
   ],
   "source": [
    "train_sp.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "5d513a55",
   "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>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": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_sp.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "c3009a22",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Transported', ylabel='count'>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#visualization\n",
    "#target column countplot with other categorical\n",
    "sns.countplot(x=\"Transported\",data=train_sp,hue=\"HomePlanet\",palette=\"winter_r\")\n",
    "#most people from class -0 (not transported) are from earth\n",
    "#same for class 1 but its more evenly distributed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "447d5a62",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Transported', ylabel='count'>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x=\"Transported\",data=train_sp,hue=\"Destination\",palette=\"winter_r\")\n",
    "#most people from both classes 0,1 had same destination -TRAPPIST-1e"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "cda35185",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Transported', ylabel='count'>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x=\"Transported\",data=train_sp,hue=\"CryoSleep\",palette=\"winter_r\")\n",
    "#most people from class 0 were in cryosleep"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "dbff8305",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Transported', ylabel='count'>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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/1g033CBJGjlypJYvX27u4+vr69ZHRkaG3n33Xa1Zs0YhISGaOnWqEhMTVVBQYP5ikpOTdejQIeXk5EiSJk6cqJSUFL377rvNMU0AANw4HA5JMkMRfp527dqZv9OG8mgguuOOO9zWn3vuOS1ZskT5+flmIPLz8zvvJF0ul1555RWtXLlScXFxkqRVq1bJ6XTqo48+Unx8vIqKipSTk6P8/Hzzy+OWLVummJgY7d27Vz169Dhn39XV1aqurjbXKysrf/Z8AQCQJJvNpvDwcIWGhrp94Skun4+PT6N8GWyL+S6zuro6vfnmmzpx4oRiYmLM9o0bNyo0NFTt2rVTbGysnnvuOYWGhkqSCgoKVFtbqxEjRpj1ERERioqK0ubNmxUfH68tW7bIbre7fZPuwIEDZbfbtXnz5vMGoqysLD377LNNNFsAAH64fOaJb3ZHfR6/qXrnzp1q27at/Pz89PDDD2vdunXq1auXJCkhIUGrV6/Whg0bNHfuXG3fvl233XabeeamtLRUvr6+at++vVufYWFhKi0tNWt+DFA/FRoaatacy8yZM+Vyuczl4MGDjTVlAADQwnj8DFGPHj1UWFioo0eP6q233tK4ceOUl5enXr16acyYMWZdVFSU+vfvr86dO+v999/XqFGjztunYRhuN6id62a1s2vO5ufnJz8/vwbOCgAAXEk8fobI19dXXbt2Vf/+/ZWVlaU+ffroxRdfPGdteHi4OnfurH379kn64aa0mpoaVVRUuNWVlZUpLCzMrDl8+HC9vsrLy80aAABgbR4PRGczDMPtZuafOnLkiA4ePKjw8HBJUr9+/eTj46Pc3FyzpqSkRLt27dKgQYMkSTExMXK5XNq2bZtZs3XrVrlcLrMGAABYm0cvmT3xxBNKSEiQ0+nUsWPHtGbNGm3cuFE5OTk6fvy4MjMzdc899yg8PFz79+/XE088oQ4dOujuu++WJNntdqWmpmrq1KkKCQlRcHCwpk2bpujoaPOps549e2rkyJGaMGGCli5dKumHx+4TExPPe0M1AACwFo8GosOHDyslJUUlJSWy2+3q3bu3cnJyNHz4cFVVVWnnzp167bXXdPToUYWHh2vo0KFau3atAgMDzT7mz58vb29vjR492nwxY3Z2tttd+6tXr1Z6err5NFpSUpIWLVrU7PMFAAAtk83gy1QuSWVlpex2u1wul4KCgprsc0IXL26yvnF5ytLSPD0EXGU4vlsOjm/ruNS/3y3uHiIAAIDmRiACAACWRyACAACWRyACAACWRyACAACWRyACAACWRyACAACWRyACAACWRyACAACWRyACAACWRyACAACWRyACAACWRyACAACWRyACAACWRyACAACWRyACAACWRyACAACWRyACAACWRyACAACWRyACAACWRyACAACWRyACAACWRyACAACWRyACAACWRyACAACWRyACAACWRyACAACWRyACAACWRyACAACWRyACAACWRyACAACW59FAtGTJEvXu3VtBQUEKCgpSTEyMPvzwQ3O7YRjKzMxURESE/P39NWTIEO3evdutj+rqak2ePFkdOnRQQECAkpKSdOjQIbeaiooKpaSkyG63y263KyUlRUePHm2OKQIAgCuARwNRp06d9Ic//EE7duzQjh07dNttt+nOO+80Q8+cOXM0b948LVq0SNu3b5fD4dDw4cN17Ngxs4+MjAytW7dOa9as0aZNm3T8+HElJiaqrq7OrElOTlZhYaFycnKUk5OjwsJCpaSkNPt8AQBAy2QzDMPw9CB+Kjg4WC+88IIeeOABRUREKCMjQzNmzJD0w9mgsLAwPf/883rooYfkcrnUsWNHrVy5UmPGjJEkffvtt3I6nfrggw8UHx+voqIi9erVS/n5+RowYIAkKT8/XzExMfqf//kf9ejR45zjqK6uVnV1tbleWVkpp9Mpl8uloKCgJpt/6OLFTdY3Lk9ZWpqnh4CrDMd3y8HxbR2VlZWy2+0X/fvdYu4hqqur05o1a3TixAnFxMSouLhYpaWlGjFihFnj5+en2NhYbd68WZJUUFCg2tpat5qIiAhFRUWZNVu2bJHdbjfDkCQNHDhQdrvdrDmXrKws8xKb3W6X0+ls7CkDAIAWwuOBaOfOnWrbtq38/Pz08MMPa926derVq5dKS0slSWFhYW71YWFh5rbS0lL5+vqqffv2F6wJDQ2t97mhoaFmzbnMnDlTLpfLXA4ePPiz5gkAAFoub08PoEePHiosLNTRo0f11ltvady4ccrLyzO322w2t3rDMOq1ne3smnPVX6wfPz8/+fn5Xeo0AADAFczjZ4h8fX3VtWtX9e/fX1lZWerTp49efPFFORwOSap3FqesrMw8a+RwOFRTU6OKiooL1hw+fLje55aXl9c7+wQAAKzJ44HobIZhqLq6WpGRkXI4HMrNzTW31dTUKC8vT4MGDZIk9evXTz4+Pm41JSUl2rVrl1kTExMjl8ulbdu2mTVbt26Vy+UyawAAgLV59JLZE088oYSEBDmdTh07dkxr1qzRxo0blZOTI5vNpoyMDM2ePVvdunVTt27dNHv2bLVp00bJycmSJLvdrtTUVE2dOlUhISEKDg7WtGnTFB0drbi4OElSz549NXLkSE2YMEFLly6VJE2cOFGJiYnnfcIMAABYi0cD0eHDh5WSkqKSkhLZ7Xb17t1bOTk5Gj58uCRp+vTpqqqqUlpamioqKjRgwACtX79egYGBZh/z58+Xt7e3Ro8eraqqKg0bNkzZ2dny8vIya1avXq309HTzabSkpCQtWrSoeScLAABarBb3HqKW6lLfY/Bz8Z6SloP3lKCxcXy3HBzf1nHFvYcIAADAUwhEAADA8ghEAADA8ghEAADA8ghEAADA8ghEAADA8ghEAADA8ghEAADA8ghEAADA8ghEAADA8ghEAADA8ghEAADA8ghEAADA8ghEAADA8ghEAADA8ghEAADA8ghEAADA8ghEAADA8ghEAADA8ghEAADA8ghEAADA8ghEAADA8ghEAADA8ghEAADA8ghEAADA8ghEAADA8ghEAADA8ghEAADA8ghEAADA8ghEAADA8jwaiLKysnTzzTcrMDBQoaGhuuuuu7R37163mvHjx8tms7ktAwcOdKuprq7W5MmT1aFDBwUEBCgpKUmHDh1yq6moqFBKSorsdrvsdrtSUlJ09OjRpp4iAAC4Ang0EOXl5WnSpEnKz89Xbm6uTp8+rREjRujEiRNudSNHjlRJSYm5fPDBB27bMzIytG7dOq1Zs0abNm3S8ePHlZiYqLq6OrMmOTlZhYWFysnJUU5OjgoLC5WSktIs8wQAAC2btyc/PCcnx219+fLlCg0NVUFBgQYPHmy2+/n5yeFwnLMPl8ulV155RStXrlRcXJwkadWqVXI6nfroo48UHx+voqIi5eTkKD8/XwMGDJAkLVu2TDExMdq7d6969OjRRDMEAABXghZ1D5HL5ZIkBQcHu7Vv3LhRoaGh6t69uyZMmKCysjJzW0FBgWprazVixAizLSIiQlFRUdq8ebMkacuWLbLb7WYYkqSBAwfKbrebNWerrq5WZWWl2wIAAK5OLSYQGYahKVOm6JZbblFUVJTZnpCQoNWrV2vDhg2aO3eutm/frttuu03V1dWSpNLSUvn6+qp9+/Zu/YWFham0tNSsCQ0NrfeZoaGhZs3ZsrKyzPuN7Ha7nE5nY00VAAC0MB69ZPZTjzzyiL744gtt2rTJrX3MmDHmz1FRUerfv786d+6s999/X6NGjTpvf4ZhyGazmes//fl8NT81c+ZMTZkyxVyvrKwkFAEAcJVqEWeIJk+erHfeeUcff/yxOnXqdMHa8PBwde7cWfv27ZMkORwO1dTUqKKiwq2urKxMYWFhZs3hw4fr9VVeXm7WnM3Pz09BQUFuCwAAuDp5NBAZhqFHHnlEf/vb37RhwwZFRkZedJ8jR47o4MGDCg8PlyT169dPPj4+ys3NNWtKSkq0a9cuDRo0SJIUExMjl8ulbdu2mTVbt26Vy+UyawAAgHV59JLZpEmT9Prrr+vvf/+7AgMDzft57Ha7/P39dfz4cWVmZuqee+5ReHi49u/fryeeeEIdOnTQ3XffbdampqZq6tSpCgkJUXBwsKZNm6bo6GjzqbOePXtq5MiRmjBhgpYuXSpJmjhxohITE3nCDAAAeDYQLVmyRJI0ZMgQt/bly5dr/Pjx8vLy0s6dO/Xaa6/p6NGjCg8P19ChQ7V27VoFBgaa9fPnz5e3t7dGjx6tqqoqDRs2TNnZ2fLy8jJrVq9erfT0dPNptKSkJC1atKjpJwkAAFo8jwYiwzAuuN3f31//+Mc/LtpP69attXDhQi1cuPC8NcHBwVq1atVljxEAAFz9WsRN1QAAAJ7UoEB02223nfN7wCorK3Xbbbf93DEBAAA0qwYFoo0bN6qmpqZe+6lTp/TJJ5/87EEBAAA0p8u6h+iLL74wf96zZ4/bW57r6uqUk5OjX/ziF403OgAAgGZwWYGob9++stlsstls57w05u/vf8EbmwEAAFqiywpExcXFMgxD1157rbZt26aOHTua23x9fRUaGur2qDsAAMCV4LICUefOnSVJZ86caZLBAAAAeEKD30P0n//8Rxs3blRZWVm9gPT000//7IEBAAA0lwYFomXLluk3v/mNOnToIIfDUe9b5QlEAADgStKgQPT73/9ezz33nGbMmNHY4wEAAGh2DXoPUUVFhe69997GHgsAAIBHNCgQ3XvvvVq/fn1jjwUAAMAjGnTJrGvXrnrqqaeUn5+v6Oho+fj4uG1PT09vlMEBAAA0hwYFoj/96U9q27at8vLylJeX57bNZrMRiAAAwBWlQYGouLi4sccBAADgMQ26hwgAAOBq0qAzRA888MAFt7/66qsNGgwAAIAnNCgQVVRUuK3X1tZq165dOnr06Dm/9BUAAKAla1AgWrduXb22M2fOKC0tTddee+3PHhQAAEBzarR7iFq1aqXHHntM8+fPb6wuAQAAmkWj3lT91Vdf6fTp043ZJQAAQJNr0CWzKVOmuK0bhqGSkhK9//77GjduXKMMDAAAoLk0KBD9+9//dltv1aqVOnbsqLlz5170CTQAAICWpkGB6OOPP27scQAAAHhMgwLRj8rLy7V3717ZbDZ1795dHTt2bKxxAQAANJsG3VR94sQJPfDAAwoPD9fgwYN16623KiIiQqmpqTp58mRjjxEAAKBJNSgQTZkyRXl5eXr33Xd19OhRHT16VH//+9+Vl5enqVOnNvYYAQAAmlSDLpm99dZb+utf/6ohQ4aYbbfffrv8/f01evRoLVmypLHGBwAA0OQadIbo5MmTCgsLq9ceGhrKJTMAAHDFaVAgiomJ0TPPPKNTp06ZbVVVVXr22WcVExPTaIMDAABoDg26ZLZgwQIlJCSoU6dO6tOnj2w2mwoLC+Xn56f169c39hgBAACaVIPOEEVHR2vfvn3KyspS37591bt3b/3hD3/Ql19+qRtuuOGS+8nKytLNN9+swMBAhYaG6q677tLevXvdagzDUGZmpiIiIuTv768hQ4Zo9+7dbjXV1dWaPHmyOnTooICAACUlJenQoUNuNRUVFUpJSZHdbpfdbldKSoqOHj3akOkDAICrTIPOEGVlZSksLEwTJkxwa3/11VdVXl6uGTNmXFI/eXl5mjRpkm6++WadPn1aTz75pEaMGKE9e/YoICBAkjRnzhzNmzdP2dnZ6t69u37/+99r+PDh2rt3rwIDAyVJGRkZevfdd7VmzRqFhIRo6tSpSkxMVEFBgby8vCRJycnJOnTokHJyciRJEydOVEpKit59992G/AoAAMBVxGYYhnG5O3Xp0kWvv/66Bg0a5Na+detW/epXv1JxcXGDBlNeXq7Q0FDl5eVp8ODBMgxDERERysjIMENWdXW1wsLC9Pzzz+uhhx6Sy+VSx44dtXLlSo0ZM0aS9O2338rpdOqDDz5QfHy8ioqK1KtXL+Xn52vAgAGSpPz8fMXExOh//ud/1KNHj4uOrbKyUna7XS6XS0FBQQ2a36UIXby4yfrG5SlLS/P0EHCV4fhuOTi+reNS/3436JJZaWmpwsPD67V37NhRJSUlDelSkuRyuSRJwcHBkqTi4mKVlpZqxIgRZo2fn59iY2O1efNmSVJBQYFqa2vdaiIiIhQVFWXWbNmyRXa73QxDkjRw4EDZ7Xaz5mzV1dWqrKx0WwAAwNWpQYHI6XTq008/rdf+6aefKiIiokEDMQxDU6ZM0S233KKoqChJPwQvSfUe8Q8LCzO3lZaWytfXV+3bt79gTWhoaL3PDA0NNWvOlpWVZd5vZLfb5XQ6GzQvAADQ8jXoHqIHH3xQGRkZqq2t1W233SZJ+uc//6np06c3+E3VjzzyiL744gtt2rSp3jabzea2bhhGvbaznV1zrvoL9TNz5kxNmTLFXK+srCQUAQBwlWpQIJo+fbq+//57paWlqaamRpLUunVrzZgxQzNnzrzs/iZPnqx33nlH//rXv9SpUyez3eFwSKp/ia6srMw8a+RwOFRTU6OKigq3s0RlZWXmPU4Oh0OHDx+u97nl5eXnfMGk9MOlOT8/v8ueCwAAuPI06JKZzWbT888/r/LycuXn5+vzzz/X999/r6effvqy+jEMQ4888oj+9re/acOGDYqMjHTbHhkZKYfDodzcXLOtpqZGeXl5Ztjp16+ffHx83GpKSkq0a9cusyYmJkYul0vbtm0za7Zu3SqXy1XvxnAAAGA9DTpD9KO2bdvq5ptvbvD+kyZN0uuvv66///3vCgwMNO/nsdvt8vf3l81mU0ZGhmbPnq1u3bqpW7dumj17ttq0aaPk5GSzNjU1VVOnTlVISIiCg4M1bdo0RUdHKy4uTpLUs2dPjRw5UhMmTNDSpUsl/fDYfWJi4iU9YQYAAK5uPysQ/Vw/fgnsT78kVpKWL1+u8ePHS/rh8lxVVZXS0tJUUVGhAQMGaP369eY7iCRp/vz58vb21ujRo1VVVaVhw4YpOzvbfAeRJK1evVrp6enm02hJSUlatGhR004QAABcERr0HiIr4j1E1sN7StDYOL5bDo5v62jS9xABAABcTQhEAADA8ghEAADA8ghEAADA8ghEAADA8ghEAADA8ghEAADA8ghEAADA8ghEAADA8ghEAADA8ghEAADA8ghEAADA8ghEAADA8ghEAADA8ghEAADA8ghEAADA8ghEAADA8ghEAADA8ghEAADA8ghEAADA8ghEAADA8ghEAADA8ghEAADA8ghEAADA8ghEAADA8ghEAADA8ghEAADA8ghEAADA8ghEAADA8ghEAADA8ghEAADA8jwaiP71r3/pjjvuUEREhGw2m95++2237ePHj5fNZnNbBg4c6FZTXV2tyZMnq0OHDgoICFBSUpIOHTrkVlNRUaGUlBTZ7XbZ7XalpKTo6NGjTTw7AABwpfBoIDpx4oT69OmjRYsWnbdm5MiRKikpMZcPPvjAbXtGRobWrVunNWvWaNOmTTp+/LgSExNVV1dn1iQnJ6uwsFA5OTnKyclRYWGhUlJSmmxeAADgyuLtyQ9PSEhQQkLCBWv8/PzkcDjOuc3lcumVV17RypUrFRcXJ0latWqVnE6nPvroI8XHx6uoqEg5OTnKz8/XgAEDJEnLli1TTEyM9u7dqx49ejTupAAAwBWnxd9DtHHjRoWGhqp79+6aMGGCysrKzG0FBQWqra3ViBEjzLaIiAhFRUVp8+bNkqQtW7bIbrebYUiSBg4cKLvdbtacS3V1tSorK90WAABwdWrRgSghIUGrV6/Whg0bNHfuXG3fvl233XabqqurJUmlpaXy9fVV+/bt3fYLCwtTaWmpWRMaGlqv79DQULPmXLKyssx7jux2u5xOZyPODAAAtCQevWR2MWPGjDF/joqKUv/+/dW5c2e9//77GjVq1Hn3MwxDNpvNXP/pz+erOdvMmTM1ZcoUc72yspJQBADAVapFnyE6W3h4uDp37qx9+/ZJkhwOh2pqalRRUeFWV1ZWprCwMLPm8OHD9foqLy83a87Fz89PQUFBbgsAALg6XVGB6MiRIzp48KDCw8MlSf369ZOPj49yc3PNmpKSEu3atUuDBg2SJMXExMjlcmnbtm1mzdatW+VyucwaAABgbR69ZHb8+HF9+eWX5npxcbEKCwsVHBys4OBgZWZm6p577lF4eLj279+vJ554Qh06dNDdd98tSbLb7UpNTdXUqVMVEhKi4OBgTZs2TdHR0eZTZz179tTIkSM1YcIELV26VJI0ceJEJSYm8oQZAACQ5OFAtGPHDg0dOtRc//GenXHjxmnJkiXauXOnXnvtNR09elTh4eEaOnSo1q5dq8DAQHOf+fPny9vbW6NHj1ZVVZWGDRum7OxseXl5mTWrV69Wenq6+TRaUlLSBd99BAAArMVmGIbh6UFcCSorK2W32+VyuZr0fqLQxYubrG9cnrK0NE8PAVcZju+Wg+PbOi717/cVdQ8RAABAUyAQAQAAyyMQAQAAyyMQAQAAyyMQAQAAyyMQAQAAyyMQAQAAyyMQAQAAyyMQAQAAyyMQAQAAyyMQAQAAyyMQAQAAyyMQAQAAyyMQAQAAyyMQAQAAyyMQAQAAyyMQAQAAyyMQAQAAyyMQAQAAyyMQAQAAyyMQAQAAyyMQAQAAyyMQAQAAyyMQAQAAyyMQAQAAyyMQAQAAyyMQAQAAyyMQAQAAyyMQAQAAyyMQAQAAyyMQAQAAy/NoIPrXv/6lO+64QxEREbLZbHr77bfdthuGoczMTEVERMjf319DhgzR7t273Wqqq6s1efJkdejQQQEBAUpKStKhQ4fcaioqKpSSkiK73S673a6UlBQdPXq0iWcHAACuFB4NRCdOnFCfPn20aNGic26fM2eO5s2bp0WLFmn79u1yOBwaPny4jh07ZtZkZGRo3bp1WrNmjTZt2qTjx48rMTFRdXV1Zk1ycrIKCwuVk5OjnJwcFRYWKiUlpcnnBwAArgzenvzwhIQEJSQknHObYRhasGCBnnzySY0aNUqStGLFCoWFhen111/XQw89JJfLpVdeeUUrV65UXFycJGnVqlVyOp366KOPFB8fr6KiIuXk5Cg/P18DBgyQJC1btkwxMTHau3evevTo0TyTBQAALVaLvYeouLhYpaWlGjFihNnm5+en2NhYbd68WZJUUFCg2tpat5qIiAhFRUWZNVu2bJHdbjfDkCQNHDhQdrvdrDmX6upqVVZWui0AAODq1GIDUWlpqSQpLCzMrT0sLMzcVlpaKl9fX7Vv3/6CNaGhofX6Dw0NNWvOJSsry7znyG63y+l0/qz5AACAlqvFBqIf2Ww2t3XDMOq1ne3smnPVX6yfmTNnyuVymcvBgwcvc+QAAOBK0WIDkcPhkKR6Z3HKysrMs0YOh0M1NTWqqKi4YM3hw4fr9V9eXl7v7NNP+fn5KSgoyG0BAABXpxYbiCIjI+VwOJSbm2u21dTUKC8vT4MGDZIk9evXTz4+Pm41JSUl2rVrl1kTExMjl8ulbdu2mTVbt26Vy+UyawAAgLV59Cmz48eP68svvzTXi4uLVVhYqODgYF1zzTXKyMjQ7Nmz1a1bN3Xr1k2zZ89WmzZtlJycLEmy2+1KTU3V1KlTFRISouDgYE2bNk3R0dHmU2c9e/bUyJEjNWHCBC1dulSSNHHiRCUmJvKEGQAAkOThQLRjxw4NHTrUXJ8yZYokady4ccrOztb06dNVVVWltLQ0VVRUaMCAAVq/fr0CAwPNfebPny9vb2+NHj1aVVVVGjZsmLKzs+Xl5WXWrF69Wunp6ebTaElJSed99xEAALAem2EYhqcHcSWorKyU3W6Xy+Vq0vuJQhcvbrK+cXnK0tI8PQRcZTi+Ww6Ob+u41L/fLfYeIgAAgOZCIAIAAJZHIAIAAJZHIAIAAJZHIAIAAJZHIAIAAJZHIAIAAJZHIAIAAJZHIAIAAJZHIAIAAJZHIAIAAJZHIAIAAJZHIAIAAJZHIAIAAJZHIAIAAJZHIAIAAJZHIAIAAJZHIAIAAJZHIAIAAJZHIAIAAJZHIAIAAJZHIAIAAJZHIAIAAJZHIAIAAJZHIAIAAJZHIAIAAJZHIAIAAJZHIAIAAJZHIAIAAJZHIAIAAJZHIAIAAJbXogNRZmambDab2+JwOMzthmEoMzNTERER8vf315AhQ7R79263PqqrqzV58mR16NBBAQEBSkpK0qFDh5p7KgAAoAVr0YFIkm644QaVlJSYy86dO81tc+bM0bx587Ro0SJt375dDodDw4cP17Fjx8yajIwMrVu3TmvWrNGmTZt0/PhxJSYmqq6uzhPTAQAALZC3pwdwMd7e3m5nhX5kGIYWLFigJ598UqNGjZIkrVixQmFhYXr99df10EMPyeVy6ZVXXtHKlSsVFxcnSVq1apWcTqc++ugjxcfHN+tcAABAy9TizxDt27dPERERioyM1K9+9St9/fXXkqTi4mKVlpZqxIgRZq2fn59iY2O1efNmSVJBQYFqa2vdaiIiIhQVFWXWnE91dbUqKyvdFgAAcHVq0YFowIABeu211/SPf/xDy5YtU2lpqQYNGqQjR46otLRUkhQWFua2T1hYmLmttLRUvr6+at++/XlrzicrK0t2u91cnE5nI84MAAC0JC06ECUkJOiee+5RdHS04uLi9P7770v64dLYj2w2m9s+hmHUazvbpdTMnDlTLpfLXA4ePNjAWQAAgJauRQeiswUEBCg6Olr79u0z7ys6+0xPWVmZedbI4XCopqZGFRUV5605Hz8/PwUFBbktAADg6nRFBaLq6moVFRUpPDxckZGRcjgcys3NNbfX1NQoLy9PgwYNkiT169dPPj4+bjUlJSXatWuXWQMAANCinzKbNm2a7rjjDl1zzTUqKyvT73//e1VWVmrcuHGy2WzKyMjQ7Nmz1a1bN3Xr1k2zZ89WmzZtlJycLEmy2+1KTU3V1KlTFRISouDgYE2bNs28BAcAACC18EB06NAh3Xffffruu+/UsWNHDRw4UPn5+ercubMkafr06aqqqlJaWpoqKio0YMAArV+/XoGBgWYf8+fPl7e3t0aPHq2qqioNGzZM2dnZ8vLy8tS0AABAC2MzDMPw9CCuBJWVlbLb7XK5XE16P1Ho4sVN1jcuT1lamqeHgKsMx3fLwfFtHZf69/uKuocIAACgKRCIAACA5RGIAACA5RGIAACA5RGIAACA5RGIAACA5RGIAACA5RGIAACA5RGIAACA5RGIAACA5RGIAACA5RGIAACA5RGIAACA5RGIAACA5RGIAACA5RGIAACA5RGIAACA5RGIAACA5RGIAACA5RGIAACA5RGIAACA5RGIAACA5RGIAACA5RGIAACA5Xl7egBAS9VpbK6nh4D/c2j1cE8PAcBVjjNEAADA8ghEAADA8ghEAADA8riHCABgOdwj2HK0lHsEOUMEAAAsj0AEAAAsz1KBaPHixYqMjFTr1q3Vr18/ffLJJ54eEgAAaAEsE4jWrl2rjIwMPfnkk/r3v/+tW2+9VQkJCTpw4ICnhwYAADzMMoFo3rx5Sk1N1YMPPqiePXtqwYIFcjqdWrJkiaeHBgAAPMwST5nV1NSooKBAv/3tb93aR4wYoc2bN59zn+rqalVXV5vrLpdLklRZWdl0A5V0pqqqSfvHpTtTe8LTQ8D/aerjrrlwfLccHN8tR1Mf3z/2bxjGBessEYi+++471dXVKSwszK09LCxMpaWl59wnKytLzz77bL12p9PZJGMEcH72Nz09AgBNpbmO72PHjslut593uyUC0Y9sNpvbumEY9dp+NHPmTE2ZMsVcP3PmjL7//nuFhIScdx9cPSorK+V0OnXw4EEFBQV5ejgAGhHHt7UYhqFjx44pIiLignWWCEQdOnSQl5dXvbNBZWVl9c4a/cjPz09+fn5ube3atWuqIaKFCgoK4n+YwFWK49s6LnRm6EeWuKna19dX/fr1U26u+5tJc3NzNWjQIA+NCgAAtBSWOEMkSVOmTFFKSor69++vmJgY/elPf9KBAwf08MMPe3poAADAwywTiMaMGaMjR47od7/7nUpKShQVFaUPPvhAnTt39vTQ0AL5+fnpmWeeqXfZFMCVj+Mb52IzLvYcGgAAwFXOEvcQAQAAXAiBCAAAWB6BCAAAWB6BCDhLdnY275wCAIshEOGqNX78eNlstnrLl19+6emhAWgE5zq+f7qMHz/e00PEFcQyj93DmkaOHKnly5e7tXXs2NFDowHQmEpKSsyf165dq6efflp79+412/z9/d3qa2tr5ePj02zjw5WFM0S4qvn5+cnhcLgtL774oqKjoxUQECCn06m0tDQdP378vH18/vnnGjp0qAIDAxUUFKR+/fppx44d5vbNmzdr8ODB8vf3l9PpVHp6uk6c4Ju0gab20+PabrfLZrOZ66dOnVK7du30xhtvaMiQIWrdurVWrVqlzMxM9e3b162fBQsWqEuXLm5ty5cvV8+ePdW6dWtdf/31Wrx4cfNNDB5BIILltGrVSi+99JJ27dqlFStWaMOGDZo+ffp568eOHatOnTpp+/btKigo0G9/+1vzX5k7d+5UfHy8Ro0apS+++EJr167Vpk2b9MgjjzTXdABcwIwZM5Senq6ioiLFx8df0j7Lli3Tk08+qeeee05FRUWaPXu2nnrqKa1YsaKJRwtP4pIZrmrvvfee2rZta64nJCTozTffNNcjIyM1a9Ys/eY3vznvvwAPHDigxx9/XNdff70kqVu3bua2F154QcnJycrIyDC3vfTSS4qNjdWSJUvUunXrJpgVgEuVkZGhUaNGXdY+s2bN0ty5c839IiMjtWfPHi1dulTjxo1rimGiBSAQ4ao2dOhQLVmyxFwPCAjQxx9/rNmzZ2vPnj2qrKzU6dOnderUKZ04cUIBAQH1+pgyZYoefPBBrVy5UnFxcbr33nt13XXXSZIKCgr05ZdfavXq1Wa9YRg6c+aMiouL1bNnz6afJIDz6t+//2XVl5eX6+DBg0pNTdWECRPM9tOnT1/SN6bjykUgwlUtICBAXbt2Nde/+eYb3X777Xr44Yc1a9YsBQcHa9OmTUpNTVVtbe05+8jMzFRycrLef/99ffjhh3rmmWe0Zs0a3X333Tpz5oweeughpaen19vvmmuuabJ5Abg0Z/8jp1WrVjr7G6t+euyfOXNG0g+XzQYMGOBW5+Xl1USjREtAIIKl7NixQ6dPn9bcuXPVqtUPt9C98cYbF92ve/fu6t69ux577DHdd999Wr58ue6++27ddNNN2r17t1voAtBydezYUaWlpTIMQzabTZJUWFhobg8LC9MvfvELff311xo7dqyHRglPIBDBUq677jqdPn1aCxcu1B133KFPP/1Uf/zjH89bX1VVpccff1y//OUvFRkZqUOHDmn79u265557JP1ww+bAgQM1adIkTZgwQQEBASoqKlJubq4WLlzYXNMCcImGDBmi8vJyzZkzR7/85S+Vk5OjDz/8UEFBQWZNZmam0tPTFRQUpISEBFVXV2vHjh2qqKjQlClTPDh6NCWeMoOl9O3bV/PmzdPzzz+vqKgorV69WllZWeet9/Ly0pEjR3T//fere/fuGj16tBISEvTss89Kknr37q28vDzt27dPt956q2688UY99dRTCg8Pb64pAbgMPXv21OLFi/Xyyy+rT58+2rZtm6ZNm+ZW8+CDD+rPf/6zsrOzFR0drdjYWGVnZysyMtJDo0ZzsBlnX0wFAACwGM4QAQAAyyMQAQAAyyMQAQAAyyMQAQAAyyMQAQAAyyMQAQAAyyMQAQAAyyMQAQAAyyMQAUALNmTIEGVkZHh6GMBVj0AEoEnZbLYLLuPHj/f0EBsdIQa48vDlrgCaVElJifnz2rVr9fTTT2vv3r1mm7+/v1t9bW2tfHx8mm18jelKHjtgdZwhAtCkHA6HudjtdtlsNnP91KlTateund544w0NGTJErVu31qpVq3TkyBHdd9996tSpk9q0aaPo6Gj95S9/cet3yJAhSk9P1/Tp0xUcHCyHw6HMzEy3mszMTF1zzTXy8/NTRESE0tPTzW1dunTRrFmzlJycrLZt2yoiIkILFy502//AgQO688471bZtWwUFBWn06NE6fPiwW/99+/bVq6++qmuvvVZ+fn4aN26c8vLy9OKLL5pnwfbv3y9J2rNnj26//Xa1bdtWYWFhSklJ0XfffWf2d+LECd1///1q27atwsPDNXfu3Eb6rwDgYghEADxuxowZSk9PV1FRkeLj43Xq1Cn169dP7733nnbt2qWJEycqJSVFW7duddtvxYoVCggI0NatWzVnzhz97ne/U25uriTpr3/9q+bPn6+lS5dq3759evvttxUdHe22/wsvvKDevXvrs88+08yZM/XYY4+Z+xuGobvuukvff/+98vLylJubq6+++kpjxoxx6+PLL7/UG2+8obfeekuFhYV66aWXFBMTowkTJqikpEQlJSVyOp0qKSlRbGys+vbtqx07dignJ0eHDx/W6NGjzb4ef/xxffzxx1q3bp3Wr1+vjRs3qqCgoCl+5QDOZgBAM1m+fLlht9vN9eLiYkOSsWDBgovue/vttxtTp04112NjY41bbrnFrebmm282ZsyYYRiGYcydO9fo3r27UVNTc87+OnfubIwcOdKtbcyYMUZCQoJhGIaxfv16w8vLyzhw4IC5fffu3YYkY9u2bYZhGMYzzzxj+Pj4GGVlZW79xMbGGo8++qhb21NPPWWMGDHCre3gwYOGJGPv3r3GsWPHDF9fX2PNmjXm9iNHjhj+/v71+gLQ+DhDBMDj+vfv77ZeV1en5557Tr1791ZISIjatm2r9evX68CBA251vXv3dlsPDw9XWVmZJOnee+9VVVWVrr32Wk2YMEHr1q3T6dOn3epjYmLqrRcVFUmSioqK5HQ65XQ6ze29evVSu3btzBpJ6ty5szp27HjRORYUFOjjjz9W27ZtzeX666+XJH311Vf66quvVFNT4zam4OBg9ejR46J9A/j5CEQAPC4gIMBtfe7cuZo/f76mT5+uDRs2qLCwUPHx8aqpqXGrO/sGZpvNpjNnzkiSnE6n9u7dq5dffln+/v5KS0vT4MGDVVtbe8Gx2Gw2ST9cMvvx5586u/3ssZ/PmTNndMcdd6iwsNBt2bdvnwYPHizDMC6pHwBNg0AEoMX55JNPdOedd+rXv/61+vTpo2uvvVb79u277H78/f2VlJSkl156SRs3btSWLVu0c+dOc3t+fr5bfX5+vnnWplevXjpw4IAOHjxobt+zZ49cLpd69ux5wc/19fVVXV2dW9tNN92k3bt3q0uXLuratavbEhAQoK5du8rHx8dtTBUVFfrPf/5z2fMGcPkIRABanK5duyo3N1ebN29WUVGRHnroIZWWll5WH9nZ2XrllVe0a9cuff3111q5cqX8/f3VuXNns+bTTz/VnDlz9J///Ecvv/yy3nzzTT366KOSpLi4OPXu3Vtjx47VZ599pm3btun+++9XbGxsvUt8Z+vSpYu2bt2q/fv367vvvtOZM2c0adIkff/997rvvvu0bds2ff3111q/fr0eeOAB1dXVqW3btkpNTdXjjz+uf/7zn9q1a5fGjx+vVq343zTQHDjSALQ4Tz31lG666SbFx8dryJAhcjgcuuuuuy6rj3bt2mnZsmX6f//v/6l379765z//qXfffVchISFmzdSpU1VQUKAbb7xRs2bN0ty5cxUfHy/ph0tnb7/9ttq3b6/BgwcrLi5O1157rdauXXvRz542bZq8vLzUq1cvdezYUQcOHFBERIQ+/fRT1dXVKT4+XlFRUXr00Udlt9vN0PPCCy9o8ODBSkpKUlxcnG655Rb169fvsuYNoGFsBheuAVhQly5dlJGRwRulAUjiDBEAAACBCAAAgEtmAADA8jhDBAAALI9ABAAALI9ABAAALI9ABAAALI9ABAAALI9ABAAALI9ABAAALI9ABAAALO//A3kyT7TAnQ03AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x=\"Transported\",data=train_sp,hue=\"VIP\",palette=\"winter_r\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "d980df5a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x1edd95cac50>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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CQru6goIChYSEXPacM2bMUHFxse119OjRmhsQAAB1CHMmAACoq5weiFzIMAy7S3N/6/jx4zp69KjCwsIkSZ06dZKnp6fS0tJsNXl5ecrMzFT37t0vew4vLy/bo37PvwAAwMWYMwEAQF3l1DVEZs6cqQEDBigyMlInTpxQamqqtm7dqk2bNunkyZNKTk7Wvffeq7CwMB0+fFgzZ85UcHCw7rnnHklSQECAxowZo6SkJDVp0kRBQUGaMmWK2rdvr7i4OGcODQAAAAAAuDCnBiI///yzRo0apby8PAUEBKhDhw7atGmT+vTpo9LSUu3du1dvv/22ioqKFBYWpjvuuEPr1q2Tn5+f7RgLFy6Uh4eHhg8frtLSUvXu3VurVq1S/fr1nTgyAAAAAADgyiyGYRjObsLZSkpKFBAQoOLiYi4FBgCgEsyZAACgrnC5NUQAAAAAAABqG4EIAAAAAAAwHaeuIQI4Q2xMjHJycyutaRYRocysLAd1BAAAAABwNAIRmE5Obq6KUlIqrQlMSnJQNwAAAAAAZ+CWGQAAAAAAYDoEIgAAAAAAwHQIRAAAAAAAgOkQiAAAAAAAANMhEAEAAAAAAKZDIAIAAAAAAEyHQAQAAAAAAJgOgQgAAAAAADAdAhEAAAAAAGA6BCIAAAAAAMB0CEQAAAAAAIDpEIgAAAAAAADTIRABAAAAAACmQyACAAAAAABMh0AEAAAAAACYDoEIAAAAAAAwHQIRAAAAAABgOgQiAAAAAADAdAhEAAAAAACA6RCIAAAAAAAA0yEQAQAAAAAApkMgAgAAAAAATIdABAAAAAAAmA6BCAAAAAAAMB0CEQAAAAAAYDoEIgAAAAAAwHQIRAAAAAAAgOkQiAAAAAAAANMhEAEAAAAAAKZDIAIAAAAAAEyHQAQAAAAAAJgOgQgAAAAAADAdAhEAAAAAAGA6BCIAAAAAAMB0CEQAAAAAAIDpeDi7AcBdxcbEKCc3t8q6ZhERyszKckBHAAAAAIDqIhABrlJObq6KUlKqrAtMSnJANwAAAACAK8EtMwAAAAAAwHQIRAAAAAAAgOkQiAAAAAAAANNhDRHUGdVd5PSU1eqAbgAAAAAAroxABHVGdRc59UhIcEA3AAAAAABX5tRbZpYtW6YOHTrI399f/v7+6tatmz755BPbfsMwlJycrPDwcHl7e6tXr17at2+f3THKyso0YcIEBQcHy8fHR0OGDFFOTo6jhwIAAAAAANyIUwORZs2aae7cudq9e7d2796tO++8U3fffbct9Jg3b54WLFigJUuWaNeuXQoNDVWfPn104sQJ2zESExO1fv16paamavv27Tp58qQGDRqkiooKZw0LAAAAAAC4OKcGIoMHD9bAgQPVpk0btWnTRi+99JJ8fX21c+dOGYahRYsWadasWRo2bJhiY2O1evVqnTp1Su+8844kqbi4WMuXL1dKSori4uJ0yy23aO3atdq7d682b97szKEBAAAAAAAX5jJPmamoqFBqaqqsVqu6deum7Oxs5efnq2/fvrYaLy8v9ezZUzt27JAkpaen68yZM3Y14eHhio2NtdVcSllZmUpKSuxeAADgYsyZAACgrnJ6ILJ37175+vrKy8tLjz32mNavX6927dopPz9fkhQSEmJXHxISYtuXn5+vBg0aqHHjxpetuZQ5c+YoICDA9oqMjKzhUQEAUDcwZwIAgLrK6YFI27ZtlZGRoZ07d+rxxx/X6NGjtX//ftt+i8ViV28YxkXbLlRVzYwZM1RcXGx7HT169NoGAQBAHcWcCQAA6iqnP3a3QYMGatWqlSSpc+fO2rVrl1555RVNnz5d0q9XgYSFhdnqCwoKbFeNhIaGqry8XIWFhXZXiRQUFKh79+6XPaeXl5e8vLxqYzgAANQpzJkAAKCucvoVIhcyDENlZWVq0aKFQkNDlZaWZttXXl6ubdu22cKOTp06ydPT064mLy9PmZmZlQYiAAAAAADA3Jx6hcjMmTM1YMAARUZG6sSJE0pNTdXWrVu1adMmWSwWJSYmavbs2WrdurVat26t2bNnq1GjRho5cqQkKSAgQGPGjFFSUpKaNGmioKAgTZkyRe3bt1dcXJwzhwYAAAAAAFyYUwORn3/+WaNGjVJeXp4CAgLUoUMHbdq0SX369JEkTZs2TaWlpRo7dqwKCwvVtWtXffbZZ/Lz87MdY+HChfLw8NDw4cNVWlqq3r17a9WqVapfv76zhgUAAAAAAFycUwOR5cuXV7rfYrEoOTlZycnJl61p2LChFi9erMWLF9dwdwAAAAAAoK5yuTVEAAAAAAAAahuBCAAAAAAAMB0CEQAAAAAAYDoEIgAAAAAAwHQIRAAAAAAAgOkQiAAAAAAAANMhEAEAAAAAAKZDIAIAAAAAAEyHQAQAAAAAAJgOgQgAAAAAADAdAhEAAAAAAGA6BCIAAAAAAMB0CEQAAAAAAIDpEIgAAAAAAADTIRABAAAAAACmQyACAAAAAABMh0AEAAAAAACYDoEIAAAAAAAwHQIRAAAAAABgOgQiAAAAAADAdAhEAAAAAACA6RCIAAAAAAAA0yEQAQAAAAAApkMgAgAAAAAATIdABAAAAAAAmI6HsxsA6rpSq1WBfn6V1jSLiFBmVpaDOgIAAAAAEIgAtazCMFSUklJpTWBSkoO6AQAAAABI3DIDAAAAAABMiEAEAAAAAACYDoEIAAAAAAAwHQIRAAAAAABgOgQiAAAAAADAdAhEAAAAAACA6RCIAAAAAAAA0/FwdgMAAABwvJiYWOXm5lRaExHRTFlZmQ7qCAAAxyIQAQAAMKHc3BylpBRVWpOUFOiQXgAAcAYCEQAAALgVrm4BANQEAhEAAAC4Fa5uAQDUBBZVBQAAAAAApkMgAgAAAAAATIdbZgAAAHBJVmup/PwCq6xjvQ4AgDsiEAEAAMAlGUZFlWt1SKzXAQBwTwQiAAAAqHU8GQYA4GoIRAAAAFDreDIMAMDVEIgAAADAJVR3zRKr9VTtNwMAqPOcGojMmTNHH374obKysuTt7a3u3bvr5ZdfVtu2bW018fHxWr16td3nunbtqp07d9rel5WVacqUKXr33XdVWlqq3r17a+nSpWrWrJnDxgIAAIBrU901SxIS+J0eAODaOfWxu9u2bdO4ceO0c+dOpaWl6ezZs+rbt6+sVqtdXf/+/ZWXl2d7bdy40W5/YmKi1q9fr9TUVG3fvl0nT57UoEGDVFFR4cjhAAAAAAAAN+HUeH3Tpk1271euXKmmTZsqPT1dPXr0sG338vJSaGjoJY9RXFys5cuXa82aNYqLi5MkrV27VpGRkdq8ebP69etXewMAAAAAAABuyalXiFyouLhYkhQUFGS3fevWrWratKnatGmjRx99VAUFBbZ96enpOnPmjPr27WvbFh4ertjYWO3YscMxjQMAAAAAALfiMjdgGoahyZMn6/bbb1dsbKxt+4ABA3TfffcpOjpa2dnZevrpp3XnnXcqPT1dXl5eys/PV4MGDdS4cWO744WEhCg/P/+S5yorK1NZWZntfUlJSe0MCgAAN8ecCQAA6iqXCUTGjx+vPXv2aPv27XbbR4wYYfs6NjZWnTt3VnR0tD7++GMNGzbsssczDEMWi+WS++bMmaPnnnuuZhoHAKAOY84EAAB1lUvcMjNhwgR99NFH2rJlS5VPhgkLC1N0dLQOHTokSQoNDVV5ebkKCwvt6goKChQSEnLJY8yYMUPFxcW219GjR2tmIAAA1DHMme4pJiZWfn6Blb54dC0AwOyceoWIYRiaMGGC1q9fr61bt6pFixZVfub48eM6evSowsLCJEmdOnWSp6en0tLSNHz4cElSXl6eMjMzNW/evEsew8vLS15eXjU3EAAA6ijmTPeUm5tT5eNreXQtAMDsnDoTjhs3Tu+8847++te/ys/Pz7bmR0BAgLy9vXXy5EklJyfr3nvvVVhYmA4fPqyZM2cqODhY99xzj612zJgxSkpKUpMmTRQUFKQpU6aoffv2tqfOAAAAAAAA/JZTA5Fly5ZJknr16mW3feXKlYqPj1f9+vW1d+9evf322yoqKlJYWJjuuOMOrVu3Tn5+frb6hQsXysPDQ8OHD1dpaal69+6tVatWqX79+o4cDgAAAAAAcBNOv2WmMt7e3vr000+rPE7Dhg21ePFiLV68uKZaAwAAAAAAdZhLLKoKAAAAAADgSAQiAAAAAADAdAhEAAAAAACA6RCIAAAAAAAA0yEQAQAAAAAApkMgAgAAAAAATIdABAAAAAAAmA6BCAAAAAAAMB0CEQAAAAAAYDoEIgAAAAAAwHQIRAAAAAAAgOl4OLsBAFKp1apAP79Ka5pFRCgzK8tBHQEAAABA3UYgAriACsNQUUpKpTWBSUkO6gYAAAAA6j5umQEAAAAAAKZDIAIAAAAAAEyHQAQAAAAAAJgOgQgAAAAAADAdAhEAAAAAAGA6PGUGbiE2JkY5ubmV1pyyWh3UDQAAAADA3RGIwC3k5OZW+Vhaj4QEB3UDAAAAAHB33DIDAAAAAABMh0AEAAAAAACYDoEIAAAAAAAwHQIRAAAAAABgOgQiAAAAAADAdAhEAAAAAACA6RCIAAAAAAAA0yEQAQAAAAAApuPh7AYAVE+p1apAP78q65pFRCgzK8sBHQEAAACA+yIQAdxEhWGoKCWlyrrApCQHdAMAAAAA7o1bZgAAAAAAgOkQiAAAAAAAANMhEAEAAAAAAKZDIAIAAAAAAEyHQAQAAAAAAJgOgQgAAAAAADAdAhEAAAAAAGA6BCIAAAAAAMB0CEQAAAAAAIDpEIgAAAAAAADTIRABAAAAAACmQyACAAAAAABMh0AEAAAAAACYjoezGwBQs0qtVgX6+VVa0ywiQplZWQ7qCAAAAABcD4EIUMdUGIaKUlIqrQlMSnJQNwAAAADgmrhlBgAAAAAAmI5TA5E5c+aoS5cu8vPzU9OmTTV06FAdPHjQrsYwDCUnJys8PFze3t7q1auX9u3bZ1dTVlamCRMmKDg4WD4+PhoyZIhycnIcORQAAAAAAOBGnBqIbNu2TePGjdPOnTuVlpams2fPqm/fvrJarbaaefPmacGCBVqyZIl27dql0NBQ9enTRydOnLDVJCYmav369UpNTdX27dt18uRJDRo0SBUVFc4YFgAAAAAAcHFOXUNk06ZNdu9Xrlyppk2bKj09XT169JBhGFq0aJFmzZqlYcOGSZJWr16tkJAQvfPOO0pISFBxcbGWL1+uNWvWKC4uTpK0du1aRUZGavPmzerXr5/DxwUAAAAAAFybS60hUlxcLEkKCgqSJGVnZys/P199+/a11Xh5ealnz57asWOHJCk9PV1nzpyxqwkPD1dsbKyt5kJlZWUqKSmxewEAgIsxZwIAgLrKZQIRwzA0efJk3X777YqNjZUk5efnS5JCQkLsakNCQmz78vPz1aBBAzVu3PiyNReaM2eOAgICbK/IyMiaHg4AAHUCcyYAAKirXCYQGT9+vPbs2aN33333on0Wi8XuvWEYF227UGU1M2bMUHFxse119OjRq28cAIA6jDkTAADUVU5dQ+S8CRMm6KOPPtKXX36pZs2a2baHhoZK+vUqkLCwMNv2goIC21UjoaGhKi8vV2Fhod1VIgUFBerevfslz+fl5SUvL6/aGAoAAHUKcyYAAKirnHqFiGEYGj9+vD788EN98cUXatGihd3+Fi1aKDQ0VGlpabZt5eXl2rZtmy3s6NSpkzw9Pe1q8vLylJmZedlABAAAAAAAmJtTrxAZN26c3nnnHf31r3+Vn5+fbc2PgIAAeXt7y2KxKDExUbNnz1br1q3VunVrzZ49W40aNdLIkSNttWPGjFFSUpKaNGmioKAgTZkyRe3bt7c9dQYAAAAAAOC3rioQadmypXbt2qUmTZrYbS8qKlLHjh31448/Vus4y5YtkyT16tXLbvvKlSsVHx8vSZo2bZpKS0s1duxYFRYWqmvXrvrss8/k5+dnq1+4cKE8PDw0fPhwlZaWqnfv3lq1apXq169/NcMDAAAAAAB13FUFIocPH1ZFRcVF28vKypSbm1vt4xiGUWWNxWJRcnKykpOTL1vTsGFDLV68WIsXL672uQEAAAAAgHldUSDy0Ucf2b7+9NNPFRAQYHtfUVGhzz//XM2bN6+x5gAAAAAAAC6nV69euvnmm7Vo0aIr/uwVBSJDhw6V9OtVG6NHj7bb5+npqebNmyslJeWKmwDgvmJjYpRTxZVhzSIilJmV5aCOALibmJhY5ebmVFkXEdFMWVmZDugIAADHsVgsle4fPXq0Vq1a5ZhmHORaQoyadEWByLlz5yT9+vSXXbt2KTg4uFaaAuA+cnJzVVRFEBqYlOSgbgC4o9zcHKWkFFVZl5QUWOu9AADgaHl5ebav161bp2eeeUYHDx60bfP29rarP3PmjDw9PR3WX01ytd6v6rG72dnZhCEAAAAAAFyj0NBQ2ysgIEAWi8X2/vTp0woMDNR7772nXr16qWHDhlq7dq2OHz+uBx54QM2aNVOjRo3Uvn17vfvuu3bH7dWrlyZOnKhp06YpKChIoaGhF63NmZycrKioKHl5eSk8PFwTJ0607WvevLleeOEFjRw5Ur6+vgoPD79o3c4jR47o7rvvlq+vr/z9/TV8+HD9/PPPdse/+eabtWLFCrVs2VJeXl4aPXq0tm3bpldeeUUWi0UWi0WHDx+WJO3fv18DBw6Ur6+vQkJCNGrUKB07dsx2PKvVqoceeki+vr4KCwu75jtUrvqxu59//rk+//xzFRQU2K4cOW/FihXX1BQAAAAAAPjV9OnTlZKSopUrV8rLy0unT59Wp06dNH36dPn7++vjjz/WqFGj1LJlS3Xt2tX2udWrV2vy5Mn69ttv9c033yg+Pl633Xab+vTpo7/85S9auHChUlNTdeONNyo/P1/fffed3Xnnz5+vmTNnKjk5WZ9++qmeeOIJxcTEqE+fPjIMQ0OHDpWPj4+2bdums2fPauzYsRoxYoS2bt1qO8YPP/yg9957Tx988IHq16+v6OhoHTp0SLGxsXr++eclSdddd53y8vLUs2dPPfroo1qwYIFKS0s1ffp0DR8+XF988YUkaerUqdqyZYvWr1+v0NBQzZw5U+np6br55puv6s/1qgKR5557Ts8//7w6d+6ssLCwKu95AgAAAAAAVycxMVHDhg2z2zZlyhTb1xMmTNCmTZv0/vvv2wUiHTp00LPPPitJat26tZYsWaLPP/9cffr00ZEjRxQaGqq4uDh5enoqKipKv/vd7+zOcdttt+nJJ5+UJLVp00Zff/21Fi5cqD59+mjz5s3as2ePsrOzFRkZKUlas2aNbrzxRu3atUtdunSRJJWXl2vNmjW67rrrbMdt0KCBGjVqpNDQUNu2ZcuWqWPHjpo9e7Zt24oVKxQZGanvv/9e4eHhWr58ud5++2316dNH0q+BT7Nmza76z/WqApHXX39dq1at0qhRo676xAAAAAAAoGqdO3e2e19RUaG5c+dq3bp1ys3NVVlZmcrKyuTj42NX16FDB7v3YWFhKigokCTdd999WrRokVq2bKn+/ftr4MCBGjx4sDw8/l9M0K1bN7vPd+vWzbYQ6oEDBxQZGWkLQySpXbt2CgwM1IEDB2yBSHR0tF0Ycjnp6enasmWLfH19L9r373//W6WlpSovL7frKSgoSG3btq3y2JdzVWuIlJeXq3v37ld9UgAAAAAAUD0XBh0pKSlauHChpk2bpi+++EIZGRnq16+fysvL7eouXMDUYrHYlryIjIzUwYMH9dprr8nb21tjx45Vjx49dObMmUp7OX+HiGEYl7xb5MLtF/Z+OefOndPgwYOVkZFh9zp06JB69OghwzCqdZwrcVWByCOPPKJ33nmnpnsBAAAAAABV+Oqrr3T33XfrwQcf1E033aSWLVvq0KFDV3wcb29vDRkyRK+++qq2bt2qb775Rnv37rXt37lzp139zp07FRMTI+nXq0GOHDmio0eP2vbv379fxcXFuuGGGyo9b4MGDVRRUWG3rWPHjtq3b5+aN2+uVq1a2b18fHzUqlUreXp62vVUWFio77///orHfd5V3TJz+vRpvfnmm9q8ebM6dOhwUeq0YMGCq24IAAAAAABcXqtWrfTBBx9ox44daty4sRYsWKD8/Pwqg4jfWrVqlSoqKtS1a1c1atRIa9askbe3t6Kjo201X3/9tebNm6ehQ4cqLS1N77//vj7++GNJUlxcnDp06KA//vGPWrRokW1R1Z49e150i8+Fmjdvrm+//VaHDx+Wr6+vgoKCNG7cOL311lt64IEHNHXqVAUHB+uHH35Qamqq3nrrLfn6+mrMmDGaOnWqmjRpopCQEM2aNUv16l3VdR6SrjIQ2bNnj20V18zMTLt9LLAKAAAAAEDtefrpp5Wdna1+/fqpUaNG+tOf/qShQ4equLi42scIDAzU3LlzNXnyZFVUVKh9+/b629/+piZNmthqkpKSlJ6erueee05+fn5KSUlRv379JP36b/8NGzZowoQJ6tGjh+rVq6f+/ftf9GjeS5kyZYpGjx6tdu3aqbS0VNnZ2WrevLm+/vprTZ8+Xf369VNZWZmio6PVv39/W+gxf/58nTx5UkOGDJGfn5+SkpKuaMwXuqpAZMuWLVd9QgAAAAAAcLH4+HjFx8fb3jdv3vySa2cEBQVpw4YNlR7rt4++Pe+3nxk6dKiGDh1a6TH8/f21bt26y+6PiorSX//618vuT05OVnJy8kXb27Rpo2+++eai7a1bt9aHH3542eP5+vpqzZo1WrNmjW3b1KlTL1tflau/tgQAAAAAAMBNXdUVInfccUelt8Z88cUXV90QAAAAAABAbbuqQOT8+iHnnTlzRhkZGcrMzNTo0aNroi8AAAAAAOAkhw8fdnYLte6qApGFCxdecntycrJOnjx5TQ0BAAAAAADUthpdQ+TBBx/UihUravKQAAAAAAAANa5GA5FvvvlGDRs2rMlDAgAAAAAA1LirumVm2LBhdu8Nw1BeXp52796tp59+ukYaAwAAAAAAqC1XFYgEBATYva9Xr57atm2r559/Xn379q2RxgAAAAAAAGrLVQUiK1eurOk+AAAAALcVExOr3NycSmsiIpopKyvTQR0BAKpyVYHIeenp6Tpw4IAsFovatWunW265pab6AgAAANxGbm6OUlKKKq1JSgp0SC8A3NuRI0d07Ngxh50vODhYUVFRtX6eVatWKTExUUVFRbV+ruq6qkCkoKBA999/v7Zu3arAwEAZhqHi4mLdcccdSk1N1XXXXVfTfQIAAAAAUKcdOXJEN8TE6FRpqcPO2cjbWweysqodisTHx2v16tUXbT906JBatWpV0+3VqqsKRCZMmKCSkhLt27dPN9xwgyRp//79Gj16tCZOnKh33323RpsEAAAAAKCuO3bsmE6Vlmrtww/rhrCwWj/fgbw8PbhihY4dO3ZFV4n079//oqU03PHCiKsKRDZt2qTNmzfbwhBJateunV577TUWVQUAAAAA4BrcEBamjg64jeVqeXl5KTQ01G7bggULtHLlSv34448KCgrS4MGDNW/ePPn6+l7yGN99950SExO1e/duWSwWtW7dWm+88YY6d+4sSdqxY4eefPJJ7dq1S8HBwbrnnns0Z84c+fj41Ng46l3Nh86dOydPT8+Ltnt6eurcuXPX3BQA54uNiVGgn1+Vr1NWq7NbBQAAAOBk9erV06uvvqrMzEytXr1aX3zxhaZNm3bZ+j/+8Y9q1qyZdu3apfT0dD355JO2nGHv3r3q16+fhg0bpj179mjdunXavn27xo8fX6M9X9UVInfeeacmTZqkd999V+Hh4ZKk3NxcPfHEE+rdu3eNNgjAOXJyc1WUklJlnUdCggO6AQBI1XuSiSRZracc0A0AwKz+/ve/2135MWDAAL3//vu29y1atNALL7ygxx9/XEuXLr3kMY4cOaKpU6cqJiZGktS6dWvbvvnz52vkyJFKTEy07Xv11VfVs2dPLVu2TA0bNqyRcVxVILJkyRLdfffdat68uSIjI2WxWHTkyBG1b99ea9eurZHGAAAAYK86TzKRpISEa3qQoGlUN2DicbkAYO+OO+7QsmXLbO99fHy0ZcsWzZ49W/v371dJSYnOnj2r06dPy2q1XvI2l8mTJ+uRRx7RmjVrFBcXp/vuu0/XX3+9pF+faPvDDz/oz3/+s63eMAydO3dO2dnZdst3XIurmi0jIyP1z3/+U2lpacrKypJhGGrXrp3i4uJqpCkAAACgtlU3YOJxuQBgz8fHx+6JMj/99JMGDhyoxx57TC+88IKCgoK0fft2jRkzRmfOnLnkMZKTkzVy5Eh9/PHH+uSTT/Tss88qNTVV99xzj86dO6eEhARNnDjxos/V5COCrygQ+eKLLzR+/Hjt3LlT/v7+6tOnj/r06SNJKi4u1o033qjXX39dv//972usQQAAAAAA4Lp2796ts2fPKiUlRfXq/bpU6XvvvVfl59q0aaM2bdroiSee0AMPPKCVK1fqnnvuUceOHbVv375af4zvFS2qumjRIj366KPy9/e/aF9AQIASEhK0YMGCGmsOAAAAAAC4tuuvv15nz57V4sWL9eOPP2rNmjV6/fXXL1tfWlqq8ePHa+vWrfrpp5/09ddfa9euXbZbYaZPn65vvvlG48aNU0ZGhg4dOqSPPvpIEyZMqNG+r+gKke+++04vv/zyZff37dtX//u//3vNTQEAAAAAYFYH8vLc6jw333yzFixYoJdfflkzZsxQjx49NGfOHD300EOXrK9fv76OHz+uhx56SD///LOCg4M1bNgwPffcc5KkDh06aNu2bZo1a5Z+//vfyzAMXX/99RoxYkSN9HveFQUiP//88yUft2s7mIeH/vvf/15zUwAAAAAAmE1wcLAaeXvrwRUrHHbORt7eCg4Ornb9qlWrLrn9iSee0BNPPGG3bdSoUbav4+PjFR8fL0lq0KCB3n333UrP06VLF3322WfV7utqXFEgEhERob179172Pp49e/YoLCysRhoDAAAArpbVWio/v8Aqang8MQDXEhUVpQNZWTp27JjDzhkcHFyjC5W6kysKRAYOHKhnnnlGAwYMuOi5v6WlpXr22Wc1aNCgGm0QAAAAuFKGUVHlE2R4PDEAVxQVFWXagMLRrmgWeOqpp/Thhx+qTZs2Gj9+vNq2bSuLxaIDBw7otddeU0VFhWbNmlVbvQIAAAAAANSIKwpEQkJCtGPHDj3++OOaMWOGDMOQJFksFvXr109Lly5VSEhIrTQKAAAAAABQU674OsHo6Ght3LhRhYWF+uGHH2QYhlq3bq3GjRvXRn8AAAAAAAA17qpvnGzcuLG6dOlSk70AAAAAAAA4RD1nNwAAAAAAAOBoBCIAAAAAAMB0CEQAAAAAAIDp8PB1AAAAAABcxJEjR3Ts2DGHnS84OFhRUVEOO58rIRABAAAAAMAFHDlyRDExN6i09JTDzunt3UhZWQeqFYpYLJZK948ePVqrVq2qoc5qH4EIcAmlVqsC/fwqrTlltTqoGwAAAABmcOzYMZWWntLDD69VWNgNtX6+vLwDWrHiQR07dqxagUheXp7t63Xr1umZZ57RwYMHbdu8vb3t6s+cOSNPT8+aa7iGEYgAl1BhGCpKSam0xiMhwUHdAAAAADCTsLAbFBXV0dltXCQ0NNT2dUBAgCwWi23b4cOHFRYWpnXr1mnp0qXauXOnli1bpp9++kkbNmxQRkaG7bOLFi3SokWLdPjwYdu2lStXat68ecrOzlbz5s01ceJEjR07tlbH49RFVb/88ksNHjxY4eHhslgs2rBhg93++Ph4WSwWu9ett95qV1NWVqYJEyYoODhYPj4+GjJkiHJychw4CgAAAAAAIEnTp0/XxIkTdeDAAfXr169an3nrrbc0a9YsvfTSSzpw4IBmz56tp59+WqtXr67VXp0aiFitVt10001asmTJZWv69++vvLw822vjxo12+xMTE7V+/XqlpqZq+/btOnnypAYNGqSKiorabh8AAAAAAPxGYmKihg0bphYtWig8PLxan3nhhReUkpJi+9ywYcP0xBNP6I033qjVXp16y8yAAQM0YMCASmu8vLzsLsv5reLiYi1fvlxr1qxRXFycJGnt2rWKjIzU5s2bq51GAQAAAACAa9e5c+crqv/vf/+ro0ePasyYMXr00Udt28+ePauAgICabs+Oy68hsnXrVjVt2lSBgYHq2bOnXnrpJTVt2lSSlJ6erjNnzqhv3762+vDwcMXGxmrHjh2XDUTKyspUVlZme19SUlK7gwAAwE0xZwIAgCvh4+Nj975evXoyDMNu25kzZ2xfnzt3TtKvt8107drVrq5+/fq11OX/31utHv0aDRgwQH/+85/1xRdfKCUlRbt27dKdd95p+8EsPz9fDRo0UOPGje0+FxISovz8/Msed86cOQoICLC9IiMja3UcAAC4K+ZMAABwLa677jrl5+fbhSK/XWA1JCREERER+vHHH9WqVSu7V4sWLWq1N5e+QmTEiBG2r2NjY9W5c2dFR0fr448/1rBhwy77OcMwKn0+8owZMzR58mTb+5KSEn7AAwDgEpgzHScmJla5uZUvDG+1nnJQNwAA1IxevXrpv//9r+bNm6c//OEP2rRpkz755BP5+/vbapKTkzVx4kT5+/trwIABKisr0+7du1VYWGj3c0hNc+lA5EJhYWGKjo7WoUOHJP36yJ/y8nIVFhbaXSVSUFCg7t27X/Y4Xl5e8vLyqvV+AQBwd8yZjpObm6OUlKJKaxIS3OpHNwDAVcrLO1BnznPDDTdo6dKlmj17tl544QXde++9mjJlit58801bzSOPPKJGjRpp/vz5mjZtmnx8fNS+fXslJibWam9uNaseP35cR48eVVhYmCSpU6dO8vT0VFpamoYPHy5JysvLU2ZmpubNm+fMVgEAAAAAuCLBwcHy9m6kFSsedNg5vb0bKTg4+Io/Fx8fr/j4eNv75s2bX7RWyHmPPfaYHnvsMbttM2fOtHs/cuRIjRw58or7uBZODUROnjypH374wfY+OztbGRkZCgoKUlBQkJKTk3XvvfcqLCxMhw8f1syZMxUcHKx77rlHkhQQEKAxY8YoKSlJTZo0UVBQkKZMmaL27dvbnjoDAAAAAIA7iIqKUlbWAR07dsxh5wwODlZUVJTDzudKnBqI7N69W3fccYft/fl7g0aPHq1ly5Zp7969evvtt1VUVKSwsDDdcccdWrdunfz8/GyfWbhwoTw8PDR8+HCVlpaqd+/eWrVqVa2vRgsAAAAAQE2LiooybUDhaE4NRHr16nXZS2ok6dNPP63yGA0bNtTixYu1ePHimmwNAAAAqFFWa6n8/AKrrIuIaKasrMzabwgATM6t1hABAAAA3JVhVFS5cK4kJSUF1novAACpnrMbAAAAAAAAcDQCEQAAAAAAYDoEIgAAAAAAwHQIRAAAAAAAgOkQiAAAAAAAANMhEAEAAAAAAKZDIAIAAAAAAEzHw9kNAAAAwL1ZraXy8wusouaUY5oBAKCaCEQAAABwTQyjQikpRZXWJCTwYycAwLUwMwEAAAAupDpX3ERENFNWVqZjGgKAOopABAAAAHAh1bniJikp0CG9AEBdxqKqAAAAAADAdAhEAAAAAACA6RCIAAAAAAAA02ENEThdbEyMcnJzK605ZbU6qBvUhlKrVYF+fpXWNIuIUGZWloM6AgAAAGB2BCKoNdUJOqRfw47y11+vtMYjIaGm2oITVBiGilJSKq0JTEpyUDcAAAAAQCCCWpSTm1vlP4Ilwg4AAAAAgOMRiABwCdW5rUbi1hoAAKorJiZWubk5VdZFRDRTVlamAzoCANdCIALAJVTnthqJW2sAAKiu3NwcpaQUVVmXlBRY670AgCviKTMAAAAAAMB0CEQAAAAAAIDpEIgAAAAAAADTIRABAAAAAACmQyACAAAAAABMh0AEAAAAAACYDoEIAAAAAAAwHQ9nNwAAAADgylitpfLzC6yi5pRjmgEAN0UgAgAAALgZw6hQSkpRpTUJCfyoDwCV4ZYZAAAAAABgOgQiAAAAAADAdAhEAAAAAACA6RCIAAAAAAAA02GlJQBupdRqVaCfX6U1zSIilJmV5aCOAAAAALgjAhEAbqXCMFSUklJpTWBSkoO6AQAAAOCuuGUGAAAAAACYDleIAAAAt2C1lsrPL7DSmoiIZsrKynRMQwAAwK0RiAAAALdgGBVKSSmqtCYpKdAhvQAAAPfHLTMAAAAAAMB0CEQAAAAAAIDpEIgAAAAAAADTIRABAAAAAACmQyACAAAAAABMh0AEAAAAAACYDoEIAAAAAAAwHacGIl9++aUGDx6s8PBwWSwWbdiwwW6/YRhKTk5WeHi4vL291atXL+3bt8+upqysTBMmTFBwcLB8fHw0ZMgQ5eTkOHAUAAAAAADA3Tg1ELFarbrpppu0ZMmSS+6fN2+eFixYoCVLlmjXrl0KDQ1Vnz59dOLECVtNYmKi1q9fr9TUVG3fvl0nT57UoEGDVFFR4ahhAAAAAAAAN+PhzJMPGDBAAwYMuOQ+wzC0aNEizZo1S8OGDZMkrV69WiEhIXrnnXeUkJCg4uJiLV++XGvWrFFcXJwkae3atYqMjNTmzZvVr18/h40FAAAAAAC4D5ddQyQ7O1v5+fnq27evbZuXl5d69uypHTt2SJLS09N15swZu5rw8HDFxsbaagAAAAAAAC7k1CtEKpOfny9JCgkJsdseEhKin376yVbToEEDNW7c+KKa85+/lLKyMpWVldnel5SU1FTbAADUKcyZAACgrnLZK0TOs1gsdu8Nw7ho24WqqpkzZ44CAgJsr8jIyBrpFQCAuoY5EwAA1FUuG4iEhoZK0kVXehQUFNiuGgkNDVV5ebkKCwsvW3MpM2bMUHFxse119OjRGu4eAIC6gTkTAADUVS4biLRo0UKhoaFKS0uzbSsvL9e2bdvUvXt3SVKnTp3k6elpV5OXl6fMzExbzaV4eXnJ39/f7gUAAC7GnAkAAOoqp64hcvLkSf3www+299nZ2crIyFBQUJCioqKUmJio2bNnq3Xr1mrdurVmz56tRo0aaeTIkZKkgIAAjRkzRklJSWrSpImCgoI0ZcoUtW/f3vbUGQAAAAAAgAs5NRDZvXu37rjjDtv7yZMnS5JGjx6tVatWadq0aSotLdXYsWNVWFiorl276rPPPpOfn5/tMwsXLpSHh4eGDx+u0tJS9e7dW6tWrVL9+vUdPh4AAAAAAOAenBqI9OrVS4ZhXHa/xWJRcnKykpOTL1vTsGFDLV68WIsXL66FDgEAAADExMQqNzen0pqIiGbKysp0UEcAcO1c9rG7AAAAAFxDbm6OUlKKKq1JSgp0SC8AUFNcdlFVAAAAAACA2kIgAgAAAAAATIdABAAAAAAAmA6BCAAAAAAAMB0WVQVgSrExMcrJza2yrllEhDKzshzQEQAAAABHIhABYEo5ubkqSkmpsi4wKckB3QAAAABwNG6ZAQAAAAAApkMgAgAAAAAATIdABAAAAAAAmA6BCAAAAAAAMB0CEQAAAAAAYDoEIgAAAAAAwHQIRAAAAAAAgOl4OLsBAHBlpVarAv38Kq1pFhGhzKwsB3UEAAAAoCYQiABAJSoMQ0UpKZXWBCYlOagbAAAAADWFW2YAAAAAAIDpEIgAAAAAAADTIRABAAAAAACmQyACAAAAAABMh0VVAQAAgEpYraXy8wusouaUY5oBANQYAhEAAACgEoZRoZSUokprEhL4sRoA3A3/5waAa1RqtSrQz6/SmmYREcrMynJQRwAAAACqQiACANeowjBUlJJSaU1gUpKDugEAAABQHQQiAACgzqjOWg8REc2UlZXpmIYAE6nO3z+Jv4MAXAeBCAAAqDOqs9ZDUlKgQ3oBzKY6f/8k/g4CcB0EIoAJVWfNi1NWq4O6wW/FxsQoJze30hrWIwEAAACuHYEIYELVWfPCIyHBQd3gt3Jyc1mPBAAAAHCAes5uAAAAAAAAwNEIRAAAAAAAgOkQiAAAAAAAANMhEAEAAAAAAKZDIAIAAAAAAEyHQAQAAAAAAJgOj90FAAAA4HZiYmKVm5tTZV1ERDNlZWU6oCMA7oZABAAAAIDbyc3NUUpKUZV1SUmBtd4LAPfELTMAAAAAAMB0CEQAAAAAAIDpEIgAAAAAAADTIRABAAAAAACmQyACAAAAAABMh6fMAAAAAHAYq7VUfn6BldaUlZ2Rl5dnFcc5VYNdATAjAhEAAAAADmMYFVU+LjchwUNLllirrAGAa8EtMwAAAAAAwHQIRAAAAAAAgOlwnRkAOECp1apAP78q605ZK788GMC1q876BZIUEdFMWVmZtd8QAABwCpcORJKTk/Xcc8/ZbQsJCVF+fr4kyTAMPffcc3rzzTdVWFiorl276rXXXtONN97ojHYB4LIqDENFKSlV1nkkJDigG8DcqrN+gSQlJQXWei8AAMB5XP6WmRtvvFF5eXm21969e2375s2bpwULFmjJkiXatWuXQkND1adPH504ccKJHQMAAAAAAFfn0leISJKHh4dCQ0Mv2m4YhhYtWqRZs2Zp2LBhkqTVq1crJCRE77zzjhKc/FvW2JgY5eTmVlrTLCJCmVlZDuoIAADUtJiYWOXm5lRZx+03AAC4HpcPRA4dOqTw8HB5eXmpa9eumj17tlq2bKns7Gzl5+erb9++tlovLy/17NlTO3bsqDQQKSsrU1lZme19SUlJjfedk5tb5eXxgUlJNX5eAABqkiPmTHeWm5vD7TcAALgpl75lpmvXrnr77bf16aef6q233lJ+fr66d++u48eP29YRCQkJsfvMb9cYuZw5c+YoICDA9oqMjKy1MQAA4M7MPGeeX3y1spfVesrZbQIAgKvk0leIDBgwwPZ1+/bt1a1bN11//fVavXq1br31VkmSxWKx+4xhGBdtu9CMGTM0efJk2/uSkhJT/YAHAEB1mXnOrM7iqwkJLv2jFAAAqIRbzeI+Pj5q3769Dh06pKFDh0qS8vPzFRYWZqspKCi46KqRC3l5ecnLy6s2WwUAoE5gzgQAAHWVS98yc6GysjIdOHBAYWFhatGihUJDQ5WWlmbbX15erm3btql79+5O7BIAAAAAALg6l75CZMqUKRo8eLCioqJUUFCgF198USUlJRo9erQsFosSExM1e/ZstW7dWq1bt9bs2bPVqFEjjRw50tmtA3CiUqtVgX5+ldacslod1I1zVOdJVxJPuwIAAIB5uXQgkpOTowceeEDHjh3Tddddp1tvvVU7d+5UdHS0JGnatGkqLS3V2LFjVVhYqK5du+qzzz6TXxX/EAJQt1UYRpVPefJw8qO5a1t1nnQl8bQrAACk6j1Cm8dnA3WPSwciqample63WCxKTk5WcnKyYxoCAAAAUOdU5xHaPD4bqHvcag0RAAAAAACAmkAgAgAAAAAATMelb5kBAAAAULus1lL5+QVWUXPKMc24sOr8OUmsNQK4EwIRAAAAwMQMo6LK9TMSEvhnQ3X+nCTWGgHcCbfMAAAAAAAA0yEQAQAAAAAApkMgAgAAAAAATIebAZ2o1GpVoJ9flXXNIiKUmZV1zeeLjYlRTm6uw84HAAAAAICrIhBxogrDUFFKSpV1gUlJVdZUJ+w4ZbWq/PXXa+R8AAAAAAC4MwKROiInN7fKcMUjIcFB3QAAAAAA4NpYQwQAAAAAAJgOV4jgItVZ24R1RgAAAAAA7oxABBepztomrDMCAAAAAHBn3DIDAAAAAABMh0AEAAAAAACYDrfMuIHqrOlxymp1UDcAAAAALsdqLZWfX2ClNRERzZSVlemYhgBcFoGIG6jOmh48UhcAAABwPsOoUEpKUaU1SUmBDukFQOUIRAAA1yw2JkY5ublV1vGEKphVdX5jbLWeckwzAABAEoEIAKAG5OTmVnklm8QTqmBe1fmNcUICP5YBAOBIzLwAYGLVWaPoTFmZPL28Kq1hHSMAAAC4GwIRAHAzNbnQcnXXKLIuWVJlDQAAqB4WXgVcA4EIALgZFloGAMC9sfAq4BrqObsBAAAAAAAARyMQAQAAAAAApkMgAgAAAAAATIc1RAAAAADUWdVZwNRqPeWYZgC4FAIRAAAAAHVWdRYwTUjgn0WAGXHLDAAAAAAAMB2iUACAw5RarQr086u0pllEhDKzshzUEQAAAMyKQAQA4DAVhqGilJRKawKTkhzUDQAAAMyMW2YAAAAAAIDpEIgAAAAAAADTIRABAAAAAACmQyACAAAAAABMh0AEAAAAAACYDoEIAAAAAAAwHR67i6tSarUq0M+v0ppTVquDugEAAAAA4MoQiOCqVBiGilJSKq3xSEhwUDcAAABA3WK1lsrPL7DKuoiIZsrKyqz9hoA6iEAEAAAAAFyMYVQoJaWoyrqkpMBa7wWoq1hDBAAAAAAAmA5XiAAA3E5sTIxycnOrrGsWEaHMrCwHdAQAgOuKiYlVbm5OpTVlZWfk5eVZ5bG4RQd1CYEIAMDt5OTmVrmOkSQFJiVVWVOdcIVgBQDgznJzc6q8/SYhwUNLllT9UARu0UFdQiACAKizqvtErPLXX6+0pjrBCgAAzlCdxVet1lOOaQZwMwQiAACXUpOP9eaJWACAuq46i68mJPDPPuBS+JsBAHApdT3E4BYdAEBdV501S1iLBK6gzgQiS5cu1fz585WXl6cbb7xRixYt0u9//3tntwUAqAOqc9WKVL0gozrrn3CLDgDAnVVnzRLWIoErqBOByLp165SYmKilS5fqtttu0xtvvKEBAwZo//79ioqKcnZ7AAA3V52rViSCDABA3ceaJahL6kQgsmDBAo0ZM0aPPPKIJGnRokX69NNPtWzZMs2ZM8fJ3QEAzKIm1z8BAMAV1dSaJdUJViRurUHtcvtApLy8XOnp6XryySfttvft21c7duxwUlcAADOq6+ufAABQU6oTrEjcWoPa5faByLFjx1RRUaGQkBC77SEhIcrPz7/kZ8rKylRWVmZ7X1xcLEkqKSmpsb4Mw1BJaWnlNVKVNdWtq6kazsf5OB/n43wucD7DqNE56Tw/Pz9ZLJYr+oyj5szS0uocrzp1NVXjqsfifJyP83E+c53v5MlT8vUNqLQmLCxc6enfVnmsK3E1cybcj8UwDMPZTVyL//znP4qIiNCOHTvUrVs32/aXXnpJa9asUdYlFrdLTk7Wc88958g2AQBwuuLiYvn7+1/RZ5gzAQBmdDVzJtyP2wci5eXlatSokd5//33dc889tu2TJk1SRkaGtm3bdtFnLvxt17lz5/TLL7+oSZMmNZIClpSUKDIyUkePHnX7v0SMxfXUlXFIjMVVMRbXVBNjqYkrRJgzL4+xuCbG4prqyljqyjgkxnIhrhAxB7e/ZaZBgwbq1KmT0tLS7AKRtLQ03X333Zf8jJeXl7y8vOy2BQYG1nhv/v7+bv8/k/MYi+upK+OQGIurYiyuydFjYc68cozFNTEW11RXxlJXxiExFpiL2wcikjR58mSNGjVKnTt3Vrdu3fTmm2/qyJEjeuyxx5zdGgAAAAAAcEF1IhAZMWKEjh8/rueff155eXmKjY3Vxo0bFR0d7ezWAAAAAACAC6oTgYgkjR07VmPHjnV2G5J+vbz42WefvegSY3fEWFxPXRmHxFhcFWNxTXVpLL9Vl8bFWFwTY3FNdWUsdWUcEmOBObn9oqoAAAAAAABXqp6zGwAAAAAAAHA0AhEAAAAAAGA6BCIAAAAAAMB0CERq2NKlS9WiRQs1bNhQnTp10ldffeXslqr05ZdfavDgwQoPD5fFYtGGDRvs9huGoeTkZIWHh8vb21u9evXSvn37nNNsFebMmaMuXbrIz89PTZs21dChQ3Xw4EG7GncZz7Jly9ShQwfb89O7deumTz75xLbfXcZxoTlz5shisSgxMdG2zV3GkpycLIvFYvcKDQ217XeXcZyXm5urBx98UE2aNFGjRo108803Kz093bbfXcbTvHnzi74vFotF48aNk+Q+45Cks2fP6qmnnlKLFi3k7e2tli1b6vnnn9e5c+dsNe40nupg3nQe5kzXG8elMG+6jrowbzJnuu544CQGakxqaqrh6elpvPXWW8b+/fuNSZMmGT4+PsZPP/3k7NYqtXHjRmPWrFnGBx98YEgy1q9fb7d/7ty5hp+fn/HBBx8Ye/fuNUaMGGGEhYUZJSUlzmm4Ev369TNWrlxpZGZmGhkZGcZdd91lREVFGSdPnrTVuMt4PvroI+Pjjz82Dh48aBw8eNCYOXOm4enpaWRmZhqG4T7j+K1//OMfRvPmzY0OHToYkyZNsm13l7E8++yzxo033mjk5eXZXgUFBbb97jIOwzCMX375xYiOjjbi4+ONb7/91sjOzjY2b95s/PDDD7YadxlPQUGB3fckLS3NkGRs2bLFMAz3GYdhGMaLL75oNGnSxPj73/9uZGdnG++//77h6+trLFq0yFbjTuOpCvOmczFnut44LsS86TrqyrzJnOm644FzEIjUoN/97nfGY489ZrctJibGePLJJ53U0ZW78Ae7c+fOGaGhocbcuXNt206fPm0EBAQYr7/+uhM6vDIFBQWGJGPbtm2GYbj/eBo3bmz83//9n1uO48SJE0br1q2NtLQ0o2fPnrYf7NxpLM8++6xx0003XXKfO43DMAxj+vTpxu23337Z/e42nt+aNGmScf311xvnzp1zu3HcddddxsMPP2y3bdiwYcaDDz5oGIZ7f18uhXnTtTBnuhbmTddSV+dN5kyYHbfM1JDy8nKlp6erb9++dtv79u2rHTt2OKmra5edna38/Hy7cXl5ealnz55uMa7i4mJJUlBQkCT3HU9FRYVSU1NltVrVrVs3txzHuHHjdNdddykuLs5uu7uN5dChQwoPD1eLFi10//3368cff5TkfuP46KOP1LlzZ913331q2rSpbrnlFr311lu2/e42nvPKy8u1du1aPfzww7JYLG43jttvv12ff/65vv/+e0nSd999p+3bt2vgwIGS3Pf7cinMm66HOdO1MG+6lro4bzJnApKHsxuoK44dO6aKigqFhITYbQ8JCVF+fr6Turp253u/1Lh++uknZ7RUbYZhaPLkybr99tsVGxsryf3Gs3fvXnXr1k2nT5+Wr6+v1q9fr3bt2tn+J+4u40hNTdU///lP7dq166J97vQ96dq1q95++221adNGP//8s1588UV1795d+/btc6txSNKPP/6oZcuWafLkyZo5c6b+8Y9/aOLEifLy8tJDDz3kduM5b8OGDSoqKlJ8fLwk9/rvS5KmT5+u4uJixcTEqH79+qqoqNBLL72kBx54QJL7jacyzJuuhTnTtTBvutY4pLo5bzJnAgQiNc5isdi9Nwzjom3uyB3HNX78eO3Zs0fbt2+/aJ+7jKdt27bKyMhQUVGRPvjgA40ePVrbtm2z7XeHcRw9elSTJk3SZ599poYNG162zh3GMmDAANvX7du3V7du3XT99ddr9erVuvXWWyW5xzgk6dy5c+rcubNmz54tSbrlllu0b98+LVu2TA899JCtzl3Gc97y5cs1YMAAhYeH2213l3GsW7dOa9eu1TvvvKMbb7xRGRkZSkxMVHh4uEaPHm2rc5fxVEddGstvudu4mDNdB/Om641DqpvzJnMmwFNmakxwcLDq169/0W+1CgoKLkol3cn5lcDdbVwTJkzQRx99pC1btqhZs2a27e42ngYNGqhVq1bq3Lmz5syZo5tuukmvvPKKW40jPT1dBQUF6tSpkzw8POTh4aFt27bp1VdflYeHh61fdxjLhXx8fNS+fXsdOnTIrb4nkhQWFqZ27drZbbvhhht05MgRSe73d0WSfvrpJ23evFmPPPKIbZu7jWPq1Kl68skndf/996t9+/YaNWqUnnjiCc2ZM0eS+42nMsybroM507Uwb7rmOOravMmc6XrjgXMQiNSQBg0aqFOnTkpLS7PbnpaWpu7duzupq2vXokULhYaG2o2rvLxc27Ztc8lxGYah8ePH68MPP9QXX3yhFi1a2O13t/FcyDAMlZWVudU4evfurb179yojI8P26ty5s/74xz8qIyNDLVu2dJuxXKisrEwHDhxQWFiYW31PJOm222676PGa33//vaKjoyW559+VlStXqmnTprrrrrts29xtHKdOnVK9evZTc/369W2PEHS38VSGedP5mDNdcxzMm645jro2bzJnut544CSOW7+17jv/+MDly5cb+/fvNxITEw0fHx/j8OHDzm6tUidOnDD+9a9/Gf/6178MScaCBQuMf/3rX7bHHs6dO9cICAgwPvzwQ2Pv3r3GAw884LKPq3r88ceNgIAAY+vWrXaPFDt16pStxl3GM2PGDOPLL780srOzjT179hgzZ8406tWrZ3z22WeGYbjPOC7lt6vlG4b7jCUpKcnYunWr8eOPPxo7d+40Bg0aZPj5+dn+jrvLOAzj10c5enh4GC+99JJx6NAh489//rPRqFEjY+3atbYadxpPRUWFERUVZUyfPv2ife40jtGjRxsRERG2Rwh++OGHRnBwsDFt2jRbjTuNpyrMm87FnOl647gc5k3nq0vzJnOma44HzkEgUsNee+01Izo62mjQoIHRsWNH26PrXNmWLVsMSRe9Ro8ebRjGr4+sevbZZ43Q0FDDy8vL6NGjh7F3717nNn0ZlxqHJGPlypW2GncZz8MPP2z7b+m6664zevfubfvBzjDcZxyXcuEPdu4ylvPPrvf09DTCw8ONYcOGGfv27bPtd5dxnPe3v/3NiI2NNby8vIyYmBjjzTfftNvvTuP59NNPDUnGwYMHL9rnTuMoKSkxJk2aZERFRRkNGzY0WrZsacyaNcsoKyuz1bjTeKqDedN5mDNdbxyXw7zpGurKvMmc6ZrjgXNYDMMwHHU1CgAAAAAAgCtgDREAAAAAAGA6BCIAAAAAAMB0CEQAAAAAAIDpEIgAAAAAAADTIRABAAAAAACmQyACAAAAAABMh0AEAAAAAACYDoEIAAAAAAAwHQIRAAAAAABgOgQiAGrMjh07VL9+ffXv39/ZrQAA4PKYNwHAuSyGYRjObgJA3fDII4/I19dX//d//6f9+/crKirK2S0BAOCymDcBwLm4QgRAjbBarXrvvff0+OOPa9CgQVq1apXd/o8++kitW7eWt7e37rjjDq1evVoWi0VFRUW2mh07dqhHjx7y9vZWZGSkJk6cKKvV6tiBAADgAMybAOB8BCIAasS6devUtm1btW3bVg8++KBWrlyp8xegHT58WH/4wx80dOhQZWRkKCEhQbNmzbL7/N69e9WvXz8NGzZMe/bs0bp167R9+3aNHz/eGcMBAKBWMW8CgPNxywyAGnHbbbdp+PDhmjRpks6ePauwsDC9++67iouL05NPPqmPP/5Ye/futdU/9dRTeumll1RYWKjAwEA99NBD8vb21htvvGGr2b59u3r27Cmr1aqGDRs6Y1gAANQK5k0AcD6uEAFwzQ4ePKh//OMfuv/++yVJHh4eGjFihFasWGHb36VLF7vP/O53v7N7n56erlWrVsnX19f26tevn86dO6fs7GzHDAQAAAdg3gQA1+Dh7AYAuL/ly5fr7NmzioiIsG0zDEOenp4qLCyUYRiyWCx2n7nw4rRz584pISFBEydOvOj4LDIHAKhLmDcBwDUQiAC4JmfPntXbb7+tlJQU9e3b127fvffeqz//+c+KiYnRxo0b7fbt3r3b7n3Hjh21b98+tWrVqtZ7BgDAWZg3AcB1sIYIgGuyYcMGjRgxQgUFBQoICLDbN2vWLG3cuFEffvih2rZtqyeeeEJjxoxRRkaGkpKSlJOTo6KiIgUEBGjPnj269dZb9T//8z969NFH5ePjowMHDigtLU2LFy920ugAAKhZzJsA4DpYQwTANVm+fLni4uIu+qFO+vU3XRkZGSosLNRf/vIXffjhh+rQoYOWLVtmWy3fy8tLktShQwdt27ZNhw4d0u9//3vdcsstevrppxUWFubQ8QAAUJuYNwHAdXCFCACneOmll/T666/r6NGjzm4FAACXx7wJADWPNUQAOMTSpUvVpUsXNWnSRF9//bXmz5+v8ePHO7stAABcEvMmANQ+AhEADnHo0CG9+OKL+uWXXxQVFaWkpCTNmDHD2W0BAOCSmDcBoPZxywwAAAAAADAdFlUFAAAAAACmQyACAAAAAABMh0AEAAAAAACYDoEIAAAAAAAwHQIRAAAAAABgOgQiAAAAAADAdAhEAAAAAACA6RCIAAAAAAAA0yEQAQAAAAAApvP/AXvg7UG5pxXqAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1093.99x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#age vs target\n",
    "facet_kws = {\"hue\":\"Transported\",\"palette\":{0:\"red\",1:\"blue\"}}\n",
    "sns.displot(x=\"Age\",data=train_sp,col=\"Transported\",**facet_kws,alpha=0.4)\n",
    "#age is more or less equally distributed among both columns "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ec82ff01",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x1ed95aec610>"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#pairplot for rest of numerical\n",
    "sns.pairplot(train_sp,hue='Transported',palette=\"magma\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "5b77ff25",
   "metadata": {},
   "outputs": [],
   "source": [
    "#converting categorical columns\n",
    "cat_ft = [\"HomePlanet\",\"CryoSleep\",\"Destination\",\"VIP\"]\n",
    "train_sp = pd.get_dummies(train_sp,columns=cat_ft,drop_first=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "6e66317c",
   "metadata": {},
   "outputs": [],
   "source": [
    "test_sp = pd.get_dummies(test_sp,columns=cat_ft,drop_first=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "94315af9",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_sp.drop([\"PassengerId\",\"Cabin\",\"Name\"],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "b769eeec",
   "metadata": {},
   "outputs": [],
   "source": [
    "test_sp.drop([\"PassengerId\",\"Cabin\",\"Name\"],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "dd3a5871",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 8693 entries, 0 to 8692\n",
      "Data columns (total 13 columns):\n",
      " #   Column                     Non-Null Count  Dtype  \n",
      "---  ------                     --------------  -----  \n",
      " 0   Age                        8693 non-null   float64\n",
      " 1   RoomService                8693 non-null   float64\n",
      " 2   FoodCourt                  8693 non-null   float64\n",
      " 3   ShoppingMall               8693 non-null   float64\n",
      " 4   Spa                        8693 non-null   float64\n",
      " 5   VRDeck                     8693 non-null   float64\n",
      " 6   Transported                8693 non-null   bool   \n",
      " 7   HomePlanet_Europa          8693 non-null   uint8  \n",
      " 8   HomePlanet_Mars            8693 non-null   uint8  \n",
      " 9   CryoSleep_True             8693 non-null   uint8  \n",
      " 10  Destination_PSO J318.5-22  8693 non-null   uint8  \n",
      " 11  Destination_TRAPPIST-1e    8693 non-null   uint8  \n",
      " 12  VIP_True                   8693 non-null   uint8  \n",
      "dtypes: bool(1), float64(6), uint8(6)\n",
      "memory usage: 467.0 KB\n"
     ]
    }
   ],
   "source": [
    "train_sp.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "6b524ffa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 4277 entries, 0 to 4276\n",
      "Data columns (total 12 columns):\n",
      " #   Column                     Non-Null Count  Dtype  \n",
      "---  ------                     --------------  -----  \n",
      " 0   Age                        4277 non-null   float64\n",
      " 1   RoomService                4277 non-null   float64\n",
      " 2   FoodCourt                  4277 non-null   float64\n",
      " 3   ShoppingMall               4277 non-null   float64\n",
      " 4   Spa                        4277 non-null   float64\n",
      " 5   VRDeck                     4277 non-null   float64\n",
      " 6   HomePlanet_Europa          4277 non-null   uint8  \n",
      " 7   HomePlanet_Mars            4277 non-null   uint8  \n",
      " 8   CryoSleep_True             4277 non-null   uint8  \n",
      " 9   Destination_PSO J318.5-22  4277 non-null   uint8  \n",
      " 10  Destination_TRAPPIST-1e    4277 non-null   uint8  \n",
      " 11  VIP_True                   4277 non-null   uint8  \n",
      "dtypes: float64(6), uint8(6)\n",
      "memory usage: 225.7 KB\n"
     ]
    }
   ],
   "source": [
    "test_sp.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "95b23782",
   "metadata": {},
   "outputs": [],
   "source": [
    "#data split\n",
    "X_train = train_sp.drop(\"Transported\",axis=1)\n",
    "y_train = train_sp[\"Transported\"]\n",
    "X_test = test_sp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "588c68eb",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Model - 1 DecisionTree\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "dtree = DecisionTreeClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "a3478273",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "DecisionTreeClassifier()"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#model-1 training\n",
    "dtree.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "dbf63a80",
   "metadata": {},
   "outputs": [],
   "source": [
    "#testing\n",
    "predictions_dtree = dtree.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "a5cdcf15",
   "metadata": {},
   "outputs": [],
   "source": [
    "test_2 = pd.read_csv(\"test_spaceship.csv\")\n",
    "pred_2 = pd.DataFrame(predictions_dtree,columns=[\"Transported\"])\n",
    "pred_2 = pd.concat([pred_2,test_2[\"PassengerId\"]],axis=1)\n",
    "pred_2.set_index(\"PassengerId\",inplace=True)\n",
    "pred_2.to_csv(\"titanic_spaceship_dtree.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "72e2ce66",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Model 2-RandomForest\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "rfc = RandomForestClassifier(n_estimators=2000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "16abe6be",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier(n_estimators=2000)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(n_estimators=2000)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "RandomForestClassifier(n_estimators=2000)"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#model - 2 training\n",
    "rfc.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "1d56c30e",
   "metadata": {},
   "outputs": [],
   "source": [
    "#model-2 test\n",
    "predictions_rfc = rfc.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "3d934b94",
   "metadata": {},
   "outputs": [],
   "source": [
    "test_2 = pd.read_csv(\"test_spaceship.csv\")\n",
    "pred_2 = pd.DataFrame(predictions_rfc,columns=[\"Transported\"])\n",
    "pred_2 = pd.concat([pred_2,test_2[\"PassengerId\"]],axis=1)\n",
    "pred_2.set_index(\"PassengerId\",inplace=True)\n",
    "pred_2.to_csv(\"titanic_spaceship_rfc.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "8144f36a",
   "metadata": {},
   "outputs": [],
   "source": [
    "#preprocessing data for log reg\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "st = StandardScaler()\n",
    "st.fit(X_train)\n",
    "X_train_lr = st.transform(X_train)\n",
    "st.fit(X_test) \n",
    "X_test_lr = st.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "5a0db612",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Model-3 Logistic regression\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "log_reg = LogisticRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "6ac6b8d4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#model-3 training\n",
    "log_reg.fit(X_train_lr,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "10a84afd",
   "metadata": {},
   "outputs": [],
   "source": [
    "#model -3 testing \n",
    "predictions_log_reg = log_reg.predict(X_test_lr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "7c9fb977",
   "metadata": {},
   "outputs": [],
   "source": [
    "test_2 = pd.read_csv(\"test_spaceship.csv\")\n",
    "pred_2 = pd.DataFrame(predictions_log_reg,columns=[\"Transported\"])\n",
    "pred_2 = pd.concat([pred_2,test_2[\"PassengerId\"]],axis=1)\n",
    "pred_2.set_index(\"PassengerId\",inplace=True)\n",
    "pred_2.to_csv(\"titanic_spaceship_log_reg.csv\")"
   ]
  }
 ],
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