Untitled.ipynb

{
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  {
   "cell_type": "code",
   "execution_count": 306,
   "id": "f4fd20f9",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "train = pd.read_csv('C:\\\\Users\\\\anish\\\\Desktop\\\\spaceship-titanic\\\\train.csv')\n",
    "test = pd.read_csv('C:\\\\Users\\\\anish\\\\Desktop\\\\spaceship-titanic\\\\test.csv')\n",
    "\n",
    "from sklearn.impute import SimpleImputer\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.model_selection import StratifiedKFold, train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 307,
   "id": "a5d81fae",
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       "      <td>151.0</td>\n",
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       "     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",
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       "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",
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       "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",
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   "source": [
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  {
   "cell_type": "code",
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       "     PassengerId HomePlanet CryoSleep     Cabin    Destination   Age    VIP  \\\n",
       "0        0013_01      Earth      True     G/3/S    TRAPPIST-1e  27.0  False   \n",
       "1        0018_01      Earth     False     F/4/S    TRAPPIST-1e  19.0  False   \n",
       "2        0019_01     Europa      True     C/0/S    55 Cancri e  31.0  False   \n",
       "3        0021_01     Europa     False     C/1/S    TRAPPIST-1e  38.0  False   \n",
       "4        0023_01      Earth     False     F/5/S    TRAPPIST-1e  20.0  False   \n",
       "...          ...        ...       ...       ...            ...   ...    ...   \n",
       "4272     9266_02      Earth      True  G/1496/S    TRAPPIST-1e  34.0  False   \n",
       "4273     9269_01      Earth     False       NaN    TRAPPIST-1e  42.0  False   \n",
       "4274     9271_01       Mars      True   D/296/P    55 Cancri e   NaN  False   \n",
       "4275     9273_01     Europa     False   D/297/P            NaN   NaN  False   \n",
       "4276     9277_01      Earth      True  G/1498/S  PSO J318.5-22  43.0  False   \n",
       "\n",
       "      RoomService  FoodCourt  ShoppingMall     Spa  VRDeck              Name  \n",
       "0             0.0        0.0           0.0     0.0     0.0   Nelly Carsoning  \n",
       "1             0.0        9.0           0.0  2823.0     0.0    Lerome Peckers  \n",
       "2             0.0        0.0           0.0     0.0     0.0   Sabih Unhearfus  \n",
       "3             0.0     6652.0           0.0   181.0   585.0  Meratz Caltilter  \n",
       "4            10.0        0.0         635.0     0.0     0.0   Brence Harperez  \n",
       "...           ...        ...           ...     ...     ...               ...  \n",
       "4272          0.0        0.0           0.0     0.0     0.0       Jeron Peter  \n",
       "4273          0.0      847.0          17.0    10.0   144.0     Matty Scheron  \n",
       "4274          0.0        0.0           0.0     0.0     0.0       Jayrin Pore  \n",
       "4275          0.0     2680.0           0.0     0.0   523.0    Kitakan Conale  \n",
       "4276          0.0        0.0           0.0     0.0     0.0  Lilace Leonzaley  \n",
       "\n",
       "[4277 rows x 13 columns]"
      ]
     },
     "execution_count": 308,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 309,
   "id": "e6e3dc7c",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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": 309,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 310,
   "id": "3ae51e57",
   "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.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 311,
   "id": "bde2ea6a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CryoSleep       217\n",
       "ShoppingMall    208\n",
       "VIP             203\n",
       "HomePlanet      201\n",
       "Name            200\n",
       "Cabin           199\n",
       "VRDeck          188\n",
       "FoodCourt       183\n",
       "Spa             183\n",
       "Destination     182\n",
       "RoomService     181\n",
       "Age             179\n",
       "PassengerId       0\n",
       "Transported       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 311,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.isna().sum().sort_values(ascending = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 312,
   "id": "2f0b9150",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 4277 entries, 0 to 4276\n",
      "Data columns (total 13 columns):\n",
      " #   Column        Non-Null Count  Dtype  \n",
      "---  ------        --------------  -----  \n",
      " 0   PassengerId   4277 non-null   object \n",
      " 1   HomePlanet    4190 non-null   object \n",
      " 2   CryoSleep     4184 non-null   object \n",
      " 3   Cabin         4177 non-null   object \n",
      " 4   Destination   4185 non-null   object \n",
      " 5   Age           4186 non-null   float64\n",
      " 6   VIP           4184 non-null   object \n",
      " 7   RoomService   4195 non-null   float64\n",
      " 8   FoodCourt     4171 non-null   float64\n",
      " 9   ShoppingMall  4179 non-null   float64\n",
      " 10  Spa           4176 non-null   float64\n",
      " 11  VRDeck        4197 non-null   float64\n",
      " 12  Name          4183 non-null   object \n",
      "dtypes: float64(6), object(7)\n",
      "memory usage: 434.5+ KB\n"
     ]
    }
   ],
   "source": [
    "test.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 313,
   "id": "83c9d258",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "FoodCourt       106\n",
       "Spa             101\n",
       "Cabin           100\n",
       "ShoppingMall     98\n",
       "Name             94\n",
       "CryoSleep        93\n",
       "VIP              93\n",
       "Destination      92\n",
       "Age              91\n",
       "HomePlanet       87\n",
       "RoomService      82\n",
       "VRDeck           80\n",
       "PassengerId       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 313,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.isna().sum().sort_values(ascending = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 314,
   "id": "2a71532c",
   "metadata": {},
   "outputs": [],
   "source": [
    "train['CryoSleep'].fillna(False, inplace=True)\n",
    "train['Cabin'].fillna('0/0/0', inplace=True)\n",
    "train['HomePlanet'].fillna(train.HomePlanet.mode()[0], inplace=True)\n",
    "train['Destination'].fillna(train.Destination.mode()[0], inplace=True)\n",
    "train['VIP'].fillna(train.VIP.mode()[0], inplace=True)\n",
    "train['Age'].fillna(train.Age.mean(), inplace=True)\n",
    "train[['RoomService', 'FoodCourt', 'ShoppingMall', 'Spa', 'VRDeck']] = train[['RoomService', 'FoodCourt', 'ShoppingMall', 'Spa', 'VRDeck']].fillna(0)\n",
    "\n",
    "\n",
    "test['CryoSleep'].fillna(False, inplace=True)\n",
    "test['Cabin'].fillna('0/0/0', inplace=True)\n",
    "test['HomePlanet'].fillna(test.HomePlanet.mode()[0], inplace=True)\n",
    "test['Destination'].fillna(test.Destination.mode()[0], inplace=True)\n",
    "test['VIP'].fillna(test.VIP.mode()[0], inplace=True)\n",
    "test['Age'].fillna(test.Age.mean(), inplace=True)\n",
    "test[['RoomService', 'FoodCourt', 'ShoppingMall', 'Spa', 'VRDeck']]= test[['RoomService', 'FoodCourt', 'ShoppingMall', 'Spa', 'VRDeck']].fillna(0)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 315,
   "id": "e295c123",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Deck and Side possibly useful, Cabin and Cabin Num useless based on EDA from prior notebook\n",
    "#Note: well... i kinda reset the other notebook to a single blank cell by accident...\n",
    "test[['Deck', 'Num', 'Side']] = test['Cabin'].str.split('/', expand=True)\n",
    "train[['Deck', 'Num', 'Side']] = train['Cabin'].str.split('/', expand=True)\n",
    "test.drop(['Cabin', 'Num'] , axis = 1, inplace = True)\n",
    "train.drop(['Cabin', 'Num'] , axis = 1, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 316,
   "id": "0f0a046e",
   "metadata": {},
   "outputs": [],
   "source": [
    "#grouping Age into groups so it's more useful\n",
    "train['AgeGroup'] = 7\n",
    "for i in range(6):\n",
    "    train.loc[(train.Age >= 10*i) & (train.Age < 10*(i + 1)), 'AgeGroup'] = i\n",
    "test['AgeGroup'] = 7\n",
    "for i in range(6):\n",
    "    test.loc[(test.Age >= 10*i) & (test.Age < 10*(i + 1)), 'AgeGroup'] = i"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 317,
   "id": "9d4e300b",
   "metadata": {},
   "outputs": [
    {
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Kurta Mondalley</td>\n",
       "      <td>False</td>\n",
       "      <td>G</td>\n",
       "      <td>S</td>\n",
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       "      <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",
       "      <td>G</td>\n",
       "      <td>S</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>8691</th>\n",
       "      <td>9280_01</td>\n",
       "      <td>Europa</td>\n",
       "      <td>False</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",
       "      <td>E</td>\n",
       "      <td>S</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8692</th>\n",
       "      <td>9280_02</td>\n",
       "      <td>Europa</td>\n",
       "      <td>False</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",
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       "      <td>S</td>\n",
       "      <td>4</td>\n",
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       "  </tbody>\n",
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       "<p>8693 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId HomePlanet  CryoSleep    Destination   Age    VIP  \\\n",
       "0        0001_01     Europa      False    TRAPPIST-1e  39.0  False   \n",
       "1        0002_01      Earth      False    TRAPPIST-1e  24.0  False   \n",
       "2        0003_01     Europa      False    TRAPPIST-1e  58.0   True   \n",
       "3        0003_02     Europa      False    TRAPPIST-1e  33.0  False   \n",
       "4        0004_01      Earth      False    TRAPPIST-1e  16.0  False   \n",
       "...          ...        ...        ...            ...   ...    ...   \n",
       "8688     9276_01     Europa      False    55 Cancri e  41.0   True   \n",
       "8689     9278_01      Earth       True  PSO J318.5-22  18.0  False   \n",
       "8690     9279_01      Earth      False    TRAPPIST-1e  26.0  False   \n",
       "8691     9280_01     Europa      False    55 Cancri e  32.0  False   \n",
       "8692     9280_02     Europa      False    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 Deck Side  AgeGroup  \n",
       "0           False    B    P         3  \n",
       "1            True    F    S         2  \n",
       "2           False    A    S         5  \n",
       "3           False    A    S         3  \n",
       "4            True    F    S         1  \n",
       "...           ...  ...  ...       ...  \n",
       "8688        False    A    P         4  \n",
       "8689        False    G    S         1  \n",
       "8690         True    G    S         2  \n",
       "8691        False    E    S         3  \n",
       "8692         True    E    S         4  \n",
       "\n",
       "[8693 rows x 16 columns]"
      ]
     },
     "execution_count": 317,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 318,
   "id": "78773cd2",
   "metadata": {},
   "outputs": [
    {
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       "</table>\n",
       "<p>4277 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId HomePlanet  CryoSleep    Destination        Age    VIP  \\\n",
       "0        0013_01      Earth       True    TRAPPIST-1e  27.000000  False   \n",
       "1        0018_01      Earth      False    TRAPPIST-1e  19.000000  False   \n",
       "2        0019_01     Europa       True    55 Cancri e  31.000000  False   \n",
       "3        0021_01     Europa      False    TRAPPIST-1e  38.000000  False   \n",
       "4        0023_01      Earth      False    TRAPPIST-1e  20.000000  False   \n",
       "...          ...        ...        ...            ...        ...    ...   \n",
       "4272     9266_02      Earth       True    TRAPPIST-1e  34.000000  False   \n",
       "4273     9269_01      Earth      False    TRAPPIST-1e  42.000000  False   \n",
       "4274     9271_01       Mars       True    55 Cancri e  28.658146  False   \n",
       "4275     9273_01     Europa      False    TRAPPIST-1e  28.658146  False   \n",
       "4276     9277_01      Earth       True  PSO J318.5-22  43.000000  False   \n",
       "\n",
       "      RoomService  FoodCourt  ShoppingMall     Spa  VRDeck              Name  \\\n",
       "0             0.0        0.0           0.0     0.0     0.0   Nelly Carsoning   \n",
       "1             0.0        9.0           0.0  2823.0     0.0    Lerome Peckers   \n",
       "2             0.0        0.0           0.0     0.0     0.0   Sabih Unhearfus   \n",
       "3             0.0     6652.0           0.0   181.0   585.0  Meratz Caltilter   \n",
       "4            10.0        0.0         635.0     0.0     0.0   Brence Harperez   \n",
       "...           ...        ...           ...     ...     ...               ...   \n",
       "4272          0.0        0.0           0.0     0.0     0.0       Jeron Peter   \n",
       "4273          0.0      847.0          17.0    10.0   144.0     Matty Scheron   \n",
       "4274          0.0        0.0           0.0     0.0     0.0       Jayrin Pore   \n",
       "4275          0.0     2680.0           0.0     0.0   523.0    Kitakan Conale   \n",
       "4276          0.0        0.0           0.0     0.0     0.0  Lilace Leonzaley   \n",
       "\n",
       "     Deck Side  AgeGroup  \n",
       "0       G    S         2  \n",
       "1       F    S         1  \n",
       "2       C    S         3  \n",
       "3       C    S         3  \n",
       "4       F    S         2  \n",
       "...   ...  ...       ...  \n",
       "4272    G    S         3  \n",
       "4273    0    0         4  \n",
       "4274    D    P         2  \n",
       "4275    D    P         2  \n",
       "4276    G    S         4  \n",
       "\n",
       "[4277 rows x 15 columns]"
      ]
     },
     "execution_count": 318,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 319,
   "id": "dc14ba95",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\anish\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "C:\\Users\\anish\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "C:\\Users\\anish\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "C:\\Users\\anish\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "C:\\Users\\anish\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
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2J1ke6tUqAZEpCwDAdMurxeLsSkoSEAAAlWo2aUtJwvwSAICNWElzndNP1Essi3ZgAACMM0wCSpSUT5CZfwJA95AENCdZhPplhExsyrUDANAClYAAAFivWnRNbFlmERYAgA3I8nywWbOXUFEPAIBx0kE7MMtVJSDiJgB0DklAc5LmoaScXNomSAIAMEVE1JKAggecAACUqpBoFdWAiJAAALSX1iq29xLWaQEAGCfLi7XZJLES2oEBQGeRBDQnWZ6rTzswAABaWymr/5y7VPz3JM2InQAASMMkoGohlkVYAADay/MYrNP2Eg+qHAAAgLWq4uz9xCpDJ883AaCDSAKakzQb7jBhkRYAgOmqKkCv29OXNEwKAgBg0Q3bgRU/wSIsAACtFZWAimVw2oEBADBeVQmot6YSEHETALqGJKA5ySOoBAQAwAYsr1ZJQD1J0ipJQAAASKrPJ120m2Z+CQBAa9lIJSDiKAAAzapqef3Eg0IHOUu0ANA5JAHNSZqHkjWVgJhcAgAwyXKaSZLOWyqSgFZSZpgAAEhSNZusKgGxCAsAQHtFJaAyCci0AwMAYJyqWl5COzAA6DSSgOakvsPEtoiRAABMNmwHRiUgAADqqvZfdlEJiOklAADtZfUkINqBAQAwVlarBEQ7MADoLpKA5qQ+uUwSgiQAANMM24H1JUkrJAEBACBJg00lVSWgYH4JAEBro+3ASAICAKBZVS2vl3jQ7YSwCQDdQxLQnNQnl0U7sG2+IQAAdrhBO7CqElBK8AQAQBouuiY27aYBANig0UpAxFEAAJoNKwElMu3AAKCzSAKak3qvafpmAgAw3Wg7MCoBAQBQqM8lbXZiAgCa2b7W9lO2T9i+reF92769fP9x21dOG2v7g7aP285t768dv9T2q7YfK38O1957u+2vlte63a4eI26PNM/XJAGlBFIAABpVSUCJpV7VDoy4CQCdQxLQnKxpB0bfTAAAphpNAlolCQgAAEn1dmCWbdqBAQDWsd2TdIek6yRdLukG25ePnHadpH3lz0FJd7YY+4SkD0j6i4aPfToirih/DtWO31lev/qsa8/+G27eaCUg2oEBANCsipkuq9BKPNsEgC4iCWhOinZgxT9vtdmFOAkAwHjLq0U7sNft6UuSVlKSgAAAkDRI+kmSIhGIuSUAoMFVkk5ExMmIWJF0r6QDI+cckHR3FB6WdL7tCyaNjYgnI+KptjdRXu/1EfHlKALY3ZLed7Zf7mykeahfJQGZJCAAAMZJGwscbOcdAQA2gySgOcma2oERKQEAGKuqBHTeEpWAAACoq6aSlpWYnZgAgEYXSnqm9vep8libc9qMbXKZ7Udtf8n2O2qfcarNtWwftH3M9rEzZ860+LjNGa0ExBotAADNsjwftAEr6xyQPAsAHUQS0JzUe02TLQsAwHSj7cBWSAICAECSFCorAVmymFsCABq54dhoxBh3Tpuxo05LuiQifkzSL0u6x/brN3KtiLgrIvZHxP69e/dO+bjNq1ds7yVWSiAFAKBRlmtQPY92YADQXf3tvoHdqqkSUBAoAQAYazkt2oEt9RP1E2s1JW4CACDVKgG5WIhlERYA0OCUpItrf18k6dmW5+xpMXaNiFiWtFy+/ortpyW9pfyMizZyrXlLRyoBUdEAAIBmWZ6r11ubBMT0EwC6h0pAc5LVek27DJQZkRIAgLGWV4vKP0tJoqVeQiUgAABK1YYS27LNIiwAoMkjkvbZvsz2HknXSzoycs4RSTe5cI2k5yPidMuxa9jea7tXvn6zpH2STpbXe9H2NS4WRW+S9NkZfs8NW9MOzGaNFgCAMdLas82qwAGbUACge6gENAd5HspDayaXEtmyAABMUrUD6/esPf1EqyQBAQAgaTiXTGwlpsosAGC9iEht3yrpQUk9SZ+KiOO2D5XvH5Z0VNJ7JJ2Q9IqkD08aK0m23y/pdyXtlfQ5249FxLslvVPSb9hOJWWSDkXE98rb+UVJ/1TSeZI+X/5sm9FKQDmVgAAAaJTlMagAlCS0AwOAriIJaA6q3SRV8o/JlgUAYKqqHVgvcVEJKCUJCAAAaTiXtKp2YNt7PwCAnSkijqpI9KkfO1x7HZJuaTu2PP6ApAcajt8v6f4x1zom6W0bufd5ymtVDXqJlRJIAQBolK2pBFQmAbFECwCdQzuwOaj6So/2zWR+CQDAeMtprj29RImtPT1TCQgAgFK9EpDNBhMAADZitBJQxiItAACNsjxqzzbLY8w/AaBzSAKag2oiOciWLf+VWagFAGC85dVc5/SLoLnUT7RCEhAAAJJqlYAs2RYzSwAA2svyXP0eSUAAAEyT5qF++VBzWOCAuAkAXUMS0BxUJWV7ZaA0gRIAgKmW00x7yiSgPb1Eq7QDAwBAkgZJP3axGzOYWwIA0Fqax+BBZi8xa7QAAIyR5TGoAESXEwDoLpKA5mDQDoxACQBAa8tprRJQL9FqRuAEAEAaJv0kthKbuSUAABuQ5TGo2F4kAbFZEwCAJlm9ElDV5YQJKAB0DklAc5DmReWCXq8qmVccZ7cmAADjLae5zlnqSZL20A4MAICBas21aAfGg0sAADYizWJQsb1XbtakJRgAAOuleahXPtSkHRgAdBdJQHNQ5gANdphQCQgAgOmWV7NaJSDTDgwAgFK15jqoBMTkEgCA1vJYWwlIoqoBAABNsjxvSALazjsCAGwGSUBzMKgENAiUxXEmlwAAjFdvB9ZLrIxdJgAASBruvKwqAREhAQBoL81Dvd7aJCAqAQEAsF4WDc82WaMFgM4hCWgOqklkVV62ypalHRgAAOMtp5nO6RftwHo2E0wAAErVXNIqKgERIgEAaC/LY7BOWz3YTAmmAACsk+X5oHqebSW0owaATiIJaA7SMgmo3xsGSqnIoAUAAM2W01znLBX/NUlsdmYCAFCqImJisQgLAMAGpdmwtUmVDMR8EwCA9dIsBjFTKp5vls1PAAAdQhLQHFRtv0ZL5lEJCACA8ZZXh+3AksT0mwYAoFTNMW0Xi7DMLQEAaC3LY1DVoFqvzZlwAgCwTpavTQJiEwoAdBNJQHMwqASUrG0HxtwSAIDx6u3AEptFWQAASvVKQLZoBwYAwAZkEer1RtqBMd8EAGCdLEaTgNiEAgBdRBLQHFTlZKvknypeEigBABhvOR1WAuolxQNPYicAAMMNJbaV2CQBAQCwAfVKQNVv2oEBALBePWZKJAEBQFeRBDQHg0pA5Q6TpAyYtAMDAGC85TTXOUtlOzBToh0AgEo1l7Qpxw4AwEaleajnteu0JAEBALBemoV6yfDRcZJYeb6NNwQA2BSSgOagmkRWgdK0AwMAYKrl1WE7sKrsbMZDTgAABpV/ElsWOzEBAGgrz0MRw3Xaaq5JLAUAYL0sD/VqT457bEIBgE4iCWgOqiSgqmQe7cAAAJiu3g5sWAloO+8IAICdoZpLJmUlIGaWAAC0M1qxvUoCStmtCQDAOlmE+vVKQDYFDgCgg0gCmoO0fGJZPcBMqAQEAMBEEbE2CYhKQAAADFRzScuyrQjaTQMA0EaVSFsl/1RtwWgHBgDAekUlIA/+ThIq0QJAF5EENAfZyA4TUwkIAICJVrIigfacpaIdGFX0AAAYirL2jz3cZEKIBABguqriT5X8M2g9TRIQAADrpHm+JgnIYn0WALqIJKA5qCaRoztMciaXAAA0WknLJKCyEhCxEwCAoWrN1SZRFgCAjciykXVakoAAABgrz7W+EhAxEwA6hySgORhUAkqqSkDs1AQAYJLlkSSgqh0Yc0wAAIatvxK7Vml2G28IAICOSPNirllVbO/RehoAgLHSPB8825SKjZrMPQGge0gCmoOqzGxVpp2dmgAATFYlAS311lYCYncmAKCJ7WttP2X7hO3bGt637dvL9x+3feW0sbZ/2/bXy/MfsH1+7b2Pluc/Zfvdc/+CI6pwWCQBlZtMRIwEAGCacRXbmWsCALBelsfaSkDm2SYAdBFJQHMwqATUq5KAqGYAAMAkVYn2fm+0EhDBEwCwlu2epDskXSfpckk32L585LTrJO0rfw5KurPF2IckvS0iflTSX0v6aDnmcknXS3qrpGsl/X55nS1TxUNrOL8kRAIAMF1V8adPOzAAAKZKR5OAErM+CwAdRBLQHIy2A+NBJgAAkw1KtA92ZxbHWZgFADS4StKJiDgZESuS7pV0YOScA5LujsLDks63fcGksRHxZxGRluMflnRR7Vr3RsRyRPyNpBPldbZMNZW0qTQLAMBGpNnaiu0kAQEAMN76SkBWuWwLAOgQkoDmYFhmtqxmUB5nkRYAMMmcWpt80PZx27nt/SPX29bWJnXrquiRQAsAGO9CSc/U/j5VHmtzTpuxkvRzkj6/gc+TJNk+aPuY7WNnzpyZ8jXaiwjZkm15kAQ0s8sDALBrjc41++V6LUlAAICtXIu1/S7bX7H91fL3T9Te+2J5rcfKnzfN83tPkuUx2KQp0Q4MALqKJKA5SKskoHJ11pRrBwBMMcfWJk9I+oCkvxj5vG1vbVKXjlbRq1ppsjALAFjPDcdGA8a4c6aOtf2rklJJn9nA5xUHI+6KiP0RsX/v3r1Np2xKHsObGLYDI0YCADBNOrpZs1wNJwkIABbbVq/FSvqupJ+KiB+RdLOkPxp5/8aIuKL8+c4MvuKmFO3Aho+OE9MODAC6aK5JQHPKov1t218vz3/A9vm193ZERYOsrI3XG1QzKI4TKAEAE8yrtcmTEfFUw+dte2uTutEqeoMS7YROAMB6pyRdXPv7IknPtjxn4ljbN0t6r4oF2CoKtfm8uQrFIPmn2mTCs0sAAKbLRjacDOeaBFIAWHBbuhYbEY9GRDWPPC7pXNvnzOerbV7RDmz4d5KYuScAdNDckoDmmEX7kKS3RcSPSvprSR8tx+yYigZZ2R9zXTUDAiUAYLytaG2y0c+bW1uTUWMrAbEwCwBY7xFJ+2xfZnuPinngkZFzjki6qdx4co2k5yPi9KSxtq+V9CuSfjoiXhm51vW2z7F9mYr561/O8wuOykODNmBVZXYqAQEAMN1ww0mZBFQGVCoBAcDC2+q12LqfkfRoRCzXjv1h2QrsY652fmyxiCiTgOqVgFifBYAummcloHll0f5ZRKTl+IdV7MKsrrUjKhoMKgGNPMhkkRYAMMFcW5ts8vPm1tZk1GjsLIvpsTALAFinnA/eKulBSU9Kui8ijts+ZPtQedpRSSdVzAs/Kekjk8aWY35P0vdLeqhcfD1cjjku6T5JX5P0p5JuiYhs/t90KGJYAYhNJgAAtDdIAqpV1LN4oAkA2PK12OKC9lsl/ZakX6gdvrFsE/aO8udDDePmvlGzmmNWmzSlsh0Yk08A6Jz+HK/dlAl7dYtzxmXRjo6VpJ+T9M9r13q44Vpr2D6oouqQLrnkkmnfYVPSdZPL4jhlZgEAE5xNa5M9LcZu5vO2TJqNVAJKqAQEABgvIo6qSPSpHztcex2Sbmk7tjz+wxM+7+OSPr7Z+z1bETGoAFTNL4mRAABMl1YbTnq1B5qJVR4GACyurV6Lle2LJD0g6aaIeLo6HhHfKn+/aPseFQUO7q6PjYi7JN0lSfv375/LZDAd2aQplUlATD0BoHPmWQlorlm0tn9VUirpMxv4vC2paDDYYdIb7tS0it2bAACMMZfWJhNse2uTutES7YMqB8wyAQBQHiFrWMFAYn4JAEAb2UjraYnWJgAASVu8Fmv7fEmfk/TRiPjXteN9228sXy9Jeq+kJ2byDTdodH1WKmImBQ4AoHvmWQloblm0tm9WEQh/MoY9tnZMRYOmyaXNg0wAwHgRkdqu2pP0JH2qam1Svn9YRdWC96hobfKKpA9PGitJtt8v6Xcl7ZX0OduPRcS7y2tXrU1SbUNrk7qqil6/t7YSUEboBABAERpUAqp+024aAIDp0sYHmiYJCAAW3FavxapoS/3Dkj5m+2PlbfxdSS9LerBMAOpJ+nMVLa23XGPibEI7MADoonkmAQ0yYSV9S0Um7M+OnHNE0q2271XR7uv5iDht+8y4sbavlfQrkv6ziHhl5Fr32P4dST+kbaxoMH5yuR13AwDoijm1NnlARZnZpjHb2tqkbrjTpChS2KMSEAAAA3kMq+QNquURIgEAmCofPNAcFsRnnRYAIG3tWmxE/Kak3xxzK29vf9fz01wJyOtbrgAAdry5JQHNK4tW0u9JOkfSQ2UZ9Icj4tBOqmgwCJRmhwkAAG2sZkXP6f6gHVhxnNgJAEAZD8vY6PoxAAAw0XCz5vBYkrBOCwDAqLEFDsicBYDOmWcloHll0f7whM/bERUNmrJlbcq1AwAwzmjsrH5nTDIBAJC0vhIQERIAgOlGq85KUs9UnQUAYFTemATEBhQA6KJk+inYqCwP9RLLI5WAMuIkAACN0pGe07Q6AQBgKI9QNb2sfvPwEgCA6UbnmhLtwAAAaNIYMxOebQJAF5EENAdpmQRUl1AJCACAsardmf2yRntSVQIidgIAoIiGSkCESAAApsryovV0b+SBJlUNAABYq6l6XkL1PADoJJKA5iDLc/U8kgSUsMMEAIBxRneaVHGUSSYAAEUloOrZZbUey8NLAACmy4ocoJFKQMRRAABGpYMkoOGxonoeMRMAuoYkoDnI8rUTS4lACQDAJKO7M3nACQDAULEWW8RIq2qZSYwEAGCatJxrJqPtwNhwAgDAGs2VgHi2CQBdRBLQHGR5rl6PdmAAALQ1rhJQxsIsAACShpWAqqKzREgAAKbLRuaaUvVAc7vuCACAnYmYCQC7B0lAc5Dmsa4SkAmUAACMNdxpUlUCosoBAACVPC8WXyXpy08/J0n6V489q3v+zTcHPwAA2L7W9lO2T9i+reF92769fP9x21dOG2v7g7aP285t768df5ftr9j+avn7J2rvfbG81mPlz5vm+b0nSUfmmlJReZa5JgAAa6UjldqlMmbycBMAOqe/3TewG2V5DBZoK/SaBgBgvDSrdpoU+cmJi2YnWb6NNwUAwA6RRwwqALl8wewSAFBnuyfpDknvknRK0iO2j0TE12qnXSdpX/lztaQ7JV09ZewTkj4g6Z+MfOR3Jf1URDxr+22SHpR0Ye39GyPi2Ky/50YNqxrQ2gQAgEkGmzS9thJQqOh04pHnngCAnYskoDnIGioB0WsaAIDxBpPM3mi5WWInAAAhrdtoQogEAIy4StKJiDgpSbbvlXRAUj0J6ICkuyMiJD1s+3zbF0i6dNzYiHiyPLbmwyLi0dqfxyWda/uciFiex5fbrNGqs1K1TrtddwQAwM40bn1WkvKQeuQAAUBn0A5sDrI81gRJib6ZAABMkjb1nKbcLAAAktZWla0iZVALCACw1oWSnqn9fUprK/NMOqfN2El+RtKjIwlAf1i2AvuYt7F0wLgkoIxsWgAA1sga1merR51s1ASAbiEJaA7SPNaUmJWKdmBBkAQAoFHW1HOaSkAAABSiSI6VirmlRCUgAMA6TYk2o9Fi3DltxjZ/qP1WSb8l6Rdqh2+MiB+R9I7y50Njxh60fcz2sTNnzrT5uA1Lm5KAEh5mAgAwKm1oB+ZBJSDiJgB0CUlAc5DloZFuYDKVgAAAGKtpktlL2J0JAIBULLhWZdirRVgiJABgxClJF9f+vkjSsy3PaTN2HdsXSXpA0k0R8XR1PCK+Vf5+UdI9KlqVrRMRd0XE/ojYv3fv3mkftynVhpO1VQ1M1VkAAEbkjYmzZRIQbTQBoFNIApqDLI81QVIqdmuSKQsAQLM0KxJok9FKQEwwAQBQHutLNFBpFgAw4hFJ+2xfZnuPpOslHRk554ikm1y4RtLzEXG65dg1bJ8v6XOSPhoR/7p2vG/7jeXrJUnvlfTETL7hJjRWAmKzJgAA61Qxs9+rx8ziN883AaBbSAKagyxCvXXtwGhpAgDAOE2tNKkEBABAIaRaJaDyGCESAFATEamkWyU9KOlJSfdFxHHbh2wfKk87KumkpBOSPinpI5PGSpLt99s+JenHJX3O9oPltW6V9MOSPmb7sfLnTZLOkfSg7cclPSbpW+VnbYuqqkE/WftAk3VaAADWygaJs8M12oR2YADQSf3tvoHdqKgEtPYY7cAAABgvy/M1u0ykcmGW4AkAgPKIQfKP19UEAgCgEBFHVST61I8drr0OSbe0HVsef0BFy6/R478p6TfH3Mrb29/1fDVWAkrYrAkAwKhBzPTaFpqSeL4JAB1DJaA5yPJYEyQlKUnIlAUAYJy0sZUmlYAAAJCK1l9eVwmIGAkAwDRZXrSetmk9DQDAJFlj4mzxm+ebANAtJAHNQR6hpOFBJjESAIBmWR5ryrNL5e5MtpkAAKCIokKeVEsC2r7bAQCgM5paT9MODACA9aokoHq19kE7MNZoAaBTSAKag8YHmUwuAQAYq6gEtPa/JT1aaQIAIKlsB1a2Aat+EyMBAJguG1N1lnVaAADWSssyeWsqAdEODAA6iSSgOUjzGATGCpNLAADGy7KmSkDDHSgAACyyiGEFoJGpJgAAmGBs1VmmmgAArDFoB1ZvoZlUSUAETgDoEpKA5iAfs8OEGAkAQLO0IXb2SKAFAEBSseuy2mhSRcsgRgIAMFWWx+ABZiUxracBABg1SAKqxc3qFWu0ANAtJAHNQRbrH2TaVDMAAGCcLM/X9JuWip0mGRNMAAAUEbVKQMULIiQAANOleb6+EpDFXBMAgBHVM8z6Gm31rLPsFAYA6AiSgOYgH9MOjLklAADNmioBFbszt+mGAADYQUJaN8dkfgkAwHRZ01wzoeosAACj0qZ2YOVL4iYAdAtJQHOQNvWaNkESAIBxsobY2WNhFgAAScVcsgqT1W/agQEAMF2aNcw1bVGwHQCAtZragVWbUVijBYBuIQloDsb2miZIAgDQqKgEtPa/JSTQAgBQyEOq+oFVM00iJAAA02V5qDfaetpFJXcAADA0aAdWW6OtnnUSNgGgW0gCmoM8Yk25PKlKAtqmGwIAYIdLs7xxd2ZG8AQAQFGrBORyrkmeLAAA02Vj12kJpAAA1A0qAfWoBAQAXUcS0Bw09Zq2KdcOAMA4aUPsTGgHBgCApCLhp4qS1XPMoBYQAABTjZ9rslYLAEBdWiUBuZ4EVPymgh4AdAtJQHPQlARUTS4BAMB6WR5aWlei3crybbohAAB2kFAMdmAOjjG/BABgqiyLNW1NpOEDTUIpAABD1WbM+vNNKgEBQDeRBDQHWTQkAZkgCQDAOE27M3tUAgIAQJKU58PFV48kAwEAgPEaKwFVDzTZsQkAwECaTUoC2pZbAgBsEklAc1BfoK3QaxoAgPGyvGl3plmUBQBA5YaScopZzTRpYQIAwHRZnqvfUHVW4oEmAAB1WV6UZF+TBJRU7xE0AaBLSAKag6Id2NpjRRLQ9twPAAA7XfPuzKK6HgAAiy40bF1S7TdhfgkAwHRZNG3WLH6zYRMAgKEsQv1x1fOImQDQKSQBzUHRDmztP63NTk0AAMbJ8nzdJLOXUAkIAACpmEsO2oGVtYCIkAAATNc010wS2oEBADAqzWMQIytUzwOAbiIJaA7GVgLKt+d+AADY6dKsqRKQqQQEAICKBVePVAJikwkAANONm2tKEku1AAAM5XmoR/U8ANgVSAKag2xMoCRIAgDQLMtD/V5DJSBCJwAAI5WAAABAW01zzUESEBNOAAAG0ryhHVj5N5tQAKBbSAKag3xMybwQgRIAgCZFFb21/y1JzKIsAADS2tLrdrUIu003AwBAh6Rj5poSGzYBAKgb92yzeG877ggAsFkkAc1BFuuzZU3fTAAAxhq304RFWQAApNBw8XXQDkzESAAApskjNFIIaPCAk3VaAACGmp5tVn9mrNECQKeQBDQHaUO2bDXZpBIQAADrFZWA1u80yYPYCQBA0Q6seO3BsW27HQAAOiPNmioB0Q4MAIBRWeOzzSpxlpgJAF1CEtAc5HkMAmOFSkAAAIy3muXrdpr02J0JAICkYsHVg0pAZTuw7bwhAAA6ImuqOktVAwAA1mmKmTzbBIBuIgloDrJoqmZQ/CZbFgCA9ZoqAVUJtRmzTADAgosQlYAAANiENM/V662vOiuxTgsAQF2axyBGVqpielTPA4BuIQloxvI8FKH1SUAJk0sAAMZJJ+zOJHYCABZdHqpVAiqO0S4TAIDpmqoaUHUWAID18jzUJ3EWAHYFkoBmrCojSzswAADay/JQv7f2vyWDBFqCJwBgwUWEPHpsW+4EAIBuySLWrdMONpww1wQAYCDNm2ImSUAA0EWtkoBs32/7/2abpKEpqpYlyZhqBuzWBIDdb7Nx0/a1tp+yfcL2bQ3v2/bt5fuP275y2ljbb7D9kO1vlL9/oDy+ZPvTtr9q+0nbHz2b73y20jxvqARUtgMjdgLArsZ8c7qiHVhVCciDYwCA3YnYODtZtr71NA80AWB3IW7ORh5NMbN6bxtuCACwaW0D4p2SflbSN2x/wvZ/Osd76rRq8jh+crnltwQA2Hobjpu2e5LukHSdpMsl3WD78pHTrpO0r/w5WH7OtLG3SfpCROyT9IXyb0n6oKRzIuJHJL1d0i/YvnRzX/fsZfn6SSYl2gFgYTDfnCKPGLQBG7QDoxYQAOxmxMYZSZtamzDXBIDdZlNxc04bMj9o+7jt3Pb+ket9tDz/Kdvvrh1/e7lR80T5eaOFYLdE2pA4a1uJSZwFgK5plQQUEX8eETdKulLS30p6yPb/avvDtpfmeYNdU1UCGlfNgEAJALvfJuPmVZJORMTJiFiRdK+kAyPnHJB0dxQelnS+7QumjD0g6dPl609Lel91m5K+z3Zf0nmSViS9cFZf/CykeYyvBMTKLADsasw3pwvVKgFVxwiPALBrERtnp2nDCeu0ALC7bCZuznFD5hOSPiDpL0Y+73JJ10t6q6RrJf1+eR2V1z1Y+6xrN/HPcNbyiEGMrEts5fk23BAAYNNal8az/R9K+oeS/gtJj0r6H1QE1IfmcmcdNWgHRq9pAFhom4ibF0p6pvb3qfJYm3Mmjf3BiDgtSeXvN5XH/4WklyWdlvRNSf/fiPheu283W3keipB6ydr/lvTKP1mYBYDdj/nmZGsrAZXtwLbxfgAA80dsnI00D/VYpwWAXW8TcXMuGzIj4smIeKrh8w5IujciliPibySdkHRVeb3XR8SXIyIk3a3hJs4t1VQ9TyqTgFifBYBO6bc5yfa/lPSfSvojST9VPUyU9M9tH5vXzXVRlQS0bocJZWYBYGFsMm42lXkdjRrjzmkzdtRVkjJJPyTpByT9z7b/PCJOrvlA+6CKnSi65JJLplxyc9Kqit5oiXYqAQHAQmC+OV3EMPlnWAmI+AgAuxWxcXbyPNZtOKESEADsLpuMm02bKq9ucc64DZmjY5s+7+GGa62Wr0ePr7EVa7RZPqYSUELMBICuaZUEJOkPIuJo/YDtc8qM1f3jBi2irAyECWVmAWCRbSZunpJ0ce3viyQ92/KcPRPGftv2BRFxutxZ8p3y+M9K+tOIWJX0Hdv/WtJ+SWuSgCLiLkl3SdL+/fvnEsTGJtASOwFgUTDfnCIiBlULGtZkAQC7D7FxRpqqGrBZEwB2nc3Eza3ekHlW19qKNdo8Qv1k/e1YVAICgK5p2w7sNxuOfXmWN7JbVH0xRwNlNdckUALAQthM3HxE0j7bl9neo6JH9JGRc45IusmFayQ9X+5smTT2iKSby9c3S/ps+fqbkn6ivNb3SbpG0tfbf8XZScvguS52Vguz9JwGgN2O+eYUeQyTY12uETO1BIBdjdg4I1keDRtOhu8BAHaFzcTNs9mQ2WZs2887Vb7eyLXmIs1iXYEDqUieZX0WALplYiUg2/+RirJz59n+MQ0zUl8v6XVzvrdOqh5kru81zYNMANjtziZuRkRq+1ZJD0rqSfpURBy3fah8/7Cko5Leo6Jn9CuSPjxpbHnpT0i6z/bPq0j8+WB5/A5JfyjpifI+/zAiHj/Lf4JNoRIQACwm5pvt5RGDf5xqqkk7MADYfYiNs5fm+boNJ8w1AWB3OMu4OdhUKelbKjZV/uzIOUck3Wr7XhXtvp4vq62faTF21BFJ99j+HUk/JGmfpL+MiMz2i+WGz38j6SZJvzvtu89DHqGl3vraET0TMwGga6a1A3u3pH+oIvP0d2rHX5T0X8/pnjqtSvJZ1w4sYXIJAAvgrOJmWbb26Mixw7XXIemWtmPL489J+smG4y9pmBC0rVazIjauW5gt55zszgSAXYv5ZksRkgeVgMpj23c7AID5ITbOUESsqaZX6Q2SgLbjrgAAM7TpuDmvDZm2368iiWevpM/Zfiwi3l1e+z5JX5OUSrolIrLydn5R0j+VdJ6kz5c/Wy7NQ+cuNVQCMu3AAKBrJiYBRcSnJX3a9s9ExP1bdE+dlkVVzWDt8WquSaAEgN2LuLk5w0pAa4Nnj92ZALCrETfbi4jBnLJKBiI8AsDuQ2ycrWquuX7DCXNNANgNzjZuzmlD5gOSHhgz5uOSPt5w/Jikt23k3uchb2ihKZXtwAiZANAp09qB/YOI+GeSLrX9y6PvR8TvNAxbaNXkcnSHSfV3xuQSAHYt4ubmVK00x5VoJ3YCwO5E3GwvJCWDJKDhMQDA7kJsnK202nDSG51rFr9znmgCQKcRN2crzWOwKbMuoR0YAHTO+uaOa31f+fs/kPT9DT8YMdxhsvafNmG3JgAsAuLmJgxi58jCbLXzpGq1CQDYdTYdN21fa/sp2yds39bwvm3fXr7/uO0rp421/UHbx23ntvfXjl9q+1Xbj5U/h0c/b97yiMGcctAOjMklAOxGzClnaGwlINqBAcBuQdycoWxcJSCbxFkA6Jhp7cD+Sfn717fmdrpv2NJk7fEeO0wAYNcjbm7OYHcmJdoBYKFsNm7a7km6Q9K7JJ2S9IjtIxHxtdpp10naV/5cLelOSVdPGfuEpA9I+icNH/t0RFyxkfucpTy0vh3Ydt0MAGBumFPOVjqm9TRzTQDYHYibszUxCYiQCQCdMq0SkCTJ9j+y/XrbS7a/YPu7tv/BvG+ui6rJ47p2YEwuAWBhEDc3ZnwVvbXvAwB2p03EzasknYiIkxGxIuleSQdGzjkg6e4oPCzpfNsXTBobEU9GxFMz/4IzEDFM/hkeIz4CwG7FnHI2Bps1R55nDtqBEUsBYFcgbs5GFjF4lllHOzAA6J5WSUCS/m5EvCDpvSp2S75F0v9rbnfVYdm4agaUmQWARULc3IA0a46dPZNACwALYqNx80JJz9T+PlUea3NOm7FNLrP9qO0v2X7HuJNsH7R9zPaxM2fOtLhsOxExaANW5QIRHQFgV9vUnHIr22WW7320PP8p2++uHX+77a+W793u0UzWLTJYp+2NbjipWk8TTQFgl2AtdgayPNa10JSKIgeszwJAt7RNAloqf79H0h9HxPfaDNrKiaftS22/avux8udwy+82U2NbmpSTS6oZAMBC2FTcXFTDSkDNVfSInQCw6200bjY9SBwNFuPOaTN21GlJl0TEj0n6ZUn32H5904kRcVdE7I+I/Xv37p1y2fbyiMGcsvoCrMECwK624TllreXldZIul3SD7ctHTqu3yzyool3mtLFVu8y/GPm8yyVdL+mtkq6V9PvldVRe92Dts65t9a1nbOxck82aALDbsBY7A1keg02ZdYmtPN+GGwIAbFrbJKB/ZfvrkvZL+oLtvZJemzRgqyeepacj4ory51DL7zZTVTbs+iSgte8DAHa1DcfNRZaWs8heb1wloC2/JQDA1tpo3Dwl6eLa3xdJerblOW3GrhERyxHxXPn6K5KeVrGzdMuEhnNK27JIAgKAXW4zc8qtbpd5QNK9ZZz8G0knJF1VXu/1EfHlKHpX3i3pfRv/Jzh7g7nmug0nxW/WaQFg12AtdgbyPNbFTKmYi2bETADolFZJQBFxm6Qfl7Q/IlYlvaz1k8hRWz3x3BGGvaabqxnwIBMAdr9Nxs2FNa0SECXaAWB320TcfETSPtuX2d6jogrBkZFzjki6qaw+e42k5yPidMuxa9jeW1U2sP1mFZtYTm74i56FPA+NdlIJGoIBwK61yTnlVrfLnHStU22uNa82mpVplYB4oAkAuwNrsbORjk0CsoKYCQCd0t/Auf8nSZfaro+5e8L5TRPBq1ucM27iOTq2yWW2H5X0gqT/JiL+59ETbB9UUXVIl1xySYtLbkz1oDIZW2aWQAkAC2KjcXNhjW+lWfxmYRYAFkLruBkRqe1bJT0oqSfpUxFx3Pah8v3Dko6qKAV/QtIrkj48aawk2X6/pN+VtFfS52w/FhHvlvROSb9hO5WUSTq01eXlQ1I9B8jW9CZmAICu2+iccqvbZZ71tSLiLkl3SdL+/ftnHtnGzzWLqnq0NgGAXYW12LOUx5gkoMRKUyagANAlrZKAbP+RpP9E0mMqFj2lYvK2kyaepyVdEhHP2X67pD+x/daIeGHNReY8uaweVK7fYVL8ppoBAOx+m4ybC2u4O3NtgcIeCbQAsBA2Ezcj4qiKRJ/6scO11yHplrZjy+MPSHqg4fj9ku6f8jXmKmK4sUQqWoIRHQFg99rknPJs2mXuaTG27eedKl9v5FpzkY9JApKKuMpcEwB2B9ZiZ2N8JSDWZwGga9pWAtov6fLYWL23LZ14RsSypOXy9VdsPy3pLZKObeCez1o6pRIQcRIAFsJm4ubCWs2K7ZfrdmfSDgwAFgVxc4o8Ys1OGUuUYweA3W0zsXHQ8lLSt1S0vPzZkXOOSLrV9r0qqq4/HxGnbZ9pMXbUEUn32P4dST+kol3mX0ZEZvvFsh3nv5F0k4pKe1suHdMOTCqq6vFAEwB2DeaUM5BNaAfG+iwAdEsy/RRJ0hOS/qMNXnsw8bS9R8Xk8cjIOUck3eTCNSonni3HrmF7r+1e+frNKiaeJzd4z2dtsMPEzQ8yaWkCAAthM3FzYWVjFmarBNqM0AkAux1xc4qItRtNbDaYAMAut+HYGBGppKrl5ZOS7qvaZVYtM1VUwjupol3mJyV9ZNJYqWiXafuUpB9X0S7zwXLMcUn3SfqapD+VdEtEVNUXflHSH5Sf87Skz2/4X2AGskEloPVL4EnCA00A2EWYU85Alse6Z5tSVT1vG24IALBpbSsBvVHS12z/pcpqO5IUET89bkBEpLaryWNP0qeqiWf5/mEVE8/3qJgQviLpw5PGSsXEU8Xukb0qJp6PRcS7Jb1T0m/YTlWU+zsUEd9r+f1mJhvba7r4zQ4TAFgIG46biywdEzurv4mdALDrETenWF8JiHZgALDLbSo2bmW7zPK9j0v6eMPxY5LeNulet8KkSkBFa5OtviMAwJwwp5yBLA/1es0xkwIHANAtbZOAfm0zF9/KiWdE3C/p/s3c5yxVDyrXJwFVDzK3/JYAAFvv17b7BrqkSqBd6q3dnVmF0ozgCQC73a9t9w3sdBGSPVoJiPgIALvYr233DewGWV60nk7GtTYhlgLAbvFr230Du8HYSkBUzwOAzmmVBBQRX7L9H0vaFxF/bvt1Kir0YMS4agaDJCACJQDsesTNjRkXO22XuzOJnQCwmxE3pwuF6mHSFpWAAGAXIzbORlbkADVWAuqRBAQAuwZxczayiDHV84iZANA16xsiN7D9f5f0LyT9k/LQhZL+ZE731GlVtYJkJFu2aj1NoASA3Y+4uTHV7syxk0wSaAFgVyNuTpdHkfhTsSymlgCwexEbZyMt55qjG06ksqoBsRQAdgXi5tnL81DEpOp523BTAIBNa5UEpKJl19+R9IIkRcQ3JL1pXjfVZdPbgREpAWABEDc3IM2aY6dUTDxpBwYAux5xc4qIWLfRJKgFBAC7GbFxBqq5ZPOGEyq2A8AuQtw8S1n1bLOpHRjtqAGgc9omAS1HxEr1h+2+qD7eaFyZ2cSWJbJlAWAxEDc3YLAw2xuzMMu/HADsdsTNKYpKQMM4aYtKQACwuxEbZ6BqPT2+qgH/pACwSxA3z1K1PttrWp9NrIx/TQDolLZJQF+y/V9LOs/2uyT9j5L+1fxuq7uqliZNk0uzwwQAFgVxcwOqhdmmSkA9e7ATBQCwaxE3J6h2XNajpG1WtAFgdyM2zkBWPrFcStYvgdPaBAB2FeLmWRokATVWAjLPNgGgY9omAd0m6Yykr0r6BUlHJf0387qpLqsqAY0NlDzIBIBFQNzcgGGJ9vX/LeklTDIBYAEQNyeoppD1dmCJqAQEALscsXEGJm04SRKxTgsAuwdx8ywN2oE1btIkZgJA1/TbnBQRue0/kfQnEXFmvrfUbVWgbHiOqSRhhwkALALi5sZMXJglgRYAdj3i5mRVHKzvMynagREfAWC3IjbOxuTW02w4AYDdgrh59qrqeazPAsDuMLESkAu/Zvu7kr4u6SnbZ2z/t1tze92TT6hmkJAtCwC7GnFzc6pWmv3G3Zkk0ALAbkXcbKcKg6NhkvAIALsPsXG20nKuOf6B5lbfEQBgloibs1MVOGhanzUxEwA6Z1o7sF+S9Hck/V8i4j+MiDdIulrS37H9/5z3zXVROq1vJklAALCb/ZKImxs2rRJQxiwTAHarXxJxc6phJaBhnLRNFhAA7E6/JGLjzKRlVYMlNmsCwG71SyJuzkS1/pqMa6HJ+iwAdMq0JKCbJN0QEX9THYiIk5L+QfkeRuT5+HZgPVvlBhQAwO5E3NyEamG2aadJLxFJQACwexE3W6ieT65rB0YWEADsRsTGGarmkr2mdmCJB1UPAACdRdyckUELzTGbNEO0pAaALpmWBLQUEd8dPVj21Fyazy11WzV5bKxmkFAJCAB2OeLmJkyqBNSjih4A7GbEzRaqMJjUKwHVjgMAdhVi4wylUx5oslkTADqPuDkjg0pAY7qcSKIlGAB0yLQkoJVNvrewJgdKyswCwC5H3NyELM/VT7ymzUmll9AODAB2MeJmC9UcMllTCcjUAQKA3YnYOENZmeXDhhMA2LWImzMyqATUUD2vOkTcBIDu6E95//9s+4WG45Z07hzup/OmlczjOSYA7GrEzU1I82hclJXKEu0ETwDYrYibLVQLrdZoJSDiIwDsQsTGGVqd0Ho6SaQ8JZYCQMcRN2cknVTgIKkqARE3AaArJiYBRURvq25kt8gmtDSxeZAJALsZcXNzsiwaF2UlKgEBwG5G3GynioJeUwlIVAICgF2I2Dhbw6oG64vhJ1QCAoDOI27OThUTm55tDtqB0UYTADpjWjswbFAeIVuNLU1oBwYAwHqTKgH1SKAFACy4KBda63NMy2JqCQDAZOm0iu08zAQAQJKUTqqeRzswAOgckoBmLMtDvYYEIKnIoCVGAgCwVpZH485MqYidOUlAAIAFFqrKsg+P2bQDAwBgmqzM8mmuasDDTAAAKlVMbGoHVm1IIW4CQHeQBDRj2YRqBpSZBQBgvYmVgBIPdm8CALCIqjA4uhhLdAQAYLJqLtm0YTNJWKcFAKAybKG5Pmb2BklAW3pLAICzQBLQjE1OAiJTFgCAUVmeN5aalUigBQCgioP155eJqTILAMA0aRZKXCT8jCrmmttwUwAA7EBV4mxTJaCkfJJMtXYA6A6SgGYsi/HtwOg1DQDAepMqAfUTD3aiAACwiKpkH9fmmTaVgAAAmCbNQ/2kefm7WKclmgIAIA03nzTFzYR2YADQOSQBzVieR+PuEqnYdZIRJAEAWCPLY3wlIJKAAAALLqLakTk85tpxAADQLMvzxrYmEhXbAQCoS7Ny3tnw1DihHRgAdA5JQDOWxYQHmUwuAQBYZ1IloJ6tjNAJAFhg1UKrtTYLiKklAACTTZprFps1t/iGAADYoSZWAkqoBAQAXUMS0IxlkyoBUWYWAIB10iwfW6K9l1gZvTQBAAss1FQJqHnOCQAAhiZWnWWdFgCAgbSMib3GSkDFb5KAAKA7SAKasSwP9TxhckmMBABgjWxSJaDEIgcIALDIBpWA6klAHiYHAQCAZqtZqDduwwkV2wEAGMgHSUANlYCqdmCs0QJAZ5AENGNZrvFlZplcAgCwTpqHlnrjk4AyYicAYIFFGQddywKyaAcGAMA0WZ5PrgREMAUAQFKtElBDkYNBEhBxEwA6gySgGctjcq9pYiQAAGtNqgSU2MryGDwABQBg0VQhMKknAZm5JQAA06R5qD9mw0mSULEdAIBKVgbFpgJ6tAMDgO4hCWjG0mkPMgmSAACskWah/rgS7eVhFmcBAIuqWmitzzItKacdGAAAE2V5TKgENGx9AgDAoquSgJrWaJMylvJ8EwC6gySgGcvz0Ji5JZNLAAAaTKoEVPWhzoifAIAFNagEVJu92xI5QAAATDZts2aIqgYAAEjDBJ9eYyWgsh1YvpV3BAA4GyQBzdi0liZMLAEA49i+1vZTtk/Yvq3hfdu+vXz/cdtXThtr+w22H7L9jfL3D9Te+1HbX7Z93PZXbZ87/2+5XprnY0u09yg3CwBYcFUMXNcObLtuCACAjsgmVJ2tqhow1QQAYFjAoNdUCaicigZBEwA6gySgGcsiGoOkRK9pAMB4tnuS7pB0naTLJd1g+/KR066TtK/8OSjpzhZjb5P0hYjYJ+kL5d+y3Zf0zyQdioi3SvrPJa3O6/tNMrkSkAfnAACwiJpCoMUCLAAA06R5PnGzpsSGEwBYZFu5IdP2jbYfq/3ktq8o3/tiea3qvTdtwddfI62SgLw+bhIzAaB7SAKaseJBZvN7VAICAExwlaQTEXEyIlYk3SvpwMg5ByTdHYWHJZ1v+4IpYw9I+nT5+tOS3le+/ruSHo+IfytJEfFcRGRz+m4TpXmoP25hliQgAMDCW18JSKZyAQAA06R5aGlM1dlqCpoz1wSAhbTVGzIj4jMRcUVEXCHpQ5L+NiIeq33WjdX7EfGdWX/faQaVgBriZrU+S8gEgO4gCWjGsjwaM2WlYnLJxBIAMMaFkp6p/X2qPNbmnEljfzAiTktS+bvaSfIWSWH7Qdt/Zfv/3XRTtg/aPmb72JkzZzbxtaabWAmojKkZTzoBAAuqmkLWp5mJaAcGAMA0k+aaw6oGW3lHAIAdZKs3ZNbdIOmPZ/ptztLkSkDFbzZpAkB3kAQ0Y3nEICt2VFEJaItvCADQFU3BYzRqjDunzdhRfUn/V0k3lr/fb/sn110k4q6I2B8R+/fu3TvlkptTVAJq/i8J7cAAAIuuyoOtVwKyaQcGAMA0aTZ+rjl4oEk8BYBFtdUbMuv+vtYnAf1h2QrsY/b6TJx5b9Ss4mFT8iztwACge0gCmrFsUksTEyQBAGOdknRx7e+LJD3b8pxJY79d7lBR+bsqJ3tK0pci4rsR8Yqko5Ku1DaYWAmIJCAAwIKr5pD1UGlPz/YFAGDRTawElPBAEwAW3FZvyCwuaF8t6ZWIeKJ2+MaI+BFJ7yh/PrTu4nPeqJlluaRpSUAz/1gAwJyQBDRjaR5rdmjWJYmZWAIAxnlE0j7bl9neI+l6SUdGzjki6SYXrpH0fLmjZNLYI5JuLl/fLOmz5esHJf2o7dfZ7kv6zyR9bV5fbpLVLB+bQNtjYRYAsOCGMXBtrCQ0AgAw2Wqeq9+b0g6MJ5oAsKi2ekNm5XqNVAGKiG+Vv1+UdI+KdmNbKivDYXMSUPGb9VkA6A6SgGYsn9JrmnklAKBJRKSSblWRnPOkpPsi4rjtQ7YPlacdlXRS0glJn5T0kUljyzGfkPQu29+Q9K7yb0XE/ybpd1QkED0m6a8i4nPz/p5N0izGLsxWfaipBAQAWFTDdmDDY5YV1AICAGCiiZWAyrkmzzMBYGFt9YZM2U4kfVDSvbVjfdtvLF8vSXqvpHqVoC2R5RMqASUkzgJA1/S3+wZ2myymJAERJAEAY0TEURWJPvVjh2uvQ9ItbceWx5+T9JNjxvwzSf/sLG55JtI8V7/XnJec0A4MALDghklAw3mmLfqBAQAwRZqF+smYuSZVDQBgoUVEarvaVNmT9KlqQ2b5/mEVa63vUbEh8xVJH540trz0JyTdZ/vnJX1TRdJP5Z2STkXEydqxcyQ9WCYA9ST9uYrNn1uq7AY22JBZRzswAOgekoBmLJ/YDqxYp81j/DkAACya1Sy0NKUdWMbCLABgQVUPJ+tTSNvkAAEAMEWWx9jW0wlzTQBYeNuwIfOLkq4ZOfaypLdv8NZnbmIlIBJnAaBzaAc2Y+mEyWWPMrMAAKyTZuMrAdEODACw6KoIuKYSkJhXAgDWs32t7adsn7B9W8P7tn17+f7jtq+cNtb2G2w/ZPsb5e8fKI/faPux2k9u+4ryvS+W16ree9MWfP110jxXb0zraaoaAAAwNKgE1PB8szeImQRNAOgKkoBmLMtjsJNkVEKgBABgndU81B+zMNuj5zQAYMEN5o9rKgExrwQArGW7J+kOSddJulzSDbYvHzntOkn7yp+Dku5sMfY2SV+IiH2SvlD+rYj4TERcERFXSPqQpL+NiMdqn3Vj9X5EfGfW37eNyZs1i9/MNQEAGFbGawqb1TNPQiYAdAdJQDOWRzT2zJRqJfOIlAAADKRZrqVkTCWghEpAAIDFVuX6jFYCAgBgxFWSTkTEyYhYkXSvpAMj5xyQdHcUHpZ0vu0Lpow9IOnT5etPS3pfw2ffIOmPZ/ptZiDNorGigcRmTQAA6rI8Vy+x3PB8cxAzWZ8FgM4gCWjGsnzC5JJsWQAA1sjzUB4aWwmoip0ZsRMAsKCiYUembdqBAQBGXSjpmdrfp8pjbc6ZNPYHI+K0JJW/m1p7/X2tTwL6w7IV2Mfc9ERxC2R5jN1wwjotAABDWd7cCkyqFThgEgoAnUES0IxNSgKq5rsZgRIAAEnSal40nF7qNf+XpG8qAQEAFls+6AZWqwRkKURsBACs0bQgORosxp3TZmzzh9pXS3olIp6oHb4xIn5E0jvKnw+NGXvQ9jHbx86cOdPm4zYkzUO9cRtOqGoAAMBAludju5yY6nkA0DkkAc1YFpPKzBa/CZQAABTSssRPf0oVPZKAAACLqqkSUHF8G24GALCTnZJ0ce3viyQ92/KcSWO/XbYMU/n7OyPXvF4jVYAi4lvl7xcl3aOi3dg6EXFXROyPiP179+6d+OU2I8vz8XPN8jCbNQEAKCoBTYuZLM8CQHeQBDRjeT7cSTKqyqJlbgkAQGGQBDSmElCVWMvuTADAohpUAqrNMxObOkAAgFGPSNpn+zLbe1Qk5xwZOeeIpJtcuEbS82WLr0ljj0i6uXx9s6TPVheznUj6oKR7a8f6tt9Yvl6S9F5J9SpBWyadULGduSYAAENZng82Y46yrcTETADokv5238BuU7QDa36PMrMAAKw1bAc2eWE2JYMWALCgqkpA9b0mrh0HAECSIiK1faukByX1JH0qIo7bPlS+f1jSUUnvkXRC0iuSPjxpbHnpT0i6z/bPS/qmiqSfyjslnYqIk7Vj50h6sEwA6kn6c0mfnMd3nibNYmxVgx5VZwEAGMhifMyUiuebhEwA6A6SgGasaAfWnAVUHaYdGAAAhWE7sDGVgEigBQAsuCoC1ivO2qISEABgnYg4qiLRp37scO11SLql7djy+HOSfnLMmC9Kumbk2MuS3r7BW5+LLI+pVWdT5poAACjLY2wlIKlKAiJmAkBX0A5sxtpUAqLXNAAAhdWsqATUn1IJiN2ZAIBFlTdWAjJtpgEAmCLNcyoBAQDQQpbHYDNmkyShwAEAdAlJQDM2KVAO2oERJwEAkDTcdTmuHVhVIIgEWgDAoqpC4JpnmKYdGAAAk+R5KI9hss+oqhotSUAAABRrtONipkQlIADoGpKAZiyfUDIvoaUJAABrpFUloCntwFiYBQAsqmEloFo7sO26GQAAOqLaSEIlIAAApsvbJAHlW3hDAICzQhLQjGURYyeX1fNNsmUBACisZpMrAdlWYhZmAQCLq5o+rikEZNqBAQAwSVrONXvjNpyU67cpARUAAGUxPnFWKirT8mwTALqDJKAZS9tUAiJOAgAgSVqdUglIKhZnqaIHAFhUoSIGJvVKQJbYhAkAwHhpWa5g3IaTPpWAAAAYyPJ87LNNiXZgANA1JAHNWJ7HoHXJKNqBAQCwVrUw2x+zMCsVSUAZk0wAQI3ta20/ZfuE7dsa3rft28v3H7d95bSxtj9o+7jt3Pb+ket9tDz/Kdvvnu+3W6squZ6MtAMLYiMAAGNVyT3jWpvQDgwAgKEsH9/lRJKSxBQ4AIAOIQloxrIY3zezOpyLSAkAgFRvBzb+vySJzcIsAGDAdk/SHZKuk3S5pBtsXz5y2nWS9pU/ByXd2WLsE5I+IOkvRj7vcknXS3qrpGsl/X55nS1R7bas7zWxRTswAAAmSMs55LgHmsMkIGrrAQCQ5bFm48moxCTOAkCXkAQ0Q3keihi/w2RYCWgr7woAgJ0rzSYvzEplJSAmmQCAoasknYiIkxGxIuleSQdGzjkg6e4oPCzpfNsXTBobEU9GxFMNn3dA0r0RsRwRfyPpRHmdLVFFwDVJQBofNwEAQL0SUPPyd+IimjLXBACgrAQ0oVJ7YlONFgA6ZK5JQItUol3SoFXJ2HZg5QNO+mYCAFBYLTNjl/rj/0tCEhAAYMSFkp6p/X2qPNbmnDZjN/N5kiTbB20fs33szJkzUy7bTrXQWk/8saWgwiwAAGOtZmXraTacAAAwVTq1EhDtwACgS+aWBLRoJdql4c6RZFo7MJKAAACQNKwEtDRmd6ZUJNdmxE4AwFDThGs0UIw7p83YzXxecTDirojYHxH79+7dO+Wy7VQhsB4qLdqBAQAwybASEElAAABMk0dMTJxNEp5tAkCXzLMS0EKVaJeGAZB2YAAAtJNWuzMnlJvtJVbOwiwAYOiUpItrf18k6dmW57QZu5nPm5sqBNZ3ZdomCQgAgAnSMoBOm2umzDUBAFCaxdgCB1JVCYiYCQBdMc8koB1Zon0e5dkr1c6RcdmytAMDAGCt1TJ2Lk1ZmGV3JgCg5hFJ+2xfZnuPioqwR0bOOSLpprIF9TWSno+I0y3Hjjoi6Xrb59i+TEUl27+c5ReaJB+0AxuyaAcGAMAkw3Xa8cvffeaaAABIKuadvWntwChwAACdMc8koB1Zon0e5dkrg3ZgYwIl7cAAAFhrUAlowsJsQjswAEBNRKSSbpX0oKQnJd0XEcdtH7J9qDztqKSTKirEflLSRyaNlSTb77d9StKPS/qc7QfLMccl3Sfpa5L+VNItEZFtyZfVcFLrNZWAaAcGAMAkVetp2oEBADBdmsfE6nmJxfosAHRIf47XPpsS7XtajN3M583VtF7Tg3ZgBEoAACQNF2anlWhnYRYAUBcRR1Uk+tSPHa69Dkm3tB1bHn9A0gNjxnxc0sfP4pY3LaLabDI8Zps6QAAATJDm1YYT2oEBADBNnsfYAgcSmzQBoGvmWQlooUq0S8Ms2HF9MwdJQJTMAwBAkrRaBsWl3vj/khRJQFt1RwAA7CyDdmAjC7LBAiwAAGNVyT29CRtO+knChhMAAFQ835yUOJskVk7MBIDOmFsloIhIbVdl1nuSPlWVaC/fP6xi9+V7VJRof0XShyeNlYoS7ZJ+V9JeFSXaH4uId5fXrkq0p9riEu3SMLlnXKCkHRgAAGsNKgFN2p1pa4UMWgDAgqqmj2srAW3PvQAA0BVVcs/ShNbTVJ0FAKCQZjG2wIFUzEeJmADQHfNsB7ZQJdqlYZnZ3pgV2SqAMrcEAKCwWpb46U+rBLRK8AQALKZq/mgN55mWxd4SAADGqzac9Ka0A6O1CQAARfGCiZWATCUgAOiSebYDWzhVkYKp7cCYXAIAIGlYon1pQon2hN2ZAIAFFoN2YMNjLndh0hIMAIBm1RyyP2GuSSUgAAAKaT6tEpApcAAAHUIS0AxVO0fGFTPokQQEAMAaaVUJaFqJdmInAGBBDdqB1RZkq4QgoiMAAM0GFdunVQLiiSYAAMrzKZWAWJ8FgE4hCWiGqkljMq4dWHmYknkAABRWs+mVgHoWC7MAgIVVbSKpR8rqNWuwAAA0q9qBTXqg2TNJQAAASEUloN6YZ5tS8XyTZ5sA0B0kAc3QoMzsmGoG1c5N4iQAAIU0z9VLLE+YZPYSek4DABZXFQHrm00mxU0AADBsPT216ixzTQAAlOcxuXqeTZcTAOgQkoBmqJo0jmsHltAODACANdJscqlZiYVZAMBiq+aP9XA5rAREfAQAoMlgs+akqrOJB23DAABYZOmUJCDbFDgAgA4hCWiGhouzzYGyOkzfTAAACqtZaGlc9mwpMT2nAQCLa7DQWk8CKieXREcAAJpVyT2THmj22XACAICk4vlmMiFmJqbAAQB0CUlAMzSsBNQcKBNblsQGEwAACmmeT9yZKbEwCwBYcA2bTbz2LQAAMGJQCWhSaxPmmgAASCoqAU2KmUli5cRMAOgMkoBmKJ2SBCQVC7dkywIAUFjNQv1kSiUgFmYBAAusCoH1WWaVDxTUAgIAoFGaTV+nLdqBEUsBAMjyGNvlRKqebW7hDQEAzgpJQDNUJfdMTAJKpCAJCAAASVKa5VqaUgmoRwItAGCBBZWAAADYsHRQCWj88jdVZwEAKORTKgH1aAcGAJ1CEtAMDdqBkS0LAEAraR5T24H1kiJ2MtEEACyiav64JgmofE1oBACgWZbnkjRxvkk7MAAACmkedDkBgF2EJKAZqvphJlMCZUagBABAkrSa5Vqa0g6smoDSdxoAsIgGC621aSbtwAAAmGxYCYgkIAAApsljShJQQoEDAOgSkoBmqErumTS5TMxDTAAAKmnWrhKQJJJoAQALrWmaSWgEAKDZoGL7lCSgEFVnAQCYXgmIZ5sA0CUkAc1Q2qYSENmyAAAMpHmu/pRKQFX7E3ZoAgAWUfVg0k3twLbljgAA2PlWs2qz5vj5Zq98j7kmAGCR5XkoYnLibGISZwGgS0gCmqEqC7Zn+mYCANDGahZaalsJiIVZAMACqsJffT22ehnMLQEAaJTluaTplYCKc4mnAIDFVVVfn/Rsc7ARhZAJAJ1AEtAMtSkzS8k8AACG0jxXvzf5vyMszAIAFlkMkoDqlYDK97bhfgAA6IKqYnu/RRJQylwTALDABs82J2zUrN6iyAEAdANJQDNUBb9pJfMIkgCAJravtf2U7RO2b2t437ZvL99/3PaV08bafoPth2x/o/z9AyPXvMT2S7b/q/l+u2arWUxclJWGu1BYlwUALKKm+aPFLkwAACbJ8lBiKZkw3+zTehoAFtpWrsXavtT2q7YfK38O18a83fZXy2vdbk8oyTMHWZsuJ2zSBIBOIQlohtJWlYDMQ0wAwDq2e5LukHSdpMsl3WD78pHTrpO0r/w5KOnOFmNvk/SFiNgn6Qvl33X/WNLnZ/6FWkqzXEtUAgIAYKqmSkAAAKBZmof6CXNNAECzbVqLfToirih/DtWO31lev/qsa2f2RVvIWhQ4qCq5Uz0PALqBJKAZqiaMycRsWcrlAQAaXSXpREScjIgVSfdKOjByzgFJd0fhYUnn275gytgDkj5dvv60pPdVF7P9PkknJR2fz1eaLs1D/QmlZiV2mgAAFls+mGcOj1Uvg7klAACNsjwmPsyU6u3A8q24JQDAzrLla7FNyuu9PiK+HMUE7+5pY2Yty6YnAS1VMTMjZgJAF5AENEPt24Ft1R0BADrkQknP1P4+VR5rc86ksT8YEaclqfz9Jkmy/X2SfkXSr0+6KdsHbR+zfezMmTMb+kJtFO3ApuzOrEq086ATALCAqvmj11QCoh0YAACTrGb59NbTbDgBgEW2pWuxpctsP2r7S7bfUfuMU1PuY67aVQKqkoCImQDQBSQBzVCVADtpgpnYg52cAADUNAWP0YAx7pw2Y0f9uqR/HBEvTTopIu6KiP0RsX/v3r1TLrlxq1mupSmVgFiYBQAsslBDJSBX7wEAgCZZi6qzfeaaALDItnot9rSkSyLixyT9sqR7bL++7bXmuVGzemY5MQmo3MS5SvU8AOiE/nbfwG6SlcEvmZgERDswAECjU5Iurv19kaRnW56zZ8LYb9u+ICJOl+Vlv1Mev1rS37P9jySdLym3/VpE/N4svkxbaZYPekqPQxIQAGCRNVYCKn/TDgwAgGZpHupNqzrLXBMAFtmWrsVGxLKk5fL1V2w/Lekt5WdcNOU+FBF3SbpLkvbv3z/TwJVWSUAe/2xziUpAANApVAKaoaoS0KRAmSQmCQgA0OQRSftsX2Z7j6TrJR0ZOeeIpJtcuEbS82VZ2Uljj0i6uXx9s6TPSlJEvCMiLo2ISyX995L+u61OAJKKdmBLU0q0V+u2xE8AwCKKCI1OMakEBADAZFkWtAMDAEyypWuxtvfa7pWv3yxpn6ST5fVetH2Ni50fN1VjtkrWphJQuYkzJWYCQCeQBDRDVd/MSZtMElvESADAqIhIJd0q6UFJT0q6LyKO2z5k+1B52lFJJyWdkPRJSR+ZNLYc8wlJ77L9DUnvKv/eMdI8n16i3SzMAgAWV0Qxj6xzWQuI/FgAQJ3ta20/ZfuE7dsa3rft28v3H7d95bSxtt9g+yHb3yh//0B5/FLbr9p+rPw5XBvzdttfLa91uz1hx+ScFJWASAICADTbhrXYd0p63Pa/lfQvJB2KiO+V7/2ipD8oP+dpSZ+fz7du1ioJqHxvNaMdGAB0Ae3AZigrg19/QhZQzyZIAgAaRcRRFZPL+rHDtdch6Za2Y8vjz0n6ySmf+2ubuN2ZSLOY2g4sYWEWALDA8giNLsUOKgGRBQQAKJXVBe5Q8cDxlKRHbB+JiK/VTrtOReWBfSpaRN8p6eopY2+T9IWI+ESZHHSbpF8pr/d0RFzRcDt3Sjoo6WEV89RrtcUPNNtsOCEJCAAW21auxUbE/ZLuH3OtY5LetpF7n6WqwEGrSkC0AwOATqAS0AytlsFvacIE06adCQAAldUsn9oOjIVZAMAiC62vBFR/DwCA0lWSTkTEyYhYkXSvpAMj5xyQdHcUHpZ0vu0Lpow9IOnT5etPS3rfpJsor/f6iPhy+fD07mlj5mEjlYBobQIAWGRtKgEtDWImRQ4AoAtIApqhlbLCz57++H9W2oEBADCU5qGlKZWAelU7MJJoAQALKI/QaA5Q1VWFyAgAqLlQ0jO1v0+Vx9qcM2nsD0bEaUkqf7+pdt5lth+1/SXb76h9xqkp9yFJsn3Q9jHbx86cOTPt+21IloWWJlRrl9hwAgCANIyDfSoBAcCuQRLQDFVtviZNMJPEVAICAKDUph1YtTCbszALAFhAEVqfBDR4j9gIABhoenI3GijGndNm7KjTki6JiB+T9MuS7rH9+o1cKyLuioj9EbF/7969Uz5uY9pUAuqXa7gkAQEAFlkVB8dVoJU0aLG5SiUgAOiE/nbfwG6ykubqJ1YyYYKZ0A4MAICB1Tyf2EZTYncmAGCxRcS6xVhiIwCgwSlJF9f+vkjSsy3P2TNh7LdtXxARp8tWX9+RpIhYlrRcvv6K7aclvaX8jIum3MfcZXk+eGA5DvEUAIC27cCoBAQAXUIloBlazfKpLU0SWyTKAgBQTDAjhrsvx6mSa2kHBgBYRHmsL6nQ56ElAGC9RyTts32Z7T2Srpd0ZOScI5JucuEaSc+XLb4mjT0i6eby9c2SPitJtvfa7pWv3yxpn6ST5fVetH2Ni/6VN1VjtlKbSkDV+ylzTQDAAktbJAFVibVpxgNOAOgCKgHN0GoW2tOf3tKESkAAAAzbaE7bnVk96GSnCQBgEeVNlYCqBViSgAAApYhIbd8q6UFJPUmfiojjtg+V7x+WdFTSeySdkPSKpA9PGlte+hOS7rP985K+KemD5fF3SvoN26mkTNKhiPhe+d4vSvqnks6T9PnyZ0ulWQzmkuP0TFItAADVM8tJSUDVe6vETADoBJKAZmilZSUgJpYAAAwfXE5rB1bF1lV2mgAAFlCENJIDNKiix9wSAFAXEUdVJPrUjx2uvQ5Jt7QdWx5/TtJPNhy/X9L9Y651TNLbNnLvs5blMbXqbLUhhXgKAFhk1cbL3ujEsyax1U/MJk0A6Ajagc3QSpprz7RqBj2TKQsAgIblY6cuzCaWRRIQAGAxRYQ8WgmIKnkAAEyU5vnUqrO9QXtN5poAgMVVrblO63TS71kpMRMAOoEkoBlazXItTQmSS4npmQkAgIo2mtL0SkC2tdRPtJISPwEAiyckjVZlH7TKZAEWAIBGWR4T25pIRVUDi0pAAIDFVq25Tk0CShI2ogBAR5AENEOrWa49U9qB9XsESQAApOGDy/6U2CkVLcFWiZ8AgAWURygZqQTUT2hfAgDAJKtZDOLlJL3ExFMAwEJboRIQAOw6JAHN0EoaWpryIHOpZ2URyoPJJQBgsVVJsW0WZvf0PJiQAgCwSPKQRnKAhu3AeGgJAECjNpWAJJKAAACo2oFNe77ZT9ikCQBdQRLQDK20aQdWBtFVHmQCABZc2wmmVOxEoR0YAGARpVm+Llb2qAQEAMBEaZ63qjrbS0xSLQBgoS1X7cBaFDlIebYJAJ1AEtAMraa59vQm7zCpqh3QEgwAsOiqhdb+lNgpVe3AmGQCABbPahbrYmU/KabyPLQEAKBZlrdrB9anEhAAYMFVGy/PmdYOjMRZAOgMkoBmaDXLp/bMpBIQAACFKhZWDzIn2dNLaAcGAFhIqw2VgKqkoIzYCABAo5R2YAAAtFIlAU17vtnv0Q4MALqCJKAZalqcHVWVoaUSEABg0VWxcIlKQAAAjLWa5VpKmtuBsQsTAIBmbSsB0Q4MALDoqjXX6UUOrDRnfRYAuoAkoBlayWJ6ElA5+VwlUAIAFlw1aexPiZ1SMQldSVmYBQAsnjQLLfXXPsRMbCUWlQsAABhjNQv1WlSdpRIQAGDRVZWApj/fTChwAAAdQRLQDK2kmfZMCZJLVAICAECSBuVjl1rsztxDJSAAwIJayfLG1plULgAAYLwsz1tVAuonCUlAAICFtpLlsjU1bvZ7Zn0WADqCJKAZWs1iakuT6n0CJQBg0VUJsW0qAS31PdiVAgDAIkmzaNxs0k8SkoAAABgjzUP9Fq2ne4mVBfEUALC4VtJce3qJ7CnPN5mDAkBnkAQ0Q6tZ3qJnZlKeS6AEACy21UE7MCoBAQAwzmqWN8bKfmJltJkGAKBRlkerSkC0AwMALLqVFs82pWINN2V9FgA6gSSgGVrN8uk9M6kEBACApGEloKWGFiejlnrFThMWZwEAi2bcPLPXM22mAQAYI81DvRZzTZKAAACLrqoENE2fltQA0BkkAc3Qcjo9Cah60EmgBAAsumrnSKtKQOVulFdXs7neEwAAO03RdrohCci0LwEAYJw0y9tVAjJJQACAxbaStq0ElLARBQA6giSgGWrTDqx60EnJPADAolstF1qXWiQBVQ8/X10hCQgAsFiKSkAN7cCoBAQAQKM8D+VRVPmZhkpAAIBFt5F2YFlQqR0AuoAkoBlazWJqybzqISbtwAAAi25QCahFifY9JAEBABZUmjdXAuonCYuvAAA0qCrltdlw0kusNGedFgCwuNq2A6s6nSynrM8CwE5HEtCMZHmR/TqtHVhVCWiVHZsAgAVXVS9o0w5sqdyN8spqOtd7AgBgp1lJ88ZYyUNLAACaVUmySYtKQH0qAQEAFlxRfbZdJSBJWl5lHgoAOx1JQDNSVfZZ6k+eXFaBlMVaAMCiWy1jYZtJ5p5ykkklIADAoknz5l2ZPLQEAKDZclrMNc/p96aeSzswAMCiW07btQMbVgLi+SYA7HQkAc3ISpkENK1kXmKrZ1MJCACw8AaVgFrszqwqAZEEBABYNKtZTKgExLwSAIBRy6vFvPGcFg80iacAgEW30jIJaFAJiHZgALDjkQQ0I6tl5mvbQJlmZMoCABZbVUWv36oSUJkEtMokEwCwWFbT5tLsVAICAKBZVaHg3CUqAQEAMM1KlrdKnK3WcKkEBAA7H0lAM1JV9mnXNzPRKpNLAMCCq3ZbLjVUNxhVxddXqAQEAFgwq3lzElCvlwyq6gEAgKHXNlAJiKRaAMCiW82aW1CPqqq5v8YmTQDY8UgCmpGVMvO1TRLQEpWAAAAYVNHrJy0qAdEODABQY/ta20/ZPmH7tob3bfv28v3HbV85baztN9h+yPY3yt8/UB6/1Parth8rfw5vzbcsrGbRmDDbT6wseGgJAMAoKgEBANDeypjqs6OWqAQEAJ1BEtCMrGRVElCLagZJMqgcBADAolrdRCUg2oEBAGz3JN0h6TpJl0u6wfblI6ddJ2lf+XNQ0p0txt4m6QsRsU/SF8q/K09HxBXlz6H5fLP18jyU5dFcCShhcwkAAE02UgmolyQKDTd4AgCwaFbSfLABc5KqEtDyKjETAHa6uSYBLdbuzCLoteub6cH5AAAsqjTL1Usse3oS0B7agQEAhq6SdCIiTkbEiqR7JR0YOeeApLuj8LCk821fMGXsAUmfLl9/WtL75vw9plrNx1ec7ScetNYEAABDG6kEdO5SEWNfWk7nek8AAOxUrZOAyo2cyynrswCw080tCWiRdmdKwySgtiXzWKwFACy6NI/BDpJpqkkmlYAAAJIulPRM7e9T5bE250wa+4MRcVqSyt9vqp13me1HbX/J9jvO/iu0k2bjq+b1aV8CAECjjVQCqhKFXnh1da73BADATrWSRcskINqBAUBXzLMS0MLszpSGJWPbJAFRCQgAgCKBtk3clKTE1lLPenWF3ZkAADVlkI5mw4w7p83YUaclXRIRPybplyXdY/v1jTdmH7R9zPaxM2fOTLnsdNW8sZ80tQNjcwkAAE02UgnovPKcF19jrgkAWEwraTaowj7JUkIlIADoinkmAe3I3ZmzXpStrGykElCSDHZ0AgCwqNIsBhV+2tjTS2gHBgCQivnhxbW/L5L0bMtzJo39drkpReXv70hSRCxHxHPl669IelrSW5puLCLuioj9EbF/7969m/hqa61WlYAadmX2ykpAEcwtAQCo20gloHPKdmAvvEYlIADAYlrJ2rYDK855bZUiBwCw080zCWhH7s6c9aJspVqcbds3k0pAAIBFl+btKwFJxQNQ2oEBACQ9Immf7cts75F0vaQjI+cckXSTC9dIer7cRDJp7BFJN5evb5b0WUmyvbdsWS3bb1bRzvrk/L7e0KDtdEP7zCqRNiMJCACANTZXCYgkIADAYlpJ841VAmJ9FgB2vP4cr302uzP3TBj7bdsXRMTp0d2ZkpbL11+xXe3OPDabrzPZajm5bBUoe5RtBwBgNYvGh5rj7OklepVKQACw8CIitX2rpAcl9SR9KiKO2z5Uvn9Y0lFJ75F0QtIrkj48aWx56U9Ius/2z0v6pqQPlsffKek3bKeSMkmHIuJ7W/BVh0lADfPMfhlDsyzUYi8KAAALYyOVgKpEoRdepR0YAGDxZHkoj7YFDopzqmRbAMDONc8koMEOS0nfUrHD8mdHzjki6Vbb90q6WuXuTNtnJoytdmd+QiO7MyV9LyKyrd6dKdXagfWnP8xcohIQAABaSXOd02JnZmWpRyUgAEAhIo6qSPSpHztcex2Sbmk7tjz+nKSfbDh+v6T7z/KWN2VaOzBJSvPQOVt6VwAA7GwbqQR0br9MAqISEABgAa2k4zeejKrmoCQBAcDON7ckoEXanSlN3qE5qt9LSAICACy8V1ayVouylT39RK9QCQgAsEAmtgNLirlnRpVZAADW2EgloHOWElnSC69RCQgAsHiqJKA2lYB6iZVYWk5ZnwWAnW6elYAWZnemVAuULftmphkLtQCAxfbaaqbzltr3L9nTSwaLuQAALIJq3ti02aReCQgAAAy9tpprTy9R0qL9dGJrTz/RC69SCQgAsHiWs2KttU0SkFQUOVhepcgBAOx07Z+8YaKqTHvbvplpHipyoAAAWEyvrKR63Z72+chLPVMJCACwUKq20/1eUyWgKgmIBVgAAOqW06xVFaDKeUs92oEBABZS9WzznBYFDqSiyMFrVAICgB2PJKAZWSmDXpt2YNU59M0EACyyV1fzDbcDe5UkIADAAkmz8RVnq0pAtAMDAGCt11ZznbOBuea5Sz29SDswAMAC2kg7MIlKQADQFSQBzcjqoEz79DKz1Y5NWpoAABbZqyupXren/cLsUi/Rq8ROAMACqeaZ/YYkoKo6EElAAACstdFKQOcu0Q4MALCYqiSgNgUOpOL5JgUOAGDnIwloRqoy7W2yZatg+hrZsgCAGtvX2n7K9gnbtzW8b9u3l+8/bvvKaWNtv8H2Q7a/Uf7+gfL4u2x/xfZXy98/sTXfcujV1UznbaQSUC/RKyvszgQALI7VvFqQXb/ZpKoElGYkAQEAULe8muvcpY0kAVEJCAAW0U5Zi7X9xfJaj5U/b5r3d69stBLQUi/RMu3AAGDHIwloRlbLJKClpE0SULFYS6AEAFRs9yTdIek6SZdLusH25SOnXSdpX/lzUNKdLcbeJukLEbFP0hfKvyXpu5J+KiJ+RNLNkv5oTl9trFdXMp23kUpA/USvrebKqXgAAFgQqxN2ZfbLuWdKXAQAYI2iElD7ueZ5Sz298BqVgABgkezAtdgbI+KK8uc7s/umk22kwIFUVKSlEhAA7HwkAc3ISpqrn1hJ0qIdGJWAAADrXSXpREScjIgVSfdKOjByzgFJd0fhYUnn275gytgDkj5dvv60pPdJUkQ8GhHPlsePSzrX9jlz+m6NXl3dWBLQnip+kkQLAFgQw7bTTUlAVTsw5pUAANS9tsFKQOcs9WgHBgCLZ+HWYpsMKgFtpB0YzzYBYMcjCWhGVrO8dc/MpXKx9rVVHmICAAYulPRM7e9T5bE250wa+4MRcVqSyt9N5WR/RtKjEbE8+obtg7aP2T525syZDXydyVazXKtZbKgd2FK5I+WVFeInAGAxpG3agVEJCABQoq1JYeOVgBK9tJxSdRYAFstOW4v9wzJmfsz2ugngvNZoh5WAphc4kIoNKmzQBICdjySgGVnNYgPl8orzKJkHAKhpmmmNrkCOO6fN2OYPtd8q6bck/ULT+xFxV0Tsj4j9e/fubXPJVl4tE2Fft6FKQMXXfJUkIADAgliZ2A6sqgTEA0sAAG1N6jZaCejcpZ7ykF5eSed4VwCAHWYnrcXeWMbTd5Q/H1p38Tmt0Q4rAbVbo93TT/TSa8RLANjpSAKakZWNVALqUQkIALDOKUkX1/6+SNKzLc+ZNPbbZZlalb8Hi6+2L5L0gKSbIuLpGXyH1l4rE3nO3UgloDLOvkr8BAAsiKrKT9Nck0pAAIARtDUpbbQSUDUvfYGHmgCwSHbMWmxEfKv8/aKke1TE5S0xSAJqWeTg+8/t69svvDbPWwIA/P/bu/P4SK7y3v+fpze1dmmk2ffx7hlv4wU7dsDBGBtjIAkETMhlCQmQsN38fiSBcHEgAV6EmxtCAgkJYPbF7DZgrsGAbcA79ow94/E6nn3XzEijrddz/6gqTY9GW6urperW9/169Uut6urqU6VWP32qnvOcECgJKCTZfHGkQsFkghO4SgISEZESDwKnmdlqM0sBNwC3jlrnVuD1fgn3S4Fev6zsRM+9FW9UJv7PWwDMrAP4MfA+59xvqrhfY5pOJaDmhgQAh/pPmrVMRESkLuX80uyJMfqaQYXZQkFJQCIiAtTYtCZQvalNplMJCODYcC60NoiISORF4lysmSXMrNu/nwSuBzaFvrfjyBXKSwJqSyfpG86rUruISMQpCSgkuUKR5FSnA/NHbA5rOjAREfE55/LAO4DbgS3At5xzm83sbWb2Nn+124CtwDPAZ4G/nOi5/nM+BlxtZk8DV/u/469/KvAB/8TsBjMb62RuVQz6HcXGMioBtaWTABptIiIic0auMJVKQOpXiogIUGPTmkD1pjbJ5AtlVZ0NEob6hlQJSERkrojQudgG4HYzexTYAOz2X2tGHJ+CempFDtoadX5WRKQWJGa7AfUiV9Z0YN56GVUCEhGREs652/A6l6XLPlNy3wFvn+pz/eU9wFVjLP8w8OEKmzxtQSWgxjIqAbU1el9b9vWqEpCIiMwNwajMsU7IBoNLCpoOTEREPJVMa5Ka4Ln7zWyxc27vdKY1MbNgWpMvV7BvZRnOFWmY4mBNOD44RZWARETmlgidi71w6q0OV2YalYAA9vUNs6q7uWrtEhGRyqgSUEiyeUdqiklAQSl3VQISEZG5amgalYAaEnFaGzTvtIiIzB25kVGZE1UCUhKQiIgAmtZkRNmVgBLeun1KAhIRkTkmqATUEJ9a3GxLe4M0dX5WRCTaVAkoJNkypgNTJSAREZnrRpKAyqgEBLCwPc2+XnUyRURkbsj5CT5B1Z9SSgISEZFSzrm8mQVTk8SBm4JpTfzHP4NXseA6vGlNBoE3TfRcf9MfA75lZm8GdgB/5C8vndbkA/6yFwMDeNOaJP1t3cEMTmvinCu7ElDa75dqOjAREZlrcuVWAvKnA9P5WRGRaFMSUEhy+SKpKc6ZOVIJSElAIiIyRwXTgTWVmwTU1sD+Y+pkiojI3OBNO22YndzXjJkRN9N0YCIiMkLTmngDNQEayqoE5F341HRgIiIy1wSVgKaaBJROxmlOxdnfl6lms0REpEKaDiwk3snZqR3OuBkGZDQdmIiIzFFBJaBySrQDLGxLs18jTUREZI7IT9LPjMeVBCQiIlJqOOcnAZVRCSgRj9GQiNE3rEpAIiIyt2TzRWJ2vNLsVCxsS2s6MBGRiFMSUEhyheKUM2XNjGQ8pkpAIiIyZx2vBFReUcJFbWkOHMtQ1AVPERGZA3IFN+ZUYIFEzMgXNbhEREQkkMlPb8BJW2OSviFVAhIRkbklW8a1zcDCtjT7lAQkIhJpSgIKSSY/9UpA4E0JFoxMERERmWsG/UpAjWWemF3UniZfdBwaUMlZERGpf5MNNonHjHxBibEiIiKBzDQqAQG0phMcUyUgERGZY7L5Iqkyrm2Cd35WlYBERKJNSUAhyRXKC5SqBCQiInNZUAmo3BOzC9vSAOzvVRKQiIjUv1yhSCI2fqxMxDQdmIiISKlpVwJKJ+kbViUgERGZW7xKQOXFzIVtaQ70ZXBOfVERkahSElBIcgVHMj71OTObUnF6BrJVbJGIiEh0DWXzNCbjxMqYbxq86cAAlZwVEZE5IV9wJBPjx8p4LEZeSUAiIiIjhqdZCWhBawO7jwxVo0kiIiKR5VUCKu/87MK2BrKFIod1jVNEJLKUBBSSycq0j9bZlGLXkcEqtkhERCS6hnIFGlPljTIBr9wsKAlIRETmhmyhSFKVgERERKZsupWAzl3WztZDA/QOqRqQiIjMHdl8edc2QYM0RURqgZKAQpLNF0mWMR1YZ1OSXUeGVC5PRETmpKFskcYyT8oCdDWniBkcUCdTRETmgFxh4n5mPGbki8UZbJGIiEi0TbcS0HnLOwB4bFdv2E0SERGJrHILHAAs9AdpHujLVKNJIiISAiUBhSQ7ycnZ0TqaUgxmCxwZ1OgSERGZe4Zy+WlVAkrEY8xvbWBfr5KARESk/k02HVgiZpoOTEREpMT0KwF1ALBx19GQWyQiIhJdqgQkIlKflAQUkulMBwZoSjAREZmThrIFmqaRBAReR1OdTBERmQuyhSKJCaYDi8eNQkFJQCIiIoGRSkDJ8k57tzcmWTO/mQ07j1ahVSIiItFUboEDgPmtDZihQZoiIhGmJKCQ5AqOVDnTgTUnAdh1ZKhaTRIREYmswWyh7JGZgYVtafYrCUhEROaA/CT9zETMKGiKaRERkREjlYAS5fc3z1/WwYadR3GKrSIiMkdk8sWyrm0CJOMxVs5rYtNuTaEpIhJVSgIKQaHoKBRdedOBNaoSkIiIzF3DuQKN00wCWtyeZs/RYZ2YFRGRupcrFEnEJ5oOLEZelYBERERGTLcSEMB5yzs4eCzDXlU2EBGROaLcWU4Czz99Pvc828NwrlCFVomISKWUBBSCXMHrXCYT45+cHa0xFactnVAlIBERmZOGctOfDuysxW30Z/Js61EirYiI1LfcJINN4jEjX1QSkIiISCCTq6AS0PIOADZqSjAREZkjsvkiDdNIAvq9MxYwlCvwwHOHq9AqERGplJKAQpD1k4DKLZm3rLNJSUAiIjInDWanXwno/BUdAGzYeSTEFomIiERPLl8kOWElIKNQLM5gi0RERKJtOD/9SkBnLm6lIRHjnmd7wm6WiIhIJGXz06sEdOmaLlKJGL988kAVWiUiIpVSElAIcn7nstxAubSzUdOBiYjInDScK9A4zUpApy1opSkVZ8OOo+E2SkREJGJyhaIqAYmIiJQhE0wHNo1KQA2JONesXcQtG3ZrehMREZkTspP0OcfTmIpz2Zou7nryYBVaJSIilVISUAiCSkDlBsplnY3sOjKEczppKyIic0sllYDiMeOcpe1s2NUbcqtERESiJV90JCboZybiRkFJQCIiIiOG8wWScSMeG7+S3li+fv8Ovn7/Dua3NtA3nOfGWzaPLBMREalX2Xyx7FlOAr93xny2Hhpge89AyK0SEZFKKQkoBLm8d9K1/CSgJgazBY4O5qrRLBERkUhyzjGUK9A0zUpAAOcv72DLnj4yeY3OFBGR+pWdZDqwxmSC4VyBjKoViIiIAF4loPQ0qgAFVnc309mU5KHth0NslYiISDTlCuVPBxYkyR4bzgPwT//3SSXOiohEjJKAQnC8ElB5I0yWdTYCsOvIUOhtEhERiapMvohzkK4wCShbKLJl77EQWyYiIhIt+eLEozLXzG+m6GDrIY28FBERAa8SUENy+qe8Y2ZcuHIeWw8OcHggG2LLREREoieTLz8JKNDV0sCitjSb96hau4hI1CgJKATBHNHlzjUdJAFtU6k8ERGZQ4ayXtycznRgwaiSbT2DAHzuV1s10kREROpWruBITDDYZOW8JpJx4+kD/TPYKhERkejK5Ipln6Mdbf2KDgA27dZFTRERqW+VTAcGsHZJGzt6Bjk2rBlPRESiRElAIQhGhXS1pMp63inzW5jf2sB/3f0seb+akIiISL0b8pNnK5kOrC2doDWdYOfhwbCaJSIiEjm5QnHCaacT8Riru5t55oAq44mIiEDllYAAOppSLO1oVGUDERGpa845stOYDqzU2iXtOODxvX3hNUxERCqmJKAQ9AxkAOhqLi8JKJ2M88GXrWXT7j6+eM+2KrRMREQkegb9SkDpaVQCCpgZp8xv4ekD/RSdC6tpIiIikTJZEhDAaQtaOdSfHUmMLRYdWw+qMpCIiMxNmVyRdIWVgADOXtLGziND9KmygYiI1KmBbAHnoKUhMe1tLGxroKs5xeN7lAQkIhIlSgIKQU9/UAmooeznXnfOIl501gI+fvuTvOPrD/OF3zzHrRv38MiOIwxk8mE3VUREZNYNj1QCmn4HE+DMRa0MZgvs6FE1IBERqU/5giM5wXRgAKcuaAHg188c4lB/hjd+8UFe+H/u4tsP7ZyJJoqIiERKJoRKQABnL24DYIsqG4iISJ06dMwrcDC/tfxrmwEzY+2Sdp492M+QP/BTRERmX2VX3wSAnoEsybjRli7vcH79/h0AXLxqHocHctz15EF+9OjekcfN4Nq1i/irq0/n9IWtobZZRERktgSVgBorqAQEcPrCVuJmbNnXx6ru5jCaJiIiEhnFoiNfdCRiE1/IXNDaQFs6wfu+9xjv//5jJOIxTpnfzId++DiXndLFss6mGWqxiIjI7AurEtCCVq+ygZKARESkXh3s95KAuqdR4KDU2iVt3P30QbbsU8wUEYkKJQGFoKc/w7zmFGYTj9AcT2s6yasuXIZzjoFsgf5MnsP9WbYfHuDnTxzg9s37uOHiFaxb2s4fP29FyK0XERGZWUN+JaDGVGUnZtPJOGvmN7Nlbx8vWbc4jKaJiIhERq5YBCCVmDgJyMx49cXL2XpwgELRcd6yDhoSMT75i6d5/U0P8GdXrFE/UkRE5oyhXIHullTF2zEzzl7cxj3P9tA3nKMtnQyhdSIiItERRiUggKWdjbQ3JtmsKcFERCJD04GFoKc/S1dzZUESvM5lS0OCRW1pzl7SxkvWLeavX3wGyzqbuPmhnTxzoD+E1oqIiMyuoZAqAQGcubiNQ/1ZDvqdVhERkXqRLziASacDA1jT3cKLzlrINWsXsag9TWdziqvOXMDWgwPs7xuudlNFREQiY3/fcMUXMwNnL2mj4Bx3PnkwlO2JiIhESViVgGJ+4uzT+48xkMmH0TQREamQkoBCcGggS1cII0zG0tyQ4A2XrWJ+SwNfu387u44MVuV1REREZspQzusMVloJCOCsRd50mY/uPlrxtkRERKIkV/AqAU02Hdh4zlvegQGbdveG2CoREZHoyhWKHOzPsKi9MZTtLZ/XREtDgp9u3hfK9kRERKLk0LEMMYN5zZVf31y7pI18UYmzIiJRoSSgEPT0ZyrOlJ1IYyrO/7h0JQ54z7c3Uiy6qr2WiIhItR0eyAHQ3lh5OfWOphSnLWjhga2HyeQLFW9PREQkKnJBJaBJpgMbT1s6ycquZh5TEpCIiMwR+/uGcQ6WtKdD2V7MjLMWt3LnkwfV3xQRkbpzsD/DvOYG4rHJq89OZlV3M82pOD/ZtDeElomISKWUBBSCwwNZukLIlJ1IZ3OK689ZzH1bD3PTb56r6muJiIhU087Dg7Q0JOhsqjwJCOCKU7s5lsnzw43qZIqISP0IKgElKzghe86ydg4cy/DU/mNhNUtERCSy9vV6U2Au7ginEhDA2Yvb6c/kuefZntC2KSIiEgUHj2VDm0IzZsa6pe387PH99PjTjImIyOxRElCFBrN5BrMFuqpYCShw4cpOXnTWQj5++5M6iSsiIjVr5+FBlnU2Ylb5KBOAUxe0sKC1gc/9aivOqVqeiIjUh3xQCSg+/W77uiVtGPDjR5UoKyIi9W9PkAQUUiUggFPme5UNNCWYiIjUm4P9GbpbwitwcNkpXWTyRb56347QtikiItOjJKAK9fRnAegKMVCOx8z42CvPobUhwV/dvIFsvlj11xQREQnbjsODrJjXFNr2zIwrTu3miX3HuHXjntC2KyIiMpuyfiWgRHz6SbOt6SSrupu57TElAYmISP3b1zsEhJsElIjHuPrshfzo0b0cG86Ftl0REZHZduhYJrRKQAALWtO88MwFfOW+bQznNI2miMhsUhJQhXoG/CSgKk8HFuhuaeCjf3gOm/f08b9+8BjFoioeiIhI7XDOsfPIIMtDTAICuGBFJ+cv7+DGWzazv2841G2LiIjMhnzRSwJKVVAJCGDd0naePtCvarIiIlL39hwdpqUhQWs6nKmnA396xWqODef55gM7Q92uiIjIbHHOcbA/w/yQZzn5sytWc6g/y3d+uyvU7YqISHmUBFShYG7LmZgOLHDN2kW884Wn8q2HdnHjrZsoKBFIRERqxMH+DMO5YqiVgADiMeNfXn0emXyB93x7I/mCquWJiEhty+W9fl6i0iSgJW2YaUowERGpf3t7h0KtAhQ4d1kHl63p4vO/fk6V2UVEpC70DefJ5ouhVgICb0qwi1Z28tHbtrBlb1+o2xYRkalTElCFRqYDm6FKQF+/fwdfv38Hi9rS/O5p3Xz1vh08/+O/5GM/eWJGXl9ERKQSOw975dmXz2sMfdtr5rdw4/Vr+dXTh/jb76panoiI1LZgOrBkBdOBgTcl2CWr5mlKMBERqXv7eodZVIUkIIC3vmAN+/qG+dZDqgYkIiK175Bf4KA75AIHZsanX7ee1nSCP/vSQ+w5OhTq9kVEZGqUBFShkenAWmYmCShgZly7dhGvuWg5fUM5PnPXs1z/77/iK/duo3dI81OLiEg07Tw8CBB6JSDwEmUBrjprAd99eBev/q97+cq920N/HRERkZkQVLWrdDowgOvPXawpwUREpO7t6R1mSXv4A04AXnD6fC5ZPY8P/XAzP9+yvyqvISIiMlMOHvOSgMKuBASwsC3NZ19/EUcGs1z7r3dz68Y9OKfBmiIiM0lJQBXq6c/QlIrTlErM+GubGect7+Cvrj6d689dTKEIH7hlM5d85A4+etsW+jP5GW+TiIjIRIIkoGWd4ScBBV54xgJ+74z5PLT9CF+85zmO+Am7IiIitSRXCGc6MIBr1i0iZvCF32yreFsiIiJRlM0XOdSfqVolIDPjs6+/iLMWt/EXX32YG2/ZxG+3HyanqahFRKQGVasSUDCbyabdfbztBafQ3pjkXd94hGv+9W629wyE+loiIjK+mc9cqTM9A9kZrwI0WjoZ53dO6eayNV3sOTrMvVsP8d93b+WbD+zgDy5YyhmL2vjj562Y1TaKiIgA7Dg8yILWBtLJeNVew8y4+uxFzGtu4AeP7ObqT9zFB64/m5edu4RYrLIpVURERGZKrhjOdGAAC1rTvOny1Xz+189x/bmLufzU7oq3KSIiEiX7+4ZxDpZ0hJ8EFFSdBXj5eUuI2V6+fv8OvnzvdtLJGBevmsc1axdx3TmLmdc8u+eJRUREpqKalYAC3S0NvOX5p3Dv1h7u2LKfqz9xN297wSn85ZWnVPXcsIiIKAmoYof6M3Q1Vy9IlsPMWNrZyKsuXM4lq7v4/iO7+NK927l4VScvWbeITnVCRURklu08MsjyKkwFNpYLV3aypCPN3U8d5N3f3MAn73ia116ygivPmM+pC1owU0KQiIhEVy4fJAGFU8D3r685g18+cYC/+c6j3PqOy+kKecSniIjIbNrXNwzAoipNBxZoSiV49UXLefl5S3j6QD/bewZ4fE8fv3r6EH9/62bWLmnjktXzeP91Z6nPKSIikXWoP0M8ZnQ0Jqv6OvGYccWp3Zy7tJ3H9/bxbz9/mm8+sIO3vuAUXrV+Ge1N1X19EZG5SklAFerpz1ZlhEmlVsxr4u1XnsrPtuznN88c4sp/vpM/vXw116xbyCnzW0I7kSwiIlKOnYeHuGT1vBl7vcXtjXzvLy/nhxv38MV7tvGR27bwkdu20NmUZO2SdtYuaWPtUu/n6q5mVQoSEZHIyBe96cDC6LsFFQyuWbuIz/5qK1f9y1287nkrWdrRqKqxIiJSF/YcHQJgSZWmAxstnYxzztJ2zlnazkvPcezrG+ahbUd4ZOcRHt3Vyy+eOMAfX7KCP1y/TNWBREQkcg4ey9Ddkpqxc6FtjUkuXdPFwrY0d2zZzz/+6HE+8uPHWdPdwsvPX8L6FZ2ct7yd1rSSgkREwqAkoAodHsiybmnbbDdjTIl4jJesW8wFKzrZuPMon7jjKT5xx1MANCRitKaTtKYTtDQk6G5JsbKrmZVdTf6tmWWdjTQkVJJPRETCkc0X2ds7xPLO6o7MHO3mB3cC8OqLlvOisxby7IF+dh4ZZOuhfu5/rodcwbvI2pCIsbSjkXnNKRqSMQpFx1CuyHC2wFCuwHCuQEMyxqquZs5e3Mb6lZ1cuLIz9LmzRUREAHIFrxJQIoTpwALL5zXxluev4av3befTv3yGtnSCXzyxn3X+Rcx1S9tZ0NqgygUiIlJz9vUGlYBmfrCmmbG4vZGXndfINWsXsWl3L8/1DPDhH2/hYz95gitO6+aqsxZy6ep5qkorIiKRcKg/W9WpwMazuruZP//dNew6Msim3X08ub+PT9zxFM5BzOD0ha1cuLKT3ztjAVec1q1pw0REpqmqSUBmdi3wSSAOfM4597FRj5v/+HXAIPBG59zDEz3XzOYBNwOrgG3Aq51zR/zH3ge8GSgA73LO3V7N/QM4c3ErZy2OZhJQYFFbmkVrF3HZKV08vf8YvUN5MvkCw7mi/7PAE/uO8Ztne8j6JecBknHj7MVtXLCikwtWdLB2STsru5pURUhEpErqPW7uOTpE0TFj04GNZV5zinmr53GxX40oXyxyoC/D3t4h9vdlODqY5fBAlnzRETMjlTCS8RjzUimScSOTL3J0MMcXfrON/7p7KwBLOxpZ0pGmpSFBwUGhWKRYhLbGBAta05y+sIWzFrdx5uI2WhqUfy0iEoZ6j5kAV56+gB+98wqWdoSbPLuss4l3vPA0Nuw8yp6jQ2zrGeTnTxzAeTmxtKYTnDK/hTXzmzlrURvrV3p9QZ18FRGJvqjERzO7EPgi0AjcBrzbuSDSVMfe3mFaGxKzXkEglYixfmUn61d2cvmp3WzYcZSNO49y55MHAWhKxbn81G5OX9jCss6mkQGarekE6WSchkSchkSM5oYE7Y1J4qpWKyJSNXM5bsYMlnXM3jnaZZ1NLOts4tp1ixjOFdh5eJDthwfZeXiQ7/x2F1+7fwcNiRjXrlvES9Yt5soz5qtPKiJSBqtWHDGzOPAUcDWwC3gQeK1z7vGSda4D3okXQJ8HfNI597yJnmtmHwcOO+c+ZmbvBTqdc39rZmcD3wAuAZYAdwCnO+cK47Xxoosucg899FDo+14qKLteC5xzDGQLHO7P0DOQZX9fhp1HBtl1ZHCkSkLMYF5zA+cvb2dBW5rWdAIcuJJtxGJGV3OK7paG47fWFPOaUiSUQCQiEWFmv3XOXTTb7QjMhbh5bDjHb57p4Zxl7RNe0KyF2JkrFNlzdIjtPYPs6R3i2HCebL5IzLxRoAYM5Qr0DecYzh1PsF0xr4kzF3kJxKcvbKWrJUVHU5KOxhTNDd4J32TcNDJURCInSnGzFmImVK+/WY04mckX2Ht0mD29Qxw8luFQf4aDxzL0DecBSMVjrFvaxoUrOzlnWQcLWhvoak4xrzlFR1NKFyhFREaZjbgZpfhoZg8A7wbuw7uY+W/OuZ9M1P5K4+bhgSz7+4YnHKw5W31N5xyHB7Js6xnguUOD7Dg8yOGBDMVJTsubQXtjknlNXr8xiLvzmlN0NqVoSsVH+qAxM2IGsZiRjHuDWRKx2MjAluB+IhYjGS+5n4jR4N/SyTiJmPqjIjLzFDfLi5szcW0TZi9u5otFth4cYNPuXp492M+RwRxNqTgXrZrH6Qta6Gz2B2vmigznCwxlizgcqXiMhB8Dk/EYqXjMi4mJ0t+9ZelknK4W7zrmvOaUih+ISM2Yasys5nD0S4BnnHNb/QZ9E3gF8HjJOq8AvuxntN5nZh1mthgvQ3a8574CuNJ//peAO4G/9Zd/0zmXAZ4zs2f8NtxbxX2sK2ZGS4M3+mRFV/PI8kLRsb9vmP19wxw8luHAsQybdvdx7NkeMn7loNK+YdF5zzl5+9CWThKz40lDyXiMloYETak4zakEzQ1xkvEYuUKRXMGRLRTJ5ov+70ViZnS3NDC/tYHulhRNqQQpv6OaSnhBPBUEdH9ZQ8n9YB0zwznnd7YdznntdjjiZiSCLwf+l4K4GYzR/z3e0YaYGWbevufyXttzJ9zcScclEQ863/5rxryf8Zgx3f528LTSDrtxvH3B3yLmX6Qey3jnIMbLGSwUHcM5b7qcYMqcTL5IMhYjmbCRL1fB8fe+dBnJWGzc/dQJB5mD6j5utqaTXLtuUbU2P6OS8Zg/jWbzhOs55+gdyrGvd5i9fcPs6x3m4R1H+dnj+8f9rDXDPwnrjQBtSB6/H8TK5gYv/mXzRTL5ove5mysymMszlC2QL7qRmBhsK7ifTMRGYkDM8GNejFTcRmJoIh4jETPiMe9kcjwG8SBexYIOtX/fj10J/3P9hMdGTj4fj6ngJVHli45C0YuP+YIjX/SW5QpFCkUvZiZL4/oJccQwDOcfxdL4FNwtFh1F50a2VfDvx2PHY288drx9QXtLY3BppDy+LPj9xDg74ftgsscnSMqfarq+cTwBDbz2KpZKHav7mDnTGhJxVnU3s6r7xLh2bDg3MiJze88gN/1m20l9GjPo9C9IzmtO0dWcYmFbmtXdzazubmZVVzPJhFEoev2u4DPZOUehCEXnfV4X/fsF5zAgEfP6RfGReHTi51yQeHRsOM9QLo+Z0ZiMjyTXNqbGHyUa9AWLLugLjv9pawbJWIzYqEQn55wfy/yffiwrOmhMxUknYhoEIyIzLRLx0cy2AW3OuXv9bX0Z+H1gwiSgSgVxKIrMjK6WBrpaGrhwpVeVtlB0HBvOkckXyeQKDPvnP4O+USZfZDBbYCCTZzBboHcox97eYQazBQaz+ZEBm2GLGX5FIq8fmQ76o8kYaf9nsDyI/90tKVrSiZF+WRBbi36sB6/ifCoRG+mLJeNebI2XJC8F8d7rgwaJTcF3Ae/cbb5Q0s8r6e8Z3jaCflGwneB8qJX8HrPj/aVYybr42yg9z5uIeeeF43GvrUEXy/nnkZ0/ONY55//EHzDrStbzLmgPZQsM+tONJ2Mx0kkv8SqdjFftu0PRP045/xx7cA4hWyjiHMf72SXnzccamORGfWeKmZ303UikBs3puBlliViM0xe2cvrCVgpFx3OHBti0p5dn9h/jnmcOkS/pk8bMO08bXJsrFN2kSbZj6WhK0tWcojWdpKXh+LXK49cnT7xe6RwnXZtMllz7Kj2PGY/ZSHxJxGI0N8Rp8s/xNiYTI783JGIj50vzhZK+5sjP4vHfC97ne2kcM6MkfnrLEnGjOZWgxa886O2blxoQfK47/J9FL36V9pVdyeNFd2I8OB7vAYLHGYnzDSPnmc2P4d5xDM4LF0e1H06Ow8aJv48Vd4OYy8hjx5clS86PB9eGReaKaiYBLQV2lvy+Cy9TdrJ1lk7y3IXOub0Azrm9ZragZFv3jbEtqVA8ZizpaGTJFMvQO+fI5Iv0Z/L0D+e9n/5tMFug9HJWoeitm80XOdifYffR4sgFuuAWXIRMxmMUHew4PMjmPb30Z6rX4ZXaMm4i0bjrj/3I+OuPt/3yXjiM7Ze7r/XqA9efzQ2XrJjtZoRNcbMOmRkdTd5ozTNLRqRm80V6BjL+CVz/JG6+SK7oyPsnfkfu+z9zBcfB/gy7jnoJPwXnTkhmCTqbjakEcb/jmy86+obz5AtZfzvesiDpxOGdFDzeoSxOu7Ms0RXkMgfxz0aWeQ8Ev3uP2cj6Uj9ufutlrFvaPtvNCJNi5gxpTSc5e0k7Zy/x3j/5QpFD/Vn6M3kGsnkGMnkGMt4FyoFsngN9wzx3cIDeoRzZQnGSrVdXKh4DG3WSkvEHNkwm6Js6x0iyz1TakIjrE1VkLFH+z9j8D9fOdhOmKyrxMeffH71cSsRjXl9xurL5IvlCkSLHY13QvytNjjlhYMSoxwrF4xcW84XR/VH/p98nzeSK9A/nRwZu5ArFkf6shCc4Dz6Rya5fFouM/J0racdUvjuVJlvFYsfPZY4esDOyiZHfx3689FxF6ePlGusYjV401vnhk9cZazs2esGE2xhrO1M55xvlOFlq3dJ2bn7rZbPdjOlS3KwB8Zhx6oIWTl3QAuAPJvFuwYD60bxBJiVxbozYl8kXGSi5dll6v6c/QyZfpOifex3reiXAQCbP0UEvOac0xp6QqOPH4iBxRedcZ1cqHiMWGzs+TRSbqhGXxlyvrG1Ofe3ytlvGulPdclnbHGPZVOLxGOtFMZbecMkKPnD92TPyWtVMAhrr2I7+9xhvnak8dzqvh5m9BXiL/2u/mT05yXYr1Q0cqvJrVIvaPjtqte212m5Q22dDRe1+7T/Caytvw8rKNxEqxc3javV9PVXav9pWz/tXz/sGc3j/zvnHULYfpbgZyZgJMxY36/29XCkdn8npGE1Ox2hidX18rHbjZlTiY9TiZqm6fu+GTMdqanScpk7Hampq6jg9DnzrbaFsSnFzkm3pHG1k6JicTMdkbDouJ5vzx+RG/1ZiOsdkSjGzmklAu4DlJb8vA/ZMcZ3UBM/db2aL/QzaxcCBMl4P59x/A/9d3q5Mn5k9NNNzmYZFbZ8dtdr2Wm03qO2zoVbbXWWKm756f39o/2pbPe9fPe8baP/qTCRjJsxM3Jxjf+uy6fhMTsdocjpGE9PxiayoxMdd/v2J2gHoPG2U6VhNjY7T1OlYTY2O04yqqbipc7TRoGNyMh2Tsem4nEzH5GTVPCbVnKD+QeA0M1ttZingBuDWUevcCrzePJcCvX6ZvImeeyvwBv/+G4BbSpbfYGYNZrYaOA14oFo7JyIiEjLFTRERkalRzBQRETlZJOKjv71jZnapefMUvL7kOSIiIlGhuCkiInWrapWAnHN5M3sHcDsQB25yzm02s7f5j38GuA24DngGGATeNNFz/U1/DPiWmb0Z2AH8kf+czWb2LbwKhHng7c45TUosIiI1QXFTRERkahQzRUREThax+PgXwBeBRuAn/k1ERCQyFDdFRKSemXOTTVMplTCzt/hl+mqO2j47arXttdpuUNtnQ622W2ZGvb8/tH+1rZ73r573DbR/Uj/0t56Yjs/kdIwmp2M0MR0fqVV6706djtXU6DhNnY7V1Og4SZTo/XgyHZOT6ZiMTcflZDomJ6vmMVESkIiIiIiIiIiIiIiIiIiIiIhIjYvNdgNERERERERERERERERERERERKQySgKqIjO71syeNLNnzOy9EWjPcjP7pZltMbPNZvZuf/k8M/uZmT3t/+wsec77/PY/aWbXlCy/0Mwe8x/7NzOzGdqHuJk9YmY/qqW2m1mHmX3HzJ7wj/9ltdB2M/sr/72yycy+YWbpqLbbzG4yswNmtqlkWWhtNbMGM7vZX36/ma2qctv/t/9+edTMvm9mHbXS9pLH3mNmzsy6o9h2iSaLWOycKjPb5r+HN5jZQ/6ySH5eTnF/avYztYL9+6CZ7fb/hhvM7Lpa3D+bge97Ed2/evn7pc3sATPb6O/fh/zldfH3k8pYjcbIMFidxdkwjBPL9FlRYpxjVBfxIgwTxFS9j6QuzeU4GlA8HZti6tQptk6NYqzUurkWMxUfFQvHo7h3MsW4k01wTGb3veKc060KNyAOPAusAVLARuDsWW7TYmC9f78VeAo4G/g48F5/+XuBf/Lvn+23uwFY7e9P3H/sAeAywICfAC+ZoX34/4CvAz/yf6+JtgNfAv7Mv58COqLedmAp8BzQ6P/+LeCNUW038HxgPbCpZFlobQX+EviMf/8G4OYqt/3FQMK//0+11HZ/+XLgdmA70B3FtusWvRsRjJ1ltH1b8F4vWRbJz8sp7k/NfqZWsH8fBN4zxro1tX/MwPe9iO5fvfz9DGjx7yeB+4FL6+Xvp1tF742ajZEh7f826ijOhnRM6jpWV/EY1UW8COn41PV3Bt10K73N9ThachwUT8c+LoqplR0rxdaT910xVreavc3FmKn4qFhY5nGZ03FPMa6sYzKr7xVVAqqeS4BnnHNbnXNZ4JvAK2azQc65vc65h/37x4AteIker8BLUsH/+fv+/VcA33TOZZxzzwHPAJeY2WKgzTl3r/PebV8ueU7VmNky4KXA50oWR77tZtaGFyg+D+CcyzrnjtZC24EE0GhmCaAJ2BPVdjvn7gYOj1ocZltLt/Ud4KqwMrnHartz7qfOubz/633Aslppu+8TwN8ArmRZpNoukRS52FmhSH5eTkUtf6ZOxQSfXWOpqf2boe97Udy/8dTa/jnnXL//a9K/Oerk7ycVqbcYGYaajbNhqPdYHYZ6jvdhqPfvDCKjKI6Ob07HU1BMLYdi69QoxkqNU8z0zKn4qFg4NsW9kynGnSyq56uVBFQ9S4GdJb/vYuI/+Izyy0RdgDe6eKFzbi94b1Rggb/aePuw1L8/enm1/SteUkGxZFkttH0NcBD4gnlTmX3OzJqj3nbn3G7gn4EdwF6g1zn306i3e5Qw2zryHOcl5/QCXVVr+Yn+FC/j84R2jGpjZNpuZi8HdjvnNo56KPJtl1kX6dg5CQf81Mx+a2Zv8ZfV0uflVNTLZ+pE3mHeNIw3lZQsrdn9q+L3vSjuH9TJ38+86W83AAeAnznn6vLvJ2Wr5RgZhrkQZ8Ogz4qpqYt4EaZ6/84gguJoQPF06vRZWB7F1nEoxkoNmosxU/FxbPrMGp/iHopxY4nS+WolAVXPWNlXboxlM87MWoDvAv/TOdc30apjLHMTLK8aM7seOOCc++1UnzLGsllpO141nfXAfzrnLgAG8EqhjScSbfc/jF6BV4psCdBsZn8y0VPGWDZbx3wy02nrrOyHmb0fyANfm6QdkWi7mTUB7wduHOvhcdoRibZLJNTy3/ty59x64CXA283s+ROsW0ufl1NRL//b/wmcApyPl/z6f/zlNbl/Vf6+F8X9q5u/n3Ou4Jw7H68K4CVmtm6C1Wtu/2Ta5vrfbS7H2TDos+K4uokXYan37wwiPr0XPYqnldNn4ckUW8ehGCs1ai6+txQfyzPXP7MU91CMG0vUzlcrCah6dgHLS35fhjeV0qwysyTeG/Brzrnv+Yv3+yWm8H8e8JePtw+7OD41UenyaroceLmZbcMrP/hCM/sqtdH2XcAufxQ3eGW61hP9tr8IeM45d9A5lwO+B/xODbS7VJhtHXmOedOjtTP1MoDTYmZvAK4HXueXfjuhHaPaGJW2n4KXOLbR/39dBjxsZotqoO0y+yIZO6fCObfH/3kA+D5e6dxa+rycipr+TJ2Mc26/n3xRBD6L9zeEGty/Gfi+F7n9q6e/X8B508feCVxLHf39ZNpqNkaGYY7E2TDos2IS9RgvKlHv3xlESszpOBpQPC2LPgunSLF1bIqxUsPmXMxUfByXPrPGoLinGDeWKJ6vVhJQ9TwInGZmq80sBdwA3DqbDfLnhvs8sMU59y8lD90KvMG//wbglpLlN5hZg5mtBk4DHvDLeB0zs0v9bb6+5DlV4Zx7n3NumXNuFd6x/IVz7k9qpO37gJ1mdoa/6Crg8Rpo+w7gUjNr8l/vKrx5DKPe7lJhtrV0W6/Cew9WsxLTtcDfAi93zg2O2qfItt0595hzboFzbpX//7oLWO//H0S67RIJkYudU2FmzWbWGtwHXgxsorY+L6eiZj9TpyLopPj+AO9vCDW2fzP0fS9y+1dHf7/5Ztbh32/ES8p+gjr5+0lFajJGhmEOxdkw6LNiEvUSL8JQ798ZREaZs3E0oHhaNn0WTpFi68kUY6XGzamYqfg4IX1mjWGuxz3FuJNF9ny1c063Kt2A64CngGeB90egPVfglYZ6FNjg367DmzPu58DT/s95Jc95v9/+J4GXlCy/yH+zPgt8CrAZ3I8rgR/592ui7Xilvh7yj/0PgM5aaDvwIbwLT5uArwANUW038A28cmo5vMSTN4fZViANfBt4BngAWFPltj+DN79j8L/6mVpp+6jHtwHdUWy7btG8EbHYOcU2rwE2+rfNQbuj+nk5xX2q2c/UCvbvK8BjeLH6VmBxLe4fM/B9L6L7Vy9/v3OBR/z92ATc6C+vi7+fbhW/P2ouRoa033UXZ0M6LnUdq6t4jOoiXoR0fOr6O4Nuuo2+zdU4WrL/iqfjHxvF1MqOlWLrycdJMVa3mr7NpZip+DjSdsXCqR+XOR33FOPKOiaz+l4JnigiIiIiIiIiIiIiIiIiIiIiIjVK04GJiIiIiIiIiIiIiIiIiIiIiNQ4JQGJiIiIiIiIiIiIiIiIiIiIiNQ4JQGJiIiIiIiIiIiIiIiIiIiIiNQ4JQGJiIiIiIiIiIiIiIiIiIiIiNQ4JQGJiIiIiIiIiIiIiIiIiIiIiNQ4JQGJzCIzK5jZBjPbZGY/NLOOKr/e+81ss5k96r/u80La7j+Y2YvC2JaIiMh0lMTU4Laqwu2tMrNNJb9fYmZ3m9mTZvaEmX3OzJoqbri37b8LYzsiIiKBsfp+ZrbNzLqr/Lq3Tbdfa2ZXmpkzszeXLLvAX/aeSZ77wWAdM/uimb1qOm0QERGphmqdkxUREakVZnanmV0zatn/9PuQQ358fNzMvmxmSf/xK82s18we8c/J3m1m11fQhv5K90OkViRmuwEic9yQc+58ADP7EvB24CPVeCEzuwy4HljvnMv4J39TZTw/4ZzLj/WYc+7GkJopIiIyXSMxNWxmthD4NnCDc+5eMzPglUArMFjBdg0w4O+Aj4bRVhERkUr7fpVwzl1X4SYeA14DfN7//QZgY4XbFBERmTWzGZdFREQi5Bt4/bvbS5bdAPw1sMI5d76ZxYGfAa8Gvuav8yvn3PUAZnY+8AMzG3LO/XzGWi5Sg1QJSCQ67gWWghfIzOw+f3TI982sc5Lld5rZJ/ws2C1mdrGZfc/MnjazD/vbXwwccs5lAJxzh5xze/znX2hmd5nZb83sdjNbXLLdj5rZXcD7/ZGjMf+xJjPbaWbJ0pGW/mvfY2YbzewBM2s1s7iZ/W8ze9Bv+1tn7rCKiMhcNY14eqEfv+7FS8wNvB34knPuXgDn+Y5zbr+ZzTOzH/jbus/MzvW3NVKRwP99k3nVhVb5sfo/gIfxLnI2+qNdvoaIiEjlxu37Ae80s4fN7DEzOxNgklj2FTP7hd+3/HN/+ZV+3/P7/kjNz5T0E7eZWXdJvPuseZUPfmpmjf46F/uvda/fT9xU0vYdQNrMFvrJstcCPwkeNLM/9/uVG83suxZSVT4REZEqGjMu+zHzn/zzpw+Y2akAZvYyM7vfvKoHd5g3KEVERKTWfQe43swawKvCDiwBdgUrOOcKwAP410pHc85tAP4BeIe/jfl+v/BB/3a5v7zFzL7g93sfNbNXlm7H77Pea2YvDX0vRSJCSUAiEWBedutVwK3+oi8Df+ucOxdvJOTfT7IcIOucez7wGeAWvAuW64A3mlkX8FNguZk9ZWb/YWYv8F87Cfw78Crn3IXATZxYjajDOfcC59yH8EZgvsBf/jLgdudcrmQ/UsDNwLudc+cBLwKGgDcDvc65i4GLgT83s9UVHDIREZHRgkSaDWb2fX9ZufH0C8C7nHOXjdr2OuC347zuh4BH/G39nb/tyZwBfNk5d4Fz7k34VYycc6+bwnNFREQmM2bfz3fIObce+E8gSFadKJadC7wUuAy40cyW+MsvAf5/4BzgFOAPx2jHacCnnXNrgaN4VfTAi7dv8+NtYYznfQf4I+B38BJmMyWPfc85d7Hf39yC19cUERGJsonicp9z7hLgU8C/+st+DVzqnLsA+CbwNzPaWhERkSpwzvXgJfhc6y+6Ae96ogvWMbM08Dzg/06wqYeBM/37nwQ+4V97fCXwOX/5B/CuSZ7j93N/UfIaC4EfAzc6535c6X6JRJWSgERmV6OZbQB6gHnAz8ysHS/x5i5/nS8Bzx9vecm2ggSix4DNzrm9/giTrcBy51w/cCHwFuAgcLOZvRHvQuQ6/7U3AP8LWFay3ZtH3X+Nf/+GUY/hb2uvc+5BAOdcnz+F2IuB1/vbvx/owjshLCIiEpYgkeZ859wflBtPx1j+lSm+7hXBus65XwBd/rYmst05d99Ud0xERKQcE/T9AL7n//wtsMq/P1Esu8U5N+ScOwT8Ei/5B+AB59xWf6TmN/xtjPacP1Jz5PXMrANodc7d4y//+hjP+xZeEtBr/W2XWmdmvzKzx4DXAWvHOw4iIiJRMElc/kbJz2AwyjLgdj/W/TWKdSIiUj+CKcHwfwZx8JSSa6U7nHOPTrANK7n/IuBT/nNvBdrMrNVf/ulgJefcEf9uEvg58DfOuZ9Vtisi0aYkIJHZNeScOx9YiTcX9NsnXn1CwejIIieOlCwCCfBK6Tnn7nTO/T1eubxX4gXMzSUXTs9xzr245PkDJfdvBV5iZvPwOq+/4ERGSdbuqOXvLHmN1c65n05zP0VERKphvBgGsBkv7o33vNEckOfE79rpkvsDiIiIVNE4fT843lcs4PcTGT+Wlf6c6vJSpf3S4PXGeq0TN+TcPiAHXI13grbUF4F3OOfOwatglEZERCTiJojLpfEzuP/vwKf8WPdWFOtERKR+/AC4yszWA43OuYf95c/610pPBS41s5dPsI0L8KrCgnfu9bKSa49LnXPHGP88bx5vgMo1le+KSLQpCUgkApxzvcC78MqxDwJHzOx3/Yf/B3CXv85Jy6f6GmZ2hpmVVt85H9gOPAnMN7PL/PWSZjbmCBN/5MoDeCX2fuSP+iz1BLDEzC72t9VqZgngduAv/KnHMLPTzax5qm0XEREp13hxc4LlR4FeMwsqGZROzfUp4A1m9rxggZn9iZktAu4O1jWzK/GmWekDtgHr/eXrgYmmwcwFMVJERKRSE/T9xjNeLAN4hZml/SmmrwQe9JdfYmarzSyGVy3211Npmz8C85iZXeovumGcVW/Em7pzdJ+zFdjrx01NoykiIpE3SVx+TcnPe/377cBu//4bqt5AERGRGeJfY7wTuImTq77inNsLvBd431jPN7Nz8ab6Cqr8/BQvuTZ4/PxxlncGLwH8KXCmmb13+nsiEn2JyVcRkZngnHvEzDbinQR9A/AZM2vCm87rTf5q4y2fihbg3/3y63ngGeAtzrmsmb0K+De/5HsCbw7qzeNs52bg23gngEfvQ9bMXuO/TiMwhFd273N4peYfNjPDK337+2W0XUREZDrKjadvAm4ys0G8BFYAnHP7zewG4J/NbAFelb278aZU+SDwBTN7FC+RNzhJ+12OT4X5IPDUBO38b+BRM3vYOacLmiIiUqkx+37A9eOs/0HGjmXgDQL5MbAC+Efn3B4zOx3vQuXHgHPwYuL3y2jfm4HPmtkA3gng3tErlEwXNtoH8KaY3o43FXZrGa8rIiIyGyaKyw1mdj/eYO3X+ut/EPi2me0G7mPiASUiIiK15ht451THGxDyA+CDJQM4f9fMHgGagAPAu5xzQcXYdwGf9vuyCby+6duAD/vLN+FVpf2Q/5o45wr+ed4fmlmfc+4/wt5BkSgw58ab9UBEREREREREROYiM/sg0O+c++dRy68E3uOcGy+paLLttvgjQPFHXy52zr27staKiIjUFjPbBlzknDs0220RERERkfqiSkAiIiIiIiIiIjJTXmpm78M7J7UdeOPsNkdEREREREREpH6oEpCIiIiIiIiIiIiIiIiIiIiISI2LzXYDRERERERERERERERERERERESkMkoCEhERERERERERERERERERERGpcUoCEhERERERERERERERERERERGpcUoCEhERERERERERERERERERERGpcUoCEhERERERERERERERERERERGpcUoCEhERERERERERERERERERERGpcf8Pj0mtWgeA4pAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 2880x504 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, ax = plt.subplots(1, 5, figsize=(40, 7))\n",
    "sns.distplot(train.RoomService, ax=ax[0])\n",
    "sns.distplot(train.FoodCourt, ax=ax[1])\n",
    "sns.distplot(train.ShoppingMall, ax=ax[2])\n",
    "sns.distplot(train.Spa, ax=ax[3])\n",
    "sns.distplot(train.VRDeck, ax=ax[4])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 320,
   "id": "408332de",
   "metadata": {},
   "outputs": [],
   "source": [
    "#going to group all paid items into one\n",
    "test['TotalSpend'] = test['RoomService'] + test['FoodCourt'] + test['ShoppingMall'] + test['Spa'] + test['VRDeck']\n",
    "train['TotalSpend'] = train['RoomService'] + train['FoodCourt'] + train['ShoppingMall'] + train['Spa'] + train['VRDeck']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 321,
   "id": "5b1da890",
   "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>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>Deck</th>\n",
       "      <th>Side</th>\n",
       "      <th>AgeGroup</th>\n",
       "      <th>TotalSpend</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0013_01</td>\n",
       "      <td>Earth</td>\n",
       "      <td>True</td>\n",
       "      <td>TRAPPIST-1e</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Nelly Carsoning</td>\n",
       "      <td>G</td>\n",
       "      <td>S</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0018_01</td>\n",
       "      <td>Earth</td>\n",
       "      <td>False</td>\n",
       "      <td>TRAPPIST-1e</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2823.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Lerome Peckers</td>\n",
       "      <td>F</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "      <td>2832.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0019_01</td>\n",
       "      <td>Europa</td>\n",
       "      <td>True</td>\n",
       "      <td>55 Cancri e</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Sabih Unhearfus</td>\n",
       "      <td>C</td>\n",
       "      <td>S</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0021_01</td>\n",
       "      <td>Europa</td>\n",
       "      <td>False</td>\n",
       "      <td>TRAPPIST-1e</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6652.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>181.0</td>\n",
       "      <td>585.0</td>\n",
       "      <td>Meratz Caltilter</td>\n",
       "      <td>C</td>\n",
       "      <td>S</td>\n",
       "      <td>3</td>\n",
       "      <td>7418.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0023_01</td>\n",
       "      <td>Earth</td>\n",
       "      <td>False</td>\n",
       "      <td>TRAPPIST-1e</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>False</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>635.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Brence Harperez</td>\n",
       "      <td>F</td>\n",
       "      <td>S</td>\n",
       "      <td>2</td>\n",
       "      <td>645.0</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4272</th>\n",
       "      <td>9266_02</td>\n",
       "      <td>Earth</td>\n",
       "      <td>True</td>\n",
       "      <td>TRAPPIST-1e</td>\n",
       "      <td>34.000000</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Jeron Peter</td>\n",
       "      <td>G</td>\n",
       "      <td>S</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4273</th>\n",
       "      <td>9269_01</td>\n",
       "      <td>Earth</td>\n",
       "      <td>False</td>\n",
       "      <td>TRAPPIST-1e</td>\n",
       "      <td>42.000000</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>847.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>144.0</td>\n",
       "      <td>Matty Scheron</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1018.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4274</th>\n",
       "      <td>9271_01</td>\n",
       "      <td>Mars</td>\n",
       "      <td>True</td>\n",
       "      <td>55 Cancri e</td>\n",
       "      <td>28.658146</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Jayrin Pore</td>\n",
       "      <td>D</td>\n",
       "      <td>P</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4275</th>\n",
       "      <td>9273_01</td>\n",
       "      <td>Europa</td>\n",
       "      <td>False</td>\n",
       "      <td>TRAPPIST-1e</td>\n",
       "      <td>28.658146</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2680.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>523.0</td>\n",
       "      <td>Kitakan Conale</td>\n",
       "      <td>D</td>\n",
       "      <td>P</td>\n",
       "      <td>2</td>\n",
       "      <td>3203.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4276</th>\n",
       "      <td>9277_01</td>\n",
       "      <td>Earth</td>\n",
       "      <td>True</td>\n",
       "      <td>PSO J318.5-22</td>\n",
       "      <td>43.000000</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Lilace Leonzaley</td>\n",
       "      <td>G</td>\n",
       "      <td>S</td>\n",
       "      <td>4</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4277 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId HomePlanet  CryoSleep    Destination        Age    VIP  \\\n",
       "0        0013_01      Earth       True    TRAPPIST-1e  27.000000  False   \n",
       "1        0018_01      Earth      False    TRAPPIST-1e  19.000000  False   \n",
       "2        0019_01     Europa       True    55 Cancri e  31.000000  False   \n",
       "3        0021_01     Europa      False    TRAPPIST-1e  38.000000  False   \n",
       "4        0023_01      Earth      False    TRAPPIST-1e  20.000000  False   \n",
       "...          ...        ...        ...            ...        ...    ...   \n",
       "4272     9266_02      Earth       True    TRAPPIST-1e  34.000000  False   \n",
       "4273     9269_01      Earth      False    TRAPPIST-1e  42.000000  False   \n",
       "4274     9271_01       Mars       True    55 Cancri e  28.658146  False   \n",
       "4275     9273_01     Europa      False    TRAPPIST-1e  28.658146  False   \n",
       "4276     9277_01      Earth       True  PSO J318.5-22  43.000000  False   \n",
       "\n",
       "      RoomService  FoodCourt  ShoppingMall     Spa  VRDeck              Name  \\\n",
       "0             0.0        0.0           0.0     0.0     0.0   Nelly Carsoning   \n",
       "1             0.0        9.0           0.0  2823.0     0.0    Lerome Peckers   \n",
       "2             0.0        0.0           0.0     0.0     0.0   Sabih Unhearfus   \n",
       "3             0.0     6652.0           0.0   181.0   585.0  Meratz Caltilter   \n",
       "4            10.0        0.0         635.0     0.0     0.0   Brence Harperez   \n",
       "...           ...        ...           ...     ...     ...               ...   \n",
       "4272          0.0        0.0           0.0     0.0     0.0       Jeron Peter   \n",
       "4273          0.0      847.0          17.0    10.0   144.0     Matty Scheron   \n",
       "4274          0.0        0.0           0.0     0.0     0.0       Jayrin Pore   \n",
       "4275          0.0     2680.0           0.0     0.0   523.0    Kitakan Conale   \n",
       "4276          0.0        0.0           0.0     0.0     0.0  Lilace Leonzaley   \n",
       "\n",
       "     Deck Side  AgeGroup  TotalSpend  \n",
       "0       G    S         2         0.0  \n",
       "1       F    S         1      2832.0  \n",
       "2       C    S         3         0.0  \n",
       "3       C    S         3      7418.0  \n",
       "4       F    S         2       645.0  \n",
       "...   ...  ...       ...         ...  \n",
       "4272    G    S         3         0.0  \n",
       "4273    0    0         4      1018.0  \n",
       "4274    D    P         2         0.0  \n",
       "4275    D    P         2      3203.0  \n",
       "4276    G    S         4         0.0  \n",
       "\n",
       "[4277 rows x 16 columns]"
      ]
     },
     "execution_count": 321,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 322,
   "id": "f059d45a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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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>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",
       "      <th>Deck</th>\n",
       "      <th>Side</th>\n",
       "      <th>AgeGroup</th>\n",
       "      <th>TotalSpend</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>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",
       "      <td>B</td>\n",
       "      <td>P</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0002_01</td>\n",
       "      <td>Earth</td>\n",
       "      <td>False</td>\n",
       "      <td>TRAPPIST-1e</td>\n",
       "      <td>24.0</td>\n",
       "      <td>False</td>\n",
       "      <td>109.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>549.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>Juanna Vines</td>\n",
       "      <td>True</td>\n",
       "      <td>F</td>\n",
       "      <td>S</td>\n",
       "      <td>2</td>\n",
       "      <td>736.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0003_01</td>\n",
       "      <td>Europa</td>\n",
       "      <td>False</td>\n",
       "      <td>TRAPPIST-1e</td>\n",
       "      <td>58.0</td>\n",
       "      <td>True</td>\n",
       "      <td>43.0</td>\n",
       "      <td>3576.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6715.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>Altark Susent</td>\n",
       "      <td>False</td>\n",
       "      <td>A</td>\n",
       "      <td>S</td>\n",
       "      <td>5</td>\n",
       "      <td>10383.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0003_02</td>\n",
       "      <td>Europa</td>\n",
       "      <td>False</td>\n",
       "      <td>TRAPPIST-1e</td>\n",
       "      <td>33.0</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1283.0</td>\n",
       "      <td>371.0</td>\n",
       "      <td>3329.0</td>\n",
       "      <td>193.0</td>\n",
       "      <td>Solam Susent</td>\n",
       "      <td>False</td>\n",
       "      <td>A</td>\n",
       "      <td>S</td>\n",
       "      <td>3</td>\n",
       "      <td>5176.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0004_01</td>\n",
       "      <td>Earth</td>\n",
       "      <td>False</td>\n",
       "      <td>TRAPPIST-1e</td>\n",
       "      <td>16.0</td>\n",
       "      <td>False</td>\n",
       "      <td>303.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>151.0</td>\n",
       "      <td>565.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Willy Santantines</td>\n",
       "      <td>True</td>\n",
       "      <td>F</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "      <td>1091.0</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",
       "      <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>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",
       "      <td>A</td>\n",
       "      <td>P</td>\n",
       "      <td>4</td>\n",
       "      <td>8536.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8689</th>\n",
       "      <td>9278_01</td>\n",
       "      <td>Earth</td>\n",
       "      <td>True</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",
       "      <td>G</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8690</th>\n",
       "      <td>9279_01</td>\n",
       "      <td>Earth</td>\n",
       "      <td>False</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",
       "      <td>G</td>\n",
       "      <td>S</td>\n",
       "      <td>2</td>\n",
       "      <td>1873.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8691</th>\n",
       "      <td>9280_01</td>\n",
       "      <td>Europa</td>\n",
       "      <td>False</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",
       "      <td>E</td>\n",
       "      <td>S</td>\n",
       "      <td>3</td>\n",
       "      <td>4637.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8692</th>\n",
       "      <td>9280_02</td>\n",
       "      <td>Europa</td>\n",
       "      <td>False</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",
       "      <td>E</td>\n",
       "      <td>S</td>\n",
       "      <td>4</td>\n",
       "      <td>4826.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8693 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId HomePlanet  CryoSleep    Destination   Age    VIP  \\\n",
       "0        0001_01     Europa      False    TRAPPIST-1e  39.0  False   \n",
       "1        0002_01      Earth      False    TRAPPIST-1e  24.0  False   \n",
       "2        0003_01     Europa      False    TRAPPIST-1e  58.0   True   \n",
       "3        0003_02     Europa      False    TRAPPIST-1e  33.0  False   \n",
       "4        0004_01      Earth      False    TRAPPIST-1e  16.0  False   \n",
       "...          ...        ...        ...            ...   ...    ...   \n",
       "8688     9276_01     Europa      False    55 Cancri e  41.0   True   \n",
       "8689     9278_01      Earth       True  PSO J318.5-22  18.0  False   \n",
       "8690     9279_01      Earth      False    TRAPPIST-1e  26.0  False   \n",
       "8691     9280_01     Europa      False    55 Cancri e  32.0  False   \n",
       "8692     9280_02     Europa      False    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 Deck Side  AgeGroup  TotalSpend  \n",
       "0           False    B    P         3         0.0  \n",
       "1            True    F    S         2       736.0  \n",
       "2           False    A    S         5     10383.0  \n",
       "3           False    A    S         3      5176.0  \n",
       "4            True    F    S         1      1091.0  \n",
       "...           ...  ...  ...       ...         ...  \n",
       "8688        False    A    P         4      8536.0  \n",
       "8689        False    G    S         1         0.0  \n",
       "8690         True    G    S         2      1873.0  \n",
       "8691        False    E    S         3      4637.0  \n",
       "8692         True    E    S         4      4826.0  \n",
       "\n",
       "[8693 rows x 17 columns]"
      ]
     },
     "execution_count": 322,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 323,
   "id": "77fd7b9e",
   "metadata": {},
   "outputs": [],
   "source": [
    "train.drop(['PassengerId', 'Name'], axis=1, inplace=True)\n",
    "test.drop(['PassengerId', 'Name'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
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       "     HomePlanet  CryoSleep    Destination        Age    VIP  RoomService  \\\n",
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     "execution_count": 324,
     "metadata": {},
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   "source": [
    "test"
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  },
  {
   "cell_type": "code",
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   "id": "e65e9d57",
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   "outputs": [
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      "text/plain": [
       "     HomePlanet  CryoSleep    Destination   Age    VIP  RoomService  \\\n",
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       "...         ...        ...            ...   ...    ...          ...   \n",
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       "8691     Europa      False    55 Cancri e  32.0  False          0.0   \n",
       "8692     Europa      False    TRAPPIST-1e  44.0  False        126.0   \n",
       "\n",
       "      FoodCourt  ShoppingMall     Spa  VRDeck  Transported Deck Side  \\\n",
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       "\n",
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       "[8693 rows x 15 columns]"
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     },
     "execution_count": 325,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1989ac68",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 326,
   "id": "c5235b4f",
   "metadata": {},
   "outputs": [],
   "source": [
    "test['HomePlanet'].replace(to_replace = ['Earth', 'Europa', 'Mars'], value = [0,1,2], inplace = True)\n",
    "test['Destination'].replace(to_replace = ['TRAPPIST-1e', '55 Cancri e', 'PSO J318.5-22'], value = [0,1,2], inplace = True)\n",
    "test['Deck'].replace(to_replace = ['A','B','C','D','E','F','G','T'], value = [0,1,2,3,4,5,6,7], inplace = True)\n",
    "test['Side'].replace(to_replace = ['P', 'S',], value = [0,1], inplace = True)\n",
    "\n",
    "train['HomePlanet'].replace(to_replace = ['Earth', 'Europa', 'Mars'], value = [0,1,2], inplace = True)\n",
    "train['Destination'].replace(to_replace = ['TRAPPIST-1e', '55 Cancri e', 'PSO J318.5-22'], value = [0,1,2], inplace = True)\n",
    "train['Deck'].replace(to_replace = ['A','B','C','D','E','F','G','T'], value = [0,1,2,3,4,5,6,7], inplace = True)\n",
    "train['Side'].replace(to_replace = ['P', 'S',], value = [0,1], inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 327,
   "id": "7e3deac5",
   "metadata": {},
   "outputs": [],
   "source": [
    "test[\"CryoSleep\"] = test[\"CryoSleep\"].astype(int)\n",
    "test[\"VIP\"] = test[\"VIP\"].astype(int)\n",
    "\n",
    "train[\"CryoSleep\"] = train[\"CryoSleep\"].astype(int)\n",
    "train[\"VIP\"] = train[\"VIP\"].astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c98eab51",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 328,
   "id": "142e8466",
   "metadata": {},
   "outputs": [
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       "      HomePlanet  CryoSleep  Destination   Age  VIP  RoomService  FoodCourt  \\\n",
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   "id": "64643e0e",
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   "outputs": [
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       "      <td>10.0</td>\n",
       "      <td>144.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1018.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4274</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>28.658146</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4275</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>28.658146</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2680.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>523.0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>3203.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4276</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>43.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4277 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      HomePlanet  CryoSleep  Destination        Age  VIP  RoomService  \\\n",
       "0              0          1            0  27.000000    0          0.0   \n",
       "1              0          0            0  19.000000    0          0.0   \n",
       "2              1          1            1  31.000000    0          0.0   \n",
       "3              1          0            0  38.000000    0          0.0   \n",
       "4              0          0            0  20.000000    0         10.0   \n",
       "...          ...        ...          ...        ...  ...          ...   \n",
       "4272           0          1            0  34.000000    0          0.0   \n",
       "4273           0          0            0  42.000000    0          0.0   \n",
       "4274           2          1            1  28.658146    0          0.0   \n",
       "4275           1          0            0  28.658146    0          0.0   \n",
       "4276           0          1            2  43.000000    0          0.0   \n",
       "\n",
       "      FoodCourt  ShoppingMall     Spa  VRDeck Deck Side  AgeGroup  TotalSpend  \n",
       "0           0.0           0.0     0.0     0.0    6    1         2         0.0  \n",
       "1           9.0           0.0  2823.0     0.0    5    1         1      2832.0  \n",
       "2           0.0           0.0     0.0     0.0    2    1         3         0.0  \n",
       "3        6652.0           0.0   181.0   585.0    2    1         3      7418.0  \n",
       "4           0.0         635.0     0.0     0.0    5    1         2       645.0  \n",
       "...         ...           ...     ...     ...  ...  ...       ...         ...  \n",
       "4272        0.0           0.0     0.0     0.0    6    1         3         0.0  \n",
       "4273      847.0          17.0    10.0   144.0    0    0         4      1018.0  \n",
       "4274        0.0           0.0     0.0     0.0    3    0         2         0.0  \n",
       "4275     2680.0           0.0     0.0   523.0    3    0         2      3203.0  \n",
       "4276        0.0           0.0     0.0     0.0    6    1         4         0.0  \n",
       "\n",
       "[4277 rows x 14 columns]"
      ]
     },
     "execution_count": 329,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 330,
   "id": "72a0d57b",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, y_train = train.drop(['Transported'], axis=1), train['Transported']\n",
    "X_test = test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 331,
   "id": "0d74fa55",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-6 {color: black;background-color: white;}#sk-container-id-6 pre{padding: 0;}#sk-container-id-6 div.sk-toggleable {background-color: white;}#sk-container-id-6 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-6 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-6 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-6 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-6 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-6 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-6 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-6 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-6 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 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-6 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-6 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-6 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-6 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 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-6 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-6 div.sk-item {position: relative;z-index: 1;}#sk-container-id-6 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-6 div.sk-item::before, #sk-container-id-6 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-6 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-6 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-6 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-6 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-6 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-6 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-6 div.sk-label-container {text-align: center;}#sk-container-id-6 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-6 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-6\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression(max_iter=5000)</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-6\" type=\"checkbox\" checked><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression(max_iter=5000)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "LogisticRegression(max_iter=5000)"
      ]
     },
     "execution_count": 331,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "log_reg = LogisticRegression(max_iter = 5000)\n",
    "log_reg.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 332,
   "id": "f73bcdc1",
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions = log_reg.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 333,
   "id": "fcf09558",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "76.08"
      ]
     },
     "execution_count": 333,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import Perceptron\n",
    "\n",
    "perceptron = Perceptron()\n",
    "perceptron.fit(X_train, y_train)\n",
    "Y_pred = perceptron.predict(X_test)\n",
    "accuracy = round(perceptron.score(X_train, y_train) * 100, 2)\n",
    "accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 334,
   "id": "5284936b",
   "metadata": {},
   "outputs": [],
   "source": [
    "test1 = pd.read_csv('C:\\\\Users\\\\anish\\\\Desktop\\\\spaceship-titanic\\\\test.csv')\n",
    "submission = pd.DataFrame([test1['PassengerId'], predictions])\n",
    "submission = submission.transpose()\n",
    "submission.rename(columns = {'Unnamed 0':'Transported'}, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 335,
   "id": "3916b12e",
   "metadata": {},
   "outputs": [],
   "source": [
    "#submission.to_csv('submission.csv', index = False)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
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