Exploration_Preprocessing.ipynb

{
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  {
   "cell_type": "code",
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   "metadata": {
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   },
   "outputs": [],
   "source": [
    "# importing libraries\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.preprocessing import StandardScaler, OneHotEncoder\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.compose import ColumnTransformer\n",
    "from sklearn.impute import SimpleImputer\n",
    "from sklearn.metrics import mean_squared_error, mean_absolute_error\n",
    "from sklearn.preprocessing import RobustScaler\n",
    "import seaborn as sns\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
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     "height": 269
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    "outputId": "c09d9c98-936f-4233-ff4c-597dd683ab00"
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   "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>ID</th>\n",
       "      <th>Price</th>\n",
       "      <th>Levy</th>\n",
       "      <th>Manufacturer</th>\n",
       "      <th>Model</th>\n",
       "      <th>Prod. year</th>\n",
       "      <th>Category</th>\n",
       "      <th>Leather interior</th>\n",
       "      <th>Fuel type</th>\n",
       "      <th>Engine volume</th>\n",
       "      <th>Mileage</th>\n",
       "      <th>Cylinders</th>\n",
       "      <th>Gear box type</th>\n",
       "      <th>Drive wheels</th>\n",
       "      <th>Doors</th>\n",
       "      <th>Wheel</th>\n",
       "      <th>Color</th>\n",
       "      <th>Airbags</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>45654403</td>\n",
       "      <td>13328</td>\n",
       "      <td>1399</td>\n",
       "      <td>LEXUS</td>\n",
       "      <td>RX 450</td>\n",
       "      <td>2010</td>\n",
       "      <td>Jeep</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Hybrid</td>\n",
       "      <td>3.5</td>\n",
       "      <td>186005 km</td>\n",
       "      <td>6.0</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>4x4</td>\n",
       "      <td>04-May</td>\n",
       "      <td>Left wheel</td>\n",
       "      <td>Silver</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>44731507</td>\n",
       "      <td>16621</td>\n",
       "      <td>1018</td>\n",
       "      <td>CHEVROLET</td>\n",
       "      <td>Equinox</td>\n",
       "      <td>2011</td>\n",
       "      <td>Jeep</td>\n",
       "      <td>No</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>3</td>\n",
       "      <td>192000 km</td>\n",
       "      <td>6.0</td>\n",
       "      <td>Tiptronic</td>\n",
       "      <td>4x4</td>\n",
       "      <td>04-May</td>\n",
       "      <td>Left wheel</td>\n",
       "      <td>Black</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>45774419</td>\n",
       "      <td>8467</td>\n",
       "      <td>-</td>\n",
       "      <td>HONDA</td>\n",
       "      <td>FIT</td>\n",
       "      <td>2006</td>\n",
       "      <td>Hatchback</td>\n",
       "      <td>No</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>1.3</td>\n",
       "      <td>200000 km</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Variator</td>\n",
       "      <td>Front</td>\n",
       "      <td>04-May</td>\n",
       "      <td>Right-hand drive</td>\n",
       "      <td>Black</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>45769185</td>\n",
       "      <td>3607</td>\n",
       "      <td>862</td>\n",
       "      <td>FORD</td>\n",
       "      <td>Escape</td>\n",
       "      <td>2011</td>\n",
       "      <td>Jeep</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Hybrid</td>\n",
       "      <td>2.5</td>\n",
       "      <td>168966 km</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>4x4</td>\n",
       "      <td>04-May</td>\n",
       "      <td>Left wheel</td>\n",
       "      <td>White</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>45809263</td>\n",
       "      <td>11726</td>\n",
       "      <td>446</td>\n",
       "      <td>HONDA</td>\n",
       "      <td>FIT</td>\n",
       "      <td>2014</td>\n",
       "      <td>Hatchback</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>1.3</td>\n",
       "      <td>91901 km</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>Front</td>\n",
       "      <td>04-May</td>\n",
       "      <td>Left wheel</td>\n",
       "      <td>Silver</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>45802912</td>\n",
       "      <td>39493</td>\n",
       "      <td>891</td>\n",
       "      <td>HYUNDAI</td>\n",
       "      <td>Santa FE</td>\n",
       "      <td>2016</td>\n",
       "      <td>Jeep</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>2</td>\n",
       "      <td>160931 km</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>Front</td>\n",
       "      <td>04-May</td>\n",
       "      <td>Left wheel</td>\n",
       "      <td>White</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>45656768</td>\n",
       "      <td>1803</td>\n",
       "      <td>761</td>\n",
       "      <td>TOYOTA</td>\n",
       "      <td>Prius</td>\n",
       "      <td>2010</td>\n",
       "      <td>Hatchback</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Hybrid</td>\n",
       "      <td>1.8</td>\n",
       "      <td>258909 km</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>Front</td>\n",
       "      <td>04-May</td>\n",
       "      <td>Left wheel</td>\n",
       "      <td>White</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         ID  Price  Levy Manufacturer     Model  Prod. year   Category  \\\n",
       "0  45654403  13328  1399        LEXUS    RX 450        2010       Jeep   \n",
       "1  44731507  16621  1018    CHEVROLET   Equinox        2011       Jeep   \n",
       "2  45774419   8467     -        HONDA       FIT        2006  Hatchback   \n",
       "3  45769185   3607   862         FORD    Escape        2011       Jeep   \n",
       "4  45809263  11726   446        HONDA       FIT        2014  Hatchback   \n",
       "5  45802912  39493   891      HYUNDAI  Santa FE        2016       Jeep   \n",
       "6  45656768   1803   761       TOYOTA     Prius        2010  Hatchback   \n",
       "\n",
       "  Leather interior Fuel type Engine volume    Mileage  Cylinders  \\\n",
       "0              Yes    Hybrid           3.5  186005 km        6.0   \n",
       "1               No    Petrol             3  192000 km        6.0   \n",
       "2               No    Petrol           1.3  200000 km        4.0   \n",
       "3              Yes    Hybrid           2.5  168966 km        4.0   \n",
       "4              Yes    Petrol           1.3   91901 km        4.0   \n",
       "5              Yes    Diesel             2  160931 km        4.0   \n",
       "6              Yes    Hybrid           1.8  258909 km        4.0   \n",
       "\n",
       "  Gear box type Drive wheels   Doors             Wheel   Color  Airbags  \n",
       "0     Automatic          4x4  04-May        Left wheel  Silver       12  \n",
       "1     Tiptronic          4x4  04-May        Left wheel   Black        8  \n",
       "2      Variator        Front  04-May  Right-hand drive   Black        2  \n",
       "3     Automatic          4x4  04-May        Left wheel   White        0  \n",
       "4     Automatic        Front  04-May        Left wheel  Silver        4  \n",
       "5     Automatic        Front  04-May        Left wheel   White        4  \n",
       "6     Automatic        Front  04-May        Left wheel   White       12  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.read_csv(\"car_price_prediction.csv\")\n",
    "df.head(7)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "S055pdU7bnmd"
   },
   "source": [
    "# Data Exploration and Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
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    "outputId": "45e66fe7-9049-422d-d10a-fafa66b7c484"
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 19237 entries, 0 to 19236\n",
      "Data columns (total 18 columns):\n",
      " #   Column            Non-Null Count  Dtype  \n",
      "---  ------            --------------  -----  \n",
      " 0   ID                19237 non-null  int64  \n",
      " 1   Price             19237 non-null  int64  \n",
      " 2   Levy              19237 non-null  object \n",
      " 3   Manufacturer      19237 non-null  object \n",
      " 4   Model             19237 non-null  object \n",
      " 5   Prod. year        19237 non-null  int64  \n",
      " 6   Category          19237 non-null  object \n",
      " 7   Leather interior  19237 non-null  object \n",
      " 8   Fuel type         19237 non-null  object \n",
      " 9   Engine volume     19237 non-null  object \n",
      " 10  Mileage           19237 non-null  object \n",
      " 11  Cylinders         19237 non-null  float64\n",
      " 12  Gear box type     19237 non-null  object \n",
      " 13  Drive wheels      19237 non-null  object \n",
      " 14  Doors             19237 non-null  object \n",
      " 15  Wheel             19237 non-null  object \n",
      " 16  Color             19237 non-null  object \n",
      " 17  Airbags           19237 non-null  int64  \n",
      "dtypes: float64(1), int64(4), object(13)\n",
      "memory usage: 2.6+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
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     "base_uri": "https://localhost:8080/"
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ID\n",
      "[45654403 44731507 45774419 ... 45804997 45793526 45813273]\n",
      "******************************************************************************************\n",
      "Price\n",
      "[13328 16621  8467 ... 56814 63886 22075]\n",
      "******************************************************************************************\n",
      "Levy\n",
      "['1399' '1018' '-' '862' '446' '891' '761' '751' '394' '1053' '1055'\n",
      " '1079' '810' '2386' '1850' '531' '586' '1249' '2455' '583' '1537' '1288'\n",
      " '915' '1750' '707' '1077' '1486' '1091' '650' '382' '1436' '1194' '503'\n",
      " '1017' '1104' '639' '629' '919' '781' '530' '640' '765' '777' '779' '934'\n",
      " '769' '645' '1185' '1324' '830' '1187' '1111' '760' '642' '1604' '1095'\n",
      " '966' '473' '1138' '1811' '988' '917' '1156' '687' '11714' '836' '1347'\n",
      " '2866' '1646' '259' '609' '697' '585' '475' '690' '308' '1823' '1361'\n",
      " '1273' '924' '584' '2078' '831' '1172' '893' '1872' '1885' '1266' '447'\n",
      " '2148' '1730' '730' '289' '502' '333' '1325' '247' '879' '1342' '1327'\n",
      " '1598' '1514' '1058' '738' '1935' '481' '1522' '1282' '456' '880' '900'\n",
      " '798' '1277' '442' '1051' '790' '1292' '1047' '528' '1211' '1493' '1793'\n",
      " '574' '930' '1998' '271' '706' '1481' '1677' '1661' '1286' '1408' '1090'\n",
      " '595' '1451' '1267' '993' '1714' '878' '641' '749' '1511' '603' '353'\n",
      " '877' '1236' '1141' '397' '784' '1024' '1357' '1301' '770' '922' '1438'\n",
      " '753' '607' '1363' '638' '490' '431' '565' '517' '833' '489' '1760' '986'\n",
      " '1841' '1620' '1360' '474' '1099' '978' '1624' '1946' '1268' '1307' '696'\n",
      " '649' '666' '2151' '551' '800' '971' '1323' '2377' '1845' '1083' '694'\n",
      " '463' '419' '345' '1515' '1505' '2056' '1203' '729' '460' '1356' '876'\n",
      " '911' '1190' '780' '448' '2410' '1848' '1148' '834' '1275' '1028' '1197'\n",
      " '724' '890' '1705' '505' '789' '2959' '518' '461' '1719' '2858' '3156'\n",
      " '2225' '2177' '1968' '1888' '1308' '2736' '1103' '557' '2195' '843'\n",
      " '1664' '723' '4508' '562' '501' '2018' '1076' '1202' '3301' '691' '1440'\n",
      " '1869' '1178' '418' '1820' '1413' '488' '1304' '363' '2108' '521' '1659'\n",
      " '87' '1411' '1528' '3292' '7058' '1578' '627' '874' '1996' '1488' '5679'\n",
      " '1234' '5603' '400' '889' '3268' '875' '949' '2265' '441' '742' '425'\n",
      " '2476' '2971' '614' '1816' '1375' '1405' '2297' '1062' '1113' '420'\n",
      " '2469' '658' '1951' '2670' '2578' '1995' '1032' '994' '1011' '2421'\n",
      " '1296' '155' '494' '426' '1086' '961' '2236' '1829' '764' '1834' '1054'\n",
      " '617' '1529' '2266' '637' '626' '1832' '1016' '2002' '1756' '746' '1285'\n",
      " '2690' '1118' '5332' '980' '1807' '970' '1228' '1195' '1132' '1768'\n",
      " '1384' '1080' '7063' '1817' '1452' '1975' '1368' '702' '1974' '1781'\n",
      " '1036' '944' '663' '364' '1539' '1345' '1680' '2209' '741' '1575' '695'\n",
      " '1317' '294' '1525' '424' '997' '1473' '1552' '2819' '2188' '1668' '3057'\n",
      " '799' '1502' '2606' '552' '1694' '1759' '1110' '399' '1470' '1174' '5877'\n",
      " '1474' '1688' '526' '686' '5908' '1107' '2070' '1468' '1246' '1685' '556'\n",
      " '1533' '1917' '1346' '732' '692' '579' '421' '362' '3505' '1855' '2711'\n",
      " '1586' '3739' '681' '1708' '2278' '1701' '722' '1482' '928' '827' '832'\n",
      " '527' '604' '173' '1341' '3329' '1553' '859' '167' '916' '828' '2082'\n",
      " '1176' '1108' '975' '3008' '1516' '2269' '1699' '2073' '1031' '1503'\n",
      " '2364' '1030' '1442' '5666' '2715' '1437' '2067' '1426' '2908' '1279'\n",
      " '866' '4283' '279' '2658' '3015' '2004' '1391' '4736' '748' '1466' '644'\n",
      " '683' '2705' '1297' '731' '1252' '2216' '3141' '3273' '1518' '1723'\n",
      " '1588' '972' '682' '1094' '668' '175' '967' '402' '3894' '1960' '1599'\n",
      " '2000' '2084' '1621' '714' '1109' '3989' '873' '1572' '1163' '1991'\n",
      " '1716' '1673' '2562' '2874' '965' '462' '605' '1948' '1736' '3518' '2054'\n",
      " '2467' '1681' '1272' '1205' '750' '2156' '2566' '115' '524' '3184' '676'\n",
      " '1678' '612' '328' '955' '1441' '1675' '3965' '2909' '623' '822' '867'\n",
      " '3025' '1993' '792' '636' '4057' '3743' '2337' '2570' '2418' '2472'\n",
      " '3910' '1662' '2123' '2628' '3208' '2080' '3699' '2913' '864' '2505'\n",
      " '870' '7536' '1924' '1671' '1064' '1836' '1866' '4741' '841' '1369'\n",
      " '5681' '3112' '1366' '2223' '1198' '1039' '3811' '3571' '1387' '1171'\n",
      " '1365' '1531' '1590' '11706' '2308' '4860' '1641' '1045' '1901']\n",
      "******************************************************************************************\n",
      "Manufacturer\n",
      "['LEXUS' 'CHEVROLET' 'HONDA' 'FORD' 'HYUNDAI' 'TOYOTA' 'MERCEDES-BENZ'\n",
      " 'OPEL' 'PORSCHE' 'BMW' 'JEEP' 'VOLKSWAGEN' 'AUDI' 'RENAULT' 'NISSAN'\n",
      " 'SUBARU' 'DAEWOO' 'KIA' 'MITSUBISHI' 'SSANGYONG' 'MAZDA' 'GMC' 'FIAT'\n",
      " 'INFINITI' 'ALFA ROMEO' 'SUZUKI' 'ACURA' 'LINCOLN' 'VAZ' 'GAZ' 'CITROEN'\n",
      " 'LAND ROVER' 'MINI' 'DODGE' 'CHRYSLER' 'JAGUAR' 'ISUZU' 'SKODA'\n",
      " 'DAIHATSU' 'BUICK' 'TESLA' 'CADILLAC' 'PEUGEOT' 'BENTLEY' 'VOLVO' 'სხვა'\n",
      " 'HAVAL' 'HUMMER' 'SCION' 'UAZ' 'MERCURY' 'ZAZ' 'ROVER' 'SEAT' 'LANCIA'\n",
      " 'MOSKVICH' 'MASERATI' 'FERRARI' 'SAAB' 'LAMBORGHINI' 'ROLLS-ROYCE'\n",
      " 'PONTIAC' 'SATURN' 'ASTON MARTIN' 'GREATWALL']\n",
      "******************************************************************************************\n",
      "Model\n",
      "['RX 450' 'Equinox' 'FIT' ... 'E 230 124' 'RX 450 F SPORT' 'Prius C aqua']\n",
      "******************************************************************************************\n",
      "Prod. year\n",
      "[2010 2011 2006 2014 2016 2013 2007 1999 1997 2018 2008 2012 2017 2001\n",
      " 1995 2009 2000 2019 2015 2004 1998 1990 2005 2003 1985 1996 2002 1993\n",
      " 1992 1988 1977 1989 1994 2020 1984 1986 1991 1983 1953 1964 1974 1987\n",
      " 1943 1978 1965 1976 1957 1980 1939 1968 1947 1982 1981 1973]\n",
      "******************************************************************************************\n",
      "Category\n",
      "['Jeep' 'Hatchback' 'Sedan' 'Microbus' 'Goods wagon' 'Universal' 'Coupe'\n",
      " 'Minivan' 'Cabriolet' 'Limousine' 'Pickup']\n",
      "******************************************************************************************\n",
      "Leather interior\n",
      "['Yes' 'No']\n",
      "******************************************************************************************\n",
      "Fuel type\n",
      "['Hybrid' 'Petrol' 'Diesel' 'CNG' 'Plug-in Hybrid' 'LPG' 'Hydrogen']\n",
      "******************************************************************************************\n",
      "Engine volume\n",
      "['3.5' '3' '1.3' '2.5' '2' '1.8' '2.4' '4' '1.6' '3.3' '2.0 Turbo'\n",
      " '2.2 Turbo' '4.7' '1.5' '4.4' '3.0 Turbo' '1.4 Turbo' '3.6' '2.3'\n",
      " '1.5 Turbo' '1.6 Turbo' '2.2' '2.3 Turbo' '1.4' '5.5' '2.8 Turbo' '3.2'\n",
      " '3.8' '4.6' '1.2' '5' '1.7' '2.9' '0.5' '1.8 Turbo' '2.4 Turbo'\n",
      " '3.5 Turbo' '1.9' '2.7' '4.8' '5.3' '0.4' '2.8' '3.2 Turbo' '1.1' '2.1'\n",
      " '0.7' '5.4' '1.3 Turbo' '3.7' '1' '2.5 Turbo' '2.6' '1.9 Turbo'\n",
      " '4.4 Turbo' '4.7 Turbo' '0.8' '0.2 Turbo' '5.7' '4.8 Turbo' '4.6 Turbo'\n",
      " '6.7' '6.2' '1.2 Turbo' '3.4' '1.7 Turbo' '6.3 Turbo' '2.7 Turbo' '4.3'\n",
      " '4.2' '2.9 Turbo' '0' '4.0 Turbo' '20' '3.6 Turbo' '0.3' '3.7 Turbo'\n",
      " '5.9' '5.5 Turbo' '0.2' '2.1 Turbo' '5.6' '6' '0.7 Turbo' '0.6 Turbo'\n",
      " '6.8' '4.5' '0.6' '7.3' '0.1' '1.0 Turbo' '6.3' '4.5 Turbo' '0.8 Turbo'\n",
      " '4.2 Turbo' '3.1' '5.0 Turbo' '6.4' '3.9' '5.7 Turbo' '0.9' '0.4 Turbo'\n",
      " '5.4 Turbo' '0.3 Turbo' '5.2' '5.8' '1.1 Turbo']\n",
      "******************************************************************************************\n",
      "Mileage\n",
      "['186005 km' '192000 km' '200000 km' ... '140607 km' '307325 km'\n",
      " '186923 km']\n",
      "******************************************************************************************\n",
      "Cylinders\n",
      "[ 6.  4.  8.  1. 12.  3.  2. 16.  5.  7.  9. 10. 14.]\n",
      "******************************************************************************************\n",
      "Gear box type\n",
      "['Automatic' 'Tiptronic' 'Variator' 'Manual']\n",
      "******************************************************************************************\n",
      "Drive wheels\n",
      "['4x4' 'Front' 'Rear']\n",
      "******************************************************************************************\n",
      "Doors\n",
      "['04-May' '02-Mar' '>5']\n",
      "******************************************************************************************\n",
      "Wheel\n",
      "['Left wheel' 'Right-hand drive']\n",
      "******************************************************************************************\n",
      "Color\n",
      "['Silver' 'Black' 'White' 'Grey' 'Blue' 'Green' 'Red' 'Sky blue' 'Orange'\n",
      " 'Yellow' 'Brown' 'Golden' 'Beige' 'Carnelian red' 'Purple' 'Pink']\n",
      "******************************************************************************************\n",
      "Airbags\n",
      "[12  8  2  0  4  6 10  3  1 16  5  7  9 11 14 15 13]\n",
      "******************************************************************************************\n"
     ]
    }
   ],
   "source": [
    "for x in df.columns:\n",
    "  print(x)\n",
    "  print(df[x].unique())\n",
    "  print(\"*\"*90)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 206
    },
    "id": "bJEfQD_Hub6S",
    "outputId": "a495ed4f-6297-4a9b-91bb-b646e80d346b"
   },
   "outputs": [
    {
     "data": {
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       "    }\n",
       "\n",
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       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>Price</th>\n",
       "      <th>Levy</th>\n",
       "      <th>Manufacturer</th>\n",
       "      <th>Model</th>\n",
       "      <th>Prod. year</th>\n",
       "      <th>Category</th>\n",
       "      <th>Leather interior</th>\n",
       "      <th>Fuel type</th>\n",
       "      <th>Engine volume</th>\n",
       "      <th>Mileage</th>\n",
       "      <th>Cylinders</th>\n",
       "      <th>Gear box type</th>\n",
       "      <th>Drive wheels</th>\n",
       "      <th>Doors</th>\n",
       "      <th>Wheel</th>\n",
       "      <th>Color</th>\n",
       "      <th>Airbags</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>45654403</td>\n",
       "      <td>13328</td>\n",
       "      <td>1399.0</td>\n",
       "      <td>LEXUS</td>\n",
       "      <td>RX 450</td>\n",
       "      <td>2010</td>\n",
       "      <td>Jeep</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Hybrid</td>\n",
       "      <td>3.5</td>\n",
       "      <td>186005 km</td>\n",
       "      <td>6.0</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>4x4</td>\n",
       "      <td>04-May</td>\n",
       "      <td>Left wheel</td>\n",
       "      <td>Silver</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>44731507</td>\n",
       "      <td>16621</td>\n",
       "      <td>1018.0</td>\n",
       "      <td>CHEVROLET</td>\n",
       "      <td>Equinox</td>\n",
       "      <td>2011</td>\n",
       "      <td>Jeep</td>\n",
       "      <td>No</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>3</td>\n",
       "      <td>192000 km</td>\n",
       "      <td>6.0</td>\n",
       "      <td>Tiptronic</td>\n",
       "      <td>4x4</td>\n",
       "      <td>04-May</td>\n",
       "      <td>Left wheel</td>\n",
       "      <td>Black</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>45774419</td>\n",
       "      <td>8467</td>\n",
       "      <td>NaN</td>\n",
       "      <td>HONDA</td>\n",
       "      <td>FIT</td>\n",
       "      <td>2006</td>\n",
       "      <td>Hatchback</td>\n",
       "      <td>No</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>1.3</td>\n",
       "      <td>200000 km</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Variator</td>\n",
       "      <td>Front</td>\n",
       "      <td>04-May</td>\n",
       "      <td>Right-hand drive</td>\n",
       "      <td>Black</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>45769185</td>\n",
       "      <td>3607</td>\n",
       "      <td>862.0</td>\n",
       "      <td>FORD</td>\n",
       "      <td>Escape</td>\n",
       "      <td>2011</td>\n",
       "      <td>Jeep</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Hybrid</td>\n",
       "      <td>2.5</td>\n",
       "      <td>168966 km</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>4x4</td>\n",
       "      <td>04-May</td>\n",
       "      <td>Left wheel</td>\n",
       "      <td>White</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>45809263</td>\n",
       "      <td>11726</td>\n",
       "      <td>446.0</td>\n",
       "      <td>HONDA</td>\n",
       "      <td>FIT</td>\n",
       "      <td>2014</td>\n",
       "      <td>Hatchback</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>1.3</td>\n",
       "      <td>91901 km</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>Front</td>\n",
       "      <td>04-May</td>\n",
       "      <td>Left wheel</td>\n",
       "      <td>Silver</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         ID  Price    Levy Manufacturer    Model  Prod. year   Category  \\\n",
       "0  45654403  13328  1399.0        LEXUS   RX 450        2010       Jeep   \n",
       "1  44731507  16621  1018.0    CHEVROLET  Equinox        2011       Jeep   \n",
       "2  45774419   8467     NaN        HONDA      FIT        2006  Hatchback   \n",
       "3  45769185   3607   862.0         FORD   Escape        2011       Jeep   \n",
       "4  45809263  11726   446.0        HONDA      FIT        2014  Hatchback   \n",
       "\n",
       "  Leather interior Fuel type Engine volume    Mileage  Cylinders  \\\n",
       "0              Yes    Hybrid           3.5  186005 km        6.0   \n",
       "1               No    Petrol             3  192000 km        6.0   \n",
       "2               No    Petrol           1.3  200000 km        4.0   \n",
       "3              Yes    Hybrid           2.5  168966 km        4.0   \n",
       "4              Yes    Petrol           1.3   91901 km        4.0   \n",
       "\n",
       "  Gear box type Drive wheels   Doors             Wheel   Color  Airbags  \n",
       "0     Automatic          4x4  04-May        Left wheel  Silver       12  \n",
       "1     Tiptronic          4x4  04-May        Left wheel   Black        8  \n",
       "2      Variator        Front  04-May  Right-hand drive   Black        2  \n",
       "3     Automatic          4x4  04-May        Left wheel   White        0  \n",
       "4     Automatic        Front  04-May        Left wheel  Silver        4  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# By looking at the unique values of \"Levy\" feature we find it has a missing values '-'\n",
    "# Replace \"-\" with NaN\n",
    "df[\"Levy\"].replace(\"-\",np.NaN,inplace=True)\n",
    "# Change the dtype of \"Levy\" feature from object to float64\n",
    "df[\"Levy\"] = df[\"Levy\"].astype(\"float64\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 206
    },
    "id": "cvk5COtffqKw",
    "outputId": "d743f4ed-0bbc-489c-d1ab-adc8eac82812"
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>Price</th>\n",
       "      <th>Levy</th>\n",
       "      <th>Manufacturer</th>\n",
       "      <th>Model</th>\n",
       "      <th>Prod. year</th>\n",
       "      <th>Category</th>\n",
       "      <th>Leather interior</th>\n",
       "      <th>Fuel type</th>\n",
       "      <th>Engine volume</th>\n",
       "      <th>Mileage</th>\n",
       "      <th>Cylinders</th>\n",
       "      <th>Gear box type</th>\n",
       "      <th>Drive wheels</th>\n",
       "      <th>Doors</th>\n",
       "      <th>Wheel</th>\n",
       "      <th>Color</th>\n",
       "      <th>Airbags</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>45654403</td>\n",
       "      <td>13328</td>\n",
       "      <td>1399.0</td>\n",
       "      <td>LEXUS</td>\n",
       "      <td>RX 450</td>\n",
       "      <td>2010</td>\n",
       "      <td>Jeep</td>\n",
       "      <td>True</td>\n",
       "      <td>Hybrid</td>\n",
       "      <td>3.5</td>\n",
       "      <td>186005 km</td>\n",
       "      <td>6.0</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>4x4</td>\n",
       "      <td>04-May</td>\n",
       "      <td>Left wheel</td>\n",
       "      <td>Silver</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>44731507</td>\n",
       "      <td>16621</td>\n",
       "      <td>1018.0</td>\n",
       "      <td>CHEVROLET</td>\n",
       "      <td>Equinox</td>\n",
       "      <td>2011</td>\n",
       "      <td>Jeep</td>\n",
       "      <td>False</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>3</td>\n",
       "      <td>192000 km</td>\n",
       "      <td>6.0</td>\n",
       "      <td>Tiptronic</td>\n",
       "      <td>4x4</td>\n",
       "      <td>04-May</td>\n",
       "      <td>Left wheel</td>\n",
       "      <td>Black</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>45774419</td>\n",
       "      <td>8467</td>\n",
       "      <td>NaN</td>\n",
       "      <td>HONDA</td>\n",
       "      <td>FIT</td>\n",
       "      <td>2006</td>\n",
       "      <td>Hatchback</td>\n",
       "      <td>False</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>1.3</td>\n",
       "      <td>200000 km</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Variator</td>\n",
       "      <td>Front</td>\n",
       "      <td>04-May</td>\n",
       "      <td>Right-hand drive</td>\n",
       "      <td>Black</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>45769185</td>\n",
       "      <td>3607</td>\n",
       "      <td>862.0</td>\n",
       "      <td>FORD</td>\n",
       "      <td>Escape</td>\n",
       "      <td>2011</td>\n",
       "      <td>Jeep</td>\n",
       "      <td>True</td>\n",
       "      <td>Hybrid</td>\n",
       "      <td>2.5</td>\n",
       "      <td>168966 km</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>4x4</td>\n",
       "      <td>04-May</td>\n",
       "      <td>Left wheel</td>\n",
       "      <td>White</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>45809263</td>\n",
       "      <td>11726</td>\n",
       "      <td>446.0</td>\n",
       "      <td>HONDA</td>\n",
       "      <td>FIT</td>\n",
       "      <td>2014</td>\n",
       "      <td>Hatchback</td>\n",
       "      <td>True</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>1.3</td>\n",
       "      <td>91901 km</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>Front</td>\n",
       "      <td>04-May</td>\n",
       "      <td>Left wheel</td>\n",
       "      <td>Silver</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         ID  Price    Levy Manufacturer    Model  Prod. year   Category  \\\n",
       "0  45654403  13328  1399.0        LEXUS   RX 450        2010       Jeep   \n",
       "1  44731507  16621  1018.0    CHEVROLET  Equinox        2011       Jeep   \n",
       "2  45774419   8467     NaN        HONDA      FIT        2006  Hatchback   \n",
       "3  45769185   3607   862.0         FORD   Escape        2011       Jeep   \n",
       "4  45809263  11726   446.0        HONDA      FIT        2014  Hatchback   \n",
       "\n",
       "   Leather interior Fuel type Engine volume    Mileage  Cylinders  \\\n",
       "0              True    Hybrid           3.5  186005 km        6.0   \n",
       "1             False    Petrol             3  192000 km        6.0   \n",
       "2             False    Petrol           1.3  200000 km        4.0   \n",
       "3              True    Hybrid           2.5  168966 km        4.0   \n",
       "4              True    Petrol           1.3   91901 km        4.0   \n",
       "\n",
       "  Gear box type Drive wheels   Doors             Wheel   Color  Airbags  \n",
       "0     Automatic          4x4  04-May        Left wheel  Silver       12  \n",
       "1     Tiptronic          4x4  04-May        Left wheel   Black        8  \n",
       "2      Variator        Front  04-May  Right-hand drive   Black        2  \n",
       "3     Automatic          4x4  04-May        Left wheel   White        0  \n",
       "4     Automatic        Front  04-May        Left wheel  Silver        4  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Replace 'Yes' 'No' with True False in Leather interior feature\n",
    "df['Leather interior'].replace({'Yes': True, 'No':False}, inplace=True)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 206
    },
    "id": "gNxkWwX11QQR",
    "outputId": "999e277d-3168-48a1-82f9-419d061add27"
   },
   "outputs": [
    {
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       "      <th>Wheel</th>\n",
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       "      <th>Turbo Engine</th>\n",
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       "      <th>0</th>\n",
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       "      <td>12</td>\n",
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       "      <th>1</th>\n",
       "      <td>44731507</td>\n",
       "      <td>16621</td>\n",
       "      <td>1018.0</td>\n",
       "      <td>CHEVROLET</td>\n",
       "      <td>Equinox</td>\n",
       "      <td>2011</td>\n",
       "      <td>Jeep</td>\n",
       "      <td>False</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>3.0</td>\n",
       "      <td>192000 km</td>\n",
       "      <td>6.0</td>\n",
       "      <td>Tiptronic</td>\n",
       "      <td>4x4</td>\n",
       "      <td>04-May</td>\n",
       "      <td>Left wheel</td>\n",
       "      <td>Black</td>\n",
       "      <td>8</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>45774419</td>\n",
       "      <td>8467</td>\n",
       "      <td>NaN</td>\n",
       "      <td>HONDA</td>\n",
       "      <td>FIT</td>\n",
       "      <td>2006</td>\n",
       "      <td>Hatchback</td>\n",
       "      <td>False</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>1.3</td>\n",
       "      <td>200000 km</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Variator</td>\n",
       "      <td>Front</td>\n",
       "      <td>04-May</td>\n",
       "      <td>Right-hand drive</td>\n",
       "      <td>Black</td>\n",
       "      <td>2</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>45769185</td>\n",
       "      <td>3607</td>\n",
       "      <td>862.0</td>\n",
       "      <td>FORD</td>\n",
       "      <td>Escape</td>\n",
       "      <td>2011</td>\n",
       "      <td>Jeep</td>\n",
       "      <td>True</td>\n",
       "      <td>Hybrid</td>\n",
       "      <td>2.5</td>\n",
       "      <td>168966 km</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>4x4</td>\n",
       "      <td>04-May</td>\n",
       "      <td>Left wheel</td>\n",
       "      <td>White</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>45809263</td>\n",
       "      <td>11726</td>\n",
       "      <td>446.0</td>\n",
       "      <td>HONDA</td>\n",
       "      <td>FIT</td>\n",
       "      <td>2014</td>\n",
       "      <td>Hatchback</td>\n",
       "      <td>True</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>1.3</td>\n",
       "      <td>91901 km</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>Front</td>\n",
       "      <td>04-May</td>\n",
       "      <td>Left wheel</td>\n",
       "      <td>Silver</td>\n",
       "      <td>4</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         ID  Price    Levy Manufacturer    Model  Prod. year   Category  \\\n",
       "0  45654403  13328  1399.0        LEXUS   RX 450        2010       Jeep   \n",
       "1  44731507  16621  1018.0    CHEVROLET  Equinox        2011       Jeep   \n",
       "2  45774419   8467     NaN        HONDA      FIT        2006  Hatchback   \n",
       "3  45769185   3607   862.0         FORD   Escape        2011       Jeep   \n",
       "4  45809263  11726   446.0        HONDA      FIT        2014  Hatchback   \n",
       "\n",
       "   Leather interior Fuel type  Engine volume    Mileage  Cylinders  \\\n",
       "0              True    Hybrid            3.5  186005 km        6.0   \n",
       "1             False    Petrol            3.0  192000 km        6.0   \n",
       "2             False    Petrol            1.3  200000 km        4.0   \n",
       "3              True    Hybrid            2.5  168966 km        4.0   \n",
       "4              True    Petrol            1.3   91901 km        4.0   \n",
       "\n",
       "  Gear box type Drive wheels   Doors             Wheel   Color  Airbags  \\\n",
       "0     Automatic          4x4  04-May        Left wheel  Silver       12   \n",
       "1     Tiptronic          4x4  04-May        Left wheel   Black        8   \n",
       "2      Variator        Front  04-May  Right-hand drive   Black        2   \n",
       "3     Automatic          4x4  04-May        Left wheel   White        0   \n",
       "4     Automatic        Front  04-May        Left wheel  Silver        4   \n",
       "\n",
       "   Turbo Engine  \n",
       "0         False  \n",
       "1         False  \n",
       "2         False  \n",
       "3         False  \n",
       "4         False  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Making sure that we don miss anything so making everything lower first\n",
    "df['Engine volume'] = df['Engine volume'].str.lower()\n",
    "# Finding the rows which has turbo in them and assigning the results to new column Turbo. \n",
    "df['Turbo Engine'] = df['Engine volume'].str.contains('turbo')\n",
    "# Slicing engine volumes and converting type to float\n",
    "df['Engine volume'] = df['Engine volume'].str.slice(0,3)\n",
    "df['Engine volume'] = df['Engine volume'].astype('float64')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 206
    },
    "id": "xyTA8-xQ5fc8",
    "outputId": "d7d28b2c-9183-414d-c272-b4f178bdc4ca"
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>Price</th>\n",
       "      <th>Levy</th>\n",
       "      <th>Manufacturer</th>\n",
       "      <th>Model</th>\n",
       "      <th>Prod. year</th>\n",
       "      <th>Category</th>\n",
       "      <th>Leather interior</th>\n",
       "      <th>Fuel type</th>\n",
       "      <th>Engine volume</th>\n",
       "      <th>Mileage</th>\n",
       "      <th>Cylinders</th>\n",
       "      <th>Gear box type</th>\n",
       "      <th>Drive wheels</th>\n",
       "      <th>Doors</th>\n",
       "      <th>Wheel</th>\n",
       "      <th>Color</th>\n",
       "      <th>Airbags</th>\n",
       "      <th>Turbo Engine</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <th>0</th>\n",
       "      <td>45654403</td>\n",
       "      <td>13328</td>\n",
       "      <td>1399.0</td>\n",
       "      <td>LEXUS</td>\n",
       "      <td>RX 450</td>\n",
       "      <td>2010</td>\n",
       "      <td>Jeep</td>\n",
       "      <td>True</td>\n",
       "      <td>Hybrid</td>\n",
       "      <td>3.5</td>\n",
       "      <td>186005</td>\n",
       "      <td>6.0</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>4x4</td>\n",
       "      <td>04-May</td>\n",
       "      <td>Left wheel</td>\n",
       "      <td>Silver</td>\n",
       "      <td>12</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>44731507</td>\n",
       "      <td>16621</td>\n",
       "      <td>1018.0</td>\n",
       "      <td>CHEVROLET</td>\n",
       "      <td>Equinox</td>\n",
       "      <td>2011</td>\n",
       "      <td>Jeep</td>\n",
       "      <td>False</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>3.0</td>\n",
       "      <td>192000</td>\n",
       "      <td>6.0</td>\n",
       "      <td>Tiptronic</td>\n",
       "      <td>4x4</td>\n",
       "      <td>04-May</td>\n",
       "      <td>Left wheel</td>\n",
       "      <td>Black</td>\n",
       "      <td>8</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>45774419</td>\n",
       "      <td>8467</td>\n",
       "      <td>NaN</td>\n",
       "      <td>HONDA</td>\n",
       "      <td>FIT</td>\n",
       "      <td>2006</td>\n",
       "      <td>Hatchback</td>\n",
       "      <td>False</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>1.3</td>\n",
       "      <td>200000</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Variator</td>\n",
       "      <td>Front</td>\n",
       "      <td>04-May</td>\n",
       "      <td>Right-hand drive</td>\n",
       "      <td>Black</td>\n",
       "      <td>2</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>45769185</td>\n",
       "      <td>3607</td>\n",
       "      <td>862.0</td>\n",
       "      <td>FORD</td>\n",
       "      <td>Escape</td>\n",
       "      <td>2011</td>\n",
       "      <td>Jeep</td>\n",
       "      <td>True</td>\n",
       "      <td>Hybrid</td>\n",
       "      <td>2.5</td>\n",
       "      <td>168966</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>4x4</td>\n",
       "      <td>04-May</td>\n",
       "      <td>Left wheel</td>\n",
       "      <td>White</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>45809263</td>\n",
       "      <td>11726</td>\n",
       "      <td>446.0</td>\n",
       "      <td>HONDA</td>\n",
       "      <td>FIT</td>\n",
       "      <td>2014</td>\n",
       "      <td>Hatchback</td>\n",
       "      <td>True</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>1.3</td>\n",
       "      <td>91901</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>Front</td>\n",
       "      <td>04-May</td>\n",
       "      <td>Left wheel</td>\n",
       "      <td>Silver</td>\n",
       "      <td>4</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         ID  Price    Levy Manufacturer    Model  Prod. year   Category  \\\n",
       "0  45654403  13328  1399.0        LEXUS   RX 450        2010       Jeep   \n",
       "1  44731507  16621  1018.0    CHEVROLET  Equinox        2011       Jeep   \n",
       "2  45774419   8467     NaN        HONDA      FIT        2006  Hatchback   \n",
       "3  45769185   3607   862.0         FORD   Escape        2011       Jeep   \n",
       "4  45809263  11726   446.0        HONDA      FIT        2014  Hatchback   \n",
       "\n",
       "   Leather interior Fuel type  Engine volume  Mileage  Cylinders  \\\n",
       "0              True    Hybrid            3.5   186005        6.0   \n",
       "1             False    Petrol            3.0   192000        6.0   \n",
       "2             False    Petrol            1.3   200000        4.0   \n",
       "3              True    Hybrid            2.5   168966        4.0   \n",
       "4              True    Petrol            1.3    91901        4.0   \n",
       "\n",
       "  Gear box type Drive wheels   Doors             Wheel   Color  Airbags  \\\n",
       "0     Automatic          4x4  04-May        Left wheel  Silver       12   \n",
       "1     Tiptronic          4x4  04-May        Left wheel   Black        8   \n",
       "2      Variator        Front  04-May  Right-hand drive   Black        2   \n",
       "3     Automatic          4x4  04-May        Left wheel   White        0   \n",
       "4     Automatic        Front  04-May        Left wheel  Silver        4   \n",
       "\n",
       "   Turbo Engine  \n",
       "0         False  \n",
       "1         False  \n",
       "2         False  \n",
       "3         False  \n",
       "4         False  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Removing \" km\" in \"Mileage\" Feature\n",
    "df[\"Mileage\"]=df[\"Mileage\"].str.strip(\" km\")\n",
    "# Change the dtype of \"Mileage\" feature from object to int64\n",
    "df[\"Mileage\"] = df[\"Mileage\"].astype(\"int64\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 206
    },
    "id": "SUe8yh_m8m9B",
    "outputId": "b309b776-f94d-4296-f6bc-af24dcbf5deb"
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>Price</th>\n",
       "      <th>Levy</th>\n",
       "      <th>Manufacturer</th>\n",
       "      <th>Model</th>\n",
       "      <th>Prod. year</th>\n",
       "      <th>Category</th>\n",
       "      <th>Leather interior</th>\n",
       "      <th>Fuel type</th>\n",
       "      <th>Engine volume</th>\n",
       "      <th>Mileage</th>\n",
       "      <th>Cylinders</th>\n",
       "      <th>Gear box type</th>\n",
       "      <th>Drive wheels</th>\n",
       "      <th>Doors</th>\n",
       "      <th>Wheel</th>\n",
       "      <th>Color</th>\n",
       "      <th>Airbags</th>\n",
       "      <th>Turbo Engine</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>45654403</td>\n",
       "      <td>13328</td>\n",
       "      <td>1399.0</td>\n",
       "      <td>LEXUS</td>\n",
       "      <td>RX 450</td>\n",
       "      <td>2010</td>\n",
       "      <td>Jeep</td>\n",
       "      <td>True</td>\n",
       "      <td>Hybrid</td>\n",
       "      <td>3.5</td>\n",
       "      <td>186005</td>\n",
       "      <td>6.0</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>4x4</td>\n",
       "      <td>4</td>\n",
       "      <td>Left wheel</td>\n",
       "      <td>Silver</td>\n",
       "      <td>12</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>44731507</td>\n",
       "      <td>16621</td>\n",
       "      <td>1018.0</td>\n",
       "      <td>CHEVROLET</td>\n",
       "      <td>Equinox</td>\n",
       "      <td>2011</td>\n",
       "      <td>Jeep</td>\n",
       "      <td>False</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>3.0</td>\n",
       "      <td>192000</td>\n",
       "      <td>6.0</td>\n",
       "      <td>Tiptronic</td>\n",
       "      <td>4x4</td>\n",
       "      <td>4</td>\n",
       "      <td>Left wheel</td>\n",
       "      <td>Black</td>\n",
       "      <td>8</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>45774419</td>\n",
       "      <td>8467</td>\n",
       "      <td>NaN</td>\n",
       "      <td>HONDA</td>\n",
       "      <td>FIT</td>\n",
       "      <td>2006</td>\n",
       "      <td>Hatchback</td>\n",
       "      <td>False</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>1.3</td>\n",
       "      <td>200000</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Variator</td>\n",
       "      <td>Front</td>\n",
       "      <td>4</td>\n",
       "      <td>Right-hand drive</td>\n",
       "      <td>Black</td>\n",
       "      <td>2</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>45769185</td>\n",
       "      <td>3607</td>\n",
       "      <td>862.0</td>\n",
       "      <td>FORD</td>\n",
       "      <td>Escape</td>\n",
       "      <td>2011</td>\n",
       "      <td>Jeep</td>\n",
       "      <td>True</td>\n",
       "      <td>Hybrid</td>\n",
       "      <td>2.5</td>\n",
       "      <td>168966</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>4x4</td>\n",
       "      <td>4</td>\n",
       "      <td>Left wheel</td>\n",
       "      <td>White</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>45809263</td>\n",
       "      <td>11726</td>\n",
       "      <td>446.0</td>\n",
       "      <td>HONDA</td>\n",
       "      <td>FIT</td>\n",
       "      <td>2014</td>\n",
       "      <td>Hatchback</td>\n",
       "      <td>True</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>1.3</td>\n",
       "      <td>91901</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>Front</td>\n",
       "      <td>4</td>\n",
       "      <td>Left wheel</td>\n",
       "      <td>Silver</td>\n",
       "      <td>4</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         ID  Price    Levy Manufacturer    Model  Prod. year   Category  \\\n",
       "0  45654403  13328  1399.0        LEXUS   RX 450        2010       Jeep   \n",
       "1  44731507  16621  1018.0    CHEVROLET  Equinox        2011       Jeep   \n",
       "2  45774419   8467     NaN        HONDA      FIT        2006  Hatchback   \n",
       "3  45769185   3607   862.0         FORD   Escape        2011       Jeep   \n",
       "4  45809263  11726   446.0        HONDA      FIT        2014  Hatchback   \n",
       "\n",
       "   Leather interior Fuel type  Engine volume  Mileage  Cylinders  \\\n",
       "0              True    Hybrid            3.5   186005        6.0   \n",
       "1             False    Petrol            3.0   192000        6.0   \n",
       "2             False    Petrol            1.3   200000        4.0   \n",
       "3              True    Hybrid            2.5   168966        4.0   \n",
       "4              True    Petrol            1.3    91901        4.0   \n",
       "\n",
       "  Gear box type Drive wheels  Doors             Wheel   Color  Airbags  \\\n",
       "0     Automatic          4x4      4        Left wheel  Silver       12   \n",
       "1     Tiptronic          4x4      4        Left wheel   Black        8   \n",
       "2      Variator        Front      4  Right-hand drive   Black        2   \n",
       "3     Automatic          4x4      4        Left wheel   White        0   \n",
       "4     Automatic        Front      4        Left wheel  Silver        4   \n",
       "\n",
       "   Turbo Engine  \n",
       "0         False  \n",
       "1         False  \n",
       "2         False  \n",
       "3         False  \n",
       "4         False  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Replacing ['04-May','02-Mar','>5'] to [4,2,6] in \"Doors\" feature\n",
    "df[\"Doors\"].replace(['04-May','02-Mar','>5'],[4,2,5],inplace=True)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "NqCkusHJK5ML"
   },
   "source": [
    "## **2.1 Data Quality Report**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "id": "d-16jm1-BB_k"
   },
   "outputs": [],
   "source": [
    "# Writing the descriptions of features in the dataset\n",
    "df.drop(['ID'], axis = 1, inplace = True)\n",
    "features_df=pd.DataFrame(index=df.columns)\n",
    "desc = [\"Price of the Car\",\"Levy\",\"Manufacturer Details\",\"Model of the Car\",\"Production year of the Car\",\"Category of the Car\",\"Is it Leather interior\",\"Type of Fuel\",\"Volume of the engine\",\"Mileage of the car\",\"No of Cylinders\",\"Which type of Gear Box is used?\",\"Drive wheels\",\"No of Doors\",\"Wheel Type\",\"Car Color\",\"No of Airbags\",\"Is the engine type Turbo\"]\n",
    "features_df[\"Desc\"]= desc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "id": "-TOYWF1Lkzop"
   },
   "outputs": [],
   "source": [
    "continuous_features = ['Price', 'Levy', 'Prod. year', 'Engine volume','Doors', 'Mileage', 'Cylinders', 'Airbags']\n",
    "contegorical_features = ['Manufacturer','Category', 'Leather interior', 'Fuel type', 'Gear box type', 'Drive wheels', 'Wheel', 'Turbo Engine', 'Color']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 300
    },
    "id": "SxhCon4gdeav",
    "outputId": "e2ec63e2-2e0a-4f45-bebc-925b9941f951"
   },
   "outputs": [
    {
     "data": {
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Feature</th>\n",
       "      <th>Desc</th>\n",
       "      <th>Count</th>\n",
       "      <th>% of Missing</th>\n",
       "      <th>Card.</th>\n",
       "      <th>Min.</th>\n",
       "      <th>Q1</th>\n",
       "      <th>Median</th>\n",
       "      <th>Q3</th>\n",
       "      <th>Max.</th>\n",
       "      <th>Mean</th>\n",
       "      <th>Std.Dev.</th>\n",
       "      <th>Notes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Price</td>\n",
       "      <td>Price of the Car</td>\n",
       "      <td>19237</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2315.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5331.0</td>\n",
       "      <td>13172.0</td>\n",
       "      <td>22075.0</td>\n",
       "      <td>2.630750e+07</td>\n",
       "      <td>1.855593e+04</td>\n",
       "      <td>1.905813e+05</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Levy</td>\n",
       "      <td>Levy</td>\n",
       "      <td>19237</td>\n",
       "      <td>30.248999</td>\n",
       "      <td>559.0</td>\n",
       "      <td>87.0</td>\n",
       "      <td>640.0</td>\n",
       "      <td>781.0</td>\n",
       "      <td>1058.0</td>\n",
       "      <td>1.171400e+04</td>\n",
       "      <td>9.068381e+02</td>\n",
       "      <td>4.618671e+02</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Prod. year</td>\n",
       "      <td>Production year of the Car</td>\n",
       "      <td>19237</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>54.0</td>\n",
       "      <td>1939.0</td>\n",
       "      <td>2009.0</td>\n",
       "      <td>2012.0</td>\n",
       "      <td>2015.0</td>\n",
       "      <td>2.020000e+03</td>\n",
       "      <td>2.010913e+03</td>\n",
       "      <td>5.668673e+00</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Engine volume</td>\n",
       "      <td>Volume of the engine</td>\n",
       "      <td>19237</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>65.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.8</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>2.000000e+01</td>\n",
       "      <td>2.307990e+00</td>\n",
       "      <td>8.778045e-01</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Doors</td>\n",
       "      <td>No of Doors</td>\n",
       "      <td>19237</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.000000e+00</td>\n",
       "      <td>3.925872e+00</td>\n",
       "      <td>4.033993e-01</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Mileage</td>\n",
       "      <td>Mileage of the car</td>\n",
       "      <td>19237</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7687.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>70139.0</td>\n",
       "      <td>126000.0</td>\n",
       "      <td>188888.0</td>\n",
       "      <td>2.147484e+09</td>\n",
       "      <td>1.532236e+06</td>\n",
       "      <td>4.840387e+07</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Cylinders</td>\n",
       "      <td>No of Cylinders</td>\n",
       "      <td>19237</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>13.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.600000e+01</td>\n",
       "      <td>4.582991e+00</td>\n",
       "      <td>1.199933e+00</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Airbags</td>\n",
       "      <td>No of Airbags</td>\n",
       "      <td>19237</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>17.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>1.600000e+01</td>\n",
       "      <td>6.582627e+00</td>\n",
       "      <td>4.320168e+00</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Feature                        Desc  Count  % of Missing   Card.  \\\n",
       "0          Price            Price of the Car  19237      0.000000  2315.0   \n",
       "1           Levy                        Levy  19237     30.248999   559.0   \n",
       "2     Prod. year  Production year of the Car  19237      0.000000    54.0   \n",
       "3  Engine volume        Volume of the engine  19237      0.000000    65.0   \n",
       "4          Doors                 No of Doors  19237      0.000000     3.0   \n",
       "5        Mileage          Mileage of the car  19237      0.000000  7687.0   \n",
       "6      Cylinders             No of Cylinders  19237      0.000000    13.0   \n",
       "7        Airbags               No of Airbags  19237      0.000000    17.0   \n",
       "\n",
       "     Min.       Q1    Median        Q3          Max.          Mean  \\\n",
       "0     1.0   5331.0   13172.0   22075.0  2.630750e+07  1.855593e+04   \n",
       "1    87.0    640.0     781.0    1058.0  1.171400e+04  9.068381e+02   \n",
       "2  1939.0   2009.0    2012.0    2015.0  2.020000e+03  2.010913e+03   \n",
       "3     0.0      1.8       2.0       2.5  2.000000e+01  2.307990e+00   \n",
       "4     2.0      4.0       4.0       4.0  5.000000e+00  3.925872e+00   \n",
       "5     0.0  70139.0  126000.0  188888.0  2.147484e+09  1.532236e+06   \n",
       "6     1.0      4.0       4.0       4.0  1.600000e+01  4.582991e+00   \n",
       "7     0.0      4.0       6.0      12.0  1.600000e+01  6.582627e+00   \n",
       "\n",
       "       Std.Dev. Notes  \n",
       "0  1.905813e+05        \n",
       "1  4.618671e+02        \n",
       "2  5.668673e+00        \n",
       "3  8.778045e-01        \n",
       "4  4.033993e-01        \n",
       "5  4.840387e+07        \n",
       "6  1.199933e+00        \n",
       "7  4.320168e+00        "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Creating a Data Frame for Continuous features\n",
    "Quality_Continuous = pd.DataFrame({\"Feature\":{},\"Desc\":{},\"Count\":{},\"% of Missing\":{},\"Card.\":{},\"Min.\":{},\"Q1\":{},\"Median\":{},\"Q3\":{},\"Max.\":{},\"Mean\":{},\"Std.Dev.\":{},\"Notes\":{}})\n",
    "Quality_Continuous[\"Feature\"] = continuous_features\n",
    "Quality_Continuous[\"Desc\"] = features_df.loc[continuous_features].values\n",
    "Quality_Continuous[\"Count\"] = df.shape[0]\n",
    "Quality_Continuous[\"% of Missing\"] = 100*(df[continuous_features].isna().sum().values)/df.shape[0]\n",
    "n = len(continuous_features)\n",
    "for i in range(n):\n",
    "    Quality_Continuous.loc[i,\"Card.\"] = (df[continuous_features[i]].unique()).size\n",
    "    Quality_Continuous[\"Min.\"] = (df[continuous_features].min()).values\n",
    "    Quality_Continuous[\"Q1\"] = (df[continuous_features].quantile(0.25)).values\n",
    "    Quality_Continuous[\"Median\"] = (df[continuous_features].median()).values\n",
    "    Quality_Continuous[\"Q3\"] = (df[continuous_features].quantile(0.75)).values\n",
    "    Quality_Continuous[\"Max.\"] = (df[continuous_features].max()).values\n",
    "    Quality_Continuous[\"Mean\"] = (df[continuous_features].mean()).values\n",
    "    Quality_Continuous[\"Std.Dev.\"] = (df[continuous_features].std()).values\n",
    "    Quality_Continuous[\"Notes\"] = \" \"\n",
    "Quality_Continuous"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 332
    },
    "id": "rRVsnDIPgHuL",
    "outputId": "2f2856e8-3894-45ed-8bb3-1d9fd1025d35"
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Feature</th>\n",
       "      <th>Desc</th>\n",
       "      <th>Count</th>\n",
       "      <th>% of Missing</th>\n",
       "      <th>Card.</th>\n",
       "      <th>Mode</th>\n",
       "      <th>Mode Freq.</th>\n",
       "      <th>Mode %</th>\n",
       "      <th>2nd Mode</th>\n",
       "      <th>2nd Mode Freq.</th>\n",
       "      <th>2nd Mode %</th>\n",
       "      <th>Notes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Manufacturer</td>\n",
       "      <td>Manufacturer Details</td>\n",
       "      <td>19237</td>\n",
       "      <td>0.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>HYUNDAI</td>\n",
       "      <td>3769.0</td>\n",
       "      <td>19.592452</td>\n",
       "      <td>TOYOTA</td>\n",
       "      <td>3662.0</td>\n",
       "      <td>19.036232</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Category</td>\n",
       "      <td>Category of the Car</td>\n",
       "      <td>19237</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>Sedan</td>\n",
       "      <td>8736.0</td>\n",
       "      <td>45.412486</td>\n",
       "      <td>Jeep</td>\n",
       "      <td>5473.0</td>\n",
       "      <td>28.450382</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Leather interior</td>\n",
       "      <td>Is it Leather interior</td>\n",
       "      <td>19237</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>True</td>\n",
       "      <td>5283.0</td>\n",
       "      <td>27.462702</td>\n",
       "      <td>False</td>\n",
       "      <td>13954.0</td>\n",
       "      <td>72.537298</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Fuel type</td>\n",
       "      <td>Type of Fuel</td>\n",
       "      <td>19237</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>10150.0</td>\n",
       "      <td>52.762905</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>4036.0</td>\n",
       "      <td>20.980402</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Gear box type</td>\n",
       "      <td>Which type of Gear Box is used?</td>\n",
       "      <td>19237</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>13514.0</td>\n",
       "      <td>70.250039</td>\n",
       "      <td>Tiptronic</td>\n",
       "      <td>3102.0</td>\n",
       "      <td>16.125175</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Drive wheels</td>\n",
       "      <td>Drive wheels</td>\n",
       "      <td>19237</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Front</td>\n",
       "      <td>12874.0</td>\n",
       "      <td>66.923117</td>\n",
       "      <td>4x4</td>\n",
       "      <td>4058.0</td>\n",
       "      <td>21.094765</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Wheel</td>\n",
       "      <td>Wheel Type</td>\n",
       "      <td>19237</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Left wheel</td>\n",
       "      <td>17753.0</td>\n",
       "      <td>92.285699</td>\n",
       "      <td>Right-hand drive</td>\n",
       "      <td>1484.0</td>\n",
       "      <td>7.714301</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Turbo Engine</td>\n",
       "      <td>Is the engine type Turbo</td>\n",
       "      <td>19237</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>False</td>\n",
       "      <td>17306.0</td>\n",
       "      <td>89.962052</td>\n",
       "      <td>True</td>\n",
       "      <td>1931.0</td>\n",
       "      <td>10.037948</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Color</td>\n",
       "      <td>Car Color</td>\n",
       "      <td>19237</td>\n",
       "      <td>0.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>Black</td>\n",
       "      <td>5033.0</td>\n",
       "      <td>26.163123</td>\n",
       "      <td>White</td>\n",
       "      <td>4489.0</td>\n",
       "      <td>23.335239</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Feature                             Desc  Count  % of Missing  \\\n",
       "0      Manufacturer             Manufacturer Details  19237           0.0   \n",
       "1          Category              Category of the Car  19237           0.0   \n",
       "2  Leather interior           Is it Leather interior  19237           0.0   \n",
       "3         Fuel type                     Type of Fuel  19237           0.0   \n",
       "4     Gear box type  Which type of Gear Box is used?  19237           0.0   \n",
       "5      Drive wheels                     Drive wheels  19237           0.0   \n",
       "6             Wheel                       Wheel Type  19237           0.0   \n",
       "7      Turbo Engine         Is the engine type Turbo  19237           0.0   \n",
       "8             Color                        Car Color  19237           0.0   \n",
       "\n",
       "   Card.        Mode  Mode Freq.     Mode %          2nd Mode  2nd Mode Freq.  \\\n",
       "0   65.0     HYUNDAI      3769.0  19.592452            TOYOTA          3662.0   \n",
       "1   11.0       Sedan      8736.0  45.412486              Jeep          5473.0   \n",
       "2    2.0        True      5283.0  27.462702             False         13954.0   \n",
       "3    7.0      Petrol     10150.0  52.762905            Diesel          4036.0   \n",
       "4    4.0   Automatic     13514.0  70.250039         Tiptronic          3102.0   \n",
       "5    3.0       Front     12874.0  66.923117               4x4          4058.0   \n",
       "6    2.0  Left wheel     17753.0  92.285699  Right-hand drive          1484.0   \n",
       "7    2.0       False     17306.0  89.962052              True          1931.0   \n",
       "8   16.0       Black      5033.0  26.163123             White          4489.0   \n",
       "\n",
       "   2nd Mode % Notes  \n",
       "0   19.036232        \n",
       "1   28.450382        \n",
       "2   72.537298        \n",
       "3   20.980402        \n",
       "4   16.125175        \n",
       "5   21.094765        \n",
       "6    7.714301        \n",
       "7   10.037948        \n",
       "8   23.335239        "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Quality_Categorical = pd.DataFrame({\"Feature\":{},\"Desc\":{},\"Count\":{},\"% of Missing\":{},\"Card.\":{},\"Mode\":{},\"Mode Freq.\":{},\"Mode %\":{},\"2nd Mode\":{},\"2nd Mode Freq.\":{},\"2nd Mode %\":{},\"Notes\":{}})\n",
    "Quality_Categorical[\"Feature\"] = contegorical_features\n",
    "Quality_Categorical[\"Desc\"] = features_df.loc[contegorical_features].values\n",
    "Quality_Categorical[\"Count\"] = df.shape[0]\n",
    "Quality_Categorical[\"% of Missing\"] = 100*(df[contegorical_features].isna().sum().values)/df.shape[0]\n",
    "m = len(contegorical_features)\n",
    "for i in range(m):\n",
    "    counts = df[contegorical_features[i]].value_counts()\n",
    "    Quality_Categorical.loc[i,\"Card.\"] = counts.size\n",
    "    if((counts.index)[0]==\"NaN\"):\n",
    "        Quality_Categorical.loc[i,\"Mode\"] = (counts.index)[1]\n",
    "        Quality_Categorical.loc[i,\"Mode Freq.\"] = counts[1]\n",
    "        Quality_Categorical.loc[i,\"Mode %\"] = 100*counts[1]/df.shape[0]\n",
    "        Quality_Categorical.loc[i,\"2nd Mode\"] = (counts.index)[2]\n",
    "        Quality_Categorical.loc[i,\"2nd Mode Freq.\"] = counts[2]\n",
    "        Quality_Categorical.loc[i,\"2nd Mode %\"] = 100*counts[2]/df.shape[0]\n",
    "    else:\n",
    "        Quality_Categorical.loc[i,\"Mode\"] = (counts.index)[0]\n",
    "        Quality_Categorical.loc[i,\"Mode Freq.\"] = counts[0]\n",
    "        Quality_Categorical.loc[i,\"Mode %\"] = 100*counts[0]/df.shape[0]\n",
    "        Quality_Categorical.loc[i,\"2nd Mode\"] = (counts.index)[1]\n",
    "        Quality_Categorical.loc[i,\"2nd Mode Freq.\"] = counts[1]\n",
    "        Quality_Categorical.loc[i,\"2nd Mode %\"] = 100*counts[1]/df.shape[0]\n",
    "Quality_Categorical[\"Notes\"]=\" \"\n",
    "Quality_Categorical"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Z-SZbUwwMzZi"
   },
   "source": [
    "**Barplots**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "vR6-HntlM5Fn",
    "outputId": "69b299b1-0883-4d4f-c6e6-294524334372"
   },
   "outputs": [],
   "source": [
    "# plt.fig\n",
    "#for i in range(m):\n",
    "#    plt.figure(figsize=(10,6))\n",
    "#    print(\"Bar Plot of\",contegorical_features[i],\"\\n\")\n",
    "#    df_for_bar = df[contegorical_features[i]].value_counts()\n",
    "#    if(contegorical_features[i]==\"Manufacturer\" or contegorical_features[i]==\"Model\"):\n",
    "#      df_for_bar = df_for_bar[0:20] # ploting only top 20 for \"Manufacturer\" and \"Model\"\n",
    "#    fig = df_for_bar.plot.bar()\n",
    "#    plt.title(f\"Bar Plot of {contegorical_features[i]}\")\n",
    "#    plt.show(fig)\n",
    "#    print(\"\\n\",\"*\"*140,\"\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1080x864 with 9 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(nrows=3, ncols=3, figsize=(15,12))\n",
    "for i in range(m):\n",
    "    row = i // 3  # calculate the row index\n",
    "    col = i % 3   # calculate the column index\n",
    "    #print(\"Bar Plot of\", contegorical_features[i])\n",
    "    df_for_bar = df[contegorical_features[i]].value_counts()\n",
    "    if (contegorical_features[i] == \"Manufacturer\" or contegorical_features[i] == \"Model\"):\n",
    "        df_for_bar = df_for_bar[0:20]  # ploting only top 20 for \"Manufacturer\" and \"Model\"\n",
    "    df_for_bar.plot.bar(ax=axs[row, col])\n",
    "    axs[row, col].set_title(f\"Bar Plot of {contegorical_features[i]}\")\n",
    "    axs[row, col].tick_params(axis='x', labelrotation=80) \n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "SRwVbF9eM4zC"
   },
   "source": [
    "**Histograms**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "1J7Cf7jjo38s",
    "outputId": "29d5d458-d695-4f2e-a52a-4b4b96141080"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2160x1440 with 9 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[continuous_features].hist(figsize=(30,20))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "DEqVXq-3AdAJ"
   },
   "source": [
    "## **2.2 EDA**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Number of instances')"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,5), dpi=100)\n",
    "plt.hist(df['Prod. year'])\n",
    "plt.xlabel('Year')\n",
    "plt.ylabel('Number of instances')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Doors', ylabel='count'>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(data=df, x='Doors')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 758
    },
    "id": "IXBC9eQQAeHz",
    "outputId": "1d5a1284-5102-4e8d-f276-489989c19745",
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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G1HJHg5T4iIiIiIiIW1lrfwVOX6bIPcDX1mkNEGKMKQXcAoRaa8OstbHAdFfZa6bER0REREREsltp4GCy54dc0y41/Zr5uKMSERERERHJGfXfmGez+zU3v9+xN84haonGW2vHZ6IKk840e5np10yJj4iIiIiIZIoryclMopPaIaBssudlgCOA3yWmXzMNdRMRERERkew2G3jMdXe3JkCEtfYosA6oaoypaIzxAx52lb1mOuMjIiIiIiJuZYyZBrQEihpjDgFvAb4A1tqxwDygAxAKXACedM2LN8Y8BywEvIGJ1tpt7miTEh8REREREXEra+0jV5hvgb6XmDcPZ2LkVhrqJiIiIiIiHk+Jj4iIiIiIeDwNdcsDujYpT+ebylC1ZCALNh9l0KwtOd2kTDt3NoLP3h/MX+vWEBQcwhO9n6Nlu/R/DPjH76Yyc+rXxMZEc1uL1vTt/zq+fn5J81cuWci3k77g5PFjFCpchBfffJs69RpwYG8Yw4e8xdHDhwCoUr0mfV7oT7mKlbIlxktxV+y/zPqOJfPmsC8slBZt7+SlN99OWi6nYndXbFeqZ9P6Pxnz8TBOHj9GtVp1eOnNtyleshQAg17+L9u2bEwqGx8XR+ly5Rn99XcAPPnA3YSfPo2Xt/N7opp1bmTIJ6Ouy/Xw29LFTJk4jlMnTlC0RAke79WXpre3BCAuNpZxn33E6l9XEB8fT6269ej7yusULVbcrbGmZ/LMX5gw/UdiYmO5o3kTBj3fGz8/33TL7gjdy6CPRhF24BCVypXh3f59qVmlYobqmvrTPH5euJxde/fToVVz3nutX4q6F6z4nVGTp3Ps5ClKFivKC091o02zxlkSc3Zs8+WL5jPyw/eS6rEJCcTExPDpl99QtUZNpk4Yx3dfT0yxfxz51TRKlS6TJTFfjrv6wLxlqxg1eTr/ngnHz9eHZjffxJv9ehJYIACA1977lDUbtxAVHUPRQiH8p+u9PNDxjmyLMzV3xb17734+GDuZ7bv2EH72HNuW/pBi2dwUd3Z8pl2p70velSfP+BhjHMaYTcaYrcaYGcaYgEuU+yO725YVTp6L5svlofy04VBON+WqjR4+DB9fX6bOXsQrg4Ywavj77A/bk6bchrWrmTllMu99OpqJM37h2JHDTJkwLmn+xnVrmDTmc158/S1mLvqVYaO+oOQNzt/EKly0GG8MGcZ385cxbe4SmjS7nWFvv5FtMV6Ku2IvXLQYDz/+FO06dk6zbE7F7q7YLldPRHg4//fmK3Tv+QzT5y2jao1aDB30etKy7w4fwazFvyU9ata5kWat2qZ4/UHDPk6a7+6kJ7vWw78nT/DR4IE8/dyLzFi0kv88+zwfvvMm4WecP6r984xp/LP1b0ZOnsY3P82nQMFAxn7yodtjTW3Vuo1MmPYDEz96m0VTx3Lo6HFGTp6ebtnYuDj6DXyfTm1bsPqnb7inXSv6DXyf2Li4DNVVvEhhend7gC7t26Sp+/jJU7z2/me82udJ/vxlKv17P8ar733CqTPhWRJ3dmzzVu3uStG3n3l5ACVvKE2V6jWSlm/epl2KMjmR9LizDzSoU4MpI95j7ewpLJgyBkeCgxGTvk1a/ulHu7B46jj+/GUqI4e8zohJ09i2K+16zw7ujNvHx4f2LW5lcP90L5fIVXFnx2daRvq+5E15MvEBoqy19a21dYBYoE/ymcYYbwBr7a050Th3W7btOMt3HCfiQlxON+WqREdF8cfKZfTo2Qf/gABq16tP42a3s2xh2mvels6fQ7tO91C+UmUKBgXx8BM9WTJ/TtL8KRPG88iTPalRpy5eXl4ULVY86RvtwIIFKVHqBowxYC1eXl4cPXQwzWtkJ3fGfluL1jS9vSUFg4LTLJsTsbsrtivV88fKZZSrWJnmrdvily8f3f7Ti72huzm4f1+a1zl+9Ajbtmyi9Z0dsjT25LJrPfx74gQFAgvSqOltGGO45dZm5PP3TzrLd/zoEW5q3IRChYvgly8ft7e5kwN7s/7A6OdFK+hyVxuqVChHcMFA+nR/kJ8WLk+37LrN23A4Enjs/k74+fnSvUtHLLB2498ZquuO5k1o06wxwUEF09R9/N9TBAUG0LzxTRhjaNGkEf7583PwyHG3x5xd2zy9utq07+h8n+ci7uwDpYoXpVBwUFJ5by8vDhw+lvS8SoVySWdUjDEYAwePHCMnuDPuimVLc3+HtlSuUDbd5XNL3Nn1mZZeXbmx70v2y6uJT3K/AVWMMS2NMcuNMd8CfwMYY84nFjLGvGqM+dsYs9kYM9Q1rbIxZoExZoMx5jdjjL5KyAKHD+7Hy8ub0uXKJ02rWLkaB/aGpSl7YG8YFatUvViuSjXCT5/ibEQ4DoeD0H+2E3EmnJ5d7+Wx+zow5uNhxMREp6jjofYtubfNbYz99EMe6vFk1gWWAe6KPaOyM3Z3xXaleg7sDaNSsmXz+/tTqnTpdA/qly6YS+0b6yedBUz00bsDeaRTW/73Yl/Cdu+6+qDTkV3roWqNmpStUJE1q1bicDhY/esKfH39qFjZWV+7Tvew/e/NnPr3JNHR0axYNJ9GTW5za6zpCd13gOqVKyQ9r165AqfOhBMecS6dsgepVql8ioOX6hXLs2ffwUzXlVrtapWpVK4My/74E4fDwdJVa/Hz9aFapfJXXDazsmubJ3fi2FG2bd5I6/YdU0z/8/df6XpXa57p/hBzf5zpjvAyzZ19AGDD3zto3Lk7t3TqxuLf1tDj/k4p6nj3s3E07PAwnZ7oR7HChWje+Cb3B5UB7o77SnJD3Nn9mQaX7vuSN+Xpa3yMMT7AXcAC16RbgDrW2r2pyt0F3As0ttZeMMYUds0aD/Sx1u42xjQGRgOts6XxeUhUVBQBgYEpphUIDCTqQmQ6ZS8QUCAwRTmAqAsXiIuLIz4+nt9XLOWDUV/i7ePD4NdfYvpXE3i898XhAd8vWEF0VBRL5s9Jug4kp7gr9qDgkAy9XnbG7q7YrlRPVNQFgkMKpZgfEBjIhQsX0rzOsgVz6fr4UymmvTJoMJWr18BamD1jGgNf7se4qTMJLJj2rMHVyK714O3tTev2Hfjwnf8RGxuLr48PAwYPI7+/PwCly5ajWPGSPHbvXXh5e1OhUmWeeelVt8R4OVFR0UnXXwBJ/0dGRRESnHIdX4iKomCBlCOTAwMDiIyKynRdqXl7e9P5jpa8+n+fOtePrw8fD+pPgH/+qw/uErJrmyeXXlLfvPUdtL+nCyGFCrNz+1be+9+rFAgMpOUd6V9vkVXc2QcAGtatydrZUzh+8hQz5y2mdImU16kNer43bz7Xk03bd7Fu81b8fNO/piaruTvuK8kNcWf3Zxpc+gstyZvy6hkff2PMJmA9cACY4Jr+Z+qkx6UtMMlaewHAWnvaGBMI3ArMcNU1Dkj3SNEY08sYs94Ys378+PHujSQP8Pf3JyryfIppFyIj8Q8okE7ZAKIiI5OVcy7nHxCAn18+AO5+oCuFixYlOCSE+7p2Y/2a39PUk9/fnw733s/HQ95KugYiJ7gr9szIrtjdFduV6vH3D+BCZGSa+QGp1su2zZs4c/oUzVqmvP6j1o31yZcvP/nz5+ehHk8SGBjIts0bcZfsWg8b161l0ujPGfr5OH5evpqhI8czYthg9uzeCcCoj4YSFxvL9HlL+WHxb9zaojWDXv6v2+JMNGfJShp1fJRGHR+l94DB+PvnJ/LCxYO3yEjn/wVcCVlyAf7+nL+Q8kDvfGRUUtnM1JXa6g2bGT7+a776+F02Lfyerz4ezKDho9kRmt5HwrXJrm2e3LIFc2lzV8ozH+UqVqJI0WJ4e3tTq249Oj/wCL+vWHrVcWVUVvaB5EoUK0KzmxvQf8jwNPO8vb1pWLcmx0+e4rvZC681pAzJrrgvJyfiTi4nPtPS6/uSd+XVxCfxGp/61tp+1tpY1/S0Xzk4GcCmmuYFhCerp761Nt1bhVhrx1trG1lrG/Xq1ctNIeQdpcuWx+FwcPjggaRpe0N3pXvHsXIVKxEWuitZud2EFC5CUHAIBYOCKFq8BBkd4WsTEoiJjubUyRPXGsJVc1fsmZUdsbsrtivVU65iJfbuubhsdFQUxw4folzFyileY+mCOdx6e6srf6gag02zO7h62bUewnbvok69BlStUQsvLy+q1axN9Vp12LTuz6SybTp0omBQML5+ftx9f1d27dhGRHi422IF6NS2Bevnfsv6ud8ybuhAqlQox849+5Lm/xO2jyKFQtI9Q1OlQll2he3D+Zt3TrvC9iVd15CZulL7Z89eGt1YizrVq+Dl5UXdGlW5sUY11vy1+eqDvYTs2uaJtm/ZxKl/T3Jbq7Q3dUjOdYlflsvKPpBavCOBg0cvfZ2Ww+HItmtdsjPuK8nOuJPL7s+0jPZ9yTvyauKTWYuA/yTe/c0YU9haexbYa4x50DXNGGPq5WQjL8Xby+Dn44WXMXi5/vf2un4u8Mvv78+tLVox5cuxREdFsX3LJtasWpnuBeit23dk0dzZHNgbxrmzZ5k+eQJtk33T07bD3fwy63vCz5zm3Nmz/Pz9NG65tTngvOPbnl3/4HA4uBB5ni9GfkJgwYKULV8xzetkF3fG7oiPJzYmhoSEBBISHMTGxOCIjwdyJnZ3xXalem69vRX7w/bw+4qlxMbE8O2kL6hQuSply1dIqj8mJppVy5fQtsPdKV73xLFjbN+yibi4OGJjYpj17decjQinVl33vdWzaz1Uq1mLbVs2Jp3h2bPrH7Zt3pQ0hr5qzdosWzCXyPPniY+PZ+6PMyhStBjBISFuizU9ne9owaz5Swndd5CIc+cZN2UG997ZKt2yN9erjZeXF1N+mEtsbBxTf3JeEN24Qd0M1RXvcBATG+t6DyQQExtLvMMBQJ3qVdnw946kMzw7doex4e/tVKtUwe0xZ9c2T7Rk/lxua9GagFTfqq/+bQXnzp7FWsvO7Vv5ZeZ3NGnewu3xXok7+8CcJSs5cvwk1lqOHD/BiIlTaeKad+pMOPOWrSIyKgqHw8GqdRuZt3xV0rLZzZ1xW2uJiY0lLs65T4+JjSU21nlDo9wUd3Z9piW6VN+XvMvY7Ph6J5cxxpy31gammtYS6G+t7ZReOWPMAOAxnHeBm2etfcMYUxEYg3OImy8w3Vr77hVe3tZ/I/077mSVPm2q0qdN1RTTxi7dzdilu7Pl9Te959yhhZ688gXGl3LubASfvv8uG9etJSgomCf69KNlu/acOHaMZ3o8yJhvZlC8ZEkAfpw+hZlTvyYmJobbWrbmuWT3/Y+Pj2fcpx+xcskCfP3y0bx1W/7zzH/xy5eP35YtYcqXY/j35An88uWjWo1aPNGnX4qLKzOrSrGCuSb2qRPG8e2kL1LU/eiTT9Ptqd5ujz2jcbsrtkvVk2jjurWM/eQDThw7RvVatXnxzbcpUeqGpPkrFi/gq7EjmTTzlxQXD+8P28MH77zJ0cOH8PPLR6Wq1XjymX5UrVHrmmPPifXwy6zv+Pn7aYSfPk1QSCE6dXmQLo90B+BsRDjjPv2IjevWEh8fR/mKlenZ70Wq16qToRgS444/tC3DcSf6asZsJk7/kWjXb5m89UKfpDtQ9R4wmIZ1a9Kr2wOAMyEZNHw0e/YfolK50gzu35eaVStlqK5Rk6cz+uvvU7z2s489RN/HHwacv/Pzzaw5nDoTTuGQYB7p3J4nHrrniu33KVMbyJ3bPDYmhu733MkbQz6gfqNbUrRh2FtvsHHdWuLiYilarDgd73uQzg8+nOEYrmWbp+auPvDZhKn8vGg5Z89HEhRYgOaNG/LiU90JCS7I6fAIXnznQ3bu2UeCtdxQohjd7uvIg1fxezaJ2/xaY3dX3IePnaBdtxQ3qOWGEsVY/O24LIk7t3+mweX7/tVw9fdc+61x/TfmZfsB/ab3OuTa9XEpeTLxyWHZnvjkNHckPtcrdyQ+16O8Gjfk3djdeRB8vXHHweD1SNs878WeV/s6KPFJz/WY+Giom4iIiIiIeDwlPiIiIiIi4vGU+IiIiIiIiMdT4iMiIiIiIh5PiY+IiIiIiHg8JT4iIiIiIuLxlPiIiIiIiIjHU+IjIiIiIiIeT4mPiIiIiIh4PCU+IiIiIiLi8ZT4iIiIiIiIx1PiIyIiIiIiHk+Jj4iIiIiIeDwlPiIiIiIi4vGU+IiIiIiIiMdT4iMiIiIiIh5PiY+IiIiIiHg8JT4iIiIiIuLxlPiIiIiIiIjHU+IjIiIiIiIeT4mPiIiIiIh4PCU+IiIiIiLi8ZT4iIiIiIiIx1PiIyIiIiIiHk+Jj4iIiIiIeDwlPiIiIiIi4vGMtTan25DXaIWLiIiIXH9MTjfgUuq/MS/bjy83vdch166PS9EZHxERERER8Xg+Od2AvCj05LmcbkK2qlKsIAD135iXwy3Jfpve6wBA/KFtOdyS7OVTpjaQ9/o6XOzvQ5fvzuGWZK8BraoC2uZ5SeI2z2v7N8i7+7jEvp6Xt7lc33TGR0REREREPJ4SHxERERER8XhKfERERERExOMp8REREREREY+nxEdERERERDyeEh8REREREfF4SnxERERERMTjKfERERERERGPp8RHREREREQ8nhIfERERERHxeEp8RERERETE4ynxERERERERj6fER0REREREPJ4SHxERERER8XhKfERERERExOMp8REREREREY+nxEdERERERDyeEh8REREREfF4SnxERERERMTjKfERERERERGPp8RHREREREQ8nhIfERERERHxeEp8RERERETE4ynxERERERERj+eT0w3IScaY89bawJxuR2acOxvBZ+8P5q91awgKDuGJ3s/Rsl37dMv++N1UZk79mtiYaG5r0Zq+/V/H188vaf7KJQv5dtIXnDx+jEKFi/Dim29Tp14DDuwNY/iQtzh6+BAAVarXpM8L/SlXsVK2xOguXZuUp/NNZahaMpAFm48yaNaWnG6S20ye+QsTpv9ITGwsdzRvwqDne+Pn55um3L6DR/ho/GQ2bduJIyGBOtWr8MZzT1GxbOk0ZZ98eRB/btrK5kUz8PH2zo4wLstdff2XWd+xZN4c9oWF0qLtnbz05tsplv1t6WKmTBzHqRMnKFqiBI/36kvT21tmcXQZFxN5jlVff8aRHRvJFxhEw3sfp/ItLdMte+7kMdZ8P45ju7bi7eNL1VvbcvP9/wFg/vABnNy7E+PatgEhRbj/nXHZFUaGuGObx8XGMmr4UDat/5PzZ89SqkwZHu/Vl0ZNb0tTx7cTxzN14niGfDKKBjc3zurwMiyj23z3H0v4/ZsReCfbr7d9dhClqt8IQPjRg6yePoZT+0PJXzCYm7s8SfkGt2ZXGJmS0X0awI7QvQz6aBRhBw5RqVwZ3u3fl5pVKgLwzidj+WXJr0ll4x3x+Pr4sG7OtwAcPnaCwZ+NZ/P2nfj6+tLu9qYM6PufHNnnuWsfd/zoEUYPH8qOrX/j6+dHs5at6fXfl/H28SEuLo4P33mT3f/s4MSxo7w/Yiw33tQoO8O8JHdt858WLmfqj3PZf/gogQH+dGh9Oy/07Ja0TV9771PWbNxCVHQMRQuF8J+u9/JAxzuyLc68zhjTHvgM8Aa+tNYOTTX/FaCb66kPUBMoZq09bYzZB5wDHEC8tfaaO6/O+FxnRg8fho+vL1NnL+KVQUMYNfx99oftSVNuw9rVzJwymfc+Hc3EGb9w7Mhhpky4eJCzcd0aJo35nBdff4uZi35l2KgvKHmD82C4cNFivDFkGN/NX8a0uUto0ux2hr39RrbF6C4nz0Xz5fJQftpwKKeb4lar1m1kwrQfmPjR2yyaOpZDR48zcvL0dMuejYykVdObmfPV5/w6cyJ1a1Sh38ChacrNWbIShyMhq5ueKe7q64WLFuPhx5+iXcfOaZb99+QJPho8kKefe5EZi1byn2ef58N33iT8zOksjS0zVk8bg5ePLw9/MIUW/+nP6m9Hc+bI/jTlHPFxLPzsf5SqfiMPf/ANDw39isqNW6Uo0+ThPvT4bCY9PpuZ65IecM82dzgcFCtegmEjx/P9whX06PkMQwe9zvGjR1LUcfTwIVatWErhIkWzJbbMyOg2ByhWqUbSNu3x2cykpCfB4WDpmMGUrXszj348jVu7Pcevk4YTcfxwdoaSIZnZp8XGxdFv4Pt0atuC1T99wz3tWtFv4PvExsUB8NaLfVg/99ukR4dWzbmzxcVkb/Bn4ykcEsyKGROYNX4467dsY/rPC7IlztTctY8bPXwowYUKM+XnBXw+aSp/b/qLuT/OTJpf68b69B84mEJFimRLXBnhzm0eHRPDgGf/w6ofvmLayGGs3biFSd//nLT80492YfHUcfz5y1RGDnmdEZOmsW1X2vUs7meM8QZGAXcBtYBHjDG1kpex1n5ora1vra0PvA6stNYm/xBu5ZrvloxdiU8qxpjKxpgFxpgNxpjfjDE1jDHBxph9xhgvV5kAY8xBY0x1Y8xfyZataozZkFVti46K4o+Vy+jRsw/+AQHUrlefxs1uZ9nCeWnKLp0/h3ad7qF8pcoUDAri4Sd6smT+nKT5UyaM55Ene1KjTl28vLwoWqw4RYsVByCwYEFKlLoBYwxYi5eXF0cPHcyqsLLMsm3HWb7jOBEX4nK6KW7186IVdLmrDVUqlCO4YCB9uj/ITwuXp1v2xhpVub9DW0KCCuLr48Nj99/N3oOHCY84l1Tm3PlIRn/9PS/36pFdIVyRO/v6bS1a0/T2lhQMCk6z7L8nTlAgsCCNmt6GMYZbbm1GPn//pLOdOS0uJpr9G//gps7d8c3vT4kqtSlXrzF71qbd3qGrlxIQUpg6be/DN19+fHz9KFymYg60+uq4a5vn9/en21O9KVHqBry8vLjltuaUuOEGQnfuSFHHmI8/4Mln+uHjm/43zDklM9v8ciKOHeRCxGlqt7kXLy9vbqhRj+KVa7Fn7bIsavnVy8w+bd3mbTgcCTx2fyf8/Hzp3qUjFli78e80ZS9ERbP4t9Xc0+7iFwCHjx2nfctbyefnR7HChWh2cwNC9x3IqtAuyZ37uGNHj9C8dVv88uWjcJGiNGx8K/v3Og/sfX19ufehR6ldrz5eXjl/Jj+RO7f5w53b0/DGWvj5+lKiWBE6trmdjVv/SVq+SoVySWeSjDEYAwePHMvyGAWAW4BQa22YtTYWmA7cc5nyjwDTsrJBSnzSGg/0s9Y2BPoDo621EcBmoIWrzN3AQmvtTiDCGFPfNf1J4Kusatjhg/vx8vKmdLnySdMqVq7Ggb1hacoe2BtGxSpVL5arUo3w06c4GxGOw+Eg9J/tRJwJp2fXe3nsvg6M+XgYMTHRKep4qH1L7m1zG2M//ZCHejyZVWFJJoXuO0D1yhWSnlevXIFTZ8JTJDOXsmHLdooWDiEkuGDStE8nTKVr5/YULVwoK5p7VdzV16+kao2alK1QkTWrVuJwOFj96wp8ff2oWLnqFZfNDmePH8Z4eRFc4uLQxEKlKxKezrf/J/f+Q2CREiz6/C2+fflR5g8fwOnD+1KUWf/TZL59+VHmfvAKR3fmrqGfWbXNz5w+xeGDByhXsXLStN+WLcHH14ebmzZzbxBukJltDnD64B6+fflRZg3qxaa500hwOACw6RW2ljOH068nJ2Vmnxa67yDVKpV3fjGXWL5iefbsS/vl3OLfVlM4JJhGN178grl7l07MX76KqOgYjp88xW9//kWzWxq4N6AMcGd/v+fBh/l1ySKio6P59+QJNqz5nYaNc+eQxkRZtc3B+TlXpULZFNPe/WwcDTs8TKcn+lGscCGaN77JPYHIlZQGkm+oQ65paRhjAoD2wKxkky2wyHUyopc7GpSnr/FJzRgTCNwKzEj2Bsvn+vsd0BVYDjwMjHZN/xJ40hjzkmv+LVnVvqioKAICU16SVCAwkKgLkemUvUBAgcAU5QCiLlwgLi6O+Ph4fl+xlA9GfYm3jw+DX3+J6V9N4PHefZOW+X7BCqKjolgyfw7FS5bKoqgks6KiogksEJD0PPH/yKioFAlNasdO/suQEV/w6jMXk9itO0PZuO0fXn/uKY6fPJV1jc4kd/X1oOCQy76Ot7c3rdt34MN3/kdsbCy+Pj4MGDyM/P7+1x6EG8TFROHnH5Bimp9/AHHRUWnKRp45xdGdW2j77EBK1ajH9mWzWTpmMF3eHou3jy+NujxJSKmyeHn7Erb+V5aMHsw9/xtBULHc8d7Oim0eHx/Ph+8MpE37jpQtXyGpzNfjRzH445HuD8INMrPNS1atw72DRhFYuDhnjh5gxRfD8PL25sb2DxFSsgz5CwazddEsare9l6M7t3Bs91ZKVq+bXaFkWGb2aReioihYIOX6CQwMIDIq7fr5edEKOt/RIsUBc6MbazNz7mIa390NR0IC97RrRZvbsv/6Lnf29zr1G7Jg9k88eGcLEhwO2tzVKVddp5ierNrmPy5YyrZdobzb/9kU0wc935s3n+vJpu27WLd5K3657Ezv9cqVjCRPSMZba8cnL5LOYul+L4PzpMLvqYa53WatPWKMKQ4sNsb8Y6399RLLZ4jO+KTkBYQnjjV0PWq65s0G7jLGFAYaAonjBWbhHLvYCdhgrU1z9GiM6WWMWW+MWT9+/PjUszPM39+fqMjzKaZdiIzEP6BAOmUDiIqMTFbOuZx/QAB+fs5c7u4HulK4aFGCQ0K4r2s31q/5PU09+f396XDv/Xw85K1cdd1DXjJnyUoadXyURh0fpfeAwfj75yfywsUdfmSk8/8ClzlYPx0ewdOvvsvD97SnY+vmACQkJDD4s/G83vepXHEzg+Tc1devZOO6tUwa/TlDPx/Hz8tXM3TkeEYMG8ye3TuvMQL38M3nT2yqD/e46Av45k+7rb19/ShRpRZl6jTC28eXOnd0Ieb8OSKOOb9sK1axOr75A/D29aVq0zaUqFyTQ1vXZ0scGeHubZ6QkMDwwQPx9fXhmZdeS5o+dcI4Wt3ZIemaxtwmM9u8YLGSFCxaEuPlReHSFajf8WH2/eXcj3t5+9Cmz/84uHU901/twdYlP1KhYTMKhOT8NU3Xsk8L8Pfn/IWU6+d8ZFSaskdP/Mv6zdvofEfLpGkJCQn0GvAubZs3Yf3cafz+w2TOnj/P8PHfuDG6jHFXf09ISGDQS89xa4tW/LD4N6bNXcL5c2eZNGZE1gaQSdmxzZeuWssnX0xh7PsDKRQclKYeb29vGtatyfGTp/hu9kJ3hJXnWWvHW2sbJXukPsg9BCQ//VYGOEL6HibVMDdr7RHX3xPAj7jh5IISn2SstWeBvcaYBwGMUz3XvPPAnzjvTDHHWutwTY8GFgJjgEmXqDepY/TqdfVn6kqXLY/D4eDwwYvjkfeG7kr3bmvlKlYiLHRXsnK7CSlchKDgEAoGBVG0eIl00/B025+QQEx0NKdOnrjqtsvV69S2RdKFuuOGDqRKhXLs3LMvaf4/YfsoUijkkmd7Is6d5+nX3qXVrTfTu9sDSdPPX4hi2649vDx4OLc/8B+6PvsqAK27Ps2GLduzNKYrcVdfv5Kw3buoU68BVWvUwsvLi2o1a1O9Vh02rfvTLXFcq6ASpbEJjhQXpJ8+tJeQG8qnKVu4TAUwGX1X4yxrL/XFW/Zz5za31vLZ0MGcOX2aN/7vA3x8Lg5u2LRhHb/MnE63znfSrfOd/HviOEMHvc6MKV9lWWyZkZltnoYxKTZp4TIV6fDyUB4dPo07/zuYc/8eo2iFalnQ6sy5ln1alQpl2RW2D5ss0F1h+6icamjT7MUrqF+7BmVvKJk0LeLceY6d+JdH77kLPz9fQoILct+drfntzyy7NPeS3NXfz509y8kTx7n7/q74+vkRFBzCHR06s3512i8yc1JWb/Pf/vyLtz4ew8j/e4NqlS7/XnE4HLrGJ/usA6oaYyoaY/xwJjezUxcyxgTjvJzk52TTChhjCib+D7QDtl5rg/J64hNgjDmU7PESzlvqPWWM2QxsI+VFWN8B3V1/k5uKaxxiVjY2v78/t7ZoxZQvxxIdFcX2LZtYs2olre/skKZs6/YdWTR3Ngf2hnHu7FmmT55A27s6Jc1v2+Fufpn1PeFnTnPu7Fl+/n4at9zqPBOwcd0a9uz6B4fDwYXI83wx8hMCCxakbPnr50JpAG8vg5+PF17G4OX639srEweGuVTnO1owa/5SQvcdJOLcecZNmcG9d7ZKt+z5yAv0eu1dGtSuwUtPp7x5QcECASz//ktmjR/OrPHDGfPemwDMGPMhdWvm7DUu7uzrjvh4YmNiSEhIICHBQWxMDI74eACq1azFti0bk87w7Nn1D9s2b0oxnj4n+ebLT/kGTdn4y1TiYqI5HrqdA5vXprlbG0DlW1pxMmwnR3ZsIiHBwfalP5M/MIjgkmWJuXCew9s2EB8XS4LDwZ61yzm+eyula+Wece7u3OajPnqfg/v28tawT8iXL3+KZd/7bDSjvv6OzydN5fNJUylctBjPvfIGnbo8lOUxZkRmtvmhreuJOnsGgPBjB9k8bzrl6l0ctnX60F7i42KJj43m70U/EBVxhqpN22ZbLBmVmX3azfVq4+XlxZQf5hIbG8fUn5w3A2jcIOUQvtmLVqSpo1BwEGVKlWD6LwuJdzg4ez6SnxctT3GtSXZxV38PDgmhRKnSzP1xJo74eM6fO8fS+XOoWOVighsXG0tsTAzgHP4ZGxOTIonICe7c5ms2/s1r733Kp2+/wo01Uu67T50JZ96yVURGReFwOFi1biPzlq9K018ka1hr44HncJ4g2AF8b63dZozpY4zpk6zofcAia23ysZ4lgFWu4/E/gbnW2mu+BaPJ6c7vCYwx/YFga+3ADBS3oSevfBH6pZw7G8Gn77/LxnVrCQoK5ok+/WjZrj0njh3jmR4PMuabGRQv6fyG68fpU5g59WtiYmK4rWVrnkt23//4+HjGffoRK5cswNcvH81bt+U/z/wXv3z5+G3ZEqZ8OYZ/T57AL18+qtWoxRN9+l31wWCVYs5vcOq/kfZuNVmpT5uq9GmTss1jl+5m7NLd2daGTe85P8TiD21za71fzZjNxOk/Eu36/YO3XuiTdNea3gMG07BuTXp1e4CfFi7nzQ8+xz9/PpIPtZ098TNuKFEsRZ2Hj52gXbc+bvkdH58ytQHIDX196oRxfDvpixR1P/rk03R7qjfg/J2fn7+fRvjp0wSFFKJTlwfp8kj3q253Yn8futw9/SzFb7oUCKLhfc7fdDl/+gQ/vvMs9701msDCzjsy7tv4B+t/mET0uXCKlK1Mk0eeodAN5Yk+F8HikW8TfuwQXl5eBJcsQ4O7u1O6lvsu6h7Qyvley+ltfuLYUZ584G58/fzwTtaPn3vlDVq1uyvNaz75wN3897X/XdPv+OTUNv9z5gT2rF1OfEwU+YNCqHxLK+p3fBgvb+cZrnWzJrLr94UkOByUqFKbJl17E1T8Bre0ES5uc3fs3zK6TwPYsTuMQcNHs2f/ISqVK83g/n2pWfXimZJN23bS89W3WTljIgUCUg6H2hG6l2GjJ7Jzzz7nXf/q1+F//32aIoVCMtXe3LSP27N7J198Npyw0N14e3tRt0Ejnn35NUIKFQacffzEsaMpXnvijNmUKJX5vpDY13PTNn/ipUH89fd2/JL9nlXDujUZN3Qgp8MjePGdD9m5Zx8J1nJDiWJ0u68jD17F7/i4tnmu/fa0/hvzsv2AftN7HXLt+rgUJT7XyBjzI1AZaG2t/TcDi1xT4nM9yqnEJzfIqsQnt3PHQcH1yt0HwdcLdyQ+16u8vs3z2v4N8u4+zp2Jz/VGiU9a12Pio7u6XSNr7X053QYREREREbm8vH6Nj4iIiIiI5AFKfERERERExOMp8REREREREY+nxEdERERERDyeEh8REREREfF4SnxERERERMTjKfERERERERGPp8RHREREREQ8nhIfERERERHxeEp8RERERETE4ynxERERERERj6fER0REREREPJ4SHxERERER8XhKfERERERExOMp8REREREREY+nxEdERERERDyeEh8REREREfF4PjndABERERERuXoP31E1p5twXdAZHxERERER8XhKfERERERExOMp8REREREREY+nxEdERERERDyeEh8REREREfF4SnxERERERMTjGWttTrchr9EKFxEREbn+mJxuwKUMXb47248vB7SqmmvXx6XojI+IiIiIiHg8/YBpDgg9eS6nm5CtqhQrCED8oW053JLs51OmNgD135iXwy3JXpve6wDA0OW7c7gl2W9AK+ePyOW1/p7Y1/Na3JB3Y0+MO699psHFz7W8Frs+z+V6pzM+IiIiIiLi8ZT4iIiIiIiIx1PiIyIiIiIiHk+Jj4iIiIiIeDwlPiIiIiIi4vGU+IiIiIiIiMdT4iMiIiIiIh5PiY+IiIiIiHg8JT4iIiIiIuLxlPiIiIiIiIjHU+IjIiIiIiIeT4mPiIiIiIh4PCU+IiIiIiLi8ZT4iIiIiIiIx1PiIyIiIiIiHk+Jj4iIiIiIeDwlPiIiIiIi4vGU+IiIiIiIiMdT4iMiIiIiIh5PiY+IiIiIiHg8JT4iIiIiIuLxlPiIiIiIiIjHU+IjIiIiIiIeT4mPiIiIiIh4PJ+cboBkzrmzEXz2/mD+WreGoOAQnuj9HC3btU+37I/fTWXm1K+JjYnmthat6dv/dXz9/AD4ZdZ3LJk3h31hobRoeycvvfl20nIH9oYxfMhbHD18CIAq1WvS54X+lKtYKcvjy6zJM39hwvQfiYmN5Y7mTRj0fG/8/HzTlNt38AgfjZ/Mpm07cSQkUKd6Fd547ikqli2dpuyTLw/iz01b2bxoBj7e3tkRhtt1bVKezjeVoWrJQBZsPsqgWVtyuknXLCbyHKu+/owjOzaSLzCIhvc+TuVbWqYpF7ZuJRvnfEtUxBm8fX0pXbshTbr2wc8/AIDwowdZPX0Mp/aHkr9gMDd3eZLyDW7N5miuLKN9G2BH6F4GfTSKsAOHqFSuDO/270vNKhUBmLdsFaMmT+ffM+H4+frQ7OabeLNfTwILONfH1J/m8fPC5ezau58OrZrz3mv9si3GS3FX7AAHjxzj/ZETWLdlG36+vtzXvg39ez+Woo79h45wb88XaXd7U4a98UJWhnZZ7oo7NjaOj7/8hgUrficmJpa7Wjfj9b5P4evj/Mhv1PHRFHXFxMbycOc7ebPf01kb4FXI6GfekvlzmD1jOkcOHSSgQAFa3nEnj/fqi7fP9XGY4+44Dx88QN/HH+a2lm14ZdDg7AojU9zV39/5ZCy/LPk1qWy8Ix5fHx/Wzfk2adq8ZasY8813HD3xL0ULhfB/r/aj4Y21sjZAyZVy1RkfY4zDGLPJGLPVGDPDGBNwDXWtMMY0cmf7coPRw4fh4+vL1NmLeGXQEEYNf5/9YXvSlNuwdjUzp0zmvU9HM3HGLxw7cpgpE8YlzS9ctBgPP/4U7Tp2TrNs4aLFeGPIML6bv4xpc5fQpNntDHv7jSyN62qsWreRCdN+YOJHb7No6lgOHT3OyMnT0y17NjKSVk1vZs5Xn/PrzInUrVGFfgOHpik3Z8lKHI6ErG56ljt5Lpovl4fy04ZDOd0Ut1k9bQxePr48/MEUWvynP6u/Hc2ZI/vTlCteuRYdX/mA7p9+zwODv8Q6HPw1+xsAEhwOlo4ZTNm6N/Pox9O4tdtz/DppOBHHD2d3OJeVmb4dGxdHv4Hv06ltC1b/9A33tGtFv4HvExsXB0CDOjWYMuI91s6ewoIpY3AkOBgx6eIBQfEihend7QG6tG+TLbFdiTtjj42L4+lX3+GWBnVZOWMiy777grvb3p6mniEjvqBO9SpZGteVuDPuL6f/wLade/jpy0+ZO3kkO3aHMW7KzKTl18/9Nunx68yJ5PPz484WuS/5h4x/5sVER9Pr+ZeZNncJH4//ik3r1zFr2pQcaPHVcXecYz4eRrUauffA3p39/a0X+6To0x1aNU/Rn/9Yv4mPv/iaIa88x5+/TGXyJ0MoU6pEtsQpuU+uSnyAKGttfWttHSAW6JN8pjEm13/9bozJsq+XoqOi+GPlMnr07IN/QAC169WncbPbWbZwXpqyS+fPoV2neyhfqTIFg4J4+ImeLJk/J2n+bS1a0/T2lhQMCk6zbGDBgpQodQPGGLAWLy8vjh46mFVhXbWfF62gy11tqFKhHMEFA+nT/UF+Wrg83bI31qjK/R3aEhJUEF8fHx67/272HjxMeMS5pDLnzkcy+uvveblXj+wKIcss23ac5TuOE3EhLqeb4hZxMdHs3/gHN3Xujm9+f0pUqU25eo3Zszbt9g4sXIz8gRf7tfHy4uyJIwBEHDvIhYjT1G5zL15e3txQox7FK9diz9pl2RZLRmSmb6/bvA2HI4HH7u+En58v3bt0xAJrN/4NQKniRSkUHJRU3tvLiwOHjyU9v6N5E9o0a0xwUMEsjSmj3Bn7TwuXU6xIYZ54sDMB/vnJ5+dH9coVUtQxb9kqCgYWoMlNdbM4sstzZ9wrVq+ne5eOhAQVpHBIMN3u68iPC5amW9eiX1dTpFAwDevmvoPkzHzmdbzvAerUa4Cvry9FixWnVbv27Ph7cw60OvPcHefKJQspEFiQeg1vzq4QMs2d/T25C1HRLP5tNfe0a5U0bdTk73imx0PUq1UdLy8vShQrQoliRbIqNMnlclvik9xvQBVjTEtjzHJjzLfA38aY/MaYScaYv40xG40xrQCMMf7GmOnGmC3GmO8A/8tVbowpaIzZa4zxdT0PMsbsM8b4GmMqG2MWGGM2GGN+M8bUcJW52xiz1vW6S4wxJVzT3zbGjDfGLAK+zqoVcvjgfry8vCldrnzStIqVq3Fgb1iasgf2hlGxStWL5apUI/z0Kc5GhGf49R5q35J729zG2E8/5KEeT15T27NC6L4DKQ5iqleuwKkz4SmSmUvZsGU7RQuHEBJ88WDv0wlT6dq5PUULF8qK5so1OHv8MMbLi+ASF4cmFipdkfB0zvgAHA/dxpQXHmLKCw+yf+Mf1G5zDwA2vcLWcuZw+vXklMz07dB9B6lWqbzzi4rE8hXLs2ffxS8rNvy9g8adu3NLp24s/m0NPe7vlKXtvxbujH3Ljl2ULlmc3gMGc9t9j/PESwPZFXZxW5+PvMDIr6bxSp8nsiyejHJn3NZarE3W263l2MlTnDsfmaaunxctp/MdLVLUlVtk5jMvta2bN+bK4dnpcWecFyLPM+XLcfR87oWsaKrbuHsfl2jxb6spHBJMI9cwNofDwdZdezgdcZb2PZ6lddeeDBnxBdExMe4PSq4LuTLxcZ01uQtITOdvAd601tYC+gJYa+sCjwCTjTH5gWeAC9baG4H/Axpe7jWsteeAFUBH16SHgVnW2jhgPNDPWtsQ6A+MdpVZBTSx1jYApgOvJquyIXCPtTbl4Gk3ioqKIiAwMMW0AoGBRF1I+2EWFXWBgAKBKcoBRF24kOHX+37BCmYsWEGfF1+lUrUaV9nqrBMVFZ10nQKQ9H9kVNRllzt28l+GjPiCV5+5mMxt3RnKxm3/0O2+DlnTWLkmcTFRSdfoJPLzDyAuOv1tXaJKbbp/+j0Pvf8Vde7oQmAR57CGkJJlyF8wmK2LZpHgiOfw9r84tnsr8XG560MwM337QlQUBQukXDeBgQEpyjasW5O1s6ewbPoXPPnQPZQuUTyLWn7t3Bn78ZOnmL98Fd3v68jy77/k9sYN6TdoaNIQmc8nTaPLXW0oVbxoVoWTYe6Mu9ktN/HND3M4HR7BydNnmPKj88xB6oO9I8dPsn7L9hTfjucmmfnMS27x3Nns/mcHXR65Ps7euzPOb74YS7tOnSlWomSWtNVd3L2PS/TzohUpEvlTZyKIj49n8a+r+ebTIcwa/zE7QlMO/ZS8Jbdd9edvjNnk+v83YAJwK/CntXava3oz4HMAa+0/xpj9QDXgdmCEa/oWY0xGrub+Emfy8hPwJPC0MSbQ9Zozkn27kM/1twzwnTGmFOAH7E1W12xrbbpHYcaYXkAvgHHjxtH6vkcy0LS0/P39iYo8n2LahchI/AMKpFM2gKjIyGTlnMv5B2Tusqn8/v50uPd+Hu10B2OnziCkUOGraLl7zFmykrc/cV6n1LBuTfz98xN54eIqj4x0/l/A/9In+06HR/D0q+/y8D3t6di6OQAJCQkM/mw8r/d96rq9mYGn883nT2yqD7m46Av45r/siV0KFCpK6doNWfHlB9zz5md4efvQps//WPPdOP5eNIsi5atQoWEzvH3Sv6A2u1xL3w7w9+f8hZTr5nxkVLplSxQrQrObG9B/yHBmjhvuzhCuWlbGni+fHw3q1KR545sAePKhexg3dSZhBw5hLaz+awszx32UJXFdSVbG3bvb/Zw7H8n9vV7Gz8+XBzq0ZUfoXgqHpBzaPHvxCm6qUyPXXu+Qmc+8RKt/XcFXY0fyf5+OJjgkJItb6B7uinPP7p1sWv8nIyZNzcrmXpXs2McdPfEv6zdv452Xnkmali+f84ZOj97bgWJFnMcvjz/QmXFTZ/L8U93cEJlcb3Jb4hNlra2ffIIr+Uj+tcflzsenO5LlkoWt/d0YU8EY0wLwttZuNcYEAeGp2+HyOfCxtXa2MaYl8HayeZf8asZaOx7nWSQAG3ryykOx0lO6bHkcDgeHDx6gdNlyAOwN3ZXu6fxyFSsRFrqL5m3ucJXbTUjhIgQFh2T6dW1CAjHR0Zw6eSJHE59ObVvQqW2LpOev/N8n7Nyzj/YtbwPgn7B9FCmUcvhachHnzvP0a+/S6tab6d3tgaTp5y9EsW3XHl4e7DwQTEhw3tygdden+WRQf935JRcIKlEam+Ag4vjhpOFupw/tJeSG8ldYEmyCg3MnjyY9L1ymIh1evnhjizkf9KdKk5y9sP9a+naVCmWZPONnrLVJ33LuCtvHI/ekf7fHeEcCB48ez4Iork5Wxl6tUnk2bv0n3dddt3krR46foO0jvQHntQEJCQns6f1ytiSFWRl3/nz5+N9/n+Z//3Xepe37OYuoXbUS3qm+2Jm9aAU9H+mSJfG5Q2Y+8wDWr/mDER8M4e0PPqVC5Zy9WUVmuCvOvzdu4PixIzzhGsoaHXWBBEcC/90XxoiJOZsMZcc+bvbiFdSvXYOyN1w82xVcMJCSxYqQC0dySg7JlUPdruBXoBuAMaYaUA7YmWp6HeDGDNb3NTANmARgrT0L7DXGPOiqyxhj6rnKBgOJt396/JojyaT8/v7c2qIVU74cS3RUFNu3bGLNqpW0vjPt8KzW7TuyaO5sDuwN49zZs0yfPIG2d10c1++Ijyc2JoaEhAQSEhzExsTgiI8HYOO6NezZ9Q8Oh4MLkef5YuQnBBYsSNnyFdO8Tk7qfEcLZs1fSui+g0ScO8+4KTO49870h2ycj7xAr9fepUHtGrz0dMrhDwULBLD8+y+ZNX44s8YPZ8x7bwIwY8yH1K1ZNb3qcj1vL4OfjxdexuDl+t/b6/rd8/vmy0/5Bk3Z+MtU4mKiOR66nQOb11K5cdrtvWftcs6fPoG1lvOnTrDh528oVaNe0vzTh/YSHxdLfGw0fy/6gaiIM1Rt2jY7w7mizPTtm+vVxsvLiyk/zCU2No6pPzmHNTVu4LxYf86SlRw5fhJrLUeOn2DExKk0aXDxQv54h4OY2FjXviCBmNhY4h2OrA/yEtwZ+91tW7Blxy5Wb9iMw+Hg61lzKBRUkErlyvBgx3bM/2Z00vu+693tuL3xTYwfNijbYk3OnXEfP3mKE/+exlrL5u07GTdlBn2feDhFHRu3/cOJU6dz7d3cIHOfeZs3rOOjdwfy5pAPqF6rTg609uq5K872nbvw5Xc/8fmkqXw+aSp33XM/N996G4OHj8yuUDLMnf090exFK9Kt4947WzP1p3mcOhNOxLnzfDNrDi2aXPZqCPFgue2MT0aMBsYaY/4G4oEnrLUxxpgxwCTXELdNwJ+JCxhjvgTGWmvXp1PfVGAIzuQnUTdgjDHmf4Avzut5NuM8wzPDGHMYWANkeybw7MsD+PT9d3n07jsICgqm78uvU75SZU4cO8YzPR5kzDczKF6yJI2a3MoDj/bg9f/2ISYmhttatqb7U72T6pk+eQLfTvoi6fnyhfN59Mmn6fZUb86fO8/YTz7k35Mn8MuXj2o1avHu8M/xy5cvvSblmOa33MR/ut7Lf14eRLTrdwCee/zih3vvAYNpWLcmvbo9wJJVa9m6M5Q9+w+muHPM7ImfcUOJYhRLdkOD2Fjn+P8ihUOu26FvT7eqQp82F5O2Tg1KM3bpbsYu3Z2Drbo2TR95llVff8b0V7qRr0AQTR99lkI3lOf86RP8+M6z3PfWaAILFyf86EHW//gVsRfO4xcQSJk6jWh478XvKfasXc6u3xeS4HBQokpt7nx+MN6+OTvULbXM9G0/X18+f3cAg4aP5pMvp1CpXGk+f3cAfq6Y9uw/xMdffMPZ85EEBRageeOGvPhU96S6xk2Zweivv096/suSlTz72EP0fTzlgXJ2cWfsFcuWZujrL/DOp+M4HR5BraqVGDnkded8X/DPf3GflnjXt9TDwbKLO+M+ePQYrw8dwenwCEoWK8qLPbtzW6P6KV7v54XLadusCQUCLj9cNKdl9DNv2ldfEhl5nrdeeT5p2do3NuDd4SNysPUZ54448+fPT/78+ZOm+/v74+uXj+BCue+GPe7s7wCbtu3k+L+n0k3k+/R4kDNnz9Lx8efw8/OjfctbU4z6kLzFpLjzSx5kjHkA500JsusqyKse6na9qlLMeeo6/tC2HG5J9vMpUxuA+m+kvS2pJ9v0nvObyqHLr99E62oNaOVMOPNaf0/s63ktbsi7sSfGndc+0+Di51pei12f55e93CJHDV2+O9sP6Ae0qppr18elXI9nfNzGGPM5zrvH6VZeIiIiIiIe7Hq8xsdtrLX9rLVVrLW7crotIiIiIiKexBjT3hiz0xgTaowZkM78lsaYCGPMJtdjUEaXvRp5+oyPiIiIiIi4nzHGGxgF3AEcAtYZY2Zba7enKvqbtbbTVS6bKXn6jI+IiIiIiGSJW4BQa22YtTYW583C7smGZS9JiY+IiIiIiLhbaeBgsueHXNNSa2qM2WyMmW+MqZ3JZTNFiY+IiIiIiGSKMaaXMWZ9skev1EXSWSz13ef+Aspba+sBnwM/ZWLZTNM1PiIiIiIikinW2vHA+MsUOQSUTfa8DHAkVR1nk/0/zxgz2hhTNCPLXg2d8REREREREXdbB1Q1xlQ0xvgBDwOzkxcwxpQ0xhjX/7fgzE1OZWTZq6EzPiIiIiIi4lbW2nhjzHPAQsAbmGit3WaM6eOaPxZ4AHjGGBMPRAEPW2stkO6y19omJT4iIiIiIuJ21tp5wLxU08Ym+38kMDKjy14rDXUTERERERGPp8RHREREREQ8nhIfERERERHxeEp8RERERETE4ynxERERERERj6fER0REREREPJ4SHxERERER8XhKfERERERExOMp8REREREREY+nxEdERERERDyeEh8REREREfF4SnxERERERMTjKfERERERERGPp8RHREREREQ8nhIfERERERHxeEp8RERERETE4xlrbU63Ia/RChcRERG5/picbsClDF2+O9uPLwe0qppr18el6IyPiIiIiIh4PJ+cbkBeFHryXE43IVtVKVYQyHtxw8XYhy7fncMtyV4DWlUFoP4b83K4Jdlv03sdgLzX3xP7evyhbTnckuznU6Y2kHe3eV6LG/Ju7Hqfy/VOZ3xERERERMTjKfERERERERGPp8RHREREREQ8nhIfERERERHxeEp8RERERETE4ynxERERERERj6fER0REREREPJ4SHxERERER8XhKfERERERExOMp8REREREREY+nxEdERERERDyeEh8REREREfF4SnxERERERMTjKfERERERERGPp8RHREREREQ8nhIfERERERHxeEp8RERERETE4ynxERERERERj6fER0REREREPJ4SHxERERER8XhKfERERERExOMp8REREREREY+nxEdERERERDyeEh8REREREfF4PlcqYIw5b60NvJYXMca0BGKttX+4nn8FzLHWzryWepPV/yXwsbV2+2XK3AvsulyZSyzXGahlrR16ba3MmHNnI/js/cH8tW4NQcEhPNH7OVq2a59u2R+/m8rMqV8TGxPNbS1a07f/6/j6+WWonk3r/2TMx8M4efwY1WrV4aU336Z4yVIADHr5v2zbsjGpbHxcHKXLlWf0198B8OQDdxN++jRe3s68uWadGxnyyagsWR+J3LVefpn1HUvmzWFfWCgt2t7JS2++nWLZ35YuZsrEcZw6cYKiJUrweK++NL29ZZbGllkxkedY9fVnHNmxkXyBQTS893Eq39IyTbmwdSvZOOdboiLO4O3rS+naDWnStQ9+/gEAhB89yOrpYzi1P5T8BYO5ucuTlG9wazZH415dm5Sn801lqFoykAWbjzJo1pacbtI1y2jfXzJ/DrNnTOfIoYMEFChAyzvu5PFeffH2ce7m77+jeYrysTExdLjvAZ558dVsiSMzJs/8hQnTfyQmNpY7mjdh0PO98fPzTbfsjtC9DPpoFGEHDlGpXBne7d+XmlUqAjBv2SpGTZ7Ov2fC8fP1odnNN/Fmv54EFghIUcf+Q0e4t+eLtLu9KcPeeCGrw8s0d/WB60FGY90XFsqEkZ8SunMHZyMimLtqfYr5V9rX5zbuivt6ep9fTmb2AW99PIb1m7ex//BRBvfvy33tW2dza+V6kl17w5bAeeCPa63IGGMAY61NSJxmre2ZgUXvBeYAGU58jDE+1trZwOxMLhOf0fKpjR4+DB9fX6bOXkTY7l28/erzVKxSlfKVKqcot2HtamZOmcx7n42hcNFi/N8b/ZkyYRxPPtPvivVEhIfzf2++wn9fG0jj25rzzZdjGTrodT4e/xUA7w4fkeK1BjzXixsb3pxi2qBhH9Pg5sZXG2amuWu9FC5ajIcff4q//lxNTExMimX/PXmCjwYPZOD7w2nY5FbWrf6doQNfY+LMXwgpVDjbYr2S1dPG4OXjy8MfTOH0oTAWj3yHwmUqUuiG8inKFa9ci46vfED+wGDioqP4Y+pI/pr9DU269ibB4WDpmMFUv/0u7nx+MMd2bWXp6HfpfEN5gkuUzqHIrt3Jc9F8uTyUptWKkt/HO6eb4xYZ7fsx0dH0ev5lqteqQ0T4Gd597SUCC07hoR5PADBr8W9JZaOjoujWuR3NW7XNzlAyZNW6jUyY9gMTh79DsSKFef6tYYycPJ2Xnu6RpmxsXBz9Br5Pj/vv5pHO7fl+ziL6DXyfeV+Pws/XlwZ1ajBlxHsUCg4iMiqKdz4Zy4hJ3/LGcyk/MoaM+II61atkV4iZ5q4+cD3IaKw+Pj40a92WDvc9wJDX+6ep53L7+tzIXXFfL+/zy8nMPgCgeqUK3NXyNj7+4ptsbmnu8kCdkjndhOvCVQ11M8ZUNsYsMMZsMMb8Zoyp4Zp+tzFmrTFmozFmiTGmhDGmAtAHeNEYs8kYk/h1xO3GmD+MMWHGmAeS1f2KMWadMWaLMeYd17QKxpgdxpjRwF9A2VTtWWGMaeT6/7wx5v+MMZuNMWtcbbgV6Ax86GpD5cvE8JUx5mNjzHJgmDHmCWPMSNe88saYpa62LTXGlEtvmatZp+DcSf2xchk9evbBPyCA2vXq07jZ7SxbOC9N2aXz59Cu0z2Ur1SZgkFBPPxET5bMn5Ohev5YuYxyFSvTvHVb/PLlo9t/erE3dDcH9+9L8zrHjx5h25ZNtL6zw9WGdc3ctV4AbmvRmqa3t6RgUHCaZf89cYICgQVp1PQ2jDHccmsz8vn7c/TwoSyNLzPiYqLZv/EPburcHd/8/pSoUpty9RqzZ+3yNGUDCxcjf+DFOI2XF2dPHAEg4thBLkScpnabe/Hy8uaGGvUoXrkWe9Yuy7ZYssKybcdZvuM4ERficropbpGZvt/xvgeoU68Bvr6+FC1WnFbt2rPj783p1rtqxVKCQwpTu16DrA4h035etIIud7WhSoVyBBcMpE/3B/lpYdr+DbBu8zYcjgQeu78Tfn6+dO/SEQus3fg3AKWKF6VQcFBSeW8vLw4cPpaijnnLVlEwsABNbqqbZTFdi6zqA7lRZmItU64Cd3a6l/IVK6dT0+X39bmNO+NOLje/zy8nM/sAgEfvvYsmN914yTNCIsld7TU+44F+1tqGQH9gtGv6KqCJtbYBMB141Vq7DxgLfGKtrW+tTfw6ohTQDOgEDAUwxrQDqgK3APWBhsaY213lqwNfW2sbWGv3X6ZtBYA11tp6wK/A064hdrOBV1xt2HOZGACqAW2ttS+nqnukqw03AlOBERlYJsMOH9yPl5c3pctd/Oa+YuVqHNgblqbsgb1hVKxS9WK5KtUIP32KsxHhV6znwN4wKiVbNr+/P6VKl+bA3j1pXmfpgrnUvrE+JW9IeRbgo3cH8kintvzvxb6E7d51tSFniLvWy5VUrVGTshUqsmbVShwOB6t/XYGvrx8VK1e94rLZ5ezxwxgvrxRnZQqVrkj4kfTfEsdDtzHlhYeY8sKD7N/4B7Xb3AOATa+wtZw5fLm3lmS3zPT91LZu3ki5ipXSnbd0/hzatO+A8wR67hK67wDVK1dIel69cgVOnQknPOJcOmUPUq1S+RRxVK9Ynj37DiY93/D3Dhp37s4tnbqx+Lc19Li/U9K885EXGPnVNF7p80SWxOIOWdUHcqNrifV6llVx5+b3+eVkZh8gklmZHupmjAkEbgVmJHsz5XP9LQN8Z4wpBfgBey9T1U+u4WrbjTElXNPauR6JF5gE4kyEDgD7rbVrMtDEWJxD2gA2AHdkMgaAGdZaRzp1NwW6uP7/BvggA8tkWFRUFAGBKS+nKhAYSNSFyHTKXiCgQGCKcgBRFy5csZ6oqAsEhxRKMT8gMJALFy6keZ1lC+bS9fGnUkx7ZdBgKlevgbUwe8Y0Br7cj3FTZxJYsGAmos04d62XoOCQy76Ot7c3rdt34MN3/kdsbCy+Pj4MGDyM/P7+1x6Em8TFRCVdo5PIzz+AuOiodMuXqFKb7p9+T+SZf9m1aiGBRZxvtZCSZchfMJiti2ZRu+29HN25hWO7t1Kyeu781juvykzfT27x3Nns/mcH/31tYJp5J44dY+umv3h+QNp5uUFUVHSKa3AS/4+MiiIkOOU+5kJUFAVTXa8TGBhAZNTF90PDujVZO3sKx0+eYua8xZQuUTxp3ueTptHlrjaUKl40K0Jxi6zoA7nV1cZ6vcuKuHP7+/xyMrMPEMmsqznj4wWEu86cJD5quuZ9Doy01tYFegP5L1NP8kG3Jtnf95PVW8VaO8E1L6N7gDhrbeIX2g7ST+4uF0NmXiv5F+eXXMYY08sYs94Ys378+PGXrMzf35+oyPMppl2IjMQ/oEA6ZQOIioxMVs65nH9AwBXr8fcP4EJkZJr5AQEpDyC2bd7EmdOnaNayTYrptW6sT758+cmfPz8P9XiSwMBAtm3eSFZx13q5ko3r1jJp9OcM/XwcPy9fzdCR4xkxbDB7du+8xgjcxzefP7FRKZOcuOgL+Oa/fHJWoFBRStduyIovnbm6l7cPbfr8j4Nb1zP91R5sXfIjFRo2o0BI7j0AzIsy0/cTrf51BV+NHcm7H40gOCQkzfxlC+ZQK52zuDllzpKVNOr4KI06PkrvAYPx989P5IWLfTwy0vl/gXS+gAjw9+f8hZTvh/ORUemWLVGsCM1ubkD/IcMB500RVv+1hcceuNud4bhdVvSB3OpqYvUEWRF3bnufX8617ANEMivTiY+19iyw1xjzIDhvNmCMqeeaHQwcdv3/eLLFzgEZSdMXAv9xnZHBGFPaGFP8CstkVFIbrhDD5fwBPOz6vxvOoX1XZK0db61tZK1t1KtXr0uWK122PA6Hg8MHDyRN2xu6K92hCuUqViIsdFeycrsJKVyEoOCQK9ZTrmIl9u65uGx0VBTHDh+iXKoxw0sXzOHW21tdOWkwBpv+4Cm3cNd6uZKw3buoU68BVWvUwsvLi2o1a1O9Vh02rfvTLXG4Q1CJ0tgEBxHHDydNO31oLyGpbmyQHpvg4NzJo0nPC5epSIeXh/Lo8Gnc+d/BnPv3GEUrVMuSdsvVyUzfB1i/5g9GfDCEQcM+pkLl9C/WX7pgHm3ad8yS9l6NTm1bsH7ut6yf+y3jhg6kSoVy7NyzL2n+P2H7KFIoJN1veqtUKMuusH1c/K4LdoXto3KFsmnKAsQ7Ejh49DgA6zZv5cjxE7R9pDe3P/AfJn0/m8W/reGB3lc9WjlLZEUfyK0yG6unyIq4c9v7/HKuZR8gklkZSXwCjDGHkj1ewnnQ/5QxZjOwDbjHVfZtnMPHfgP+TVbHL8B9qW5ukIa1dhHwLbDaGPM3MJOMJUwZMR14xXXjhcqXieFy/gs8aYzZAvQAnndT2wDntTa3tmjFlC/HEh0VxfYtm1izamW6NxZo3b4ji+bO5sDeMM6dPcv0yRNoe1enDNVz6+2t2B+2h99XLCU2JoZvJ31BhcpVKVu+QlL9MTHRrFq+hLYdUn4beuLYMbZv2URcXByxMTHM+vZrzkaEU6tuRvLGnF0vAI74eGJjYkhISCAhwUFsTAyOeOdN+KrVrMW2LRuTzvDs2fUP2zZvSnHNUE7zzZef8g2asvGXqcTFRHM8dDsHNq+lcuNWacruWbuc86dPYK3l/KkTbPj5G0rVuLidTh/aS3xcLPGx0fy96AeiIs5Qten1dfef1Ly9DH4+XngZg5frf2+v62t8e3KZ6fubN6zjo3cH8uaQD6heq0669W3/ezOn/j1Bs9a5dzt3vqMFs+YvJXTfQSLOnWfclBnce2fa/g1wc73aeHl5MeWHucTGxjH1J+fF4I0bOIdszlmykiPHT2Kt5cjxE4yYOJUmrnkPdmzH/G9GM2v8cGaNH07Xu9txe+ObGD9sUPYEmkHu7gO5WWZitdYSGxNDfJzzRiaxMTHExcYmzb/cvj63cWfccH28zy8nM/sAcN7dMSY2Fmst8Q4HMbGxJCQkXLK85G0m+Tdlki1s6MlLX6B37mwEn77/LhvXrSUoKJgn+vSjZbv2nDh2jGd6PMiYb2ZQvKTzloU/Tp/CzKlfExMTw20tW/Ncqt/xSa+eRBvXrWXsJx9w4tgxqteqzYtvvk2JUjckzV+xeAFfjR3JpJm/pLgwcn/YHj54502OHj6En18+KlWtxpPP9KNqjVqXjKlKMWfuerm4r8Rd62XqhHF8O+mLFHU/+uTTdHuqN+D87Yefv59G+OnTBIUUolOXB+nySPerbndi7EOX777qOlJL8Ts+BYJoeJ/zd3zOnz7Bj+88y31vjSawcHE2/PQ1oWuWEnvhPH4BgZSp04iG9z5O/kDnXa7WzZrIrt8XkuBwUKJKbZp07U1Q8Ruu8OoZM6CVM1ms/0bauxJlpT5tqtKnTcpEdezS3Yxd6r71fyWb3nMerFxLf08uo31/QL/ebNuyCT9XXweofWODFLen//yD/yMmJpr+Awe7pW3JJfb1+EPbrrmur2bMZuL0H4l2/YbHWy/0SbpjU+8Bg2lYtya9ujlvBrpjdxiDho9mz/5DVCpXmsH9+1KzqvOb8s8mTOXnRcs5ez6SoMACNG/ckBef6p7uN8ejJk/nwOFjV/U7Pj5lagPu2+apubMPuJM79u2pZTTW40eP8J8HO6dYtnjJUkya+Qtw5X39tXJ37O6KG66f9/nlZGYf8MRLA1m3OWV7Jg1/l1vquzf5d73Pc+03aaEnz2X7AX2VYgVz7fq4FCU+2e+yiY8nyooPx+tFViQ+14OcSnxyA3cnPteL7Dogyo2yOvHJrbRvz3ux632uxCe56zHxudrbWYuIiIiIiFw3lPiIiIiIiIjHU+IjIiIiIiIeT4mPiIiIiIh4PCU+IiIiIiLi8ZT4iIiIiIiIx1PiIyIiIiIiHk+Jj4iIiIiIeDwlPiIiIiIi4vGU+IiIiIiIiMdT4iMiIiIiIh5PiY+IiIiIiHg8JT4iIiIiIuJ2xpj2xpidxphQY8yAdOZ3M8ZscT3+MMbUSzZvnzHmb2PMJmPMene0x8cdlYiIiIiIiCQyxngDo4A7gEPAOmPMbGvt9mTF9gItrLVnjDF3AeOBxsnmt7LW/uuuNumMj4iIiIiIuNstQKi1NsxaGwtMB+5JXsBa+4e19ozr6RqgTFY2SImPiIiIiIhkijGmlzFmfbJHr1RFSgMHkz0/5Jp2KU8B85M9t8AiY8yGdOq+KhrqJiIiIiIimWKtHY9zaNqlmPQWS7egMa1wJj7Nkk2+zVp7xBhTHFhsjPnHWvvrVTcYnfERERERERH3OwSUTfa8DHAkdSFjzI3Al8A91tpTidOttUdcf08AP+IcOndNlPiIiIiIiIi7rQOqGmMqGmP8gIeB2ckLGGPKAT8APay1u5JNL2CMKZj4P9AO2HqtDdJQNxERERERcStrbbwx5jlgIeANTLTWbjPG9HHNHwsMAooAo40xAPHW2kZACeBH1zQf4Ftr7YJrbZMSHxERERERcTtr7TxgXqppY5P93xPomc5yYUC91NOvlYa6iYiIiIiIx1PiIyIiIiIiHk+Jj4iIiIiIeDwlPiIiIiIi4vGMten+jpBkHa1wERERketPej/ImSuEnjyX7ceXVYoVzLXr41J0xkdERERERDyebmedA0JPnsvpJmSrKsUKAjB0+e4cbkn2G9CqKgDxh7blcEuyl0+Z2kDe6+twsb/Xf2PeFUp6lk3vdQDyXl+Hi/1d2zzvyKv7uMT9W17e5nJ90xkfERERERHxeEp8RERERETE4ynxERERERERj6fER0REREREPJ4SHxERERER8XhKfERERERExOMp8REREREREY+nxEdERERERDyeEh8REREREfF4SnxERERERMTjKfERERERERGPp8RHREREREQ8nhIfERERERHxeEp8RERERETE4ynxERERERERj6fER0REREREPJ4SHxERERER8XhKfERERERExOMp8REREREREY+nxEdERERERDyeEh8REREREfF4SnxERERERMTjKfERERERERGPp8RHREREREQ8nk9ON0Au79zZCD57fzB/rVtDUHAIT/R+jpbt2qdb9sfvpjJz6tfExkRzW4vW9O3/Or5+fhmq57eli5kycRynTpygaIkSPN6rL01vbwlAXGws4z77iNW/riA+Pp5adevR95XXKVqseJbHfykxkedY9fVnHNmxkXyBQTS893Eq39Iy3bLnTh5jzffjOLZrK94+vlS9tS033/8fAOYPH8DJvTsx3t4ABIQU4f53xmVXGBk2eeYvTJj+IzGxsdzRvAmDnu+Nn59vumV3hO5l0EejCDtwiErlyvBu/77UrFIRgHnLVjFq8nT+PROOn68PzW6+iTf79SSwQAAAU3+ax88Ll7Nr7346tGrOe6/1y7YYrySj74Ul8+cwe8Z0jhw6SECBArS8404e79UXbx/n7u7+O5qnKB8bE0OH+x7gmRdfzZY4skrXJuXpfFMZqpYMZMHmowyatSWnm5Rh7urfGalr3rJVjPnmO46e+JeihUL4v1f70fDGWoTuO8gbw0Zw8MgxAGpVq8zrfZ+iSoWyWRt8OoL8fXm7S12aVi3Kmcg4Pl+0k/mbj6Qp9+Y9dehY/4ak5z7ehjiH5bZ3FgFwQ4g/b9xTmxvLFSI2PoElW4/y4dwdOBJstsWSUe7qA7v37ueDsZPZvmsP4WfPsW3pDymWbdTx0RTPY2JjebjznbzZ7+msCSyD3LF/i4uNZdTwoWxa/yfnz56lVJkyPN6rL42a3pYDEV1ZXt/mkjMydcbHGOMwxmxK9hhwtS9sjPnjape9FsaYr4wxD+TEa1+N0cOH4ePry9TZi3hl0BBGDX+f/WF70pTbsHY1M6dM5r1PRzNxxi8cO3KYKRPGZaief0+e4KPBA3n6uReZsWgl/3n2eT58503Cz5wG4OcZ0/hn69+MnDyNb36aT4GCgYz95MPsWQGXsHraGLx8fHn4gym0+E9/Vn87mjNH9qcp54iPY+Fn/6NU9Rt5+INveGjoV1Ru3CpFmSYP96HHZzPp8dnMXJn0rFq3kQnTfmDiR2+zaOpYDh09zsjJ09MtGxsXR7+B79OpbQtW//QN97RrRb+B7xMbFwdAgzo1mDLiPdbOnsKCKWNwJDgYMenbpOWLFylM724P0KV9m2yJLTMy+l6IiY6m1/MvM23uEj4e/xWb1q9j1rQpSfNnLf4t6TF19iL88uWjeau22RlKljh5Lpovl4fy04ZDOd2UTHFn/75SXX+s38THX3zNkFee489fpjL5kyGUKVUCgOJFC/PJW6/wx09fs+qHr2jV9GZeGfJx1q+AdLzeuTZxjgRav7eUN77fxBv31KZy8cA05f7v563c+s6ipMeCzUdZ/PfRpPlv3FOb0+djafv+Urp+/hsNKxbhocblsjOUDHFnH/Dx8aF9i1sZ3L9vusuvn/tt0uPXmRPJ5+fHnS1uzbLYMsod+zeHw0Gx4iUYNnI83y9cQY+ezzB00OscP5o2ac5p2uaSUzI71C3KWls/2WPo1b6wtVa97gqio6L4Y+UyevTsg39AALXr1adxs9tZtnBemrJL58+hXad7KF+pMgWDgnj4iZ4smT8nQ/X8e+IEBQIL0qjpbRhjuOXWZuTz9+foYecB1PGjR7ipcRMKFS6CX7583N7mTg7sTbtDzi5xMdHs3/gHN3Xujm9+f0pUqU25eo3Zs3Z5mrKhq5cSEFKYOm3vwzdffnx8/ShcpmI6teZePy9aQZe72lClQjmCCwbSp/uD/LQwbawA6zZvw+FI4LH7O+Hn50v3Lh2xwNqNfwNQqnhRCgUHJZX39vLiwOFjSc/vaN6ENs0aExxUMEtjyqzMvBc63vcAdeo1wNfXl6LFitOqXXt2/L053XpXrVhKcEhhatdrkNUhZLll246zfMdxIi7E5XRTMsWd/ftKdY2a/B3P9HiIerWq4+XlRYliRShRrAgAQYEFKF2yOMYYLODl5cWBI0fTaUXWyu/rTdvaJRm1eDdRsQ427T/Dyh0n6Nig9BWXa1OnJL9sPJw07YZCASz6+yix8QmcOh/LH7tOUrlE7npvg3v7QMWypbm/Q1sqZ+BM3aJfV1OkUDAN69ZyZziZ5q79W35/f7o91ZsSpW7Ay8uLW25rTokbbiB0547sDumK8vo2l5zjlmt8jDH7jDHvGGP+Msb8bYyp4ZpezBiz2DV9nDFmvzGmqGveedfflsaYFcaYmcaYf4wxU40xxjWvoTFmpTFmgzFmoTGmVKrXDXa9tpfreYAx5qAxxtcYU98Ys8YYs8UY86MxptAl2p3YnkbGmBWu/982xkw2xixylelijPnAFdsCY4xvRtp3rQ4f3I+Xlzely5VPmlaxcjUO7A1LU/bA3jAqVql6sVyVaoSfPsXZiPAr1lO1Rk3KVqjImlUrcTgcrP51Bb6+flSs7KyvXad72P73Zk79e5Lo6GhWLJpPoyY5d+r87PHDGC8vgktcPBAoVLoi4emc8Tm59x8Ci5Rg0edv8e3LjzJ/+ABOH96Xosz6nybz7cuPMveDVzi6M/cNDwrdd4DqlSskPa9euQKnzoQTHnEunbIHqVapPK63kLN8xfLs2Xcw6fmGv3fQuHN3bunUjcW/raHH/Z2ytP3ukJn3QmpbN2+kXMVK6c5bOn8Obdp3SLG+JHu5s39fri6Hw8HWXXs4HXGW9j2epXXXngwZ8QXRMTEpXqNJ5+7c1L4r7438kqcfvd+9wWZA+aIFcFjLgVORSdN2HT2b7hmf5NrWKcmZyFg27D2dNO3bP/Zx542lyO/rRfGgfNxWvRi/7zqZZW2/Wu7ex2XUz4uW0/mOFjn+/s+q/duZ06c4fPAA5SpWdltb3SWvb3PJOZlNfPxTDXXrmmzev9bam4AxQH/XtLeAZa7pPwKXOsfeAHgBqAVUAm5zJRefAw9YaxsCE4H/S76QtTYC2Ay0cE26G1horY0DvgZes9beCPztaktmVAY6AvcAU4Dl1tq6QBTQMSPtu1ZRUVEEBKb8sCsQGEjUhch0yl4goEBginIAURcuXLEeb29vWrfvwIfv/I97W9/Kh++8yXOvvEF+f38ASpctR7HiJXns3rt48M4WHNy/l0ee7OnOUDMlLiYKP/+AFNP8/AOIi45KUzbyzCnC1v1KrVZ303XYZMrUvZmlYwbjiHd+K96oy5M8OORLug6dTLXm7VkyejBnT2b/t7yXExUVnXQNDpD0f2RU2ngvREVRsEDKdRMYGJCibMO6NVk7ewrLpn/Bkw/dQ+kSOXetVkZl5r2Q3OK5s9n9zw66PNIjzbwTx46xddNftLkr9yd+nsyd/ftydZ06E0F8fDyLf13NN58OYdb4j9kRGsa4KTNT1Ldm9hTWzJ7Cm/16prh2KLsE5PPmfHR8imnno+MpkO/yl+TefVNp5iQ72wOwYe8pKpcoyKpB7Vg0oA3bD0WwfPtxt7f5Wrl7H5cRR46fZP2W7dzTrtWVC2exrNi/xcfH8+E7A2nTviNly1dwZ3PdIq9vc8k51zrU7btk8xKvJtsAVHD93wyYDmCtXQCcuUS9f1prD1lrE4BNruWrA3WAxcaYTcD/gDLpLPsdkJiAPQx8Z4wJBkKstStd0ycDt2ciToD5rgTqb8AbWOCa/ncm24cxppcxZr0xZv348eMz3AB/f3+iIs+nmHYhMhL/gALplA0gKjIyWTnncv4BAVesZ+O6tUwa/TlDPx/Hz8tXM3TkeEYMG8ye3TsBGPXRUOJiY5k+byk/LP6NW1u0ZtDL/81wHO7mm8+f2FQ7vLjoC/jm909T1tvXjxJValGmTiO8fXypc0cXYs6fI+KY85uiYhWr45s/AG9fX6o2bUOJyjU5tHV9tsRxKXOWrKRRx0dp1PFReg8YjL9/fiIvXIw3MtL5fwH/tPEG+Ptz/kLKdXM+MirdsiWKFaHZzQ3oP2S4myNwv8y8FxKt/nUFX40dybsfjSA4JCTN/GUL5lDrxvqUvOHyQ4jEvbKyf1+urnz5nDd6efTeDhQrUphCwUE8/kBnfv3zr3ReJz9d776T14eO4NSZ8GsLOJMuxDjSJDkF8vsQGRN/iSWgRHB+GlYozC9/Xby+yxgY/eQtLNt2jKZvL6LF4MUU9PflhfbVs6ztGZVd+7jLmb14BTfVqZF0jVdOcvf+LSEhgeGDB+Lr68MzL72WFU3ONG1zyS3ceTvrxPECDi7eLS6j5xKTjzVIXN4A25IlWXWtte3SWXY2cJcxpjDQEFiWiTbHc3Ed5E+vTa5kLM5am3gbnIRMtg9r7XhrbSNrbaNevXpluHGly5bH4XBw+OCBpGl7Q3ele1q7XMVKhIXuSlZuNyGFixAUHHLFesJ276JOvQZUrVELLy8vqtWsTfVaddi07s+ksm06dKJgUDC+fn7cfX9Xdu3YRkR4eIZjcaegEqWxCQ4ijl/8dvP0ob2E3FA+TdnCZSo4jwAyyhiwOXvHo05tWyRdiDlu6ECqVCjHzj37kub/E7aPIoVCCAlOO1a/SoWy7Arbh00Ww66wfZcc+xzvSODg0dz3DXBqmXkvAKxf8wcjPhjCoGEfU6FylXTLLF0wjzbtO2ZJe+XSsrJ/X66u4IKBlCxWJMO7gwRriY6J5cS/p69c2I32/xuJj5ehXJGL33BXK1mQPSfOX3KZuxuUZvOBcA6fuXhwGOzvS6kQf6av3k+cI4GIqDhmbzhEs+o5f4Y3O/dxlzJ70Ypc882/O/dv1lo+GzqYM6dP88b/fYCPT+64ea+2ueQWWf07PquAhwCMMe2ANNfZXMZOoJgxpqlreV9jTO3Uhay154E/gc+AOdZah2sI3BljTOJ9a3sAK1MvC+zDmSwBZHYwd4bady3y+/tza4tWTPlyLNFRUWzfsok1q1bS+s4Oacq2bt+RRXNnc2BvGOfOnmX65Am0dQ3huVI91WrWYtuWjUlnePbs+odtmzclXTNUtWZtli2YS+T588THxzP3xxkUKVos3W/Rs4NvvvyUb9CUjb9MJS4mmuOh2zmweW2au7UBVL6lFSfDdnJkxyYSEhxsX/oz+QODCC5ZlpgL5zm8bQPxcbEkOBzsWbuc47u3UrrWTTkQ1aV1vqMFs+YvJXTfQSLOnWfclBnce2f6O++b69XGy8uLKT/MJTY2jqk/OS+ObdygLuD81u3I8ZNYazly/AQjJk6liWseQLzDQUxsLAkJCSQkJBATG0u8w5H1QV5BZt4Lmzes46N3B/LmkA+oXqtOuvU5r1k7QbPW1//d3BJ5exn8fLzwMgYv1//eXrl/HLs7+/eV6rr3ztZM/Wkep86EE3HuPN/MmkOLJs6PgD/Wb2LH7jAcDgfnIy/wwZhJBBUsQKXy6Z7IzzLRcQ6Wbj/GM22rkd/Xm/rlCtGyVgnmphrGllynBqWZ/VfKu/mFX4jj0OkLPNi4HN5ehoL5fbj7ptLsOno2q0PINHf2AWstMbGxxMU5z5DFxMYSG5vyhh8bt/3DiVOnc82dvdy5fxv10fsc3LeXt4Z9Qr58qb/PzT3y+jaXnGNsJr7dNsY4cA71SrTAWjvAGLMPaGSt/dcY0wj4yFrb0hhTHJiGM+FZiXNIWkVrbYwx5ry1NtAY0xLob63t5HqNkcB6a+1Xxpj6wAggGOdZlk+ttV+k064HgBlAy8Thba5lxwIBQBjwpLX2jDHmK5wJ0kxXYjQBOA6sdcXQ0hjzNnDeWvuRq67z1tpA1/9J8zLavlRs6Mm0F+9dyrmzEXz6/rtsXLeWoKBgnujTj5bt2nPi2DGe6fEgY76ZQfGSJQH4cfoUZk79mpiYGG5r2ZrnUv2OT3r1JPpl1nf8/P00wk+fJiikEJ26PEiXR7oDcDYinHGffsTGdWuJj4+jfMXK9Oz34iUPKlOrUsz5Dc7Q5bszHPeVpPgdnwJBNLzP+Ts+50+f4Md3nuW+t0YTWNj5zea+jX+w/odJRJ8Lp0jZyjR55BkK3VCe6HMRLB75NuHHDuHl5UVwyTI0uLs7pWu57w5fA1o5k8f4Q9uuqZ6vZsxm4vQfiXb93sFbL/RJ+r2D3gMG07BuTXp1c96lfcfuMAYNH82e/YeoVK40g/v3pWZV5zeHn02Yys+LlnP2fCRBgQVo3rghLz7VPelbtlGTpzP66+9TvPazjz1E38cfzlR7fco4vwPITF+/koy+Fwb06822LZvwc/V9gNo3NuDd4SOSnn/+wf8RExNN/4GD3da+RIn9vf4bae/IlJX6tKlKnzZVU0wbu3Q3Y5e67313OZvecx6kXU1fd1f/vlJdcfHxvD9qAvOW/oafnx/tW97Ky70eI5+fHwtX/sHnk6Zx7OQp8ufzo071KrzYs3uKC7AvJbG/u2ubB/n78s79dWlSpSjhF+IYsdD5Oz4lg/Pzwwu30+XTXzkWEQ3AjWVDGPfULbR5bykXYlN+SVG9VEFe6ViLaqWCcCRY1oed4r3Z2zgTGeuWdl7LNk/NXX3g8LETtOvWJ0XdN5QoxuJvL/5UwdsfjyE6Jpahrz9/1e119z7OHfu3E8eO8uQDd+Pr54e367fpAJ575Q1atbvLLe1M3L/l4W2ea79NCj15LtuHq1QpVjDXro9LyVTik+nKjckHOKy18a4zI2OstfWz7AWvD5lKfDxBViQ+1wt3JT7Xm6xIfK4XOZX45DR3HgRfb9yd+FwvtM3z3j7OnYnP9UaJT1rXY+KT1YM/ywHfu243HQvoZ3JFRERERCTbZWniY63djfNW1SIiIiIiIjkmq29uICIiIiIikuOU+IiIiIiIiMdT4iMiIiIiIh5PiY+IiIiIiHg8JT4iIiIiIuLxlPiIiIiIiIjHU+IjIiIiIiIeT4mPiIiIiIh4PCU+IiIiIiLi8ZT4iIiIiIiIx1PiIyIiIiIiHk+Jj4iIiIiIeDwlPiIiIiIi4vGU+IiIiIiIiMdT4iMiIiIiIh5PiY+IiIiIiHg8JT4iIiIiIuLxlPiIiIiIiIjHU+IjIiIiIiIeT4mPiIiIiIh4PCU+IiIiIiLi8ZT4iIiIiIiI2xlj2htjdhpjQo0xA9KZb4wxI1zztxhjbsrosldDiY+IiIiIiLiVMcYbGAXcBdQCHjHG1EpV7C6gquvRCxiTiWUzTYmPiIiIiIi42y1AqLU2zFobC0wH7klV5h7ga+u0BggxxpTK4LKZZqy111qHZI5WuIiIiMj1x+R0Ay4l9OS5bD++rFKs4GXXhzHmAaC9tban63kPoLG19rlkZeYAQ621q1zPlwKvARWutOzV8LmWhUVEREREJGdViDmQ7a9pTJ1eOIenJRpvrR2fvEg6i6VO0C5VJiPLZpoSnxwQf2hbTjchW/mUqQ1A6MlzOdyS7FelWEEg727zvBY35N3YE+Ou/8a8HG5J9tv0Xgcg727zvBY35N3Y82rccDF2uciV5Iy/TJFDQNlkz8sARzJYxi8Dy2aarvERERERERF3WwdUNcZUNMb4AQ8Ds1OVmQ085rq7WxMgwlp7NIPLZprO+IiIiIiIiFtZa+ONMc8BCwFvYKK1dpsxpo9r/lhgHtABCAUuAE9ebtlrbZMSHxERERERcTtr7TycyU3yaWOT/W+Bvhld9lppqJuIiIiIiHg8JT4iIiIiIuLxlPiIiIiIiIjHU+IjIiIiIiIeT4mPiIiIiIh4PCU+IiIiIiLi8ZT4iIiIiIiIx1PiIyIiIiIiHk+Jj4iIiIiIeDwlPiIiIiIi4vGU+IiIiIiIiMdT4iMiIiIiIh5PiY+IiIiIiHg8JT4iIiIiIuLxlPiIiIiIiIjHU+IjIiIiIiIeT4mPiIiIiIh4PCU+IiIiIiLi8ZT4iIiIiIiIx1PiIyIiIiIiHk+Jj4iIiIiIeDyfnG7AtTDGWGCKtbaH67kPcBRYa63tZIzpDNSy1g41xrwNnLfWfpRzLb56k2f+woTpPxITG8sdzZsw6Pne+Pn5plt2R+heBn00irADh6hUrgzv9u9LzSoVM1TX1J/m8fPC5ezau58OrZrz3mv9UtS9YMXvjJo8nWMnT1GyWFFeeKobbZo1zrrAXc6djeCz9wfz17o1BAWH8ETv52jZrn26ZX/8biozp35NbEw0t7VoTd/+r+Pr50dcbCyjhg9l0/o/OX/2LKXKlOHxXn1p1PS2NHV8O3E8UyeOZ8gno2hwc9bHdyXu3P4Hjxzj/ZETWLdlG36+vtzXvg39ez+Woo79h45wb88XaXd7U4a98UJWhnZF7op93rJVjJo8nX/PhOPn60Ozm2/izX49CSwQkKKOnIw9u97n4FwfY775jqMn/qVooRD+79V+NLyxFqH7DvLGsBEcPHIMgFrVKvN636eoUqFs1gZ/lbo2KU/nm8pQtWQgCzYfZdCsLTndpEzJq/v2vNzXsyv2PfsPMWTEeLbvDqNQcBD9ez9G22ZNANi8fSefT5rGtt1heHt5cXO92rzx3FMUK1L4uo/7cn398LETtOvWB//8+ZOmPfXwvTzT46Esilpym+v9jE8kUMcY4+96fgdwOHGmtXa2tXZojrTMjVat28iEaT8w8aO3WTR1LIeOHmfk5Onplo2Ni6PfwPfp1LYFq3/6hnvataLfwPeJjYvLUF3FixSmd7cH6NK+TZq6j588xWvvf8arfZ7kz1+m0r/3Y7z63iecOhOeJXEnN3r4MHx8fZk6exGvDBrCqOHvsz9sT5pyG9auZuaUybz36WgmzviFY0cOM2XCOAAcDgfFipdg2MjxfL9wBT16PsPQQa9z/OiRFHUcPXyIVSuWUrhI0SyPKyPcuf1j4+J4+tV3uKVBXVbOmMiy777g7ra3p6lnyIgvqFO9SpbGlRHujL1BnRpMGfEea2dPYcGUMTgSHIyY9G2aenIq9ux8n/+xfhMff/E1Q155jj9/mcrkT4ZQplQJAIoXLcwnb73CHz99zaofvqJV05t5ZcjHWb8CrtLJc9F8uTyUnzYcyummZFpe3bfn5b6eXbHHOxz0G/g+LZo04o8fJ/P2S88w4P3P2HfQ+Xl39lwkD3Zqx+KpY1n87TgKBPjz5ocjr/u44fJ9PdGa2d+wfu63rJ/7rZKePOZ6T3wA5gMdXf8/AkxLnGGMecIYk+adbIypbIxZYIzZYIz5zRhTwzX9bmPMWmPMRmPMEmNMCdf0YsaYxcaYv4wx44wx+40xRV3zuhtj/jTGbHLN83Z3gD8vWkGXu9pQpUI5ggsG0qf7g/y0cHm6Zddt3obDkcBj93fCz8+X7l06YoG1G//OUF13NG9Cm2aNCQ4qmKbu4/+eIigwgOaNb8IYQ4smjfDPn5+DR467O+QUoqOi+GPlMnr07IN/QAC169WncbPbWbZwXpqyS+fPoV2neyhfqTIFg4J4+ImeLJk/B4D8/v50e6o3JUrdgJeXF7fc1pwSN9xA6M4dKeoY8/EHPPlMP3x80/8mKru5c/v/tHA5xYoU5okHOxPgn598fn5Ur1whRR3zlq2iYGABmtxUN4sjuzJ3xl6qeFEKBQcllff28uLA4WMp6sjJ2LPzfT5q8nc80+Mh6tWqjpeXFyWKFaFEsSIABAUWoHTJ4hhjsICXlxcHjhzN6vCv2rJtx1m+4zgRF+JyuimZllf37Xm5r2dX7HsPHOLEqTM8/sDdeHt706RBXRrUrsHsJSsAaN74Ju5scSuBBQLwz5+PR++5i41b/7nu44bL93URT0h8pgMPG2PyAzcCazOwzHign7W2IdAfGO2avgpoYq1t4Kr3Vdf0t4Bl1tqbgB+BcgDGmJpAV+A2a219wAF0c0dQyYXuO5Di4LR65QqcOhNOeMS5dMoepFql8hhjLpavWJ49+w5muq7UalerTKVyZVj2x584HA6WrlqLn68P1SqVv/rgMuDwwf14eXlTutzF16lYuRoH9oalKXtgbxgVq1S9WK5KNcJPn+JsRHiasmdOn+LwwQOUq1g5adpvy5bg4+vDzU2buTeIa+DO7b9lxy5KlyxO7wGDue2+x3nipYHsCtufVPZ85AVGfjWNV/o8kWXxZIY7YwfY8PcOGnfuzi2durH4tzX0uL9T0rycjj273ucOh4Otu/ZwOuIs7Xs8S+uuPRky4guiY2JSvEaTzt25qX1X3hv5JU8/er97gxUg7+7b83Jfz67YrU372tZaQvceSLdd6//enqVD/HJLX090xyO9ad21J29+8DlnIs5mPiC5bl3X1/gAWGu3GGMq4Dzbk/YUQCrm/9u77/AoyrWP4987DUILHRSlIyigKCgqKlVAQFTsYjsWxIO9nGM5YsHXjl0RpIiCoIAoTWlSxApIB+lIL9IJISHJ8/6xm5CQUAJbktnf57pysTs7M3vfu7MPc88884xZMeBiYFiWH1Uh/7+nAV+Z2SlAHLDaP/0S4Br/+/1gZjv901sCDYGZ/nXFA1tPMqUckpIOZLsOIeNxYlISJROyH9HYn5RE8cOuWShWrAiJSUl5XtfhoqOj6Xh5M/7zf++SkpJCbGwMb3d/giLxhY+63MlKSkqiSLFi2aYVLVaMpP2Jucy7nyJFi2WbDyBp/35KJJTMnJ6amsqbLz5Hy7btOb1K1cx5Pu/zET3eDt7p/hMRyO9/y7bt/DF3IR/2eJrG59Vn0DdjebD7a4we8D5xsbF8MGAIna5oySnl80c3v0DmDtCw/pn8PmoQW7ZtZ/i4iVSqUD7ztXDnHqrfecrBg6SmpjJx+q988e7LxMTE8MBzr9J70HAevvvQcZvfRg1if9IBvpswhVMrlAtssgJEbtseydt6qHKvVrkSZUol0P+rb7n9uiv5Y+5CZs5fzAUN6uWIaenKNfT6YhgfvvRUYJLMRX7Z1ksmFOerj9+gTs1q7Nq9l5ff78N/XnmXT1/vfuLJSYFS4Asfv1HAW0AzoMwx5o0CdvnP0BzuA+Bt59woM2sGvOCfbrnMmzF9oHPu6aO9oZl1AboA9O7dm7va5byYPqsxk6bxwju+61Ia1j+T+PjCJO4/tPOWmOh7XDQ+PseyReLj2ZdlXoB9iUmZ8+ZlXYf7dfY8evb5nM/efomzalVn0bKVPPDcq3zy6nPZLjoMtPj4eJIS92Wbtj8xkfgiRXOZtwhJiYlZ5vMtF1/kUCOZnp5Ozx7PERsbw/2P/Tdz+uB+vWneph0VT60U6BTyJJjff6FCcZxb70wubXweAP+64Sp6Dx7OqrXrcQ5+/XM+w3uHb/yPYOaeVYVyZbjk/HN54uWeDO/dkyUrVoc893D9zi3K15zdcnW7zAuZ77iuI70HZ98Z9L1PYW68sg2XdLqT0QPep0ypkieTcsSL1LY9krf1cOUeGxPD+y/+l1c+7Eu/oSOpW7smbZteTNxhXbj/3rCJrk+/zNPd7qLh2WcFIGOf/LqtF42Pz7yGs2zpkjz70L00u/5u9iXuzzHQjXiTF7q6AfQHXnLOLTjWjM65PcBqM7sewHzO8b+cwKHBEe7IstgM4Ab//K2BUv7pk4HrzKy8/7XSZpajb4Bzro9zrpFzrlGXLl2OmUyHVk0zL7rr/dpz1KxamaUr12S+/teqNZQpVTLXIxs1q57OslVrcFnOcy9btYYa/lPYeVnX4f5auZpGZ59Fvdo1iYqKon6dWpxd5wx++3PeMZc9GZVOr0JaWhob1h06Rb96xTIqV6ueY97K1aqzasWyLPMtp2TpMplne5xzvPdaD3bu2MEz//cGMTGHav+5s2cyevhQOndsQ+eObfhn6xZe6/40wwZ9FrTcchPM79/XfSD39505byEbt2yl1c33cdl1dzHg61FM/Ok3rrvv8YDmdzTBzP1wqWnprNvku4YhHLmH63eeULwYFcuVOeJ2cLh05ziQnMLWf3acUJ5ySKS27ZG8rYfzO69doyoD33mZX779nE9f7876TVuoX+fQwC0bt2zlnidfoOtt19Px8mYByznceeeF+Y9ru9z6BooneaLwcc6td869l4dFOgN3m9k8YBFwlX/6C/i6wP0E/JNl/heB1mb2J3AFviGz9zrnFgP/AyaY2XxgInDKSSWTi46XN2XE95NZsWYdu/fuo/egYVzdpnmu855/Tl2ioqIY9M1YUlIOMvhbX++/xufWP651paalkZySQnp6Ounp6SSnpJCalgZAvdq1mL1gCUtW+HoALlm+itkLFnNG9aqBTjmbwvHxXNy0OYP6fsKBpCQWz5/LbzOm0aJNuxzztmjbngljR7F29Sr27tnD0IH9aHXFoes4PnrrVdatWc3zr79DoULZu3G88t7HfPT5V3wwYDAfDBhM6bLleODJZ+jQKbwjvgTy+7+yVVPmL1nGr7PnkZaWxucjxlCqRHGqVz6N69u35vsvPmZEn56M6NOTG69szWWNz6NPGLsABDL3MZOmsXHLNpxzbNyylff7D+ZC/2v5IfdQ/s6vbtOCwd+OY/vOXezeu48vRoyh6YUNAd8oWEuWryItLY19ift5o9cAShQvSvUqpwX5Ezgx0VFGXEwUUWZE+R9HRx3nnm6YRWrbHsnbeihzX7pyDckpKSQdSGbA19+ybcdOrm7TAvB1e77r8ee5+aoruPHKNkHLN0N+2dbnL1nG6nUbSE9PZ9fuvbz6UV/OP6cexYvl7EEi3mSqco/NzAoBac65VDO7COh1hK5yx8Olrl+U54U+GzaK/kNHcsA/Zv3zj3TNHLP+vqd60LD+mXTpfB3g+0+re8+PWfn3eqpXrkSPJ7pxZq3qx7WujwYO5ePPv8723v++/Qa63XET4Bsf/4sRY9i+cxelSyZwc8e23HnDVRxNzGl1AVix7fgvPDzc3j27effVl5gz83dKlEjgzq4P0qx1W7Zu3sz9t11Pry+GUb5iRQBGDh3E8MGfk5ycTJNmLXjAfx+frZs38a/rriQ2Lo7o6EOD7z3w5DM0b31Fjvf813VX8tB//3dS9/GpWc53BOpEvvOsAvn9T/zpN3r2+Zwdu3ZzVq3q/O+he6lZtXKO9/xo4FDWbth8QveyyfjOTzZvCFzu7/UbzHcTprBnXyIlihXl0sYNefTuW3M9Shiu3EP1Oz+YmsqrH/Vj3OSfiIuLo22zi3m8y+0Uiotj/LRf+GDAEDZv207hQnHUq12TR++5Ncfof0fKu8Ezx7zUMqC6tqxF15a1sk37ZPJyPpm8PGQxzH3FdxAmP3/nwWzb83PewdjWC0rub/UeyIhxkziYmkbD+mfyzIP3UKWS79jsx59/xUcDv8p2PxuAWWNzDvFf0PI+2rY+9sefeK/fYHbs2k3RIvFc3PAcHutyO+VKl+JY/Lnn26MqqesXhXyHPua0uvn28zgSFT7HwcxqAV/jO0OWAvzbOTfzBFd3QoVPQRaIwqegClThU9AEsvApaCI193AVPvnByRQ+BVmkbusQublHat6gwic3BbHw8crgBkHlnFsOnBvuOERERERE5MR44hofERERERGRo1HhIyIiIiIinqfCR0REREREPE+Fj4iIiIiIeJ4KHxERERER8TwVPiIiIiIi4nkqfERERERExPNU+IiIiIiIiOep8BEREREREc9T4SMiIiIiIp6nwkdERERERDxPhY+IiIiIiHieCh8REREREfE8FT4iIiIiIuJ5KnxERERERMTzVPiIiIiIiIjnqfARERERERHPU+EjIiIiIiKep8JHREREREQ8T4WPiIiIiIh4ngofERERERHxPBU+IiIiIiLieSp8RERERETE81T4iIiIiIiI55lzLtwxRBp94CIiIiIFj4U7gCNJXb8o5PuXMafVzbefx5HojI+IiIiIiHheTLgDiEQrtu0NdwghVbNccQBem7I8zJGE3lPNawGQun5RmCMJrZjT6gKRt63Doe29wTPjwhxJaM19pR0Qeds6HNre9Z1Hjkht4zLat0jLGw7lLgWbzviIiIiIiIjnqfARERERERHPU+EjIiIiIiKep8JHREREREQ8T4WPiIiIiIh4ngofEREREREJGTMrbWYTzWy5/99SucxzuplNMbMlZrbIzB7O8toLZrbBzOb6/9odz/uq8BERERERkVB6CpjsnKsFTPY/P1wq8Lhz7kzgQqCbmZ2V5fV3nHMN/H/HdT8BFT4iIiIiIhJKVwED/Y8HAlcfPoNzbpNz7k//473AEqDSybypCh8REREREQmlCs65TeArcIDyR5vZzKoC5wK/Z5n8gJnNN7P+uXWVy40KHxERERERyRMz62Jms7L8dTns9UlmtjCXv6vy+D7FgBHAI865Pf7JvYAaQANgE9DzeNYVk5c3FhERERERcc71Afoc5fVWR3rNzLaY2SnOuU1mdgqw9QjzxeIregY7577Jsu4tWeb5FBhzPDHrjI+IiIiIiITSKOAO/+M7gO8On8HMDOgHLHHOvX3Ya6dkeXoNsPB43lSFj4iIiIiIhNJrwOVmthy43P8cMzvVzDJGaGsC3Aa0yGXY6jfMbIGZzQeaA48ez5uqq5uIiIiIiISMc2470DKX6RuBdv7HMwA7wvK3ncj76oyPiIiIiIh4ngofERERERHxPBU+IiIiIiLieSp8RERERETE81T4iIiIiIiI56nwERERERERz1PhIyIiIiIinqfCR0REREREPE+Fj4iIiIiIeJ4KHxERERER8byYcAcgR7d3z27ee7UHf878jRIJJbnzvgdo1rptrvOO/Gowwwd/TkryAZo0bUG3J54mNi7umOuZMuF7Pnzzlcz1uPR0kpOTebfvF9SqcyaD+/Xmq8/7Z64L4MPPhnBKpdOCmPnRJSfuZcbn77FxyRwKFStBw6vvoMYFzXLMt/yXSfz8xftEZ4m91b+7c0rtswHYtWkdvw7txfa/V1C4eALnd/oXVc69OFRpHLeBw0fTb+hIklNSuPzSC+n+8H3ExcXmOu+SFavp/tZHrFq7nuqVT+OlJ7pxZs1qAKSkHOTtvl/ww9SfSU5O4YoWl/B0t7uJjfE1BY3a35JtXckpKdzUsQ3PPnhvcBM8Acf725j0/RhGDRvKxvXrKFK0KM0ub8MdXboRHVMwmr8S8bG80Kk+F9Uqy87Eg3wwYSnfz9uYY75nr6pH+wanZj6PiTYOpjmavDgBgFNLxvPMVXU5u3IpUlLTmbRwE2+OXUJaugtZLkcSqO37WOsa/O04vhs/hWWr/6Zd80t55b8PZlv3D1N/5qOBQ9m8bTsVy5Xlkbs70/KSxsFL/CTdeGEVOp53GrUqFuOHeZvoPmJ+uEM6YYHaBsb9OIOPBg7ln527iIuN4ZLzz+PZB++hWNEiANz52HPMW7yM6OhoACqULc3YgR+GJsmjON72bM2qFfT78F1WLF3Cnt27GTtjVrbXt2zayMc9X2PJwgXExsVxSbMWdHno8XzV3gVqv+bNl55j3uw/OJB0gFKly3Bd59tpc+XVwLH3ayRy5fszPmZW0cyGmtlKM1tsZuPM7IwjzNvMzMb4H3c0s6fy+F6fmdl1gYg7UD7u+ToxsbEMHjWBJ7u/zEc9X+XvVStzzDf7918ZPmggr7z7Mf2HjWbzxg0M6tf7uNbTvPUVjJj4U+bf/Y8/RcVTK1Gzdp3M5S9t2TrbPOEsegB+HdKLqJhYbnpjEE3veoJfv/yYnRv/znXectXrcNt7wzP/Moqe9LQ0Jvfqwen1z+eWt4dwcecHmD6gJ7u3bAhlKsc0Y+Yc+g35hv5vvcCEwZ+wftMWPhw4NNd5Uw4e5MHnXqVDq6b8+u0XXNW6OQ8+9yopBw8C0HfoNyxaupJv+77L2IEfsmT5KnoPGp65/KyxX2b+TR/en0JxcbRpmv8KQTj+30bygQN0efhxhoydxNt9PmPurJmMGDIoDBGfmKc71uVgWjotXpnMM1/P5Zmr6lKjfLEc8/3fdwu5+MUJmX8/zNvExAWbMl9/5qq67NiXQqtXJ3PjBz/RsFoZbmhcOZSp5CqQ2/ex1lW+TGnu63wdndq2zLHuLdu2899X3+M/Xf/FH6MH88R9t/OfV95h+85dQck7ELbtPUDfKSv4dvb6cIdyUgK5DZxbrw6D3n+F30cN4odBvUhLT+P9AV9mW8ezD96T2c7lh6IHjr89i4mJ4ZIWrXjoqeeOsJ7XSChVmkHf/cAHAwazYO6fjB05PNd5wyVQ+zU33HonA4aNZviEaXR//W0+/7QXy/9aAhzffo1Epnxd+JiZASOBqc65Gs65s4BngArHWtY5N8o591qQ4wvqIZQDSUn8Mu1HbrunK/FFilD3nAY0vuQyfhw/Lse8k78fQ+sOV1Gleg2KlyjBTXfew6Tvx+R5PRnratm2Pb6PP/85mHyAv+f8wnkdbyW2cDwVatal8jmNWfn7lDytZ/fmdezfvYO6La8mKiqaU+ucQ/kaZ7Hy9x+DFPmJ+W7CVDpd0ZKaVSuTULwYXW+9nm/H557rzHmLSEtL5/ZrOxAXF8utndrjgN/nLABg6q+zuLVTe0qWKE7pkgl0vqY9I3+YnOu6Jkz/lTKlEmhY/6xgpXbC8rJNt7/mOuqdcy6xsbGULVee5q3bsmTBvDBEnXeFY6NpVbciH01cTlJKGnP/3sm0JVtpf26lYy7Xsl5FRs85VMSfWqoIExZsIiU1ne37Uvhl2TZqVCge7BSOKZDb97HWdfmlF9LyksYklMiZ95Z/tlOiWBEubXweZkbTCxsRX7gw6zZuCUregfDjoi1MWbKF3fsPhjuUkxLIbeCU8mUplVAic/7oqCjWbtgcijROWF7as9MqV6VNh6upUq1GruvavGkjl7ZoRVyhQpQuU5aGjS/m79U5i4pwCdR+DUCV6jUyz/6Y+f42b8j9IEB+36+R0MnXhQ/QHDjonPskY4Jzbi7QxcyuyphmZoPNrGPWBc3sTjP70P/4MzN738x+MbNVGWd1zOdD/5mksUD5LMs3NLNpZjbbzMab2Sn+6VPN7BUzmwY8bGbXm9lCM5tnZtMDmfyGdX8TFRVNpcpVMqdVq3EGa1evyjHv2tWrqFaz1qH5ap7Brh3b2bN7V57Ws3XzJhbNm0OLtu2zTf/j5+nceEUL7r/1hrAfPdqzZQMWFUVChUM7f6UqVWPXEc747Fi3ki8fv4UR3bswd+wQ0tPSAMi1g49z7NyQ+3rCZcWatdSuUTXzee0aVdm+cxe7du/NZd51nFG9SrbGvXa1Kqxcsw4A5xzOZcncOTZv287efYk51vXdhCl0vLxpvvyPIi/b9OEWzptD5WrVgxlewFQpW5Q051i7/dD3s2zTnlzP+GTVql5FdiamMHv1jsxpX/6yhjZnn0Lh2CjKlyhEk9rl+HnZtqDFfrwCuX3nZV2Hq3tGDapXPo0ff/mDtLQ0Js/4nbjYGM6oXuWYy8rJCeQ2ADB7wRIad7yVCzp0ZuJPv3HbtR2yrePdfoNpcs0ddH7oaf6YuzDwCeXRybRnh7vq+puYPmkCBw4c4J9tW5n92880bJx/ztoHar8mw0dvvUanlk2475brKF2mLI0uapJjPUfar5HIlH86feauHjA7l+l9gUeB78wsAbgYuAO45CjrOsX/eh1gFDAcuAaoDdTHdxZpMdDfzGKBD4CrnHPbzOxG4P+Au/zrKumcawpgZguANs65DWZW8iRyzSEpKYkixbLv4BQtVoyk/Tl3UpOS9lOkaLFs8wEk7d+fp/VM/mEsdc9uQMVTDxUVl7a4nLZXdaJkqdIsXbyQV/73H4oWK0azy3PvkxtsB5OTiIsvkm1aXHwRDh5IyjFvxVr1uLr7RxQrXZ6dm9Yy9dPXiYqO5uy2N1Cy4mkULp7AwgkjqNvqajYtnc/m5QupWLt+qFI5LklJBzL7pwOZjxOTkiiZkP3I9f6kJIoXzf7ZFCtWhMQk32dzyQXn8cU3Y7igQT3S0tMZNNJ3lO1AcjLFixXNXGbjlm3Mmr+YHk90C0pOJysv23RWE8eOYvlfS3jov7l3E8lvihSKZt+B1GzT9h1IpWihozfdV55XiTFzsnfZnL16O53OP50Z3VsTEx3FqNnrmbI4/GczArl952Vdh4uOjqbj5c34z/+9S0pKCrGxMbzd/QmKxBc+8eTkuARyGwBoWP9Mfh81iC3btjN83EQqVcg8pslj995GjSqnExsTw7gpM+j2v1cY0edtKp9aMRipHZcTbc9yU69BQ34Y9S3Xt2lKeloaLa/owEWXNQtQpCcvUPs1JRJKAtDtiafo+uiT/LVwAQvmzMp2LXKG3PZrJHLl9zM+uXLOTQNqmll54GZghHMu9RiLfeucS3fOLeZQV7nLgCHOuTTn3EYgo49TbXxF10Qzmwv8D8h6UctXWR7/DHxmZvcC0bm9sZl1MbNZZjarT58+x51nfHw8SYn7sk3bn5hIfJGiucxbhKTExCzz+ZaLL1IkT+v58YextLwi+9GxytWqU6ZsOaKjozmr/jl0vO5mfp6ae/eoUIgtFE9KUvYi5+CB/cQWjs8xb/FyFSletiIWFUXpSlVp0P4m1vz5MwBR0TG07Po/1i2cxdD/3MbCSSOp2vASipYsG5I8jmTMpGk0an8Ljdrfwn1P9SA+vjCJ+w/lm5joe1w0Pme+ReLj2bc/+2ezLzEpc977Ol/LmTWrc22Xx7n1oWdo2eQCYmJiKF0yIdsyoyZO5bx6dTjtlGP2Kg2LvGzTGX6dPpXPPvmQl956n4SSJYMcYWDsT07LUeQULRxDYvKRm7sKCYVpWLU0o/881OXDDD7+1wX8uGgzF70wgaY9JlI8PpZH2tYOWuxHEsztOy/rOtyvs+fRs8/nfPb2S8wd/zWfvd2D7j0/ZsmK1XlPUo4qmNtAVhXKleGS88/liZd7Zk47+8wzKFoknri4WK5u05xz69Xhp99zO74aOifSnuUmPT2d7o89wMVNm/PNxJ8YMnYS+/buYUCv9wMZ7kkJ1H5NVtHR0dQ9pwH/bNvKuFx6pOS2XyORK78XPouAhkd47QugM/AvYMBxrCs5y+OsfXdy6/FkwCLnXAP/X33nXOssr2f+Ep1zXfEVRqcDc82szOErc871cc41cs416tKly3GE6lPp9CqkpaWxYd3azGmrVyzLtZtO5WrVWbViWZb5llOydBlKJJQ87vUsnj+X7f9so0nznBf+ZmUGLowDQZWoUAmXnpZtEIId61dT8tTj6JJili320qdVo93jr3FLzyG0eagHe//ZTNmquY6dETIdWjXNvPC292vPUbNqZZauXJP5+l+r1lCmVMlcj2DXrHo6y1atydadbdmqNdSoejoAhQsV4n8P3cuUr/syflAvEkoUp26t6pkjHGUYNWEqV7VuHpwEAyAvvw2AWb/9wvtvvEz319+mao2aoQrzpP39TyIxUUblMof+oz+jYnFWbt13xGWuPLcS89buYsPOQzuHCfGxnFIynqG//s3BtHR2Jx1k1Oz1XFK7/BHXEyzB3L7zsq7D/bVyNY3OPot6tWsSFRVF/Tq1OLvOGfz2Z8G4HqwgCeY2cLjUtHTWbTrymU3Dwvr/GeS9PTuSvXv2sG3rFq689kZi4+IokVCSy9t1ZNavPwc65BMWqP2a3KSlpbHpsGt8jne/RiJHfi98fgQK+c+mAGBm55tZU+Az4BEA59yiE1z/dOAmM4v2X8OTsae3FChnZhf53zPWzOrmtgIzq+Gc+9051x34B18BFBCF4+O5uGlzBvX9hANJSSyeP5ffZkyjRZt2OeZt0bY9E8aOYu3qVezds4ehA/vRyn+E43jXM+n7sTRp2oIihx15+fWnqezdswfnHEsXL2T08K+48NKmgUozz2ILFabKuRcxZ/RgDiYfYMuKxayd9zs1GufcUV+/cBZJe3YCsGvzOuaNG0rlcw4NT7tj/WpSD6aQmnKABRO+IWn3Tmpd1CpkuRyPjpc3ZcT3k1mxZh279+6j96BhXN0m96Lk/HPqEhUVxaBvxpKScpDB3/q6sjU+19d9b8u27Wz9ZwfOOeYtXkrvQcPodudN2dYxZ9FfbN2+I9+O5gZ5+23Mmz2Tt156jmdffoPaZ9ULQ7Qn7sDBNCYv3sz9rc6gcGw0DSqXotlZFRg758gjD3Y4txKj/sz+n/+u/QdZv2M/1zeuTHSUUbxwDFeeV4llm/YEO4VjCuT2fax1paalkZySQnp6Ounp6SSnpJDqv+avXu1azF6wJPMMz5Llq5i9YDFnVK8axOxPTnSUERcTRZQZUf7H0VH575q8YwnkNjBm0jQ2btmGc46NW7byfv/BXOh/bc++RGbMnJP5vY+ZNI3ZCxbT5PwGIcnzSPLSnjnnSElOJtU/il1KcjIHU1IASChZkgqnVGLsyOGkpaayb+9eJn8/hmo1w3swL6tA7dfs2rmDaZPGk7R/P2lpacz+/VemTRrP2Q0bZVvHkfZrJHKZC/ehjmMws1OBd/Gd+TkArAEecc4tN7Mf8HVh+8Q/bzPgCedcBzO7E2jknHvAzD4Dxjjnhvvn2+ecK+YfNe4DoAWQcVhhkHNuuJk1AN4HEvBdC/Wuc+5TM5vqf49Z/nV9A9TCd5Zosj+2o32obsW2Y19om2Hvnt28++pLzJn5OyVKJHBn1wdp1rotWzdv5v7brqfXF8MoX9HXN3nk0EEMH/w5ycnJNGnWggcOu49PbuvJkJKczK1XteGZl9+gQaMLssXw+vPPMGfm7xw8mELZcuVpf831dLw++87y0dQs5ztq99qU5ce9zLFku49P0RI0vMZ3H599O7Yy8sV/c83zH1OsdHn+GN6Plb9PITU5icIlSlLjguY0aH8TUdG+7kMzR/Rn2c/jSU9Lo0LNulx4432UKH/qMd79+D3V3HdhZur6E63NfT4bNor+Q0dywH+Pi+cf6Zp5j4v7nupBw/pn0qWzbyT2JctX0b3nx6z8ez3VK1eixxPdOLOW72jarPmLePq199mxazcVy5Xl/tuup0Or7EXsC2/34kByCq89/fAJxxtzmu84QV629bw63t/GUw/ex6L5c4nL0ve77tnn8lLP4HT/yNjeGzyT+6iJeVUiPpYXr63PhTXLsmv/Qd4f77uPT8WEwnzzyGV0enc6m3cfAODs00vS++4LaPnKZPanpGVbT+1TivNk+7M445QSpKU7Zq3aziujFrEzMSUgcc59xbfjciLbeqC272Ot66OBQ/n486+zvfe/b7+Bbnf42rPB347jixFj2L5zF6VLJnBzx7bcecNVHEvG9h6o7/x4dW1Zi64ta2Wb9snk5XwyOXBt7dGczHd+uEBtA+/1G8x3E6awZ18iJYoV5dLGDXn07lspmVCcHbt20/WZl1m9dgPRUVFUq1yJB++8mYsbNchzvIFu4463PduyaSN3XZ9tLCfKVzyFAcNHA7By+VI+fa8nq1YsJzo6ivrnNuLfj/+XkqVKByTOjPbtZPIOxH7N7p07eeW5/7J6xTLS0x3lK1ak43U30bbjNZnvc7T9mhPhzz3fHllIXb8o5Dv0MafVzbefx5Hk+8LnSMysCLAAOM85tzvc8eRBngofLwhG4VNQBKrwKWhCUfjkV4EufAqKQO4EFzThKnzCTd955LVxgSh8CioVPjkVxMInv4/qliszawX0B94uYEWPiIiIiEhAJY4fEvL3TLj75ZC/58kqkIWPc24SEP5bjouIiIiISIGQ3wc3EBEREREROWkqfERERERExPNU+IiIiIiIiOep8BEREREREc9T4SMiIiIiIp6nwkdERERERDxPhY+IiIiIiHieCh8REREREfE8FT4iIiIiIuJ5KnxERERERMTzVPiIiIiIiIjnqfARERERERHPU+EjIiIiIiKep8JHREREREQ8T4WPiIiIiIh4ngofERERERHxPBU+IiIiIiLieSp8RERERETE81T4iIiIiIiI56nwERERERERz1PhIyIiIiIinqfCR0REREREPE+Fj4iIiIiIeJ4KHxERERER8TxzzoU7hkijD1xERESk4LFwB3Aku/v9L+T7lwl3v5xvP48j0RkfERERERHxvJhwBxCJUtcvCncIIRVzWl0g8vKGQ7mv2LY3zJGEVs1yxYHIyxsO5R5p27t+55GXe0beDZ4ZF+ZIQm/uK+2AyGvjIrV9g0PbuxRsOuMjIiIiIiKep8JHREREREQ8T4WPiIiIiIh4ngofERERERHxPBU+IiIiIiLieSp8RERERETE81T4iIiIiIiI56nwERERERERz1PhIyIiIiIinqfCR0REREREPE+Fj4iIiIiIeJ4KHxERERER8TwVPiIiIiIi4nkqfERERERExPNU+IiIiIiIiOep8BEREREREc9T4SMiIiIiIiFjZqXNbKKZLff/W+oI860xswVmNtfMZuV1+cOp8BERERERkVB6CpjsnKsFTPY/P5LmzrkGzrlGJ7h8JhU+IiIiIiISSlcBA/2PBwJXh2J5FT4iIiIiIpInZtbFzGZl+euSh8UrOOc2Afj/LX+E+RwwwcxmH7b+410+m5g8BCgiIiIiIoJzrg/Q50ivm9kkoGIuLz2bh7dp4pzbaGblgYlm9pdzbnoeQ82kwkdERERERALKOdfqSK+Z2RYzO8U5t8nMTgG2HmEdG/3/bjWzkcAFwHTguJY/nLq6iYiIiIhIKI0C7vA/vgP47vAZzKyomRXPeAy0BhYe7/K5UeEjIiIiIiKh9BpwuZktBy73P8fMTjWzcf55KgAzzGwe8Acw1jn3w9GWP5aI6OpmZmnAAiAWSMU3+sO7zrn0sAZ2ggYOH02/oSNJTknh8ksvpPvD9xEXF5vrvEtWrKb7Wx+xau16qlc+jZee6MaZNasBMO7HGXw0cCj/7NxFXGwMl5x/Hs8+eA/FihYB4L+vvMtvc+aTdCCZsqVKcteNV3Nd+8tDlmduApX7i+98wuhJh7qIpqalEhsTw8wxXwKwYfNWerzXh3mLlxIbG0vryy7iqW53ERMdHfwk82Dvnt2892oP/pz5GyUSSnLnfQ/QrHXbHPNN+n4Mo4YNZeP6dRQpWpRml7fhji7diI4pOE3A8ea6ZtUK+n34LiuWLmHP7t2MnTEr2+ujR3zFpHFjWLNqBU1bteGxZ18IUQZ5E6htffnqv3njk4EsXraSXXv2smjyN9mWbdT+lmzPk1NSuKljG5598N7gJJaLQOV6POsa9+MMen3xFZu2/kPZUiX5v/88SMOzz2LFmnU88/r7rNu4GYCzzqjB093upmbV04ObfBahatvvfOw55i1eRrS/PatQtjRjB34YmiQD4MYLq9DxvNOoVbEYP8zbRPcR88Md0gk53jYNYORXgxk++HNSkg/QpGkLuj3xNLFxcdnm2bBuLd3uuIkmzVryZPcemdN/mjyRQf17s33rVspWqMAdXbpx0WXNgpnaCQnU9p+ScpC3+37BD1N/Jjk5hStaXMLT3e4mtgD9fxcJnHPbgZa5TN8ItPM/XgWck5fljyVSzvgk+cf/rouvKmwHPH+yKzWzkP+KZsycQ78h39D/rReYMPgT1m/awocDh+Y6b8rBgzz43Kt0aNWUX7/9gqtaN+fB514l5eBBAM6tV4dB77/C76MG8cOgXqSlp/H+gC8zl7/3lk5MHNybP0YP5sOXn+b9AUNYtGxlSPLMTSBzf/7Rrswa+2XmX7vml9Km6cWZy/d4rw+lSyYwdVg/RvTpyaz5ixj63Q+5vlc4fdzzdWJiYxk8agJPdn+Zj3q+yt+rcn5HyQcO0OXhxxkydhJv9/mMubNmMmLIoDBEfOKON9eYmBguadGKh556Ltf1lC5bjpvuuJvW7TsGO+QTFshtPSYmhrZNL6bHE91yXT7r72D68P4UiovL9lsItkDmeqx1/TJrLm9/+jkvP/kAf4wezMB3Xua0UyoAUL5sad55/kl++fZzZnzzGc0vOp8nX347+B+AXyjbdoBnH7wn83svSEUPwLa9B+g7ZQXfzl4f7lBOyvG2abN//5Xhgwbyyrsf03/YaDZv3MCgfr1zzNfr7dc5o85Z2ab9s20rb/V4jnsfeJRhE6Zx178f5s0Xn2XXzh1By+tEBHL77zv0GxYtXcm3fd9l7MAPWbJ8Fb0HDQ9lOpKPRUrhk8k5txXoAjxgPoXNbID/rrBzzKw5wFGm32lmw8xsNL7h9U4xs+n+O8ouNLNLgxn/dxOm0umKltSsWpmE4sXoeuv1fDt+Sq7zzpy3iLS0dG6/tgNxcbHc2qk9Dvh9zgIATilfllIJJTLnj46KYu2GzZnPa1atnHm0xcwwI/NoaDgEMves9icdYOJPv3JV6+aZ0zZs3kLbZhdTKC6OcqVLccn557JizdpgpXZCDiQl8cu0H7ntnq7EFylC3XMa0PiSy/hx/Lgc87a/5jrqnXMusbGxlC1Xnuat27JkwbwwRH1i8pLraZWr0qbD1VSpViPXdTVp2oKLLmtG8RIJwQ77hAVyW692eiWubdeKGsdx5mLC9F8pUyqBhvXPOua8gRLIXI+1ro8GfsX9t93AOWfVJioqigrlylChXBkAShQrSqWK5TEzHBAVFcXajZuCnX6mULbtBd2Pi7YwZckWdu8/GO5QTlhe2rTJ34+hdYerqFK9BsVLlOCmO+9h0vdjss0zbdJ4ihYrzjkNz882/Z+tWylarDiNLmqCmXHBxZdQKD6eTRvyV9EYyO1/6q+zuLVTe0qWKE7pkgl0vqY9I3+YHMJsJD+LuMIHMk+dReEb87ubf1p94GZgoJkVPsp0gIuAO5xzLYBbgPHOuQb4TsfNDWbsK9aspXaNqpnPa9eoyvadu9i1e28u867jjOpVMLND81erwso16zKfz16whMYdb+WCDp2Z+NNv3HZth2zreOm93jRsdxMd7nyQcqVLcWnj8wKf1HEKdO4ZJv70K6VLJtDo7EM7e7d26sD3U2aQdCCZLdu289Mff3LJBecGNqGTtGHd30RFRVOpcpXMadVqnMHa1auOuezCeXOoXK16MMMLqJPJtSAK1rZ+LN9NmELHy5tmW1ewBTLXo60rLS2NhctWsmP3Htre9m9a3HgPL7//KQeSk7O9x4Udb+W8tjfyyod9ufeWawOb7FGEum1/t99gmlxzB50fepo/5i5EQisvbdra1auoVrPWoflqnsGuHdvZs3sXAPsT9zGob2/ueeCRHMvWqnMmp1etxm8zppGWlsav06cSGxtHtRq1cswbToHc/p1zOOcOLeAcm7dtZ+++xKDFLwVHJHd4zPjFXAJ8AOCc+8vM/gbOOMp0gInOuYzzxDOB/mYWC3zrnJsbzKCTkg5k9tMGMh8nJiVRMqF4tnn3JyVRPMu8AMWKFSExKSnzecP6Z/L7qEFs2bad4eMmUqlC9vs/dX/4Pp594B7mLl7GzHkLiYvNvb9tKAQ69wzfTZiaY2ev0dl1GT52Io2v7ExaejpXtW5OyyaNA5nOSUtKSqJIsWLZphUtVoyk/Udv3CeOHcXyv5bw0H9z7wqWH51orgVVsLb1o9m4ZRuz5i8+Ype4YAlkrkdbV8rBg6SmpjJx+q988e7LxMTE8MBzr9J70HAevrtz5jK/jRrE/qQDfDdhCqdWKBfYZI8ilG37Y/feRo0qpxMbE8O4KTPo9r9XGNHnbSqfmtvtNiQY8tKmJSXtp0jRYtnmA0jav58SCSX54tNPaN2hI+Uq5Pz+oqOjadG2HW+++D9SUlKIjYnhqR6vUzg+PsAZnZxAbv+XXHAeX3wzhgsa1CMtPZ1BI31n0Q4kJ1O8WNFgpiEFQESe8TGz6kAavjG/j3Ro82iHPDNbJv9NlC4DNgBfmNntubxf5p1t+/Q54n2ecjVm0jQatb+FRu1v4b6nehAfX5jE/Yf+c0tM9D0umksjViQ+nn37s+/87EtMynXeCuXKcMn55/LEyz1zvBYdHU3D+meyZdt2vho1Pk/xn4xQ5L5p6z/MmreIjpc3y5yWnp5Ol6deotWlFzJr7BB+/mYge/bto2efLwKY3cmLj48nKXFftmn7ExOJL3Lkhv3X6VP57JMPeemt90koWTLIEQbOieRakITqd340oyZO5bx6dTKveQmWYOZ6tHUVKuS7EPyWq9tRrkxpSiWU4I7rOjL9jz9zeZ/C3HhlG55+7X2279x1cgkfQTjb9rPPPIOiReKJi4vl6jbNObdeHX76fXagUpPjkJc2LT6+CEmJiVnm8y0XX6QIK5cvZe6sP7j6xs45lgOYM/N3Bnz8Aa990JvvpvzKax/24f3Xe7By+dIAZpN3wdz+7+t8LWfWrM61XR7n1oeeoWWTC4iJiaF0yfzbvVlCJ+IKHzMrB3wCfOh850KnA539r50BVAaWHmX64eurAmx1zn0K9ANy9AVzzvVxzjVyzjXq0qVLnuLt0Kpp5gWovV97jppVK7N05ZrM1/9atYYypUrmOCICULPq6SxbtSbbKd9lq9Ycsa9/alo66zZtOWIsaWlpIb3GJxS5j5o4lQZ163B6liOdu/fuY/PWf7jlqiuIi4ulZEJxrmnTgp/+yF87BpVOr0JaWhob1h269mj1imVH7MI267dfeP+Nl+n++ttUrVEzVGEGRF5zLWhC+Ts/klETpma7zi1Ygpnr0daVULwYFcuV4Xh78aU7x4HkFLb+E5yLwPNT224YWXsGSfDlpU2rXK06q1YsyzLfckqWLkOJhJIsmDObLZs3cue1HejcsQ3fDB3EL1N/5KG7fIXQquXLqHfOudSqcxZRUVGccWZdap9Vj7kz/wh+kkcRzO2/cKFC/O+he5nydV/GD+pFQoni1K1VPXMUQ4lskVL4xPsHH1gETAImAC/6X/sYiDazBcBXwJ3OueSjTD9cM2Cumc0BrgXeC2YiHS9vyojvJ7NizTp2791H70HDuLpN7jsr559Tl6ioKAZ9M5aUlIMM/tZ3urfxufUB3xGXjVu24Zxj45atvN9/MBf6X9u+cxfjfpxBYlISaWlpzJg5h3FTZmQuGw6BzD3DqAlTc6yjVEIJTjulAkNHjyc1LY09+xL5bsKUbP2P84PC8fFc3LQ5g/p+woGkJBbPn8tvM6bRok27HPPOmz2Tt156jmdffoPaZ9ULQ7QnJy+5OudISU4m1T/CT0pyMgdTUjJfT0tNJSU5mfT0dNLT00hJTiYtNTVkuRyPQG7rzjmSU1I4eNCXY3JKCikp2S8Kn7PoL7Zu3xHS0dwyBDLXY63r6jYtGPztOLbv3MXuvfv4YsQYml7YEPCN+LZk+SrS0tLYl7ifN3oNoETxolSvclqQPwGOK/asTqZt37MvkRkz55CckkJqWhpjJk1j9oLFNDm/QUjyDIToKCMuJoooM6L8j6OjQnddWiDkpU1r0bY9E8aOYu3qVezds4ehA/vR6grfNVttO3ai71ff8sGAwXwwYDBXXHUt51/chB49fSP1nXHmWSyaPyfzDM/KZX+xaN7cbNcM5QeB3P63bNvO1n924Jxj3uKl9B40jG533hSyXCR/M6fDPKHmUtcvOqkVfDZsFP2HjuSAf6z75x/pmjn62n1P9aBh/TPp0vk6AJYsX0X3nh+z8u/1VK9ciR5PdOPMWr4jSu/1G8x3E6awZ18iJYoV5dLGDXn07lspmVCcHbt28+iLb7J05RrSnePUCuXofE17rj+B+/jEnFYXgJPNO5C5A8xdtJR7/vMC04b1p2iR7KfTl6xYzesf92fpyjVERUVxQYN6/O+heylTqmSe4s3IfcW2nBdoBsLePbt599WXmDPzd0qUSODOrg/SrHVbtm7ezP23XU+vL4ZRvmJFnnrwPhbNn0tclvs+1D37XF7q+X5Q4qpZzneULpB5H2+uWzZt5K7rsw9VXb7iKQwYPhqAwf168+WAT7O9fsu/7qXz3fcFJM6M3PPL73zD5q207tw127pPrVCOiV8eGg73hbd7cSA5hdeefviE4z2Z33kgf9dHW9fB1FRe/agf4yb/RFxcHG2bXczjXW6nUFwc46f9wgcDhrB523YKF4qjXu2aPHrPrcd1wCNQbVyo2vauz7zM6rUbiI6KolrlSjx4581c3KhBnuPNyLvBMzlHIgumri1r0bVl9h33TyYv55PJy0MWw9xXfAXKybRxx9umAYwcOojhgz8nOTmZJs1a8EAu9/EBX/u2ccP6bPfxGT3iK777egi7duygRMlSdOh0PZ1uvvWEYg5U+5abQG3/s+Yv4unX3mfHrt1ULFeW+2+7ng6tmp50fP7tPd9W2Lv7/S/kO/QJd7+cbz+PI1HhE3onXfgUNIEsfAqaYBc++VUwCp+CIpg7BvmZfueRl3u4Cp/8IBCFT0EUqe0bqPDJTUEsfCKlq5uIiIiIiEQwFT4iIiIiIuJ5KnxERERERMTzVPiIiIiIiIjnqfARERERERHPU+EjIiIiIiKep8JHREREREQ8T4WPiIiIiIh4ngofERERERHxPBU+IiIiIiLieSp8RERERETE81T4iIiIiIiI56nwERERERERz1PhIyIiIiIinqfCR0REREREPE+Fj4iIiIiIeJ4KHxERERER8TwVPiIiIiIi4nkqfERERERExPNU+IiIiIiIiOep8BEREREREc9T4SMiIiIiIp6nwkdERERERDxPhY+IiIiIiHieCh8REREREfE8c86FO4ZIow9cREREpOCxcAdwJLv7/S/k+5cJd7+cbz+PI9EZHxERERER8byYcAcQiVLXLwp3CCEVc1pdAFZs2xvmSEKvZrniQOTlHql5Q+TmnpF3pLVvELltXKRu63Ao9wbPjAtzJKE195V2QGR/51Kw6YyPiIiIiIh4ngofERERERHxPBU+IiIiIiLieSp8RERERETE81T4iIiIiIiI56nwERERERERz9Nw1iIiIiIiBdi8Pj+G/D0vuzvkb3nSdMZHREREREQ8T4WPiIiIiIh4ngofERERERHxPBU+IiIiIiLieSp8RERERETE81T4iIiIiIiI56nwERERERERz1PhIyIiIiIinqfCR0REREREPE+Fj4iIiIiIeJ4KHxERERER8TwVPiIiIiIi4nkqfERERERExPNU+IiIiIiIiOep8BEREREREc9T4SMiIiIiIp6nwkdERERERDwvJtwBSN4NHD6afkNHkpySwuWXXkj3h+8jLi4213mXrFhN97c+YtXa9VSvfBovPdGNM2tWA2D56r9545OBLF62kl179rJo8jfZlv3vK+/y25z5JB1Ipmypktx149Vc1/7yoOeXm717dvPeqz34c+ZvlEgoyZ33PUCz1m1znXfkV4MZPvhzUpIP0KRpC7o98TSxcXEAbNm0kY97vsaShQuIjYvjkmYt6PLQ40THxHDw4EHefPFZlv+1hK2bN/Hq+59w9nmNQpnmcTnez2LS92MYNWwoG9evo0jRojS7vA13dOlGdEz2n/2GdWvpdsdNNGnWkie79whVGnl2vHmvWbWCfh++y4qlS9izezdjZ8zK9vq1l1+a7XlKcjLtrrmO+x/9T1DjP1GB+L4PpqTwUc/XmDvrD/bt2cMpp53GHV260eiiJmHIKKdAtWnHWtfKv9fz8vt9WLx8FaUSSvDEfbfT6pILAZi3eCkfDBjCouWriI6K4vxz6vLMA3dTrkzp4H8AhwnUtn609i6/CFTbnuFI7dlPkycyqH9vtm/dStkKFbijSzcuuqxZMFMLihsvrELH806jVsVi/DBvE91HzA93SHkWyO982qTxfDngU7Zt2Uyp0mV49NkXqHfOufy1cAFf9O3FiqV/ERUdRf0GDen6yJOULls2VGlKPuWJMz5mdo2ZOTOr439+qpkNP8K8zcxsTGgjDJwZM+fQb8g39H/rBSYM/oT1m7bw4cChuc6bcvAgDz73Kh1aNeXXb7/gqtbNefC5V0k5eBCAmJgY2ja9mB5PdMt1+Xtv6cTEwb35Y/RgPnz5ad4fMIRFy1YGLbej+bjn68TExjJ41ASe7P4yH/V8lb9X5Yxl9u+/MnzQQF5592P6DxvN5o0bGNSvd5b1vEZCqdIM+u4HPhgwmAVz/2TsyEObyllnN+CJ53pQqkyZkOR1Io73s0g+cIAuDz/OkLGTeLvPZ8ydNZMRQwblmK/X269zRp2zQhH6STnevGNiYrikRSseeuq5XNczYuJPmX+DR00grlAhLm3eKtjhn7BAfN9paWmUK1+B1z/sw9fjp3LbPffzWven2bJpY6jTySGQbdrR1pWalsaDz71K0wsb8cvIgbzw2P089ep7rFnn+wz27E3k+g6tmTj4EyZ+2ZuiReJ59s0PQ/MhHCZQ2/qx2rv8IFBte4bc2rN/tm3lrR7Pce8DjzJswjTu+vfDvPnis+zauSNoeQXLtr0H6DtlBd/OXh/uUE5YoL7zOTN/Y0CvD3j06ecZPmE6r3/0KRVPrQTAvr17aNuxEwOGj2LA8DEUKVKEd155MWQ5Sv7licIHuBmYAdwE4Jzb6Jy77vCZzCz/HOY6Qd9NmEqnK1pSs2plEooXo+ut1/Pt+Cm5zjtz3iLS0tK5/doOxMXFcmun9jjg9zkLAKh2eiWubdeKGlVPz3X5mlUrZx4pNTPMYN3GzUHJ62gOJCXxy7Qfue2ersQXKULdcxrQ+JLL+HH8uBzzTv5+DK07XEWV6jUoXqIEN915D5O+P1Tnbt60kUtbtCKuUCFKlylLw8YX8/dqX4MbGxvL1TfcQt1zGhAVFR2y/PIiL59F+2uuo9455xIbG0vZcuVp3rotSxbMyzbPtEnjKVqsOOc0PD9UKZyQvOR9WuWqtOlwNVWq1TjmemdMnUxCydLUPefcYIR90gL1fReOj6fz3fdR4ZRTiYqK4oIml1Lh1FNZsXRJqFPKIZBt2tHWtXrterZu38kd111JdHQ0F55bn3Pr1mHUpKkAXNr4PNo0vZhiRYsQX7gQt1x1BXMW/hWKjyCbQG7rR2vv8oNAtu1w5Pbsn61bKVqsOI0uaoKZccHFl1AoPp5NGwpe8fDjoi1MWbKF3fsPhjuUExLI73xQvz7c/K97qFOvPlFRUZQtV56y5coD0OiiJlzaohVFihajcOHCdLj2xhz//0lkKvCFj5kVA5oAd+MvfMysqpkt9D++08yGmdloYIJ/sRJmNtLMFpvZJ2YW5Z+3l5nNMrNFZvZilvdoZ2Z/mdkMM3s/44yRmTU1s7n+vzlmVjzY+a5Ys5baNapmPq9doyrbd+5i1+69ucy7jjOqV8HMDs1frQor16w77vd76b3eNGx3Ex3ufJBypUtxaePzTir+E7Fh3d9ERUVTqXKVzGnVapzB2tWrcsy7dvUqqtWsdWi+mmewa8d29uzeBcBV19/E9EkTOHDgAP9s28rs336mYeOLg55DoOTlszjcwnlzqFyteubz/Yn7GNS3N/c88EgwQg2ok8n7aCZ/P4aWbdtl+43kJ4H8vrPauWM7G9atpfJxFIfBFsg27Wjrci7nezvnWLF6ba5xzVqwmJpHOCgUTIHc1vN7exfItv1o7VmtOmdyetVq/DZjGmlpafw6fSqxsXFUq1Erx7wSXIH6ztPS0ljx12J279zFPTdeze3XtKPX26+TnHwg1/ddOO/PI7aHEh5mVtrMJprZcv+/pXKZp3aW/ey5ZrbHzB7xv/aCmW3I8lq743nfAl/4AFcDPzjnlgE7zCy3PfOLgDuccy38zy8AHgfqAzWATv7pzzrnGgFnA03N7GwzKwz0Bq5wzl0ClMuy3ieAbs65BsClQFJAM8tFUtIBihUtkvk843FiUs633p+URPEs8wIUK1Yk13mPpPvD9/HH6MF8/u7/0erSC4mLzb3ffTAlJSVRpFixbNOKFitG0v7EXObdT5GixbLNB5C0fz8A9Ro05O/Vq7i+TVPuuKYdNeucVaD6eefls8hq4thRLP9rCZ1uvi1z2heffkLrDh0pV6FiUGINpBPN+2i2bt7Mwrl/0vKKDicbXtAE8vvOkJqaypsvPkfLtu05vUrVQIZ7QgLZph1tXdUqV6JMqQT6f/UtB1NT+XnWXGbOX0xSckqO91m6cg29vhjGE13uOPkE8yiQ23p+b+8C2bYfrT2Ljo6mRdt2vPni/7i6xcW8+eKzPPDkMxSOjw9kOnIcAvWd79q5g9TUVH6eOpk3PurLBwO+ZOXypQz9rF+O9axesZwhA/pyV7eHA5yNnKSngMnOuVrAZP/zbJxzS51zDfz72Q2B/cDILLO8k/G6cy7nacNceKHwuRnI6BA+1P/8cBOdc1k78/7hnFvlnEsDhgCX+KffYGZ/AnOAusBZQB1glXNutX+eIVnW8zPwtpk9BJR0zqXmFqCZdfGfSZrVp0+fPCU3ZtI0GrW/hUbtb+G+p3oQH1+YxP2HdggSE32Pi+bSgBeJj2ff/uw7D/sSk3Kd92iio6NpWP9Mtmzbzlejxudp2UCIj48nKXFftmn7ExOJL1I0l3mLkJSYmGU+33LxRYqQnp5O98ce4OKmzflm4k8MGTuJfXv3MKDX+8FNIIDy8llk+HX6VD775ENeeut9EkqWBGDl8qXMnfUHV9/YOZjhBsyJ5H0sP/4whrPObpDZJzw/CtT3nSE9PZ2ePZ4jNjaG+x/7bzBCPqZgtmlHW1dsTAzvv/hfpv8+m6bX3cVnw0bRtunFVCyb/Xq+vzdsouvTL/N0t7toeHbor30L1LZeENq7QLXtx2rP5sz8nQEff8BrH/Tmuym/8tqHfXj/9R6sXL40gNnI8QjUdx4XVwiAK6+7kdJly5JQsiTX3NiZWb/9nG0dG9ev4/knHuK+hx+nXj7t0hzBrgIG+h8PxHci42haAiudc3+fzJsW6MLHzMoALYC+ZrYGeBK4ETi838rhhxIO7/TgzKwavjM4LZ1zZwNjgcK5rOvQQs69BtwDxAO/ZQyukMt8fZxzjZxzjbp06XJcuWXo0Kops8Z+yayxX9L7teeoWbUyS1euyXz9r1VrKFOqJCUTcvayq1n1dJatWoPL0sdj2ao1R7ym51jS0tLCco1PpdOrkJaWxoZ1h7qkrF6xLNfT1pWrVWfVimVZ5ltOydJlKJFQkr179rBt6xauvPZGYuPiKJFQksvbdWTWrz/nWE9+lZfPAmDWb7/w/hsv0/31t6lao2bm9AVzZrNl80buvLYDnTu24Zuhg/hl6o88dFf+LITymvfxmPzDOFq2bR+I8IImUN83+Lp1vfdaD3bu2MEz//cGMWEa2SuYbdqx1lW7RlUGvvMyv3z7OZ++3p31m7ZQv86hz2njlq3c8+QLdL3tejpe3iywiR+nQG3rBaG9C1Tbfqz2bNXyZdQ751xq1TmLqKgozjizLrXPqsfcmX8EP0nJJlDfefESJShbvsKRd9CArZs38ewj/+amO++mRT5v6yNUBefcJgD/v+WPMf9NZD/5APCAmc03s/65dZXLTYEufIDrgM+dc1Wcc1Wdc6cDq4HTjrHcBWZWzX9tz434BkYoga9A2m1mFYAr/PP+BVQ3s6r+5zdmrMTMajjnFjjnXgdm4Ts7FFQdL2/KiO8ns2LNOnbv3UfvQcO4uk3zXOc9/5y6REVFMeibsaSkHGTwt76zgI3PrQ/4doSSU1I4eNB3oio5JYWUFN8Fk9t37mLcjzNITEoiLS2NGTPnMG7KjMxlQ6lwfDwXN23OoL6fcCApicXz5/LbjGm0aJOzO2eLtu2ZMHYUa1evYu+ePQwd2I9W/q5MCSVLUuGUSowdOZy01FT27d3L5O/HUK3mGZnLH0xJISU5GfB1CUpJTs62kxVuefks5s2eyVsvPcezL79B7bPqZXutbcdO9P3qWz4YMJgPBgzmiquu5fyLm9CjZ3hGsTqWvOTtnCMlOZlU/0hfKcnJHEzJ3p1p8YJ5bP9nK5e0yL+juUHgvm+Aj956lXVrVvP86+9QqFDhUIR/XALZph1rXUtXriE5JYWkA8kM+Ppbtu3YydVtfD2gt2zbzl2PP8/NV13BjVe2CXLWRxaobf142rtwC1Tbfqz27Iwzz2LR/DmZZ3hWLvuLRfPmZrt+pKCIjjLiYqKIMiPK/zg6Kn9eo5ibQH3nAK3aXcnoEV+za+cO9u7Zw3dfD+GCi323K/hn21aefqgrHTpdT7urc4x1JQGStUeT/6/LYa9PMrOFufxdlcf3iQM6AsOyTO6F73KVBsAmoOdxrSs/7dTllZlNBV5zzv2QZdpD+IqW051z9czsTqCRc+4B/+vNgO7ANnzX+EwH/u2cSzezz4DGwCogGRjlnPvMzK4E3gT+Af7AV6V2NrMPgOZAGrAYuNM5l3yMsF3q+kUnlfdnw0bRf+hIDvjvU/H8I10zR1+776keNKx/Jl06+37oS5avonvPj1n593qqV65Ejye6cWYt35GVDZu30rpz12zrPrVCOSZ+2Zsdu3bz6ItvsnTlGtKd49QK5eh8TXuuP4H7+MScVheAFdtyXqx8vPbu2c27r77EnJm/U6JEAnd2fZBmrduydfNm7r/tenp9MYzyFX19u0cOHcTwwZ+TnJxMk2YteCDLuP8rly/l0/d6smrFcqKjo6h/biP+/fh/KVnKd6+Of113JVs3b8r23v2HjaLCKaeeUNw1y/mONJ9M7oc73s/iqQfvY9H8ucRluedB3bPP5aWeObu6DO7Xm40b1gfsPj7hzHvLpo3cdX3HbMuWr3gKA4aPznz+wRv/R3LyAZ54LvD3LQp07oH4vrdu3sS/rruS2Lg4oqMPjVj4wJPP0Lz1Fbm9bZ5l5H0i7Vug2rRjreut3gMZMW4SB1PTaFj/TJ558B6qVDoFgI8//4qPBn5FfOHsReGssV8eM/5AtHFZBWpbP1Z7d7ICsa0Hqm3PKrf2bPSIr/ju6yHs2rGDEiVL0aHT9XS6+dYTjjsj9wbPHNdlBQHTtWUturbMXrB9Mnk5n0xeHpL3n/uKr0DJD995amoqvd99i2mTfiA2rhCXtmjFXfc/RFyhQnzZvw+D+/fJcR3XiIk/nXDc/u8831aZ0xtfHPId+st+/+WEPw8zWwo0c85tMrNTgKnOudpHmPcqfNfUtz7C61WBMc65nEf9Dp+3IBc+oWJmxZxz+8w3lNBHwHLn3DsnuLqTLnwKmkDvFBQkwSgACoJIzRsiN/eTKXwKukht4yJ1W4fwFT7hFojCp6BS4ZPTSRY+bwLbnXOvmdlTQGnnXK53EjezocB459yALNNOyegqZ2aPAo2dczcd630Lele3ULnXzOYCi4AEfKO8iYiIiIhI3r0GXG5my4HL/c8xs1PNLPOIgpkV8b/+zWHLv2FmC8xsPr7eV48ez5sW+Bt6hoL/7M6JnuERERERERE/59x2fCO1HT59I9Auy/P9QJlc5st5v4bjoDM+IiIiIiLieSp8RERERETE81T4iIiIiIiI56nwERERERERz1PhIyIiIiIinqfCR0REREREPE+Fj4iIiIiIeJ4KHxERERER8TwVPiIiIiIi4nkqfERERERExPNU+IiIiIiIiOep8BEREREREc9T4SMiIiIiIp6nwkdERERERDxPhY+IiIiIiHieCh8REREREfE8FT4iIiIiIuJ5KnxERERERMTzVPiIiIiIiIjnqfARERERERHPU+EjIiIiIiKep8JHREREREQ8T4WPiIiIiIh4ngofERERERHxPHPOhTuGSKMPXERERKTgsXAHcCTTG18c8v3Ly37/Jd9+HkeiMz4iIiIiIuJ5MeEOIBKt2LY33CGEVM1yxQFIXb8ozJGEXsxpdYHIyz1S84bIzT1S84ZDuattjxyR/p03eGZcmCMJvbmvtAt3CBIAOuMjIiIiIiKep8JHREREREQ8T4WPiIiIiIh4ngofERERERHxPBU+IiIiIiLieSp8RERERETE81T4iIiIiIiI56nwERERERERz1PhIyIiIiIinqfCR0REREREPE+Fj4iIiIiIeJ4KHxERERER8TwVPiIiIiIi4nkqfERERERExPNU+IiIiIiIiOep8BEREREREc9T4SMiIiIiIp6nwkdERERERDxPhY+IiIiIiHieCh8REREREfE8FT4iIiIiIuJ5KnxERERERMTzVPiIiIiIiIjnqfARERERERHPiwn0Cs2sDDDZ/7QikAZs8z+/wDmXcpRlqwJjnHP1TvC9PwOaArv9k/Y75y4+wXW9BEx3zk06keWDZe+e3bz3ag/+nPkbJRJKcud9D9Csddtc5x351WCGD/6clOQDNGnagm5PPE1sXBwAo0d8xaRxY1izagVNW7XhsWdfyFxuyoTv+fDNVzKfu/R0kpOTebfvF9Sqc2ZQ8zuWgcNH02/oSJJTUrj80gvp/vB9xMXF5jrvkhWr6f7WR6xau57qlU/jpSe6cWbNagB8O34Kg0eO5e8NmyhWJJ52LS7jkXs6ExMdDcB/X3mX3+bMJ+lAMmVLleSuG6/muvaXhyzPwwUq7xff+YTRk6ZnzpualkpsTAwzx3yZOW3cjzPo9cVXbNr6D2VLleT//vMgDc8+K7gJnqC8fC7Pv92LWfMW8feGTfR4ohvXtG0R4mjzJlDf+fLVf/PGJwNZvGwlu/bsZdHkb7It26j9LdmeJ6ekcFPHNjz74L3BSSwXgcr1WOsa/O04vhs/hWWr/6Zd80t55b8PZi63YfNWWnfuSnzhwpnT7r7pau6/7YYgZZ1doNr2N196jnmz/+BA0gFKlS7DdZ1vp82VVwP5u20/XKC2iZSUg7zd9wt+mPozyckpXNHiEp7udjexMQHf/cmzQH3nANMmjefLAZ+ybctmSpUuw6PPvkC9c87lr4UL+KJvL1Ys/Yuo6CjqN2hI10eepHTZsqFKMyBuvLAKHc87jVoVi/HDvE10HzE/3CFJARXwMz7Oue3OuQbOuQbAJ8A7Gc+PUfREByiEJ7O83wkVPQDOue75regB+Ljn68TExjJ41ASe7P4yH/V8lb9Xrcwx3+zff2X4oIG88u7H9B82ms0bNzCoX+/M10uXLcdNd9xN6/YdcyzbvPUVjJj4U+bf/Y8/RcVTK1Gzdp2g5nYsM2bOod+Qb+j/1gtMGPwJ6zdt4cOBQ3OdN+XgQR587lU6tGrKr99+wVWtm/Pgc6+ScvAgAAeSk3nq33cx45vPGPLh6/w+Zz4Dvv4uc/l7b+nExMG9+WP0YD58+WneHzCERctyfs6hEMi8n3+0K7PGfpn51675pbRpeuhn8susubz96ee8/OQD/DF6MAPfeZnTTqkQkjzzKi+fC0Dt6lV57uEunFWregijPDGB/M5jYmJo2/RiejzRLdfls24P04f3p1BcXLZtItgCmeux1lW+TGnu63wdndq2PGI8v436IvPzCFXRA4Fr22+49U4GDBvN8AnT6P7623z+aS+W/7UEyL9t++ECuU30HfoNi5au5Nu+7zJ24IcsWb6K3oOGhzKdIwrUdz5n5m8M6PUBjz79PMMnTOf1jz6l4qmVANi3dw9tO3ZiwPBRDBg+hiJFivDOKy+GLMdA2bb3AH2nrODb2evDHYoUcCHp6mZmn5nZdVme7/P/28zMppjZl8AC/8sxZjbQzOab2XAzK+Kft6WZzTGzBWbW38wK5eH9X/AvM9XMVpnZQ1lee87M/jKziWY2xMyeODxmM1tjZi+a2Z/+96/jn17Uv96Z/tiuOtnP6mgOJCXxy7Qfue2ersQXKULdcxrQ+JLL+HH8uBzzTv5+DK07XEWV6jUoXqIEN915D5O+H5P5epOmLbjosmYUL5FwzPed/P0YWrZtj5kFNJ+8+m7CVDpd0ZKaVSuTULwYXW+9nm/HT8l13pnzFpGWls7t13YgLi6WWzu1xwG/z/FtZjd1bEvDs88iLjaWCuXK0L7lZcxZ+Ffm8jWrVs48umhmmMG6jZuDnmNuApl3VvuTDjDxp1+5qnXzzGkfDfyK+2+7gXPOqk1UVBQVypWhQrkywUrtpOTlcwG45eoruPC8s4941Dg/CeR3Xu30SlzbrhU1qp5+zPedMP1XypRKoGH90J3hC2Sux1rX5ZdeSMtLGpNQongoUjtugWzbq1SvkXkmwMz3t3lD7juL+aVtP1wgt4mpv87i1k7tKVmiOKVLJtD5mvaM/GFyrusKpUB+54P69eHmf91DnXr1iYqKomy58pQtVx6ARhc14dIWrShStBiFCxemw7U3smTBvJDlGSg/LtrClCVb2L3/YLhDkQIuP1zjcwHwrHMu43/a2kAf59zZwB7g32ZWGPgMuNE5Vx9fF737j7C+N81srv9vcJbpdYA2/vd73sxizawRcC1wLtAJaHSUOP9xzp0H9AKe8E97FvjROXc+0Nz/3kXzknxebFj3N1FR0VSqXCVzWrUaZ7B29aoc865dvYpqNWsdmq/mGezasZ09u3fl6T23bt7EonlzaNG2/QnHHSgr1qyldo2qmc9r16jK9p272LV7by7zruOM6lWy/Ydeu1oVVq5Zl+u6Z89fTM3Ddgxfeq83DdvdRIc7H6Rc6VJc2vi8wCSSR8HKe+JPv1K6ZAKN/N3Y0tLSWLhsJTt276Htbf+mxY338PL7n3IgOTnwSQVAXj6XgiaY2/rRfDdhCh0vbxrSHeFA5hqIbeLym++jxY338OwbH7Bz9568J3QCAt22f/TWa3Rq2YT7brmO0mXK0uiiJjnWk5/a9sMFcptwzuGcO7SAc2zetp29+xKDFv/xCNR3npaWxoq/FrN75y7uufFqbr+mHb3efp3k5AO5vu/CeX9SuVr+P+stEiz5ofD5wzm3Osvzdc65n/2PBwGX4CuGVjvnlvmnDwQuO8L6snZ165xl+ljnXLJz7h9gK1DBv+7vnHNJzrm9wOijxJnRMX42UNX/uDXwlJnNBaYChYHKR832JCQlJVGkWLFs04oWK0bS/pwNeFLSfooULZZtPoCk/fvz9J6TfxhL3bMbZJ42D6ekpAMUK1ok83nG48SkpBzz7k9KoniWeQGKFSuS67wjf5jMomUr+NcN2U/YdX/4Pv4YPZjP3/0/Wl16IXGx4TlTEKy8v5swNdtO7vadu0lNTWXi9F/54t2XGdHnbZasyD/dQg6Xl8+loAnWd340G7dsY9b8xdnOAIZCIHM9mW2iZEJxvvr4DSYO6c3Xvd4icX8S/3nl3TzncyIC3bZ3e+Iphk2Yzhsf9eXiy5pnuxYkQ35q2w8XyG3ikgvO44tvxrBj12627djJoJG+MyrhPqATqO98184dpKam8vPUybzxUV8+GPAlK5cvZehn/XKsZ/WK5QwZ0Je7uj0c4GxECo5QXd2Xir/IMt9eVtZW+PBfucvleSAOP2Zt5dLw5Z6X9WYsn7Es/uWvdc4tPdqCZtYF6ALQu3dvWlxzcx7e9pD4+HiSEvdlm7Y/MZH4IjlPMsXHFyEpMTHLfL7l4osUyTHv0fz4w1huuO1fJxDtyRszaRovvOPrx9yw/pnExxcmcf+h//gSE32Pi8bH51i2SHw8+/Zn/09yX2JSjnknz/iddz4dRN83X6BUQokc64mOjqZh/TMZM2kaX40az62dgn90NBR5b9r6D7PmLeLFxw6dOC1UyPezvOXqdpQrUxqAO67rSO/Bw3n47s6E28l8LvldKL7zYxk1cSrn1asT9Gu6gpnryWwTRePjqVe7JgBlS5fk2Yfupdn1d7MvcX+2nfBgCEbbHh0dTd1zGjBlwjjGjRxOx+tvyvZ6ONv2wwVzm7iv87Xs3ZfItV0eJy4uluvatWLJitWULnnsbt7BFKjv3Mx3/PrK627MHLDgmhs7M3RgP+6479B1fRvXr+P5Jx7ivocfp9455wY8H5GCIlRnfNYADf2PrwKOdui8spld5H98MzAD+AuoamY1/dNvA6YFIK4ZwJVmVtjMigF53asdDzzoL+Yws1xbE+dcH+dcI+dcoy5dupxwsJVOr0JaWhob1q3NnLZ6xbJcT1tXrladVSuWZZlvOSVLl6FEQsnjfr/F8+ey/Z9tNGl+5AuBg6lDq6aZFxn3fu05alatzNKVazJf/2vVGsqUKknJhJz99WtWPZ1lq9Zk6+KwbNWabNc5/PTHnzz/di8+/L9nOKN6lRzryCotLS1k1/gEO2/w7eQ2qFuH00+tmDktoXgxKpYrQz7r7p/pZD6X/C4U3/mxjJowNSRne4KZayC3CfMfF8vWTSpIgtm2p6Wlsemwa3zC3bYfLpjbROFChfjfQ/cy5eu+jB/Ui4QSxalbqzrR0YEaT+nEBOo7L16iBGXLVzjqUdytmzfx7CP/5qY7786XXRtFQilUhc+nQFMz+wNoTM6zPFktAe4ws/lAaaCXc+4A8C9gmJktANLxjRiXm6zX+Mw1s5zn+P2cczOBUcA8fF3ZZnFoKOzj0QNfETffzBb6nwdN4fh4Lm7anEF9P+FAUhKL58/ltxnTaNGmXY55W7Rtz4Sxo1i7ehV79+xh6MB+tLqiQ+braamppCQnk56eTnp6GinJyaSlpmZbx6Tvx9KkaQuK5HIEKhw6Xt6UEd9PZsWadezeu4/eg4ZxdZvcd9TOP6cuUVFRDPpmLCkpBxn8ra97Q+Nz6wPw25wF/PeVd3n3hSc5u06tbMtu37mLcT/OIDEpibS0NGbMnMO4KTMylw21QOadYdSEqbmu4+o2LRj87Ti279zF7r37+GLEGJpe2DDHfPlBXj4X8I3+lJySgnOO1LQ0klNSSE9PD2HExy+Q37lzjuSUFA4e9P2+k1NSSEnJfoHwnEV/sXX7jpCO5pYhkLkea11Zv/f09HSSU1JITUsDYP6SZaxet4H09HR27d7Lqx/15fxz6lG8WPDbv0C17bt27mDapPEk7d9PWloas3//lWmTxnN2w+yXr+a3tv1wgdwmtmzbztZ/duCcY97ipfQeNIxud96U67pCKZD/n7dqdyWjR3zNrp072LtnD999PYQLLr4UgH+2beXph7rSodP1tLv6uhzrLiiio4y4mCiizIjyP46OyqdH6SRfs1AczcrPzKyYc26ff/S46UAX59yfQXxLt2LbiV98vXfPbt599SXmzPydEiUSuLPrgzRr3Zatmzdz/23X0+uLYZSv6DuKP3LoIIYP/pzk5GSaNGvBA1nG/R/crzdfDvg027pv+de9dL77PgBSkpO59ao2PPPyGzRodMEJxwtQs5zvKF3q+kUntR6Az4aNov/QkRzw39vh+Ue6Zo7Sdd9TPWhY/0y6dPY17kuWr6J7z49Z+fd6qleuRI8nunGmfyjjOx/rzp8LFhOXpe97w/pn0vu159ixazePvvgmS1euId05Tq1Qjs7XtOf6E7iPT8xpdYGTzz1QeQPMXbSUe/7zAtOG9adokexdRw6mpvLqR/0YN/kn4uLiaNvsYh7vcjuFcrlG4GgClfex5OVzufOx55g5L3s8A3q+xAUNTui2YUeU377zjPvTZHVqhXJM/PLQcLgvvN2LA8kpvPb0iff9P5m8A7l9H21dHw0cyseff53tvf99+w10u+Mmxv74E+/1G8yOXbspWiSeixuew2Ndbqdc6VLHnXu42/bdO3fyynP/ZfWKZaSnO8pXrEjH626ibcdrMt8nv7bthwvUNjFr/iKefu19duzaTcVyZbn/tuvp0KrpSceXX75zgNTUVHq/+xbTJv1AbFwhLm3Rirvuf4i4QoX4sn8fBvfvQ+HDugmOmPjTCcWc8Z03eCbn6HPB1LVlLbq2zH6Q8pPJy/lk8vKQxTD3lXYQmEsvgmJ644tDvkN/2e+/5NvP40hU+PiG0j4L38AEA51zrwb5LU+q8CmIgvmfY34XqgIgv4nUvCFyc4/UvCEwO8EFkdr2yP3OQ1345AcqfHIqiIVP+G9dHGbOuVuOPZeIiIiIiBRk+WE4axERERERkaBS4SMiIiIiIp6nwkdERERERELGzK43s0Vmlm5mjY4yX1szW2pmK8zsqSzTS5vZRDNb7v/32CPRoMJHRERERERCayHQCd+Iyrkys2jgI+AKfAOR3WxmZ/lffgqY7JyrBUz2Pz8mFT4iIiIiIhIyzrklzrmlx5jtAmCFc26Vcy4FGApc5X/tKmCg//FA4OrjeV8VPiIiIiIikt9UAtZleb7ePw2ggnNuE4D/3/LHs8KIH85aRERERKQgC8c9dcysC9Aly6Q+zrk+WV6fBFTMZdFnnXPfHc9b5DLtpO5XpMJHRERERETyxF/k9DnK661O8i3WA6dneX4asNH/eIuZneKc22RmpwBbj2eF6uomIiIiIiL5zUyglplVM7M44CZglP+1UcAd/sd3AMdzBkmFj4iIiIiIhI6ZXWNm64GLgLFmNt4//VQzGwfgnEsFHgDGA0uAr51zi/yreA243MyWA5f7nx+TurqJiIiIiEjIOOdGAiNzmb4RaJfl+ThgXC7zbQda5vV9dcZHREREREQ8T4WPiIiIiIh4ngofERERERHxPBU+IiIiIiLieSp8RERERETE81T4iIiIiIiI56nwERERERERz1PhIyIiIiIinqfCR0REREREPE+Fj4iIiIiIeJ4KHxERERER8TwVPiIiIiIi4nkqfERERERExPPMORfuGCKNPnARERGRgsfCHYCcHJ3xCT0L15+Z3RfO91fuylu5K2/lrryVu/IuwLlLAafCJ7J0CXcAYRSpuUdq3hC5uUdq3hC5uUdq3hC5uUdq3hDZuctJUuEjIiIiIiKep8JHREREREQ8T4VPZOkT7gDCKFJzj9S8IXJzj9S8IXJzj9S8IXJzj9S8IbJzl5OkUd1ERERERMTzdMZHREREREQ8T4WPiIiIiIh4ngofEY8ws2gzGxTuOMLBn/ub4Y5DREQCy8zizax2uOMQb1DhI+IRzrk0oJyZxYU7llDz597QzCLuBnNmVsHM+pnZ9/7nZ5nZ3eGOS0LHzEqZ2dnhjkOCx8yKmlmU//EZZtbRzGLDHVewmdmVwFzgB//zBmY2KqxBSYGmwsfj/A3kZDNb6H9+tpn9L9xxhYKZzTKzbmZWKtyxhNAa4Gcze87MHsv4C3dQITIH+M7MbjOzThl/4Q4qBD4DxgOn+p8vAx4JVzChZj63mll3//PKZnZBuOMKNjObamYlzKw0MA8YYGZvhzuuUDCzJmZW1P/4VjN728yqhDuuIJsOFDazSsBk4F/4fvte9wJwAbALwDk3F6gatmikwFPh432fAk8DBwGcc/OBm8IaUejchG9ncKaZDTWzNhFwRmAjMAbfb7t4lr9IUBrYDrQArvT/dQhrRKFR1jn3NZAO4JxLBdLCG1JIfQxcBNzsf74X+Ch84YRMgnNuD9AJGOCcawi0CnNModIL2G9m5wD/Af4GPg9vSEFnzrn9+L7vD5xz1wBnhTmmUEh1zu0OdxDiHTHhDkCCrohz7o/D9vdTwxVMKDnnVgDPmtlz+HaA+wPpZtYfeM85tyOsAQaBc+7FcMcQLs65f4U7hjBJNLMygAMwswuBSNpRaOycO8/M5gA453ZGSHfPGDM7BbgBeDbcwYRYqnPOmdlV+NryfmZ2R7iDCjIzs4uAzkBGV9ZI2IdbaGa3ANFmVgt4CPglzDFJAaYzPt73j5nV4NBO0XXApvCGFDr+fu89gTeBEcB1wB7gx3DGFSxmVs7M3jSzcWb2Y8ZfuOMKBTM7zcxGmtlWM9tiZiPM7LRwxxUCjwGjgBpm9jO+I98PhjekkDpoZtEcauPK4T/75XEv4uviuMI5N9PMqgPLwxxTqOw1s6eBW4Gx/u/f69e7PIKv98ZI59wi//c9JbwhhcSDQF0gGRiC7//vR8IZkBRsuoGpx/kbxz7AxcBOYDVwq3NuTTjjCgUzm42vX3A/YIRzLjnLa9845zx3/YeZTQC+Ap4AugJ3ANucc/8Na2AhYGYTgS+BL/yTbgU6O+cuD19UoWFmMUBtwIClzrmDYQ4pZMysM3AjcB4wEN/Bjf8554aFNbAg8u/oP+SceyfcsYSDmVUEbgFmOud+MrPKQDPnnNe7u4nISVLhEyH8F4JGOef2hjuWUDGzWs65SDkCCviKPedcQzOb75w72z9tmnOuabhjCzYzm+uca3CsaV7j3wluj++C38yuL865iLjQHcDM6gAt8RV+k51zS8IcUtCZ2RTnXPNwxxFq/u19vHMuUq5nAsDMRuM/q5nFbmAW0Ns5dyD0UQWfmZ2B70BeVbK3by3CFZMUbJHQPzSimdkrwBvOuV3+56WAx51zkTCy22Qz+wHfGZAfXWRU+RlH+jeZWXt8gx1EQncv8HXrvBVfdwjwXey+PYzxhMpo4ACwgMjo4pWNf1SzrRz63jGz2Ag46/WLmX2Ir31LzJjonPszfCEFn3Muzcz2m1lChF30vgoox6Ht/EZgC3AGvkGMbgtTXME2DPgE6EtkDdoiQaIzPh5nZnOcc+ceNu1P59x54YopVMwsHt/IXjfh6wYzBhjqnJsR1sCCyMw6AD8BpwMfACWAF51znr/vgb+7y4f4Rvhy+C6Afdg593dYAwuyrGf3IpGZrcG3ve/Ed8anJL7rGLcC9zrnZoctuCAys9yu73CRcCTczL4GLgQmkr3oeyhsQQWZmU13zl2W2zQzW+Scqxuu2IIpoxdDuOMQ79AZH++LNrNCGde3+IuBQmGOKSScc0nA18DX/jNd7wHTgOiwBhZEzrkx/oe7gYjqBuOcWwt0DHccYfC9mbV2zk0IdyBh8gO+C77HA5hZa6Atvt/+x0DjMMYWNJHYzS2Lsf6/SFLOzCr727mMAz1l/a+lhC+soBttZv8GRuIb4AAAL47KKqGhwsf7BuHr8jUA31Hwu/BdABwRzKwpvi4BVwAz8Q396ln+/tC9gArOuXr+Ue06OudeDnNoQWNm/3HOvWFmH5CzD7ynjwL7/QaMNN9d3Q/iO+vhnHMlwhtWyDRyznXNeOKcm2BmrzjnHjMzzx7kMbMKwCvAqc65K8zsLOAi51y/MIcWdM65gf4hy8/wT4qEAT0eB2aY2Up8v/FqwL/91+96+f/0jGHKn8wyzQHVwxCLeIC6ukUAM7uCQxf+Tsg4Mup1ZrYamIvvyO8o51zi0Zco+MxsGr7/IHpndHE0s4XOuXrhjSx4zOxK59zoI93Hwznn5Z0CzGwVcDWwIEKuY8vGP5LhZGCof9KNwOX4zvrM9Gq3XjP7HhgAPOucO8c/st8c51z9MIcWdGbWDN/O/hp8/6+dDtzhnJsevqiCz1/I18GX819eHdBAJJh0xicCOOe+B74PdxxhcI7/zuaRJOJuWOsveqKBes65J4+5gPcsBxZGYtHjdwvwPPAtvh3CGf5p0Xj7DG9Z59zX/vvZ4JxLNbNIufi7J9DaObcUMs90DwG8fi1ILXzD1hcGzjYzvDqEt5m1cM79aGa53nbCOfdNqGMSb1Dh41FmNsM5d4mZ7SV7959I6gZT0cxGEkHdvojQG9b6R3ry+k7PkWwCpvrPAGTtAx8Rw1k75/7hyDdsXRHKWEIs0czKcOi3fiG+a/siQWxG0QPgnFtmZp6+gamZPQ80A84CxuHrvj0D3w2LvagpvhuNX5nLaw5Q4SMnRF3dxLMitNtXbjes7ez1kc0AzKwnviOiw8g+0pOn/4P07xDl4Jx7MdSxhIOZlQP+g+/u7oUzpnt9dDMzOw/fyI31gIX4hjq+zjk3P6yBhYCZ9ce385txs+LOQIxz7l/hiyq4zGwBcA6+7ozn+K/x6uucy60wEJEj0BkfD/Nf7Dzfyzv6xxBx3b6Av51zrSLxhrVAaXz37cm6w+v5I4ORUuAcxWB897LpAHTFdzH0trBGFALOuT/9g7fUxncmPxIu8M9wP9ANeAhf7tPxjeDnZUnOuXQzSzWzEviGa/f8Bf5m9lguk3cDs51zc0McjniACh8P8zeS87IOgRlhIrHb1wozGw70j4S712fl5aO9R+O/n0tuo9l5+oxHFmWcc/3M7GHn3DRgmv9srycd6ZoH4Az/NR+eLvQBnHPJ/pu3TsS37UdC0TfLzEriu1npbGAf8EdYIwqNRv6/0f7n7fGN0NrVzIY5594IW2RSIKnw8b5TgEVm9gfZu/9Ewv1OuuHr9lXHzDbg7/YV3pCC7mx8N2zt5z/j1x/fTVs9P8hDJA7l7fdElseFgWvx/pnNrDJ2eDeZWXtgI3BaGOMJtoyuTeXxdWn90f+8OTAVj5/hhNxHdTMzT4/q5pz7t//hJ2b2A1AiEro1AmWA85xz+yCza+9w4DJ8BaAKH8kTXePjcf6uEDn4j4xGhKzdvszsEefcu+GOKRTM7DJ8Ix2VxPcfRQ/nnGcv9o7Ea7qOxMymOedy/e17jZl1AH7CN6TxB0AJ4EXn3KiwBhZkZjYGuNc5t8n//BTgI+fckc4IeYaZzQZuOXxUN+ecpwc48Z/tuwTfWa4ZzrmRYQ4p6MxsCb4RWlP8zwsBc51zZ5rZnIy2XuR46YyPR5lZYXz93WsCC4B+zrlIOgqc6bD79zwGvBumUILOP6xze+BfQFV8w74OBi7FNxLQGUdcuOCLxGu6MLPSWZ5G4RvSt2KYwgk559wY/8Pd+M56RIqqGUWP3xa8/fvOKhJHdfsY3//nQ/yT7jOzVs65bmEMKxS+BH4zs+/8z68EhvgPaC4OX1hSUKnw8a6B+LqA/IRv2MuzgIfDGlH+YMeepUBbDkwB3nTO/ZJl+nD/GSAvi8RrusDX3cPh27ZT8XXpvDusEYVQBHdxnGpm4/HtCDt8XVynhDekkJllZv3IPqrb7DDGEwpN8d2rLKN9G4jvoKanOed6+Ifqb4KvjevqnJvlf9nrXdclCNTVzaPMbEHGHbz9d/T+w6t3MM8LM1vrnKsc7jiCxcyKZfSFjjSRPJR3JIvkLo7+rk+X+p9Oj4SuT5DZ3akbvm5fmaO6OeeSj7pgAWZm3wCPZrRnZlYFeM05d3N4Iws+f0+GCmQ5WB+hAzZJAOiMj3dljnDjv6N3OGMJqVxu2pr5EhAf4nBCKlKLHj93+FDeZlYt3EEFy1FG9wK8f/+iLCKyiyNkfseR8j1n8o/q9gXwhXPO00OXm9lofP+fJQBL/AMVOaAx8MvRlvUCM3sQeB5fV840/DdhxzeQj0ieqfDxrnPMLGMkLwPi/c8N3w5iifCFFlzOueLhjkHCYgS+0X+yXtM1HN81L150tBsXev7+RVlEZBdHf+H7Or7R3YwIaNvNV90+DzyAP2czSwM+cM69FNbgguetcAcQZg8DtZ1z28MdiHiDCh+Pcs5FhzsGkVAwszpAXSDhsLMgJfAN7+xJkXrfolzkNmz9reENKSTeAK6MsPt1PYLvWo/znXOrIbOLay8ze9Q59044gwsG59w0f1ev8c65VuGOJwzW4Ru4RCQgVPiIeJyZneec+zPccQRRbaADvmG7s54F2QvcG46AQsnMEvAdBc8YvGIa8JJzLiJ2Fpxzq4BsXRzDHVOIbImwogfgduBy59w/GROcc6vM7FZgAuC5wgfAOZdmZvvNLCFSftdZrMI3kMdYIPMaLufc2+ELSQoyFT4i3nc/Hi4AnHPfAd+Z2UXOuV/DHU8Y9AcWAjf4n98GDAA8fT8XM3vsCNOBiNgxmmVmXwHfkn2H0MtdHGOzFj0ZnHPbvD6cNXAAWGBmE8l+M/KHwhdSSKz1/8X5/0ROigofEY9zznm26DnMCjN7Bt/9i7KO/nNX2CIKjRrOuWuzPH/RzOaGK5gQivRr+UoA+4HWWaZ5/dqulBN8zQvG+v8iinPuxcOn+UeqFTkhGs5axAPM7KhDlXu8qxsAZvYLvvtWzcY3+g8AzrkRYQsqBMzsV+BJ59wM//MmwFvOuYvCG5lIYPkHMkjM7SWgsHPO62d9IoaZzXDOXeJ//IVz7rYsr/2p23PIiVLVLOINPf3/FgYaAfPw7QycDfyO734XXlfEOfffcAcRBvcDA/3X+oDvHkZ3hDGekDCz/zjn3jCzD8hl+HqvdgGK1LwhMgftMbOvnXM3mNkCcn7fzjl3TjjiCoGiWR4ffk+uyLk/hwScCh8RD3DONQcws6FAF+fcAv/zesAT4YwthMaYWTvn3LhwBxJiC5xz55hZCQDn3J5jLeARGRf2zzrqXN4TqXlHqof9/3bIMs2A04BnQh9OyLgjPM7tuchxU1c3EQ8xs7nOuQbHmuZF/hvXFsV3ofdBIuC+JgBmthb4AfgK+NGpURfxJDNrANyCbyCT1cAI59yHYQ0qSMxsFfA4EAW8yaEDeAa84ZyrEa7YpGBT4SPiIWY2BF8f+EH4jordChRzzt0c1sAkaMwsHt8w3jcB5wFjgKEZ1/x4lZmNOtrrzrmOoYollMxsNEc54u3VvCOVmZ2B77d9M7Ad3wGOJ5xzVcIaWJCZ2YCjva77mMmJUuEj4iFmVhjfNR8Z93SZDvRyzh0IX1TBZWZ1nHN/HWmAh0gY2CGDmZUC3gM6e/16CDPbhu/mhkPwXceWrd+/c25aOOIKNjNrerTXvZp3pDKzdHyDttztnFvhn7bKOVc9vJGJFEwqfEQ8xszi8N3U0wFLnXMHwxxSUJlZH+dcFzObksvLzjnXIuRBhZh/Z/hG4ApgJvBVBIxmFw1cju9I+Nn4hvod4pxbFNbAQsTMOgDjnHPp4Y5FgsfMrsF3xudifF1ahwJ9nXPVwhqYSAGlwkfEQ8ysGTAQWIPvCPjpwB3Ouenhi0qCycxWA3OBr4FRzrnchvv1NDMrhK8AehN4yTn3QZhDCjozGwRcBIwABjjnlhxjESnAzKwocDW+7bwFvnZ+pHNuQjjjEiloVPiIeIiZzQZucc4t9T8/A99R8IbhjUyCxcxKRNBIbtn4C572+HYGqwKjgP7OuQ3hjCtU/CP53Qz8C98Z3gH4fu97wxqYBJWZlQauB26MhDPaIoGkwkfEQ8xsvnPu7GNNEynozGwgvvt7fI9vMIeFYQ4pLMysLL5BTB7BN9R1TeD9SDjrJd5nZrFkv251GvCJ17twS/Co8BHxEP9IOOnAF/5JnYEYr4+AY2YGnOacWxfuWCQ0/Bd9Z3Try/ofWaQMY34lcBdQA9/vfaBzbquZFQGWeH3UL4kMZtYXiMXXtQ/gNiDNOXdP+KKSgkyFj4iH+Lv+dAMuwbcDOB342DmXHNbAQsDMZqtLn3idmdUEKgJd8F3kPt0//VJgo3NupZm1dM5NDmecIoFgZvOcc+cca5rI8YoKdwAiEhhmFgXMds697Zzr5Jy7xjn3TiQUPX6/mdn54Q4i1Mysgpn1M7Pv/c/PMrO7wx2XBM27wB7n3O2HDVqS5H8NFT3iIWlmlnmzUjOrDqSFMR4p4FT4iHiEf1jbeWZWOdyxhElzfMXPSjObb2YLzGx+uIMKgc+A8cCp/ufL8F3vId5U1TmXY7t2zs3CN8CDiJc8CUwxs6lmNg34EXg8zDFJARYT7gBEJKBOARaZ2R8cuv4hUu7mfkW4AwiTss65r83saQDnXKqZ6YiodxU+ymvxIYtCJAScc5PNrBa+e9MZ8FcE9WKQIFDhI+ItL4Y7gHBxzv1tZpcAtZxzA8ysHFAs3HGFQKKZlcF/gb+ZXQjsDm9IEkQzzexe59ynWSf6uzfODlNMIkFhZoWBf+O7btUBP5nZJ865A+GNTAoqDW4g4gH+/xy64hvKdgHQzzmXGt6oQsvMngcaAbWdc2eY2anAMOdckzCHFlRmdh7wAb6hnRcC5YDrcusOJQWfmVUARgIpHCp0GgFxwDXOuc3hik0k0Mzsa2AvMMg/6WaglHPu+vBFJQWZCh8RDzCzr4CDwE/4unz97Zx7OLxRhZaZzQXOBf50zp3rnxYR9zAysxgOdQVZqntceJ+ZNcdX7AIscs79GM54RIJBo7pJoKmrm4g3nOWcqw9gZv2AP8IcTzikOOecmWV0+Soa7oBC6AJ8F7bHAOeZGc65z8MbkgSTc24KMCXccYgE2Rwzu9A59xuAmTUGfg5zTFKAqfAR8YbMI/z+i9vDGUu4fG1mvYGSZnYvvps7fnqMZQo8M/sC300s53JomFcHqPARkQLJzBbga8digdvNbK3/eRVgcThjk4JNXd1EPMA/ilfGKG6Gb3Sn/UTIXewzmNnlQGt8eY93zk0Mc0hBZ2ZL8J3xU2MuIp5gZlWO9rpz7u9QxSLeosJHRKQAM7NhwEPOuU3hjkVEJFD8N+We75yrd8yZRY6TurqJiCeYWSfgdaA8vjM+kXK2qyyw2H/vpsz7W0TIvZtExKOcc+lmNs/MKjvn1oY7HvEGnfEREU8wsxXAlc65JeGOJZTMrGlu051z00Idi4hIIJnZj8D5+AbsibSbcksQqPAREU8ws5+9fs8eEZFIogM7EmgqfESkQPN3cQNoClQEviV7l69vwhBW0JnZDOfcJWa2F99oR5kvERld/ERERPJEhY+IFGhmNuAoLzvn3F0hC0ZERALmsAM7cfiGt07UgR05URrcQEQKNOfcvwDMrIlzLtuN7czM813fzKx0LpP3OucO5jJdRKTAcM4Vz/rczK7Gd8NmkROiMz4i4glm9qdz7rxjTfMaM1sDnA7sxNfNrSSwCdgK3Oucmx224EREToCZxTjnUo/w2m/OuQtDHZN4g874iEiBZmYXARcD5czssSwvlQCiwxNVSP0AjHTOjQcws9ZAW+Br4GOgcRhjExE5EX8A52W5hhMgCmhE9msaRfJEhY+IFHRxQDF87VnWbhF7gOvCElFoNXLOdc144pybYGavOOceM7NC4QxMROQkXcmhQicVWANoKGs5YerqJiKeYGZVnHN/hzuOUDOzCcBkYKh/0o3A5fjO+sz0elc/EfEeM1sPvI2v+25WDsA593bIgxJP0BkfEfGK/Wb2JlAXKJwx0TnXInwhhcQtwPP4hvE2YIZ/WjRwQ/jCEhE5YdH4zuQfXviInBSd8RERT/Cf+fgKeALoCtwBbHPO/TesgYmISJ5EwsA0Eh464yMiXlHGOdfPzB7239V7mpl5/u7eZnYGvmKvKlna9Ag40yUi3qUzPRIUKnxExCsy7luzyczaAxuB08IYT6gMAz4B+gJpYY5FRCQQWoY7APEmdXUTEU8wsw7AT/juafMBvuGsX3TOjQprYEFmZrOdcw3DHYeIiEh+p8JHRKQAM7MX8N2sdCSQnDHdObcjXDGJiIjkRyp8RMQT/Ne69AIqOOfqmdnZQEfn3MthDi2ozGx1LpOdc656yIMRERHJx1T4iIgn+AcyeBLo7Zw71z9toXOuXngjExERkfwgKtwBiIgESBHn3B+HTUsNSyQhYGb/yfL4+sNeeyX0EYmIiORvKnxExCv+MbMa+O/sbWbXAZvCG1JQ3ZTl8dOHvdY2lIGIiIgUBBrOWkS8ohvQB6hjZhuA1UDn8IYUVHaEx7k9FxERiXg64yMinuCcW+WcawWUA+o45y4BrglzWMHkjvA4t+ciIiIRT4MbiIhnmdla51zlcMcRDGaWBiTiO7sTD+zPeAko7JyLDVdsIiIi+ZG6uomIl3m2y5dzLjrcMYiIiBQk6uomIl6mU9oiIiIC6IyPiBRwZraX3AucjC5gIiIiIrrGR0REREREvE9d3URERERExPNU+IiIiIiIiOep8BEREREREc9T4SMiIiIiIp6nwkdERERERDxPhY+IiIiIiHje/wOnaJ+x7GlwZQAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1008x864 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def correlation_heatmap(df):\n",
    "    _ , ax = plt.subplots(figsize =(14, 12))\n",
    "    #colormap = sns.diverging_palette(220, 10, as_cmap = True)\n",
    "    colormap = sns.color_palette(\"RdBu\")\n",
    "    \n",
    "    _ = sns.heatmap(\n",
    "        df.corr(), \n",
    "        cmap = colormap,\n",
    "        vmin=-1,\n",
    "        square=True, \n",
    "        cbar_kws={'shrink':.9 }, \n",
    "        ax=ax,\n",
    "        annot=True, \n",
    "        linewidths=0.1,vmax=1.0, linecolor='white',\n",
    "        annot_kws={'fontsize':12 }\n",
    "    )\n",
    "    \n",
    "    plt.title('Pearson Correlation of Features', y=1.05, size=15)\n",
    "\n",
    "correlation_heatmap(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "rJQiVvEfL3WN"
   },
   "source": [
    "## **2.3 Missing Values and Outliers**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "RgEv9agXLg7k"
   },
   "source": [
    "### **Missing Values**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Price                  0\n",
       "Levy                5819\n",
       "Manufacturer           0\n",
       "Model                  0\n",
       "Prod. year             0\n",
       "Category               0\n",
       "Leather interior       0\n",
       "Fuel type              0\n",
       "Engine volume          0\n",
       "Mileage                0\n",
       "Cylinders              0\n",
       "Gear box type          0\n",
       "Drive wheels           0\n",
       "Doors                  0\n",
       "Wheel                  0\n",
       "Color                  0\n",
       "Airbags                0\n",
       "Turbo Engine           0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "id": "Wke4un-ouG9C"
   },
   "outputs": [],
   "source": [
    "c_df=df.copy()\n",
    "c_df.dropna(axis=0,inplace=True)\n",
    "arr=c_df[\"Manufacturer\"].unique()\n",
    "group=c_df.groupby(by=\"Manufacturer\")\n",
    "mean_levy = group.mean()[\"Levy\"]\n",
    "length = len(df[\"Levy\"])\n",
    "for i in range(length):\n",
    "  x=df.loc[i,\"Manufacturer\"]\n",
    "  if df.loc[i,\"Levy\"]!=df.loc[i,\"Levy\"] and x in arr:\n",
    "    df.loc[i,\"Levy\"]=mean_levy[x]\n",
    "df['Levy']=df['Levy'].fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Uo3WH4uZK0oJ",
    "outputId": "26fc7cfb-fe1c-44c4-d671-286b515343ac"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Price               0\n",
       "Levy                0\n",
       "Manufacturer        0\n",
       "Model               0\n",
       "Prod. year          0\n",
       "Category            0\n",
       "Leather interior    0\n",
       "Fuel type           0\n",
       "Engine volume       0\n",
       "Mileage             0\n",
       "Cylinders           0\n",
       "Gear box type       0\n",
       "Drive wheels        0\n",
       "Doors               0\n",
       "Wheel               0\n",
       "Color               0\n",
       "Airbags             0\n",
       "Turbo Engine        0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "FNUNO0Glf5F8"
   },
   "source": [
    "### **Outliers**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 0
    },
    "id": "rlyNfWN7cuUy",
    "outputId": "a6173f57-75dc-4473-ab0f-7358bb7cc06e"
   },
   "outputs": [],
   "source": [
    "#col_out = [\"Levy\",\"Engine volume\", \"Mileage\", \"Price\"]\n",
    "#for i in range(4):\n",
    "#    plt.figure(figsize=(10,6))\n",
    "#    print(\"Box Plot of\",col_out[i],\"\\n\")\n",
    "#    fig = df.boxplot(col_out[i])\n",
    "#    plt.title(f\"Box Plot of {col_out[i]}\")\n",
    "#    plt.show(fig)\n",
    "#    print(\"\\n\",\"*\"*140,\"\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x576 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "col_out = [\"Levy\",\"Engine volume\", \"Mileage\", \"Price\"]\n",
    "fig, axs = plt.subplots(nrows=2, ncols=2, figsize=(12, 8))\n",
    "\n",
    "for i, col in enumerate(col_out):\n",
    "    row = i // 2\n",
    "    col = i % 2\n",
    "    axs[row, col].boxplot(df[col_out[i]])\n",
    "    axs[row, col].set_title(f\"Box Plot of {col_out[i]}\")\n",
    "    axs[row, col].grid(True)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "id": "mHUKVqQTB6yf"
   },
   "outputs": [],
   "source": [
    "def outlier_IQR(con_df):\n",
    "  print(f\"{con_df}\")\n",
    "  Q1,Q3 = np.percentile(df[con_df] , [25,75])\n",
    "  ul = Q3*1.5\n",
    "  ll = Q1*1.5\n",
    "  outliers = df[(df[con_df] > ul) | (df[con_df] < ll)]\n",
    "  print(f\"Lower limit: {ll}, Upper Limit: {ul}\")\n",
    "  print(f\"Number of outliers: {len(outliers)}\")\n",
    "  print(f\"outliers: {np.array(outliers[con_df])}\")\n",
    "  print('--'*50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "GnDuVO1c85LF",
    "outputId": "7e8cbf6e-4bf4-4e14-dc0e-0a5294ba7f9b"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Levy\n",
      "Lower limit: 1060.5, Upper Limit: 1623.4487447698746\n",
      "Number of outliers: 14686\n",
      "outliers: [1018.         803.8573975  862.        ...  831.         836.\n",
      "  753.       ]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "Engine volume\n",
      "Lower limit: 2.7, Upper Limit: 3.75\n",
      "Number of outliers: 15999\n",
      "outliers: [1.3 2.5 1.3 ... 2.  2.  2.4]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "Mileage\n",
      "Lower limit: 105208.5, Upper Limit: 283332.0\n",
      "Number of outliers: 9132\n",
      "outliers: [ 91901 398069      0 ... 307325 300000  51258]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "Price\n",
      "Lower limit: 7996.5, Upper Limit: 33112.5\n",
      "Number of outliers: 8874\n",
      "outliers: [ 3607 39493  1803 ...  5802  5331   470]\n",
      "----------------------------------------------------------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "for col in col_out:\n",
    "  outlier_IQR(col)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Levy\n",
      "Extreme Outliers: 1620.0, ... ,1624.0\n",
      "Lower limit: 138.5769230769232, Upper Limit: 1609.0384615384614\n",
      "Number of outliers: 53\n",
      "----------------------------------------------------------------------------------------------------\n",
      "Engine volume\n",
      "Extreme Outliers: 4.0, ... ,5.6\n",
      "Lower limit: 0.2500000000000002, Upper Limit: 3.8499999999999996\n",
      "Number of outliers: 509\n",
      "----------------------------------------------------------------------------------------------------\n",
      "Mileage\n",
      "Extreme Outliers: 350000, ... ,350000\n",
      "Lower limit: -90381.375, Upper Limit: 342635.625\n",
      "Number of outliers: 125\n",
      "----------------------------------------------------------------------------------------------------\n",
      "Price\n",
      "Extreme Outliers: 45734, ... ,44611\n",
      "Lower limit: -18032.5, Upper Limit: 43435.5\n",
      "Number of outliers: 331\n",
      "----------------------------------------------------------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "def outlier_IQR(con_df):\n",
    "    print(f\"{con_df}\")\n",
    "    Q1, Q3 = np.percentile(df[con_df], [25, 75])\n",
    "    IQR = Q3 - Q1\n",
    "    ul = Q3 + 1.5*IQR\n",
    "    ll = Q1 - 1.5*IQR\n",
    "    outliers = df[(df[con_df] > ul) | (df[con_df] < ll)][con_df].tolist()\n",
    "    \n",
    "    extreme_outliers = []\n",
    "    other_outliers = []\n",
    "    for outlier in outliers:\n",
    "        if outlier < ll or outlier > ul:\n",
    "            extreme_outliers.append(outlier)\n",
    "        else:\n",
    "            other_outliers.append(outlier)\n",
    "    \n",
    "    if len(extreme_outliers) > 0:\n",
    "        print(f\"Extreme Outliers: {extreme_outliers[0]}, ... ,{extreme_outliers[-1]}\")\n",
    "    if len(other_outliers) > 0:\n",
    "        print(f\"Outliers: {other_outliers[0]}, ... ,{other_outliers[-1]}\")\n",
    "    print(f\"Lower limit: {ll}, Upper Limit: {ul}\")\n",
    "    print(f\"Number of outliers: {len(outliers)}\")\n",
    "    print('--'*50)\n",
    "\n",
    "\n",
    "for col in col_out:\n",
    "    outlier_IQR(col)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "-j3awkRhvJ0G"
   },
   "source": [
    "Remove Outlier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "id": "jZ7OwTzAnMWo"
   },
   "outputs": [],
   "source": [
    "def remove_outlier(df_arr, ll, ul):\n",
    "  del_id = []\n",
    "  del_id = df[(df[df_arr] < ll) | (df[df_arr] > ul)].index\n",
    "  print(f'{df_arr}: No of deleted values: {len(del_id)}')\n",
    "  print(del_id)\n",
    "  print(\"-\"*50)\n",
    "  return del_id"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "eQ7Z9Y4ZpP7m",
    "outputId": "dcabe3d4-2bde-49f3-f1be-1997c86273f5"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Levy: No of deleted values: 53\n",
      "Int64Index([  719,   759,   976,  1127,  1965,  1980,  1992,  2000,  2247,\n",
      "             2518,  2793,  3084,  3738,  3853,  3958,  4573,  5093,  5487,\n",
      "             5489,  7047,  7117,  7344,  7504,  7818,  8208,  9781,  9920,\n",
      "             9944, 10054, 10923, 11120, 11440, 11488, 11861, 12679, 13055,\n",
      "            13694, 13734, 14290, 14322, 14384, 14477, 15215, 17660, 17754,\n",
      "            17935, 18005, 18549, 18803, 18934, 19058, 19119, 19217],\n",
      "           dtype='int64')\n",
      "--------------------------------------------------\n",
      "Price: No of deleted values: 331\n",
      "Int64Index([   41,    72,   112,   172,   176,   201,   297,   313,   314,\n",
      "              561,\n",
      "            ...\n",
      "            18728, 18759, 18812, 18850, 18912, 18926, 18975, 18980, 19056,\n",
      "            19089],\n",
      "           dtype='int64', length=331)\n",
      "--------------------------------------------------\n",
      "Mileage: No of deleted values: 125\n",
      "Int64Index([   12,    91,   126,   204,   365,   576,   589,   602,  1061,\n",
      "             1108,\n",
      "            ...\n",
      "            18613, 18672, 18798, 18838, 18877, 18900, 19076, 19093, 19096,\n",
      "            19130],\n",
      "           dtype='int64', length=125)\n",
      "--------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "del_indices = []\n",
    "for col in [\"Levy\", \"Price\", \"Mileage\"] :\n",
    "  Q1,Q3 = np.percentile(df[col] , [25,75])\n",
    "  IQR = Q3 - Q1\n",
    "  # Outlier step\n",
    "  outlier_step = IQR * 1.5\n",
    "  ul = Q3 + outlier_step\n",
    "  ll = Q1 - outlier_step\n",
    "  del_indices.extend(remove_outlier(col, ll, ul))\n",
    "df.drop(df.loc[del_indices].index, axis=0, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "3evyB0FKseTh",
    "outputId": "de09dfbc-7d18-4e26-bb37-f3653e2151e3"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(16222, 18)"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "FPsGigDYAzaJ"
   },
   "source": [
    "## **2.4 Feature Selection**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "4gsrEnW-MOJk"
   },
   "source": [
    "Chi - square test on categorical columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "id": "vojXDEsxFsPh"
   },
   "outputs": [],
   "source": [
    "from scipy.stats import chi2_contingency"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "B4PZERaKFl4V",
    "outputId": "7de771b2-1795-47e0-b9e8-7946afc33359"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Manufacturer\n",
      "             Column              Hypothesis\n",
      "0      Manufacturer  Reject Null Hypothesis\n",
      "1          Category  Reject Null Hypothesis\n",
      "2  Leather interior  Reject Null Hypothesis\n",
      "3         Fuel type  Reject Null Hypothesis\n",
      "4     Gear box type  Reject Null Hypothesis\n",
      "5      Drive wheels  Reject Null Hypothesis\n",
      "6             Wheel  Reject Null Hypothesis\n",
      "7             Color  Reject Null Hypothesis\n",
      "8      Turbo Engine  Reject Null Hypothesis\n",
      "**************************************************\n",
      "Category\n",
      "             Column              Hypothesis\n",
      "0      Manufacturer  Reject Null Hypothesis\n",
      "1          Category  Reject Null Hypothesis\n",
      "2  Leather interior  Reject Null Hypothesis\n",
      "3         Fuel type  Reject Null Hypothesis\n",
      "4     Gear box type  Reject Null Hypothesis\n",
      "5      Drive wheels  Reject Null Hypothesis\n",
      "6             Wheel  Reject Null Hypothesis\n",
      "7             Color  Reject Null Hypothesis\n",
      "8      Turbo Engine  Reject Null Hypothesis\n",
      "**************************************************\n",
      "Leather interior\n",
      "             Column              Hypothesis\n",
      "0      Manufacturer  Reject Null Hypothesis\n",
      "1          Category  Reject Null Hypothesis\n",
      "2  Leather interior  Reject Null Hypothesis\n",
      "3         Fuel type  Reject Null Hypothesis\n",
      "4     Gear box type  Reject Null Hypothesis\n",
      "5      Drive wheels  Reject Null Hypothesis\n",
      "6             Wheel  Reject Null Hypothesis\n",
      "7             Color  Reject Null Hypothesis\n",
      "8      Turbo Engine  Reject Null Hypothesis\n",
      "**************************************************\n",
      "Fuel type\n",
      "             Column              Hypothesis\n",
      "0      Manufacturer  Reject Null Hypothesis\n",
      "1          Category  Reject Null Hypothesis\n",
      "2  Leather interior  Reject Null Hypothesis\n",
      "3         Fuel type  Reject Null Hypothesis\n",
      "4     Gear box type  Reject Null Hypothesis\n",
      "5      Drive wheels  Reject Null Hypothesis\n",
      "6             Wheel  Reject Null Hypothesis\n",
      "7             Color  Reject Null Hypothesis\n",
      "8      Turbo Engine  Reject Null Hypothesis\n",
      "**************************************************\n",
      "Gear box type\n",
      "             Column              Hypothesis\n",
      "0      Manufacturer  Reject Null Hypothesis\n",
      "1          Category  Reject Null Hypothesis\n",
      "2  Leather interior  Reject Null Hypothesis\n",
      "3         Fuel type  Reject Null Hypothesis\n",
      "4     Gear box type  Reject Null Hypothesis\n",
      "5      Drive wheels  Reject Null Hypothesis\n",
      "6             Wheel  Reject Null Hypothesis\n",
      "7             Color  Reject Null Hypothesis\n",
      "8      Turbo Engine  Reject Null Hypothesis\n",
      "**************************************************\n",
      "Drive wheels\n",
      "             Column              Hypothesis\n",
      "0      Manufacturer  Reject Null Hypothesis\n",
      "1          Category  Reject Null Hypothesis\n",
      "2  Leather interior  Reject Null Hypothesis\n",
      "3         Fuel type  Reject Null Hypothesis\n",
      "4     Gear box type  Reject Null Hypothesis\n",
      "5      Drive wheels  Reject Null Hypothesis\n",
      "6             Wheel  Reject Null Hypothesis\n",
      "7             Color  Reject Null Hypothesis\n",
      "8      Turbo Engine  Reject Null Hypothesis\n",
      "**************************************************\n",
      "Wheel\n",
      "             Column              Hypothesis\n",
      "0      Manufacturer  Reject Null Hypothesis\n",
      "1          Category  Reject Null Hypothesis\n",
      "2  Leather interior  Reject Null Hypothesis\n",
      "3         Fuel type  Reject Null Hypothesis\n",
      "4     Gear box type  Reject Null Hypothesis\n",
      "5      Drive wheels  Reject Null Hypothesis\n",
      "6             Wheel  Reject Null Hypothesis\n",
      "7             Color  Reject Null Hypothesis\n",
      "8      Turbo Engine  Reject Null Hypothesis\n",
      "**************************************************\n",
      "Color\n",
      "             Column              Hypothesis\n",
      "0      Manufacturer  Reject Null Hypothesis\n",
      "1          Category  Reject Null Hypothesis\n",
      "2  Leather interior  Reject Null Hypothesis\n",
      "3         Fuel type  Reject Null Hypothesis\n",
      "4     Gear box type  Reject Null Hypothesis\n",
      "5      Drive wheels  Reject Null Hypothesis\n",
      "6             Wheel  Reject Null Hypothesis\n",
      "7             Color  Reject Null Hypothesis\n",
      "8      Turbo Engine  Reject Null Hypothesis\n",
      "**************************************************\n",
      "Turbo Engine\n",
      "             Column              Hypothesis\n",
      "0      Manufacturer  Reject Null Hypothesis\n",
      "1          Category  Reject Null Hypothesis\n",
      "2  Leather interior  Reject Null Hypothesis\n",
      "3         Fuel type  Reject Null Hypothesis\n",
      "4     Gear box type  Reject Null Hypothesis\n",
      "5      Drive wheels  Reject Null Hypothesis\n",
      "6             Wheel  Reject Null Hypothesis\n",
      "7             Color  Reject Null Hypothesis\n",
      "8      Turbo Engine  Reject Null Hypothesis\n",
      "**************************************************\n"
     ]
    }
   ],
   "source": [
    "cat_attribs = ['Manufacturer','Category', 'Leather interior', 'Fuel type', 'Gear box type', 'Drive wheels', 'Wheel', 'Color', 'Turbo Engine']\n",
    "for j in cat_attribs:\n",
    "  print(j)\n",
    "  chi2_check = []\n",
    "  for i in cat_attribs:\n",
    "      if chi2_contingency(pd.crosstab(df[j], df[i]))[1] < 0.05:\n",
    "          chi2_check.append('Reject Null Hypothesis')\n",
    "      else:\n",
    "          chi2_check.append('Fail to Reject Null Hypothesis')\n",
    "  res = pd.DataFrame(data = [cat_attribs, chi2_check] \n",
    "              ).T \n",
    "  res.columns = ['Column', 'Hypothesis']\n",
    "  print(res)\n",
    "  print('*'*50)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "HO9seN58drq4"
   },
   "source": [
    "Corelation test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "00Uhr3DuLw-U",
    "outputId": "a8a158d8-7bfc-4b44-892d-f21be415f54f"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Manufacturer\n",
      "correlation  -0.06556334151754092\n",
      "Category\n",
      "correlation  -0.04244473314364189\n",
      "Leather interior\n",
      "correlation  0.07054639415665302\n",
      "Fuel type\n",
      "correlation  -0.08765469781654472\n",
      "Gear box type\n",
      "correlation  0.13649759702470005\n",
      "Drive wheels\n",
      "correlation  0.032928155506148096\n",
      "Wheel\n",
      "correlation  -0.15112614900318408\n",
      "Color\n",
      "correlation  0.030308987505018082\n",
      "Turbo Engine\n",
      "correlation  0.16392828057888967\n"
     ]
    }
   ],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "cat_attribs = ['Manufacturer','Category', 'Leather interior', 'Fuel type', 'Gear box type', 'Drive wheels', 'Wheel', 'Color', 'Turbo Engine']\n",
    "encoder=LabelEncoder()\n",
    "for x in cat_attribs:\n",
    "  df_color=pd.DataFrame()\n",
    "  df_color[x] = df[x]\n",
    "  encoder.fit(df_color[x])\n",
    "  df_color['enc_cat']=encoder.transform(df_color[x])\n",
    "  corr=df['Price'].corr(df_color['enc_cat'])\n",
    "  print(x)\n",
    "  print(\"correlation \",corr)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {
    "id": "lANyyZ4siDIu"
   },
   "source": [
    "Information Gain\n",
    "\n",
    "Selecting top 12 features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "hKhxGWdealD5",
    "outputId": "f28b983a-d44e-4b2e-b547-dda6bccb6a75",
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Mileage',\n",
       " 'Model',\n",
       " 'Levy',\n",
       " 'Prod. year',\n",
       " 'Manufacturer',\n",
       " 'Engine volume',\n",
       " 'Airbags',\n",
       " 'Color',\n",
       " 'Category',\n",
       " 'Fuel type',\n",
       " 'Gear box type',\n",
       " 'Leather interior']"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def entropy(feature, dataset):\n",
    "  prob = dataset[feature].value_counts(normalize=True)\n",
    "  entropy = -1 * np.sum(np.log2(prob) * prob)\n",
    "  return entropy\n",
    "\n",
    "def gini(feature, dataset):\n",
    "  prob = dataset[feature].value_counts(normalize=True)\n",
    "  gini = 1 - np.sum(np.square(prob))\n",
    "  return gini\n",
    "\n",
    "def IG(feature, target, dataset, measure):\n",
    "  elements = dataset[feature].unique()\n",
    "  rem = 0\n",
    "  target_entropy = 0\n",
    "  if measure == 'gini':\n",
    "    target_entropy = gini(target, dataset)\n",
    "  else:\n",
    "    target_entropy = entropy(target, dataset)\n",
    "  for x in elements:\n",
    "    dataset_x = dataset[feature] == x\n",
    "    if measure == 'gini':\n",
    "      rem += (dataset_x.sum()/dataset.shape[0])*gini(target, dataset[dataset_x])\n",
    "    else:\n",
    "      rem += (dataset_x.sum()/dataset.shape[0])*entropy(target, dataset[dataset_x])\n",
    "  return target_entropy - rem\n",
    "\n",
    "def IUFS(target, dataset, k, measure='entropy'):\n",
    "    column_lst = [x for x in dataset.columns if x != target]\n",
    "    IG_dict = {}\n",
    "    for x in column_lst:\n",
    "      IG_dict[x] = IG(x, target, dataset, measure)\n",
    "    IG_dict = sorted(IG_dict, key=IG_dict.get, reverse=True)\n",
    "    return IG_dict[0:k]\n",
    "\n",
    "feature_lst = IUFS('Price', df, 12, measure='entropy')\n",
    "feature_lst"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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