CarPriceProj.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Car Prices Prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import sklearn\n",
    "from sklearn.cluster import KMeans"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>car_ID</th>\n",
       "      <th>symboling</th>\n",
       "      <th>CarName</th>\n",
       "      <th>fueltype</th>\n",
       "      <th>aspiration</th>\n",
       "      <th>doornumber</th>\n",
       "      <th>carbody</th>\n",
       "      <th>drivewheel</th>\n",
       "      <th>enginelocation</th>\n",
       "      <th>wheelbase</th>\n",
       "      <th>...</th>\n",
       "      <th>enginesize</th>\n",
       "      <th>fuelsystem</th>\n",
       "      <th>boreratio</th>\n",
       "      <th>stroke</th>\n",
       "      <th>compressionratio</th>\n",
       "      <th>horsepower</th>\n",
       "      <th>peakrpm</th>\n",
       "      <th>citympg</th>\n",
       "      <th>highwaympg</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>alfa-romero giulia</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>two</td>\n",
       "      <td>convertible</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>88.6</td>\n",
       "      <td>...</td>\n",
       "      <td>130</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.47</td>\n",
       "      <td>2.68</td>\n",
       "      <td>9.0</td>\n",
       "      <td>111</td>\n",
       "      <td>5000</td>\n",
       "      <td>21</td>\n",
       "      <td>27</td>\n",
       "      <td>13495.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>alfa-romero stelvio</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>two</td>\n",
       "      <td>convertible</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>88.6</td>\n",
       "      <td>...</td>\n",
       "      <td>130</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.47</td>\n",
       "      <td>2.68</td>\n",
       "      <td>9.0</td>\n",
       "      <td>111</td>\n",
       "      <td>5000</td>\n",
       "      <td>21</td>\n",
       "      <td>27</td>\n",
       "      <td>16500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>alfa-romero Quadrifoglio</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>two</td>\n",
       "      <td>hatchback</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>94.5</td>\n",
       "      <td>...</td>\n",
       "      <td>152</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>2.68</td>\n",
       "      <td>3.47</td>\n",
       "      <td>9.0</td>\n",
       "      <td>154</td>\n",
       "      <td>5000</td>\n",
       "      <td>19</td>\n",
       "      <td>26</td>\n",
       "      <td>16500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>audi 100 ls</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>four</td>\n",
       "      <td>sedan</td>\n",
       "      <td>fwd</td>\n",
       "      <td>front</td>\n",
       "      <td>99.8</td>\n",
       "      <td>...</td>\n",
       "      <td>109</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.19</td>\n",
       "      <td>3.40</td>\n",
       "      <td>10.0</td>\n",
       "      <td>102</td>\n",
       "      <td>5500</td>\n",
       "      <td>24</td>\n",
       "      <td>30</td>\n",
       "      <td>13950.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>audi 100ls</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>four</td>\n",
       "      <td>sedan</td>\n",
       "      <td>4wd</td>\n",
       "      <td>front</td>\n",
       "      <td>99.4</td>\n",
       "      <td>...</td>\n",
       "      <td>136</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.19</td>\n",
       "      <td>3.40</td>\n",
       "      <td>8.0</td>\n",
       "      <td>115</td>\n",
       "      <td>5500</td>\n",
       "      <td>18</td>\n",
       "      <td>22</td>\n",
       "      <td>17450.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   car_ID  symboling                   CarName fueltype aspiration doornumber  \\\n",
       "0       1          3        alfa-romero giulia      gas        std        two   \n",
       "1       2          3       alfa-romero stelvio      gas        std        two   \n",
       "2       3          1  alfa-romero Quadrifoglio      gas        std        two   \n",
       "3       4          2               audi 100 ls      gas        std       four   \n",
       "4       5          2                audi 100ls      gas        std       four   \n",
       "\n",
       "       carbody drivewheel enginelocation  wheelbase  ...  enginesize  \\\n",
       "0  convertible        rwd          front       88.6  ...         130   \n",
       "1  convertible        rwd          front       88.6  ...         130   \n",
       "2    hatchback        rwd          front       94.5  ...         152   \n",
       "3        sedan        fwd          front       99.8  ...         109   \n",
       "4        sedan        4wd          front       99.4  ...         136   \n",
       "\n",
       "   fuelsystem  boreratio  stroke compressionratio horsepower  peakrpm citympg  \\\n",
       "0        mpfi       3.47    2.68              9.0        111     5000      21   \n",
       "1        mpfi       3.47    2.68              9.0        111     5000      21   \n",
       "2        mpfi       2.68    3.47              9.0        154     5000      19   \n",
       "3        mpfi       3.19    3.40             10.0        102     5500      24   \n",
       "4        mpfi       3.19    3.40              8.0        115     5500      18   \n",
       "\n",
       "   highwaympg    price  \n",
       "0          27  13495.0  \n",
       "1          27  16500.0  \n",
       "2          26  16500.0  \n",
       "3          30  13950.0  \n",
       "4          22  17450.0  \n",
       "\n",
       "[5 rows x 26 columns]"
      ]
     },
     "execution_count": 219,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cars = pd.read_csv ('CarPrice_Assignment.csv')\n",
    "cars.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(205, 26)"
      ]
     },
     "execution_count": 220,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cars.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>car_ID</th>\n",
       "      <th>symboling</th>\n",
       "      <th>wheelbase</th>\n",
       "      <th>carlength</th>\n",
       "      <th>carwidth</th>\n",
       "      <th>carheight</th>\n",
       "      <th>curbweight</th>\n",
       "      <th>enginesize</th>\n",
       "      <th>boreratio</th>\n",
       "      <th>stroke</th>\n",
       "      <th>compressionratio</th>\n",
       "      <th>horsepower</th>\n",
       "      <th>peakrpm</th>\n",
       "      <th>citympg</th>\n",
       "      <th>highwaympg</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>205.000000</td>\n",
       "      <td>205.000000</td>\n",
       "      <td>205.000000</td>\n",
       "      <td>205.000000</td>\n",
       "      <td>205.000000</td>\n",
       "      <td>205.000000</td>\n",
       "      <td>205.000000</td>\n",
       "      <td>205.000000</td>\n",
       "      <td>205.000000</td>\n",
       "      <td>205.000000</td>\n",
       "      <td>205.000000</td>\n",
       "      <td>205.000000</td>\n",
       "      <td>205.000000</td>\n",
       "      <td>205.000000</td>\n",
       "      <td>205.000000</td>\n",
       "      <td>205.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>103.000000</td>\n",
       "      <td>0.834146</td>\n",
       "      <td>98.756585</td>\n",
       "      <td>174.049268</td>\n",
       "      <td>65.907805</td>\n",
       "      <td>53.724878</td>\n",
       "      <td>2555.565854</td>\n",
       "      <td>126.907317</td>\n",
       "      <td>3.329756</td>\n",
       "      <td>3.255415</td>\n",
       "      <td>10.142537</td>\n",
       "      <td>104.117073</td>\n",
       "      <td>5125.121951</td>\n",
       "      <td>25.219512</td>\n",
       "      <td>30.751220</td>\n",
       "      <td>13276.710571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>59.322565</td>\n",
       "      <td>1.245307</td>\n",
       "      <td>6.021776</td>\n",
       "      <td>12.337289</td>\n",
       "      <td>2.145204</td>\n",
       "      <td>2.443522</td>\n",
       "      <td>520.680204</td>\n",
       "      <td>41.642693</td>\n",
       "      <td>0.270844</td>\n",
       "      <td>0.313597</td>\n",
       "      <td>3.972040</td>\n",
       "      <td>39.544167</td>\n",
       "      <td>476.985643</td>\n",
       "      <td>6.542142</td>\n",
       "      <td>6.886443</td>\n",
       "      <td>7988.852332</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-2.000000</td>\n",
       "      <td>86.600000</td>\n",
       "      <td>141.100000</td>\n",
       "      <td>60.300000</td>\n",
       "      <td>47.800000</td>\n",
       "      <td>1488.000000</td>\n",
       "      <td>61.000000</td>\n",
       "      <td>2.540000</td>\n",
       "      <td>2.070000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>48.000000</td>\n",
       "      <td>4150.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>5118.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>52.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>94.500000</td>\n",
       "      <td>166.300000</td>\n",
       "      <td>64.100000</td>\n",
       "      <td>52.000000</td>\n",
       "      <td>2145.000000</td>\n",
       "      <td>97.000000</td>\n",
       "      <td>3.150000</td>\n",
       "      <td>3.110000</td>\n",
       "      <td>8.600000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>4800.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>7788.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>103.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>97.000000</td>\n",
       "      <td>173.200000</td>\n",
       "      <td>65.500000</td>\n",
       "      <td>54.100000</td>\n",
       "      <td>2414.000000</td>\n",
       "      <td>120.000000</td>\n",
       "      <td>3.310000</td>\n",
       "      <td>3.290000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>95.000000</td>\n",
       "      <td>5200.000000</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>10295.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>154.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>102.400000</td>\n",
       "      <td>183.100000</td>\n",
       "      <td>66.900000</td>\n",
       "      <td>55.500000</td>\n",
       "      <td>2935.000000</td>\n",
       "      <td>141.000000</td>\n",
       "      <td>3.580000</td>\n",
       "      <td>3.410000</td>\n",
       "      <td>9.400000</td>\n",
       "      <td>116.000000</td>\n",
       "      <td>5500.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>34.000000</td>\n",
       "      <td>16503.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>205.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>120.900000</td>\n",
       "      <td>208.100000</td>\n",
       "      <td>72.300000</td>\n",
       "      <td>59.800000</td>\n",
       "      <td>4066.000000</td>\n",
       "      <td>326.000000</td>\n",
       "      <td>3.940000</td>\n",
       "      <td>4.170000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>288.000000</td>\n",
       "      <td>6600.000000</td>\n",
       "      <td>49.000000</td>\n",
       "      <td>54.000000</td>\n",
       "      <td>45400.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           car_ID   symboling   wheelbase   carlength    carwidth   carheight  \\\n",
       "count  205.000000  205.000000  205.000000  205.000000  205.000000  205.000000   \n",
       "mean   103.000000    0.834146   98.756585  174.049268   65.907805   53.724878   \n",
       "std     59.322565    1.245307    6.021776   12.337289    2.145204    2.443522   \n",
       "min      1.000000   -2.000000   86.600000  141.100000   60.300000   47.800000   \n",
       "25%     52.000000    0.000000   94.500000  166.300000   64.100000   52.000000   \n",
       "50%    103.000000    1.000000   97.000000  173.200000   65.500000   54.100000   \n",
       "75%    154.000000    2.000000  102.400000  183.100000   66.900000   55.500000   \n",
       "max    205.000000    3.000000  120.900000  208.100000   72.300000   59.800000   \n",
       "\n",
       "        curbweight  enginesize   boreratio      stroke  compressionratio  \\\n",
       "count   205.000000  205.000000  205.000000  205.000000        205.000000   \n",
       "mean   2555.565854  126.907317    3.329756    3.255415         10.142537   \n",
       "std     520.680204   41.642693    0.270844    0.313597          3.972040   \n",
       "min    1488.000000   61.000000    2.540000    2.070000          7.000000   \n",
       "25%    2145.000000   97.000000    3.150000    3.110000          8.600000   \n",
       "50%    2414.000000  120.000000    3.310000    3.290000          9.000000   \n",
       "75%    2935.000000  141.000000    3.580000    3.410000          9.400000   \n",
       "max    4066.000000  326.000000    3.940000    4.170000         23.000000   \n",
       "\n",
       "       horsepower      peakrpm     citympg  highwaympg         price  \n",
       "count  205.000000   205.000000  205.000000  205.000000    205.000000  \n",
       "mean   104.117073  5125.121951   25.219512   30.751220  13276.710571  \n",
       "std     39.544167   476.985643    6.542142    6.886443   7988.852332  \n",
       "min     48.000000  4150.000000   13.000000   16.000000   5118.000000  \n",
       "25%     70.000000  4800.000000   19.000000   25.000000   7788.000000  \n",
       "50%     95.000000  5200.000000   24.000000   30.000000  10295.000000  \n",
       "75%    116.000000  5500.000000   30.000000   34.000000  16503.000000  \n",
       "max    288.000000  6600.000000   49.000000   54.000000  45400.000000  "
      ]
     },
     "execution_count": 221,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cars.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 205 entries, 0 to 204\n",
      "Data columns (total 26 columns):\n",
      "car_ID              205 non-null int64\n",
      "symboling           205 non-null int64\n",
      "CarName             205 non-null object\n",
      "fueltype            205 non-null object\n",
      "aspiration          205 non-null object\n",
      "doornumber          205 non-null object\n",
      "carbody             205 non-null object\n",
      "drivewheel          205 non-null object\n",
      "enginelocation      205 non-null object\n",
      "wheelbase           205 non-null float64\n",
      "carlength           205 non-null float64\n",
      "carwidth            205 non-null float64\n",
      "carheight           205 non-null float64\n",
      "curbweight          205 non-null int64\n",
      "enginetype          205 non-null object\n",
      "cylindernumber      205 non-null object\n",
      "enginesize          205 non-null int64\n",
      "fuelsystem          205 non-null object\n",
      "boreratio           205 non-null float64\n",
      "stroke              205 non-null float64\n",
      "compressionratio    205 non-null float64\n",
      "horsepower          205 non-null int64\n",
      "peakrpm             205 non-null int64\n",
      "citympg             205 non-null int64\n",
      "highwaympg          205 non-null int64\n",
      "price               205 non-null float64\n",
      "dtypes: float64(8), int64(8), object(10)\n",
      "memory usage: 41.7+ KB\n"
     ]
    }
   ],
   "source": [
    "cars.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# (1) Cleaning data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    .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>car_ID</th>\n",
       "      <th>symboling</th>\n",
       "      <th>CompanyName</th>\n",
       "      <th>fueltype</th>\n",
       "      <th>aspiration</th>\n",
       "      <th>doornumber</th>\n",
       "      <th>carbody</th>\n",
       "      <th>drivewheel</th>\n",
       "      <th>enginelocation</th>\n",
       "      <th>wheelbase</th>\n",
       "      <th>...</th>\n",
       "      <th>enginesize</th>\n",
       "      <th>fuelsystem</th>\n",
       "      <th>boreratio</th>\n",
       "      <th>stroke</th>\n",
       "      <th>compressionratio</th>\n",
       "      <th>horsepower</th>\n",
       "      <th>peakrpm</th>\n",
       "      <th>citympg</th>\n",
       "      <th>highwaympg</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>alfa-romero</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>two</td>\n",
       "      <td>convertible</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>88.6</td>\n",
       "      <td>...</td>\n",
       "      <td>130</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.47</td>\n",
       "      <td>2.68</td>\n",
       "      <td>9.0</td>\n",
       "      <td>111</td>\n",
       "      <td>5000</td>\n",
       "      <td>21</td>\n",
       "      <td>27</td>\n",
       "      <td>13495.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>alfa-romero</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>two</td>\n",
       "      <td>convertible</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>88.6</td>\n",
       "      <td>...</td>\n",
       "      <td>130</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.47</td>\n",
       "      <td>2.68</td>\n",
       "      <td>9.0</td>\n",
       "      <td>111</td>\n",
       "      <td>5000</td>\n",
       "      <td>21</td>\n",
       "      <td>27</td>\n",
       "      <td>16500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>alfa-romero</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>two</td>\n",
       "      <td>hatchback</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>94.5</td>\n",
       "      <td>...</td>\n",
       "      <td>152</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>2.68</td>\n",
       "      <td>3.47</td>\n",
       "      <td>9.0</td>\n",
       "      <td>154</td>\n",
       "      <td>5000</td>\n",
       "      <td>19</td>\n",
       "      <td>26</td>\n",
       "      <td>16500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>audi</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>four</td>\n",
       "      <td>sedan</td>\n",
       "      <td>fwd</td>\n",
       "      <td>front</td>\n",
       "      <td>99.8</td>\n",
       "      <td>...</td>\n",
       "      <td>109</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.19</td>\n",
       "      <td>3.40</td>\n",
       "      <td>10.0</td>\n",
       "      <td>102</td>\n",
       "      <td>5500</td>\n",
       "      <td>24</td>\n",
       "      <td>30</td>\n",
       "      <td>13950.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>audi</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>four</td>\n",
       "      <td>sedan</td>\n",
       "      <td>4wd</td>\n",
       "      <td>front</td>\n",
       "      <td>99.4</td>\n",
       "      <td>...</td>\n",
       "      <td>136</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.19</td>\n",
       "      <td>3.40</td>\n",
       "      <td>8.0</td>\n",
       "      <td>115</td>\n",
       "      <td>5500</td>\n",
       "      <td>18</td>\n",
       "      <td>22</td>\n",
       "      <td>17450.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   car_ID  symboling  CompanyName fueltype aspiration doornumber      carbody  \\\n",
       "0       1          3  alfa-romero      gas        std        two  convertible   \n",
       "1       2          3  alfa-romero      gas        std        two  convertible   \n",
       "2       3          1  alfa-romero      gas        std        two    hatchback   \n",
       "3       4          2         audi      gas        std       four        sedan   \n",
       "4       5          2         audi      gas        std       four        sedan   \n",
       "\n",
       "  drivewheel enginelocation  wheelbase  ...  enginesize  fuelsystem  \\\n",
       "0        rwd          front       88.6  ...         130        mpfi   \n",
       "1        rwd          front       88.6  ...         130        mpfi   \n",
       "2        rwd          front       94.5  ...         152        mpfi   \n",
       "3        fwd          front       99.8  ...         109        mpfi   \n",
       "4        4wd          front       99.4  ...         136        mpfi   \n",
       "\n",
       "   boreratio  stroke compressionratio horsepower  peakrpm citympg  highwaympg  \\\n",
       "0       3.47    2.68              9.0        111     5000      21          27   \n",
       "1       3.47    2.68              9.0        111     5000      21          27   \n",
       "2       2.68    3.47              9.0        154     5000      19          26   \n",
       "3       3.19    3.40             10.0        102     5500      24          30   \n",
       "4       3.19    3.40              8.0        115     5500      18          22   \n",
       "\n",
       "     price  \n",
       "0  13495.0  \n",
       "1  16500.0  \n",
       "2  16500.0  \n",
       "3  13950.0  \n",
       "4  17450.0  \n",
       "\n",
       "[5 rows x 26 columns]"
      ]
     },
     "execution_count": 223,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cars['CarName']=cars['CarName'].apply(lambda x:x.split(' ', 1)[0]) #فانكشن مهمة\n",
    "cars.rename(columns = {'CarName':'CompanyName'}, inplace = True)\n",
    "cars.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# (2) Encoding Categorical data (Replacin` Values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>car_ID</th>\n",
       "      <th>symboling</th>\n",
       "      <th>CompanyName</th>\n",
       "      <th>fueltype</th>\n",
       "      <th>aspiration</th>\n",
       "      <th>doornumber</th>\n",
       "      <th>carbody</th>\n",
       "      <th>drivewheel</th>\n",
       "      <th>enginelocation</th>\n",
       "      <th>wheelbase</th>\n",
       "      <th>...</th>\n",
       "      <th>enginesize</th>\n",
       "      <th>fuelsystem</th>\n",
       "      <th>boreratio</th>\n",
       "      <th>stroke</th>\n",
       "      <th>compressionratio</th>\n",
       "      <th>horsepower</th>\n",
       "      <th>peakrpm</th>\n",
       "      <th>citympg</th>\n",
       "      <th>highwaympg</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>alfa-romero</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>2</td>\n",
       "      <td>convertible</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>88.6</td>\n",
       "      <td>...</td>\n",
       "      <td>130</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.47</td>\n",
       "      <td>2.68</td>\n",
       "      <td>9.0</td>\n",
       "      <td>111</td>\n",
       "      <td>5000</td>\n",
       "      <td>21</td>\n",
       "      <td>27</td>\n",
       "      <td>13495.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>alfa-romero</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>2</td>\n",
       "      <td>convertible</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>88.6</td>\n",
       "      <td>...</td>\n",
       "      <td>130</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.47</td>\n",
       "      <td>2.68</td>\n",
       "      <td>9.0</td>\n",
       "      <td>111</td>\n",
       "      <td>5000</td>\n",
       "      <td>21</td>\n",
       "      <td>27</td>\n",
       "      <td>16500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>alfa-romero</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>2</td>\n",
       "      <td>hatchback</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>94.5</td>\n",
       "      <td>...</td>\n",
       "      <td>152</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>2.68</td>\n",
       "      <td>3.47</td>\n",
       "      <td>9.0</td>\n",
       "      <td>154</td>\n",
       "      <td>5000</td>\n",
       "      <td>19</td>\n",
       "      <td>26</td>\n",
       "      <td>16500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>audi</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>4</td>\n",
       "      <td>sedan</td>\n",
       "      <td>fwd</td>\n",
       "      <td>front</td>\n",
       "      <td>99.8</td>\n",
       "      <td>...</td>\n",
       "      <td>109</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.19</td>\n",
       "      <td>3.40</td>\n",
       "      <td>10.0</td>\n",
       "      <td>102</td>\n",
       "      <td>5500</td>\n",
       "      <td>24</td>\n",
       "      <td>30</td>\n",
       "      <td>13950.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>audi</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>4</td>\n",
       "      <td>sedan</td>\n",
       "      <td>4wd</td>\n",
       "      <td>front</td>\n",
       "      <td>99.4</td>\n",
       "      <td>...</td>\n",
       "      <td>136</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.19</td>\n",
       "      <td>3.40</td>\n",
       "      <td>8.0</td>\n",
       "      <td>115</td>\n",
       "      <td>5500</td>\n",
       "      <td>18</td>\n",
       "      <td>22</td>\n",
       "      <td>17450.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   car_ID  symboling  CompanyName fueltype aspiration  doornumber  \\\n",
       "0       1          3  alfa-romero      gas        std           2   \n",
       "1       2          3  alfa-romero      gas        std           2   \n",
       "2       3          1  alfa-romero      gas        std           2   \n",
       "3       4          2         audi      gas        std           4   \n",
       "4       5          2         audi      gas        std           4   \n",
       "\n",
       "       carbody drivewheel enginelocation  wheelbase  ...  enginesize  \\\n",
       "0  convertible        rwd          front       88.6  ...         130   \n",
       "1  convertible        rwd          front       88.6  ...         130   \n",
       "2    hatchback        rwd          front       94.5  ...         152   \n",
       "3        sedan        fwd          front       99.8  ...         109   \n",
       "4        sedan        4wd          front       99.4  ...         136   \n",
       "\n",
       "   fuelsystem  boreratio  stroke compressionratio horsepower  peakrpm citympg  \\\n",
       "0        mpfi       3.47    2.68              9.0        111     5000      21   \n",
       "1        mpfi       3.47    2.68              9.0        111     5000      21   \n",
       "2        mpfi       2.68    3.47              9.0        154     5000      19   \n",
       "3        mpfi       3.19    3.40             10.0        102     5500      24   \n",
       "4        mpfi       3.19    3.40              8.0        115     5500      18   \n",
       "\n",
       "   highwaympg    price  \n",
       "0          27  13495.0  \n",
       "1          27  16500.0  \n",
       "2          26  16500.0  \n",
       "3          30  13950.0  \n",
       "4          22  17450.0  \n",
       "\n",
       "[5 rows x 26 columns]"
      ]
     },
     "execution_count": 224,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cleanup_nums = {\"doornumber\":     {\"four\": 4, \"two\": 2}}\n",
    "cars=cars.replace(cleanup_nums)\n",
    "cars.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "car_ID              0\n",
       "symboling           0\n",
       "CompanyName         0\n",
       "fueltype            0\n",
       "aspiration          0\n",
       "doornumber          0\n",
       "carbody             0\n",
       "drivewheel          0\n",
       "enginelocation      0\n",
       "wheelbase           0\n",
       "carlength           0\n",
       "carwidth            0\n",
       "carheight           0\n",
       "curbweight          0\n",
       "enginetype          0\n",
       "cylindernumber      0\n",
       "enginesize          0\n",
       "fuelsystem          0\n",
       "boreratio           0\n",
       "stroke              0\n",
       "compressionratio    0\n",
       "horsepower          0\n",
       "peakrpm             0\n",
       "citympg             0\n",
       "highwaympg          0\n",
       "price               0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 225,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cars.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>car_ID</th>\n",
       "      <th>symboling</th>\n",
       "      <th>CompanyName</th>\n",
       "      <th>fueltype</th>\n",
       "      <th>aspiration</th>\n",
       "      <th>doornumber</th>\n",
       "      <th>carbody</th>\n",
       "      <th>drivewheel</th>\n",
       "      <th>enginelocation</th>\n",
       "      <th>wheelbase</th>\n",
       "      <th>...</th>\n",
       "      <th>enginesize</th>\n",
       "      <th>fuelsystem</th>\n",
       "      <th>boreratio</th>\n",
       "      <th>stroke</th>\n",
       "      <th>compressionratio</th>\n",
       "      <th>horsepower</th>\n",
       "      <th>peakrpm</th>\n",
       "      <th>citympg</th>\n",
       "      <th>highwaympg</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>0 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [car_ID, symboling, CompanyName, fueltype, aspiration, doornumber, carbody, drivewheel, enginelocation, wheelbase, carlength, carwidth, carheight, curbweight, enginetype, cylindernumber, enginesize, fuelsystem, boreratio, stroke, compressionratio, horsepower, peakrpm, citympg, highwaympg, price]\n",
       "Index: []\n",
       "\n",
       "[0 rows x 26 columns]"
      ]
     },
     "execution_count": 226,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cars.loc[cars.duplicated()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['alfa-romero', 'audi', 'bmw', 'chevrolet', 'dodge', 'honda',\n",
       "       'isuzu', 'jaguar', 'maxda', 'mazda', 'buick', 'mercury',\n",
       "       'mitsubishi', 'Nissan', 'nissan', 'peugeot', 'plymouth', 'porsche',\n",
       "       'porcshce', 'renault', 'saab', 'subaru', 'toyota', 'toyouta',\n",
       "       'vokswagen', 'volkswagen', 'vw', 'volvo'], dtype=object)"
      ]
     },
     "execution_count": 227,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cars.CompanyName.unique()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['alfa-romero', 'audi', 'bmw', 'chevrolet', 'dodge', 'honda',\n",
       "       'isuzu', 'jaguar', 'mazda', 'buick', 'mercury', 'mitsubishi',\n",
       "       'nissan', 'peugeot', 'plymouth', 'porsche', 'renault', 'saab',\n",
       "       'subaru', 'toyota', 'volkswagen', 'volvo'], dtype=object)"
      ]
     },
     "execution_count": 228,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cars.CompanyName = cars.CompanyName.str.lower()\n",
    "\n",
    "def replace_name(a,b):\n",
    "    cars.CompanyName.replace(a,b,inplace=True)\n",
    "\n",
    "replace_name('maxda','mazda')\n",
    "replace_name('porcshce','porsche')\n",
    "replace_name('toyouta','toyota')\n",
    "replace_name('vokswagen','volkswagen')\n",
    "replace_name('vw','volkswagen')\n",
    "\n",
    "cars.CompanyName.unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visualizing data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1800x432 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(25, 6))\n",
    "\n",
    "\n",
    "explode=[0,0.1]\n",
    "colors = ['#008fd5','#fc4f30']\n",
    "\n",
    "\n",
    "plt.subplot(1,3,1)\n",
    "plt1 = cars.CompanyName.value_counts().plot('bar')\n",
    "plt.title('Companies Histogram')\n",
    "plt1.set(xlabel = 'Car company', ylabel='Frequency of company')\n",
    "\n",
    "plt.subplot(1,3,2)\n",
    "plt1 = cars.fueltype.value_counts().plot('pie',autopct='%1.1f%%',shadow = True ,explode=explode ,colors=colors ,wedgeprops={'edgecolor':'black'})\n",
    "plt.title('Fuel Type PieChart')\n",
    "plt1.set(xlabel = 'Fuel Type', ylabel='Frequency of fuel type')\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "plt.subplot(1,3,3)\n",
    "plt1 = cars.carbody.value_counts().plot('bar')\n",
    "plt.title('Car Type Histogram')\n",
    "plt1.set(xlabel = 'Car Type', ylabel='Frequency of Car type')\n",
    "\n",
    "plt.style.use('fivethirtyeight')\n",
    "plt.tight_layout()\n",
    "plt.xkcd()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Toyota seemed to be favored car company.\n",
    "#Number of gas fueled cars are more than diesel.\n",
    "#sedan is the top car type prefered."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['bmh', 'classic', 'dark_background', 'fast', 'fivethirtyeight', 'ggplot', 'grayscale', 'seaborn-bright', 'seaborn-colorblind', 'seaborn-dark-palette', 'seaborn-dark', 'seaborn-darkgrid', 'seaborn-deep', 'seaborn-muted', 'seaborn-notebook', 'seaborn-paper', 'seaborn-pastel', 'seaborn-poster', 'seaborn-talk', 'seaborn-ticks', 'seaborn-white', 'seaborn-whitegrid', 'seaborn', 'Solarize_Light2', 'tableau-colorblind10', '_classic_test']\n"
     ]
    }
   ],
   "source": [
    "print(plt.style.available)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['car_ID', 'symboling', 'CompanyName', 'fueltype', 'aspiration',\n",
       "       'doornumber', 'carbody', 'drivewheel', 'enginelocation', 'wheelbase',\n",
       "       'carlength', 'carwidth', 'carheight', 'curbweight', 'enginetype',\n",
       "       'cylindernumber', 'enginesize', 'fuelsystem', 'boreratio', 'stroke',\n",
       "       'compressionratio', 'horsepower', 'peakrpm', 'citympg', 'highwaympg',\n",
       "       'price'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 232,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cars.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Linear Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 233,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[15874.62088145],\n",
       "       [15874.62088145],\n",
       "       [ 9293.07976059],\n",
       "       [ 9293.07976059],\n",
       "       [13680.77384116],\n",
       "       [15874.62088145],\n",
       "       [15874.62088145],\n",
       "       [15874.62088145],\n",
       "       [15874.62088145],\n",
       "       [15874.62088145],\n",
       "       [15874.62088145],\n",
       "       [15874.62088145],\n",
       "       [15874.62088145],\n",
       "       [15874.62088145],\n",
       "       [15874.62088145],\n",
       "       [15874.62088145],\n",
       "       [13680.77384116],\n",
       "       [13680.77384116],\n",
       "       [13680.77384116],\n",
       "       [13680.77384116],\n",
       "       [13680.77384116],\n",
       "       [18068.46792174],\n",
       "       [ 9293.07976059],\n",
       "       [ 9293.07976059],\n",
       "       [ 9293.07976059],\n",
       "       [ 9293.07976059],\n",
       "       [ 9293.07976059],\n",
       "       [13680.77384116],\n",
       "       [15874.62088145],\n",
       "       [11486.92680088],\n",
       "       [ 9293.07976059],\n",
       "       [11486.92680088],\n",
       "       [ 9293.07976059],\n",
       "       [11486.92680088],\n",
       "       [ 9293.07976059],\n",
       "       [11486.92680088],\n",
       "       [11486.92680088],\n",
       "       [11486.92680088],\n",
       "       [11486.92680088],\n",
       "       [15874.62088145],\n",
       "       [15874.62088145],\n",
       "       [15874.62088145],\n",
       "       [15874.62088145],\n",
       "       [15874.62088145],\n",
       "       [15874.62088145],\n",
       "       [15874.62088145],\n",
       "       [15874.62088145],\n",
       "       [15874.62088145],\n",
       "       [13680.77384116],\n",
       "       [13680.77384116],\n",
       "       [13680.77384116],\n",
       "       [15874.62088145],\n",
       "       [15874.62088145],\n",
       "       [15874.62088145],\n",
       "       [15874.62088145],\n",
       "       [15874.62088145],\n",
       "       [15874.62088145],\n",
       "       [15874.62088145],\n",
       "       [15874.62088145],\n",
       "       [15874.62088145],\n",
       "       [15874.62088145],\n",
       "       [13680.77384116],\n",
       "       [13680.77384116],\n",
       "       [13680.77384116],\n",
       "       [13680.77384116],\n",
       "       [11486.92680088],\n",
       "       [11486.92680088],\n",
       "       [11486.92680088],\n",
       "       [11486.92680088],\n",
       "       [11486.92680088],\n",
       "       [11486.92680088],\n",
       "       [18068.46792174],\n",
       "       [18068.46792174],\n",
       "       [18068.46792174],\n",
       "       [18068.46792174],\n",
       "       [18068.46792174],\n",
       "       [ 9293.07976059],\n",
       "       [ 9293.07976059],\n",
       "       [18068.46792174],\n",
       "       [18068.46792174],\n",
       "       [11486.92680088],\n",
       "       [11486.92680088],\n",
       "       [11486.92680088],\n",
       "       [11486.92680088],\n",
       "       [11486.92680088],\n",
       "       [11486.92680088],\n",
       "       [11486.92680088],\n",
       "       [ 9293.07976059],\n",
       "       [ 9293.07976059],\n",
       "       [15874.62088145],\n",
       "       [15874.62088145],\n",
       "       [15874.62088145],\n",
       "       [20262.31496202],\n",
       "       [18068.46792174],\n",
       "       [20262.31496202],\n",
       "       [18068.46792174],\n",
       "       [20262.31496202],\n",
       "       [18068.46792174],\n",
       "       [18068.46792174],\n",
       "       [18068.46792174],\n",
       "       [18068.46792174],\n",
       "       [18068.46792174],\n",
       "       [18068.46792174]])"
      ]
     },
     "execution_count": 233,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = cars.loc[:101,[\"symboling\"]]\n",
    "y = cars.loc[:101,[\"price\"]]\n",
    "\n",
    "x_test = cars.loc[102:,[\"symboling\"]]\n",
    "y_actual = cars.loc[102:,[\"price\"]]\n",
    "\n",
    "from sklearn.linear_model import LinearRegression\n",
    "model=LinearRegression()\n",
    "model.fit(x,y)\n",
    "LinearRegression()\n",
    "\n",
    "y_predicted = model.predict(x_test)\n",
    "y_predicted"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(x_test,y_predicted)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 235,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-2193.84704029]])"
      ]
     },
     "execution_count": 235,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([15874.62088145])"
      ]
     },
     "execution_count": 236,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.intercept_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Multi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fueltype</th>\n",
       "      <th>aspiration</th>\n",
       "      <th>carbody</th>\n",
       "      <th>drivewheel</th>\n",
       "      <th>wheelbase</th>\n",
       "      <th>carlength</th>\n",
       "      <th>carwidth</th>\n",
       "      <th>curbweight</th>\n",
       "      <th>enginetype</th>\n",
       "      <th>cylindernumber</th>\n",
       "      <th>enginesize</th>\n",
       "      <th>boreratio</th>\n",
       "      <th>horsepower</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>convertible</td>\n",
       "      <td>rwd</td>\n",
       "      <td>88.6</td>\n",
       "      <td>168.8</td>\n",
       "      <td>64.1</td>\n",
       "      <td>2548</td>\n",
       "      <td>dohc</td>\n",
       "      <td>four</td>\n",
       "      <td>130</td>\n",
       "      <td>3.47</td>\n",
       "      <td>111</td>\n",
       "      <td>13495.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>convertible</td>\n",
       "      <td>rwd</td>\n",
       "      <td>88.6</td>\n",
       "      <td>168.8</td>\n",
       "      <td>64.1</td>\n",
       "      <td>2548</td>\n",
       "      <td>dohc</td>\n",
       "      <td>four</td>\n",
       "      <td>130</td>\n",
       "      <td>3.47</td>\n",
       "      <td>111</td>\n",
       "      <td>16500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>hatchback</td>\n",
       "      <td>rwd</td>\n",
       "      <td>94.5</td>\n",
       "      <td>171.2</td>\n",
       "      <td>65.5</td>\n",
       "      <td>2823</td>\n",
       "      <td>ohcv</td>\n",
       "      <td>six</td>\n",
       "      <td>152</td>\n",
       "      <td>2.68</td>\n",
       "      <td>154</td>\n",
       "      <td>16500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>sedan</td>\n",
       "      <td>fwd</td>\n",
       "      <td>99.8</td>\n",
       "      <td>176.6</td>\n",
       "      <td>66.2</td>\n",
       "      <td>2337</td>\n",
       "      <td>ohc</td>\n",
       "      <td>four</td>\n",
       "      <td>109</td>\n",
       "      <td>3.19</td>\n",
       "      <td>102</td>\n",
       "      <td>13950.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>sedan</td>\n",
       "      <td>4wd</td>\n",
       "      <td>99.4</td>\n",
       "      <td>176.6</td>\n",
       "      <td>66.4</td>\n",
       "      <td>2824</td>\n",
       "      <td>ohc</td>\n",
       "      <td>five</td>\n",
       "      <td>136</td>\n",
       "      <td>3.19</td>\n",
       "      <td>115</td>\n",
       "      <td>17450.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  fueltype aspiration      carbody drivewheel  wheelbase  carlength  carwidth  \\\n",
       "0      gas        std  convertible        rwd       88.6      168.8      64.1   \n",
       "1      gas        std  convertible        rwd       88.6      168.8      64.1   \n",
       "2      gas        std    hatchback        rwd       94.5      171.2      65.5   \n",
       "3      gas        std        sedan        fwd       99.8      176.6      66.2   \n",
       "4      gas        std        sedan        4wd       99.4      176.6      66.4   \n",
       "\n",
       "   curbweight enginetype cylindernumber  enginesize  boreratio  horsepower  \\\n",
       "0        2548       dohc           four         130       3.47         111   \n",
       "1        2548       dohc           four         130       3.47         111   \n",
       "2        2823       ohcv            six         152       2.68         154   \n",
       "3        2337        ohc           four         109       3.19         102   \n",
       "4        2824        ohc           five         136       3.19         115   \n",
       "\n",
       "     price  \n",
       "0  13495.0  \n",
       "1  16500.0  \n",
       "2  16500.0  \n",
       "3  13950.0  \n",
       "4  17450.0  "
      ]
     },
     "execution_count": 237,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Variables for prediction of price\n",
    "\n",
    "auto = cars[['fueltype', 'aspiration', 'carbody', 'drivewheel', 'wheelbase', 'carlength', 'carwidth', 'curbweight', 'enginetype',\n",
    "       'cylindernumber', 'enginesize',  'boreratio', 'horsepower', 'price']]\n",
    "auto.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Categorical Variables are converted into Neumerical Variables with the help of Dummy Variable "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 238,
   "metadata": {},
   "outputs": [],
   "source": [
    "cyl_no = pd.get_dummies(auto['cylindernumber'], drop_first = True)\n",
    "auto = pd.concat([auto, cyl_no], axis = 1)\n",
    "\n",
    "eng_typ = pd.get_dummies(auto['enginetype'], drop_first = True)\n",
    "auto = pd.concat([auto, eng_typ], axis = 1)\n",
    "\n",
    "drwh = pd.get_dummies(auto['drivewheel'], drop_first = True)\n",
    "auto = pd.concat([auto, drwh], axis = 1)\n",
    "\n",
    "carb = pd.get_dummies(auto['carbody'], drop_first = True)\n",
    "auto = pd.concat([auto, carb], axis = 1)\n",
    "\n",
    "asp = pd.get_dummies(auto['aspiration'], drop_first = True)\n",
    "auto = pd.concat([auto, asp], axis = 1)\n",
    "\n",
    "fuelt = pd.get_dummies(auto['fueltype'], drop_first = True)\n",
    "auto = pd.concat([auto, fuelt], axis = 1)\n",
    "\n",
    "auto.drop(['fueltype', 'aspiration', 'carbody', 'drivewheel', 'enginetype', 'cylindernumber'], axis = 1, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 239,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "    .dataframe thead th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>wheelbase</th>\n",
       "      <th>carlength</th>\n",
       "      <th>carwidth</th>\n",
       "      <th>curbweight</th>\n",
       "      <th>enginesize</th>\n",
       "      <th>boreratio</th>\n",
       "      <th>horsepower</th>\n",
       "      <th>price</th>\n",
       "      <th>five</th>\n",
       "      <th>four</th>\n",
       "      <th>...</th>\n",
       "      <th>ohcv</th>\n",
       "      <th>rotor</th>\n",
       "      <th>fwd</th>\n",
       "      <th>rwd</th>\n",
       "      <th>hardtop</th>\n",
       "      <th>hatchback</th>\n",
       "      <th>sedan</th>\n",
       "      <th>wagon</th>\n",
       "      <th>turbo</th>\n",
       "      <th>gas</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>157</th>\n",
       "      <td>95.7</td>\n",
       "      <td>166.3</td>\n",
       "      <td>64.4</td>\n",
       "      <td>2109</td>\n",
       "      <td>98</td>\n",
       "      <td>3.19</td>\n",
       "      <td>70</td>\n",
       "      <td>7198.000</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>163</th>\n",
       "      <td>94.5</td>\n",
       "      <td>168.7</td>\n",
       "      <td>64.0</td>\n",
       "      <td>2169</td>\n",
       "      <td>98</td>\n",
       "      <td>3.19</td>\n",
       "      <td>70</td>\n",
       "      <td>8058.000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>96.9</td>\n",
       "      <td>173.6</td>\n",
       "      <td>65.4</td>\n",
       "      <td>2420</td>\n",
       "      <td>108</td>\n",
       "      <td>3.62</td>\n",
       "      <td>82</td>\n",
       "      <td>8013.000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>93.7</td>\n",
       "      <td>157.3</td>\n",
       "      <td>63.8</td>\n",
       "      <td>2191</td>\n",
       "      <td>98</td>\n",
       "      <td>3.03</td>\n",
       "      <td>102</td>\n",
       "      <td>8558.000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111</th>\n",
       "      <td>107.9</td>\n",
       "      <td>186.7</td>\n",
       "      <td>68.4</td>\n",
       "      <td>3075</td>\n",
       "      <td>120</td>\n",
       "      <td>3.46</td>\n",
       "      <td>95</td>\n",
       "      <td>15580.000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>166</th>\n",
       "      <td>94.5</td>\n",
       "      <td>168.7</td>\n",
       "      <td>64.0</td>\n",
       "      <td>2300</td>\n",
       "      <td>98</td>\n",
       "      <td>3.24</td>\n",
       "      <td>112</td>\n",
       "      <td>9538.000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>99.5</td>\n",
       "      <td>178.2</td>\n",
       "      <td>67.9</td>\n",
       "      <td>3053</td>\n",
       "      <td>131</td>\n",
       "      <td>3.13</td>\n",
       "      <td>160</td>\n",
       "      <td>17859.167</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153</th>\n",
       "      <td>95.7</td>\n",
       "      <td>169.7</td>\n",
       "      <td>63.6</td>\n",
       "      <td>2280</td>\n",
       "      <td>92</td>\n",
       "      <td>3.05</td>\n",
       "      <td>62</td>\n",
       "      <td>6918.000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>158</th>\n",
       "      <td>95.7</td>\n",
       "      <td>166.3</td>\n",
       "      <td>64.4</td>\n",
       "      <td>2275</td>\n",
       "      <td>110</td>\n",
       "      <td>3.27</td>\n",
       "      <td>56</td>\n",
       "      <td>7898.000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>112</th>\n",
       "      <td>107.9</td>\n",
       "      <td>186.7</td>\n",
       "      <td>68.4</td>\n",
       "      <td>3252</td>\n",
       "      <td>152</td>\n",
       "      <td>3.70</td>\n",
       "      <td>95</td>\n",
       "      <td>16900.000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132</th>\n",
       "      <td>99.1</td>\n",
       "      <td>186.6</td>\n",
       "      <td>66.5</td>\n",
       "      <td>2658</td>\n",
       "      <td>121</td>\n",
       "      <td>3.54</td>\n",
       "      <td>110</td>\n",
       "      <td>11850.000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>122</th>\n",
       "      <td>93.7</td>\n",
       "      <td>167.3</td>\n",
       "      <td>63.8</td>\n",
       "      <td>2191</td>\n",
       "      <td>98</td>\n",
       "      <td>2.97</td>\n",
       "      <td>68</td>\n",
       "      <td>7609.000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>98.8</td>\n",
       "      <td>177.8</td>\n",
       "      <td>66.5</td>\n",
       "      <td>2425</td>\n",
       "      <td>122</td>\n",
       "      <td>3.39</td>\n",
       "      <td>84</td>\n",
       "      <td>11245.000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>103.3</td>\n",
       "      <td>174.6</td>\n",
       "      <td>64.6</td>\n",
       "      <td>2535</td>\n",
       "      <td>122</td>\n",
       "      <td>3.34</td>\n",
       "      <td>88</td>\n",
       "      <td>8921.000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>97.0</td>\n",
       "      <td>173.5</td>\n",
       "      <td>65.4</td>\n",
       "      <td>2455</td>\n",
       "      <td>108</td>\n",
       "      <td>3.62</td>\n",
       "      <td>94</td>\n",
       "      <td>10198.000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125</th>\n",
       "      <td>94.5</td>\n",
       "      <td>168.9</td>\n",
       "      <td>68.3</td>\n",
       "      <td>2778</td>\n",
       "      <td>151</td>\n",
       "      <td>3.94</td>\n",
       "      <td>143</td>\n",
       "      <td>22018.000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>93.7</td>\n",
       "      <td>157.3</td>\n",
       "      <td>63.8</td>\n",
       "      <td>1876</td>\n",
       "      <td>90</td>\n",
       "      <td>2.97</td>\n",
       "      <td>68</td>\n",
       "      <td>6377.000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194</th>\n",
       "      <td>104.3</td>\n",
       "      <td>188.8</td>\n",
       "      <td>67.2</td>\n",
       "      <td>2912</td>\n",
       "      <td>141</td>\n",
       "      <td>3.78</td>\n",
       "      <td>114</td>\n",
       "      <td>12940.000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>101.2</td>\n",
       "      <td>176.8</td>\n",
       "      <td>64.8</td>\n",
       "      <td>2765</td>\n",
       "      <td>164</td>\n",
       "      <td>3.31</td>\n",
       "      <td>121</td>\n",
       "      <td>21105.000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>115.6</td>\n",
       "      <td>202.6</td>\n",
       "      <td>71.7</td>\n",
       "      <td>3770</td>\n",
       "      <td>183</td>\n",
       "      <td>3.58</td>\n",
       "      <td>123</td>\n",
       "      <td>31600.000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>100.4</td>\n",
       "      <td>181.7</td>\n",
       "      <td>66.5</td>\n",
       "      <td>3095</td>\n",
       "      <td>181</td>\n",
       "      <td>3.43</td>\n",
       "      <td>152</td>\n",
       "      <td>13499.000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>115.6</td>\n",
       "      <td>202.6</td>\n",
       "      <td>71.7</td>\n",
       "      <td>3740</td>\n",
       "      <td>234</td>\n",
       "      <td>3.46</td>\n",
       "      <td>155</td>\n",
       "      <td>34184.000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>96.3</td>\n",
       "      <td>172.4</td>\n",
       "      <td>65.4</td>\n",
       "      <td>2405</td>\n",
       "      <td>122</td>\n",
       "      <td>3.35</td>\n",
       "      <td>88</td>\n",
       "      <td>8189.000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>94.5</td>\n",
       "      <td>171.2</td>\n",
       "      <td>65.5</td>\n",
       "      <td>2823</td>\n",
       "      <td>152</td>\n",
       "      <td>2.68</td>\n",
       "      <td>154</td>\n",
       "      <td>16500.000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>120.9</td>\n",
       "      <td>208.1</td>\n",
       "      <td>71.7</td>\n",
       "      <td>3900</td>\n",
       "      <td>308</td>\n",
       "      <td>3.80</td>\n",
       "      <td>184</td>\n",
       "      <td>40960.000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>100.4</td>\n",
       "      <td>184.6</td>\n",
       "      <td>66.5</td>\n",
       "      <td>3296</td>\n",
       "      <td>181</td>\n",
       "      <td>3.43</td>\n",
       "      <td>152</td>\n",
       "      <td>14399.000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>95.3</td>\n",
       "      <td>169.0</td>\n",
       "      <td>65.7</td>\n",
       "      <td>2385</td>\n",
       "      <td>70</td>\n",
       "      <td>3.33</td>\n",
       "      <td>101</td>\n",
       "      <td>13645.000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>105.8</td>\n",
       "      <td>192.7</td>\n",
       "      <td>71.4</td>\n",
       "      <td>3086</td>\n",
       "      <td>131</td>\n",
       "      <td>3.13</td>\n",
       "      <td>140</td>\n",
       "      <td>23875.000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>94.5</td>\n",
       "      <td>165.3</td>\n",
       "      <td>63.8</td>\n",
       "      <td>1951</td>\n",
       "      <td>97</td>\n",
       "      <td>3.15</td>\n",
       "      <td>69</td>\n",
       "      <td>7299.000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>174</th>\n",
       "      <td>102.4</td>\n",
       "      <td>175.6</td>\n",
       "      <td>66.5</td>\n",
       "      <td>2480</td>\n",
       "      <td>110</td>\n",
       "      <td>3.27</td>\n",
       "      <td>73</td>\n",
       "      <td>10698.000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>184</th>\n",
       "      <td>97.3</td>\n",
       "      <td>171.7</td>\n",
       "      <td>65.5</td>\n",
       "      <td>2264</td>\n",
       "      <td>97</td>\n",
       "      <td>3.01</td>\n",
       "      <td>52</td>\n",
       "      <td>7995.000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>110.0</td>\n",
       "      <td>197.0</td>\n",
       "      <td>70.9</td>\n",
       "      <td>3505</td>\n",
       "      <td>209</td>\n",
       "      <td>3.62</td>\n",
       "      <td>182</td>\n",
       "      <td>36880.000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>93.1</td>\n",
       "      <td>166.8</td>\n",
       "      <td>64.2</td>\n",
       "      <td>1950</td>\n",
       "      <td>91</td>\n",
       "      <td>3.08</td>\n",
       "      <td>68</td>\n",
       "      <td>7395.000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>94.5</td>\n",
       "      <td>170.2</td>\n",
       "      <td>63.8</td>\n",
       "      <td>2037</td>\n",
       "      <td>97</td>\n",
       "      <td>3.15</td>\n",
       "      <td>69</td>\n",
       "      <td>7999.000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>108</th>\n",
       "      <td>107.9</td>\n",
       "      <td>186.7</td>\n",
       "      <td>68.4</td>\n",
       "      <td>3197</td>\n",
       "      <td>152</td>\n",
       "      <td>3.70</td>\n",
       "      <td>95</td>\n",
       "      <td>13200.000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>175</th>\n",
       "      <td>102.4</td>\n",
       "      <td>175.6</td>\n",
       "      <td>66.5</td>\n",
       "      <td>2414</td>\n",
       "      <td>122</td>\n",
       "      <td>3.31</td>\n",
       "      <td>92</td>\n",
       "      <td>9988.000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>172</th>\n",
       "      <td>98.4</td>\n",
       "      <td>176.2</td>\n",
       "      <td>65.6</td>\n",
       "      <td>2975</td>\n",
       "      <td>146</td>\n",
       "      <td>3.62</td>\n",
       "      <td>116</td>\n",
       "      <td>17669.000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>93.3</td>\n",
       "      <td>157.3</td>\n",
       "      <td>63.8</td>\n",
       "      <td>2240</td>\n",
       "      <td>108</td>\n",
       "      <td>3.62</td>\n",
       "      <td>73</td>\n",
       "      <td>7603.000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>116</th>\n",
       "      <td>107.9</td>\n",
       "      <td>186.7</td>\n",
       "      <td>68.4</td>\n",
       "      <td>3252</td>\n",
       "      <td>152</td>\n",
       "      <td>3.70</td>\n",
       "      <td>95</td>\n",
       "      <td>17950.000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154</th>\n",
       "      <td>95.7</td>\n",
       "      <td>169.7</td>\n",
       "      <td>63.6</td>\n",
       "      <td>2290</td>\n",
       "      <td>92</td>\n",
       "      <td>3.05</td>\n",
       "      <td>62</td>\n",
       "      <td>7898.000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>123</th>\n",
       "      <td>103.3</td>\n",
       "      <td>174.6</td>\n",
       "      <td>64.6</td>\n",
       "      <td>2535</td>\n",
       "      <td>122</td>\n",
       "      <td>3.35</td>\n",
       "      <td>88</td>\n",
       "      <td>8921.000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>95.1</td>\n",
       "      <td>162.4</td>\n",
       "      <td>63.8</td>\n",
       "      <td>2008</td>\n",
       "      <td>97</td>\n",
       "      <td>3.15</td>\n",
       "      <td>69</td>\n",
       "      <td>8249.000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>115</th>\n",
       "      <td>107.9</td>\n",
       "      <td>186.7</td>\n",
       "      <td>68.4</td>\n",
       "      <td>3075</td>\n",
       "      <td>120</td>\n",
       "      <td>3.46</td>\n",
       "      <td>97</td>\n",
       "      <td>16630.000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>197</th>\n",
       "      <td>104.3</td>\n",
       "      <td>188.8</td>\n",
       "      <td>67.2</td>\n",
       "      <td>3042</td>\n",
       "      <td>141</td>\n",
       "      <td>3.78</td>\n",
       "      <td>114</td>\n",
       "      <td>16515.000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>99.8</td>\n",
       "      <td>176.6</td>\n",
       "      <td>66.2</td>\n",
       "      <td>2337</td>\n",
       "      <td>109</td>\n",
       "      <td>3.19</td>\n",
       "      <td>102</td>\n",
       "      <td>13950.000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>180</th>\n",
       "      <td>104.5</td>\n",
       "      <td>187.8</td>\n",
       "      <td>66.5</td>\n",
       "      <td>3131</td>\n",
       "      <td>171</td>\n",
       "      <td>3.27</td>\n",
       "      <td>156</td>\n",
       "      <td>15690.000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>93.7</td>\n",
       "      <td>150.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>1940</td>\n",
       "      <td>92</td>\n",
       "      <td>2.91</td>\n",
       "      <td>76</td>\n",
       "      <td>6529.000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>96.5</td>\n",
       "      <td>163.4</td>\n",
       "      <td>64.0</td>\n",
       "      <td>2010</td>\n",
       "      <td>92</td>\n",
       "      <td>2.91</td>\n",
       "      <td>76</td>\n",
       "      <td>7295.000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>97.2</td>\n",
       "      <td>172.0</td>\n",
       "      <td>65.4</td>\n",
       "      <td>2340</td>\n",
       "      <td>108</td>\n",
       "      <td>3.62</td>\n",
       "      <td>94</td>\n",
       "      <td>9960.000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>165</th>\n",
       "      <td>94.5</td>\n",
       "      <td>168.7</td>\n",
       "      <td>64.0</td>\n",
       "      <td>2265</td>\n",
       "      <td>98</td>\n",
       "      <td>3.24</td>\n",
       "      <td>112</td>\n",
       "      <td>9298.000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179</th>\n",
       "      <td>102.9</td>\n",
       "      <td>183.5</td>\n",
       "      <td>67.7</td>\n",
       "      <td>3016</td>\n",
       "      <td>171</td>\n",
       "      <td>3.27</td>\n",
       "      <td>161</td>\n",
       "      <td>15998.000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>187</th>\n",
       "      <td>97.3</td>\n",
       "      <td>171.7</td>\n",
       "      <td>65.5</td>\n",
       "      <td>2319</td>\n",
       "      <td>97</td>\n",
       "      <td>3.01</td>\n",
       "      <td>68</td>\n",
       "      <td>9495.000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>167</th>\n",
       "      <td>98.4</td>\n",
       "      <td>176.2</td>\n",
       "      <td>65.6</td>\n",
       "      <td>2540</td>\n",
       "      <td>146</td>\n",
       "      <td>3.62</td>\n",
       "      <td>116</td>\n",
       "      <td>8449.000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>101.2</td>\n",
       "      <td>176.8</td>\n",
       "      <td>64.8</td>\n",
       "      <td>2395</td>\n",
       "      <td>108</td>\n",
       "      <td>3.50</td>\n",
       "      <td>101</td>\n",
       "      <td>16925.000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>204</th>\n",
       "      <td>109.1</td>\n",
       "      <td>188.8</td>\n",
       "      <td>68.9</td>\n",
       "      <td>3062</td>\n",
       "      <td>141</td>\n",
       "      <td>3.78</td>\n",
       "      <td>114</td>\n",
       "      <td>22625.000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142</th>\n",
       "      <td>97.2</td>\n",
       "      <td>172.0</td>\n",
       "      <td>65.4</td>\n",
       "      <td>2190</td>\n",
       "      <td>108</td>\n",
       "      <td>3.62</td>\n",
       "      <td>82</td>\n",
       "      <td>7775.000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>95.9</td>\n",
       "      <td>173.2</td>\n",
       "      <td>66.3</td>\n",
       "      <td>2926</td>\n",
       "      <td>156</td>\n",
       "      <td>3.59</td>\n",
       "      <td>145</td>\n",
       "      <td>14489.000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>96.0</td>\n",
       "      <td>172.6</td>\n",
       "      <td>65.2</td>\n",
       "      <td>2734</td>\n",
       "      <td>119</td>\n",
       "      <td>3.43</td>\n",
       "      <td>90</td>\n",
       "      <td>11048.000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>93.1</td>\n",
       "      <td>166.8</td>\n",
       "      <td>64.2</td>\n",
       "      <td>1945</td>\n",
       "      <td>91</td>\n",
       "      <td>3.03</td>\n",
       "      <td>68</td>\n",
       "      <td>6695.000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>192</th>\n",
       "      <td>100.4</td>\n",
       "      <td>180.2</td>\n",
       "      <td>66.9</td>\n",
       "      <td>2579</td>\n",
       "      <td>97</td>\n",
       "      <td>3.01</td>\n",
       "      <td>68</td>\n",
       "      <td>13845.000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>143 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     wheelbase  carlength  carwidth  curbweight  enginesize  boreratio  \\\n",
       "157       95.7      166.3      64.4        2109          98       3.19   \n",
       "163       94.5      168.7      64.0        2169          98       3.19   \n",
       "148       96.9      173.6      65.4        2420         108       3.62   \n",
       "27        93.7      157.3      63.8        2191          98       3.03   \n",
       "111      107.9      186.7      68.4        3075         120       3.46   \n",
       "166       94.5      168.7      64.0        2300          98       3.24   \n",
       "9         99.5      178.2      67.9        3053         131       3.13   \n",
       "153       95.7      169.7      63.6        2280          92       3.05   \n",
       "158       95.7      166.3      64.4        2275         110       3.27   \n",
       "112      107.9      186.7      68.4        3252         152       3.70   \n",
       "132       99.1      186.6      66.5        2658         121       3.54   \n",
       "122       93.7      167.3      63.8        2191          98       2.97   \n",
       "64        98.8      177.8      66.5        2425         122       3.39   \n",
       "28       103.3      174.6      64.6        2535         122       3.34   \n",
       "147       97.0      173.5      65.4        2455         108       3.62   \n",
       "125       94.5      168.9      68.3        2778         151       3.94   \n",
       "22        93.7      157.3      63.8        1876          90       2.97   \n",
       "194      104.3      188.8      67.2        2912         141       3.78   \n",
       "13       101.2      176.8      64.8        2765         164       3.31   \n",
       "70       115.6      202.6      71.7        3770         183       3.58   \n",
       "101      100.4      181.7      66.5        3095         181       3.43   \n",
       "71       115.6      202.6      71.7        3740         234       3.46   \n",
       "86        96.3      172.4      65.4        2405         122       3.35   \n",
       "2         94.5      171.2      65.5        2823         152       2.68   \n",
       "73       120.9      208.1      71.7        3900         308       3.80   \n",
       "102      100.4      184.6      66.5        3296         181       3.43   \n",
       "57        95.3      169.0      65.7        2385          70       3.33   \n",
       "8        105.8      192.7      71.4        3086         131       3.13   \n",
       "94        94.5      165.3      63.8        1951          97       3.15   \n",
       "174      102.4      175.6      66.5        2480         110       3.27   \n",
       "..         ...        ...       ...         ...         ...        ...   \n",
       "184       97.3      171.7      65.5        2264          97       3.01   \n",
       "17       110.0      197.0      70.9        3505         209       3.62   \n",
       "54        93.1      166.8      64.2        1950          91       3.08   \n",
       "97        94.5      170.2      63.8        2037          97       3.15   \n",
       "108      107.9      186.7      68.4        3197         152       3.70   \n",
       "175      102.4      175.6      66.5        2414         122       3.31   \n",
       "172       98.4      176.2      65.6        2975         146       3.62   \n",
       "140       93.3      157.3      63.8        2240         108       3.62   \n",
       "116      107.9      186.7      68.4        3252         152       3.70   \n",
       "154       95.7      169.7      63.6        2290          92       3.05   \n",
       "123      103.3      174.6      64.6        2535         122       3.35   \n",
       "98        95.1      162.4      63.8        2008          97       3.15   \n",
       "115      107.9      186.7      68.4        3075         120       3.46   \n",
       "197      104.3      188.8      67.2        3042         141       3.78   \n",
       "3         99.8      176.6      66.2        2337         109       3.19   \n",
       "180      104.5      187.8      66.5        3131         171       3.27   \n",
       "33        93.7      150.0      64.0        1940          92       2.91   \n",
       "35        96.5      163.4      64.0        2010          92       2.91   \n",
       "143       97.2      172.0      65.4        2340         108       3.62   \n",
       "165       94.5      168.7      64.0        2265          98       3.24   \n",
       "179      102.9      183.5      67.7        3016         171       3.27   \n",
       "187       97.3      171.7      65.5        2319          97       3.01   \n",
       "167       98.4      176.2      65.6        2540         146       3.62   \n",
       "11       101.2      176.8      64.8        2395         108       3.50   \n",
       "204      109.1      188.8      68.9        3062         141       3.78   \n",
       "142       97.2      172.0      65.4        2190         108       3.62   \n",
       "84        95.9      173.2      66.3        2926         156       3.59   \n",
       "46        96.0      172.6      65.2        2734         119       3.43   \n",
       "53        93.1      166.8      64.2        1945          91       3.03   \n",
       "192      100.4      180.2      66.9        2579          97       3.01   \n",
       "\n",
       "     horsepower      price  five  four  ...  ohcv  rotor  fwd  rwd  hardtop  \\\n",
       "157          70   7198.000     0     1  ...     0      0    1    0        0   \n",
       "163          70   8058.000     0     1  ...     0      0    0    1        0   \n",
       "148          82   8013.000     0     1  ...     0      0    0    0        0   \n",
       "27          102   8558.000     0     1  ...     0      0    1    0        0   \n",
       "111          95  15580.000     0     1  ...     0      0    0    1        0   \n",
       "166         112   9538.000     0     1  ...     0      0    0    1        0   \n",
       "9           160  17859.167     1     0  ...     0      0    0    0        0   \n",
       "153          62   6918.000     0     1  ...     0      0    1    0        0   \n",
       "158          56   7898.000     0     1  ...     0      0    1    0        0   \n",
       "112          95  16900.000     0     1  ...     0      0    0    1        0   \n",
       "132         110  11850.000     0     1  ...     0      0    1    0        0   \n",
       "122          68   7609.000     0     1  ...     0      0    1    0        0   \n",
       "64           84  11245.000     0     1  ...     0      0    1    0        0   \n",
       "28           88   8921.000     0     1  ...     0      0    1    0        0   \n",
       "147          94  10198.000     0     1  ...     0      0    1    0        0   \n",
       "125         143  22018.000     0     1  ...     0      0    0    1        0   \n",
       "22           68   6377.000     0     1  ...     0      0    1    0        0   \n",
       "194         114  12940.000     0     1  ...     0      0    0    1        0   \n",
       "13          121  21105.000     0     0  ...     0      0    0    1        0   \n",
       "70          123  31600.000     1     0  ...     0      0    0    1        0   \n",
       "101         152  13499.000     0     0  ...     1      0    1    0        0   \n",
       "71          155  34184.000     0     0  ...     1      0    0    1        0   \n",
       "86           88   8189.000     0     1  ...     0      0    1    0        0   \n",
       "2           154  16500.000     0     0  ...     1      0    0    1        0   \n",
       "73          184  40960.000     0     0  ...     1      0    0    1        0   \n",
       "102         152  14399.000     0     0  ...     1      0    1    0        0   \n",
       "57          101  13645.000     0     0  ...     0      1    0    1        0   \n",
       "8           140  23875.000     1     0  ...     0      0    1    0        0   \n",
       "94           69   7299.000     0     1  ...     0      0    1    0        0   \n",
       "174          73  10698.000     0     1  ...     0      0    1    0        0   \n",
       "..          ...        ...   ...   ...  ...   ...    ...  ...  ...      ...   \n",
       "184          52   7995.000     0     1  ...     0      0    1    0        0   \n",
       "17          182  36880.000     0     0  ...     0      0    0    1        0   \n",
       "54           68   7395.000     0     1  ...     0      0    1    0        0   \n",
       "97           69   7999.000     0     1  ...     0      0    1    0        0   \n",
       "108          95  13200.000     0     1  ...     0      0    0    1        0   \n",
       "175          92   9988.000     0     1  ...     0      0    1    0        0   \n",
       "172         116  17669.000     0     1  ...     0      0    0    1        0   \n",
       "140          73   7603.000     0     1  ...     0      0    0    0        0   \n",
       "116          95  17950.000     0     1  ...     0      0    0    1        0   \n",
       "154          62   7898.000     0     1  ...     0      0    0    0        0   \n",
       "123          88   8921.000     0     1  ...     0      0    1    0        0   \n",
       "98           69   8249.000     0     1  ...     0      0    1    0        1   \n",
       "115          97  16630.000     0     1  ...     0      0    0    1        0   \n",
       "197         114  16515.000     0     1  ...     0      0    0    1        0   \n",
       "3           102  13950.000     0     1  ...     0      0    1    0        0   \n",
       "180         156  15690.000     0     0  ...     0      0    0    1        0   \n",
       "33           76   6529.000     0     1  ...     0      0    1    0        0   \n",
       "35           76   7295.000     0     1  ...     0      0    1    0        0   \n",
       "143          94   9960.000     0     1  ...     0      0    1    0        0   \n",
       "165         112   9298.000     0     1  ...     0      0    0    1        0   \n",
       "179         161  15998.000     0     0  ...     0      0    0    1        0   \n",
       "187          68   9495.000     0     1  ...     0      0    1    0        0   \n",
       "167         116   8449.000     0     1  ...     0      0    0    1        1   \n",
       "11          101  16925.000     0     1  ...     0      0    0    1        0   \n",
       "204         114  22625.000     0     1  ...     0      0    0    1        0   \n",
       "142          82   7775.000     0     1  ...     0      0    1    0        0   \n",
       "84          145  14489.000     0     1  ...     0      0    1    0        0   \n",
       "46           90  11048.000     0     1  ...     0      0    0    1        0   \n",
       "53           68   6695.000     0     1  ...     0      0    1    0        0   \n",
       "192          68  13845.000     0     1  ...     0      0    1    0        0   \n",
       "\n",
       "     hatchback  sedan  wagon  turbo  gas  \n",
       "157          1      0      0      0    1  \n",
       "163          0      1      0      0    1  \n",
       "148          0      0      1      0    1  \n",
       "27           0      1      0      1    1  \n",
       "111          0      1      0      0    1  \n",
       "166          1      0      0      0    1  \n",
       "9            1      0      0      1    1  \n",
       "153          0      0      1      0    1  \n",
       "158          0      1      0      0    0  \n",
       "112          0      1      0      1    0  \n",
       "132          1      0      0      0    1  \n",
       "122          0      1      0      0    1  \n",
       "64           1      0      0      0    1  \n",
       "28           0      0      1      0    1  \n",
       "147          0      0      1      0    1  \n",
       "125          1      0      0      0    1  \n",
       "22           1      0      0      0    1  \n",
       "194          0      1      0      0    1  \n",
       "13           0      1      0      0    1  \n",
       "70           0      1      0      1    0  \n",
       "101          0      1      0      0    1  \n",
       "71           0      1      0      0    1  \n",
       "86           0      1      0      0    1  \n",
       "2            1      0      0      0    1  \n",
       "73           0      1      0      0    1  \n",
       "102          0      0      1      0    1  \n",
       "57           1      0      0      0    1  \n",
       "8            0      1      0      1    1  \n",
       "94           0      1      0      0    1  \n",
       "174          0      1      0      1    0  \n",
       "..         ...    ...    ...    ...  ...  \n",
       "184          0      1      0      0    0  \n",
       "17           0      1      0      0    1  \n",
       "54           0      1      0      0    1  \n",
       "97           0      0      1      0    1  \n",
       "108          0      1      0      1    0  \n",
       "175          1      0      0      0    1  \n",
       "172          0      0      0      0    1  \n",
       "140          1      0      0      0    1  \n",
       "116          0      1      0      1    0  \n",
       "154          0      0      1      0    1  \n",
       "123          0      0      1      0    1  \n",
       "98           0      0      0      0    1  \n",
       "115          0      1      0      0    1  \n",
       "197          0      0      1      0    1  \n",
       "3            0      1      0      0    1  \n",
       "180          0      1      0      0    1  \n",
       "33           1      0      0      0    1  \n",
       "35           0      1      0      0    1  \n",
       "143          0      1      0      0    1  \n",
       "165          0      1      0      0    1  \n",
       "179          1      0      0      0    1  \n",
       "187          0      1      0      1    0  \n",
       "167          0      0      0      0    1  \n",
       "11           0      1      0      0    1  \n",
       "204          0      1      0      1    1  \n",
       "142          0      1      0      0    1  \n",
       "84           1      0      0      1    1  \n",
       "46           1      0      0      0    1  \n",
       "53           0      1      0      0    1  \n",
       "192          0      1      0      1    0  \n",
       "\n",
       "[143 rows x 28 columns]"
      ]
     },
     "execution_count": 239,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xx=auto\n",
    "yy=cars.price\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "xx_train, xx_test, yy_train, yy_test = train_test_split(xx,yy,test_size=.3)\n",
    "xx_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 240,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "157     7198.000\n",
       "163     8058.000\n",
       "148     8013.000\n",
       "27      8558.000\n",
       "111    15580.000\n",
       "166     9538.000\n",
       "9      17859.167\n",
       "153     6918.000\n",
       "158     7898.000\n",
       "112    16900.000\n",
       "132    11850.000\n",
       "122     7609.000\n",
       "64     11245.000\n",
       "28      8921.000\n",
       "147    10198.000\n",
       "125    22018.000\n",
       "22      6377.000\n",
       "194    12940.000\n",
       "13     21105.000\n",
       "70     31600.000\n",
       "101    13499.000\n",
       "71     34184.000\n",
       "86      8189.000\n",
       "2      16500.000\n",
       "73     40960.000\n",
       "102    14399.000\n",
       "57     13645.000\n",
       "8      23875.000\n",
       "94      7299.000\n",
       "174    10698.000\n",
       "         ...    \n",
       "184     7995.000\n",
       "17     36880.000\n",
       "54      7395.000\n",
       "97      7999.000\n",
       "108    13200.000\n",
       "175     9988.000\n",
       "172    17669.000\n",
       "140     7603.000\n",
       "116    17950.000\n",
       "154     7898.000\n",
       "123     8921.000\n",
       "98      8249.000\n",
       "115    16630.000\n",
       "197    16515.000\n",
       "3      13950.000\n",
       "180    15690.000\n",
       "33      6529.000\n",
       "35      7295.000\n",
       "143     9960.000\n",
       "165     9298.000\n",
       "179    15998.000\n",
       "187     9495.000\n",
       "167     8449.000\n",
       "11     16925.000\n",
       "204    22625.000\n",
       "142     7775.000\n",
       "84     14489.000\n",
       "46     11048.000\n",
       "53      6695.000\n",
       "192    13845.000\n",
       "Name: price, Length: 143, dtype: float64"
      ]
     },
     "execution_count": 240,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "yy_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 241,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x1cc5246fa58>"
      ]
     },
     "execution_count": 241,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 540x180 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pairplot(auto, x_vars=[\"carlength\",\"carwidth\",\"curbweight\"],y_vars=[\"price\"], diag_kind=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 242,
   "metadata": {},
   "outputs": [],
   "source": [
    "#import statsmodels.formula.api as sm \n",
    "#lr=sm.ols(formula = 'price ~ fuelt+asp+carb+drwh+wheelbase+carlength+carwidth+curbweight+eng_typ+cyl_no+enginesize+boreratio+horsepower', data=auto).fit()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# FeatureScaling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 243,
   "metadata": {},
   "outputs": [],
   "source": [
    "xxx = auto\n",
    "yyy = cars.price"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 244,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.70875569, -0.76907997, -0.99935266, ..., -0.3423266 ,\n",
       "        -0.52610428, -2.81735692],\n",
       "       [-0.70875569, -0.76907997, -0.99935266, ..., -0.3423266 ,\n",
       "        -0.52610428,  0.3549426 ],\n",
       "       [-0.70875569, -0.73613257, -0.90936058, ..., -0.3423266 ,\n",
       "        -0.52610428,  0.3549426 ],\n",
       "       ...,\n",
       "       [ 1.50532592,  0.99360616,  1.07046529, ..., -0.3423266 ,\n",
       "        -0.52610428,  0.3549426 ],\n",
       "       [ 0.26610113,  0.45821084,  0.39552465, ..., -0.3423266 ,\n",
       "        -0.52610428,  0.3549426 ],\n",
       "       [-0.70875569, -1.54334397, -1.08934475, ..., -0.3423266 ,\n",
       "        -0.52610428,  0.3549426 ]])"
      ]
     },
     "execution_count": 244,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "xxx_train, xxx_test, yyy_train, yyy_test = train_test_split(xxx,yyy,test_size=.3)\n",
    "\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "sc = StandardScaler()\n",
    "xxx_train_scale = sc.fit_transform(xxx_train)\n",
    "xxx_test_scale = sc.transform(xxx_train)\n",
    "xxx_test_scale"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 245,
   "metadata": {},
   "outputs": [],
   "source": [
    "#from sklearn.neighbors import KNeighborsClassifier\n",
    "#model = KNeighborsClassifier(n_neighbors=4)\n",
    "#model.fit(xxx_train_scale,yyy_train)\n",
    "#KNeighborsClassifier()\n",
    "#yyy_predict = model.predict(xxx_test_scale)\n",
    "#yyy_predict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 246,
   "metadata": {},
   "outputs": [],
   "source": [
    "#z = auto\n",
    "#c = cars.price\n",
    "#from sklearn.model_selection import train_test_split\n",
    "#z_train, z_test, c_train, c_test = train_test_split(z,c,test_size=.3)\n",
    "#from sklearn.naive_bayes import GaussianNB\n",
    "#GNB = GaussianNB()\n",
    "#GNB.fit(z_train, c_train)\n",
    "#GaussianNB()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 247,
   "metadata": {},
   "outputs": [],
   "source": [
    "#from sklearn.preprocessing import MinMaxScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 248,
   "metadata": {},
   "outputs": [],
   "source": [
    "#MNS = MinMaxScaler()\n",
    "#xxx_train_MN= MNS.fit_transform(xxx_train)\n",
    "#xxx_train_MN"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Before Creatig Elbow Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 249,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CompanyName</th>\n",
       "      <th>citympg</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>alfa-romero</td>\n",
       "      <td>21</td>\n",
       "      <td>13495.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>alfa-romero</td>\n",
       "      <td>21</td>\n",
       "      <td>16500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>alfa-romero</td>\n",
       "      <td>19</td>\n",
       "      <td>16500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>audi</td>\n",
       "      <td>24</td>\n",
       "      <td>13950.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>audi</td>\n",
       "      <td>18</td>\n",
       "      <td>17450.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   CompanyName  citympg    price\n",
       "0  alfa-romero       21  13495.0\n",
       "1  alfa-romero       21  16500.0\n",
       "2  alfa-romero       19  16500.0\n",
       "3         audi       24  13950.0\n",
       "4         audi       18  17450.0"
      ]
     },
     "execution_count": 249,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "NewCars = cars[['CompanyName','citympg','price']]\n",
    "NewCars.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 250,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>citympg</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>21</td>\n",
       "      <td>13495.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>21</td>\n",
       "      <td>16500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>19</td>\n",
       "      <td>16500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>24</td>\n",
       "      <td>13950.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>18</td>\n",
       "      <td>17450.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   citympg    price\n",
       "0       21  13495.0\n",
       "1       21  16500.0\n",
       "2       19  16500.0\n",
       "3       24  13950.0\n",
       "4       18  17450.0"
      ]
     },
     "execution_count": 250,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Q = NewCars.iloc[:,[1,2]]\n",
    "Q.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 251,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    alfa-romero\n",
       "1    alfa-romero\n",
       "2    alfa-romero\n",
       "3           audi\n",
       "4           audi\n",
       "Name: CompanyName, dtype: object"
      ]
     },
     "execution_count": 251,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "W = NewCars.iloc[:,0]\n",
    "W.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 252,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize =(5,5))\n",
    "plt.scatter(cars.citympg,cars.CompanyName)\n",
    "plt.xlabel('citympg')\n",
    "plt.ylabel('CompanyName')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 253,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize =(5,5))\n",
    "plt.scatter(cars.price,cars.CompanyName)\n",
    "plt.xlabel('Price of the vehicles')\n",
    "plt.ylabel('CompanyName')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Scaling using standarization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 254,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>citympg</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.646553</td>\n",
       "      <td>0.027391</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.646553</td>\n",
       "      <td>0.404461</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.953012</td>\n",
       "      <td>0.404461</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.186865</td>\n",
       "      <td>0.084485</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-1.106241</td>\n",
       "      <td>0.523668</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>-0.953012</td>\n",
       "      <td>0.247610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>-0.953012</td>\n",
       "      <td>0.556293</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>-0.953012</td>\n",
       "      <td>0.708125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>-1.259470</td>\n",
       "      <td>1.329882</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>-1.412700</td>\n",
       "      <td>0.575011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>-0.340094</td>\n",
       "      <td>0.395677</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>-0.340094</td>\n",
       "      <td>0.457790</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>-0.646553</td>\n",
       "      <td>0.965360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>-0.646553</td>\n",
       "      <td>0.982300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>-0.799782</td>\n",
       "      <td>1.416464</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>-1.412700</td>\n",
       "      <td>2.193818</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>-1.412700</td>\n",
       "      <td>3.518268</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>-1.565929</td>\n",
       "      <td>2.961761</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>3.337410</td>\n",
       "      <td>-1.019621</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>1.958346</td>\n",
       "      <td>-0.876071</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>1.958346</td>\n",
       "      <td>-0.840936</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>1.805116</td>\n",
       "      <td>-0.966794</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>0.885740</td>\n",
       "      <td>-0.865782</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>-0.186865</td>\n",
       "      <td>-0.667522</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>0.885740</td>\n",
       "      <td>-0.884353</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>0.885740</td>\n",
       "      <td>-0.826255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>0.885740</td>\n",
       "      <td>-0.711189</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>-0.186865</td>\n",
       "      <td>-0.592108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>-0.186865</td>\n",
       "      <td>-0.546558</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>-0.953012</td>\n",
       "      <td>-0.039239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>175</th>\n",
       "      <td>0.272823</td>\n",
       "      <td>-0.412670</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176</th>\n",
       "      <td>0.272823</td>\n",
       "      <td>-0.298483</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>177</th>\n",
       "      <td>0.272823</td>\n",
       "      <td>-0.254564</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>178</th>\n",
       "      <td>-0.799782</td>\n",
       "      <td>0.411739</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179</th>\n",
       "      <td>-0.953012</td>\n",
       "      <td>0.341470</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>180</th>\n",
       "      <td>-0.799782</td>\n",
       "      <td>0.302822</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181</th>\n",
       "      <td>-0.953012</td>\n",
       "      <td>0.310350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>182</th>\n",
       "      <td>1.805116</td>\n",
       "      <td>-0.690359</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>183</th>\n",
       "      <td>0.272823</td>\n",
       "      <td>-0.665263</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>184</th>\n",
       "      <td>1.805116</td>\n",
       "      <td>-0.662754</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>185</th>\n",
       "      <td>0.272823</td>\n",
       "      <td>-0.637657</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>186</th>\n",
       "      <td>0.272823</td>\n",
       "      <td>-0.600013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>187</th>\n",
       "      <td>1.805116</td>\n",
       "      <td>-0.474532</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>188</th>\n",
       "      <td>0.119594</td>\n",
       "      <td>-0.411792</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>189</th>\n",
       "      <td>-0.186865</td>\n",
       "      <td>-0.211022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>190</th>\n",
       "      <td>-0.186865</td>\n",
       "      <td>-0.413674</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>191</th>\n",
       "      <td>-0.953012</td>\n",
       "      <td>0.002295</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>192</th>\n",
       "      <td>1.192199</td>\n",
       "      <td>0.071309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>193</th>\n",
       "      <td>-0.033636</td>\n",
       "      <td>-0.123813</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194</th>\n",
       "      <td>-0.340094</td>\n",
       "      <td>-0.042251</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>195</th>\n",
       "      <td>-0.340094</td>\n",
       "      <td>0.017353</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196</th>\n",
       "      <td>-0.186865</td>\n",
       "      <td>0.339838</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>197</th>\n",
       "      <td>-0.186865</td>\n",
       "      <td>0.406343</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>198</th>\n",
       "      <td>-1.259470</td>\n",
       "      <td>0.645384</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199</th>\n",
       "      <td>-1.259470</td>\n",
       "      <td>0.711889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>200</th>\n",
       "      <td>-0.340094</td>\n",
       "      <td>0.447752</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>201</th>\n",
       "      <td>-0.953012</td>\n",
       "      <td>0.723810</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>202</th>\n",
       "      <td>-1.106241</td>\n",
       "      <td>1.029983</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>203</th>\n",
       "      <td>0.119594</td>\n",
       "      <td>1.153582</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>204</th>\n",
       "      <td>-0.953012</td>\n",
       "      <td>1.173031</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>205 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      citympg     price\n",
       "0   -0.646553  0.027391\n",
       "1   -0.646553  0.404461\n",
       "2   -0.953012  0.404461\n",
       "3   -0.186865  0.084485\n",
       "4   -1.106241  0.523668\n",
       "5   -0.953012  0.247610\n",
       "6   -0.953012  0.556293\n",
       "7   -0.953012  0.708125\n",
       "8   -1.259470  1.329882\n",
       "9   -1.412700  0.575011\n",
       "10  -0.340094  0.395677\n",
       "11  -0.340094  0.457790\n",
       "12  -0.646553  0.965360\n",
       "13  -0.646553  0.982300\n",
       "14  -0.799782  1.416464\n",
       "15  -1.412700  2.193818\n",
       "16  -1.412700  3.518268\n",
       "17  -1.565929  2.961761\n",
       "18   3.337410 -1.019621\n",
       "19   1.958346 -0.876071\n",
       "20   1.958346 -0.840936\n",
       "21   1.805116 -0.966794\n",
       "22   0.885740 -0.865782\n",
       "23  -0.186865 -0.667522\n",
       "24   0.885740 -0.884353\n",
       "25   0.885740 -0.826255\n",
       "26   0.885740 -0.711189\n",
       "27  -0.186865 -0.592108\n",
       "28  -0.186865 -0.546558\n",
       "29  -0.953012 -0.039239\n",
       "..        ...       ...\n",
       "175  0.272823 -0.412670\n",
       "176  0.272823 -0.298483\n",
       "177  0.272823 -0.254564\n",
       "178 -0.799782  0.411739\n",
       "179 -0.953012  0.341470\n",
       "180 -0.799782  0.302822\n",
       "181 -0.953012  0.310350\n",
       "182  1.805116 -0.690359\n",
       "183  0.272823 -0.665263\n",
       "184  1.805116 -0.662754\n",
       "185  0.272823 -0.637657\n",
       "186  0.272823 -0.600013\n",
       "187  1.805116 -0.474532\n",
       "188  0.119594 -0.411792\n",
       "189 -0.186865 -0.211022\n",
       "190 -0.186865 -0.413674\n",
       "191 -0.953012  0.002295\n",
       "192  1.192199  0.071309\n",
       "193 -0.033636 -0.123813\n",
       "194 -0.340094 -0.042251\n",
       "195 -0.340094  0.017353\n",
       "196 -0.186865  0.339838\n",
       "197 -0.186865  0.406343\n",
       "198 -1.259470  0.645384\n",
       "199 -1.259470  0.711889\n",
       "200 -0.340094  0.447752\n",
       "201 -0.953012  0.723810\n",
       "202 -1.106241  1.029983\n",
       "203  0.119594  1.153582\n",
       "204 -0.953012  1.173031\n",
       "\n",
       "[205 rows x 2 columns]"
      ]
     },
     "execution_count": 254,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "scaler = StandardScaler()\n",
    "scaler.fit(Q)\n",
    "scaled_Q = pd.DataFrame(scaler.transform(Q), columns= Q.columns)\n",
    "scaled_Q"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 267,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "k_rng = range(1,10)\n",
    "sse1 = []  #the sum of the squared Euclidean distances of each point to its closest centroid.\n",
    "for k1 in k_rng:\n",
    "    km1 = KMeans(n_clusters = k1)\n",
    "    km1.fit(scaled_Q[['citympg']])\n",
    "    sse1.append(km1.inertia_)\n",
    "    \n",
    "plt.xlabel('K_Values')\n",
    "plt.ylabel('CompanyName')\n",
    "plt.plot(k_rng, sse1, marker = 'o')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#The optimum value for K in citympg attribute is 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 269,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "k_rng = range(1,10)\n",
    "sse2 = []  \n",
    "for k2 in k_rng:\n",
    "    km2 = KMeans(n_clusters = k2)\n",
    "    km2.fit(scaled_Q[['price']])\n",
    "    sse2.append(km2.inertia_)\n",
    "    \n",
    "plt.xlabel('K_Values')\n",
    "plt.ylabel('price')\n",
    "plt.plot(k_rng, sse2, marker = 'o')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 270,
   "metadata": {},
   "outputs": [],
   "source": [
    "#The optimum value for K in price attribute is 3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# (Applying Clustering Algorithm)\n",
    "I wanna find out which company should we go for, in terms of its price & citympg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 278,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>citympg</th>\n",
       "      <th>CompanyName</th>\n",
       "      <th>Cluster(CompanyName vs citympg)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.646553</td>\n",
       "      <td>alfa-romero</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.646553</td>\n",
       "      <td>alfa-romero</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.953012</td>\n",
       "      <td>alfa-romero</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.186865</td>\n",
       "      <td>audi</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-1.106241</td>\n",
       "      <td>audi</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>-0.953012</td>\n",
       "      <td>audi</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>-0.953012</td>\n",
       "      <td>audi</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>-0.953012</td>\n",
       "      <td>audi</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>-1.259470</td>\n",
       "      <td>audi</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>-1.412700</td>\n",
       "      <td>audi</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>-0.340094</td>\n",
       "      <td>bmw</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>-0.340094</td>\n",
       "      <td>bmw</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>-0.646553</td>\n",
       "      <td>bmw</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>-0.646553</td>\n",
       "      <td>bmw</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>-0.799782</td>\n",
       "      <td>bmw</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>-1.412700</td>\n",
       "      <td>bmw</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>-1.412700</td>\n",
       "      <td>bmw</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>-1.565929</td>\n",
       "      <td>bmw</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>3.337410</td>\n",
       "      <td>chevrolet</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>1.958346</td>\n",
       "      <td>chevrolet</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>1.958346</td>\n",
       "      <td>chevrolet</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>1.805116</td>\n",
       "      <td>dodge</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>0.885740</td>\n",
       "      <td>dodge</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>-0.186865</td>\n",
       "      <td>dodge</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>0.885740</td>\n",
       "      <td>dodge</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>0.885740</td>\n",
       "      <td>dodge</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>0.885740</td>\n",
       "      <td>dodge</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>-0.186865</td>\n",
       "      <td>dodge</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>-0.186865</td>\n",
       "      <td>dodge</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>-0.953012</td>\n",
       "      <td>dodge</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>175</th>\n",
       "      <td>0.272823</td>\n",
       "      <td>toyota</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176</th>\n",
       "      <td>0.272823</td>\n",
       "      <td>toyota</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>177</th>\n",
       "      <td>0.272823</td>\n",
       "      <td>toyota</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>178</th>\n",
       "      <td>-0.799782</td>\n",
       "      <td>toyota</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179</th>\n",
       "      <td>-0.953012</td>\n",
       "      <td>toyota</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>180</th>\n",
       "      <td>-0.799782</td>\n",
       "      <td>toyota</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181</th>\n",
       "      <td>-0.953012</td>\n",
       "      <td>toyota</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>182</th>\n",
       "      <td>1.805116</td>\n",
       "      <td>volkswagen</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>183</th>\n",
       "      <td>0.272823</td>\n",
       "      <td>volkswagen</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>184</th>\n",
       "      <td>1.805116</td>\n",
       "      <td>volkswagen</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>185</th>\n",
       "      <td>0.272823</td>\n",
       "      <td>volkswagen</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>186</th>\n",
       "      <td>0.272823</td>\n",
       "      <td>volkswagen</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>187</th>\n",
       "      <td>1.805116</td>\n",
       "      <td>volkswagen</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>188</th>\n",
       "      <td>0.119594</td>\n",
       "      <td>volkswagen</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>189</th>\n",
       "      <td>-0.186865</td>\n",
       "      <td>volkswagen</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>190</th>\n",
       "      <td>-0.186865</td>\n",
       "      <td>volkswagen</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>191</th>\n",
       "      <td>-0.953012</td>\n",
       "      <td>volkswagen</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>192</th>\n",
       "      <td>1.192199</td>\n",
       "      <td>volkswagen</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>193</th>\n",
       "      <td>-0.033636</td>\n",
       "      <td>volkswagen</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194</th>\n",
       "      <td>-0.340094</td>\n",
       "      <td>volvo</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>195</th>\n",
       "      <td>-0.340094</td>\n",
       "      <td>volvo</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196</th>\n",
       "      <td>-0.186865</td>\n",
       "      <td>volvo</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>197</th>\n",
       "      <td>-0.186865</td>\n",
       "      <td>volvo</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>198</th>\n",
       "      <td>-1.259470</td>\n",
       "      <td>volvo</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199</th>\n",
       "      <td>-1.259470</td>\n",
       "      <td>volvo</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>200</th>\n",
       "      <td>-0.340094</td>\n",
       "      <td>volvo</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>201</th>\n",
       "      <td>-0.953012</td>\n",
       "      <td>volvo</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>202</th>\n",
       "      <td>-1.106241</td>\n",
       "      <td>volvo</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>203</th>\n",
       "      <td>0.119594</td>\n",
       "      <td>volvo</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>204</th>\n",
       "      <td>-0.953012</td>\n",
       "      <td>volvo</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>205 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      citympg  CompanyName  Cluster(CompanyName vs citympg)\n",
       "0   -0.646553  alfa-romero                                0\n",
       "1   -0.646553  alfa-romero                                0\n",
       "2   -0.953012  alfa-romero                                0\n",
       "3   -0.186865         audi                                2\n",
       "4   -1.106241         audi                                0\n",
       "5   -0.953012         audi                                0\n",
       "6   -0.953012         audi                                0\n",
       "7   -0.953012         audi                                0\n",
       "8   -1.259470         audi                                0\n",
       "9   -1.412700         audi                                0\n",
       "10  -0.340094          bmw                                2\n",
       "11  -0.340094          bmw                                2\n",
       "12  -0.646553          bmw                                0\n",
       "13  -0.646553          bmw                                0\n",
       "14  -0.799782          bmw                                0\n",
       "15  -1.412700          bmw                                0\n",
       "16  -1.412700          bmw                                0\n",
       "17  -1.565929          bmw                                0\n",
       "18   3.337410    chevrolet                                1\n",
       "19   1.958346    chevrolet                                1\n",
       "20   1.958346    chevrolet                                1\n",
       "21   1.805116        dodge                                1\n",
       "22   0.885740        dodge                                1\n",
       "23  -0.186865        dodge                                2\n",
       "24   0.885740        dodge                                1\n",
       "25   0.885740        dodge                                1\n",
       "26   0.885740        dodge                                1\n",
       "27  -0.186865        dodge                                2\n",
       "28  -0.186865        dodge                                2\n",
       "29  -0.953012        dodge                                0\n",
       "..        ...          ...                              ...\n",
       "175  0.272823       toyota                                2\n",
       "176  0.272823       toyota                                2\n",
       "177  0.272823       toyota                                2\n",
       "178 -0.799782       toyota                                0\n",
       "179 -0.953012       toyota                                0\n",
       "180 -0.799782       toyota                                0\n",
       "181 -0.953012       toyota                                0\n",
       "182  1.805116   volkswagen                                1\n",
       "183  0.272823   volkswagen                                2\n",
       "184  1.805116   volkswagen                                1\n",
       "185  0.272823   volkswagen                                2\n",
       "186  0.272823   volkswagen                                2\n",
       "187  1.805116   volkswagen                                1\n",
       "188  0.119594   volkswagen                                2\n",
       "189 -0.186865   volkswagen                                2\n",
       "190 -0.186865   volkswagen                                2\n",
       "191 -0.953012   volkswagen                                0\n",
       "192  1.192199   volkswagen                                1\n",
       "193 -0.033636   volkswagen                                2\n",
       "194 -0.340094        volvo                                2\n",
       "195 -0.340094        volvo                                2\n",
       "196 -0.186865        volvo                                2\n",
       "197 -0.186865        volvo                                2\n",
       "198 -1.259470        volvo                                0\n",
       "199 -1.259470        volvo                                0\n",
       "200 -0.340094        volvo                                2\n",
       "201 -0.953012        volvo                                0\n",
       "202 -1.106241        volvo                                0\n",
       "203  0.119594        volvo                                2\n",
       "204 -0.953012        volvo                                0\n",
       "\n",
       "[205 rows x 3 columns]"
      ]
     },
     "execution_count": 278,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#As the optimum value for K=3 for both attributes >> Create 3 clusters of classification.\n",
    "km1 = KMeans(n_clusters=3)\n",
    "km1.fit(scaled_Q[['citympg']])\n",
    "W_predict = km1.predict(scaled_Q[['citympg']])\n",
    "MC_cars = pd.concat([scaled_Q['citympg'],cars.CompanyName], axis=1)\n",
    "MC_cars['Cluster(CompanyName vs citympg)'] = W_predict\n",
    "MC_cars"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 282,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'CompanyName')"
      ]
     },
     "execution_count": 282,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "MC_cars1 = MC_cars[MC_cars['Cluster(CompanyName vs citympg)'] == 0]\n",
    "MC_cars2 = MC_cars[MC_cars['Cluster(CompanyName vs citympg)'] == 1]\n",
    "MC_cars3 = MC_cars[MC_cars['Cluster(CompanyName vs citympg)'] == 2]\n",
    "\n",
    "plt.subplots(figsize=([15,10]))\n",
    "plt.scatter(MC_cars1['citympg'],MC_cars1['CompanyName'], color='red')\n",
    "plt.scatter(MC_cars2['citympg'],MC_cars2['CompanyName'], color='green')\n",
    "plt.scatter(MC_cars3['citympg'],MC_cars3['CompanyName'], color='blue')\n",
    "\n",
    "plt.title('KMeans Clustering of vehicles City Mileage')\n",
    "plt.xlabel('city mileage')\n",
    "plt.ylabel('CompanyName')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 283,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>price</th>\n",
       "      <th>CompanyName</th>\n",
       "      <th>Cluster(CompanyName vs citympg)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.027391</td>\n",
       "      <td>alfa-romero</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.404461</td>\n",
       "      <td>alfa-romero</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.404461</td>\n",
       "      <td>alfa-romero</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.084485</td>\n",
       "      <td>audi</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.523668</td>\n",
       "      <td>audi</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.247610</td>\n",
       "      <td>audi</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.556293</td>\n",
       "      <td>audi</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.708125</td>\n",
       "      <td>audi</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1.329882</td>\n",
       "      <td>audi</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.575011</td>\n",
       "      <td>audi</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.395677</td>\n",
       "      <td>bmw</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.457790</td>\n",
       "      <td>bmw</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.965360</td>\n",
       "      <td>bmw</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.982300</td>\n",
       "      <td>bmw</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1.416464</td>\n",
       "      <td>bmw</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2.193818</td>\n",
       "      <td>bmw</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>3.518268</td>\n",
       "      <td>bmw</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2.961761</td>\n",
       "      <td>bmw</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>-1.019621</td>\n",
       "      <td>chevrolet</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>-0.876071</td>\n",
       "      <td>chevrolet</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>-0.840936</td>\n",
       "      <td>chevrolet</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>-0.966794</td>\n",
       "      <td>dodge</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>-0.865782</td>\n",
       "      <td>dodge</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>-0.667522</td>\n",
       "      <td>dodge</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>-0.884353</td>\n",
       "      <td>dodge</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>-0.826255</td>\n",
       "      <td>dodge</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>-0.711189</td>\n",
       "      <td>dodge</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>-0.592108</td>\n",
       "      <td>dodge</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>-0.546558</td>\n",
       "      <td>dodge</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>-0.039239</td>\n",
       "      <td>dodge</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>175</th>\n",
       "      <td>-0.412670</td>\n",
       "      <td>toyota</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176</th>\n",
       "      <td>-0.298483</td>\n",
       "      <td>toyota</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>177</th>\n",
       "      <td>-0.254564</td>\n",
       "      <td>toyota</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>178</th>\n",
       "      <td>0.411739</td>\n",
       "      <td>toyota</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179</th>\n",
       "      <td>0.341470</td>\n",
       "      <td>toyota</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>180</th>\n",
       "      <td>0.302822</td>\n",
       "      <td>toyota</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181</th>\n",
       "      <td>0.310350</td>\n",
       "      <td>toyota</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>182</th>\n",
       "      <td>-0.690359</td>\n",
       "      <td>volkswagen</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>183</th>\n",
       "      <td>-0.665263</td>\n",
       "      <td>volkswagen</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>184</th>\n",
       "      <td>-0.662754</td>\n",
       "      <td>volkswagen</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>185</th>\n",
       "      <td>-0.637657</td>\n",
       "      <td>volkswagen</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>186</th>\n",
       "      <td>-0.600013</td>\n",
       "      <td>volkswagen</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>187</th>\n",
       "      <td>-0.474532</td>\n",
       "      <td>volkswagen</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>188</th>\n",
       "      <td>-0.411792</td>\n",
       "      <td>volkswagen</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>189</th>\n",
       "      <td>-0.211022</td>\n",
       "      <td>volkswagen</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>190</th>\n",
       "      <td>-0.413674</td>\n",
       "      <td>volkswagen</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>191</th>\n",
       "      <td>0.002295</td>\n",
       "      <td>volkswagen</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>192</th>\n",
       "      <td>0.071309</td>\n",
       "      <td>volkswagen</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>193</th>\n",
       "      <td>-0.123813</td>\n",
       "      <td>volkswagen</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194</th>\n",
       "      <td>-0.042251</td>\n",
       "      <td>volvo</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>195</th>\n",
       "      <td>0.017353</td>\n",
       "      <td>volvo</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196</th>\n",
       "      <td>0.339838</td>\n",
       "      <td>volvo</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>197</th>\n",
       "      <td>0.406343</td>\n",
       "      <td>volvo</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>198</th>\n",
       "      <td>0.645384</td>\n",
       "      <td>volvo</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199</th>\n",
       "      <td>0.711889</td>\n",
       "      <td>volvo</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>200</th>\n",
       "      <td>0.447752</td>\n",
       "      <td>volvo</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>201</th>\n",
       "      <td>0.723810</td>\n",
       "      <td>volvo</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>202</th>\n",
       "      <td>1.029983</td>\n",
       "      <td>volvo</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>203</th>\n",
       "      <td>1.153582</td>\n",
       "      <td>volvo</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>204</th>\n",
       "      <td>1.173031</td>\n",
       "      <td>volvo</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>205 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        price  CompanyName  Cluster(CompanyName vs citympg)\n",
       "0    0.027391  alfa-romero                                2\n",
       "1    0.404461  alfa-romero                                2\n",
       "2    0.404461  alfa-romero                                2\n",
       "3    0.084485         audi                                2\n",
       "4    0.523668         audi                                2\n",
       "5    0.247610         audi                                2\n",
       "6    0.556293         audi                                2\n",
       "7    0.708125         audi                                1\n",
       "8    1.329882         audi                                1\n",
       "9    0.575011         audi                                2\n",
       "10   0.395677          bmw                                2\n",
       "11   0.457790          bmw                                2\n",
       "12   0.965360          bmw                                1\n",
       "13   0.982300          bmw                                1\n",
       "14   1.416464          bmw                                1\n",
       "15   2.193818          bmw                                1\n",
       "16   3.518268          bmw                                1\n",
       "17   2.961761          bmw                                1\n",
       "18  -1.019621    chevrolet                                0\n",
       "19  -0.876071    chevrolet                                0\n",
       "20  -0.840936    chevrolet                                0\n",
       "21  -0.966794        dodge                                0\n",
       "22  -0.865782        dodge                                0\n",
       "23  -0.667522        dodge                                0\n",
       "24  -0.884353        dodge                                0\n",
       "25  -0.826255        dodge                                0\n",
       "26  -0.711189        dodge                                0\n",
       "27  -0.592108        dodge                                0\n",
       "28  -0.546558        dodge                                0\n",
       "29  -0.039239        dodge                                2\n",
       "..        ...          ...                              ...\n",
       "175 -0.412670       toyota                                2\n",
       "176 -0.298483       toyota                                2\n",
       "177 -0.254564       toyota                                2\n",
       "178  0.411739       toyota                                2\n",
       "179  0.341470       toyota                                2\n",
       "180  0.302822       toyota                                2\n",
       "181  0.310350       toyota                                2\n",
       "182 -0.690359   volkswagen                                0\n",
       "183 -0.665263   volkswagen                                0\n",
       "184 -0.662754   volkswagen                                0\n",
       "185 -0.637657   volkswagen                                0\n",
       "186 -0.600013   volkswagen                                0\n",
       "187 -0.474532   volkswagen                                2\n",
       "188 -0.411792   volkswagen                                2\n",
       "189 -0.211022   volkswagen                                2\n",
       "190 -0.413674   volkswagen                                2\n",
       "191  0.002295   volkswagen                                2\n",
       "192  0.071309   volkswagen                                2\n",
       "193 -0.123813   volkswagen                                2\n",
       "194 -0.042251        volvo                                2\n",
       "195  0.017353        volvo                                2\n",
       "196  0.339838        volvo                                2\n",
       "197  0.406343        volvo                                2\n",
       "198  0.645384        volvo                                1\n",
       "199  0.711889        volvo                                1\n",
       "200  0.447752        volvo                                2\n",
       "201  0.723810        volvo                                1\n",
       "202  1.029983        volvo                                1\n",
       "203  1.153582        volvo                                1\n",
       "204  1.173031        volvo                                1\n",
       "\n",
       "[205 rows x 3 columns]"
      ]
     },
     "execution_count": 283,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#KMeans for price & CompanyName\n",
    "km2 = KMeans(n_clusters=3)\n",
    "km2.fit(scaled_Q[['citympg']])\n",
    "W_predict = km1.predict(scaled_Q[['price']])\n",
    "MC_cars = pd.concat([scaled_Q['price'],cars.CompanyName], axis=1)\n",
    "MC_cars['Cluster(CompanyName vs citympg)'] = W_predict\n",
    "MC_cars"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 284,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'CompanyName')"
      ]
     },
     "execution_count": 284,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "MC_cars1 = MC_cars[MC_cars['Cluster(CompanyName vs citympg)'] == 0]\n",
    "MC_cars2 = MC_cars[MC_cars['Cluster(CompanyName vs citympg)'] == 1]\n",
    "MC_cars3 = MC_cars[MC_cars['Cluster(CompanyName vs citympg)'] == 2]\n",
    "\n",
    "plt.subplots(figsize=([15,10]))\n",
    "plt.scatter(MC_cars1['price'],MC_cars1['CompanyName'], color='red')\n",
    "plt.scatter(MC_cars2['price'],MC_cars2['CompanyName'], color='green')\n",
    "plt.scatter(MC_cars3['price'],MC_cars3['CompanyName'], color='blue')\n",
    "\n",
    "plt.title('KMeans Clustering of Price')\n",
    "plt.xlabel('Price')\n",
    "plt.ylabel('CompanyName')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Creating Dendrogram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 287,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'euclidean distance')"
      ]
     },
     "execution_count": 287,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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//fXXS5LOP/98//0n10FFRYUk6dFHH1VjY6PmzZunzTbbLFCuwdvy4sWLZWb60Y9+pN7eXl1++eX60pe+pIsvvlg1NTW66667uJUiAHyOCOwAgJRcRz9VkJPWXJ3dhdi3335b7e3tqqio0Pbbb6/Zs2frySef1De/+U29+OKL+tWvfhW4z3Rra6sk6YgjjgiEF/e8Lvi423O5AYLB+vv7h1zMTZJmzJgR+H9lZaWOO+445eXlaeXKlVq5cqUmTJigyZMnB9arr6+XJDU1NUmS/v73v0uSvva1rw15DVe2adOmpX3tqqoqHXvssQqFQnr++eclSd/4xjeGXJm9tLQ0EIzdxcJmz54deM7KykpFIpHAxcQ22mgjSUMD4mfhyrrzzjsHlrs6Wr169ZC/Gfze99prL22zzTZpX+ef//ynOjo6NH/+/LTbnDRwUUFJCofDwz7uAmtPT8+Qx+bPn6+tttpKXV1duuaaa9Tf36/f/OY3kgaC8WiYmSorK3XMMcf4y1wgTr443sSJEyUN/Ty6u7sVCoUCg2HuPSW/92effVaStGTJEk2ePFkXXHCBiouLddVVV2nZsmXacccd/XWff/555eXlad999w28lgvUbpt68sknJY1uW3av/+yzz2qjjTbS2WefrUQioUsuuURvv/229tprr3TVBAD4jPJHXgUAsKFyASLdDPujjz4qSZo5c2bgb/7+978rLy9PRx55pM466yxtscUWw/69C+yDQ4YLsYOvQu3WHywUCvkzlMlsFLf1Gi4cdnZ2SlozEOFCWPLMpSufu8r6pEmTRv3a6W7hVVdXF7iFm3vPNTU1gfUaGhr03nvvqampSQ0NDZLWBMS333475WsP5gZQhqu/ZIPraXAdJRtNvQ+2cuVKSUOPVBiOG6RIdRu6VDPs0sD7/elPf6q5c+fql7/8pTbZZBN99NFHmj59uubOnTvia7e3t8vM/CMfHPcZJF+lv6ioSA0NDfroo48UjUb971IikRhS9uG+b27Z9ddfr0022URXXnmlvvOd7wy7zfb19cnzvCFHoLg7J7htLtW2LElLly6VtGbgx73+TTfdpIkTJ+qiiy7Scccdx6w6AKwnzLADAFIabsYvWUdHh6688kpJAzPk0kBozcvL08Ybb6x3331XN99885Cwft999+mjjz6StCY8TJgwIbCOC4EuxO2www6SBg6tHhwGe3p6ZGZ+eUdrzJgxqq2t1fvvv6/GxsbAY4sWLZIk7bnnnpKkKVOmSFozA+n86le/8g/bHxyo09l4440lDdz6brAJEyaou7vbD+1ulj/5EHNpTUB0den+VtKQ2+el4wJ7qluZudlyN9vquDpaV7OsruyvvPLKiOsOvuXgYOkCuzQwQLTTTjupo6NDJ5xwgiTpjDPOCBz9kYr7PFwQdsaMGSMp+HlIA4MoiUQisLywsFDxeDxwdMRwgX2TTTaRJJ133nl66623dMIJJwS+j8uXL9fdd98taWCb6u/v9w9rd1y9uoGEVNvyww8/rOeee07Smm3Zvf7JJ5+sd999VyeffHIgrDc1NenWW2/93O53DwAbOgI7ACAldzjxcIear1y5Uvvvv78++ugj7b777v4MeUNDgw488EB98MEHuvLKKwOHS3/44Yc65ZRTdNBBB/mHIDuDZ5pdyHCBdtasWZo5c6YeffRRLVy40A9Nr7/+uvbcc0/19PSoubl5yCxxuiCRl5enI444QvF4XMccc4xaW1vV19en6667TjfddJPGjx/vn8986KGHShq41/fy5cvV19enG2+8UWeddZZCoZCKi4uHzFime+099thDoVBIDz74oF599VV/+euvv65ly5ZJkv72t79JWvM5DH5+FxA//vhjf1l1dbWKior08ccfD3uP7uG4cg53n25p4Lx/STrttNP05ptv+veWP+200xQKhTR//vyUz7k2dtllF02ZMkW333677rnnHv85urq6dPHFF+uAAw7wP18XalMdFeBmr1MFds/zdMkll0gaCP3jx4/XYYcdNqpyrs3nIUnjx4+XtOY6D9Kaw9OTBxxcWZMD+8KFC+V5nm666SYtXrzY/4z6+vq0ePFi7bHHHjr00EPV1tamvffeW5J02WWX+XXX19enhx56SNKa7elb3/qWQqGQrrvuOr366qsyMy1evFgLFizwByxcYD/66KNVUFCg22+/XQ899JDfFsTjcT3++OPaa6+9dOSRRwbeGwBgHVrfV7kDAHxxuNs2FRcX28EHH2xnnnmmnXDCCTZnzhzLz883SbbJJpvY+++/H/i7t99+28aPH+/fsmzjjTf2/y/Jpk2bZitXrjQzszlz5pgke+qppwLP8dprr/m3UHP+/e9/B+4dnnw/dHe/aVcWd5X45CvPD+ejjz6yhoYG//Zo7hZvoVDI/vznP/vrJRIJO+qoo/zXc/dOnzdvnm222WY2btw4f113lfjkq6cP5/jjj/frd7fddrNZs2YF7pu97777mtmaq38ff/zxgb93t8m77rrrAsvdLdiS73efzty5c4fcni1ZLBazXXbZZch7V9KV8R13lfgVK1aM6rUHe+CBB/xb/G200Ua28847+5/zpEmT/FuY/fvf/zZJNnfu3GGfx92C75e//GXa15sxY4ZJsssvv3zUZVy+fLm/7SdbsmSJf/uzZN/73vcCt/MzM5s8ebJJCtw28I477jBJ9t3vfjfw9xdccIFf35WVlbbpppsGPoPLLrvMzMxeffVVKy4u9m85t/fee/vbtvt59dVXzczs8ssv95eFw2GTZF/5ylf82ykm3wnBXWXefec23XRT/4r6kuzss88edd0BANYOgR0AkNJjjz3m36pq8E8oFLJ58+b5wXuwpqYmu/TSS23HHXe0mpoa22ijjWz27Nl2ww03BG45ddlll9n48ePt448/Dvx9d3e3VVZW2g477BBY/sEHH9gZZ5xhm2yyiVVXV9usWbPsuuuus4cfftgOPPBA/7lffvll8zzPrrjiihHf59tvv22HHHKI5eXl+bcKG+7WXvF43P7yl7/YggULbN9997Urr7zSotGo1dfX28yZM/31XnzxRfM8z66++uq0rxuLxewXv/iFzZw507/v+8KFC+3ll1+23Xff3caOHWuJRMI6Oztt/PjxdtVVVwX+3t0ze/DruPt2v/HGGyO+dzOzbbfdNjDYMZyOjg479dRTraKiwqSBe6zfdtttFo/HA+sddthhVllZOeQe52vjySeftLlz5/qDQnV1dXbyySfb8uXL/XX6+vrs8ssvt2eeeWbY5/j5z39uoVDI3nnnnZSv85///MdCoZCVlZUNuVXfSObNm+cPqDjPPPOMSbI5c+YElv/3f//3kFvVPfjgg3bjjTcG1nv99ddtk002sQceeGDI6y1ZssSOPvpomzx5slVXV9uMGTPspJNOsueffz6w3nPPPWcLFiywcDhsnufZTjvtZLfeeqvddtttlp+fH7il4r/+9S879thjbc6cOXbOOedYS0uL7b777paXl+ffls75xz/+Yccff7z/vZs+fbqdcMIJQwbaAADrlmfGSUcAgPQ+/PBDLV26VMuXL1dxcbE233xz7bjjjv5trD4vK1asUDgcTnnrrpF88sknGjdu3KjOS5YGzuGOx+Mprzw+nKamJtXX12vvvff2DzmWBg6LHj9+/KhfOx6PKxQK+Ydy9/f3KxKJpH3vb7zxhs477zz99Kc/1eabbx4o01tvvTXkqu6pzJ49W++8847eeust5eXljVjO7u5ulZWVDXvBt56eHkUikSHnd38a3d3d6uzsVENDQ8qLy6ViZmptbU17XYFzzjlHl112mX74wx/q2muv/azFVW9vr0444QTNnTtX3/rWt/zl7vDx3XbbLXD1/8+TmSmRSAQ+z+bmZtXU1KSsSzPTuHHjlJeXp08++WS9lBMAkB6BHQCAz+D222/XEUccoTPOOENXXHFFpovzqcTjcZnZkKuL57Le3l5NmjRJTU1Nev311zV9+vRMFynjXnrpJW299dbad9999Ze//CXTxQEAiNu6AQAwKjZwGllgxvxf//qXzjrrLEnSggULMlW0z2ykWfVc9OCDD6qpqUm77LLLBhnWE4lEYFt+5513dPTRR0v6Ym/LAJBrCOwAAIzCUUcdpbvvvlvTpk3T+PHjFYlE9NRTTymRSOg73/mOtt5660wXEWvB3ZLu8MMPz3BJ1r+f/exnOvfcc7Xppptq8uTJ6u/v1zPPPKPu7m599atf1cEHH5zpIgIA/g+BPYuZmdrb29Xc3Kz29nZFIhG1t7ertbVVzc3N6uzsVDQaVSwWUywWU19fn7q7uxWJRNTT06NYLKb+/v4ht/XxPE95eXnKz89XYWGhCgoKlJ+fr4KCAhUUFCgcDqumpkYVFRUqLy9XZWWlSktLVVVVpcrKShUXF6u4uFilpaWqrKwcciumXNHf36+2tjZ1dXUpEomoo6PDr9uenh719vaqq6tLnZ2d6u7u9n9isZii0ah6e3vV19en/v5+/yeRSCiRSPi323HnEbp6T67boqIiFRQUqKysTJWVlaqsrFRFRYUqKir83xsaGlRZWbnW53Zmi87OTrW0tCgSifg/7pzVzs5Ov37d765Oe3t7FY1G1dfXp1gsFtjGPc/zt+3CwkKVlJSovLzc/0muv6qqKlVVVfm/V1dX58T2HI1GtXz5crW2tqqlpUWrVq3yt9/e3l5/W41Go/427bZV929ynYZCIRUUFKiwsNCv26KiIuXn56ukpERlZWUqLS31t19Xl66+a2trNXbs2MCtqr6I5syZo3/84x967bXX9OqrryoUCmn77bfXgQceqFNOOUXSwO3H2tratHr1aq1YsUKrV69WU1OTVq9erfb2dnV0dKirq8tvn/v7+/32ILme3b9lZWV+W+y213A4rLKyMtXU1PjLxowZM+pz5bNVIpFQU1OTGhsb1d7eru7ubvX09Kirq8u/J31LS4vfJrv21u3/4vG4/+OEQiHl5+crLy9PBQUFKi4u1iWXXKKpU6fqk08+UVlZmX+u+Z/+9CdFo1EVFxeroqJCY8aMUV1dnSoqKlRcXPyFbWeH444qePPNN/17sc+YMUNz587Vj3/8YxUWFuq5557TokWL1NnZqUgkolgsFmgvotFoYPv1PM+v73A4rJKSEr/9dfs011aUl5eroaFBNTU1fl+iqqpKRUVFOVXP0sDpJk1NTWpublZra6va2tr8foTbxt1+zvXdYrGYent7/W3btReuD5HM9efy8vKUl5en4uJihcNhFRYWqrS01P8sysrKAm2z69+Vl5errKxM5eXlOVf30kBfOhaLqbu7298ntrW1qa2tza/3SCQS6M+5Pob7DOLxuN93Sz6TOBQK+fWevG8Mh8MqLS31+2/Jbfa4ceNUX1/vr1NaWpqT9Z5IJPxbrbp94PLly9Xe3q5oNKpoNKrOzk51dHT4/ZHktjyRSATqXVrTxrj6Ts4xyfvLqqoqlZWV+f9PXl5fX6/Kysov3FFlOXsOeyKR0KmnnqrXXntNJSUlqqqqUk1Njd9Aucarurra77zX1NT4O491dR6f22CTN8qOjg6/07Zq1SqtWrVKK1euVHNzs/9Ya2urVqxYkfL+sY7neX4wceGktLRUJSUlKioq8hsSz/PkeZ5/EZp4PK7+/n7/y+E66i70t7W1pbwf72Cug15bW+vvjGtqavyOTlVVlRoaGlRbW6vS0lI/MLmgVFJSss4bq1gsptWrV6ulpcUPe83NzWpubvaDX1dXl1pbW9XR0aH29na/0XYNd1NT06jrQJLfOXENdnFxsT8Y4n5CoZD/4yQSCfX19QUGAtwOw+2o0yksLFRDQ4Pq6+vV0NCgcePGacyYMRozZozC4bCqqqpUV1en6upq1dXV+Q3Zuurcm5mi0ag/WOQ60m6wacWKFVq5cqX/78qVK9XS0uJ/FqPhOnolJSXKz89XcXGxP6BRWFjob+PSQH26bdt1etz3z907OR23Yy0vL/frtLa2VjU1NQqHw6qvr1ddXZ2/rVdWVqq6utoPTuuiXpM7GF1dXero6NDq1avV2trq/9+9JzeI58JhY2Nj4L7nw8nLy1M4HFZRUZHfXiQP3LnOXygU8juIyZ1GNyjV39xlsh4AACAASURBVN/vX2AsGo2O+L7c55jcaaypqdGYMWP8Nri2tjbQZid38CsqKtb5gIqZBQbgVq9e7W+bPT09amlpUSgU0sKFC/2LhcXjcfX29qq4uDiw0z///PN18cUXD/s6BQUFqqqq8jvHbvDDtQvueV09u3/d5x2JRNK+j/z8fNXU1KiyslJ1dXWqr6/XxIkT/Y6h+6moqPDbZvf5l5eXq6SkRMXFxetk+43H437n15W/tbXV3981NjaqqalJ7e3tamtrU2trq78Nj9Te5eXl+Z3c0tLSwACH29+5bdfM/H2dq9toNKrjjz9ep556qiKRiDo7OzV27Fj985//1C677JLydUOhkMrLy1VXV+fv6+rr6zV27FiVlZX5A62u7XBtgqtzty2vy31dPB73w4fbXl0n2AVE1z64n+7ubt1xxx2aMGGCpDV9FFeHzm233aajjjpKiURiSAB07YUbtHN17foXrl1IDqORSGRU+9OioiKNGTNG48ePV0NDg9+fmDBhgj9A7erZDWS5NiIcDq/zvoRrH3p6evxttbOzU21tbX5/rampSR9//LEaGxsD27xbZzRto3vvrg9RWFio4uJi/3fXLrv+g3ufg7dx1za54O/2yYMnboYTCoX8Oq2urtb48eNVX1/vt8VuX1dZWamGhga/X1FbW/u5hR8z8/sTrr7d9pQ8aeLeZ/L+sKmpSZ2dnWpsbFR3d/eIr5WXl+dvS0VFRX4/zvUxXL27f13du5/kfaMbFO/q6lJ/f/+Ir508oeD2EyUlJYH+SHV1tb/fHDyR4/okhYWF6+w74Pohbvt323RLS4va29vV2Niojz/+2G/TV6xY4U/AuD7sSEpKSvwcUFRUFGjLXTB323xyG+PqOznHJO8vR1PnlZWVGj9+vGpra1VZWana2lo1NDT4Aymu7XZ139DQ4H8GxcXF66KK10rOBnZJOvnkk/Xcc8+pt7fX36l1dnaOquFyO6PCwkK/o+NCwuDO7OBOlgt8o91g8/Ly1NDQoIaGBn9AoaqqSmPHjtW4ceNUV1fn76QqKytVU1Oj6upqVVRUKD8//3MZmUskEv5MZ1tbmyKRiNra2tTe3q7e3l719vb6jaObJU2e+WhublZLS8uodlauA+YGHFwoczP+ySOYyR3b5AbSlcl9YUcTBF2YdY1eeXm5/0V1o/+uw+CWuc6K+3EN6brq6A6nr68vsPPv7OxUe3u72tvb/YbSdYBdKG5sbFRfX1/K5/Q8z28kkzu8bht3AdjtmFxoc4232xG52ZaRmpFQKKSGhgaNHz9eY8eOVV1dnWpqavzGMnmkObmRdCOk6yqkxePxwACNG2V39etmP9you6vX1atXq62tbcSdvqvX5CCRvBNybYcri9uG3Wiz6xR2dXWNWKfSQEhz7cWYMWP8up0wYYImTJjgd6jGjBnjj/K7gaR13W709/f738PkenUdWjfL7wbK3OCDq9+Ojo6026zjOjHFxcV+UEpuK5I7s25k3g04uB9XTjeYM5rX3XLLLXXBBRdo55131pgxY/zlK1eu1LJly/TKK6+os7PT347d9l1fX6/6+npVVFR8pjpPJBJ+8HWDYy6QNTc36+OPP/b3c83NzX5nqrW1da1ep6CgIDAj6rbj5I5Ucv3G43F/cMkdidTe3p52+y0uLlZDQ0PgyJbq6mqNHTtWEydO1JgxY/yw6wahXbvweRxRZGbq7OxUa2urP/jU1tamVatW+Z1TF+4bGxv9bde1taP9vrr37o5GcW2uC2luf+72JckzTG5f5444cBMAIwmFQn5n1AWtmTNn6qCDDtLUqVNVUVHhv5577lgsplAo5Pd91lX729/fHzhirbGx0W/v3D7NDex88sknampqUiQS8Y9OGUly4HIDUK4/kTxYnjx54YJu8qSFG0B3ZR1poMHzPI0ZM0bjxo3z+wTJR3K59qChocEf2HV9iOTZ78/z7gEuvA+eWXb7Qrc/TO7TffLJJ37frqOjI+3zl5WV+e/FhV23TQ+ue1f/7scFLzegljwB0NPTM6o+uzTw+VdUVPj7vDFjxgQG2ZInMFzfzwVl9538vAZ8XC5obm7WypUr/W07eWbf9e/coJALysmDnyNxs8yuzRw8oOnqP3lA02UXt927z2A0++TCwkK/rseNG+dPerrvoDti1w3wjBs3zj+SxuWpz4PLL66tcW1mS0uLmpqa/PbHDTK4SZGmpqZRTeyEQqEh+dBt78l9vcEDPK4v7bZ3l1/6+vo0a9Ys3XfffSlfM2cDeywW80NeMjda574Era2t/shdU1OTv8NOPjzDjZS5Q2RcA29m/uHlyV8KFzKSOxrJjYI7rNmN0NfW1gYaieSGLPkQnME/bt3kf5P/frSSX9v97r7U7mfwsuT/p1rf6e7u9js5LuS7mRXXQLlOgpvpSt5xuk6Lq3NpzWFI7nAYdyiua6xqamr8mVAX/Kqrq1VfX+83zIMDdnJdpqvv4ep+pM9jbT+L0dRx8k/yjlBa07mPRCL+IWDuCIPk+neHNroG223jrq7dj3sNNwOQfBh0cqfa/d9t527EsqGhwa/vVNv3cHU52joeTX2Oph7Tbe+uXt3MlevMuU6mG3l2A1zJ4TD5SBbX+Uveht3Oy3UoXBB1bYerSzdL5wY0Bh+dklyfqdqOVPW5tnU6uH6Hq+/B2+hwjyW/tgukPT09gcET16lxbXVyG57cXgw+XDT5tV2odyHJ/biOtRsAcLPTrg13s0vDdSxGqt+RPofR1HO6bdLVafL3f/A6bgbV7cdcoHeDqa5j4/Z5yUEl+ZSr5EMVk8uYl5fnd1zctulm4ZL3eW6GwnWih9vnJQ+wpGpnP21bkKpeXUcq1fc/1XY7uPzJg/WuY+4O63fbtTvKJ/kIKndElTt6Jfn9ufp1h5kPd4qPG8B322txcbG/3M26DS7rSO2u+w6lq+uR6nxt2obB9T54fyYN9CPcwJ6rZzcgmBx6XH/C9SV6enqGPR3NvZY7ysX149yP28cl9+fchIkbaEqefUtuH1Jtq8PV++B/h2uP17abvrb9iHSfi5NIJAL7usbGxkC/IvmUK9ePGHwY/+CBD/f87jNwE2XJh5S7ek8+JTA5YLsBEjfAmK4vPdxnsy76GWtb94O375H6HU4sFgvsE5MHuQYffedyTHL/Lrlf596ba79dhnGTNsXFxaqsrPT7Iq7dcadhuSM26+rq/LKOtC8cqW1P/tw+TZ0Prr+16fMN3tZdO+3aGBf0k+t6cD5023vyaYXJ217y55+8vbvsMn36dJ1xxhmp36etbc18QVx++eU6++yz/RGpeDy+Vh+ie3y4fwf/nqqBTff7cI342hx+ne3W5gsyXJ0P/n3w/wdvtsM1AKk6Jek+j1yQqvM+0nY++PfB/0/XyI7Uuf6ib+PJs7bJv69N++Ek79ySpWsjRrMj/KJuw2vTRozUZgz3/9G0FSO1EekCTLYaTccleZn7ffCy5OWDf5dG/u4nLxs8oJT8d19Ea1vHa9u3kIbffj9N2/BFbSOGCzlr0y6kq1vps7UPbvkXff+WTqp2ebh+hls/+d/BzzWcVH1o9+9Ifeh0bc8X3Wj2iaNpawY/pzOauh9uWa707ZIN17dL/n2k/af7/2Dpsosk/1S8jTfeOO2Rujl70Tl3caNoNKqioqKc+gJ/EVDfmZMLDWe2Sa7T0R6eh9Ghrfh8UK+fP+r48zd4gAfrF9t4ZlH/608m2phwOKybbrpJp556qpqbm1VTU5Ny3S/2JWXTqKyslCS1tbWlHNUDAAAAAGB9MrNAXk0nZwP7+PHjJUkrVqz4wt/qBgAAAACQG8wskFfTydkkW19fL0lqampihh0AAAAAkBUSiUQgr6aTs4G9rq5OktTY2EhgBwAAAABkBTML5NV0cjaw19bWSpJaWloI7AAAAACArGBmgbyaTs4G9pKSEkkD9+4ksAMAAAAAsoGZBfJqOjkb2EOhkIqLi9XT05PpogAAAAAA4BttXs3ZwC4N3N8uEokwww4AAAAAyApmJmlNXk0npwN7aWkpgR0AAAAAkDVcYHd5NZ2cDuw1NTVcdA4AAAAAkHVcXk0npwP7uHHjtGLFCgI7AAAAACArmJnMzM+r6eR0YG9oaNCqVaskDZzUDwAAAABApplZIK+mktMpdsyYMVq1apXMjFl2AAAAAEBWSCQSgbyaSk4H9okTJyoWi6mpqYkZdgAAAABAVjCzQF5NJadTbENDgySpqamJGXYAAAAAQFZIJBKBvJpKTgf2yspKSVJrayuBHQAAAACQNZLzaio5HdhramokSW1tbQR2AAAAAEBWSCQSgbyaSk4H9qqqKkniXuwAAAAAgKySnFdTyenAXlFRIUnq7OzMcEkAAAAAABhgZqPKqzkd2EtKSiRJPT09zLADAAAAALKCmQXyaiobRGDv7e3NcEkAAAAAAFhjNHk1pwN7YWGhCgoK1NXVxQw7AAAAACArmFkgr6aS04Hd8zxVVFSovb2dwA4AAAAAyApmFsirqeR0YJekoqIiDokHAAAAAGSdkfJqVgT2V199VW+99dawj0WjUT3++OOf+rmLiooUjUaZYQcAAAAAZAUzk7Qmr6aS0cBuZvr1r3+trbfeWrfddtuQx3t7e7X//vvr29/+thKJxKd6jZKSEmbYAQAAAABZZ6S8mtHAftlll+mkk06S53kqLi4OPNbf369DDz1UL730khYtWqRQ6NMVtaCgQLFYjBl2AAAAAEBWcDPsLq+mktHAvuOOO+rxxx/XhAkThhTy+uuv19KlS/XEE0/oy1/+8qd+jYKCAvX19X3WogIAAAAAsE6NlFfz12NZhthrr70kSR0dHSorK/OX9/f368orr9QVV1yhaDSqZcuWadq0acPOkr/xxhtatmyZQqGQioqKVFVVpXA4rEmTJqm6ulqFhYVpRywAAAAAAMiEkfJqRgO7JMXjcbW1tamhocFfdu+99+qDDz7Qj3/8Y61evVqSNH36dN17772aMWNG4O/vueceXXjhhUOe1/M8JRIJ5eXlKR6Pc0g8AAAAACCruLyaSsavEt/S0iIz09ixY/1lixcvVm1trS666CK99NJL+tOf/qRoNKoTTzxxyN+nenPunADP8wjrAAAAAICsM1JezfgMu7tJfE1Njb+ssbFR8+bN0/e+9z1J0pZbbqnu7m4dccQRikQiKi0t9dedPn26DjjgAMXjcUWjUbW2tqqrq0uffPKJJCmRSHzqC9YBAAAAAPB5GSmvZjywu9GE5FGFmpoaNTU1BdYrKSmRmQ25vduCBQu0YMGClM9vZvI8z59xBwAAAAAgG7i8mkrGp57dzHpjY6Mfqrfbbjs9/vjj/uy7JD300EPafvvtVV5evlbPP1IFAAAAAACQCVkd2E8//XTV19dLkvbdd1//HPWjjjpK4XBYO+20ky6//HIdfPDBuvnmm3X++eev9WvE43Hl5eWt03IDAAAAAPBZjZRXM3pI/Iknnqh99tnHP8x95syZkqTq6mo9//zzuvrqq/Xwww9r44031tNPP63tt99+rV+jr69PBQUF67TcAAAAAAB8ViPl1YwG9o033lgbb7zxsI+NHz9eV1999Wd+jVgspsLCws/8PMicgw8O65FHGHQBACCV//f/+nTPPd2ZLgYAYJTcYfAj5dWMn8P+eXMjFlx07ouLsA4AQHrsKwHgiymrZ9jXh+7uboXD4UwXA+tAW1v7yCsBALCBqaqqzHQRAACf0kh5Nedn2N1925lhBwAAAABkA3dIvMurqeR0YDczdXV1qby8nMAOAAAAAMgKnucF8moqOR3YY7GYYrGYKioqCOwAAAAAgKzgeV4gr6aS04G9q6tLklRWVpbhkgAAAAAAsMZo8mpOB/ampiZJA/d1Z4YdAAAAAJANQqFQIK+mXG99FSgTOjo6JBHYAQAAAADZJTmvppLTgb29feA2YJWVlQR2AAAAAEBW8DwvkFdTIbADAAAAALAeEdi15iR+7sMOAAAAAMgWnucF8moqOR3YOzs7JSntfe0AAAAAAFifPM8bVV7N6cDe2toqSaqqqmKGHQAAAACQFTzPC+TVVHI+sJeVlamwsJDADgAAAADICi6wu7yaSk4H9s7OTv/wAgI7AAAAACBbJOfVVHI6sHd1damsrEwSgR0AAAAAkB3cRedcXk0lpwN7d3e3SkpKJBHYAQAAAADZwfO8QF5NJacDe09Pz4gVAAAAAADA+jaavJrTgd2dE8DsOgAAAAAgW7jbum3Q57C3tbWpurqawA4AAAAAyAqe50lak1fTyenA3tHRwQw7AAAAACBruMDu8mo6OR3YOzs7VVFRkeliAAAAAAAQMJq8mrOB3cw4hx0AAAAAkFU8zwvk1XRyNrBHIhFJUllZGYEdAAAAAJAVPM8L5NV0cj6wl5aWZrgkAAAAAAAMSA7sI+XVnA3s3d3dkgYqgBl2AAAAAEC2SM6r6eRsYF+9erUkqa6ujsAOAAAAAMgKoVAokFfTrrs+CpQJK1askCSNHTtWiUQiw6UBAAAAAGDgkPjkvJpOzgb2lpYWSVJtbS0z7AAAAACArOB5XiCvppOzgb2rq0sSV4kHAAAAAGQPz/MCeTWdnA3soz2JHwAAAACA9WmDv+hce3u78vPzVVJSwgw7AAAAACAreJ4XyKvp5Gxg7+rqUllZmTzPI7ADAAAAALKCOyTe5dV0cjawRyIRhcPhTBcDAAAAAACf53mjzqs5G9i7u7v9CmCGHQAAAACQLZLzajo5G9ij0aiKiooyXQwAAAAAAAJGm1dzNrDHYjEVFhZKYoYdAAAAAJAdPM8L5NV0cjaw9/f3Kz8/P9PFAAAAAAAgYLR5NWcDezweV15eXqaLAQAAAABAwGjzKoEdAAAAAID1aIMP7IlEgsAOAAAAAMgqnueNOq/mbGCXNOJN6AEAAAAAyITR5NWcDuxcHR4AAAAAkI1Gk1dzNrC7wwwAAAAAAMgmo82rOR3YmWEHAAAAAGQTMxt1Xs3ZwB4KhZhhBwAAAABkndHm1ZwN7Hl5eYrH45kuBgAAAAAAAaPNqzkb2AsLCxWLxTJdDAAAAAAAAkabV3M2sBcXF6u3t1cSt3cDAAAAAGQHMwvk1XRyNrAXFRUpGo1muhgAAAAAAASMNq/mbGAvLi72K4AZdgAAAABANnAz7Bt0YA+Hw4pEIpkuBgAAAAAAAaPNqzkb2MvLy9XV1eXf4w4AAAAAgEwzs0BeTSdnA3tZWZni8bh6e3sJ7AAAAACArGBmgbyaTs4G9tLSUknisHgAAAAAQFYZbV7N2cBeXl4uSers7GSGHQAAAACQFdwh8dJAXk2HwA4AAAAAwHpCYJdUVVUlSWpvbyewAwAAAACygpkF8mo6OR/Y29raCOwAAAAAgKyQHNjb2trSrpuzgb2mpkaS1NTURGAHAAAAAGQFMwvk1XRyNrBXV1dLYoYdAAAAAJA9zCyQV9PJ2cAeDoclSd3d3RkuCQAAAAAAa4w2r+ZsYC8sLFQ4HFZzc7NCoZx9mwAAAACALxAzC+TVdHI2yXqep4aGBs5hBwAAAABkjUQiEcir6eRsYJcG7sXOfdgBAAAAANnG5dV0cjqw19XVadWqVQR2AAAAAEBWMDOZmZ9X08npwD5u3Di/AgjtAAAAAIBsYGaBvJpKTgf26upqtba2SiKwAwAAAACyg7u1m8urqeR0YK+srFRHR4fMjMAOAAAAAMgKZhbIq6nkdGAvLS1VPB5XNBolsAMAAAAAsoKZBfJqKjkf2CUpEolkuCQAAAAAAKwxmrya04G9vLxckri1GwAAAAAga5hZIK+mktOBvaysTJLU1dVFYAcAAAAAZAUzC+TVVHI6sBcXF0uSent7M1wSAAAAAADWGE1ezenAnnxOADPsAAAAAIBs4C46J3EOO+ewAwAAAACyBuewS6qoqJAkdXR0ENgBAAAAAFkjOa+mssEEdgAAAAAAsoGZEdgrKyslSe3t7cywAwAAAACygpkF8moqOR3Yw+Gw8vLyCOwAAAAAgKxhZoG8mkpOB3bP81RSUqKenh4COwAAAAAgK5hZIK+mktOBXRq4tVu6y+QDAAAAAJAJI+XVnA/s4XBY3d3dzLADAAAAALKCmUlak1dTyfnAXlRUpGg0muliAAAAAAAQMFJe3WACOzPsAAAAAIBs4GbYN/jAXlBQoL6+vkwXAwAAAACAgJHyas4H9vz8fPX392e6GAAAAAAABIyUVzeYwM4h8QAAAACAbLLBB3aCOgAAAAAgG42UVzeIwO5O6AcAAAAAIFuMlFc3iMAOAAAAAEC22eBn2JldBwAAAABko5HyalYE9vfff18ffvhhysdXr16tpUuXfqrnJrADAAAAALJR1gf222+/XVtssYVuvPHGYR9vbm7Wdtttp912202NjY1r/fyJREKhUIjgDgAAAADIKi6vppLRwH7ttdfqiCOOUDQaVUlJybDr/OAHP1Bra6ukgTeztkaqAAAAAAAAMiGrA/uUKVP0wAMPaKONNlI0Gh3y+F133aX77rtPl1xyyad+DQI7AAAAACAbjZRX89djWYbYf//9JUnHHHOMwuFw4LHly5fr+9//vk477TRtvfXWKZ/jjTfe0LJlyxQKhVRUVKSqqiqFw2FNmjRJ1dXV6u/vV35+Rt8mAAAAAABDjJRXM55kE4mEWltb1dDQ4C8zMx133HGqr6/XBRdcoBdeeCHl399zzz268MILhyyvrq5WS0uLYrGYCgsLP4+iAwAAAACw1tzt3EbKqxkP7K2trTIzjR071l/2i1/8QosWLdLixYsVCoXSnrsej8eHXV5UVCRJikajKioq4qJzAAAAAICs4vJqKhkP7G1tbZKk2tpaSVJTU5POPPNMSdLs2bMD626yySa67LLLdMopp/jLpk+frgMOOEDxeFzRaFStra3q6uryRyyi0aiKi4vXx1sBAAAAAGDURsqrGQ/sbuY7Ly9P0kBwf/XVV7V69Wr19fUpGo3q5Zdf1jnnnKOrrrpKc+fODfz9ggULtGDBgpTP39PTo+LiYmbYAQAAAABZwU0wu7yaSsYDe01NjSSppaVF0kDBp0+frunTp/vrVFZWSpLmz58fOHR+NLq7u1VaWrqOSgsAAAAAwLoxUl7N6P3Ozj//fE2dOlWStN9+++n0008fdr1Pc/91aeD89q6uLlVUVDDDDgAAAADICp7nBfJqKhmdYT/kkEO0xRZb+GF6m222GXa9HXbYQddcc03gSvKj0dzcLGngMHsCOwAAAAAgG4RCoUBeTSWjgX3GjBmaMWPGiOsVFhbq5JNPXuvnb21tlURgBwAAAABkD8/zAnk1lYweEv95a2pqkjRwnjyBHQAAAACQDTzPC+TVVHI6sDc2NkqSGhoaPvV58AAAAAAArEue5wXyaio5Hdjb29slSVVVVcywAwAAAACygud5gbyaSk4HdndOQHV1NYEdAAAAAJAVks9hr66uTrleTgd2N2JRWVlJYAcAAAAAZIXkGfbKysqU6+V0YG9paVFFRYVCoRCBHQAAAACQFTzPC+TVVHI6sLe1tflX3COwAwAAAACyged5gbyaSk4H9tWrVxPYAQAAAABZxfO8QF5NJacD+/LlyzVhwgTCOgAAAAAgK3ieJ8/z/LyaTk4H9tbWVtXU1BDYAQAAAABZxeXVdHI6sHd2dqq8vJzADgAAAADICp7nSVqTV9PJ2cBuZuro6OCWbgAAAACArOF5XiCvppOzgb2zs1OJREJVVVUEdgAAAABAVvA8L5BX08nZwN7V1SVJHBIPAAAAAMganucF8mo6ORvYOzs7JY1cAQAAAAAArC9uhl3agAN7e3u7JHEOOwAAAAAgqyTn1XRyNrAnj1gQ2AEAAAAA2YAZdhHYAQAAAADZh8CugZvQS+Iq8QAAAACArOF5XiCvppOzgb2trU0SgR0AAAAAkD08zwvk1XRyNrC7y+SXlZVluCQAAAAAAAxIvq3bSHk1ZwN7T0+P8vLyVFBQwAw7AAAAACBrJOfVdHI2sHd3d6u0tFSSCOwAAAAAgKzgeV4gr6aTs4E9EomMqgIAAAAAAFifRptXczawx2IxFRYWSmKGHQAAAACQHTzPC+TVdHI2sHd3d6ukpCTTxQAAAAAAIGC0eTVnA3tvb6+Ki4slMcMOAAAAAMgOnucF8mo6ORvY4/G48vLyMl0MAAAAAAACRptXCewAAAAAAKxHG3xg7+/vV35+fqaLAQAAAABAwGjzas4GdmbYAQAAAADZxvM8ZtilgYoAAAAAACDbjCav5nRgBwAAAADgiyqnAzu3cwMAAAAAZKPR5NWcDeyhUEiJRCLTxQAAAAAAwGdmo86rBHYAAAAAANajDT6w5+fnq7+/P9PFAAAAAAAgYLR5NWcDe2Fhofr6+jJdDAAAAAAAAkabV3M2sBcUFCgWi0ni9m4AAAAAgOxgZoG8mk7OBvbCwsJRVQAAAAAAAOvTaPNqzgb24uJi9fb2SmKGHQAAAACQPZLzajo5G9iZYQcAAAAAZBszY4adwA4AAAAAyEYbfGAvKSlRT0+PJA6JBwAAAABkBzML5NV0cjawl5eXKxqNKhaLEdgBAAAAAFkjOa+mk9OBXZK6uroyXBIAAAAAAAaY2ajzas4G9pKSEklST08PM+wAAAAAgKzgDomXNOJh8Tkb2MPhsCSpu7s7wyUBAAAAAGCN0ebVnA3szLADAAAAALINM+zSqCsAAAAAAID1aYMP7FVVVZKktrY2ZtgBAAAAAFnBzAJ5NZ2cDezV1dWSCOwAAAAAgOxhZoG8mk7OBvba2lpJUmNjo0KhnH2bAAAAAIAvEDML5NV0cjbJ1tXVSZJaWlqYYQcAAAAAZIVEIhHIq+nkbGDPy8tTUVGRIpFIposCAAAAAIBvtHk1ZwO7JFVUVKijo4MZdgAAAABAVjAzSWvyajo5HdirqqrU3t5OYAcAAAAAZAUX2F1eTSenAzsz7AAAq3IGPAAAIABJREFUAACAbMIM+/8pKytTZ2cngR0AAAAAkFVcXk0npwN7eXm5urq6Ml0MAAAAAAB8ZjaqvJrTgb2hoUErVqyQJO7FDgAAAADICmYWyKup5HSKnThxolatWqX+/n4OiwcAAAAAZIVEIhHIq6nkdGCvq6uTmam1tZUZdgAAAABAVjCzQF5NJadTbEVFhSSNeCI/AAAAAADri5mNKq9uEIGdW7sBAAAAALJFcmBPd2u3nA7sVVVVkqS2tjYCOwAAAAAgK5hZIK+mktOBvby8XBIz7AAAAACA7OFu6yZtwDPs1dXVkqSWlhYCOwAAAAAgK5hZIK+mktOB3R1i0N7eTmAHAAAAAGSF5EPi29vbU66X04G9pKREktTT05PhkgAAAAAAsMZo8mrOB/ZQKKSuri5m2AEAAAAAWcHMAnk1lZwO7J7nqbS0lMAOAAAAAMgaZhbIq6nkdGCXpLKysrQVAAAAAABAJoyUV3M+sJeUlKinp4cZdgAAAABAVjAzSWvyaio5H9gLCgrU19eX6WIAAAAAABAwUl7dYAI7M+wAAAAAgGxCYGeGHQAAAACQRdwh8Rt8YM/Pz1c8Hs90MQAAAAAACBgpr+Z8YM/Ly1N/f3+miwEAAAAAQMBIeTXnA3t+fr76+/s5hx0AAAAAkFVcXk0l5wN7KBTyzw8AAAAAACBbjJRXcz6wM7MOAAAAAMhGI+XVnA/szK4DAAAAALLRSHk15wN7IpFglh0AAAAAkHVGyqsbRGDnPHYAAAAAQLZxeTWVDSawAwAAAACQTTb4wN7f36/8/PxMFwMAAAAAgICR8mrOB/aenh6VlJRwSDwAAAAAICu489ZdXk0l5wN7b2+viouLM10MAAAAAAACRsqrOR/YmWEHAAAAAGQTZtj/TyQSUVlZWaaLAQAAAABAwEh5NacDu5mpq6tL5eXlzLADAAAAALKC53mBvJpKVgT2xsZGNTY2DvtYT0+Pent7P9Xzdnd3Kx6PE9gBAAAAAFnD87xAXk0l44H9z3/+s6ZOnapf//rXgeXvvfee9ttvP5WWliocDuvrX/+6Pvzww7V67kgkIkkcEg8AAAAAyBqe540qr2Y0sN90003af//91dHRETjR/s0339SsWbO0evVq3X///br99tv1yiuv6Pjjj1+r5+/q6pIklZaWMsMOAAAAAMgayXk1ldR3aF8PwuGw/vCHP+jCCy9UNBr1lxcUFOiMM87QKaecoqKiIknSiy++qFtvvXWtnr+zs1OSVFFRQWAHAAAAAGQFz/MCeTWVjAb2BQsWSJJOP/30/8/enUe5Vdf/H399kkxmssy+tEMpS1uQYtmRqq1IC2WTb4uiQBUUEAT6la9s/iiCWoqCRXEDBQQpfPErfOuGoiIIKktFW1kUDq0iS6GtnaUzk21mMjPJ/f0x36RzbZNJ0kxzuX0+zvEcSe5c3snxeM4zn8+917bCPm3aNF111VXZf04mk/rlL3+pQw89dLtzrFu3TuvXr5fH41F1dbUaGhoUDAa13377KRqNSiLYAQAAAADOYYyx9WouFQ12SUqn0+rt7VVbW9sO39+8ebMWL16sN954Q/fcc892769atUrLli3b7vUVK1booIMOksQ17AAAAAAA5zDGZLfEO/Yadknq6+tTOp3WpEmTtnvvoYce0sEHH6xNmzbpD3/4g4466qjtjkmlUjs8bygUyl7EzzXsAAAAAAAnGduruVR8hb2vr0+S1NLSYnv9W9/6li699FL953/+p1asWJHzQ8ycOVOLFi1SKpVSMplUb2+v4vG4mpubFYlEJLElHgAAAADgHMYYW6/mUvFgz6yQ+3zbRunt7dVnP/tZfe5zn9OXv/zlvH+/ePHi7LXw/+7GG2+UJLW2thLsAAAAAABHMMaos7NT0miv5lLxLfFNTU2SRiM94/e//72Gh4d15ZVXKplMqru7Wx0dHUVH96ZNm1RfX69gMKh0Ol3WuQEAAAAAKIXH47H1as7jduFM21mxYoVmz54tSTr99NP1hS98QZKyj3jbe++9VVNTo9bWVk2ePFmzZs0qKrx7enqyv1awwg4AAAAAcAJjjK1Xc6nolvh58+apvr4+G+Hvec97JEkLFy7U97//ffl8PjU3NyscDme3DHg8hf/GkEgkste+E+wAAAAAACcwxth6NZeKBvtRRx21wzu/h0IhnXfeeTt9/s7OTrW0tBDrAAAAAADHyCxI//vN1/9dxa9hn0ibN2/WlClTCHYAAAAAgCNkdo1nejXvsbtioEqwLEsdHR2aNGkSN5wDAAAAADiCMcbWq/m4NtiTyaSSyaQaGxtZYQcAAAAAOIIxxtar+bg22Pv6+iRJ9fX1FZ4EAAAAAIBRxpiCe9W1wR6JRCSNfgGssAMAAAAAnMAYY+vVfFwb7IlEQpIUDocJdgAAAACAY4zt1XxcG+w9PT2SpIaGBoIdAAAAAOAIxhhbr+bj2mDv7OyUJLW1tRHsAAAAAABH8Hg8tl7Ne+yuGKgSYrGYJKmuro5gBwAAAAA4xthezce1wZ65JiAUChHsAAAAAABHMMbYejUf1wZ7PB6XxE3nAAAAAADOYYyx9Wo+rg32ZDIpj8cjn89X6VEAAAAAAMgqtFddG+yJRCK7vYAVdgAAAACAE2S2xI+3HV5ycbDHYjHV1tZWegwAAAAAAGwK7VXXBvvAwIACgYAkVtgBAAAAAM5gjLH1aj6uDvZgMFjpMQAAAAAAyMoEeyG96tpg7+/vZ4UdAAAAAOA4Y3s1H9cG+8jICHeIBwAAAAA4TqG96tpgT6fT8nhc+/EAAAAAAG9DxpiCe9W1RWtZlowxlR4DAAAAAACbQnvVtcEuiWAHAAAAADjSbh3smW0GAAAAAAA4SaG96tpg9/l8GhkZqfQYAAAAAABkWZZVcK+6Ntirq6uVTCYrPQYAAAAAADaF9qprg72qqkrDw8OSuJYdAAAAAOAMlmXZejUf1wZ7IBDQwMBApccAAAAAAMCm0F51bbCHQiElEglJrLADAAAAAJzBsixbr+bj2mD3+/0aGhqq9BgAAAAAANgU2quuDfZQKKT+/n5JrLADAAAAAJwhs8Ke6dV8XBvstbW1GhwcLOhCfgAAAAAAdpVCe9W1wR4MBiVJ/f39rLADAAAAABzBsixbr+bj2mAPhUKSCHYAAAAAgHNktsRLu3Gwh8NhSVI8Hq/wJAAAAAAAbFNor7o22JuamiRJ3d3d8nhc+zEBAAAAAG8jlmXZejUf15Zsa2urpNEvgC3xAAAAAAAnSKfTtl7Nx7XBnrkmoJCH0QMAAAAAsKsU2quuDfaamhpJ0uDgICvsAAAAAABHsCzL1qv5uDbYx17ET7ADAAAAAJxit7/pXG1trSQpFotVeBIAAAAAAEZZllVwr7o22GtqauTxeJRIJFhhBwAAAAA4QmZLfKZX83FtsBtj1NzcrK6uLoIdAAAAAOAIlmXZejUf1wa7JE2dOlVvvfUWz2EHAAAAADhCOp2WtK1X83F1yba1tamzs5MVdgAAAACAY1iWle3VfFwd7I2NjYpEIpJEtAMAAAAAHMGyLFuv5uL6YN+6daskgh0AAAAA4AyZYM/0ai6uD/ZIJJK9qB8AAAAAgEobu8JuWVbO41wd7PX19Uqn04rH4wQ7AAAAAMARLMuy9Wourg725uZmSVJ3dzfBDgAAAABwBMuybL2ai6uDvaWlRZLU09NDsAMAAAAAHMGyLFuv5uLqYK+trZUkxWIxgh0AAAAA4AiWZdl6NRdXB3t1dbUkaXBwsMKTAAAAAACwTSG96upgDwQCkqSBgQFW2AEAAAAAjmBZlq1Xc3F1sIfDYUniLvEAAAAAAMewLMvWq7m4OtgbGhokSb29vQQ7AAAAAMAxxvZqLq4O9qamJkncJR4AAAAA4BzpdNrWq7m4Oti9Xq+CwSB3iQcAAAAAOMrYXs3F1cEuSXV1dXm/AAAAAAAAdiXLsiSN36uuD/ZwOMwKOwAAAADAMTLBnunVXFwf7IFAgMe6AQAAAAAcJ9Orubg+2P1+v4aGhio9BgAAAAAAkratsI/Xq64Pdp/Pp5GRkUqPAQAAAACAzXi96vpg93g8SqfTlR4DAAAAAACb8XrV9cHu9XqVSqW4hh0AAAAA4CiZXs3F9cHu8Xiy1wcAAAAAAOAU4/Wq64MdAAAAAIC3I9cHezqdZjs8AAAAAMBxxutV1we7ZVkyxrAtHgAAAADgKJlezcX1wT4yMiKfz1fpMQAAAAAAsBmvV10f7ENDQ/L7/ZUeAwAAAAAAm/F61fXBPjAwoGAwyJZ4AAAAAIAjZLbBZ3o1F9cH++DgoGpqaio9BgAAAAAANuP1quuDfXh4WFVVVaywAwAAAAAcIbPCnunVXFwf7KywAwAAAACcaLdfYU8kElzDDgAAAABwjMwKe6ZXc3F1sFuWpcHBQQUCgUqPAgAAAABAViG96upgj8fjkqS6ujpW2AEAAAAAjmCMsfVqLq4O9p6eHklSY2MjwQ4AAAAAcARjjK1Xc3F1sEejUUmssAMAAAAAnMMYY+vVXFwd7P39/ZKU9yJ+AAAAAAB2tUJ61dXB3tfXJ0mqr69nhR0AAAAA4AjGGFuv5uLqYO/t7ZUkNTc3E+wAAAAAAEcwxth6NRdXB3ssFpMkhcNhgh0AAAAA4AjGGFuv5uLqYM/cJp9gBwAAAAA4xdjHuu22wZ5IJCRJoVCowpMAAAAAADDKGFNQr7o62AcHB2WMUVVVFSvsAAAAAADHGNurubg62OPxuMLhsIwxBDsAAAAAwBEyW+IzvZqLq4N9y5YtamtrkySCHQAAAADgCMYYW6/m4upg7+rq0uTJkyUR7AAAAAAAZzDG2Ho1F1cHe2aLAbEOAAAAAHCKsVvi83F1sG/dulWNjY0EOwAAAADAETLXrGd6NR9XB3t3d7fa2toIdgAAAACAI2SCPdOr+bg22C3LUiwWU21tLcEOAAAAAHCEzFPMMr2ajyOCPRaLKRKJbPe6ZVnauHGjurq6ij7n4OCg0um0QqEQwQ4AAAAAcARjjK1X86l4sD/55JOaPn26br75Ztvrzz33nObMmaOpU6eqra1NH/3oR9XZ2VnweaPRqCSpvr6+rPMCAAAAAFAqY0zBvVrRYH/ggQc0f/58dXV1KRAIZF/fsGGD5s+fr+rqaj355JP6xS9+oWeffVZnn312wefu7u6WJDU1NSmdTpd9dgAAAAAAimWMsfVqPr5dMVAu8XhcX/3qV/XNb35TyWQy+/pNN92k1tZW/eY3v1F1dbWk0Q8yd+5cvfDCCzr00EPHPXdHR4ckadKkSWyJBwAAAAA4gjHG1qv5VDTYzz//fEnSDTfcoJqaGkmj163/+te/1oUXXpiNdUl673vfq4aGBj399NO2YF+3bp3Wr18vj8ej6upqNTQ0aPr06dktBg0NDQQ7AAAAAMARxm6Jb2hoyHtsRYNdktLptHp7e9Xa2ipJSiQSeuONNzRr1izbccYYTZ48WW+99Zbt9VWrVmnZsmW212677bbsBx8b/QAAAAAAVNrg4KCk8Xu14jedi0QiSqVSmjx5sqRtz6TzeLYfLZVKyev1bvfavwuFQurv78/+d1bYAQAAAABOYIyx9Wo+FV9h7+vrkyS1tLRIGh04GAxu9yi3dDqtjo4O7bXXXrbXZ86cqUWLFimVSimZTKq3t1dTp07Vc889J4kt8QAAAAAA5zDGZDvY8VviR0ZGJEl+vz/72jvf+U798Y9/1Cc+8Ynsay+++KKi0ahmz55t+/vFixdr8eLF2533d7/7nSSptrY2++sFAAAAAACVNDbYa2tr8x5b8S3xjY2NkrattEvSaaedph/96EfatGmTJCmZTOqLX/yiJk2atN217bnE43GFw+Edbq0HAAAAAKASjDEF92pFa/Y73/mOTjzxREnSOeecoxUrVkiSlixZoilTpmjWrFk677zzNHv2bP3iF7/QN77xDVVVVRV07oGBAdud5wEAAAAAcIKxvZpPRbfEH3jggTrttNP0wQ9+UJI0Z84cSaPbAv7yl7/oG9/4hp566ikdfvjhuu+++3TQQQcVfO7h4eGC4x4AAAAAgF2l0F6taLDPmzdP8+bN2+F71dXVWrp0qZYuXVrSuZPJZPYW+aywAwAAAACcwBhj69V8XHuBdzKZLGiLAQAAAAAAu1KhveraYB8cHGSFHQAAAADgKMYYW6/m49pg5xp2AAAAAIATFdqrrg32VColr9db6TEAAAAAALAptFddG+wjIyPy+Sp6Tz0AAAAAALZTaK+6NtjT6TQr7AAAAAAARzHGFNyrrg12afSLAAAAAADAaQrpVVcHezqdrvQIAAAAAABsp5BedW2we71epVKpSo8BAAAAAECWZVkF92rZgr23t1dXXXWVOjo6ynXKnUKwAwAAAACcaEKDfe3atTr11FO1bNmy7L8kHo9r5cqVOumkkxyxFZ1gBwAAAAA40YQF+8DAgI4//ng9//zzuu6663T99ddLkqZOnarbb79dzz//vJ555pniJy6zqqoqDQ8PV3oMAAAAAABsCu3VooP96aefVl9fn2677Tadc845+upXv6quri5J0sknnyyv16vf//73xU9cZn6/X0NDQ5K4WzwAAAAAwBksy7L1aj5FB3smzmfMmKHly5drZGRE3/72tyVJNTU1CofD2rp1a7GnLTu/388KOwAAAADAcQrt1aKDff/995ckPfHEE5o6darOPfdc3XLLLYpGo3rzzTcViUQ0ffr04icus2AwqEQiIYkVdgAAAACAM1iWZevVfHzFnvzII4/UiSeeqEsvvVSJREIXXXSR7rzzTt1yyy1as2aNvF6vFi5cWNLg5RQKhQr6AgAAAAAA2JUK7dWig12SfvCDH+jjH/+4LrvsMlVVVcnr9eraa6+VMUY333yz9tprr1JOW1bhcFiJREKWZbHCDgAAAABwBMuyCu7VkoK9ublZv/rVr/TPf/5TDz/8sDZs2KDGxkYtXrxY06ZNK3nwcmpqalIqlVIkElFVVVWlxwEAAAAAQJZl2Xq1oaEh57ElBXvGjBkzdMkll9j+xU5Z0a6vr5ckRaNRtbS0VHgaAAAAAABGu3lsr+YL9qJvOidJV155pT71qU9t9/pFF12kBQsWKJ1Ol3LasgoGg5Kk/v5+R/yAAAAAAACAZO/VfIoO9pdeekk333yzJk+evN17c+fO1eOPP66f//znxZ627MLhsCRx4zkAAAAAgGNkrmGXxu/VooP9sccekzS6mv7vFi9erKamJj344IPFnrbsAoGAJGlgYIAVdgAAAACAI1iWZevVfIoO9mQyKUmKRCLbvefz+VRTU6NoNFrsacuu0C0GAAAAAADsShO2Jf7cc89VW1ubTjnlFK1du9b23lNPPaXNmzdr/vz5xZ627DIX8ff19bHCDgAAAABwhLE3nevr68t7bNF3iW9ra9PPfvYzffjDH9ZRRx2l97///Zo3b54ikYi+973vaY899tDHPvax0iYvo7a2NklSR0eHPJ6S7q0HAAAAAEBZWZZl69V8SirZ9773vVq/fr1uvPFGRaNRLVu2TLfeeqsWLlyoJ598Uk1NTaWctqxaWlrk9/u1ceNGVtgBAAAAAI6QTqdtvZpPyc9hr6ur09KlS7V06VINDQ3J6/XK6/WWerqy83g8am9v15YtW1hhBwAAAAA4QjqdtvVqPiUHe29vrzZu3Ji9Cd3WrVv12muvaePGjZozZ45OPvnkUk9dNo2Njert7WWFHQAAAADgCJZlSdrWq/kUHezxeFynnnqqHn/88ZzHnHnmmQQ7AAAAAAA7YFnWxAT7rbfeqscff1yLFy/WggULJI0+6i0UCmmPPfbQvvvuq3322aekocutoaFBr7zyiiTJGJP9JQMAAAAAgEqxLMvWq7kUHezpdFqSdNFFF+noo48ubbpdpL6+3hHPhAcAAAAAYKxCerXou7EtWbJE7e3tuvDCC7Vp06aSh9sVWlpa1NnZKcuyuPEcAAAAAMARLMuy9WouRVdsOBzW17/+db322muaOXOmli1bpldffTX7L0mlUqVPXWZtbW0aHBxULBbjOnYAAAAAgCOk02lbr+ZS9Jb48847T/fdd58kaWhoSNddd52uu+46VVVVqaamRrFYTF/5yld01VVXlT59mUyaNEnS6MPop0yZUuFpAAC7o+Dpp6vq0UcrPQZcbXTRpL6hocJzwK2Gjz9e/atWVXoMwFUsy7L1al1d3Q6PKynYp0+frmAwqKGhIUUiEfX29ioSiSidTqu5uVnHHHPMTg1fLpkvoLOzU1OnTq3wNACA3RGxjolmiV2EmFj8/xhQfmODvbOzU/vtt98Ojys62I855pi8Qe6kO7E3NjZKEo92AwBUXKSvr9IjAEDR2LkBTIx0Om3r1VyKDnZJWr9+vV588UUdcMABCoVC8ng86uvr05NPPqm7775bRx11lL73ve+VNnkZZbYVRKNRgh0AAAAA4BhjezWXooN9eHhYJ598sl5//fUdvh8MBnXLLbcUe9oJEQ6HJUmJRKLCkwAAAAAAMMqyrIJ6tehgX716tV5//XVdf/31qq2tlWVZqq+vV3t7u1avXq0VK1Zo7733Ln3yMqqtrZUk7hIPAAAAAHAMy7JsvZpL0cHe0dEhSTrzzDM1Y8YM23vHHXecbr/9dt1zzz36whe+UOypyy4cDssYo0gkQrADAAAAABwhs8Ke6dVcin4O+xFHHCFJ2Ue7jeXz+dTc3Jxzu/yu5vV6VV9fz03nAAAAAACOYVmWrVdzKTrYZ8yYoVNOOUU33HCDbrnlFqVSqex7jzzyiF555RUdcsghpU09Aerq6rjpHAAAAADAMTJPV8v0ai5FB7sk3Xnnndpvv/30X//1X5o8ebLmzJmjd7zjHTrxxBPV2Nios846q7SpJ0AoFFI8Hq/0GAAAAAAA2IzXqyUF++TJk7V27Vrdfvvtesc73qHNmzerrq5OV1xxhdasWaOWlpaSBy63YDCo/v5+VtgBAAAAAI6QWWHP9GouRd90bmRkRK+//rr2228/XXjhhbrwwgtLn3IX8Pv9GhoaqvQYAAAAAADYjNerRa+wr1y5Uvvvv7/WrVu3U4PtKl6vV6lUihV2AAAAAICjZHo1l6KDPXPL+XzL9k7i8Xiy2w0AAAAAAHCK8Xq1oC3xlmXpoYce0j/+8Q89++yzkqRvfetb2nPPPTU4OJj9TzKZVCAQ0Kc+9SkdddRR5fkEAAAAAADshgoK9hdeeEGLFi2yvfbvz2H3eDzyeDwaGRmRz+dzTLBblsV2eAAAAACA44zXqwUF+6GHHqo///nPsixLv/3tb/X5z39ed999t0466SQFAgEFAgFVVVXJGKNUKiWPp6Sbz0+IVCqlqqoqtsUDAAAAABwl06u5FBTsxpjsivkzzzwjSZo5c6YmT5683bFer7eUOSfM8PCwamtrKz0GAAAAAAA24/Vq0Uvh73//+3X++efrsMMO26nBdpWhoSH5/f5KjwEAAAAAgM14vVp0sB922GG69dZbd2qoXSnzBbAlHgAAAADgBJnr1ssa7L29vTrooIMUCoV09NFHZ19/6KGHNG/ePL3nPe/RT3/60xJHnhjDw8OssAMAAAAAHGe8Xi0q2NPptF5++WW1t7fr6quvliStWrVKCxcu1N/+9je9+uqrOu200/Tcc8/t3NRllEwmVV1dXekxAAAAAACwGa9Xiwr25uZmHXfccYpEIjr55JMlSddff71aW1v1/PPP67e//a0k6f7779+JkcsrkUgoFAqxJR4AAAAA4AiZLfGZXs2l6GvYFy1apFgspt/85jd688039dJLL+mMM87QXnvtpYMPPlher1cbNmwoffIyi8ViCofDBDsAAAAAwBEywZ7p1VyKDvYPfvCD8vl8uuSSS3T77bdLUvZ69ng8rlQqpWAwWMrMZZdKpTQ8PKxAIFDpUQAAAAAAyCqkV4sO9vb2dn31q1/Vpk2bdOONN2ry5Mk66aSTJEk//vGPJUmzZ88uceTySiaTkqTq6mpW2AEAAAAAjmCMsfVqLr5STn7ppZfquOOO01//+lcdc8wx2SX83/3ud5o5c6Y++tGPlnLashsYGJAkx6z4AwAAAAAgFdarJQW7JM2aNUuzZs2yvXbvvfcqnU7L5yv5tGUVi8UkSbW1taywAwAAAAAcwRhj69VcCtoSf/XVV+snP/lJ9p87Ojq0ceNGRSIRpdNpSZJlWUqlUjszc9klEglJo79YEOwAAAAAACcwxth6NZdxl8Lj8bi+8pWvaO+999Zpp52m119/XdOnT7cFcFVVlVKplNLptPx+v1566SXtt99+ZfgYO6eQawIAAAAAANjVynINezgc1iOPPKJ99tlHkjR16lR9+9vfVnd3t6LRqKLRqJLJpLxer2pqalRXV6f29vbyfIKdFI1GJUl1dXWssAMAAAAAHMEYY+vVXAq62Pz444/f9gc+nz796U/v5Hi7Rnd3tySpubmZYAcAAAAAOIIxxtaruYx7DfvIyIiSyeTbMngzv1g0NDS8LecHAAAAALjP2BX2hoaGnMeNu8I+b948Pf300/L5fGpsbFRLS4uam5vV1NSkcDgsr9eroaEhJZNJdXR0qKOjQ3fccYeOO+648n2aEvX390vipnMAAAAAAOcwxth6NZdxg/3666/XE088oa6uLsViMSUSCa1Zs0ZPP/202trasjFsjNEbb7yhSZMmaf/99y/fJ9kJhXwBAAAAAADsamUJ9mOOOUbHHHNM9p87Ozs1Y8YMnXrqqfrf//1f+f3+7HsXXXSR7rjjDnV2dmqvvfbaidHLI/MFBAKB7H8HAAAAAKCSxq6wBwKBnMcV9Bz2sVavXq1YLKYrrrjCFuuS9KUvfUmS9NBDDxV72gnR1dWlxsZGeTwetsQDAAAAABzBGGPr1VyKDnafb3RRPpVKbfdeJuAzD4CvtI0bN2rPPfeUJIIdAAAAAOC/fZCNAAAgAElEQVQIHo/H1qs5jyv2xPPmzVN9fb2+//3vbxfBd911lyTZttBXUldXl9ra2mRZFsEOAAAAAHCEzAp7W1tb3uMKeg77WOFwWF/84hd1+eWXa8OGDTr11FPV3Nysp556SnfddZcOO+wwnXTSSSUPXk7d3d067LDDiHUAAAAAgCMYYyRt69V8ig52Sbrsssu055576rvf/a6uvPJKpdNphcNhnX/++frKV74ir9dbymnLLhKJ8Ax2AAAAAIBjZII906v5lBTskvSRj3xEH/nIRxSPx9XX16eWlhbV1NSUerqyS6fT6unpUVNTE8EOAAAAAHAEY4ytV/MpOdgzwuGwwuHwzp6m7Do6OjQyMqI999yTYAcAAAAAOILH47H1at5jd9FMu1xfX58kqbm5Wel0usLTAAAAAAAwusI+tlfzcX2w19fXV3gSAAAAAABGjQ328XrVtcHe09MjiRV2AAAAAIBzGGNsvZqPa4M9Ho9LkkKhUIUnAQAAAABgm0J7teSbzo2MjKi7u1vJZFKStHXrVr322mvauHGj3vWud2nOnDmlnros+vv7JUnBYJCbzgEAAAAAHMEYY+vVfIoO9sHBQZ177rl68MEHNTg4uMNjFi5cqJ///OfFnrqsMtcENDY2EuwAAAAAAEcYew17Y2Nj3mOLDvbbbrtNDzzwgI4//ngde+yxkqRkMqlQKKQ99thD++67rw455JASxi6vzBdQV1eX/fUCAAAAAIBKGhvsdXV1eY8tOtgjkYgkafny5Zo9e3YJ4+0asVhMoVBIHo9rL9MHAAAAALzNGGMK7tWia/biiy9WU1OTLrnkkuyvAk4UjUazv1awJR4AAAAA4BRjezWfooN90qRJuuuuu/S3v/1NBxxwgL7zne9kb0nvJPF4XOFwWBLBDgAAAABwBmOMrVfzKTrYlyxZog996ENKJpPq6OjQpz/9abW2tmqPPfbQ9OnT1dzcrO985zslDV5OQ0ND8vv9lR4DAAAAAACbQnu16GvYTzjhBHk8HgWDQQ0NDSkSiai3t1eRSETpdFqHHHKIZs6cWdLQ5TQ8PKyqqipJrLADAAAAAJzBGGPr1XyKDvZFixZp0aJFJQ22K7HCDgAAAABwokJ7taRbqG/cuFF/+MMftGXLFsViMSUSCW3atEn333+/FixYoCuuuKKU05ZVob9YAAAAAACwK03YCnsqldIJJ5ygl19+eYfv+/1+XXrppcWetuzS6bS8Xm+lxwAAAAAAIMsYU3CvFh3sf/rTn/Tyyy/rsssuUzAYlGVZqq+vV3t7u5555hmtXLlSRxxxREmDAwAAAACAUUUH+1tvvSVJuuSSS7Tvvvva3jvjjDN0//3365577tHSpUvLM2GJjDFKpVIVnQEAAAAAgH9XaK8WfQ37wQcfLEn60Y9+tN17fr9f7e3t+vvf/17sabfzz3/+U2eeeab2339/zZkzRy+++GJRf+/z+TQyMrLTcwAAAAAAUC6WZRXcq0UH+4EHHqj58+dr2bJleuCBB2yPTFu7dq3+8Y9/6IADDij2tDY9PT1697vfrWQyqWuuuUaHHnqo5s2bp1deeaXgcxDsAAAAAAAnmrBgl6Q777xTLS0tWrx4sQ444ACdeuqpmjt3rmbPnq1AIKCzzz67lNNm3X///ZKkVatW6ROf+IRuvfVWSdJvf/vbgs/h9/s1NDS0U3MAAAAAAFBuhfZq0dewS9K0adP03HPP6fbbb9d9992nxx57TC0tLTr77LN1+eWXa4899ijltFlDQ0NKpVLq7u5We3u7Nm3apHg8vsPb3q9bt07r16+Xx+NRdXW1GhoadMQRRygcDisej0savT5g7E4AAAAAAAAqwbIsW6/mU1KwS1JLS4uuvfZaXXvttZJGH6P2zDPP6KCDDir1lFnnnHOObrrpJh1++OGaP3++fvWrX+l973ufzjjjjO2OXbVqlZYtW2Z7bd26daqrq1MsFpNEsAMAAAAAnMGyLFuv5lPSlvj+/n6tXLlSa9asyb728ssva+7cufr0pz9dyiltQqGQDjvsMHV0dOiNN95Qf3+/IpGIIpHIdsfu6M56kUhE9fX16uvrk2VZMsbs9EwAAAAAAOyszKPRM72aT9HBblmWjj76aJ1//vl6z3veo1/+8peSpFmzZum6667Tbbfdptdee620yf/P3XffrT/+8Y9as2aNVq9erZdffllbtmzRJz/5ye2OnTlzphYtWqRTTjlFCxYs0JFHHqmBgQG1trZqeHhY0WiUYAcAAAAAOIJlWbZezafoYP/LX/6iZ599Vt/85jd12GGH6TOf+YyGh4clSRdddJEkZSO+VPfee68+/vGP68gjj5QkzZgxQ5/61Kf01FNPbfcLxOLFi/Xggw/qoYce0qOPPqq1a9fqmGOOUUtLiySpq6uLYAcAAAAAOIJlWbZezafoYH/11VclSccdd5y+/vWv67XXXtMPf/hDSVJbW5saGhq0YcOGYk9rMzQ0lP0RICMajcrnK/yS+9bWVkmjX4DHU9LOfwAAAAAAyiqzwi6NH+xF33Surq5OkrR582Yde+yxmj9/vlasWKGzzz5b/f39ikajamhoKGHsbc466yxdeeWVamxs1JFHHql169bpu9/9ri6++OKCV8vb29uzcx5++OE7NQ8AAAAAAOWQTqdtvZpP0cG+YMECzZgxQ5dccol+9rOf6eqrr9aCBQv085//XK+88orS6bROOOGE0ib/P5/5zGdUXV2t++67Tz/84Q/V3t6uG264QRdffHHB58j8YtHT08OWeAAAAACAI4xdYe/p6cl7bNHBXlVVpZUrV2rhwoWaNWuW5s+fr0AgoIsvvlgdHR36wAc+oHe9612lTf5/PB6PlixZoiVLlpR8jmAwKElKJBIEOwAAAADAESzLsvVqPiVd3D137lytX79eK1asUDqd1pQpU7TXXnvplltu0Y9//GNHBHIoFJI0/hcAAAAAAMCuVGivFr3CntHW1qbLL79cl19+eamnmFB+v1/V1dWKRCKO+AEBAAAAAADLsmy9ms+4wX7vvffqpZdeUjgcVmNjo1paWtTc3KympiaFw2F5vV4NDQ0pmUyqo6NDHR0dWrRokZqamsr2gUphjFF9fT3BDgAAAABwDMuybL2az7jB/vDDD+unP/3pdo9Zy+dnP/uZTj311IKPnyiBQEADAwMEOwAAAADAESzLkrStV/MZN9gfeOABWZalaDSqWCymeDyu008/XW+99Za+9KUvqaGhIfsLwdKlSzU0NKRjjz22PJ9kJzU1NWnr1q0EOwAAAADAETLBnunVfAq6hj2zXF9fX6/Vq1frxRdf1MqVK3XOOefYjps8ebKOO+44PfDAA7rgggtKm76Mmpub1dvbS7ADAAAAABzDsqxsr+ZT9F3iN23aJEk6+OCDt3vv/e9/vyTp5ZdfLva0EyIUCmXvuke0AwAAAACcYmyv5lJ0sB9wwAGSpLVr12733ksvvSRJam9vL/a0E6K+vl59fX2VHgMAAAAAgCzLsgrq1aKD/eCDD9bRRx+t5cuX69FHH83uv3/99dd1zjnnyOv16mMf+1hpU5dZfX29otGoJFbYAQAAAADOkAn2TK/mUtJz2H/wgx9o4cKFOuGEExQKhdTU1KS33npLPp9Pd955p6ZMmVLS0OUWDAbV398viWAHAAAAADiDZVm2Xs2lpGCfOnWqnn32WT3zzDP69a9/ra1bt2ratGn62Mc+5phYl0aDfWhoSKlUqtKjAAAAAACQNbZXvV7vDo8pKdglyePxaM6cOZozZ07JA060xsZGSdLWrVsVDocJdwAAAABAxVmWZevVtra2HR5X0DXsN998sx577LHsPycSCcViMaXT6TKMOnGampokSX19fWyJBwAAAAA4gmVZtl7NZdwV9ng8riuvvFJTp07Vm2++qQ0bNmjGjBkaGRmRMUa1tbXy+/1KpVJKJpOqqanR2rVrNW3atPJ9mhIFAgFJ0sDAQIUnAQAAAABglGVZBfXquMEeDof1gx/8QAceeKCk0Ue2LVu2TN3d3YpGo4pEIhoaGpLX61V1dbXq6urU3Nxcpo+xc8LhsKTRHx1YYQcAAAAAOIFlWbZezaWga9jHPqbN7/frmmuu2cnxdo3MDwddXV0EOwAAAADAESzLsvVqLkU/h/3tpLW1VZLU3d1NsAMAAAAAHMGyLFuv5jLuCvsdd9yh1157TV6vV1VVVaqqqpLP55PH45Hf71dNTU32P36/X+FwWHPnzlUwGCzfpykRW+IBAAAAAE5Tti3x1113nf71r38V9S9fvny5Pv/5zxf1NxOhpqZGkjQ4OFjhSQAAAAAA2KaQXh032F999VXF43GlUikNDw9reHhYIyMjSqVS2TvDJxIJDQwMqL+/XyMjIzr22GPL9yl2QiAQUFVVlSKRCCvsAAAAAABHsCxLwWAw26u5jBvsgUAge7v5t5vMY+ei0SjBDgAAAABwBMuybL2aS0k3nfv73/+uNWvWbPf6Y489pocffriUU06YmpoatsQDAAAAABxnvF4tOtjj8biOPvpoXX311du995Of/EQf+tCHtGHDhmJPO2EyXwAr7AAAAAAAJ7AsS9IEBPvDDz+szs5OLV++fLv3rrrqKg0NDenuu+8u9rQThhV2AAAAAIATlT3Y169fL0maNWvWdu/ts88+2mefffTXv/612NNOmKqqKg0PD7PCDgAAAABwhMwKe6ZXcyk62I844ghJ0s0335z9l2TEYjFt3rxZU6ZMKfa0E2a8LwAAAAAAgEooe7CffPLJOu+883T99dfrxBNP1GOPPaZkMqmenh5dcMEFGhwc1Ec/+tGdGrqcCHYAAAAAgBON16vjPtZtR+68807NmjVLy5cv14IFC+TxeJROpyVJ1157rebMmVPatBNg7GwAAAAAADjFeL1aUrB7PB5ddtllWrJkiR577DGtWbNGgUBAH/7whzVjxoySh50IxpjsM+4AAAAAAHCKTK/mUlKwZ1RXV+sDH/iAPvCBD+zMaSaUx+PJ+wUAAAAAAFAJ4/Vq0cH+61//Wp/97GcVDAYVDocVDAYljd7lLp1Oa3BwUHPnztWXvvSl0qcGAAAAAGA3V3SwNzQ0qKGhQR0dHfrXv/6l4eFheTweeTweJRIJRSIRHX744RMxa0nYDg8AAAAAcKLxerXoYH/ve9+r1atX7/C9Rx55RCeeeKLe9773FXvaCZNOp+Xz+dgWDwAAAABwlEyv5lL0Y93yOfDAAyVJf/rTn8p52p2STqfl8ZT1YwIAAAAAsNPG69WylmxmKX9gYKCcp90pw8PDqqqqqvQYAAAAAADYjNerRW+Jf/bZZ3XXXXepvb1d7e3tMsaov79fW7Zs0RNPPCFJeve73136xGVGsAMAAAAAnKjswf7CCy/o9ttv3+F7ra2tWrp0qc4888xiTzthBgcHVVNTwzXsAAAAAABHyOxOz/RqLkUH+yc/+UmdeeaZ2rBhgyKRiHw+n2prazVlyhTV1taWPvEEicfjCofDlR4DAAAAAABJ24J9vF4tOtglKRQKZW8wlxGNRvXlL39ZS5YsUWNjYymnnRCDg4MKBAKssAMAAAAAHCXTq7mUdNO5l156SZ/4xCf09a9/PRvCW7du1de+9jWdcsopjorj/v5+BYNBR80EAAAAANh9ZVbYM72aS9HBnkwmNX/+fD3yyCO64oordNNNN0mS9t13X333u9/VH//4R61Zs6bEsctreHhYAwMDqqurq/QoAAAAAABIGg32Qnq16GBfvXq1urq6dMcdd+iMM87QDTfcoN7eXknSokWL5PV69fjjj5c+eRlFo1FJUn19PSvsAAAAAADHGNuruRQd7Fu2bJEkzZw5UzfccIP6+/t16623SpKCwaDC4bC6urpKmbfsYrGYJCkcDhPsAAAAAABHMMbYejWXooN92rRpkqQ//elPmjZtms466yx961vfUiKR0ObNmxWJRLT33nuXOHZ5JRIJSaM3ySPYAQAAAABOYIyx9WouRd8lfvbs2Zo3b54uueQSjYyM6LLLLtO9996rO+64Q2vXrpUxRv/xH/9R+uRlFI/HJcmRj5sDAAAAAOyejDEF9WrRwW6M0f/8z//ojDPO0Cc/+UmFw2FVVVXpyiuvlGVZWr58uaZPn1765GXU09MjSWpoaGCFHQAAAADgCMYYW6/mUtJz2Nvb2/XEE0/o+eef18MPP6w33nhDjY2NOvvss3XQQQeVNvEE6OvrkyQ1NjYS7AAAAAAAxxjbq7mUFOzS6C8Chx9+uA4//PBSTzHhBgYGJEmBQIBgBwAAAAA4gjHG1qu5FH3TubeTrVu3SmKFHQAAAADgHMYYW6/m4upg7+npUVVVlerq6gh2AAAAAIAjZK5hz/RqLq4O9mg0qrq6OhljCHYAAAAAgCMYY2y9mourg72jo0Otra2SRLADAAAAABzBGGPr1VxcHew9PT1qaWmRRLADAAAAAJwhsyU+06u5uDrYY7GYwuEwsQ4AAAAAcAxjTLZX83F1sPf29nKHeAAAAACAY2SuWc/0aj6uDva+vj6CHQAAAADgGJlgz/RqPq4O9ng8zpZ4AAAAAIBjZII906v5uDbYk8mkkslk3mfaAQAAAACwKxljCu5V1wb7li1bJEmTJk1SOp2u8DQAAAAAAIwG+9hezce1wb5161ZJUktLC1viAQAAAACOYIyx9Wo+rg32aDQqSaqrqyPYAQAAAACOYIyx9Wo+rg32WCwmiWAHAAAAADhH5hns0m4c7L29vZKkhoYGgh0AAAAA4AjGGFuv5uPaYO/r65NEsAMAAAAAnMMYY+vVfFwb7IODg5KkmpqaCk8CAAAAAMA2hfbqbhHsrLADAAAAAJzAGEOwJxIJVVdXy+fzVXoUAAAAAACyCu1V1wZ7PB5XKBSSJFbYAQAAAACOYIyx9Wo+rg32gYEBBQKBSo8BAAAAAECWMabgXnVtsI+MjGS3F7DCDgAAAABwirG9mo9rgz2ZTKq6urrSYwAAAAAAYFNor7o22IeGhuT3+ys9BgAAAAAAWcaYgnvVtcHOlngAAAAAgBPt9lviWWEHAAAAADjRbr/CPjw8rKqqqkqPAQAAAACATaG96tpgT6fT8nhc+/EAAAAAAG9DxpiCe9XVRUuwAwAAAACcaLcP9nQ6XekRAAAAAADYTiG96tpg93q9SqVSlR4DAAAAAIAsy7IK7lXXBrvP59PIyEilxwAAAAAAwKbQXnVtsFdVVWl4eLjSYwAAAAAAYFNor7o22Mf+YmGMqfA0AAAAAACMYoWdFXYAAAAAgMNYlsUKeyAQ0MDAQKXHAAAAAADAptBedW2wh0IhJRIJSWyJBwAAAAA4g2VZtl7Nx7XBXl1drWQyWekxAAAAAACwKbRXXRvswWBQ/f39siyLFXYAAAAAgCNYlmXr1XxcHeypVErDw8MEOwAAAADAETLBnunVfFwb7DU1NZKkwcHBCk8CAAAAAMA2hfaqa4M9FApJkvr7+1lhBwAAAAA4Quamc9Jor+bj2mCvq6uTJEUiEYIdAAAAAOAYY3s1H9cG+6RJkyRJHR0dBDsAAAAAwBHS6bStV/N5WwR7Op0u+m+ampokSb29vQQ7AAAAAMARLMuy9Wo+jg/2G264Qfvtt1/Rf9fY2ChJ6u7uJtgBAAAAAI5gWZatV/Px7YqBSvW1r31N1157re69996i/3aPPfaQJG3atEkej+N/lwAAAAAA7AYsy7L1aj6ODfZf/epXWrp0qf77v/9bZ511VtF/X11drdbWVm3atIkVdgAAAACAI6TTaVuv5uPIYLcsS8uXL9fnPve5cWN93bp1Wr9+vTwej6qrq9XQ0KDa2lq9853vVFtbG1viAQAAAACOYVmWJGV7NR9HBvsf/vAHrVmzRvvuu6/23Xdf1dbW6vTTT9f/+3//T36/33bsqlWrtGzZMttrXq9XyWRSra2t2rJlC1viAQAAAACOYFmWLMvK9mo+jizZe+65R5IUjUZ11lln6cADD9QXv/hFXXPNNdsdm0qldvhaZ2en2tvbs7fJZ5UdAAAAAOAElmXZejUXR66wv/nmm7rgggv0ve99L/vatGnTdNddd+mmm26yxffMmTO1aNEipVIpJZNJ9fb2Kh6Pq6+vT42Njerr65M0GuyZrQcAAAAAAFRK5k7xmV7NxZHB7vP5ttvGfsghh6irq0upVEo+37axFy9erMWLF+/wPPX19YpEIrIsixV2AAAAAIAjWJZVUK86ckv8jBkz9Oc//9m2Iv7Xv/5V73jHO2yxPp66ujqNjIxoYGCAYAcAAAAAOIJlWbZezcWRwX7uuefqhRde0DXXXKNXX31V9913n7797W/rggsuKOo8tbW1kqRYLEawAwAAAAAcY2yv5uLILfFHHXWUVq5cqWuvvVY33nijWlpadNVVV+nSSy8t6jx1dXWSRm9el/kyJsLpD56uR994dMLOj9GdFg3fbKjwHO52/D7Ha9Wpqyo9BgAAAOB6mRV2abRXJ02atMPjHBnsknTOOefo7LPPVl9fn+rr64vaCp8RDoclSfF4fEJX2In1CbaM3RG7Av87BgAAAHYNy7JsvZqLY4NdGn2eenNzc8l/39jYKEnq6enZJVvi+y7Nf4c/wKnYvQAAAADsOpm7xEujvZqLI69hL5fW1lZJ0tatW7mGHQAAAADgCJZl2Xo1F1cH+9hrAgh2AAAAAIAT/Ps17Lm4Otgz1wRwl3gAAAAAgFOMvYY9313iXR3swWBQktTf31/hSQAAAAAA2KaQXnV1sPv9fhljNDg4yAo7AAAAAMARLMuy9Wourg52Y4zC4fCEP9YNAAAAAIBijO3VXFwd7JLU0NCgvr4+gh0AAAAA4AiWZUna1qu5uD7YA4EA17ADAAAAABwjE+zj9arrg726ulrJZJIVdgAAAACAo2R6NZfdJtgBAAAAAHCCzAr7bh/sPp9PIyMjlR4DAAAAAACb8XrV9cHu9XqVSqUqPQYAAAAAADbj9epuEezpdJpr2AEAAAAAjpLp1VxcH+wAAAAAALwduT7YU6mUPB5P9qJ+AAAAAACcINOrubg+2EdGRuTz+So9BgAAAAAANuP1quuDfXh4WFVVVZUeAwAAAAAAm/F61fXBPjQ0JL/fX+kxAAAAAACQpOxN0cfrVdcHezKZVE1NDdewAwAAAAAcJdOrubg+2IeGhtgSDwAAAABwnPF61fXBnrkmgBV2AAAAAIATZLbE7/bXsCcSCYVCoUqPAQAAAACAzXi96upgT6fTikajamhoYIUdAAAAAOAIxhhbr+bi6mDv6+uTZVlqamoi2AEAAAAAjmCMsfVqLq4PdkmssAMAAAAAHCMT7JJ23xX23t5eSVJjYyPBDgAAAABwBGOMrVdzcXWwRyIRSVJ9fT3BDgAAAABwBGOMrVdzcXWwJxIJSVIoFCLYAQAAAACOYIyx9Wourg72eDwuSQqHwxWeBAAAAACAbQrpVVcHe3d3tySpubmZFXYAAAAAgCN4PB5br+Y8blcNVAldXV2SpJaWFqXT6QpPAwAAAADA6Jb4sb2ai6uDvb+/X8FgUB6Pqz8mAAAAAOBtxBhTUK+6umR7enqyz7RjSzwAAAAAwAmMMbZezcXVwb5161a1trZKElviAQAAAACOYIyx9Wourg72zs5OtbS0sLoOAAAAAHAMY0y2V/NxdbB3dXWpra2NYAcAAAAAOIIxRtK2Xs3H1cEei8VUW1tLsAMAAAAAHCET7JlezcfVwR6Px8f9AgAAAAAA2FUywV5Ir7o22IeHh9Xf36+GhgZW2AEAAAAAjmCMsfVqPq4N9t7eXklSY2MjwQ4AAAAAcARjjK1X83FtsPf19UkSK+wAAAAAAMcwxth6NR/XBns0GpUk1dXVEewAAAAAAMcY26v5uDbYWWEHAAAAADgNK+wi2AEAAAAAzkOwa9sWA57DDgAAAABwCmOMrVfzcW2wRyIRSVJ9fT3BDgAAAABwBGOMrVfzcW2wc9M5AAAAAIDTjF1h321vOheLxRQIBOT1eis9CgAAAAAAkkaDvdBedXWwZ36tYIUdAAAAAOAUY3s1H1cHe+YCfoIdAAAAAOAEmRX28W44J7k42AcGBlRTU1PpMQAAAAAAyDLGFNyrrg32oaEh+f1+SaywAwAAAACcY2yv5rNbBDsAAAAAAE6x2wf7yMiIfD5fpccAAAAAACDLGFNwr7o22C3Lksfj2o8HAAAAAHibKrRXKVoAAAAAABzI1cHOzeYAAAAAAE5USK+6Nti9Xq9SqVSlxwAAAAAAIMuyrIJ71bXB7vf7NTQ0VOkxAAAAAACwKbRXXRvsVVVVGh4ervQYAAAAAADYFNqrrg326upqJZNJSaO3zQcAAAAAoNIsy7L1aj6uDfaamhoNDg5WegwAAAAAAGwK7VXXBntVVZVGRkYkscIOAAAAAP+fvTuPc6o6+D/+vZktyUyS2YcBiruICLhTrdhWW1pt1dqX0trnZa3tY32srbW/ahef9qmIuC/YWmtb3OpCpSqoVSuiQmvFBZeKFlxwQRBmn8wks2W5vz+m55oAyQSFmcudz/v1mhcwZDIn556ce77nnHsD98jMq/l4NrD7/X719vaOdDEAAAAAAHDYtl1wXvVsYK+oqFAsFpPECjsAAAAAwB1s287Kq/l4NrCHQiH19/crkUgQ2AEAAAAArpGZV/PxdGCXVNCsBQAAAAAAw8G27YLzqmcDe0VFhaTBCmCFHQAAAADgBmZLvDSKA7uZseju7iawAwAAAABcIXOFvbu7O+9jPR/Yu7q6RrgkAAAAAAB8qNC86tnAHgwGJUk9PT2ssAMAAAAAXMG27ay8mo9nA3t5ebkkKR6PE9gBAAAAAK5g23ZWXs1nVAR2AAAAAADcgsD+nwpgSzwAAAAAwC0yV9hH7Zb46upqSVJbWxuBHQAAAADgCrZtZ+XVfDwb2EOhkCoqKvTBBx/I5/PsywQAAIz8HR4AACAASURBVAAA7ETMx7qZvJqPp5NsTU2N2tvbWWEHAAAAALhCOp2W9GFezcfTgb26ulqtra0EdgAAAACAK9i2LenDvJqPpwP7mDFjtGnTJgI7AAAAAMA1bNt28mo+ng/s5poArmMHAAAAALiBCeyj+hr2xsZGNTc3K51Os8oOAAAAAHCFdDqdlVdz8XRgHzNmjNLptJqbm1lhBwAAAAC4gllhN3k1F0+n2MbGRklSc3MzK+wAAAAAAFcwK+ySRm9gr6mpkTT4YfQEdgAAAACAG9i2nZVXc/F0YI9EIpKkrq4uAjsAAAAAwBVs287Kq7l4OrAHg0FJUjweJ7ADAAAAAFwjM6/m4unAXl5eLknq6ekZ4ZIAAAAAADDItu2C8uqoCOyssAMAAAAA3CIzsI/aFfZCthgAAAAAADDcRv2W+LKyMlmWpd7eXlbYAQAAAACuYNt2Vl7NxdOB3bIsBQIBrmEHAAAAALhKIXnV04FdGryOnWvYAQAAAABuYdu2pA/zai6uD+zJZFInnXSSxo4dq0Qisc0/X1FRoVgsRmAHAAAAALiCCewmr+ZSPFwF+qguvfRS3XvvvZKkRCKhkpKSbfr5YDCY95oAAAAAAABGwlB51dUr7C+++KIuuuginX766R/5OQKBADedAwAAAAC4hllhN3k1F9eusPf19enUU0/Vscceq5NOOkm33HLLVh+3evVqrVmzRj6fT2VlZaqsrFQwGNQnPvEJVVVVqbS0VP39/cNcegAAAAAA8hsqr7o2sP/yl7/U+vXr9eijj2rVqlU5H7dw4UJdeOGFW3zf5/MplUrJ5/MpnU6zwg4AAAAAcBWTV3P+/zCWpWBPPvmkrr76al133XUaP3583semUqmtft+8aJ/P52w3AAAAAADALYbKq65bYW9vb9fJJ58sy7J055136p577lF7e7sk6YwzztA555yj6dOnO4+fNGmSTjjhBKVSKfX396ujo0OxWEzr16+XNHhtgM/nynkJAAAAAMAoNlRedV1gLy4u1ve//311dHRoYGBA/f39zgfJd3d3q7OzM+vxp5xyik455ZScz5dOp1VcXMwqOwAAAADAVUxezcV1gT0cDm9xTfojjzyiJ598Un/+858VDAa36fls2+b6dQAAAACA6wyVV3eKveL5LsIfSiqVUlFR0XYsDQAAAAAAH99QeXWnCOz77ruvPv3pT6usrGybf7a/v/8j/RwAAAAAADvSUHnVdVvit2a33XbTsmXLPtLP9vX1ye/3cw07AAAAAMAVzDZ4k1dz2SlW2D+ORCKhkpKSkS4GAAAAAABZhsqrng/sAwMDKi0tHeliAAAAAACQZai86vnAbmYs2BIPAAAAAHADsyV+1K+w9/b2KhAIjHQxAAAAAADIMlRe9XRgT6fT6urqUmVlJSvsAAAAAABXsCwrK6/m4unAHovFZNu2IpEIgR0AAAAA4AqWZWXl1Vw8Hdg7OzslicAOAAAAAHANy7Ky8moung7sra2tkqSamhoCOwAAAADAFSzLysqruXg6sHd0dEgisAMAAAAA3MOyrKy8mounA7uZsaiuriawAwAAAABcIXOFvbq6OufjPB3YzTUBVVVVBHYAAAAAgCtkXsNeVVWV83GeDuw9PT2SpPLycgI7AAAAAMAVLMvKyqu5eDqwNzU1qaSkROFwmMAOAAAAAHAFy7Ky8moung/s9fX18vl8BHYAAAAAgCv4fL6svJrzccNYpmG3ceNGjRkzRpKUTqdHuDQAAAAAAAyusGfm1Vw8Hdibm5vV2NjI6joAAAAAwDUsy3Lyaj6eDuwtLS2qra0lsAMAAAAAXMFsgTd5NZ/i4SjQSLBtW83Nzaqvr2c7PHZKsxbP0pJ3lwzr76ycVzksv2fmrjO18CsLh+V3AQAAAG5iWVZWXs3Hsyvs0WhUAwMDqq+vZ4UdO6XhDuvDycuvDQAAAMjHsqysvJqPZ1fYm5ubJUkNDQ0EduzUOs/tHOkibFfDtYoPAAAAuJHP58vKq3kfOxwFGgldXV2SpEgkQmAHAAAAALhGZl7Nx7OBPRqNSiKwAwAAAADcw2yJl0ZxYDczFqFQiMAOAAAAAHAFy7Ky8mo+ng/s4XCYwA4AAAAAcIXMwB4Oh/M+1rOB3WwxqKysJLADAAAAAFwhc0t8ZWX+GzJ7PrAPNWMBAAAAAMBwyQzso3aFPRaLqbS0VCUlJaywAwAAAABcIzOv5uPZwJ5IJJwXT2AHAAAAALiBZVlZeTUfzwb2/v5++f3+kS4GAAAAAABZCs2rng3s8XhcwWBQEivsAAAAAAB3sCwrK6/m49nA3tfXxwo7AAAAAMB1Cs2rng7sgUBAEivsAAAAAAB3sCwrK6/m49nA3tPTU1AFAAAAAAAwnArNq54N7IXedQ8AAAAAgOHCXeL/w+fz9MsDAAAAAOykCsmrnk20XLcOAAAAAHCjQvOqZwM7AAAAAAA7M88GdsuylE6nR7oYAAAAAABkKTSvejaw+3w+AjsAAAAAwFVs2y44rxYPQ3lGBIEdAABg5xWcNUslS5aMdDHwH5HKypEuwqiXmDlTPQsXjnQxsJ0Umlc9u8JeXFysZDI50sUAAADAR0BYB7LxnvCWQvOqZ1fYCewAAAA7v2hn50gXARhx7HDwnkLz6qhYYbcsa4RLAwAAAADAoFEf2EtKSpRIJEa6GAAAAAAAOGzbLjivejaw+/1+9fX1jXQxAAAAAADIUmhe9WxgLysrU39/vyS2xAMAAAAA3MG27ay8mo9nA3tpaakGBgZGuhgAAAAAAGQpNK96NrAHg0H19vZKYoUdAAAAAOAOtm1n5dV8PB/YC/kwegAAAAAAhkuhedXTgV2S+vr6WGEHAAAAALiCWWGXNOSN5zwb2EOhkCSpu7ubwA4AAAAAcI3MvJqPZwN7RUWFJCkWi41wSQAAAAAAGGTbdsF51bOB3e/3S5J6e3tZYQcAAAAAuIJt21l5NR/PBvZAICBp6AoAAAAAAGA4FZpXR0VgZ4UdAAAAAOAGtm0T2MvLyyVJ8XicwA4AAAAAcI3MvJqPZwN7OByWNPRd9wAAAAAAGC62bRecV4uHo0AjgRV2ANg+grNmqWTJkpEuxk4vUlk50kXYaSVmzlTPwoUjXQwAALYL27ZZYTe3ySewA8DHQ1jHSKMNAgC8JPNj3YYK7J5dYa+srJTP51NzczOBHQC2g2hn50gXAaMQOxMAAF5j23ZWXs3HsyvsxcXFqq2tJbADAAAAAFzDtu2svJqPZwO7NLgtvru7m8AOAAAAAHAF27YlfZhX8/F0YC8vL+cadgAAAACA65i8mo/nA3tPT89IFwMAAAAAAEkfrrAXklc9HdhDoRBb4gEAAAAArmLbtpNX8/F0YI9EIopGo5JEaAcAAAAAuIJt21l5NRdPB/ZwOExgBwAAAAC4im3bWXk1F08H9qqqKnX+53ODCewAAAAAALfIzKu5eDqwV1RUqKenR+l0eqSLAgAAAACApMEV9kLyqqcDu9/vlyT19fWxwg4AAAAAcAXbtrPyai6eDuwVFRWSxGexAwAAAABcJTOv5uLpwF5TUyNJamlpIbADAAAAAFzBtu2svJrLqAjsHR0dBHYAAAAAgCuk0+msvJqLpwO72WIQi8VGuCQAAAAAAHyokLzq6cAeCoUkSd3d3aywAwAAAABcwbbtrLyai6cDe3V1tSSptbWVwA4AAAAAcAXbtrPyai6eDux1dXWSBi/i9/k8/VIBAAAAADsJ27az8mounk6xpaWlqqioUHt7OyvsAAAAAABXSKfTWXk1F08HdmnwQn5uOgcAAAAAcJuh8qrnA3tpaakGBgZYYQcAAAAAuIJt25I+zKu5eD6w+/1+9fX1jXQxAAAAAADIMlReHTWBnRV2AAAAAIAbmBX2UR/Yh9piAAAAAADASBj1W+KLi4uVTCZHuhgAAAAAAGQZKq96PrAXFRUplUqNdDEAAAAAAMgyVF4dFYE9nU5zDTsAAAAAwFVMXs3F84EdAAAAAICdkecDO6vrAAAAAAA3Giqvej6wp1IpFRUVObfNBwAAAADADUxezWXUBHYAAAAAANxk1Af2dDotn8/zLxMAAAAAsJMZKq96PskmEgmVlJSMdDEAAAAAAMgyVF4dNYGda9gBAAAAAG5gbjS30wb2ZDKpG2+8UV/60pf0la98RXfcccdHCt3JZJIVdgAAAACA6wyVV10Z2Ds7O3X00Ufrxz/+sWpra5VOp3Xqqadq7ty52/xcvb298vv9O6CUAAAAAAB8dEPl1eJhLEvBVqxYoUQioZdeekl77723JOm0007T7bffrl/84hfb9Fy9vb0KBAJsiQcAAAAAuILZEm/yai6uDOzHHHOMjjnmGOfftm3rvffeU0NDwxaPXb16tdasWSOfz6eysjJVVlYqGAxq1113VTgc1sDAgEpLS4ez+AAAAAAADGmovOrKwJ4pnU5rzpw5Wr58ue66664t/n/hwoW68MILt/j+jBkztHz5csXjcVVUVLDCDgAAAABwBcuyZNu2k1dzcXVg37hxo771rW/p8ccf15VXXqlTTjlli8ekUqmt/mxFRYV6e3uVSqUUCoUI7AAAAAAAV7AsKyuv5uLawP7000/rK1/5impra7VixQodcsghW33cpEmTdMIJJyiVSqm/v18dHR2KxWJqaGhQV1eXJCkcDg9n0QEAAAAAyMmyrILyqisDe29vr44//ngdeuihuvfee/NehH/KKadsdeVdktasWSNJqqysZIUdAAAAAOAKlmWps7NT0mBezcWVH+u2ZMkStbW16Q9/+EPesD6UaDQqSYpEIgR2AAAAAIArWJaVlVdzceUK+/r161VSUqJf/OIXam1tVXd3t2zb1jHHHKOf//znBT+P2WJAYAcAAAAAuElmXs3FlYH9yCOP1NFHH63Ozk7V19dr9913Vzqd1uuvv75NzxOPxyVJ5eXlBHYAAAAAgCtYlpWVV3NxZWCfMmWKHnnkkY/9PG1tbZKkqqoqAjsAAAAAwBUsy8rKq7m48hr27aW5uVmS1NDQQGAHAAAAALiCZVlZeTUXTwf2zs5OlZWVKRAIENgBAAAAAK5g7hJv8moung7sXV1dzmfaEdgBAAAAAG5gPoc932ewSx4P7K2traqurpZEYAcAAAAAuINlWVl5NRdPB/b29nbV1NRIIrADAAAAANzBsqysvJqLpwN7PB7nI90AAAAAAK5iPtYt30e6SR4P7LFYTBUVFQR2AAAAAIArWJYl6cO8mo+nA3tbW5uqq6sJ7AAAAAAAVzCB3eTVfDwd2Ds7OwnsAAAAAADXMIHd5NV8PBvYE4mE+vr6FAqFRrooAAAAAABIGgzsheZVzwb2aDQqSYpEIqywAwAAAABcIzOv5uPZwB6PxyWJu8QDAAAAAFzD3CFe0ui9S3xfX58kye/3j3BJAAAAAAAYZFlWwXl1VAR2VtgBAAAAAG4x6gM717ADAAAAANzGsiyuYe/s7JQkVVZWEtgBAAAAAK5gWVZWXs3Hs4G90Iv4AQAAAAAYTqP+pnNmiwEr7AAAAAAAt8jcEj9qV9i7u7slSaFQiMAOAAAAAHAFy7Ky8mo+ng3sXV1d8vl8CgaDBHYAAAAAgCtYlpWVV/PxbGBvb29XZWWlfD4fgR0AAAAA4AqWZWXl1Xw8G9h7enqGnK0AAAAAAGA4WZZVcF71bGBPJBIqKSmRJFbYAQAAAACukZlX8xkVgR0AAAAAALcoNK8WD0NZRkQymVRxsWdfHgAAAIACBGfNUsmSJSNdjO0iMsRHgO0MEjNnqmfhwpEuxogrNK+OihV2tsQDAAAAo5NXwrpXcDwGr2Ef9SvsAwMDKi0tHeliAAAAAHCBaGfnSBdh1PPCDoHtpdC86tkVdrbEAwAAAADcaNRviU+lUioqKhrpYgAAAAAA4LAsq+C86tnAbtv2kB9CDwAAAADAcCs0r3o60VqWNdJFAAAAAABgC4XkVU8Hdu4ODwAAAABwo0LyKoEdAAAAAIBhNqoDe1FRkVKp1EgXAwAAAAAAh23bBedVzwb24uJiAjsAAAAAwHUKzaueDeylpaXq7+8f6WIAAAAAAJCl0Lzq2cAeCATU29sribvFAwAAAADcwbbtrLyaT/EwlGdElJeXKx6PSxoM7NyADijcrMWztOTdJTv0d1TOq9whzztz15la+JWFO+S5AQwKzpqlkiU7to/YXKRyx/QZm0vMnKmehfQhAIAdKzOv5uPZFfZgMFjQjAWALe3osL4j7cxlB3YWwx3Wh5OXXxsAwB1s2y44r3p2hb2kpEQDAwMjXQxgp9Z5budIF2Gb7KhVewBbF+3cufqIoQzXKj4AAIXmVc+usJeWljoVwDXsAAAAAAA3sG07K6/m4/nAzrXrAAAAAAA3KTSvenZLfFlZmWzbVjKZZIUdQEFG4kZaOxO2C28dNykDAADbKjOvlpSU5HycZ1fYQ6GQJKmrq2uESwJgZ0FYx0dBuwEAANvCtu2C86pnV9hramokSR0dHRo7duwIlwbAzsRrN9LCjsOuAwAAsK1s287Kq+bvW+PZFfaqqipJUnt7O1viAQAAAACuYNt2Vl7Nx7OBPRKJSJKi0SiBHQAAAADgCrZtZ+XVfDwb2MvLyyVJ8XicwA4AAAAAcAXbtrPyaj6eDeyssAMAAAAA3IYVdn1407nW1lYCOwAAAADAFTJvOtfa2pr3sZ4N7JFIRH6/Xxs3bpTP59mXCQAAAADYiZgVdpNX8/FskrUsS42Njdq0aRMr7AAAAAAAV0in01l5NR/PBnZp8KPdOjs7CewAAAAAAFewbVvSh3k1H08H9nA4zE3nAAAAAACuYtu2k1fzKR6m8oyIcDis9957b6SLAQAAsEMEZ81SyZIlI12MHSpSWTnSRdghEjNnqmfhwpEuBoARVEhe9fQKe01NjZqbmyWJG88BAADP8XpY9zKOHTC6mTvFm7yai6dX2MeMGaPm5mbZts22eAAA4FnRIa6BhLt4ddcAgMKl0+mC8qqnl50bGhqUSqXU1tbGCjsAAAAAwBVs287Kq7l4OsU2NDRIklpaWlhhBwAAAAC4QjqdzsqruXg6sFdUVEiSYrHYCJcEAAAAAIAPFZJXPR3Yw+GwJKmrq4sVdgAAAACAK5iPdZMG82ouBHYAAAAAAIZRoYHd03eJDwaDkqR4PE5gBwAAADDqBGfNct3HCLrlkxISM2eqZ+HCEfndtm1n5dVcPB3YzYxFd3f3CJcEAACMhI8yUC1kIDmSgzwA2BZuC+tuMtJ1U0he9XRgD4VCkgYrgBV2AABGnx01GBvpQR4AbKtoZ+dIF8FVRnqV37btrLyai6cDeyAQkCT19PQQ2AEAGMW250B1pAd5AICdX+aW+J6enpyP8/RN53w+n/x+f95rAgAAAAAAGG6F5FVPB3Zp8MZzvb29rLADAAAAAFzBtm1JH+bVXDy9JV4a/DD6WCy2RWCftXiWlry7fa8/q5z38bfIzdx1phZ+hZvYAAAAAIBXmcBu8moung/s5eXlW62A7R3Wtxe3lgs7Xq5JJDMRxGQOAAAA4C258qrh+cBeUlKiRCKRc0t857nuuVvi9lihx85rqMkaJnMAAAAAbzAr7Cav5uL5wF5aWqqBgYGRLgZQsK1NIjGZAwAAAHjPUHnV84F9qBkLAAAAAMMnOGuWSpYM/87B4fpIxsTMmepZyGWMKMxQedXzd4kvKipSKpXiLvEAAACAC4xEWB9OXn992L5MXs3F8yvsPp/PuT4AAAAAgDtEO91zL6ntZbhW8eEdQ+VVzwf2dDqt4mLPv0xg1Mn30YyZ1/xzd30AAAC41VB51fNb4lOplIqKilhlBzym0Lvmc3d9AAAAuJXJq7l4fuk5mUyywo5RJ9/q87b4OHenH66V7Xwfzcjd9QEAAOBmQ+VVzyfZ/v5+lZWVjXQxgGHlhlVlN5QBAIDhtK13Px/qemfuNg5431B51fOBva+vT36/ny3xGJXyrT7vSKxsAwBGo+19d3DuNg54l/kUM5NXc/F8YO/p6VEwGBzpYgAAXGR7fwbw9rgr8EddSRupzzOWhv9uyKw2YmexPe5+zt3GAW8zgX2ovDpqAjsr7NgZbX4teuW8Su56DmwHbly1+qhlcuNr2VGG87UWMhGSK1AxsQAA7vBx+nJpePrzUR/YBwYGVFpaOtLFAD6SrV0HzrXhwPbjls8A3h4raW55LTvKcK82fpzJgdE0iQIAbvZx++Ph6M+HyqueD+zmIn5W2LEzM9eic204AAyvbZ0IYRszALjPR5nU3tH9udkSP9RN5zz9OezJZFKJRIJr2AEAAAAArlJIXvV0YI/H45Kk8vJyVtgBAAAAAK5gWVZWXs3F01vi29vbJUlVVVWuC+yb30ws0+bbnrnJGAAAAAB4h2VZWXk1F0+vsJsKqK2tdV1g35Ybh3GTMQAAAADwjszAXltbm/Nxnl5h7+rqkiSFw2HXBXbD3EwsF24yBgDAttnax/iYmwfxkWsAADewLCsrr+bi6RX2aDQqSYpEIq4N7AC2zazFs7ImsirnVWrW4lnb/BgA3pXvY3j4yDUAgBtYlpWVV3Px9Ap7R0eHJHdeww7goynks+n5/HoA0pYf48NHrgEA3MKyrKy8mounV9hjsZgkqaKigsAOeEznuZ1DXlJSyGMAAACA4WZZVlZezcXTgb23t1eSFAgERrgkAAAAAAB8qJC86trA3tvbq0suuUSHHnqojj76aD300EPbvEoejUZVVFSkYDDICjsAAAAAwBXMNewmr+biysAei8X0yU9+UldccYVmzJihMWPG6Pjjj9dVV121Tc/T3d2tUCgky7II7AAAAAAAV7AsKyuv5uLKm85dddVVev/99/XKK69owoQJkqSDDjpIs2fP1plnnpn3tveZotGoKv9zg5mdLbBbliWfz6cTJp6guvI6lZaWyrKsLb4yH7+1Pzf/u5FZH5vXjW3bW3xlfj/X3zO/t7PKrL9c9b35Yzb/3tb+3NrvyGTbtjp+2qG+ZJ9CwZBTjyu+s0I9iR5nl8i8L85Td3+3SktLJUnpdNqp93Q6vd3qYUcz7Xtrdbu1Y5D573XnrpO/2K9wcLAfSP4yqSJfUdbzD/xiQAOpAQVLBuvt9e+/ru7+bpWXl+dt3/rc56RoVD6fb6dsz6aONq9bn8/n/H/m4zb/3ubPZWytj8j8e1Ydaut9SK5639nl6iO2dhzy9d2ybSmdVvg//x6qTjO/p298Q1q/fqdtt9KHfUJmnZnvO3X04INSdbVznV/etvufOghvXhdNTVJ/f9a1gjn7g7PPllpbVVxc7Km2m6vvzfw/871tPccV2jdIH56/Mr+/M57PtlBRIYXDWXWZr87z9setrVJJicKhkPP0+eo889+F9MtDfX9nlq9N5+u3M38233lRynNuXLtWisWcMYf5v83bd75x9s5mqHHc1vr2rT0+1/MahYxHtqjPuXOlri6VlpZmjZvdUN/mc9grh7ghqisD+1/+8hedeeaZTliXpNNPP10//vGPtWTJEp100knO91evXq01a9bI5/OprKxMlZWVCgaDmjJlim677TalUilJH954znzdc/I9iifi8vv9BXVWhR7UfA028+9Xfv5KhcpCCgaDWQ058zls29atX7xVbW1tWr16teLxuKLRqDo6OtTW1qbu7m719/drYGBAAwMDSiQS6unpUTweV29vrwYGBpRMJp06yCxjUVGRiouLVVpaqpKSEhUXF6ukpEQlJSUKBoOqrq5WOBxWKBRSJBJReXm5KisrFYlE5Pf75ff7VV5erkgkopKSkqznL2SQXmidFyrXoCOz7vMNRDYfREtSMplUZ2enYrGY4vG4urq6nLrt7e1VX1+fYrGYuru71dPT43wNDAyov79ffX19SiQSSiaTzlc6nc4apJjfaeo9s27LyspUUlKiiooKvRp5VZFIRBPCExQOh/Xaa68pHA6rvr5ekUgkq83Ytq1nvvOMmuJNCgQCBR2LbanzoU5ulmXp/x32/xQpG2wrpt4376ylwV0w7e3tisfjzldPT4+6u7vV3d3t1K/5u6nTvr4+9ff3K5FIaGBgIKuNW5bltO3S0lIFAgGFQiGFQiH9NfRXhcNhRSIRhcNhVVZWqrKy0vl7ycMPq6SkRKGM17u1etvayfbj1GshdZpvgLH59/v7+/XBBx+oo6ND7e3tampqctpvX1+f01b7+/udNm3aqvkzs059Pp9KSkpUWlrq1G1ZWZmKi4sVCARUUVGh8vJyp/1WVlaqqqpKoVBI4XBYNTU1GjNmjMrKyrJe97b2E5nf2/zvhXLa3557Sn6/ioqKhqzrXH1FZlu2bVsDAwNOG25padHGjRvV0tKi1tZWtbS0KBqNqqurS7FYzOmfk8mk8xoy69n8WVFR4fTFpr0Gg0FVVFSourp68HtXXaWGhgaFfL6s8mQOTAqtz63V60fpF3L1v1s735m/p9Nptba2qrm5WdFoVD09Pert7VUsFlNPT4+i0aja29sVu/tup54zz3+pVMr5Mnw+n4qLi1VUVKSSkhL5/X6VlZU5/atpv5l16/f7FQ6H1XDWWaqtrVXY55Pf79/ieOesyxtvlDo6nInVXH3DtgajrdWZz+eTzjhD+s85I9cx2Px42Latvr6+rL41Ho+rpaVlsI7/c94z/XE8Hnfat+kv+vv7s9qvObbFxcUKBoMKBAJO/2vOaaavCIVCqq+vV3V1tTOWqKysVFlZ2Rbns6HaciF1vC31m6uu89Vp1vmtu1uSnPNIKpVSa2ur2tra1NHRoc7OTmccYdq4Oc+ZsdvAwID6+vqctm36CzOGyGTGc0VFRSoqKpLf71cwGFRpaanKy8udY2HauembzfguFAqpoqJiixW+ocZymx+bzet7yLq3rMGvrRyDXHWe65jkOj6mHAMDA+rp6XHOiZ2dners7HTqPR6PZ43nzBjDHINUKrVFG5QG33+mNu65tAAAIABJREFU3jPPjcFgUOXl5aqoqFAkEsnqsxsbG1VXV+c8pry8PGcoHWrcbP4v1wRA1nEoLnYmMrd+OPIH6aw+R8o7tjNl6u3tVWtrq3MO/OCDDxSNRtXf36/+/n51d3erq6vLGY9k9uXpdDqr3k05TJ0XFxdn5ZjM82VlZaUqKiqcf1cccsjg99etU11dnSKRiIqKPlzo2drYzvlz3jwpGlVpael268sz69yyLJWVlenAAw/M/1h7W599B2tvb1dNTY0eeeQRffGLX8z6v5qaGv385z/Xeeed53xv9uzZuvDCC7d4nnQ6rXPPPVevvvqqAoGAKisrVV1d7XRQpvOqqqpyBu/V1dXOyaO4ePvMZZgGm9kou7q6nEFbU1OTmpqatGnTJrW1tTn/19HRoY0bN6qvry/v81uW5QQTE07Ky8sVCARUVlbmdCSmUZgGmUqllEwmnTeHGaib0N/Z2VnwDLcZoNfU1Dgn4+rq6sGBzn/eOPX19aqpqVF5ebkTmExQCgQCW7zRP66BgQFn4GEGJG1tbWpra3MGJ7FYTB0dHerq6lI0GnU6bdNxt7a2btMsvxmcmA7b7/c7kyHmy6wg+TIG1ul0WolEImsiwJwwzIk6n9LSUtXX16uurk719fVqbGxUQ0ODGhoaFAwGVVlZqdraWlVVVam2ttbpyDLL8HHYtq3+/n5nssgM8qLRqNra2rRx40Zt2rTJ+XPTpk1qb293jkUhzEAvEAiouLjYGXSbTjozdKXTaadtm0GPef+ZG3vkY06soVDIqdOamhpVV1crGAyqrq5OtbW1TluPRCKqqqpyBvzbo14zBxixWExdXV1qaWlRR0eH82/zmswkngmHzc3Namlpyfv85lqpsrIyp7/InLgzgz+fz+cMEDMHjWZSKplMqre3V/F4XP39/UO+LnMcMweN1dXVamhocPrgmpqarD47c4AfDoe3mCD8uGzbzpqAa2lpcdpmb2+v2tvb1dHR4UxyRKNRRaNRxeNxtbW1qb29Xb29vc4gJJeSkhJVVlY6g2Mz+WH6BWlwYG/q2fxpjnc8Hs/7OoqLi1VdXa1IJKLa2lrV1dVp/PjxzsDQfIXDYadvNsc/FAopEAg4E2wfVyqVcga/pvwdHR3O+a65uVmtra2KRqPq7OxUR0eH04aH6u+KioqcQW55eXnWBIc535m2a9u2c64zdWsG4WbCzxz3ofh8PoVCIdXW1jrnurq6Oo0ZM0YVFRXORKvpO0yfYOrctOXtea5LpVJO+DDt1QyCTUA0/YP5am5u1vvvv6+2trZtOsdnBkDTX5hJu8ydHab/3TyMxuPxgn5fWVmZGhoaNHbsWNXX1zvjiXHjxjkT1KaezUSW6SPMAsj2ZPqH3t5ep612d3ers7PTGa+1trZq/fr1am5uzmrz5jGF9I3mtZsxRGlpqfx+v/N30y9n7kIx5cts46lUSn19fU7wN+fkzRdutsbn8zl1WlVVpbFjx6qurs7pi825LhKJqL6+3hlX1NTUZIWf7cm2baffNfVt2lPmool5nZnnw9bWVnV3d6u5uVk9PT1D/q6ioiKnLZlJPXMMTJ+SGVBN3ZuvzHOjOV/EYjElk8khf7eZQDFjZtMnZ45HqqqqnPOmmQgw+cWMScxu3O3BjENM+zdtur29XdFoVM3NzVq/fr3Tp2/cuNFZgDFj2KEEAgEnB5SVlWX15SaYmzaf2ceY+s7MMZnny0LqPBKJaOzYsaqpqVEkElFNTY3q6+udiRTTd5u6r6+vd46B3+/fHlW8TVy3wm4GJVv78PhgMLhFA9haJ5Q5E97X16fOzk699tprzmxaIR2XORmVlpY6Ax0TEjYfzG4+yDKBr9AGW1RUpPr6etXX1ysUCqmxsVGTJk3SmDFj1NjYqNraWuckFYlEVF1draqqKoXDYRUXF2/3E5Q0GHrMzHpnZ6fi8bg6OzsVjUbV19fnzMaboNve3p41K79q1Sq1t7cXdLIyAzAz4WBCmVnxz5zBzBzYZnaQpkzmDVvIAMyEWdPphUIhNTQ0ZM3+mwGD+Z4ZrJgv05Fur4Hu1iQSiayTf3d3txMaTEdpBsAbN27Uq6++qubmZiUSiZzPaVmW00lmDnhNGzcB2JyYTGgznbc5EZnVlqHm/Xw+n+rr6zV27Fg1NjZqypQpqq6udjrLzJnmzE7SzJBur5CWSqWyJmjMLLupX7P6YWbdm5ub9d5772nlypXq7Owc8qRv6jUzSGSehEzfYcpi2rCZbTaDwlgsVtBMbXFxsTP51dDQoIkTJ+pTn/qUxo0bp3HjxjkDqoaGBmeW30wkbe9+I5lMOu/DzHo1A1qzym8myszkw2uvvaZly5apq6srb5s1zCDG7/c7QSmzr8gczJqZeTPhYL5MOc1kTiG/15wLzICqvLxcjY2N2m+//RQIBJx+xLRj077r6upUV1encDj8seo8nU47wddMjplA1tbWpvXr1zvhra2tTe+8847+8Y9/OJ/vWqiSkpKsFVHTjjMHUpn1m0qlnMklsxMpGo3mbb9+v1/19fVOXX7iE5/Q1KlTNWbMGI0fP14NDQ1O2DWT0KbuM3cUbS/pdDrrXNff36/Ozk41NTU5g9N4PO70Cabtrly5Us3NzQW/X81rN7tRTJ9rQpo5n5tzSeYKkznXmR0HZgFgKD6fzxmMRiIRNTQ06OCDD1ZdXZ1zXsuccKitrXUG/2bss73632QymbVjrbm52envzDnNTOxs2LBB7733nrPqH41Gh3z+zMBlJqDMeCJzsjxz8cIE3cxFCzOBbso61ESDZVlqaGhQY2OjKioq1NDQoL322ssJU6Y/qK+vdyZ2zRgic/Xb7MjYEUx433xl2ZwLTfvPHNNt2LBBa9asccZy+Zj2YoKXOe9tre5N/ZsvE7zMhFrmAkBvb29BY3Zp8PiHw2HnnLfHHntkTbJlLmCYsZ8JyuY9uaMmfEwuaGtr06ZNm9Ta2pq1QGQm4Lu7u52FpKamJqcdmomgoZhVZtNnbj6hmbkjxEw4mOxi2r05BoWck0tLS50FogkTJmj//fd3zoGmfZu2X1tbq8bGRmcnjclTO0LmziAz7ujq6lJ7e7taW1ud/sdMMmzcuFGvvPKKWltbC1rY8fl8W+RD094zx3qbT/CYsbRp7ya/JBIJHXroobrnnnty/k7XrbD39PSovLxcDz30kI499ljn+7ZtKxQK6corr9RZZ53lfH/BggW6++67lUql1N/fr46ODpWVlekf//iHpK1f+5C5za6jo8OZuWttbVVHR4czO2S2Z5iZMjMrbzp427ZlWZaz1c68KUzIyBxoZHYK4XB4cIvGf2boa2pqdkjodouenh5nkGNCvllZMR2U2SJqVroyT5xm0GLqXPpwG5LZDmO24prOqrq62lkJNQOSqqoqZ5CyIwO2G5jBfTwed7aAmR0GmfVvtjaaDtu0cVPX5st0OmYFIHMbdOag2vzbtHMzY1lfX++J+jZbds0KbOaMvpl5NoP+zHCYuZPFDP4y27A5eZkBhRlAm77D1KVZpTMTGjtid8pIsW3bCaS9vb1ZkydmUGP66sw+PLO/2Hy7aObgxIR6E5LMlwmBZgLArE6bPtysLu2ogcWOZnZ5mfOYCfRmMtUMbMw5LzOoZF5ylblV0TDnPzNwMW3TrMJlnvPMCkVDQ8OQN9fZ2di2nTVZbwbmZlu/addml0/mDiqzo8rsXsncbmnq12wz3/wSH7NDpaqqymmvfr/f+b4J3l6oa3OZiTmvmQF45r8zL58yY4ne3t6tXo4mfXj5VOYlE+bLnOMyx3NmwcRMNGWuvu2s/UOh0ul01rmuubk5a1yRecmVGUdsvo1/84kP0y7NMTALZZlbyjMnQ83YOTNgm0UTM8Hohbaey8DAQNY5MXOSa/PddybHZI7vMsd10mC/Zfpvk2HMoo3f71ckEnHGIqbfMZdhmR2btbW1nqvzzHOm6WNM0M+s683zoWnvmZcVZm6zzxyPZLZ3k10mTZqk888/P2e5XBfYJamurk4XXHCBfvSjHznfe+ONNzRx4kQ999xzOuSQQ0awdAAAAAAA7HiuXPL60pe+pLvvvjtrJvT2229XMBjU1KlTR7BkAAAAAAAMD1fu4fnBD36g6dOna9asWfr2t7+tJ598UldeeaUuuOCCLe4yDAAAAACAF7lyS7wk/fOf/9RZZ52lVatWqaqqSj/96U/14x//2PPXCQEAAAAAILk4sBs9PT2eurESAAAAAACFcH1gBwAAAABgNHLlTecAAAAAABjtCOwAAAAAALgQgR0AAAAAABcaNYG9v79f6XRakpRIJAr6me15eX8ymVQymdxuz/dxtbW1qa2tLe//f5zXH4/HP/LPGv39/XnrLB6Pq7e3t+Dnc9vtGvr7+5VKpSQNlm3Tpk3q6urK+zObH7dkMqkNGzaor69vm39/MpnM+16wbXuLOuvs7FRzc3NWebYm87XtaB/1uNq2rZ6eno/9fPF43PmZbW2Tm+vp6fnY7TSVSmW1h3zv880VctzS6fTHeo25DAwMaGBgwPl3d3f3dv8dm9se/dTWxGKxIfv75uZmdXR0bPH9zDrYXK42u7nMNrk129ImtlWh59ePw9RRX1/fVuurp6fHOd/nk+9xPT09W7wXdmS9SUP3PYX+/q6uLm3atMl5Ptu2P1JbTyQS2rBhQ1Yd9/b25q3bjo6Ogvv+QvqSzeukr69vh5xbPuqxNefuaDTqfC+ZTKq/v3+bn7upqUlvvvnmkO0gmUxmHZOPct5Ip9PasGFD3v5ka+Xu7+/Xhg0bsvq3vr6+rNc73My4JLO9b+11bY9xSSqV0oYNG/K221zHO51OZ9VbT09P3v5+OKXTabW3t2/X58xsk4X0Jel0Wh988EHWsds8B2zvPnh7jLmGS9GFF1544UgXYkdavHixFixYoNtvv11f/epXJUmzZs1SaWmp9tlnH1mWpf7+fhUVFem6667TbrvtpoqKCt15551atGiRXnnlFU2fPl09PT3q7+/XH//4R5WUlKixsVELFizQpk2bdMcdd+gzn/mMJOmqq67SY489ppaWFk2ePFmPP/64br75Zi1atEgHHHCAwuGwrr32Wj322GNavXq1Dj74YPX19enKK6/UM888o0AgoMbGRt1333266aabNHnyZIXDYcViMc2fP1+vvfaaUqmUxo4dq5deekm/+c1vdO+99+pLX/qSJOn//u//9MADD+gzn/mMiouL9a9//Us33HCDnnnmGU2bNk1lZWW64YYbtGrVKr311ls68MAD1dfXp7lz5+qee+5RcXGx9thjD82bN08PPvig9tlnH0UiEaXTabW1tenRRx/V22+/rb333lv33XefBgYGNG/ePB1++OEqKSnR7NmztWLFCt1yyy366le/qq6uLl188cX661//qnHjxqm+vl633HKLbrvtNu2yyy6qq6tTLBbTRRddpKefflrJZFK77babli1bpj/96U+64oordOqpp0qSfv/732vRokV66aWXdPjhh+vZZ5/V+eefr1122UXjx4+XNDgIbmpq0m9/+1sdeeSRampq0q9//Ws99dRTeu2113TwwQfrxRdf1E033aSnn35aRxxxhCRpzpw5+uc//6kjjjhClmXpX//6lxYvXqw333xT48aNUyAQ0LXXXqulS5dq/fr1mjp1qqLRqCzL0jXXXKO6ujrV1NRowYIFeuutt7Ro0SLNmDFDyWRS55xzjlasWKHPfOYzsixL999/vxYsWKA77rjDaZMXXXSRNmzYoHfeeUdTp05VS0uLLr/8ct1///2aPn26AoGAc9zWrVunadOmqaurS//7v/+rWCym3t5e7bLLLlq2bJnmz5+vX//61/r6178u27Z19913a9GiRYrH49prr730xBNP6KabbtKiRYt03HHHSZIuv/xy/eUvf3F+V3t7u372s5/p5ptv1rRp01RbW6vbbrtNzzzzjN566y0dfPDBkqSjjz5awWBQkydPzvnafvWrX+mBBx7QkUceqZKSkqzXtuuuu6q2tlZ/+MMf9NBDD+n111/XQQcd5Lzf5s+fr2g0qt13311PPvmkbr75Zj3yyCOaOXOmJGnu3Llavny51q5dqwMOOECbNm3SFVdcoSeffFKf/exns17bYYcdJr/fr3//+9+6+OKL9dxzz8nn82n33XfXzTffrPvvv1/PPvusjjjiCMViMc2ePVtPP/20fD6fdtllFz322GO6/fbb9dxzz+lTn/qUJOnCCy/UM888owcffFBf+MIX9Nxzz+m8887TLrvsok984hNZbfJ3v/udZsyYoXfeeUerVq3S9ddfr3A4rPHjx+uOO+7Q4sWL9fvf/16zZs2SbdtatGiRHnzwQb399tuaNm2a3n33XV1zzTVaunSp9tlnH4XDYf3ud7/T448/ro0bN2ry5MlqaWnRBRdcoDvuuEMTJkzQ+PHjNWfOHD355JPab7/9VFFRoUQioa6uLv3lL39RR0eHdt11160etxtuuEGPPvqo3nzzTR144IF6/vnndf3112vp0qUaP368GhoadPfdd+u+++7TY489pqOOOkrxeFw//OEPtWrVKs2YMUOStugnTZtcvHixNm3apMmTJ+vll1/Wb37zG82bN08nnXSSiouL9d3vflfr16/X/vvvr+LiYsXjcVmWpeuvv14VFRWqr6/XXXfdpZtvvll77723qqqqFI1G9dRTT+lPf/qTLMvSrrvuqgULFqi3t1e/+93vdNRRR0mSfvrTn2rlypVO37lp0yZdfvnleuCBB7T//vuroqJC119/ve68805NnjxZkUhEyWRSl1xyiZ544gmlUintvvvuevDBB3XjjTeqvr5ejY2NkqRf/vKXWr16tdra2jRx4kQ9//zzuvXWW/XKK6/o0EMPlSRdffXVevPNN9XS0qJ9991Xb7zxhubNm6d//vOfSiaT2mOPPfS3v/1N8+fP1wMPPKBjjjlGqVRK5557rh577DGtXbtW06dP10svvaRf//rXuu+++5xzgOmDFy5c6Ly2yy67THfffbemTZumSCSin/zkJ1q5cqWmTp2qQCCQ9X4rKipyzm9NTU26/fbbnfPbeeedp6VLl+qzn/2sfD6fVqxYofnz5+vZZ5/VgQceqJKSEn3nO99RV1eX9ttvP/l8PiUSCVmWpfnz5yudTmvcuHF64oknNH/+fC1btkxHHXWUuru79dxzz+nPf/6zurq6tNdee+nhhx/Wb3/7W/X19WmfffZxXtvTTz+tv/71r5o5c6aeffZZ/fjHP9akSZPU0NDgnCeWL1+uSy+9VP/1X/+lRCKhiy66SI888oiCwaAmTJigO++803m/fe1rX5MkXXvttbr//vu1du1aHXzwwXrooYd02WWXacqUKaqurpYkfe9739Mbb7yh/fffX6WlpWpqatL555+vt99+W4cccogsy9Jdd92lRYsW6fnnn9fhhx8u27b129/+Vk888YSam5s1adIkRaNRlZaW6rzzztMXvvAFSdKll16qhx9+WCtXrtQRRxyhf/zjH+ru7tY111yjvffeW5FIxGm3Wztu6XRa48eP16JFi7R06VKtW7dOBx10kNPen3rqKee5X3jhBV166aWKxWLaa6+9VFRUpHnz5mnJkiV67733NG3aNG3atElz5sxRd3e3gsGg6urqdOedd2rJkiW68MILddppp8m2bV166aVavHix2tvbNWXKFN1xxx2aP3++9tprL9XW1jp9gDl3Tp48WStXrtRvfvMbLV26VGPHjtWYMWOcvuTRRx/V0Ucfrd7eXp133nm66667FAwGteeee+rBBx/Ufffdp7lz52rWrFkqKipSa2urli5dqmeffVb777+/3nrrLV1zzTV68MEHNWXKFIXDYee1vfHGGzrooIMUjUZ1ySWXaOHChaqvr9e4ceN08cUX64knntDkyZNVUVEh27Z11VVXafny5Vq3bp2mTp2qF198Uddff33WOWjOnDlav369NmzYoMmTJ+vdd9/VnDlz9Pvf/16HH364KisrneM2ZcoUBQIB9ff365JLLtE999yjcDisCRMm6O6779bKlSsVjUa13377ZfWT3d3d2nvvvfXkk0/qpptu0uLFi51z99VXX60VK1bo+uuv18knn6yuri7NnTtXCxcuVENDg8aNG6e77rpL9913n1566SUddthhSiaTOv/889XV1aWmpiZNnDhRL7/8sq677rqsvuSb3/ymNm3a5PTBb775pq655hpFo1GNHz9eFRUVuvnmm/XEE08449mWlhZde+21euGFF1RUVKRx48ZpyZIlzjn2mGOOkSTNmzdPixcv1rp163TAAQeopaVFgUBAP/vZz3TQQQcpEAhowYIFWrFihZ566il98pOfVHd3tzOe3GWXXVRbW6vbb79dK1as0Ntvv62DDjpI6XRaP/rRj7R06VKnH3rggQd011136c4779SJJ57onLvvv/9+Z1yyatUq/fa3v9Uzzzyj3XbbTeFwWH/+85/1l7/8RbfeeqtOPPFEdXd36+c//7m6u7sVjUa1xx57aNmyZbr55pv18MMPO23ixBNPVF9fn6ZNmyafz6ennnpKt9xyixYtWuTU7W9+8xutWLFCb731lg444AB1dXXJtm3NmzdPNTU1qq2t1YIFC7R27Vrde++9OvLII5VKpfTDH/5QTz/9tDOefPzxx/WnP/1Jzz77rCZOnKjy8nI1Nzdr5cqVeuqppzR16lQtW7ZMHR0duvrqq516W7hwoe69917n/dbV1aXjjjtO1dXVmjhxoizLUnNzs3w+n84991x96Utfkm3b+vOf/6wHHnjAaZOPP/640w4PPPBA+f1+3XjjjXrsscd0991369hjj83qSwKBgHPufvTRRzV37lydeuqpTpuMRqPq6OjQnnvuqb///e+67bbbsnKAaZPTpk1TcXGxmpqadNlll2nhwoVOn2Ne28MPP6zPf/7zzjngb3/7mwKBgCZMmOD003/84x918sknS5LOP/98Zyzj8/n0zDPP6I9//KOeffZZTZgwQZFIRM3NzXrrrbd0yy23aMaMGWpubtZ5552ntWvX6tBDD5VlWYpGo+rr69PPfvYzffGLX9xqP/nUU0/pmmuuUWlpqVpaWtTS0qIxY8YoH89/qPmMGTP0wgsvOCcOSTr44INVWVmpW2+9VaeffrrWrl2r+++/X729vc5J//XXX9eFF14on29wE0I6ndbs2bPV1dWlM888U5L09ttva926dTr77LOd547FYtpzzz2dTuHQQw/V008/raqqKidUTpkyRYcddpiKiookSatXr9Z+++2nV199VQcccIAk6bXXXtPs2bM1Z84cXXbZZSovL5dlWTrqqKNUWloqSXrppZd09NFH6w9/+IPz+1OplC644ALdeOONOvfcczVp0iQdfvjh+tznPuf8vo0bN+qCCy6Q3++XJOeE/P777+uwww6TJFmWpV/96le66aab9N3vfldlZWW67rrr9M4772j+/PmSBmeDW1tbdeKJJ6qsrEyS9MUvflEPPPCADjzwQElywmV3d7f23Xdf5/fPmTNHc+fO1WWXXaaKigodeuih+vKXv+zMpL355pv6/ve/nzUbumnTJiUSCScIJBIJnXfeeVq3bp1CoZAmT56sRx55RM8//7y+8Y1vSJKqqqpUXFysPffcU3vssYck6dFHH9Vpp52mpUuXShqcBUwmkzruuOP0xBNP6Oijj9aYMWMUCoV0yCGHOK/NHDfTJhKJhObOnau+vj7nudeuXSvbtvWjH/1I0uBKUGNjo4477jg9/PDD+vKXv6wjjjhCzz//vGpqapzX1tPTo//+7/92/m1OfvPnz1dlZeVWj1t7e7v22GMPnX766c6q1r/+9S/Nnj1bl1xyiXMc16xZo0QioWOPPVaSdMghh+if//ynqqqqstrtnDlzdPPNN+vcc8/VunXrdPzxx6upqclpt+vWrdMPf/hDhcNhSYPviRNOOEG777677r33Xp188sk64ogjtHLlyqzXZtrkggUL9J3vfEerV6/WQQcdpJtuukl77rmnJGnq1Kk64YQTsp579uzZikajOuOMMyRJK1eu1JlnnqkXXnjBed5EIqHMOcfly5frpJNO0sUXX5z12i6++GLNmzdPv/zlL7XPPvto8uTJamtrc97vRx55pHw+n3bbbTdJUkVFhaZPn64vf/nLzozw/vvvr/LycmeyQpICgYBOP/10LV++3GkT559/vt5//32FQiHtt99++tvf/qbnnntO3/zmNyUNzhi/+uqramho0Cc/+UlJ0lFHHaXXXntNdXV1znFbvXq1BgYGnBPVM888o2984xt64oknNG7cOEnShAkTdNhhhzkzxJs2bdLnP/95tba2auLEiZKkcDis//mf/9Gtt96qH/3oR7JtW1dccYU++OADp++YMWOGVq5cmdVPtrS0aNq0aU7InTp1qp588kklk0ntvffekqQ1a9bo+OOPd/qkWCymSZMmaf/999fbb7+t3XffXW+//bbef/99p580bTKZTGrWrFmSBvvb7373u7rllluc59pnn300c+ZM3XTTTfre976nWCyma6+9VolEwulL3nnnHV188cW67LLLNGfOHKVSKb3++uvy+Xz69Kc/LUl699131dnZqdNPP13JZFLFxcX6whe+oOXLlzvHcs2aNTr66KP1zjvvOG2ira1Nl1xyia6++mr93//9n1pbW1VXV6dUKuXUyeuvv65f/epXuvzyy3XggQcqlUo5A0ZzTPbaay+1tbU5Yd089+zZs1VcPHgK3nXXXRWJRBQKhZwy/fvf/9bUqVP1+uuvSxrsS3bffXcddthhCgQCkqSXX35Zn/vc53TjjTc6z+33+7Pa5Ntvv619991Xd911lzOpMG7cOH3729/W7bffrnPOOUe2beuiiy5SZ2envvvd7zo/9/777+t73/te1nOfccYZuv3223X66acdZrTHAAAgAElEQVRrypQp6u3t1YwZM1RSUiJJmjhxovbZZx/dcMMN+sEPfqCmpib9/ve/VzKZdPq4l156SdOnT9e///1v532zatWqrH6qqalJZ599th544AH19PQoGAzq4IMP1ssvv6xJkyY5P3fuuefqhRde0OTJk1VUVKRYLKazzz7baUcdHR1qaGhQIBDIer+9+uqrzvtNGuxP29vb9Z3vfMd57vPPP19LlizRhAkTVFpaqokTJ+rrX/+67rrrLp1xxhnq6OjQ4Ycfrkgkong8rlAopGnTpunQQw91+s1EIqGenh6tXr1a3//+9yVJr776qv7+979nvd9KSkp0wQUXOAPg1tZWvfXWW/r0pz/tTP6NHTt2yOP25ptv6lvf+pbq6+ud566urtbXvvY1vfvuu5IG+/LTTjtNK1ascOppypQpmj59utMmP/jgAx144IH6xje+4ZyXW1padPbZZzurqWYlef369frJT37ivN7Zs2dr8eLFTh/0+uuvO8fK/C7Tl5jHmL7EtKOOjg5Nnz5dDQ0Nmjx5slPuH/zgB84iizQYfNauXeuMS15++WV9/etf18qVK51+0rw28zMtLS1qbGzUiy++qClTpkiSQqFQVj+ZSCSUTCZVVVWlU045RZL07LPP6qyzztKyZcucuo3H41nn7nfffVezZs3S008/7fQl5rjdcccdOueccxSLxeT3+52wKknr16/XySef7BzrzHO3CXnm3G0mkKTB8+BZZ52lhQsXShqcIB4zZoxefPFF532y//7765BDDnGeu7e3V5WVlfr2t7/9/9s77/goq7RhX5NJMpPeSE8mhUxIJwUkJCQhQBBFA9jbq1jWXVdfFRVXAUUUFbD9bKgoXcXQBEEpUUroLaGTBEISUiCkENInycx8f8zvOZsR3N3ve90X1u9cf8FkZp7nPOc+97nrGTG3RUVF5OTk8Nlnn4nvTklJYcyYMaxevZp7772X8vJyRowYwejRo4V+q6io4JVXXhHz1q9fPyIjI7n55pvFzzEfP36cJ554gmPHjonvbmxspKmpSdhzTU1NfPLJJ6hUKjG+06dP093dzYwZM8QziouLo6OjQ+xBFRUVPP3007i5uQEWPRkWFkZ6errI3ip2Sd/11tvby9SpU/nss894/vnnGTBgAEOHDiUnJ0esgfPnz/Paa6/x6aefCpkMCwvjkUceEXaBYpccOHBAfPfw4cMZMmQI3377LQ8++CBxcXHk5+cL/at898yZM63syZkzZ9LZ2SnsorKyMgCeeeYZ8R4fHx8mTJjA2rVrmTBhAgMHDkSr1TJ48GDx3UqA+KOPPgIsFQilpaUMGzZMPLdTp04xbtw4MUc9PT089NBDuLu7U1BQQFZWFidOnOCXX34R+61KpRJrWZHJsrIyKioquPPOO3FxcQEgKCiI5uZmEhISAItdkpSUxP333y/k7eLFizz11FNijvrKpGL3nz59mieffNLKD0hJSeGmm27iu+++Y+LEiZSXlxMdHc2yZcuELjl16hTjx48Xz7a5uRkfHx+cnJyEj5Odnc2xY8esZEKj0fD444+zePFiHn30UeLj42lvbyczM1PI+8qVKykqKhL6rrm5maFDh+Lp6cnp06cZMGAAmzdv5siRI2RmZgJ/r3qsqanhb3/7G2BZEy+88AILFy7E29tb6KF/xB8+w24ymbjxxhtpaGgQwnPp0iXGjBkjFKW3tzcRERGcPHlSZFz9/f354osv2LFjB5mZmdjb2zNy5EhMJhNGoxFfX1+MRiMXLlzAZDIJhy0yMpL09HS6u7uxtbVFo9GQlZVFW1sbkZGRqFQqKioqKCkpYd26dQwbNgxfX19MJhO33Xab+Fx4eDhdXV0MGzYMJycnOjo6cHd3Z+fOnZSUlJCcnExCQgJnz57l1ltvFYaHklVTKgXa2trIy8vDy8sLb29vbGxsiImJEdmNzMxMnJyccHR0ZOzYsbS2ttKvXz9MJhN6vZ7U1FRsbW2xsbEhOzsbT09Pzp49S0REBHZ2dpSWlmIwGPD29sbFxYWgoCBSU1MpLy8nMTERb29vPDw8yMjIoKWlBa1WS3x8PAaDgVGjRmFvb4/ZbGbTpk0sX76cS5cukZCQQGxsLN999x1ZWVnC8PH19WXAgAEiA9TW1oa3tzfp6enCOElMTCQiIoJt27aRmpqKWq0mPT2dCxcuEBwcjKurq3g2fYMTDg4OwlD28PCgtLSUG264gSVLlpCUlISDgwN1dXUUFhby888/k5aWhqOjI8OHD6e9vR1fX1+cnJxoa2ujqakJOzs7dDqdqMZQFJ6XlxeOjo6MGDHCSiaDg4OZP38+J06cYPDgweh0OjG3XV1deHh4EBsby7x58ygoKCAjIwN3d3eam5tZvnw5/v7+eHl5ERISwmeffUZqaqpwPo8cOUJvby9JSUloNBohk62trYSGhoqAhqurK1FRUTg6OuLr68uePXswGo0MGjQIlUrFwIEDmT9/Ptu2bSMjI0MosPT0dGFQKWNrbGwkLi4OlUqFTqcjMDCQwMBAnJ2drcYGFoeyuLiYn376ibS0NGxtbbGzsxOyV1tbS//+/YmNjcXJyUkoNhsbGwwGA3l5edTV1REbG0tkZCQbN24Usurg4CDGlpCQIAJfCQkJdHd3079/f5ycnDh27Bhr165l165dQslu2rSJvLw8WlpaiI+Pp6enhzVr1nD27FkSExMBsLW1JT8/n7vvvhs7Ozt6e3vx8fEhNTVVyOTAgQPR6/Xs27ePlJQUbGxsKCwspKuri+TkZOzt7XFxcWHkyJHU1taKrNjOnTvp7OwkNTUVe3t7IiIiMJlMjBgxgt7eXmxsbGhpaaGhoUEYOb6+vpw8eVJ8TqVSYTKZCAsLIyUlBTs7O9RqNSNHjqS3t5ft27eLqorMzEyrrFxkZCQxMTFotVrUajW2trakp6djNBrp168fWq2WAQMG0L9/f2GYOjs74+npSXJyMk5OTqjVanp7e7lw4QJGo1EYIkePHqWnp0fIZFRUFGvXriU5OZnw8HDAUmZ5ww03MHjwYIxGIy4uLqSlpXHhwgXq6urQ6/VER0fT2dlJVlYWWq1WZBw6OzvJzMxErVZjZ2dHeXk5BoMBNzc3oQOSk5M5d+4csbGx6HQ6tFqtlZ6Kjo7GxsaG1NRUtFotzs7OeHl5MXr0aNra2tBoNMTHx9PU1MTNN9+M0WjE3t4eBwcHlixZwo4dO0hKSsLNzY2IiAjhZANERETw5Zdfsnv3btLT01Gr1aSlpdHZ2UlAQAAajYawsDDa29sZOHAgAQEB2NnZ0dPTw8mTJ8nJycHGxoaEhATKysoYN26c2AM8PDz48ccfuf3229FoNAQFBXH58mVuv/12nJ2dsbOzw2QyERsby5AhQwCwt7dnxIgRmM1mTCYTPj4+mEwmzp8/j8lkEvOmVFYEBgbi6uoq9M++ffvEurl06RIjRowQ3+3q6kpiYiKlpaXU19cTFRVFTEwMZ8+eJTo6Gp1OB8D69etpampixIgR2NrakpCQQGNjIykpKVRWVuLv709kZCRRUVF0dHQQERFBV1cX3t7eItukUqkICQnh22+/FdkiR0dH3N3dGTVqlJg3gJtuuonz588LeQ8JCSE5OZn29nYCAgLo7OwkNDRUyJFiTwwcOJD4+HjUajXu7u6o1WoyMzNF4Hvx4sWYTCbi4uIAUKvVdHV1iaC8Im8RERGcOnWKyMhIHB0dsbe3Z+XKldx1111oNBrMZjOHDh2ipaWFlJQU1Go1JpOJmJgYEXjoO29VVVVERESQlJTEkiVL+OWXX4QzpNVqKSgoYNy4cajVanQ6HQaDQWQ7lcoVPz8/QkJChF106tQp1q1bh1arJSgoCL1ez9KlSxk/fjxeXl6o1Wp8fX2Fzg8ICKC7u5uIiAirANWZM2cwGAxCF/fVJT4+Pmg0mit0iYuLC2fOnKGqqorMzExUKhXBwcEsX76cm2++mYCAAFQqFcOHD6dfv37s27ePhIQEUTGgBDL72lzLli1j5MiReHp6YjKZmDBhgtAtJpOJqKgo8TlbW1sCAwMZPXo07e3t2NvbExsbi42NjZBtsARDlixZwqFDhxg6dCghISHs2LGDoKAgse//er05Ojri6OjILbfcQklJCaGhoaSkpLB48WLy8vLEvBw9epTe3l7hlNnb25ORkWHlsPr5+bFy5UrGjRuHi4uL1diqqqoIDAykubmZrVu3UlxcTGJiIhqNBjs7O77++mvOnj1LUlKSsCdzc3OFLjGZTKSkpIg9Nzw8nAMHDrB+/Xqxn0RERPD5559TWFhIamoq7e3ttLe309railarxcnJiYSEBEwmE35+fjg7O1utt1OnThEdHU2/fv0YPHgw58+fFwmf6upqOjo6CAgIEDasp6cnmZmZYk4GDhzIggUL2LRpk6gs7enp4cSJE9x8883Y2Njg4OBAdnb2FXZJQEAAERERODs7o1ar0ev1rFq1Stgzrq6ufPXVV+Tk5ODr64ubmxutra3k5eXR1dVFREQEsbGxaLVaBg4cKGTCZDIxZMgQoqOjUavVaLVasrOzqampEXpBp9Mxb948tm/fTmZmprAnOzo6cHZ2xsPDg/b2dhobG7GzsyMkJARbW1sCAgKIjo7G3t4eT09PYZeUl5cDluBQeno6QUFBHD58mJiYGNRqNSdPnsRgMDBgwACr9aYEEUwmEwaDgeHDhxMaGgpYgi/JycmcOHFCrGdFJm+44Qbs7OwwGAwUFRVRX19PfHw89vb2REZGotfr2b17NxkZGfj5+VFeXs6SJUtwc3MjMDAQvV7PkiVLGDduHF5eXmg0Guzt7Vm6dKnwHxQ/IDMzU/gBJpOJ5ORkYYP13d+URFFgYCBRUVEiY+3k5ERQUBAZGRk0Njbi5OR0VZsrKCiI8PBw/P39cXFxwd7envDwcJYuXUq/fv1wd3dn8ODBhIWFCXvVy8sLBwcHhg4dSnt7O+7u7sTGxhIYGCiqeG1sbPDz8yMlJYULFy4QEhJCQkICFy9e5NZbb6WyspJBgwbh5OTEP+IP38P+9ddf88wzz4hsC1giSR9//DGrV68GLFm5pqYmmpubqaurAyzZDb1eL6KGvb29vP322xw+fFj0gnl7e9Pb2yuyBACrVq1i9uzZLFy4ELBkgF555RX27dsn+pMLCwvZt2+fUFxHjx7FYDDw0ksviQj4gQMHOHz4MEuXLgUs0aft27dTW1vLww8/DFiy8AUFBSxbtkxcPy8vj7/97W8iQrljxw5yc3NZuXKliGz98MMP1NbWikxCZ2cnixYt4vXXXxdRw46ODl599VVuvPFGEYGbNm0a33//PaWlpQC4u7tz/vx51q9fLxb9hx9+yKxZsygoKAAsxtusWbO44447OHfuHACbN29m7ty53H///YAlK+fg4MATTzwhFlxZWRmVlZUiagwwZcoUjhw5IhRjc3Mzzz33HMePHxfvuXz5MlqtVmQAKisr2bt3L/n5+aL3et++fRw9elREbc1mM+fPn2fOnDkUFxeL96xYsYLbbrtNZKJ//vlnq4hwfX09kyZNoqWl5YpMjV6vByyRtblz5zJz5kzx2vbt23n22WeFAgKLc2ZraysiuydPnqSoqIgdO3aI669fv56Ojg4xXoPBQGZmJt7e3qLnZ/fu3aSmprJjxw4A4di5u7uzYsUKAAoKCnjttdfYvHmzmO+dO3fy8ccfC3mrqKigsrKS5uZmsRH89NNPVFVVcccddwAWI2/v3r1MnTqVxYsXC5mcMmUKhw4dEnKzY8cO3njjDbHe+o5NkZtdu3Zx77338sknn4i5HTt2LG5ubowaNUpcT61WiyyN0pdpZ2cn1tLmzZsZOnQoW7ZsEc9tzZo1vPXWW0yfPh2wRLZffvllqqurxT0WFBQwffp0pk2bJmRSo9Hw17/+VTgUS5cu5YEHHhDPFmDOnDk4OjoKPeHu7s68efOYMmWKyIx+//33rFy5kqqqKsDi1NbV1REcHCwU9Lp163j++edF0MhoNKLT6YRTCRY9sXDhQj755BMRLFm/fj01NTWcPn1ayOTOnTtFm4Qy3vfff58vvvhCyMSLL75IbW2tcMQKCgp44403rHrHFi9ezOeffy7mtr6+nldffZU1a9YIXXbw4EFee+01ETXu7u5m+fLlvPjii5w6dUq8RzGsFZ588klycnL48ccfhUwcPnyYtrY2oYNzcnLE+xWHydHRkccee0xkYb29vfH39xfZFa1WyyuvvMLbb78tMof+/v50dHRQU1Mj1txXX33FvHnzhL5tbGzkiy++YMqUKeL6u3fvZu7cuSK7cvHiRb755hs++OADsSZ27tzJsmXLWLJkCR9//DEAaWlpTJ06lalTp4qMw6/Zu3ev1TkD5eXlvPnmmxQWFnLhwgXAokt3794t5shkMrFmzRr279/PkiVLAIue2rp1K88//7z4LqXMVHFMKysrRbmto6MjAKWlpUyaNInz588DFn0/adIkysvLhUFpa2tLS0uLVeZo6dKlfPnllxw9ehSwyPZTTz3F1KlTxXt6e3tZuHCh0CVHjx6lsrKS+vp6Dh48SFtbG+7u7kyYMEEEyI1GI7m5uaLEFGDr1q2sWrWKDRs2sHDhQnGvPj4+oozcaDSydOlSPvroI+rr68X429rahExevnyZRx55hC1btoj5+OSTT5g6dSrjxo0T9zx//nzWrVtHYWEhYNEvc+fOFToJYP/+/bz11ls88cQTYt4mT57Mvn37RKamt7cXjUYj9E1LSwsmk4ndu3cLOd2+fTsLFixAq9WK5zRo0CAmT54s7lGr1aLT6QgLCxP6paysjBkzZoi2la6uLp577jlWrVolslA7duzg4sWLosJCkZMLFy6waNEiAPLz83nzzTcpKysTOlBx0OfMmSPGtmDBAqvgi62tLQ888ABffvmleP46nY4jR44IeY6Li8PW1pYXXnhBXN9oNOLq6kpeXh5gyeZOnz6djRs3Cl2yfv16nn/+eSH/9fX1nDhxgo6ODjEnixYtoqamRhjiRqORqVOnsmvXLtF/u3fvXubOncvTTz8tXisqKmLPnj0iaNbY2MjSpUvZsmWL2INqa2uZNGkSpaWlbNy4EbPZLIKHyv5y6NAhZsyYwdatW8XY3nrrLXx8fPjv//5vwLJ33HPPPRw7dkzYXImJiVRVVQmZMBgMREVFUVBQINbpTz/9hNFotMq0GQwGLl26xDfffANY9OQLL7zA3Llzxd79/fff09bWxvvvvw9YspLr16/nu+++E+s9Ly+PuLg4cS6I2Wxm586dAEK2SkpK2Lp1q6hUUGRgxowZomKtsbGRU6dOUVdXR01NDYBYr4rtZmNjw7fffkt1dbV4/ps3b2bx4sWsX79efPemTZtYuXIlzc3NgGWdOjk5CZ0McM8993D//fezZcsWMbbPPvuMmTNnikq7NWvW0NXVxcGDB8XY9u7dy8WLF1m3bp34biUA1tbWBljkfd++fWzYsAGw6KmlS5dy/Phxsb+WlJSINkJlPtLT00WCBizyNnv2bCudtGnTJt5++22R4T548CBvvvkmDQ0N4tySLVu2YGNjI5zjtrY23n77bfbu3Suu7+vri8FgYN++fYClGnbhwoW89dZbQgYVu2Tbtm3C8Vy0aBEbNmwQvom/vz/t7e00NzfT0NAAWGR5+vTpvPTSS+K+p06dypYtW8T1P/30Uz788ENRCafM28iRI8Va6u3t5dFHH6W+vl7YD6tXr2bDhg3CBmtoaKCgoAA/Pz9RIbp161YaGxuFnm5tbRXzVVtbC1gCfdXV1cJOAIsOfOedd5g9ezZgkclffvmFRYsWUV1dDcBHH33EO++8I3w1g8GAh4cHCxYsEHbJ5cuX6e7u5sSJE0JufH192bhxI3v27AEsdqkSDFHswHfffZf169eL/ejMmTMsXbqUmTNnCptv9erVrFq1SvhhRqORBQsWsHv3bqEn9+7dy8qVK/nyyy8pLS0VNtw/4g/vsPf09PDSSy8JZwUspVcNDQ1i06uvr2f//v1kZ2eL6G5xcTElJSU899xzgMVY8/X1xcfHR2xeP/zwA/fcc49QuGBxUL28vESJ1N69e3nggQcIDAwUztgLL7zA+PHjhUHd0tLC4cOHiYuLsyo1PXbsmCgj1Wg0HDlyhMLCQrExb9u2jSeffFIseLAI5rPPPitKJEeOHElDQwNTpkwR5SG1tbXMnDmT+fPnAxaBHzp0KA8++KDICp45c4Zp06aJSL7ZbMbDw4O//e1vYkPZtm0bL7/8MgkJCVZlNVOnThVObWVlJbm5udx2223C8KytrWXq1KkiQubi4kJvby9LliwRn9u1axfjx48XCxcs7QVJSUnC6FBK7Pbv3y8U0/Lly/nwww9FL5Hi4Pr6+oqKipKSEg4ePCgcP6X8uL29XTgCgYGBlJSUsGrVKjFPQ4cO5dFHHxWlR3V1dWRkZODq6ioOnDl48CDTpk0TxrvBYBAGpqIETpw4wYsvvmi16dfW1qLT6UQvTXh4OHFxcTz44IOiJL65uZmpU6eK63d1dfHKK69w5MgRkan/9diUDKetra1QsPHx8aKXVClBP3PmDAkJCaKVIzg4mAEDBjBx4kTh5Cjlx8qG3tPTg8Fg4OzZs4wfPx6wODm/nrfq6mq6u7vFuuk7NkV5DR8+HCcnJ5GlU8qzWlpahOG5a9cu3nnnHRH9VkoG29vbRUaiurqavXv3iucIFidyypQpQpZra2sZNGgQDz/8sDCOc3Nzef3110UrgbOzM0ajkcWLF4tne+eddzJv3jwht2ApUUxKSuLbb78FLJv+yJEjCQoKEmv52LFjjBo1SgQ66urq6N+/P7a2tkLpl5SU8Oqrr4oAlVqt5vz588TFxQlnNTIykpSUFO6//35hZL322mvcdtttYvzl5eUMGzaMp59+Wuil0tJSnn32WSETbW1teHl58cwzz4h1UlhYyF//+lfxHrDopeeee04E9pQ2jbNnz4rSypMnTxIeHi5KL5ubm3F2dub06dNi/Ioz9vrrr9PT04PRaOSll15i+fLlon+4uLiYxx57jJKSEjZu3CgM9N+D77//nkcffdTqELuamhpCQkJEe9OxY8fEvCmBtaNHjzJq1ChhiHl7exMZGcljjz0mdG5ERAQ5OTnccccdpKWlifn8Z5SVleHq6ioycH3Xm2IYuLq68vjjj4tAS1dXF+Hh4Tz55JMi23Hs2DFefvllq2xmV1cXkyZNErJUUVHB+PHjaWtrE60zNTU1zJw5kwULFgAW42XAgAHExcWJYMT27duZNm2aCGL1LfdX5O3222/n448/5u233xbXLy4uprKyUgRklf0tJiaG119/XRixfenu7qawsJD29nbGjBkjvuf+++/n0Ucf5dZbb7UKjCv01SWKU3XDDTfQ3t4uyiN7enp48MEHaW1ttXJilXJEsDiiAwcOFBUSYGllUErClT1g8ODBGAwGMW89PT38+c9/pry8XDgQ3t7ejBkzRgSEe3t7KS0tRaVSifWmVOLdfffdZGdnC0OvL+7u7tTX16PT6YQBfenSJaZOnSoSEE1NTQwZMoSHHnpI7G+lpaVWezdYDOb6+nohy0q5f35+vniPj48PDz74oHC0e3p6+Mtf/kJ5eblwGDZu3Mh7770nzvDo7e1lxowZ/PLLL+J6x48fZ9asWXh5eYn7trW1pbu7Wzg5kZGR2Nvb4+fnJ2wuRSaVvVMprf7Tn/4kzjBQ5k2xAdRqNYmJidjZ2QnjWOn/DgwMFM7/888/z+233y7slLq6OtLT0/H09BSHWCn727x589i9e/dVD708ceIEkydPFgkJsARaUlJSROCloaGB6dOnC0cUYMWKFXz44YfCvjAYDLz66qsUFRWJtXvmzBkeeughq/J6Ozs7pk+fLgIHR44cYdKkSQwdOlSs5cGDB4tKE7Bkrt3d3fH19RVrYsSIEQwcOFAEmhQ97OnpKdbp7t27mTBhgtXeXVVVRW9vr7jv4uJikpOTKSsrE5UYOp2O+++/n5ycHKqqqnB0dGTu3LnceuutYt6uNjalb1cJyJ87d4633npLtCgCzJo1i4ULFwq5DQkJwc3NDR8fH2G7DBkyhJaWFqGTlVYGT09PEZA7fvw4s2fPxsvLSziMH330EV999RXjxo2js7OThIQEBgwYQGNjoyjTLi4upri4WLSydHd38+qrr1JYWCjWwOnTp63WW1+ZVGwnRZfcf//9wokNDg7mT3/6k9CltbW1BAcHc+bMGfFst2zZwujRo4XN7+zsjF6vx9XVVQRj7rjjDubNmyeqEpR5MxqN3HLLLYAlAThixAgOHjwontOJEyfQ6/VCthSbq62tTQQozGYzkydPFusNLBWVVVVVrFq1SjyjTZs2cddddwkZLC4upqKiQrTpVFZWMnToUJ5++mlhv1VXVzNt2jRx333Hpti427dv54knnhB7ovK50NBQq707OzubwMBAAgICAEuVwb333iv8oL7zpgSJ9+zZw5w5c4iKisJoNGI2m5k9ezZ5eXniuaWnp+Ph4UFjY6NInPx6vSm97T4+PsLHCQ8PZ/z48cKebm9vx8PDg2effVbIe2RkJGlpadxzzz2MGzfOqkX1t/jDl8R3dHQQExMjFhdYIkutra2i1FmJvCmvqdVqKioqaGhowN3dnYCAAFxcXPDw8GD06NE0Nzfj4OBAYGAgbm5uZGZm0tbWhr29PcHBwaLc1NbWFr1eL8pYu7u7sbe35+DBg5hMJtLS0nB1dcVsNrNnzx5aW1tJT08Xhz3U1tZib29PSEgIarWa5ORkJkyYgKOjIyqViujoaMxmsxBAsGy64eHhovy7qamJ9vZ2Ojo68Pb2Rq1WC2dB6cl1dXWltLSUmpoaMjIyUKlU6PV65s2bJ4xYlUpFT08P+fn5jB8/Hjs7O8LDw5k3bx7+/v7CiXF1dRUHV7m4uODn53dFaasC0wsAAB3HSURBVHVISAjz5s0jNzcXDw8P1Go1gwcPturPj4+P5+DBg9x44404ODjg4OBAV1cX6enpYoF3dXXh4+MjSuJUKhUpKSn4+PiwZ88ecQjQ9u3bxbNVehwrKiro6ekRTkVBQQGdnZ0MGTJElAvdeOONVr2Z4eHh2NjYiE3fx8cHs9nMiBEjOHfuHF5eXgQFBdHd3c2gQYNwc3PDzs4Od3d3kpKSRAm/UgqTm5tLU1MTTk5OBAcHk5GRgUaj4cKFC3h4eBAVFSU2F7CUvi1YsIAJEybg6emJVqtl+PDhNDc34+Hhgbu7O+3t7VRUVIhfJfDx8RF99ydOnBD9r9nZ2VRVVREWFibkdvDgwbi7u1NZWYmnp6dVOTQgyo9HjBghXndwcCA9PV30KyvzNmbMGNE/GBAQgK+vLx0dHYSHh9PU1ERXVxddXV10dnbi6upKeHg4jo6O4jNGoxGVSsWoUaPEBqPX69HpdFabpTJv6enp2NnZYTabOXLkCK2trUJO3Nzc+Prrr5kwYQIuLi4EBASIgwG9vLzw9fWlvLycs2fP8swzz6DRaMRJxPHx8bi4uODo6Eh9fT3e3t7ExMQIZa3IpCL/ISEhqFQqcnJyMJlM4sC66OhokRVyc3MTB7xUVVWJ/qVLly5x8803i3GEhISIQ+lUKhUNDQ10dnbS3d2Ni4sLdnZ23HXXXRQVFXHs2DFSU1MJDAykqKiI9evXo9PpcHd3JyYmhs8//1y0Umi1Wjo7O8nLy6OjowO9Xk9iYiKLFi1i69atImjWr18/FixYwP79+0lLS0On01FWVkZubi5lZWXodDr0ej3Nzc2i7NvJyYmenh5ycnI4f/48gYGBIrA1cuRIenp6RFlneXk5+/fvZ8iQIURFRdHU1MStt95KRUUFer1eZCP/p4SGhmI0GkXPnTJPw4YNQ6PRiPLIX89baGgo0dHRBAYGcubMGby8vIiNjRVtJWDJKgUGBorDzH4ro361e7KxsaGrqwuVSoWPj49Yb0pgVzm0SNHvdnZ2NDU1UVRUxE033YSNjQ3R0dEsWLCAIUOGiBYYHx8fwsLC8PT0xM3NjZCQEDo6OrjpppswGAzY2dnRv39/1Go1SUlJojxQKevr6OjA1dWViIgIPv74Y3bt2kVGRoYoLV2yZAlNTU1izhSdpgR7T506xaVLl+jfvz/u7u6YzWbhAA0YMABXV1cR8FFQDlm8cOECo0ePxtbWlqSkJPLy8vj5559Rq9VW/ZYKfXVJV1cXnp6eBAYGMnToUHEQnDKffXWJUqI+bNgwjEYjNjY2xMXFERQUJMoxtVoter0eBwcHsQeEhoaSnJxMTU0NCQkJ9PT04OTkxIgRI4Sx2NdwBqzaNNLS0rh8+TK+vr6Ehobi6OiIn5+fVR+lgtJz7+fnJ8bt7+/P/PnzGTduHJ6enlZl41lZWahUKiIjI632brCs5ejoaNrb2wkKChLl/spaB0sFh1IF5ObmZjU25Xvi4uIIDAwUB7Ta2tqKdX306FHi4+Otyv1DQ0NxcXEhOTkZLy8vjh07RlpaGvb29mRmZtLY2EhYWJhoy1Kr1aSmpuLo6GhVWq3IpzJvSjm9jY2NaDEoKysjMTFR7G/V1dWEhIQIRz4gIEAEhBX9nZ2dTUVFBd7e3kRHR9PR0cGoUaNobGxk4MCBYu9TiI+PF2WsSh+6sgckJSUJJ3jQoEGcO3eOiooK4uLiSE5OxsfHh/z8fHQ6Hb6+vmLvvnDhgmhlWLp0qVUrw4ULF1i3bh233367aEFatmwZUVFRohJGKacvLS1l8ODBor1GKSV3cnKitbWVyspKurq66NevH2q1mqCgIHJycqzaew4cOMCYMWMwmUyidNnb21scFhgcHEx5eTm5ubn8+OOPJCUlERYWhpubG87Ozr+ps682NkUmi4uLiYmJEWcVlJeXC7kZNmwYdXV1bNmyhaysLKuxubu7069fP7y9vcnKyhItrX3H1tLSgq2tLaGhofTv359Tp05hMBgIDw8nMzMTZ2dn1qxZg4uLi2jbU8rowRJEqK+vF3a60n7T3NyMwWAQAd558+YxYsQIAgMDUalU4ln19vYSGhpKY2Oj2LsVm1dpZwPLPuLl5UVTUxNjx46luLiYsLAwgoOD2bx5M0FBQZw9e5aoqCiSkpJwd3fn4sWL4oBEpWdeITAwkH79+lFfX8+AAQPQ6XSihFs5zyMyMpKmpib8/f3FHqnYXH3b0mJjY638p7q6Ou666y4RtGxra6OwsJCamhrS0tIAi8Pe3NxMcHAwXl5eBAQEcOTIEdavXy/OfAoNDWXevHmMHTtWnDWljK21tZXw8HCioqIwm81kZGSI6tng4GCrvTs4OBgbGxtycnKEnlTaDZX70Wg0DB8+nMuXL3Pu3DkGDBhAREQEoaGhFBUVicOms7Ozsbe3p6SkhKioKGpqamhpaWHYsGH069cPBwcHsd5KSkqs1ltnZyf+/v44OTmJim1FzjUaDd3d3Xz33XcYDAb69+9PQ0OD+CWS5uZmcbDnP+IP77BXVlaybNkyjh8/LqI2586dw9/fH41Gg6+vL62trbS2tuLr60tPTw9+fn4MHDiQ7OxssQkDIgKiROA9PDzEvxVHU3mPYnTZ2toKI055zxdffIGjoyMtLS30799flKoEBgYSHh6Ora0tFy9eRKPRoNVqRT/7f/3Xf2EwGOjo6CAsLIw9e/bw/fffW41t6dKlotRJr9fT1NTEggULcHV1xcvLC2dnZ9auXcvWrVs5d+4cBQUFDB06lOjoaOFQK2NMS0uzKtsODQ1l6NChwnjRaDRWzgpYjMX09HQxZqVfODExUXy3i4uLKCv69YaooFarhbGsPGPlnAAFb29vsUH0NeT8/f3F83B0dCQzM5Ps7GxxLb1eT1ZWlnDWwXLo2KhRo6yqJf4VlOyAUg6mGMl9Ny7lPcpitLW1FZumknFQ5EalUv2m0e/h4SGyAmCp+vjoo4/o7e0lISEBFxcX6uvrsbGxwcnJicOHD6PT6XB2diYgIED07Cso/UZ9rw+IjP6vUQx7JYjQ1tbGN998Q01NDXq9nsDAQHbs2EFhYSFNTU1iDhYtWkRVVRVeXl5WMunm5kZlZSUVFRVWGRRA9N7+1jMAi1NfVVVFcHAwjo6OeHl5cfnyZTo7O4UjZW9vz+rVq+nq6qK1tZXY2FgaGhpYsGABYWFhjBkzBpVKJQ42+/DDD8VJ5nPmzMHf31/8KkNxcTH5+fniHAiwlOPt3LmT1tZWYaQuX76cw4cPC6Pjhx9+YMOGDVRVVREfH09jYyOFhYUiS+Hu7s4vv/xCfn4+paWlDBo0CJPJxAMPPEBPTw/nz58nKiqK6upqVqxYgVarpaOjg6CgIMaOHUtOTo7YmMrLy9mxYwdOTk5ERETg6enJhg0bxAF7ycnJtLW1sWrVKrRaLVFRUcIALysrEz1aAN98843ojS8pKSE+Pp7IyEj8/f1FxNvZ2ZnIyEgRWQaLM+zv7y+CL8qzgr/rRTs7O4YMGSKMDBsbG3x8fIQT+ns562DJgBw8eJDq6mqRPV+4cCE7duwQh4T2nbeQkBCreSsuLiYvL4/c3Nzf7Z6WLVsmSj9/+OEHkcHqy+HDh1m7di2lpaVi3lavXo2NjY3I3iinDLe1tYl5+/rrr9m5cyc2NjbibJa1a9cKmXR1dRV7gHJi7cWLF/n22285dOiQyHqeOHEClUpFeHg4ERERGI1GvvnmG9HXFxYWRk1NDXl5eTg4OAgDLSUlhREjRgg94u7uTkZGBhkZGSI4+Wvs7OwYPXo0ubm5QkZ27drFzp07ycnJwcfHRxwe15dLly4xf/583NzcqKio4OTJk8TFxWFnZyeex9V0Sd+9RDHOweKo9t3ProZWqxVVNy4uLlaHu10NW1tbsrKyGDVqFHZ2diIz9v/C+vXruXz5Mh0dHWLeNm7ciEqlwtnZGV9fX7Zt20Z1dTWXL19m0KBBohyzurpanHFTU1PDd999x9GjR0Ww9Msvv0Sr1dLa2kr//v1pbGxkxYoVFBUV4enpiZeXF6tWrWLnzp2it7W9vV2UniYmJhIUFMT27dtZu3at1aGty5cvp66ujkGDBuHv78+JEyf48ssvaW9vFxmqtWvXipP0ExIS6O3t5b333sPX15cLFy4QFhZmpWf+2bz1Pd3/avx6X3Z0dBT7W0JCwlVtk757t7IPXk2WNBoNgwcPFk41WALuytkzYLFxBg4cKMa0adMmCgsLue222wgICMBoNFJYWCjKiH18fCgqKuL48eN0dXWJvf2LL75g586dIjBcUVHBp59+SmNjI/7+/nh7e5Ofn09FRYVIYLW3tzNv3jwOHDhAWVkZ3d3d4vNKIgos9kxoaKhVVZter8ff359BgwZZzcE/YtOmTRQVFTFhwgQxNkUm9Xo9oaGhHDx4kLy8PLRaLTExMbi4uLBu3Tp6e3tJSUkhKCjIamx6vV7I5Lp166xkUhnb2bNn2bt3LzfccAMuLi4MHTpUOKOKbsvIyBB71a9JSEgQ2Vslo6/Mm6JTtm/fLtabct7KrFmzOHXqFH5+flZ60tHRURx+9tVXX6HVaqmpqRG/shEeHi4+Axa5Tk1NJS4uTlQMASJ4/FssXrxY+Dl6vZ7KykouXrwozqJwdHRk9erVnDlzBrVaTXx8vPjlmEOHDhEUFISbmxunT59m+fLl4heWjEYjhw8fpqSkBI1Gg6enJ3Z2dnh7exMbGyt0YU1NDR4eHkL3VlRUUFBQgKOjIwaDgcjISDw9Pa/qB3h5eYk50mg0wkZW1ltfWxkQLZ992b17t2jhjI+Pp76+no8++oienh4SExPx8/Nj48aNbN68GTc3NxHYnTVrFk1NTYSFhREWFkZtba2YN6WFUTnbTFkvlZWVfPrppzQ0NBAQEIC3tzc///wz5eXltLe3i/Wm2G5KQlWpJlSr1ZSWluLp6XnVvbEvf/hT4vV6PSqViri4OJH1Pn/+vFV/pLu7Ow0NDcTGxtLW1sa+ffusolW/N0qpXN/rK/ekRLyUngtXV1fKysro378/P/74o5VjqhxiFxcXh8FgQKPREBISwk033SR+dkKn0/HBBx9YXS84OJgRI0ZgZ2cnyrQl/3koJbpKSRUgjPl+/frR0NDwb/1tVKWEaezYsaKEra9MdnV1odVq/yWZ/H9BKRsfO3asKGFzdXWltbWV6Ohourq6xGEjyk+lgSVroUT5T506RWxsLLm5ubS1tXHbbbexfft2srKyrE7LBUtLxK+DHsrYlJJZpUQ3KytLBLZ0Oh3Z2dkiGOTq6kptbS033XST1Uni3t7ehIWFUV1dTVBQEOvWrRPr3Ww2ExMTw6xZs6yu/2vjXymtzsnJEZtgeHg4sbGxIqh0tXnT6/WYzWbi4+OvOm9Kf+B/Ig4ODvT09ODr60tbW5s4+PBfnTd7e/t/Gvn+vyU4OJi77rpLnPp7NcLCwoiJibli3m655RZRDnu1efu/GZsik/9Ml5jNZlGddeutt9LS0kJHRwfR0dGil/D3Rmkd+vLLL/Hx8bmqIx0cHPy76JL/FHQ6HcOHD7/qvCm6JCIiQhi9v543pQVJr9fT0NDAkCFDxGtKf7VCUFDQFbokLCxMHBAGliDur2UyMjJSyKRiZJ8/f14c2gu/LZPDhw/H3t5eBGLnzp37b3uW1xtKW9ScOXPEIYPK/qbsAxEREdTX14uDSMEyT3fccYd4tn3nrbu7G7PZLH41R6HvvLW2tnL48OF/+9heeeUV5syZI6pYfy2TfcemyM3FixdJSkoSgYGryaTSknU1mfzfGJsSGA8PD8dkMl2hJ9vb26+qJ1999dV/2z3pdDpuvPFGYf8p7TWxsbGivF6n04nDWsGSCdbr9dx8881WukQZG1zd5mptbeWHH34gNTUVLy8v/P39qa+vJzEx8arz1tnZye7du618sH/H+H89tqvpSQ8PD/r37y+qsH49b1FRUVfMW3BwsNV6CwwMvGK9Kb8qodBXJtva2jAYDFYJi38Vlfk/5RfjJRKJ5Hfi6NGj4vdJlRJNiURy/WAwGKioqCAsLIw1a9aQnZ39uwdOJJLrhdbWVhoaGvD19aWoqMiqDPk/nb5j27NnDyNHjrzWtySR/MchHXaJRCKRSCQSiUQikUiuQ/7wp8RLJBKJRCKRSCQSiUTyn4h02CUSiUQikUgkEolEIrkOkQ67RCKRSCQSiUQikUgk1yHSYZdIJBKJRPK7ceDAAY4ePXqtb0MikUgkkj8E0mGXSCQSieQ6ZsWKFSxZsuRa38ZVqaur47nnnuPy5cvitYkTJ/LUU09dw7uSSCQSieSPg3TYJRKJRCK5jrnvvvuYOHHiFa9PmzaN/fv3/+/fUB/eeustPvjgAz777DPxWnd3t/idWolEIpFIJP8zbK/1DUgkEolEIvltdu7cSU9Pj9VrZrOZN998kzNnzvDdd99dozuDN954g8GDB3P77beL13p7e7Gzs7tm9ySRSCQSyR8J6bBLJBKJRHIdM2TIkCteU6lUODo60tnZ+btd58CBAyQlJWFr+6+bBq6urjzwwANWr/X09EiHXSKRSCSS3wlZEi+RSCQSyTWksbGR++67j6CgILKysvjiiy8wm82AJZM+c+ZMDh06BMDWrVuJiooiNTUVg8HAzp07iYmJITo6mq1bt/LnP/+ZgoKCK65x/vx57r77biorK696D+fOnSM1NZW8vDzOnj3LLbfcQkZGBsePH2fr1q3cdtttZGdns3XrVqvP7dq1i3feecfqtas57J2dnbz77ruMHDmSm266ienTp3Px4kWr95hMJn766SfuvvtuRo0axUsvvURtbe0V78nPz2fy5Mk89dRTLF269Iry++rqap566imGDx/OhAkTmDdv3hUVChKJRCKR/MdglkgkEolEck1oamoyh4WFmR0cHMx33nmnOTk52QyY58+fbzabzebLly+bAfMLL7xgNpvN5oMHD5qHDBli1ul0ZpVKZdZoNOaBAweaBw0aZM7Pzze7ubmZw8LCzB0dHVbXefjhh82Aef/+/Ve9j59//tkMmGfPnm329/c3a7Vas1qtNvv7+5ttbGzMDg4OZjs7O7O9vb25rq5OfO6RRx4xq9Vqq+/y8vIy33rrreL/jY2N5qioKLNarTanpqaak5KSzCqVypySkmI2Go1ms9ls7ujoMI8ePdoMmIOCgswjR44029ramgMCAswmk8lsNpvNxcXF5gEDBpgBc3JysjktLc2sUqnMw4cPF+PduXOn2cHBwezq6mrOysoyh4WFmQHzlClT/ifTJJFIJBLJNUNm2CUSiUQiuUa8/PLLVFVVkZ+fz/Llyzl06BBr1qzh5ptvBqC9vR0AjUYDQEpKCnv37qWyshKdTsfQoUM5fPgwBw4cYNSoUbz22muUl5fz5ptvimuUl5ezZMkShg0bxuDBg696Hw4ODgDMnDkTW1tbioqKGDRoEOfPn2fixIlUVVUxZ84curu72bdvn/hce3s7Wq3W6ruMRiM2Nn83L1577TXOnDnDli1b2LNnDwcPHuSRRx6hsLCQjo4O8Rw2b97MK6+8wtmzZ/n5559JTEzE29sblUpFb28vubm5tLe3U1RUxKFDh9i1axeTJ09m27ZtXLhwAbPZzMMPP0xwcDAlJSVs27aNQ4cOER0dzY4dO/6nUyWRSCQSyTVBOuwSiUQikVwDWltbWbRoERMnTiQ9PV28Pm7cOPz8/ADEz6W5ubld8XkHBwe6urqsXnviiScIDg7mvffe48KFCwB88MEHGI1GXnzxxd+8F09PTwBsbGzYtGkTUVFR9OvXj/DwcObPn4+Xlxe33HILAGfPnhWfa25uvuLeenp6hBNvMplYsWIFjz32GMOGDeOHH34gKSmJ+fPn89RTT+Hs7Exrayvz588nKyuLGTNmiHL6ixcv4uPjA8CWLVsoLS1lzpw5JCYmims1NDRgY2NDQEAAR44c4fTp07zzzju4uLjw/vvvo9frOX36NM8///xvjl0ikUgkkusZ6bBLJBKJRHIN2Lx5MwaDgccee+w339PW1gaAs7PzFX+7msOu0Wh49dVX6erq4t1336WlpYWFCxcSExPD2LFjf/M6ipP87LPPEh0dDYCLiwsGg0G8JyQkBJVKZeWwt7e34+TkJP5vNpvp6uoSDvuZM2dE4GDgwIGMGzcOtVrN2rVr+fDDDwHYt28fbW1t3HnnnahUKvFdWq1WfM9PP/0EICoPFE6fPk1oaCgajYaCggJsbW05cOAAISEhvPDCC2RnZ3P48GHGjRv3m2OXSCQSieR6RjrsEolEIpFcA6qrqwGIiIj4zfcoB6r1LTFXuJrDDvDQQw8RERHBZ599xjvvvENbWxuTJ0++6ncoXLp0CUBktJV/19XVYTQaAYtT7+fnx5kzZ8R7jEYjarVa/L+npwej0Sgc7dbWVgA+//xzPD092bBhA4cOHSI3N1c4593d3QB4eHhY3VO/fv3EtS9fvoxarcbFxUX8vbOzk0OHDqHT6cS1ent7mTVrFrm5uZw8eZIVK1YQGxv7m+OWSCQSieR6RzrsEolEIpFcA/R6PWA5ab0vHR0d4lRzJfN9tVPOHRwcrvqzbnZ2dsyYMYOOjg5mzpxJQEAA99133z+8l8bGRgC8vLzEaz4+PvT29lJXVydeCwwMtDpp3t7eXjjcyr3D33vu+/fvj4ODA4888gjbt29nzJgxwlE/ePAg06dPJyoqCoBjx45Z3ZOfnx8VFRUAxMXFYTQaOXHihPj766+/TkdHhyjnj4+PB2DZsmUsWLBAfC/AggULWL169T98BhKJRCKRXI/I32GXSCQSieQacOONNxIREcHEiROZNm0aCQkJ7N+/n/fff5/HHnuMWbNmYW9vD2DlFCsYjUZ6e3uv+t333HMPb7/9NsePH2fSpEnie/4ZSkYbwNfXF4Da2loCAgIACAgIYMuWLZjNZlQq1RUOu5LxVxx2d3d3HnvsMT7++GNaWlq45ZZbsLW1ZdeuXSxZsoTExERmzJhBdHQ0n3zyCXq9ngEDBpCfn8+6devo6emhtLSU++67j9dff5277rqLv/zlLxw6dIjvv/8ed3d34bCPHTuWqKgoHnzwQfbt20dKSgqXL19mw4YNrF27lpkzZ/5Lz0AikUgkkusJ6bBLJBKJRHINUKvV/PjjjzzwwANWh6KNHDmSxx9/HIDg4GDS0tJITk6+4vNms1lk6X+NjY0Nrq6uuLi48Kc//emf3ktKSgparVaUsAO4uroCf8++gyVj/sMPP9Da2oqrqyujRo0SJelgcdQ1Gg0hISHitXfffRcHBwcWLVrEypUrAYsj/+CDD/LGG28A8PXXXzNx4kQeffRRwJLp/+tf/8oXX3zBsmXLmD59Oj///DNTpkxh8uTJhIWFsWbNGu69915RSq9Wq9m8eTOTJk3ik08+EcGDiIgI3n33XSZNmvRPn4NEIpFIJNcbKrPZbL7WNyGRSCQSyf/PnDx5koqKCqKioggPD/+XPlNWVoaNjQ1hYWFX/K2wsJCUlBSee+453nvvvX/p+86ePUtISIjoST9z5gyPPPIIixcvFtc4d+4cW7Zs4aGHHrI6IK4vdXV1eHt7X9EzbzQaKS8vR61WExwcjK2tdc7AbDZTUlJCa2srCQkJaDQaTp48iZeXl8j2K+9TqVS0trbi7u7O7NmzeeGFF6y+q729nXPnzuHh4YGvr+9v3qtEIpFIJNc70mGXSCQSieQPxiOPPMLChQs5ffr0PzzU7j+ZVatWcccdd5Cfn8+oUaOu9e1IJBKJRPJvQTrsEolEIpH8gejq6sLT05MbbriBbdu2Xevb+V2oqamhtLSUqKgonJyc2Lp1K48//jj29vaUl5dfka2XSCQSieSPgtzhJBKJRCL5A7Fnzx46Ozu59957r/Wt/G7MmTOHjz76yOo1f39/vvvuO+msSyQSieQPjcywSyQSiUTyB6KpqYnPPvuMSZMm4ejoeK1v53fBYDCwZcsWqqqqUKlU6HQ6srKyxO+9SyQSiUTyR0U67BKJRCKRSCQSiUQikVyH2Pzzt0gkEolEIpFIJBKJRCL530Y67BKJRCKRSCQSiUQikVyHSIddIpFIJBKJRCKRSCSS6xDpsEskEolEIpFIJBKJRHIdIh12iUQikUgkEolEIpFIrkOkwy6RSCQSiUQikUgkEsl1yP8BetNBUr96EF4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1080x1080 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (15,15))\n",
    "import scipy.cluster.hierarchy as sch\n",
    "dendrograms = sch.dendrogram(sch.linkage(scaled_Q[['citympg']], method = 'ward')) #Ward's method is a criterion applied in hierarchical clustering\n",
    "\n",
    "plt.title('Dendrogram for city mileage')\n",
    "plt.xlabel('city mileage')\n",
    "plt.ylabel('euclidean distance')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 288,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'euclidean distance')"
      ]
     },
     "execution_count": 288,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (15,15))\n",
    "import scipy.cluster.hierarchy as sch\n",
    "dendrograms = sch.dendrogram(sch.linkage(scaled_Q[['price']], method = 'ward')) \n",
    "\n",
    "plt.title('Dendrogram for car price')\n",
    "plt.xlabel('car price')\n",
    "plt.ylabel('euclidean distance')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
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