5_preprocessing.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "d8afb6fc",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn import preprocessing\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "3b686b65",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>1</th>\n",
       "      <th>CountryName</th>\n",
       "      <th>CountryCode</th>\n",
       "      <th>Population growth</th>\n",
       "      <th>Total population</th>\n",
       "      <th>Area (sq. km)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>BRA</td>\n",
       "      <td>0.817555711</td>\n",
       "      <td>207652865</td>\n",
       "      <td>8358140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>Switzerland</td>\n",
       "      <td>CHE</td>\n",
       "      <td>1.077221168</td>\n",
       "      <td>8372098</td>\n",
       "      <td>39516</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4</td>\n",
       "      <td>Germany</td>\n",
       "      <td>DEU</td>\n",
       "      <td>1.193866758</td>\n",
       "      <td>82667685</td>\n",
       "      <td>348900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5</td>\n",
       "      <td>Denmark</td>\n",
       "      <td>DNK</td>\n",
       "      <td>0.834637611</td>\n",
       "      <td>?</td>\n",
       "      <td>42262</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6</td>\n",
       "      <td>Spain</td>\n",
       "      <td>ESP</td>\n",
       "      <td>-0.008048086</td>\n",
       "      <td>46443959</td>\n",
       "      <td>500210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>7</td>\n",
       "      <td>France</td>\n",
       "      <td>FRA</td>\n",
       "      <td>0.407491036</td>\n",
       "      <td>66896109</td>\n",
       "      <td>547557</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>8</td>\n",
       "      <td>Japan</td>\n",
       "      <td>JPN</td>\n",
       "      <td>-0.115284177</td>\n",
       "      <td>126994511</td>\n",
       "      <td>364560</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>9</td>\n",
       "      <td>Greece</td>\n",
       "      <td>GRC</td>\n",
       "      <td>-0.687542545</td>\n",
       "      <td>10746740</td>\n",
       "      <td>128900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>10</td>\n",
       "      <td>Iran</td>\n",
       "      <td>IRN</td>\n",
       "      <td>1.1487886</td>\n",
       "      <td>80277428</td>\n",
       "      <td>1628760</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>11</td>\n",
       "      <td>Kuwait</td>\n",
       "      <td>KWT</td>\n",
       "      <td>2.924206194</td>\n",
       "      <td>4052584</td>\n",
       "      <td>?</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>12</td>\n",
       "      <td>Morocco</td>\n",
       "      <td>MAR</td>\n",
       "      <td>?</td>\n",
       "      <td>35276786</td>\n",
       "      <td>446300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>13</td>\n",
       "      <td>Nigeria</td>\n",
       "      <td>NGA</td>\n",
       "      <td>2.619033526</td>\n",
       "      <td>185989640</td>\n",
       "      <td>910770</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>14</td>\n",
       "      <td>Qatar</td>\n",
       "      <td>QAT</td>\n",
       "      <td>3.495069918</td>\n",
       "      <td>2569804</td>\n",
       "      <td>11610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>15</td>\n",
       "      <td>Sweden</td>\n",
       "      <td>SWE</td>\n",
       "      <td>?</td>\n",
       "      <td>9903122</td>\n",
       "      <td>407310</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>16</td>\n",
       "      <td>India</td>\n",
       "      <td>IND</td>\n",
       "      <td>1.148214693</td>\n",
       "      <td>1324171354</td>\n",
       "      <td>2973190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>17</td>\n",
       "      <td>World</td>\n",
       "      <td>WLD</td>\n",
       "      <td>1.18167997</td>\n",
       "      <td>7442135578</td>\n",
       "      <td>129733172.7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     1  CountryName CountryCode Population growth Total population  \\\n",
       "0    2       Brazil         BRA       0.817555711        207652865   \n",
       "1    3  Switzerland         CHE       1.077221168          8372098   \n",
       "2    4      Germany         DEU       1.193866758         82667685   \n",
       "3    5      Denmark         DNK       0.834637611                ?   \n",
       "4    6        Spain         ESP      -0.008048086         46443959   \n",
       "5    7       France         FRA       0.407491036         66896109   \n",
       "6    8        Japan         JPN      -0.115284177        126994511   \n",
       "7    9       Greece         GRC      -0.687542545         10746740   \n",
       "8   10         Iran         IRN         1.1487886         80277428   \n",
       "9   11       Kuwait         KWT       2.924206194          4052584   \n",
       "10  12      Morocco         MAR                 ?         35276786   \n",
       "11  13      Nigeria         NGA       2.619033526        185989640   \n",
       "12  14        Qatar         QAT       3.495069918          2569804   \n",
       "13  15       Sweden         SWE                 ?          9903122   \n",
       "14  16        India         IND       1.148214693       1324171354   \n",
       "15  17        World         WLD        1.18167997       7442135578   \n",
       "\n",
       "   Area (sq. km)  \n",
       "0        8358140  \n",
       "1          39516  \n",
       "2         348900  \n",
       "3          42262  \n",
       "4         500210  \n",
       "5         547557  \n",
       "6         364560  \n",
       "7         128900  \n",
       "8        1628760  \n",
       "9              ?  \n",
       "10        446300  \n",
       "11        910770  \n",
       "12         11610  \n",
       "13        407310  \n",
       "14       2973190  \n",
       "15   129733172.7  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "countries=pd.read_csv('country.csv',header=1)\n",
    "countries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "6fd7ce32",
   "metadata": {},
   "outputs": [],
   "source": [
    "countries.rename(columns={'CountryName':'Name','CountryCode':'Code','Population growth':'Pop_growth',\n",
    "                          \t'Total population':'Pop',\t'Area (sq. km)':'Area'},inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "3f972b83",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
       "      <th>1</th>\n",
       "      <th>Name</th>\n",
       "      <th>Code</th>\n",
       "      <th>Pop_growth</th>\n",
       "      <th>Pop</th>\n",
       "      <th>Area</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>BRA</td>\n",
       "      <td>0.817555711</td>\n",
       "      <td>207652865</td>\n",
       "      <td>8358140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>Switzerland</td>\n",
       "      <td>CHE</td>\n",
       "      <td>1.077221168</td>\n",
       "      <td>8372098</td>\n",
       "      <td>39516</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4</td>\n",
       "      <td>Germany</td>\n",
       "      <td>DEU</td>\n",
       "      <td>1.193866758</td>\n",
       "      <td>82667685</td>\n",
       "      <td>348900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5</td>\n",
       "      <td>Denmark</td>\n",
       "      <td>DNK</td>\n",
       "      <td>0.834637611</td>\n",
       "      <td>?</td>\n",
       "      <td>42262</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6</td>\n",
       "      <td>Spain</td>\n",
       "      <td>ESP</td>\n",
       "      <td>-0.008048086</td>\n",
       "      <td>46443959</td>\n",
       "      <td>500210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>7</td>\n",
       "      <td>France</td>\n",
       "      <td>FRA</td>\n",
       "      <td>0.407491036</td>\n",
       "      <td>66896109</td>\n",
       "      <td>547557</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>8</td>\n",
       "      <td>Japan</td>\n",
       "      <td>JPN</td>\n",
       "      <td>-0.115284177</td>\n",
       "      <td>126994511</td>\n",
       "      <td>364560</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>9</td>\n",
       "      <td>Greece</td>\n",
       "      <td>GRC</td>\n",
       "      <td>-0.687542545</td>\n",
       "      <td>10746740</td>\n",
       "      <td>128900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>10</td>\n",
       "      <td>Iran</td>\n",
       "      <td>IRN</td>\n",
       "      <td>1.1487886</td>\n",
       "      <td>80277428</td>\n",
       "      <td>1628760</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>11</td>\n",
       "      <td>Kuwait</td>\n",
       "      <td>KWT</td>\n",
       "      <td>2.924206194</td>\n",
       "      <td>4052584</td>\n",
       "      <td>?</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>12</td>\n",
       "      <td>Morocco</td>\n",
       "      <td>MAR</td>\n",
       "      <td>?</td>\n",
       "      <td>35276786</td>\n",
       "      <td>446300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>13</td>\n",
       "      <td>Nigeria</td>\n",
       "      <td>NGA</td>\n",
       "      <td>2.619033526</td>\n",
       "      <td>185989640</td>\n",
       "      <td>910770</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>14</td>\n",
       "      <td>Qatar</td>\n",
       "      <td>QAT</td>\n",
       "      <td>3.495069918</td>\n",
       "      <td>2569804</td>\n",
       "      <td>11610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>15</td>\n",
       "      <td>Sweden</td>\n",
       "      <td>SWE</td>\n",
       "      <td>?</td>\n",
       "      <td>9903122</td>\n",
       "      <td>407310</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>16</td>\n",
       "      <td>India</td>\n",
       "      <td>IND</td>\n",
       "      <td>1.148214693</td>\n",
       "      <td>1324171354</td>\n",
       "      <td>2973190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>17</td>\n",
       "      <td>World</td>\n",
       "      <td>WLD</td>\n",
       "      <td>1.18167997</td>\n",
       "      <td>7442135578</td>\n",
       "      <td>129733172.7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     1         Name Code    Pop_growth         Pop         Area\n",
       "0    2       Brazil  BRA   0.817555711   207652865      8358140\n",
       "1    3  Switzerland  CHE   1.077221168     8372098        39516\n",
       "2    4      Germany  DEU   1.193866758    82667685       348900\n",
       "3    5      Denmark  DNK   0.834637611           ?        42262\n",
       "4    6        Spain  ESP  -0.008048086    46443959       500210\n",
       "5    7       France  FRA   0.407491036    66896109       547557\n",
       "6    8        Japan  JPN  -0.115284177   126994511       364560\n",
       "7    9       Greece  GRC  -0.687542545    10746740       128900\n",
       "8   10         Iran  IRN     1.1487886    80277428      1628760\n",
       "9   11       Kuwait  KWT   2.924206194     4052584            ?\n",
       "10  12      Morocco  MAR             ?    35276786       446300\n",
       "11  13      Nigeria  NGA   2.619033526   185989640       910770\n",
       "12  14        Qatar  QAT   3.495069918     2569804        11610\n",
       "13  15       Sweden  SWE             ?     9903122       407310\n",
       "14  16        India  IND   1.148214693  1324171354      2973190\n",
       "15  17        World  WLD    1.18167997  7442135578  129733172.7"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "countries\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "9a8fca88",
   "metadata": {},
   "outputs": [],
   "source": [
    "countries.drop(['1','Code'],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "a34017df",
   "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>Name</th>\n",
       "      <th>Pop_growth</th>\n",
       "      <th>Pop</th>\n",
       "      <th>Area</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Brazil</td>\n",
       "      <td>0.817555711</td>\n",
       "      <td>207652865</td>\n",
       "      <td>8358140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Switzerland</td>\n",
       "      <td>1.077221168</td>\n",
       "      <td>8372098</td>\n",
       "      <td>39516</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Germany</td>\n",
       "      <td>1.193866758</td>\n",
       "      <td>82667685</td>\n",
       "      <td>348900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Denmark</td>\n",
       "      <td>0.834637611</td>\n",
       "      <td>?</td>\n",
       "      <td>42262</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Spain</td>\n",
       "      <td>-0.008048086</td>\n",
       "      <td>46443959</td>\n",
       "      <td>500210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>France</td>\n",
       "      <td>0.407491036</td>\n",
       "      <td>66896109</td>\n",
       "      <td>547557</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Japan</td>\n",
       "      <td>-0.115284177</td>\n",
       "      <td>126994511</td>\n",
       "      <td>364560</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Greece</td>\n",
       "      <td>-0.687542545</td>\n",
       "      <td>10746740</td>\n",
       "      <td>128900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Iran</td>\n",
       "      <td>1.1487886</td>\n",
       "      <td>80277428</td>\n",
       "      <td>1628760</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Kuwait</td>\n",
       "      <td>2.924206194</td>\n",
       "      <td>4052584</td>\n",
       "      <td>?</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Morocco</td>\n",
       "      <td>?</td>\n",
       "      <td>35276786</td>\n",
       "      <td>446300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Nigeria</td>\n",
       "      <td>2.619033526</td>\n",
       "      <td>185989640</td>\n",
       "      <td>910770</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Qatar</td>\n",
       "      <td>3.495069918</td>\n",
       "      <td>2569804</td>\n",
       "      <td>11610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Sweden</td>\n",
       "      <td>?</td>\n",
       "      <td>9903122</td>\n",
       "      <td>407310</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>India</td>\n",
       "      <td>1.148214693</td>\n",
       "      <td>1324171354</td>\n",
       "      <td>2973190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>World</td>\n",
       "      <td>1.18167997</td>\n",
       "      <td>7442135578</td>\n",
       "      <td>129733172.7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Name    Pop_growth         Pop         Area\n",
       "0        Brazil   0.817555711   207652865      8358140\n",
       "1   Switzerland   1.077221168     8372098        39516\n",
       "2       Germany   1.193866758    82667685       348900\n",
       "3       Denmark   0.834637611           ?        42262\n",
       "4         Spain  -0.008048086    46443959       500210\n",
       "5        France   0.407491036    66896109       547557\n",
       "6         Japan  -0.115284177   126994511       364560\n",
       "7        Greece  -0.687542545    10746740       128900\n",
       "8          Iran     1.1487886    80277428      1628760\n",
       "9        Kuwait   2.924206194     4052584            ?\n",
       "10      Morocco             ?    35276786       446300\n",
       "11      Nigeria   2.619033526   185989640       910770\n",
       "12        Qatar   3.495069918     2569804        11610\n",
       "13       Sweden             ?     9903122       407310\n",
       "14        India   1.148214693  1324171354      2973190\n",
       "15        World    1.18167997  7442135578  129733172.7"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "countries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "3310e473",
   "metadata": {},
   "outputs": [],
   "source": [
    "countries.rename(index=countries.Name,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "30aa65e6",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Name</th>\n",
       "      <th>Pop_growth</th>\n",
       "      <th>Pop</th>\n",
       "      <th>Area</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Brazil</th>\n",
       "      <td>Brazil</td>\n",
       "      <td>0.817555711</td>\n",
       "      <td>207652865</td>\n",
       "      <td>8358140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Switzerland</th>\n",
       "      <td>Switzerland</td>\n",
       "      <td>1.077221168</td>\n",
       "      <td>8372098</td>\n",
       "      <td>39516</td>\n",
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       "    <tr>\n",
       "      <th>Germany</th>\n",
       "      <td>Germany</td>\n",
       "      <td>1.193866758</td>\n",
       "      <td>82667685</td>\n",
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       "    </tr>\n",
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       "      <td>?</td>\n",
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       "    <tr>\n",
       "      <th>Spain</th>\n",
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       "      <td>46443959</td>\n",
       "      <td>500210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>France</th>\n",
       "      <td>France</td>\n",
       "      <td>0.407491036</td>\n",
       "      <td>66896109</td>\n",
       "      <td>547557</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Japan</th>\n",
       "      <td>Japan</td>\n",
       "      <td>-0.115284177</td>\n",
       "      <td>126994511</td>\n",
       "      <td>364560</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Greece</th>\n",
       "      <td>Greece</td>\n",
       "      <td>-0.687542545</td>\n",
       "      <td>10746740</td>\n",
       "      <td>128900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Iran</th>\n",
       "      <td>Iran</td>\n",
       "      <td>1.1487886</td>\n",
       "      <td>80277428</td>\n",
       "      <td>1628760</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kuwait</th>\n",
       "      <td>Kuwait</td>\n",
       "      <td>2.924206194</td>\n",
       "      <td>4052584</td>\n",
       "      <td>?</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Morocco</th>\n",
       "      <td>Morocco</td>\n",
       "      <td>?</td>\n",
       "      <td>35276786</td>\n",
       "      <td>446300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nigeria</th>\n",
       "      <td>Nigeria</td>\n",
       "      <td>2.619033526</td>\n",
       "      <td>185989640</td>\n",
       "      <td>910770</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Qatar</th>\n",
       "      <td>Qatar</td>\n",
       "      <td>3.495069918</td>\n",
       "      <td>2569804</td>\n",
       "      <td>11610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sweden</th>\n",
       "      <td>Sweden</td>\n",
       "      <td>?</td>\n",
       "      <td>9903122</td>\n",
       "      <td>407310</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>India</th>\n",
       "      <td>India</td>\n",
       "      <td>1.148214693</td>\n",
       "      <td>1324171354</td>\n",
       "      <td>2973190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>World</th>\n",
       "      <td>World</td>\n",
       "      <td>1.18167997</td>\n",
       "      <td>7442135578</td>\n",
       "      <td>129733172.7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Name    Pop_growth         Pop         Area\n",
       "Brazil            Brazil   0.817555711   207652865      8358140\n",
       "Switzerland  Switzerland   1.077221168     8372098        39516\n",
       "Germany          Germany   1.193866758    82667685       348900\n",
       "Denmark          Denmark   0.834637611           ?        42262\n",
       "Spain              Spain  -0.008048086    46443959       500210\n",
       "France            France   0.407491036    66896109       547557\n",
       "Japan              Japan  -0.115284177   126994511       364560\n",
       "Greece            Greece  -0.687542545    10746740       128900\n",
       "Iran                Iran     1.1487886    80277428      1628760\n",
       "Kuwait            Kuwait   2.924206194     4052584            ?\n",
       "Morocco          Morocco             ?    35276786       446300\n",
       "Nigeria          Nigeria   2.619033526   185989640       910770\n",
       "Qatar              Qatar   3.495069918     2569804        11610\n",
       "Sweden            Sweden             ?     9903122       407310\n",
       "India              India   1.148214693  1324171354      2973190\n",
       "World              World    1.18167997  7442135578  129733172.7"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "countries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "f7045786",
   "metadata": {},
   "outputs": [],
   "source": [
    "countries.drop('Name',axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "a06b7ea0",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>?</td>\n",
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       "      <td>10746740</td>\n",
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       "      <th>Iran</th>\n",
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       "      <td>80277428</td>\n",
       "      <td>1628760</td>\n",
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       "    <tr>\n",
       "      <th>Kuwait</th>\n",
       "      <td>2.924206194</td>\n",
       "      <td>4052584</td>\n",
       "      <td>?</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Morocco</th>\n",
       "      <td>?</td>\n",
       "      <td>35276786</td>\n",
       "      <td>446300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nigeria</th>\n",
       "      <td>2.619033526</td>\n",
       "      <td>185989640</td>\n",
       "      <td>910770</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Qatar</th>\n",
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       "      <td>2569804</td>\n",
       "      <td>11610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sweden</th>\n",
       "      <td>?</td>\n",
       "      <td>9903122</td>\n",
       "      <td>407310</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>India</th>\n",
       "      <td>1.148214693</td>\n",
       "      <td>1324171354</td>\n",
       "      <td>2973190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>World</th>\n",
       "      <td>1.18167997</td>\n",
       "      <td>7442135578</td>\n",
       "      <td>129733172.7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Pop_growth         Pop         Area\n",
       "Brazil        0.817555711   207652865      8358140\n",
       "Switzerland   1.077221168     8372098        39516\n",
       "Germany       1.193866758    82667685       348900\n",
       "Denmark       0.834637611           ?        42262\n",
       "Spain        -0.008048086    46443959       500210\n",
       "France        0.407491036    66896109       547557\n",
       "Japan        -0.115284177   126994511       364560\n",
       "Greece       -0.687542545    10746740       128900\n",
       "Iran            1.1487886    80277428      1628760\n",
       "Kuwait        2.924206194     4052584            ?\n",
       "Morocco                 ?    35276786       446300\n",
       "Nigeria       2.619033526   185989640       910770\n",
       "Qatar         3.495069918     2569804        11610\n",
       "Sweden                  ?     9903122       407310\n",
       "India         1.148214693  1324171354      2973190\n",
       "World          1.18167997  7442135578  129733172.7"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "countries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "4630c0c4",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Denmark</th>\n",
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       "      <td>?</td>\n",
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       "      <th>Spain</th>\n",
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      ],
      "text/plain": [
       "               Pop_growth        Pop     Area\n",
       "Brazil        0.817555711  207652865  8358140\n",
       "Switzerland   1.077221168    8372098    39516\n",
       "Germany       1.193866758   82667685   348900\n",
       "Denmark       0.834637611          ?    42262\n",
       "Spain        -0.008048086   46443959   500210"
      ]
     },
     "execution_count": 12,
     "metadata": {},
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    }
   ],
   "source": [
    "countries.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "eb5a0714",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Nigeria</th>\n",
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       "      <th>Qatar</th>\n",
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       "      <td>1324171354</td>\n",
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       "    </tr>\n",
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      "text/plain": [
       "          Pop_growth         Pop         Area\n",
       "Nigeria  2.619033526   185989640       910770\n",
       "Qatar    3.495069918     2569804        11610\n",
       "Sweden             ?     9903122       407310\n",
       "India    1.148214693  1324171354      2973190\n",
       "World     1.18167997  7442135578  129733172.7"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "countries.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "73b45ede",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 16 entries, Brazil to World\n",
      "Data columns (total 3 columns):\n",
      " #   Column      Non-Null Count  Dtype \n",
      "---  ------      --------------  ----- \n",
      " 0   Pop_growth  16 non-null     object\n",
      " 1   Pop         16 non-null     object\n",
      " 2   Area        16 non-null     object\n",
      "dtypes: object(3)\n",
      "memory usage: 512.0+ bytes\n"
     ]
    }
   ],
   "source": [
    "countries.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "e0c516be",
   "metadata": {},
   "outputs": [
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       "       Pop_growth        Pop     Area\n",
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   "cell_type": "code",
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   "id": "c3e3cd25",
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   "outputs": [],
   "source": [
    "countries.replace('?',0,inplace=True)"
   ]
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   "id": "06ad451c",
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       "               Pop_growth         Pop         Area\n",
       "Brazil        0.817555711   207652865      8358140\n",
       "Switzerland   1.077221168     8372098        39516\n",
       "Germany       1.193866758    82667685       348900\n",
       "Denmark       0.834637611           0        42262\n",
       "Spain        -0.008048086    46443959       500210\n",
       "France        0.407491036    66896109       547557\n",
       "Japan        -0.115284177   126994511       364560\n",
       "Greece       -0.687542545    10746740       128900\n",
       "Iran            1.1487886    80277428      1628760\n",
       "Kuwait        2.924206194     4052584            0\n",
       "Morocco                 0    35276786       446300\n",
       "Nigeria       2.619033526   185989640       910770\n",
       "Qatar         3.495069918     2569804        11610\n",
       "Sweden                  0     9903122       407310\n",
       "India         1.148214693  1324171354      2973190\n",
       "World          1.18167997  7442135578  129733172.7"
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     "execution_count": 17,
     "metadata": {},
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   "source": [
    "countries"
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  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "85ce51e8",
   "metadata": {},
   "outputs": [
    {
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       "        Pop_growth        Pop     Area\n",
       "count           16         16       16\n",
       "unique          15         16       16\n",
       "top              0  207652865  8358140\n",
       "freq             2          1        1"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "countries.describe(include='all')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "c64bb399",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pop_growth    object\n",
       "Pop           object\n",
       "Area          object\n",
       "dtype: object"
      ]
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     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
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    "countries.dtypes"
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  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "79e88e0a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(countries)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "29859d60",
   "metadata": {},
   "outputs": [],
   "source": [
    "countries.Pop_growth= countries['Pop_growth'].astype(float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "be313869",
   "metadata": {},
   "outputs": [],
   "source": [
    "countries['Pop']=countries['Pop'].astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "d3ae321f",
   "metadata": {},
   "outputs": [],
   "source": [
    "countries['Area']=countries['Area'].astype(complex)"
   ]
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  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "52fe576d",
   "metadata": {},
   "outputs": [
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      "text/plain": [
       "             Pop_growth         Pop  \\\n",
       "Brazil         0.817556   207652865   \n",
       "Switzerland    1.077221     8372098   \n",
       "Germany        1.193867    82667685   \n",
       "Denmark        0.834638           0   \n",
       "Spain         -0.008048    46443959   \n",
       "France         0.407491    66896109   \n",
       "Japan         -0.115284   126994511   \n",
       "Greece        -0.687543    10746740   \n",
       "Iran           1.148789    80277428   \n",
       "Kuwait         2.924206     4052584   \n",
       "Morocco        0.000000    35276786   \n",
       "Nigeria        2.619034   185989640   \n",
       "Qatar          3.495070     2569804   \n",
       "Sweden         0.000000     9903122   \n",
       "India          1.148215  1324171354   \n",
       "World          1.181680  7442135578   \n",
       "\n",
       "                                                       Area  \n",
       "Brazil       8.358140e+06+0.000000e+                    00j  \n",
       "Switzerland  3.951600e+04+0.000000e+                    00j  \n",
       "Germany      3.489000e+05+0.000000e+                    00j  \n",
       "Denmark      4.226200e+04+0.000000e+                    00j  \n",
       "Spain        5.002100e+05+0.000000e+                    00j  \n",
       "France       5.475570e+05+0.000000e+                    00j  \n",
       "Japan        3.645600e+05+0.000000e+                    00j  \n",
       "Greece       1.289000e+05+0.000000e+                    00j  \n",
       "Iran         1.628760e+06+0.000000e+                    00j  \n",
       "Kuwait       0.000000e+00+0.000000e+                    00j  \n",
       "Morocco      4.463000e+05+0.000000e+                    00j  \n",
       "Nigeria      9.107700e+05+0.000000e+                    00j  \n",
       "Qatar        1.161000e+04+0.000000e+                    00j  \n",
       "Sweden       4.073100e+05+0.000000e+                    00j  \n",
       "India        2.973190e+06+0.000000e+                    00j  \n",
       "World        1.297332e+08+0.000000e+                    00j  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "countries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "6ada4741",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/mohammad-hossein-abedi/Documents/Code/Machine_learning_Puyan_Rajiyan/lib/python3.12/site-packages/pandas/core/dtypes/astype.py:133: ComplexWarning: Casting complex values to real discards the imaginary part\n",
      "  return arr.astype(dtype, copy=True)\n"
     ]
    }
   ],
   "source": [
    "countries['Area']=countries['Area'].astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "63b84008",
   "metadata": {},
   "outputs": [
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       "      <th>mean</th>\n",
       "      <td>1.002306</td>\n",
       "      <td>6.021344e+08</td>\n",
       "      <td>9.152572e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.160152</td>\n",
       "      <td>1.852081e+09</td>\n",
       "      <td>3.222189e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-0.687543</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>9.520366e+06</td>\n",
       "      <td>1.072405e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.955929</td>\n",
       "      <td>5.667003e+07</td>\n",
       "      <td>4.268050e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.184727</td>\n",
       "      <td>1.417433e+08</td>\n",
       "      <td>1.090268e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>3.495070</td>\n",
       "      <td>7.442136e+09</td>\n",
       "      <td>1.297332e+08</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Pop_growth           Pop          Area\n",
       "count   16.000000  1.600000e+01  1.600000e+01\n",
       "mean     1.002306  6.021344e+08  9.152572e+06\n",
       "std      1.160152  1.852081e+09  3.222189e+07\n",
       "min     -0.687543  0.000000e+00  0.000000e+00\n",
       "25%      0.000000  9.520366e+06  1.072405e+05\n",
       "50%      0.955929  5.667003e+07  4.268050e+05\n",
       "75%      1.184727  1.417433e+08  1.090268e+06\n",
       "max      3.495070  7.442136e+09  1.297332e+08"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "countries.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "443ff847",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 16 entries, Brazil to World\n",
      "Data columns (total 3 columns):\n",
      " #   Column      Non-Null Count  Dtype  \n",
      "---  ------      --------------  -----  \n",
      " 0   Pop_growth  16 non-null     float64\n",
      " 1   Pop         16 non-null     int64  \n",
      " 2   Area        16 non-null     int64  \n",
      "dtypes: float64(1), int64(2)\n",
      "memory usage: 512.0+ bytes\n"
     ]
    }
   ],
   "source": [
    "countries.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "ee0ce0ea",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pop_growth</th>\n",
       "      <th>Pop</th>\n",
       "      <th>Area</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>World</th>\n",
       "      <td>1.18168</td>\n",
       "      <td>7442135578</td>\n",
       "      <td>129733172</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Pop_growth         Pop       Area\n",
       "World     1.18168  7442135578  129733172"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "countries[countries['Pop']==countries['Pop'].max()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "936b0531",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7442135578"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "countries['Pop'].max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "2ea7e414",
   "metadata": {},
   "outputs": [],
   "source": [
    "countries.drop('World',inplace=True,axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "a7f84d5c",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pop_growth</th>\n",
       "      <th>Pop</th>\n",
       "      <th>Area</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Brazil</th>\n",
       "      <td>0.817556</td>\n",
       "      <td>207652865</td>\n",
       "      <td>8358140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Switzerland</th>\n",
       "      <td>1.077221</td>\n",
       "      <td>8372098</td>\n",
       "      <td>39516</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Germany</th>\n",
       "      <td>1.193867</td>\n",
       "      <td>82667685</td>\n",
       "      <td>348900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Denmark</th>\n",
       "      <td>0.834638</td>\n",
       "      <td>0</td>\n",
       "      <td>42262</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Spain</th>\n",
       "      <td>-0.008048</td>\n",
       "      <td>46443959</td>\n",
       "      <td>500210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>France</th>\n",
       "      <td>0.407491</td>\n",
       "      <td>66896109</td>\n",
       "      <td>547557</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Japan</th>\n",
       "      <td>-0.115284</td>\n",
       "      <td>126994511</td>\n",
       "      <td>364560</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Greece</th>\n",
       "      <td>-0.687543</td>\n",
       "      <td>10746740</td>\n",
       "      <td>128900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Iran</th>\n",
       "      <td>1.148789</td>\n",
       "      <td>80277428</td>\n",
       "      <td>1628760</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kuwait</th>\n",
       "      <td>2.924206</td>\n",
       "      <td>4052584</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Morocco</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>35276786</td>\n",
       "      <td>446300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nigeria</th>\n",
       "      <td>2.619034</td>\n",
       "      <td>185989640</td>\n",
       "      <td>910770</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Qatar</th>\n",
       "      <td>3.495070</td>\n",
       "      <td>2569804</td>\n",
       "      <td>11610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sweden</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>9903122</td>\n",
       "      <td>407310</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>India</th>\n",
       "      <td>1.148215</td>\n",
       "      <td>1324171354</td>\n",
       "      <td>2973190</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Pop_growth         Pop     Area\n",
       "Brazil         0.817556   207652865  8358140\n",
       "Switzerland    1.077221     8372098    39516\n",
       "Germany        1.193867    82667685   348900\n",
       "Denmark        0.834638           0    42262\n",
       "Spain         -0.008048    46443959   500210\n",
       "France         0.407491    66896109   547557\n",
       "Japan         -0.115284   126994511   364560\n",
       "Greece        -0.687543    10746740   128900\n",
       "Iran           1.148789    80277428  1628760\n",
       "Kuwait         2.924206     4052584        0\n",
       "Morocco        0.000000    35276786   446300\n",
       "Nigeria        2.619034   185989640   910770\n",
       "Qatar          3.495070     2569804    11610\n",
       "Sweden         0.000000     9903122   407310\n",
       "India          1.148215  1324171354  2973190"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "countries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "5b827577",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pop_growth</th>\n",
       "      <th>Pop</th>\n",
       "      <th>Area</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>15.000000</td>\n",
       "      <td>1.500000e+01</td>\n",
       "      <td>1.500000e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.990347</td>\n",
       "      <td>1.461343e+08</td>\n",
       "      <td>1.113866e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.199851</td>\n",
       "      <td>3.325660e+08</td>\n",
       "      <td>2.150749e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-0.687543</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>9.137610e+06</td>\n",
       "      <td>8.558100e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.834638</td>\n",
       "      <td>4.644396e+07</td>\n",
       "      <td>4.073100e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.171328</td>\n",
       "      <td>1.048311e+08</td>\n",
       "      <td>7.291635e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>3.495070</td>\n",
       "      <td>1.324171e+09</td>\n",
       "      <td>8.358140e+06</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Pop_growth           Pop          Area\n",
       "count   15.000000  1.500000e+01  1.500000e+01\n",
       "mean     0.990347  1.461343e+08  1.113866e+06\n",
       "std      1.199851  3.325660e+08  2.150749e+06\n",
       "min     -0.687543  0.000000e+00  0.000000e+00\n",
       "25%      0.000000  9.137610e+06  8.558100e+04\n",
       "50%      0.834638  4.644396e+07  4.073100e+05\n",
       "75%      1.171328  1.048311e+08  7.291635e+05\n",
       "max      3.495070  1.324171e+09  8.358140e+06"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "countries.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "6734dd6f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pop_growth</th>\n",
       "      <th>Pop</th>\n",
       "      <th>Area</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>India</th>\n",
       "      <td>1.148215</td>\n",
       "      <td>1324171354</td>\n",
       "      <td>2973190</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Pop_growth         Pop     Area\n",
       "India    1.148215  1324171354  2973190"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "countries[countries['Pop']==countries['Pop'].max()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "0445dfda",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "    .dataframe tbody tr th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pop_growth</th>\n",
       "      <th>Pop</th>\n",
       "      <th>Area</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Brazil</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Switzerland</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Germany</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Denmark</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Spain</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>France</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Japan</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Greece</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Iran</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kuwait</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Morocco</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nigeria</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Qatar</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sweden</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>India</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Pop_growth    Pop   Area\n",
       "Brazil            False  False  False\n",
       "Switzerland       False  False  False\n",
       "Germany           False  False  False\n",
       "Denmark           False  False  False\n",
       "Spain             False  False  False\n",
       "France            False  False  False\n",
       "Japan             False  False  False\n",
       "Greece            False  False  False\n",
       "Iran              False  False  False\n",
       "Kuwait            False  False  False\n",
       "Morocco           False  False  False\n",
       "Nigeria           False  False  False\n",
       "Qatar             False  False  False\n",
       "Sweden            False  False  False\n",
       "India             False  False  False"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "countries.isnull()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "c384fed9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pop_growth    0\n",
       "Pop           0\n",
       "Area          0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "countries.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "4f960f31",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pop           1\n",
       "Area          1\n",
       "Pop_growth    2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(countries[['Pop','Area','Pop_growth']]==0).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "3c5d0b81",
   "metadata": {},
   "outputs": [],
   "source": [
    "countries_copy=countries.copy(deep=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "88cd45fa",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pop_growth</th>\n",
       "      <th>Pop</th>\n",
       "      <th>Area</th>\n",
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       "  <tbody>\n",
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       "      <th>Brazil</th>\n",
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       "      <td>8358140</td>\n",
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       "    <tr>\n",
       "      <th>Switzerland</th>\n",
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       "    <tr>\n",
       "      <th>Germany</th>\n",
       "      <td>1.193867</td>\n",
       "      <td>82667685</td>\n",
       "      <td>348900</td>\n",
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       "    <tr>\n",
       "      <th>Denmark</th>\n",
       "      <td>0.834638</td>\n",
       "      <td>0</td>\n",
       "      <td>42262</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Spain</th>\n",
       "      <td>-0.008048</td>\n",
       "      <td>46443959</td>\n",
       "      <td>500210</td>\n",
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       "    <tr>\n",
       "      <th>France</th>\n",
       "      <td>0.407491</td>\n",
       "      <td>66896109</td>\n",
       "      <td>547557</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Japan</th>\n",
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       "      <td>126994511</td>\n",
       "      <td>364560</td>\n",
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       "    <tr>\n",
       "      <th>Greece</th>\n",
       "      <td>-0.687543</td>\n",
       "      <td>10746740</td>\n",
       "      <td>128900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Iran</th>\n",
       "      <td>1.148789</td>\n",
       "      <td>80277428</td>\n",
       "      <td>1628760</td>\n",
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       "    <tr>\n",
       "      <th>Kuwait</th>\n",
       "      <td>2.924206</td>\n",
       "      <td>4052584</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Morocco</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>35276786</td>\n",
       "      <td>446300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nigeria</th>\n",
       "      <td>2.619034</td>\n",
       "      <td>185989640</td>\n",
       "      <td>910770</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Qatar</th>\n",
       "      <td>3.495070</td>\n",
       "      <td>2569804</td>\n",
       "      <td>11610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sweden</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>9903122</td>\n",
       "      <td>407310</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>India</th>\n",
       "      <td>1.148215</td>\n",
       "      <td>1324171354</td>\n",
       "      <td>2973190</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Pop_growth         Pop     Area\n",
       "Brazil         0.817556   207652865  8358140\n",
       "Switzerland    1.077221     8372098    39516\n",
       "Germany        1.193867    82667685   348900\n",
       "Denmark        0.834638           0    42262\n",
       "Spain         -0.008048    46443959   500210\n",
       "France         0.407491    66896109   547557\n",
       "Japan         -0.115284   126994511   364560\n",
       "Greece        -0.687543    10746740   128900\n",
       "Iran           1.148789    80277428  1628760\n",
       "Kuwait         2.924206     4052584        0\n",
       "Morocco        0.000000    35276786   446300\n",
       "Nigeria        2.619034   185989640   910770\n",
       "Qatar          3.495070     2569804    11610\n",
       "Sweden         0.000000     9903122   407310\n",
       "India          1.148215  1324171354  2973190"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "countries_copy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "8baa87d6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Denmark'], dtype='object')"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "countries_copy[countries_copy['Pop']==0].index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "99d9abbb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "         Pop_growth  Pop   Area\n",
       "Denmark    0.834638    0  42262"
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     "execution_count": 40,
     "metadata": {},
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   "source": [
    "countries_copy[countries_copy['Pop']==0]"
   ]
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  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "89843501",
   "metadata": {},
   "outputs": [],
   "source": [
    "countries_copy.drop(countries_copy[countries_copy['Pop']==0].index,axis=0,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "f2d00b19",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index([], dtype='object')"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
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   "source": [
    "countries_copy[countries_copy['Pop']==0].index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "aee7ced5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Kuwait'], dtype='object')"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "countries_copy[countries_copy['Area']==0].index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "b99da61e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Morocco', 'Sweden'], dtype='object')"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "countries_copy[countries_copy['Pop_growth']==0].index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "48cc78a7",
   "metadata": {},
   "outputs": [],
   "source": [
    "countries_copy.drop(countries_copy[countries_copy['Area']==0].index,axis=0,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "aa73062a",
   "metadata": {},
   "outputs": [],
   "source": [
    "countries_copy.drop(countries_copy[countries_copy['Pop_growth']==0].index,axis=0,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "9d9ff917",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>126994511</td>\n",
       "      <td>364560</td>\n",
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       "    <tr>\n",
       "      <th>Greece</th>\n",
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       "      <td>10746740</td>\n",
       "      <td>128900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Iran</th>\n",
       "      <td>1.148789</td>\n",
       "      <td>80277428</td>\n",
       "      <td>1628760</td>\n",
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       "    <tr>\n",
       "      <th>Nigeria</th>\n",
       "      <td>2.619034</td>\n",
       "      <td>185989640</td>\n",
       "      <td>910770</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Qatar</th>\n",
       "      <td>3.495070</td>\n",
       "      <td>2569804</td>\n",
       "      <td>11610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>India</th>\n",
       "      <td>1.148215</td>\n",
       "      <td>1324171354</td>\n",
       "      <td>2973190</td>\n",
       "    </tr>\n",
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       "</table>\n",
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      ],
      "text/plain": [
       "             Pop_growth         Pop     Area\n",
       "Brazil         0.817556   207652865  8358140\n",
       "Switzerland    1.077221     8372098    39516\n",
       "Germany        1.193867    82667685   348900\n",
       "Spain         -0.008048    46443959   500210\n",
       "France         0.407491    66896109   547557\n",
       "Japan         -0.115284   126994511   364560\n",
       "Greece        -0.687543    10746740   128900\n",
       "Iran           1.148789    80277428  1628760\n",
       "Nigeria        2.619034   185989640   910770\n",
       "Qatar          3.495070     2569804    11610\n",
       "India          1.148215  1324171354  2973190"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "countries_copy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "1832b250",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>66896109</td>\n",
       "      <td>547557</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Japan</th>\n",
       "      <td>-0.115284</td>\n",
       "      <td>126994511</td>\n",
       "      <td>364560</td>\n",
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       "    <tr>\n",
       "      <th>Greece</th>\n",
       "      <td>-0.687543</td>\n",
       "      <td>10746740</td>\n",
       "      <td>128900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Iran</th>\n",
       "      <td>1.148789</td>\n",
       "      <td>80277428</td>\n",
       "      <td>1628760</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kuwait</th>\n",
       "      <td>2.924206</td>\n",
       "      <td>4052584</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Morocco</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>35276786</td>\n",
       "      <td>446300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nigeria</th>\n",
       "      <td>2.619034</td>\n",
       "      <td>185989640</td>\n",
       "      <td>910770</td>\n",
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       "    <tr>\n",
       "      <th>Qatar</th>\n",
       "      <td>3.495070</td>\n",
       "      <td>2569804</td>\n",
       "      <td>11610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sweden</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>9903122</td>\n",
       "      <td>407310</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>India</th>\n",
       "      <td>1.148215</td>\n",
       "      <td>1324171354</td>\n",
       "      <td>2973190</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Pop_growth         Pop     Area\n",
       "Brazil         0.817556   207652865  8358140\n",
       "Switzerland    1.077221     8372098    39516\n",
       "Germany        1.193867    82667685   348900\n",
       "Denmark        0.834638           0    42262\n",
       "Spain         -0.008048    46443959   500210\n",
       "France         0.407491    66896109   547557\n",
       "Japan         -0.115284   126994511   364560\n",
       "Greece        -0.687543    10746740   128900\n",
       "Iran           1.148789    80277428  1628760\n",
       "Kuwait         2.924206     4052584        0\n",
       "Morocco        0.000000    35276786   446300\n",
       "Nigeria        2.619034   185989640   910770\n",
       "Qatar          3.495070     2569804    11610\n",
       "Sweden         0.000000     9903122   407310\n",
       "India          1.148215  1324171354  2973190"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "countries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "8c9e99d9",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_37131/1618553936.py:1: FutureWarning: The 'method' keyword in Series.replace is deprecated and will be removed in a future version.\n",
      "  countries['Pop_growth'].replace(0,method='bfill')\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Brazil         0.817556\n",
       "Switzerland    1.077221\n",
       "Germany        1.193867\n",
       "Denmark        0.834638\n",
       "Spain         -0.008048\n",
       "France         0.407491\n",
       "Japan         -0.115284\n",
       "Greece        -0.687543\n",
       "Iran           1.148789\n",
       "Kuwait         2.924206\n",
       "Morocco        2.619034\n",
       "Nigeria        2.619034\n",
       "Qatar          3.495070\n",
       "Sweden         1.148215\n",
       "India          1.148215\n",
       "Name: Pop_growth, dtype: float64"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "countries['Pop_growth'].replace(0,method='bfill')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "beea86e2",
   "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>Pop_growth</th>\n",
       "      <th>Pop</th>\n",
       "      <th>Area</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Brazil</th>\n",
       "      <td>0.817556</td>\n",
       "      <td>207652865</td>\n",
       "      <td>8358140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Switzerland</th>\n",
       "      <td>1.077221</td>\n",
       "      <td>8372098</td>\n",
       "      <td>39516</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Germany</th>\n",
       "      <td>1.193867</td>\n",
       "      <td>82667685</td>\n",
       "      <td>348900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Denmark</th>\n",
       "      <td>0.834638</td>\n",
       "      <td>0</td>\n",
       "      <td>42262</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Spain</th>\n",
       "      <td>-0.008048</td>\n",
       "      <td>46443959</td>\n",
       "      <td>500210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>France</th>\n",
       "      <td>0.407491</td>\n",
       "      <td>66896109</td>\n",
       "      <td>547557</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Japan</th>\n",
       "      <td>-0.115284</td>\n",
       "      <td>126994511</td>\n",
       "      <td>364560</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Greece</th>\n",
       "      <td>-0.687543</td>\n",
       "      <td>10746740</td>\n",
       "      <td>128900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Iran</th>\n",
       "      <td>1.148789</td>\n",
       "      <td>80277428</td>\n",
       "      <td>1628760</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kuwait</th>\n",
       "      <td>2.924206</td>\n",
       "      <td>4052584</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Morocco</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>35276786</td>\n",
       "      <td>446300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nigeria</th>\n",
       "      <td>2.619034</td>\n",
       "      <td>185989640</td>\n",
       "      <td>910770</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Qatar</th>\n",
       "      <td>3.495070</td>\n",
       "      <td>2569804</td>\n",
       "      <td>11610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sweden</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>9903122</td>\n",
       "      <td>407310</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>India</th>\n",
       "      <td>1.148215</td>\n",
       "      <td>1324171354</td>\n",
       "      <td>2973190</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Pop_growth         Pop     Area\n",
       "Brazil         0.817556   207652865  8358140\n",
       "Switzerland    1.077221     8372098    39516\n",
       "Germany        1.193867    82667685   348900\n",
       "Denmark        0.834638           0    42262\n",
       "Spain         -0.008048    46443959   500210\n",
       "France         0.407491    66896109   547557\n",
       "Japan         -0.115284   126994511   364560\n",
       "Greece        -0.687543    10746740   128900\n",
       "Iran           1.148789    80277428  1628760\n",
       "Kuwait         2.924206     4052584        0\n",
       "Morocco        0.000000    35276786   446300\n",
       "Nigeria        2.619034   185989640   910770\n",
       "Qatar          3.495070     2569804    11610\n",
       "Sweden         0.000000     9903122   407310\n",
       "India          1.148215  1324171354  2973190"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "countries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "155e7ac2",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.impute import SimpleImputer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "8bfebfe3",
   "metadata": {},
   "outputs": [],
   "source": [
    "simple_imputer=SimpleImputer(missing_values=0,strategy='mean')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "18dc9f33",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-1 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-1 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-1 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-1 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SimpleImputer(missing_values=0)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;SimpleImputer<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.impute.SimpleImputer.html\">?<span>Documentation for SimpleImputer</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>SimpleImputer(missing_values=0)</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "SimpleImputer(missing_values=0)"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "simple_imputer.fit(countries)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "4afa9a84",
   "metadata": {},
   "outputs": [],
   "source": [
    "countries_copy2=simple_imputer.transform(countries)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "3ad7c4b2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 8.17555711e-01,  2.07652865e+08,  8.35814000e+06],\n",
       "       [ 1.07722117e+00,  8.37209800e+06,  3.95160000e+04],\n",
       "       [ 1.19386676e+00,  8.26676850e+07,  3.48900000e+05],\n",
       "       [ 8.34637611e-01,  1.56572478e+08,  4.22620000e+04],\n",
       "       [-8.04808600e-03,  4.64439590e+07,  5.00210000e+05],\n",
       "       [ 4.07491036e-01,  6.68961090e+07,  5.47557000e+05],\n",
       "       [-1.15284177e-01,  1.26994511e+08,  3.64560000e+05],\n",
       "       [-6.87542545e-01,  1.07467400e+07,  1.28900000e+05],\n",
       "       [ 1.14878860e+00,  8.02774280e+07,  1.62876000e+06],\n",
       "       [ 2.92420619e+00,  4.05258400e+06,  1.19342750e+06],\n",
       "       [ 1.14270849e+00,  3.52767860e+07,  4.46300000e+05],\n",
       "       [ 2.61903353e+00,  1.85989640e+08,  9.10770000e+05],\n",
       "       [ 3.49506992e+00,  2.56980400e+06,  1.16100000e+04],\n",
       "       [ 1.14270849e+00,  9.90312200e+06,  4.07310000e+05],\n",
       "       [ 1.14821469e+00,  1.32417135e+09,  2.97319000e+06]])"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "countries_copy2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "9306b1fe",
   "metadata": {},
   "outputs": [],
   "source": [
    "countries_copy2= pd.DataFrame(countries_copy2,columns=countries.columns,index=countries.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "92c99475",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pop_growth</th>\n",
       "      <th>Pop</th>\n",
       "      <th>Area</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Brazil</th>\n",
       "      <td>0.817556</td>\n",
       "      <td>207652865</td>\n",
       "      <td>8358140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Switzerland</th>\n",
       "      <td>1.077221</td>\n",
       "      <td>8372098</td>\n",
       "      <td>39516</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Germany</th>\n",
       "      <td>1.193867</td>\n",
       "      <td>82667685</td>\n",
       "      <td>348900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Denmark</th>\n",
       "      <td>0.834638</td>\n",
       "      <td>0</td>\n",
       "      <td>42262</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Spain</th>\n",
       "      <td>-0.008048</td>\n",
       "      <td>46443959</td>\n",
       "      <td>500210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>France</th>\n",
       "      <td>0.407491</td>\n",
       "      <td>66896109</td>\n",
       "      <td>547557</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Japan</th>\n",
       "      <td>-0.115284</td>\n",
       "      <td>126994511</td>\n",
       "      <td>364560</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Greece</th>\n",
       "      <td>-0.687543</td>\n",
       "      <td>10746740</td>\n",
       "      <td>128900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Iran</th>\n",
       "      <td>1.148789</td>\n",
       "      <td>80277428</td>\n",
       "      <td>1628760</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kuwait</th>\n",
       "      <td>2.924206</td>\n",
       "      <td>4052584</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Morocco</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>35276786</td>\n",
       "      <td>446300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nigeria</th>\n",
       "      <td>2.619034</td>\n",
       "      <td>185989640</td>\n",
       "      <td>910770</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Qatar</th>\n",
       "      <td>3.495070</td>\n",
       "      <td>2569804</td>\n",
       "      <td>11610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sweden</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>9903122</td>\n",
       "      <td>407310</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>India</th>\n",
       "      <td>1.148215</td>\n",
       "      <td>1324171354</td>\n",
       "      <td>2973190</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Pop_growth         Pop     Area\n",
       "Brazil         0.817556   207652865  8358140\n",
       "Switzerland    1.077221     8372098    39516\n",
       "Germany        1.193867    82667685   348900\n",
       "Denmark        0.834638           0    42262\n",
       "Spain         -0.008048    46443959   500210\n",
       "France         0.407491    66896109   547557\n",
       "Japan         -0.115284   126994511   364560\n",
       "Greece        -0.687543    10746740   128900\n",
       "Iran           1.148789    80277428  1628760\n",
       "Kuwait         2.924206     4052584        0\n",
       "Morocco        0.000000    35276786   446300\n",
       "Nigeria        2.619034   185989640   910770\n",
       "Qatar          3.495070     2569804    11610\n",
       "Sweden         0.000000     9903122   407310\n",
       "India          1.148215  1324171354  2973190"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "countries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "2df34f74",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pop_growth</th>\n",
       "      <th>Pop</th>\n",
       "      <th>Area</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Brazil</th>\n",
       "      <td>0.817556</td>\n",
       "      <td>2.076529e+08</td>\n",
       "      <td>8358140.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Switzerland</th>\n",
       "      <td>1.077221</td>\n",
       "      <td>8.372098e+06</td>\n",
       "      <td>39516.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Germany</th>\n",
       "      <td>1.193867</td>\n",
       "      <td>8.266768e+07</td>\n",
       "      <td>348900.0</td>\n",
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       "      <th>Denmark</th>\n",
       "      <td>0.834638</td>\n",
       "      <td>1.565725e+08</td>\n",
       "      <td>42262.0</td>\n",
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       "    <tr>\n",
       "      <th>Spain</th>\n",
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       "      <td>4.644396e+07</td>\n",
       "      <td>500210.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>France</th>\n",
       "      <td>0.407491</td>\n",
       "      <td>6.689611e+07</td>\n",
       "      <td>547557.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Japan</th>\n",
       "      <td>-0.115284</td>\n",
       "      <td>1.269945e+08</td>\n",
       "      <td>364560.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Greece</th>\n",
       "      <td>-0.687543</td>\n",
       "      <td>1.074674e+07</td>\n",
       "      <td>128900.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Iran</th>\n",
       "      <td>1.148789</td>\n",
       "      <td>8.027743e+07</td>\n",
       "      <td>1628760.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kuwait</th>\n",
       "      <td>2.924206</td>\n",
       "      <td>4.052584e+06</td>\n",
       "      <td>1193427.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Morocco</th>\n",
       "      <td>1.142708</td>\n",
       "      <td>3.527679e+07</td>\n",
       "      <td>446300.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nigeria</th>\n",
       "      <td>2.619034</td>\n",
       "      <td>1.859896e+08</td>\n",
       "      <td>910770.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Qatar</th>\n",
       "      <td>3.495070</td>\n",
       "      <td>2.569804e+06</td>\n",
       "      <td>11610.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sweden</th>\n",
       "      <td>1.142708</td>\n",
       "      <td>9.903122e+06</td>\n",
       "      <td>407310.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>India</th>\n",
       "      <td>1.148215</td>\n",
       "      <td>1.324171e+09</td>\n",
       "      <td>2973190.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Pop_growth           Pop       Area\n",
       "Brazil         0.817556  2.076529e+08  8358140.0\n",
       "Switzerland    1.077221  8.372098e+06    39516.0\n",
       "Germany        1.193867  8.266768e+07   348900.0\n",
       "Denmark        0.834638  1.565725e+08    42262.0\n",
       "Spain         -0.008048  4.644396e+07   500210.0\n",
       "France         0.407491  6.689611e+07   547557.0\n",
       "Japan         -0.115284  1.269945e+08   364560.0\n",
       "Greece        -0.687543  1.074674e+07   128900.0\n",
       "Iran           1.148789  8.027743e+07  1628760.0\n",
       "Kuwait         2.924206  4.052584e+06  1193427.5\n",
       "Morocco        1.142708  3.527679e+07   446300.0\n",
       "Nigeria        2.619034  1.859896e+08   910770.0\n",
       "Qatar          3.495070  2.569804e+06    11610.0\n",
       "Sweden         1.142708  9.903122e+06   407310.0\n",
       "India          1.148215  1.324171e+09  2973190.0"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "countries_copy2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "f658219b",
   "metadata": {},
   "outputs": [],
   "source": [
    "dup_data=pd.read_csv('mydata.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "fe709bc9",
   "metadata": {},
   "outputs": [
    {
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       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>b</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>b</td>\n",
       "      <td>B</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>4</td>\n",
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       "      <td>B</td>\n",
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       "</table>\n",
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      ],
      "text/plain": [
       "   columns1 columns2 columns3\n",
       "0         1        a        A\n",
       "1         2        a        A\n",
       "2         2        a        A\n",
       "3         3        b        B\n",
       "4         4        b        B\n",
       "5         4        b        B"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dup_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "cdab6fe3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    False\n",
       "1    False\n",
       "2     True\n",
       "3    False\n",
       "4    False\n",
       "5     True\n",
       "dtype: bool"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dup_data.duplicated()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "f1e04f2f",
   "metadata": {},
   "outputs": [
    {
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       "      <td>15</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>reza</td>\n",
       "      <td>6</td>\n",
       "      <td>14</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        0  1   2   3\n",
       "0   babak  1  13  17\n",
       "1    raha  2  14  14\n",
       "2    sara  3  17  12\n",
       "3    reza  4  12  18\n",
       "4   baran  2  15  20\n",
       "5    sara  3  14  19\n",
       "6   arash  4  20  19\n",
       "7  mehran  5  15  18\n",
       "8    reza  6  14  12"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "source"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "3be32817",
   "metadata": {},
   "outputs": [],
   "source": [
    "smartphones=pd.read_csv('smartphones.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "2dbbdb66",
   "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",
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       "    }\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>Name</th>\n",
       "      <th>OS</th>\n",
       "      <th>Capacity</th>\n",
       "      <th>Ram</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Company</th>\n",
       "      <th>inch</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Galaxy S10</td>\n",
       "      <td>Android</td>\n",
       "      <td>128</td>\n",
       "      <td>8</td>\n",
       "      <td>163.0</td>\n",
       "      <td>Samsung</td>\n",
       "      <td>6.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Lumia 950</td>\n",
       "      <td>windows</td>\n",
       "      <td>32</td>\n",
       "      <td>3</td>\n",
       "      <td>150.0</td>\n",
       "      <td>Microsoft</td>\n",
       "      <td>5.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Xpreia 1 ll</td>\n",
       "      <td>Android</td>\n",
       "      <td>256</td>\n",
       "      <td>8</td>\n",
       "      <td>181.0</td>\n",
       "      <td>Sony</td>\n",
       "      <td>6.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>iphone 7</td>\n",
       "      <td>ios</td>\n",
       "      <td>128</td>\n",
       "      <td>2</td>\n",
       "      <td>138.0</td>\n",
       "      <td>Apple</td>\n",
       "      <td>4.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>U Ultra</td>\n",
       "      <td>Android</td>\n",
       "      <td>64</td>\n",
       "      <td>4</td>\n",
       "      <td>170.0</td>\n",
       "      <td>HTC</td>\n",
       "      <td>5.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Galaxy S5</td>\n",
       "      <td>Android</td>\n",
       "      <td>16</td>\n",
       "      <td>2</td>\n",
       "      <td>145.0</td>\n",
       "      <td>Samsung</td>\n",
       "      <td>5.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>iphone 11 pro max</td>\n",
       "      <td>ios</td>\n",
       "      <td>256</td>\n",
       "      <td>4</td>\n",
       "      <td>226.0</td>\n",
       "      <td>Apple</td>\n",
       "      <td>6.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Moto G5</td>\n",
       "      <td>Android</td>\n",
       "      <td>16</td>\n",
       "      <td>3</td>\n",
       "      <td>144.5</td>\n",
       "      <td>Motorola</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Pixel</td>\n",
       "      <td>Android</td>\n",
       "      <td>128</td>\n",
       "      <td>4</td>\n",
       "      <td>143.0</td>\n",
       "      <td>Google</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Name        OS  Capacity  Ram  Weight    Company  inch\n",
       "0         Galaxy S10   Android       128    8   163.0    Samsung   6.2\n",
       "1          Lumia 950  windows         32    3   150.0  Microsoft   5.2\n",
       "2        Xpreia 1 ll   Android       256    8   181.0       Sony   6.5\n",
       "3          iphone 7        ios       128    2   138.0      Apple   4.7\n",
       "4            U Ultra   Android        64    4   170.0        HTC   5.7\n",
       "5          Galaxy S5   Android        16    2   145.0    Samsung   5.1\n",
       "6  iphone 11 pro max       ios       256    4   226.0      Apple   6.5\n",
       "7            Moto G5   Android        16    3   144.5   Motorola   5.0\n",
       "8             Pixel    Android       128    4   143.0     Google   5.0"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "smartphones"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "af119c86",
   "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>Capacity</th>\n",
       "      <th>Ram</th>\n",
       "      <th>Weight</th>\n",
       "      <th>inch</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>9.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>9.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>113.777778</td>\n",
       "      <td>4.222222</td>\n",
       "      <td>162.277778</td>\n",
       "      <td>5.544444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>93.104481</td>\n",
       "      <td>2.279132</td>\n",
       "      <td>27.833333</td>\n",
       "      <td>0.698411</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>16.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>138.000000</td>\n",
       "      <td>4.700000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>32.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>144.500000</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>128.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>5.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>128.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>170.000000</td>\n",
       "      <td>6.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>256.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>226.000000</td>\n",
       "      <td>6.500000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Capacity       Ram      Weight      inch\n",
       "count    9.000000  9.000000    9.000000  9.000000\n",
       "mean   113.777778  4.222222  162.277778  5.544444\n",
       "std     93.104481  2.279132   27.833333  0.698411\n",
       "min     16.000000  2.000000  138.000000  4.700000\n",
       "25%     32.000000  3.000000  144.500000  5.000000\n",
       "50%    128.000000  4.000000  150.000000  5.200000\n",
       "75%    128.000000  4.000000  170.000000  6.200000\n",
       "max    256.000000  8.000000  226.000000  6.500000"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "smartphones.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "a420a873",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Name         object\n",
       "OS           object\n",
       "Capacity      int64\n",
       "Ram           int64\n",
       "Weight      float64\n",
       "Company      object\n",
       "inch        float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "smartphones.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "9bfa09a3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "OS\n",
       "Android     6\n",
       "ios         2\n",
       "windows     1\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "smartphones.OS.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "3dc13245",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Company\n",
       "Samsung      2\n",
       "Apple        2\n",
       "Microsoft    1\n",
       "Sony         1\n",
       "HTC          1\n",
       "Motorola     1\n",
       "Google       1\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "smartphones.Company.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "0320d9a2",
   "metadata": {},
   "outputs": [],
   "source": [
    "cat_os=smartphones.groupby(smartphones.OS)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "c99abd76",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<pandas.core.groupby.generic.DataFrameGroupBy object at 0x72b6efb60bf0>"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cat_os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "ac695e1a",
   "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>Capacity</th>\n",
       "      <th>Ram</th>\n",
       "      <th>Weight</th>\n",
       "      <th>inch</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OS</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Android</th>\n",
       "      <td>101.333333</td>\n",
       "      <td>4.833333</td>\n",
       "      <td>157.75</td>\n",
       "      <td>5.583333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ios</th>\n",
       "      <td>192.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>182.00</td>\n",
       "      <td>5.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>windows</th>\n",
       "      <td>32.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>150.00</td>\n",
       "      <td>5.200000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Capacity       Ram  Weight      inch\n",
       "OS                                              \n",
       "Android   101.333333  4.833333  157.75  5.583333\n",
       "ios       192.000000  3.000000  182.00  5.600000\n",
       "windows    32.000000  3.000000  150.00  5.200000"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cat_os.mean(numeric_only=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "1825162c",
   "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>Capacity</th>\n",
       "      <th>Ram</th>\n",
       "      <th>Weight</th>\n",
       "      <th>inch</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OS</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Android</th>\n",
       "      <td>96.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>154.0</td>\n",
       "      <td>5.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ios</th>\n",
       "      <td>192.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>182.0</td>\n",
       "      <td>5.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>windows</th>\n",
       "      <td>32.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>5.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Capacity  Ram  Weight  inch\n",
       "OS                                   \n",
       "Android       96.0  4.0   154.0   5.4\n",
       "ios          192.0  3.0   182.0   5.6\n",
       "windows       32.0  3.0   150.0   5.2"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cat_os.median(numeric_only=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "42ee31af",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Android          Axes(0.1,0.559091;0.363636x0.340909)\n",
       "ios         Axes(0.536364,0.559091;0.363636x0.340909)\n",
       "windows              Axes(0.1,0.15;0.363636x0.340909)\n",
       "dtype: object"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x800 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "cat_os.boxplot(figsize=(12,8))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "4d8f3893",
   "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>Name</th>\n",
       "      <th>Capacity</th>\n",
       "      <th>Ram</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Company</th>\n",
       "      <th>inch</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OS</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Android</th>\n",
       "      <td>Xpreia 1 ll</td>\n",
       "      <td>256</td>\n",
       "      <td>8</td>\n",
       "      <td>181.0</td>\n",
       "      <td>Sony</td>\n",
       "      <td>6.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ios</th>\n",
       "      <td>iphone 7</td>\n",
       "      <td>256</td>\n",
       "      <td>4</td>\n",
       "      <td>226.0</td>\n",
       "      <td>Apple</td>\n",
       "      <td>6.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>windows</th>\n",
       "      <td>Lumia 950</td>\n",
       "      <td>32</td>\n",
       "      <td>3</td>\n",
       "      <td>150.0</td>\n",
       "      <td>Microsoft</td>\n",
       "      <td>5.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Name  Capacity  Ram  Weight    Company  inch\n",
       "OS                                                           \n",
       "Android   Xpreia 1 ll       256    8   181.0       Sony   6.5\n",
       "ios         iphone 7        256    4   226.0      Apple   6.5\n",
       "windows     Lumia 950        32    3   150.0  Microsoft   5.2"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cat_os.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "777cdc1a",
   "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>Name</th>\n",
       "      <th>Capacity</th>\n",
       "      <th>Ram</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Company</th>\n",
       "      <th>inch</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OS</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Android</th>\n",
       "      <td>Galaxy S10</td>\n",
       "      <td>16</td>\n",
       "      <td>2</td>\n",
       "      <td>143.0</td>\n",
       "      <td>Google</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ios</th>\n",
       "      <td>iphone 11 pro max</td>\n",
       "      <td>128</td>\n",
       "      <td>2</td>\n",
       "      <td>138.0</td>\n",
       "      <td>Apple</td>\n",
       "      <td>4.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>windows</th>\n",
       "      <td>Lumia 950</td>\n",
       "      <td>32</td>\n",
       "      <td>3</td>\n",
       "      <td>150.0</td>\n",
       "      <td>Microsoft</td>\n",
       "      <td>5.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       Name  Capacity  Ram  Weight    Company  inch\n",
       "OS                                                                 \n",
       "Android          Galaxy S10        16    2   143.0     Google   5.0\n",
       "ios       iphone 11 pro max       128    2   138.0      Apple   4.7\n",
       "windows           Lumia 950        32    3   150.0  Microsoft   5.2"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cat_os.min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "e86db790",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Ram</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>8</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OS</th>\n",
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       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Android</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ios</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>windows</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
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      ],
      "text/plain": [
       "Ram       2  3  4  8\n",
       "OS                  \n",
       "Android   1  1  2  2\n",
       "ios       1  0  1  0\n",
       "windows   0  1  0  0"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(smartphones.OS,smartphones.Ram)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "3a771df9",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Company</th>\n",
       "      <th>Apple</th>\n",
       "      <th>Google</th>\n",
       "      <th>HTC</th>\n",
       "      <th>Microsoft</th>\n",
       "      <th>Motorola</th>\n",
       "      <th>Samsung</th>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>Name</th>\n",
       "      <th></th>\n",
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       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Galaxy S10</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Galaxy S5</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lumia 950</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>Moto G5</th>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pixel</th>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>U Ultra</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Xpreia 1 ll</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>iphone 11 pro max</th>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>iphone 7</th>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Company            Apple  Google  HTC  Microsoft  Motorola  Samsung  Sony\n",
       "Name                                                                     \n",
       "Galaxy S10           NaN     NaN  NaN        NaN       NaN      8.0   NaN\n",
       "Galaxy S5            NaN     NaN  NaN        NaN       NaN      2.0   NaN\n",
       "Lumia 950            NaN     NaN  NaN        3.0       NaN      NaN   NaN\n",
       "Moto G5              NaN     NaN  NaN        NaN       3.0      NaN   NaN\n",
       "Pixel                NaN     4.0  NaN        NaN       NaN      NaN   NaN\n",
       "U Ultra              NaN     NaN  4.0        NaN       NaN      NaN   NaN\n",
       "Xpreia 1 ll          NaN     NaN  NaN        NaN       NaN      NaN   8.0\n",
       "iphone 11 pro max    4.0     NaN  NaN        NaN       NaN      NaN   NaN\n",
       "iphone 7             2.0     NaN  NaN        NaN       NaN      NaN   NaN"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.pivot_table(data=smartphones,index='Name',columns='Company',values='Ram')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "91a807d9",
   "metadata": {},
   "outputs": [],
   "source": [
    "smartphones.rename(index=smartphones.Name,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "9605a8db",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Galaxy S10</th>\n",
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       "    <tr>\n",
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       "      <td>windows</td>\n",
       "      <td>32</td>\n",
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       "      <td>181.0</td>\n",
       "      <td>Sony</td>\n",
       "      <td>6.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>iphone 7</th>\n",
       "      <td>iphone 7</td>\n",
       "      <td>ios</td>\n",
       "      <td>128</td>\n",
       "      <td>2</td>\n",
       "      <td>138.0</td>\n",
       "      <td>Apple</td>\n",
       "      <td>4.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>U Ultra</th>\n",
       "      <td>U Ultra</td>\n",
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       "      <td>64</td>\n",
       "      <td>4</td>\n",
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       "    <tr>\n",
       "      <th>Galaxy S5</th>\n",
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       "      <td>16</td>\n",
       "      <td>2</td>\n",
       "      <td>145.0</td>\n",
       "      <td>Samsung</td>\n",
       "      <td>5.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>iphone 11 pro max</th>\n",
       "      <td>iphone 11 pro max</td>\n",
       "      <td>ios</td>\n",
       "      <td>256</td>\n",
       "      <td>4</td>\n",
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       "      <td>Apple</td>\n",
       "      <td>6.5</td>\n",
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       "    <tr>\n",
       "      <th>Moto G5</th>\n",
       "      <td>Moto G5</td>\n",
       "      <td>Android</td>\n",
       "      <td>16</td>\n",
       "      <td>3</td>\n",
       "      <td>144.5</td>\n",
       "      <td>Motorola</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pixel</th>\n",
       "      <td>Pixel</td>\n",
       "      <td>Android</td>\n",
       "      <td>128</td>\n",
       "      <td>4</td>\n",
       "      <td>143.0</td>\n",
       "      <td>Google</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                Name        OS  Capacity  Ram  Weight  \\\n",
       "Galaxy S10                Galaxy S10   Android       128    8   163.0   \n",
       "Lumia 950                  Lumia 950  windows         32    3   150.0   \n",
       "Xpreia 1 ll              Xpreia 1 ll   Android       256    8   181.0   \n",
       "iphone 7                   iphone 7        ios       128    2   138.0   \n",
       "U Ultra                      U Ultra   Android        64    4   170.0   \n",
       "Galaxy S5                  Galaxy S5   Android        16    2   145.0   \n",
       "iphone 11 pro max  iphone 11 pro max       ios       256    4   226.0   \n",
       "Moto G5                      Moto G5   Android        16    3   144.5   \n",
       "Pixel                         Pixel    Android       128    4   143.0   \n",
       "\n",
       "                     Company  inch  \n",
       "Galaxy S10           Samsung   6.2  \n",
       "Lumia 950          Microsoft   5.2  \n",
       "Xpreia 1 ll             Sony   6.5  \n",
       "iphone 7               Apple   4.7  \n",
       "U Ultra                  HTC   5.7  \n",
       "Galaxy S5            Samsung   5.1  \n",
       "iphone 11 pro max      Apple   6.5  \n",
       "Moto G5             Motorola   5.0  \n",
       "Pixel                 Google   5.0  "
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "smartphones"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1040f5ea",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "a9254549",
   "metadata": {},
   "outputs": [],
   "source": [
    "smartphones.drop(['Name','Company'],inplace=True,axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "015a116e",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>163.0</td>\n",
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       "      <th>iphone 11 pro max</th>\n",
       "      <td>ios</td>\n",
       "      <td>256</td>\n",
       "      <td>4</td>\n",
       "      <td>226.0</td>\n",
       "      <td>6.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Moto G5</th>\n",
       "      <td>Android</td>\n",
       "      <td>16</td>\n",
       "      <td>3</td>\n",
       "      <td>144.5</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pixel</th>\n",
       "      <td>Android</td>\n",
       "      <td>128</td>\n",
       "      <td>4</td>\n",
       "      <td>143.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         OS  Capacity  Ram  Weight  inch\n",
       "Galaxy S10          Android       128    8   163.0   6.2\n",
       "Lumia 950          windows         32    3   150.0   5.2\n",
       "Xpreia 1 ll         Android       256    8   181.0   6.5\n",
       "iphone 7                ios       128    2   138.0   4.7\n",
       "U Ultra             Android        64    4   170.0   5.7\n",
       "Galaxy S5           Android        16    2   145.0   5.1\n",
       "iphone 11 pro max       ios       256    4   226.0   6.5\n",
       "Moto G5             Android        16    3   144.5   5.0\n",
       "Pixel               Android       128    4   143.0   5.0"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "smartphones"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "871580c4",
   "metadata": {},
   "outputs": [],
   "source": [
    "smartphones_dummy=pd.get_dummies(smartphones)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "56294979",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>8</td>\n",
       "      <td>163.0</td>\n",
       "      <td>6.2</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lumia 950</th>\n",
       "      <td>32</td>\n",
       "      <td>3</td>\n",
       "      <td>150.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Xpreia 1 ll</th>\n",
       "      <td>256</td>\n",
       "      <td>8</td>\n",
       "      <td>181.0</td>\n",
       "      <td>6.5</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>iphone 7</th>\n",
       "      <td>128</td>\n",
       "      <td>2</td>\n",
       "      <td>138.0</td>\n",
       "      <td>4.7</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>U Ultra</th>\n",
       "      <td>64</td>\n",
       "      <td>4</td>\n",
       "      <td>170.0</td>\n",
       "      <td>5.7</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Galaxy S5</th>\n",
       "      <td>16</td>\n",
       "      <td>2</td>\n",
       "      <td>145.0</td>\n",
       "      <td>5.1</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>iphone 11 pro max</th>\n",
       "      <td>256</td>\n",
       "      <td>4</td>\n",
       "      <td>226.0</td>\n",
       "      <td>6.5</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Moto G5</th>\n",
       "      <td>16</td>\n",
       "      <td>3</td>\n",
       "      <td>144.5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pixel</th>\n",
       "      <td>128</td>\n",
       "      <td>4</td>\n",
       "      <td>143.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Capacity  Ram  Weight  inch  OS_Android  OS_ios  \\\n",
       "Galaxy S10              128    8   163.0   6.2        True   False   \n",
       "Lumia 950                32    3   150.0   5.2       False   False   \n",
       "Xpreia 1 ll             256    8   181.0   6.5        True   False   \n",
       "iphone 7                128    2   138.0   4.7       False    True   \n",
       "U Ultra                  64    4   170.0   5.7        True   False   \n",
       "Galaxy S5                16    2   145.0   5.1        True   False   \n",
       "iphone 11 pro max       256    4   226.0   6.5       False    True   \n",
       "Moto G5                  16    3   144.5   5.0        True   False   \n",
       "Pixel                   128    4   143.0   5.0        True   False   \n",
       "\n",
       "                   OS_windows   \n",
       "Galaxy S10               False  \n",
       "Lumia 950                 True  \n",
       "Xpreia 1 ll              False  \n",
       "iphone 7                 False  \n",
       "U Ultra                  False  \n",
       "Galaxy S5                False  \n",
       "iphone 11 pro max        False  \n",
       "Moto G5                  False  \n",
       "Pixel                    False  "
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "smartphones_dummy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "a47a8e2c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Capacity', 'Ram', 'Weight', 'inch', 'OS_Android', 'OS_ios',\n",
       "       'OS_windows '],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "smartphones_dummy.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "40d03b41",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\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>Capacity</th>\n",
       "      <th>Ram</th>\n",
       "      <th>Weight</th>\n",
       "      <th>inch</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>9.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>9.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>113.777778</td>\n",
       "      <td>4.222222</td>\n",
       "      <td>162.277778</td>\n",
       "      <td>5.544444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>93.104481</td>\n",
       "      <td>2.279132</td>\n",
       "      <td>27.833333</td>\n",
       "      <td>0.698411</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>16.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>138.000000</td>\n",
       "      <td>4.700000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>32.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>144.500000</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>128.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>5.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>128.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>170.000000</td>\n",
       "      <td>6.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>256.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>226.000000</td>\n",
       "      <td>6.500000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Capacity       Ram      Weight      inch\n",
       "count    9.000000  9.000000    9.000000  9.000000\n",
       "mean   113.777778  4.222222  162.277778  5.544444\n",
       "std     93.104481  2.279132   27.833333  0.698411\n",
       "min     16.000000  2.000000  138.000000  4.700000\n",
       "25%     32.000000  3.000000  144.500000  5.000000\n",
       "50%    128.000000  4.000000  150.000000  5.200000\n",
       "75%    128.000000  4.000000  170.000000  6.200000\n",
       "max    256.000000  8.000000  226.000000  6.500000"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "smartphones_dummy.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "d7d6cd3f",
   "metadata": {},
   "outputs": [],
   "source": [
    "smartphones_dummy[['OS_windows ','OS_ios','OS_Android']]= smartphones_dummy[['OS_windows ','OS_ios','OS_Android']].astype(int )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "2d747a5c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
       "      <th>Capacity</th>\n",
       "      <th>Ram</th>\n",
       "      <th>Weight</th>\n",
       "      <th>inch</th>\n",
       "      <th>OS_Android</th>\n",
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       "      <th>Galaxy S10</th>\n",
       "      <td>128</td>\n",
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       "      <td>163.0</td>\n",
       "      <td>6.2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lumia 950</th>\n",
       "      <td>32</td>\n",
       "      <td>3</td>\n",
       "      <td>150.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Xpreia 1 ll</th>\n",
       "      <td>256</td>\n",
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       "      <td>181.0</td>\n",
       "      <td>6.5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>iphone 7</th>\n",
       "      <td>128</td>\n",
       "      <td>2</td>\n",
       "      <td>138.0</td>\n",
       "      <td>4.7</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>U Ultra</th>\n",
       "      <td>64</td>\n",
       "      <td>4</td>\n",
       "      <td>170.0</td>\n",
       "      <td>5.7</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Galaxy S5</th>\n",
       "      <td>16</td>\n",
       "      <td>2</td>\n",
       "      <td>145.0</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>iphone 11 pro max</th>\n",
       "      <td>256</td>\n",
       "      <td>4</td>\n",
       "      <td>226.0</td>\n",
       "      <td>6.5</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <th>Moto G5</th>\n",
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       "      <td>3</td>\n",
       "      <td>144.5</td>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>Pixel</th>\n",
       "      <td>128</td>\n",
       "      <td>4</td>\n",
       "      <td>143.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Capacity  Ram  Weight  inch  OS_Android  OS_ios  \\\n",
       "Galaxy S10              128    8   163.0   6.2           1       0   \n",
       "Lumia 950                32    3   150.0   5.2           0       0   \n",
       "Xpreia 1 ll             256    8   181.0   6.5           1       0   \n",
       "iphone 7                128    2   138.0   4.7           0       1   \n",
       "U Ultra                  64    4   170.0   5.7           1       0   \n",
       "Galaxy S5                16    2   145.0   5.1           1       0   \n",
       "iphone 11 pro max       256    4   226.0   6.5           0       1   \n",
       "Moto G5                  16    3   144.5   5.0           1       0   \n",
       "Pixel                   128    4   143.0   5.0           1       0   \n",
       "\n",
       "                   OS_windows   \n",
       "Galaxy S10                   0  \n",
       "Lumia 950                    1  \n",
       "Xpreia 1 ll                  0  \n",
       "iphone 7                     0  \n",
       "U Ultra                      0  \n",
       "Galaxy S5                    0  \n",
       "iphone 11 pro max            0  \n",
       "Moto G5                      0  \n",
       "Pixel                        0  "
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "smartphones_dummy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "083d9cef",
   "metadata": {},
   "outputs": [],
   "source": [
    "smartphones_dummy.drop(['OS_windows '],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "2b6a7ea9",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Galaxy S10</th>\n",
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       "      <th>Galaxy S5</th>\n",
       "      <td>16</td>\n",
       "      <td>2</td>\n",
       "      <td>145.0</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>iphone 11 pro max</th>\n",
       "      <td>256</td>\n",
       "      <td>4</td>\n",
       "      <td>226.0</td>\n",
       "      <td>6.5</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Moto G5</th>\n",
       "      <td>16</td>\n",
       "      <td>3</td>\n",
       "      <td>144.5</td>\n",
       "      <td>5.0</td>\n",
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       "    <tr>\n",
       "      <th>Pixel</th>\n",
       "      <td>128</td>\n",
       "      <td>4</td>\n",
       "      <td>143.0</td>\n",
       "      <td>5.0</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Capacity  Ram  Weight  inch  OS_Android  OS_ios\n",
       "Galaxy S10              128    8   163.0   6.2           1       0\n",
       "Lumia 950                32    3   150.0   5.2           0       0\n",
       "Xpreia 1 ll             256    8   181.0   6.5           1       0\n",
       "iphone 7                128    2   138.0   4.7           0       1\n",
       "U Ultra                  64    4   170.0   5.7           1       0\n",
       "Galaxy S5                16    2   145.0   5.1           1       0\n",
       "iphone 11 pro max       256    4   226.0   6.5           0       1\n",
       "Moto G5                  16    3   144.5   5.0           1       0\n",
       "Pixel                   128    4   143.0   5.0           1       0"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "smartphones_dummy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "5878b2b6",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>9.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>9.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>113.777778</td>\n",
       "      <td>4.222222</td>\n",
       "      <td>162.277778</td>\n",
       "      <td>5.544444</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.222222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>93.104481</td>\n",
       "      <td>2.279132</td>\n",
       "      <td>27.833333</td>\n",
       "      <td>0.698411</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.440959</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>16.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>138.000000</td>\n",
       "      <td>4.700000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>32.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>144.500000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>128.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>5.200000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>128.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>170.000000</td>\n",
       "      <td>6.200000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>256.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>226.000000</td>\n",
       "      <td>6.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Capacity       Ram      Weight      inch  OS_Android    OS_ios\n",
       "count    9.000000  9.000000    9.000000  9.000000    9.000000  9.000000\n",
       "mean   113.777778  4.222222  162.277778  5.544444    0.666667  0.222222\n",
       "std     93.104481  2.279132   27.833333  0.698411    0.500000  0.440959\n",
       "min     16.000000  2.000000  138.000000  4.700000    0.000000  0.000000\n",
       "25%     32.000000  3.000000  144.500000  5.000000    0.000000  0.000000\n",
       "50%    128.000000  4.000000  150.000000  5.200000    1.000000  0.000000\n",
       "75%    128.000000  4.000000  170.000000  6.200000    1.000000  0.000000\n",
       "max    256.000000  8.000000  226.000000  6.500000    1.000000  1.000000"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "smartphones_dummy.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "bcccad39",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import scale"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "c139551a",
   "metadata": {},
   "outputs": [],
   "source": [
    "smartphones_scale=scale(smartphones_dummy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "081b2b4d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.16202168,  1.75809815,  0.02752212,  0.99557678,  0.70710678,\n",
       "        -0.53452248],\n",
       "       [-0.93162467, -0.56879646, -0.46787604, -0.52309966, -1.41421356,\n",
       "        -0.53452248],\n",
       "       [ 1.62021682,  1.75809815,  0.71345804,  1.45117971,  0.70710678,\n",
       "        -0.53452248],\n",
       "       [ 0.16202168, -1.03417538, -0.92516666, -1.28243788, -1.41421356,\n",
       "         1.87082869],\n",
       "       [-0.56707589, -0.10341754,  0.29427498,  0.23623856,  0.70710678,\n",
       "        -0.53452248],\n",
       "       [-1.11389906, -1.03417538, -0.6584138 , -0.67496731,  0.70710678,\n",
       "        -0.53452248],\n",
       "       [ 1.62021682, -0.10341754,  2.42829784,  1.45117971, -1.41421356,\n",
       "         1.87082869],\n",
       "       [-1.11389906, -0.56879646, -0.67746757, -0.82683495,  0.70710678,\n",
       "        -0.53452248],\n",
       "       [ 0.16202168, -0.10341754, -0.7346289 , -0.82683495,  0.70710678,\n",
       "        -0.53452248]])"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "smartphones_scale"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "a2c3175e",
   "metadata": {},
   "outputs": [],
   "source": [
    "smartphones_scale=pd.DataFrame(data=smartphones_scale,index=smartphones_dummy.index,columns=smartphones_dummy.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "9141284e",
   "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>Capacity</th>\n",
       "      <th>Ram</th>\n",
       "      <th>Weight</th>\n",
       "      <th>inch</th>\n",
       "      <th>OS_Android</th>\n",
       "      <th>OS_ios</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Galaxy S10</th>\n",
       "      <td>0.162022</td>\n",
       "      <td>1.758098</td>\n",
       "      <td>0.027522</td>\n",
       "      <td>0.995577</td>\n",
       "      <td>0.707107</td>\n",
       "      <td>-0.534522</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lumia 950</th>\n",
       "      <td>-0.931625</td>\n",
       "      <td>-0.568796</td>\n",
       "      <td>-0.467876</td>\n",
       "      <td>-0.523100</td>\n",
       "      <td>-1.414214</td>\n",
       "      <td>-0.534522</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Xpreia 1 ll</th>\n",
       "      <td>1.620217</td>\n",
       "      <td>1.758098</td>\n",
       "      <td>0.713458</td>\n",
       "      <td>1.451180</td>\n",
       "      <td>0.707107</td>\n",
       "      <td>-0.534522</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>iphone 7</th>\n",
       "      <td>0.162022</td>\n",
       "      <td>-1.034175</td>\n",
       "      <td>-0.925167</td>\n",
       "      <td>-1.282438</td>\n",
       "      <td>-1.414214</td>\n",
       "      <td>1.870829</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>U Ultra</th>\n",
       "      <td>-0.567076</td>\n",
       "      <td>-0.103418</td>\n",
       "      <td>0.294275</td>\n",
       "      <td>0.236239</td>\n",
       "      <td>0.707107</td>\n",
       "      <td>-0.534522</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Galaxy S5</th>\n",
       "      <td>-1.113899</td>\n",
       "      <td>-1.034175</td>\n",
       "      <td>-0.658414</td>\n",
       "      <td>-0.674967</td>\n",
       "      <td>0.707107</td>\n",
       "      <td>-0.534522</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>iphone 11 pro max</th>\n",
       "      <td>1.620217</td>\n",
       "      <td>-0.103418</td>\n",
       "      <td>2.428298</td>\n",
       "      <td>1.451180</td>\n",
       "      <td>-1.414214</td>\n",
       "      <td>1.870829</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Moto G5</th>\n",
       "      <td>-1.113899</td>\n",
       "      <td>-0.568796</td>\n",
       "      <td>-0.677468</td>\n",
       "      <td>-0.826835</td>\n",
       "      <td>0.707107</td>\n",
       "      <td>-0.534522</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pixel</th>\n",
       "      <td>0.162022</td>\n",
       "      <td>-0.103418</td>\n",
       "      <td>-0.734629</td>\n",
       "      <td>-0.826835</td>\n",
       "      <td>0.707107</td>\n",
       "      <td>-0.534522</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Capacity       Ram    Weight      inch  OS_Android  \\\n",
       "Galaxy S10         0.162022  1.758098  0.027522  0.995577    0.707107   \n",
       "Lumia 950         -0.931625 -0.568796 -0.467876 -0.523100   -1.414214   \n",
       "Xpreia 1 ll        1.620217  1.758098  0.713458  1.451180    0.707107   \n",
       "iphone 7           0.162022 -1.034175 -0.925167 -1.282438   -1.414214   \n",
       "U Ultra           -0.567076 -0.103418  0.294275  0.236239    0.707107   \n",
       "Galaxy S5         -1.113899 -1.034175 -0.658414 -0.674967    0.707107   \n",
       "iphone 11 pro max  1.620217 -0.103418  2.428298  1.451180   -1.414214   \n",
       "Moto G5           -1.113899 -0.568796 -0.677468 -0.826835    0.707107   \n",
       "Pixel              0.162022 -0.103418 -0.734629 -0.826835    0.707107   \n",
       "\n",
       "                     OS_ios  \n",
       "Galaxy S10        -0.534522  \n",
       "Lumia 950         -0.534522  \n",
       "Xpreia 1 ll       -0.534522  \n",
       "iphone 7           1.870829  \n",
       "U Ultra           -0.534522  \n",
       "Galaxy S5         -0.534522  \n",
       "iphone 11 pro max  1.870829  \n",
       "Moto G5           -0.534522  \n",
       "Pixel             -0.534522  "
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "smartphones_scale"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "a64a9b82",
   "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>Capacity</th>\n",
       "      <th>Ram</th>\n",
       "      <th>Weight</th>\n",
       "      <th>inch</th>\n",
       "      <th>OS_Android</th>\n",
       "      <th>OS_ios</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>9.000000e+00</td>\n",
       "      <td>9.000000e+00</td>\n",
       "      <td>9.000000e+00</td>\n",
       "      <td>9.000000e+00</td>\n",
       "      <td>9.000000e+00</td>\n",
       "      <td>9.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>4.009139e-17</td>\n",
       "      <td>-4.934325e-17</td>\n",
       "      <td>2.467162e-16</td>\n",
       "      <td>-1.060880e-15</td>\n",
       "      <td>7.401487e-17</td>\n",
       "      <td>3.700743e-17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.060660e+00</td>\n",
       "      <td>1.060660e+00</td>\n",
       "      <td>1.060660e+00</td>\n",
       "      <td>1.060660e+00</td>\n",
       "      <td>1.060660e+00</td>\n",
       "      <td>1.060660e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-1.113899e+00</td>\n",
       "      <td>-1.034175e+00</td>\n",
       "      <td>-9.251667e-01</td>\n",
       "      <td>-1.282438e+00</td>\n",
       "      <td>-1.414214e+00</td>\n",
       "      <td>-5.345225e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-9.316247e-01</td>\n",
       "      <td>-5.687965e-01</td>\n",
       "      <td>-6.774676e-01</td>\n",
       "      <td>-8.268350e-01</td>\n",
       "      <td>-1.414214e+00</td>\n",
       "      <td>-5.345225e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.620217e-01</td>\n",
       "      <td>-1.034175e-01</td>\n",
       "      <td>-4.678760e-01</td>\n",
       "      <td>-5.230997e-01</td>\n",
       "      <td>7.071068e-01</td>\n",
       "      <td>-5.345225e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.620217e-01</td>\n",
       "      <td>-1.034175e-01</td>\n",
       "      <td>2.942750e-01</td>\n",
       "      <td>9.955768e-01</td>\n",
       "      <td>7.071068e-01</td>\n",
       "      <td>-5.345225e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.620217e+00</td>\n",
       "      <td>1.758098e+00</td>\n",
       "      <td>2.428298e+00</td>\n",
       "      <td>1.451180e+00</td>\n",
       "      <td>7.071068e-01</td>\n",
       "      <td>1.870829e+00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Capacity           Ram        Weight          inch    OS_Android  \\\n",
       "count  9.000000e+00  9.000000e+00  9.000000e+00  9.000000e+00  9.000000e+00   \n",
       "mean   4.009139e-17 -4.934325e-17  2.467162e-16 -1.060880e-15  7.401487e-17   \n",
       "std    1.060660e+00  1.060660e+00  1.060660e+00  1.060660e+00  1.060660e+00   \n",
       "min   -1.113899e+00 -1.034175e+00 -9.251667e-01 -1.282438e+00 -1.414214e+00   \n",
       "25%   -9.316247e-01 -5.687965e-01 -6.774676e-01 -8.268350e-01 -1.414214e+00   \n",
       "50%    1.620217e-01 -1.034175e-01 -4.678760e-01 -5.230997e-01  7.071068e-01   \n",
       "75%    1.620217e-01 -1.034175e-01  2.942750e-01  9.955768e-01  7.071068e-01   \n",
       "max    1.620217e+00  1.758098e+00  2.428298e+00  1.451180e+00  7.071068e-01   \n",
       "\n",
       "             OS_ios  \n",
       "count  9.000000e+00  \n",
       "mean   3.700743e-17  \n",
       "std    1.060660e+00  \n",
       "min   -5.345225e-01  \n",
       "25%   -5.345225e-01  \n",
       "50%   -5.345225e-01  \n",
       "75%   -5.345225e-01  \n",
       "max    1.870829e+00  "
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "smartphones_scale.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "c6018c75",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import normalize"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "1c69c6df",
   "metadata": {},
   "outputs": [],
   "source": [
    "smartphones_normalize_l1=normalize(smartphones_dummy,norm='l1',axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "a158a10d",
   "metadata": {},
   "outputs": [],
   "source": [
    "smartphones_normalize_l1=pd.DataFrame(data=smartphones_normalize_l1,index=smartphones_dummy.index,columns=smartphones_dummy.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "e4816b1a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Capacity</th>\n",
       "      <th>Ram</th>\n",
       "      <th>Weight</th>\n",
       "      <th>inch</th>\n",
       "      <th>OS_Android</th>\n",
       "      <th>OS_ios</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Galaxy S10</th>\n",
       "      <td>0.125000</td>\n",
       "      <td>0.210526</td>\n",
       "      <td>0.111606</td>\n",
       "      <td>0.124248</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lumia 950</th>\n",
       "      <td>0.031250</td>\n",
       "      <td>0.078947</td>\n",
       "      <td>0.102705</td>\n",
       "      <td>0.104208</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Xpreia 1 ll</th>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.210526</td>\n",
       "      <td>0.123930</td>\n",
       "      <td>0.130261</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>iphone 7</th>\n",
       "      <td>0.125000</td>\n",
       "      <td>0.052632</td>\n",
       "      <td>0.094488</td>\n",
       "      <td>0.094188</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>U Ultra</th>\n",
       "      <td>0.062500</td>\n",
       "      <td>0.105263</td>\n",
       "      <td>0.116398</td>\n",
       "      <td>0.114228</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Galaxy S5</th>\n",
       "      <td>0.015625</td>\n",
       "      <td>0.052632</td>\n",
       "      <td>0.099281</td>\n",
       "      <td>0.102204</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>iphone 11 pro max</th>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.105263</td>\n",
       "      <td>0.154742</td>\n",
       "      <td>0.130261</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Moto G5</th>\n",
       "      <td>0.015625</td>\n",
       "      <td>0.078947</td>\n",
       "      <td>0.098939</td>\n",
       "      <td>0.100200</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pixel</th>\n",
       "      <td>0.125000</td>\n",
       "      <td>0.105263</td>\n",
       "      <td>0.097912</td>\n",
       "      <td>0.100200</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Capacity       Ram    Weight      inch  OS_Android  OS_ios\n",
       "Galaxy S10         0.125000  0.210526  0.111606  0.124248    0.166667     0.0\n",
       "Lumia 950          0.031250  0.078947  0.102705  0.104208    0.000000     0.0\n",
       "Xpreia 1 ll        0.250000  0.210526  0.123930  0.130261    0.166667     0.0\n",
       "iphone 7           0.125000  0.052632  0.094488  0.094188    0.000000     0.5\n",
       "U Ultra            0.062500  0.105263  0.116398  0.114228    0.166667     0.0\n",
       "Galaxy S5          0.015625  0.052632  0.099281  0.102204    0.166667     0.0\n",
       "iphone 11 pro max  0.250000  0.105263  0.154742  0.130261    0.000000     0.5\n",
       "Moto G5            0.015625  0.078947  0.098939  0.100200    0.166667     0.0\n",
       "Pixel              0.125000  0.105263  0.097912  0.100200    0.166667     0.0"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "smartphones_normalize_l1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "cdf47af2",
   "metadata": {},
   "outputs": [],
   "source": [
    "smartphones_normalize_l2=normalize(smartphones_dummy,norm='l2',axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "a6672e0c",
   "metadata": {},
   "outputs": [],
   "source": [
    "smartphones_normalize_l2=pd.DataFrame(data=smartphones_normalize_l2,index=smartphones_dummy.index,columns=smartphones_dummy.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "5b5fdf22",
   "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",
       "        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>Capacity</th>\n",
       "      <th>Ram</th>\n",
       "      <th>Weight</th>\n",
       "      <th>inch</th>\n",
       "      <th>OS_Android</th>\n",
       "      <th>OS_ios</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Galaxy S10</th>\n",
       "      <td>0.296908</td>\n",
       "      <td>0.562878</td>\n",
       "      <td>0.330523</td>\n",
       "      <td>0.370144</td>\n",
       "      <td>0.408248</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lumia 950</th>\n",
       "      <td>0.074227</td>\n",
       "      <td>0.211079</td>\n",
       "      <td>0.304163</td>\n",
       "      <td>0.310444</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Xpreia 1 ll</th>\n",
       "      <td>0.593816</td>\n",
       "      <td>0.562878</td>\n",
       "      <td>0.367023</td>\n",
       "      <td>0.388055</td>\n",
       "      <td>0.408248</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>iphone 7</th>\n",
       "      <td>0.296908</td>\n",
       "      <td>0.140720</td>\n",
       "      <td>0.279830</td>\n",
       "      <td>0.280593</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.707107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>U Ultra</th>\n",
       "      <td>0.148454</td>\n",
       "      <td>0.281439</td>\n",
       "      <td>0.344718</td>\n",
       "      <td>0.340294</td>\n",
       "      <td>0.408248</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Galaxy S5</th>\n",
       "      <td>0.037113</td>\n",
       "      <td>0.140720</td>\n",
       "      <td>0.294024</td>\n",
       "      <td>0.304474</td>\n",
       "      <td>0.408248</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>iphone 11 pro max</th>\n",
       "      <td>0.593816</td>\n",
       "      <td>0.281439</td>\n",
       "      <td>0.458272</td>\n",
       "      <td>0.388055</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.707107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Moto G5</th>\n",
       "      <td>0.037113</td>\n",
       "      <td>0.211079</td>\n",
       "      <td>0.293010</td>\n",
       "      <td>0.298503</td>\n",
       "      <td>0.408248</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pixel</th>\n",
       "      <td>0.296908</td>\n",
       "      <td>0.281439</td>\n",
       "      <td>0.289968</td>\n",
       "      <td>0.298503</td>\n",
       "      <td>0.408248</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Capacity       Ram    Weight      inch  OS_Android  \\\n",
       "Galaxy S10         0.296908  0.562878  0.330523  0.370144    0.408248   \n",
       "Lumia 950          0.074227  0.211079  0.304163  0.310444    0.000000   \n",
       "Xpreia 1 ll        0.593816  0.562878  0.367023  0.388055    0.408248   \n",
       "iphone 7           0.296908  0.140720  0.279830  0.280593    0.000000   \n",
       "U Ultra            0.148454  0.281439  0.344718  0.340294    0.408248   \n",
       "Galaxy S5          0.037113  0.140720  0.294024  0.304474    0.408248   \n",
       "iphone 11 pro max  0.593816  0.281439  0.458272  0.388055    0.000000   \n",
       "Moto G5            0.037113  0.211079  0.293010  0.298503    0.408248   \n",
       "Pixel              0.296908  0.281439  0.289968  0.298503    0.408248   \n",
       "\n",
       "                     OS_ios  \n",
       "Galaxy S10         0.000000  \n",
       "Lumia 950          0.000000  \n",
       "Xpreia 1 ll        0.000000  \n",
       "iphone 7           0.707107  \n",
       "U Ultra            0.000000  \n",
       "Galaxy S5          0.000000  \n",
       "iphone 11 pro max  0.707107  \n",
       "Moto G5            0.000000  \n",
       "Pixel              0.000000  "
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "smartphones_normalize_l2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "c81b89c6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\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>Capacity</th>\n",
       "      <th>Ram</th>\n",
       "      <th>Weight</th>\n",
       "      <th>inch</th>\n",
       "      <th>OS_Android</th>\n",
       "      <th>OS_ios</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>9.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>9.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.111111</td>\n",
       "      <td>0.111111</td>\n",
       "      <td>0.111111</td>\n",
       "      <td>0.111111</td>\n",
       "      <td>0.111111</td>\n",
       "      <td>0.111111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.090922</td>\n",
       "      <td>0.059977</td>\n",
       "      <td>0.019057</td>\n",
       "      <td>0.013996</td>\n",
       "      <td>0.083333</td>\n",
       "      <td>0.220479</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.015625</td>\n",
       "      <td>0.052632</td>\n",
       "      <td>0.094488</td>\n",
       "      <td>0.094188</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.031250</td>\n",
       "      <td>0.078947</td>\n",
       "      <td>0.098939</td>\n",
       "      <td>0.100200</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.125000</td>\n",
       "      <td>0.105263</td>\n",
       "      <td>0.102705</td>\n",
       "      <td>0.104208</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.125000</td>\n",
       "      <td>0.105263</td>\n",
       "      <td>0.116398</td>\n",
       "      <td>0.124248</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.210526</td>\n",
       "      <td>0.154742</td>\n",
       "      <td>0.130261</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Capacity       Ram    Weight      inch  OS_Android    OS_ios\n",
       "count  9.000000  9.000000  9.000000  9.000000    9.000000  9.000000\n",
       "mean   0.111111  0.111111  0.111111  0.111111    0.111111  0.111111\n",
       "std    0.090922  0.059977  0.019057  0.013996    0.083333  0.220479\n",
       "min    0.015625  0.052632  0.094488  0.094188    0.000000  0.000000\n",
       "25%    0.031250  0.078947  0.098939  0.100200    0.000000  0.000000\n",
       "50%    0.125000  0.105263  0.102705  0.104208    0.166667  0.000000\n",
       "75%    0.125000  0.105263  0.116398  0.124248    0.166667  0.000000\n",
       "max    0.250000  0.210526  0.154742  0.130261    0.166667  0.500000"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "smartphones_normalize_l1.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "0b368edd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\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>Capacity</th>\n",
       "      <th>Ram</th>\n",
       "      <th>Weight</th>\n",
       "      <th>inch</th>\n",
       "      <th>OS_Android</th>\n",
       "      <th>OS_ios</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>9.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>9.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.263918</td>\n",
       "      <td>0.297075</td>\n",
       "      <td>0.329059</td>\n",
       "      <td>0.331007</td>\n",
       "      <td>0.272166</td>\n",
       "      <td>0.157135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.215964</td>\n",
       "      <td>0.160359</td>\n",
       "      <td>0.056439</td>\n",
       "      <td>0.041696</td>\n",
       "      <td>0.204124</td>\n",
       "      <td>0.311805</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.037113</td>\n",
       "      <td>0.140720</td>\n",
       "      <td>0.279830</td>\n",
       "      <td>0.280593</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.074227</td>\n",
       "      <td>0.211079</td>\n",
       "      <td>0.293010</td>\n",
       "      <td>0.298503</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.296908</td>\n",
       "      <td>0.281439</td>\n",
       "      <td>0.304163</td>\n",
       "      <td>0.310444</td>\n",
       "      <td>0.408248</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.296908</td>\n",
       "      <td>0.281439</td>\n",
       "      <td>0.344718</td>\n",
       "      <td>0.370144</td>\n",
       "      <td>0.408248</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>0.593816</td>\n",
       "      <td>0.562878</td>\n",
       "      <td>0.458272</td>\n",
       "      <td>0.388055</td>\n",
       "      <td>0.408248</td>\n",
       "      <td>0.707107</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Capacity       Ram    Weight      inch  OS_Android    OS_ios\n",
       "count  9.000000  9.000000  9.000000  9.000000    9.000000  9.000000\n",
       "mean   0.263918  0.297075  0.329059  0.331007    0.272166  0.157135\n",
       "std    0.215964  0.160359  0.056439  0.041696    0.204124  0.311805\n",
       "min    0.037113  0.140720  0.279830  0.280593    0.000000  0.000000\n",
       "25%    0.074227  0.211079  0.293010  0.298503    0.000000  0.000000\n",
       "50%    0.296908  0.281439  0.304163  0.310444    0.408248  0.000000\n",
       "75%    0.296908  0.281439  0.344718  0.370144    0.408248  0.000000\n",
       "max    0.593816  0.562878  0.458272  0.388055    0.408248  0.707107"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "smartphones_normalize_l2.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "92509323",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import minmax_scale"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "63e30db2",
   "metadata": {},
   "outputs": [],
   "source": [
    "smartphones_minmax_scale=minmax_scale(smartphones_dummy,feature_range=(0,1),axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "7aac2931",
   "metadata": {},
   "outputs": [],
   "source": [
    "smartphones_minmax_scale=pd.DataFrame(data=smartphones_minmax_scale,index=smartphones_dummy.index,columns=smartphones_dummy.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "id": "9c41bea3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Capacity</th>\n",
       "      <th>Ram</th>\n",
       "      <th>Weight</th>\n",
       "      <th>inch</th>\n",
       "      <th>OS_Android</th>\n",
       "      <th>OS_ios</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Galaxy S10</th>\n",
       "      <td>0.466667</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.284091</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lumia 950</th>\n",
       "      <td>0.066667</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.136364</td>\n",
       "      <td>0.277778</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Xpreia 1 ll</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.488636</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>iphone 7</th>\n",
       "      <td>0.466667</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>U Ultra</th>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.363636</td>\n",
       "      <td>0.555556</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Galaxy S5</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.079545</td>\n",
       "      <td>0.222222</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>iphone 11 pro max</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Moto G5</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.073864</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pixel</th>\n",
       "      <td>0.466667</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.056818</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Capacity       Ram    Weight      inch  OS_Android  OS_ios\n",
       "Galaxy S10         0.466667  1.000000  0.284091  0.833333         1.0     0.0\n",
       "Lumia 950          0.066667  0.166667  0.136364  0.277778         0.0     0.0\n",
       "Xpreia 1 ll        1.000000  1.000000  0.488636  1.000000         1.0     0.0\n",
       "iphone 7           0.466667  0.000000  0.000000  0.000000         0.0     1.0\n",
       "U Ultra            0.200000  0.333333  0.363636  0.555556         1.0     0.0\n",
       "Galaxy S5          0.000000  0.000000  0.079545  0.222222         1.0     0.0\n",
       "iphone 11 pro max  1.000000  0.333333  1.000000  1.000000         0.0     1.0\n",
       "Moto G5            0.000000  0.166667  0.073864  0.166667         1.0     0.0\n",
       "Pixel              0.466667  0.333333  0.056818  0.166667         1.0     0.0"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "smartphones_minmax_scale"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "id": "41397fb8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Capacity</th>\n",
       "      <th>Ram</th>\n",
       "      <th>Weight</th>\n",
       "      <th>inch</th>\n",
       "      <th>OS_Android</th>\n",
       "      <th>OS_ios</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>9.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>9.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.407407</td>\n",
       "      <td>0.370370</td>\n",
       "      <td>0.275884</td>\n",
       "      <td>0.469136</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.222222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.387935</td>\n",
       "      <td>0.379855</td>\n",
       "      <td>0.316288</td>\n",
       "      <td>0.388006</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.440959</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.066667</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.073864</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.466667</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.136364</td>\n",
       "      <td>0.277778</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.466667</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.363636</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Capacity       Ram    Weight      inch  OS_Android    OS_ios\n",
       "count  9.000000  9.000000  9.000000  9.000000    9.000000  9.000000\n",
       "mean   0.407407  0.370370  0.275884  0.469136    0.666667  0.222222\n",
       "std    0.387935  0.379855  0.316288  0.388006    0.500000  0.440959\n",
       "min    0.000000  0.000000  0.000000  0.000000    0.000000  0.000000\n",
       "25%    0.066667  0.166667  0.073864  0.166667    0.000000  0.000000\n",
       "50%    0.466667  0.333333  0.136364  0.277778    1.000000  0.000000\n",
       "75%    0.466667  0.333333  0.363636  0.833333    1.000000  0.000000\n",
       "max    1.000000  1.000000  1.000000  1.000000    1.000000  1.000000"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "smartphones_minmax_scale.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "id": "d7cef38e",
   "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>Capacity</th>\n",
       "      <th>Ram</th>\n",
       "      <th>Weight</th>\n",
       "      <th>inch</th>\n",
       "      <th>OS_Android</th>\n",
       "      <th>OS_ios</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Galaxy S10</th>\n",
       "      <td>0.125000</td>\n",
       "      <td>0.210526</td>\n",
       "      <td>0.111606</td>\n",
       "      <td>0.124248</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lumia 950</th>\n",
       "      <td>0.031250</td>\n",
       "      <td>0.078947</td>\n",
       "      <td>0.102705</td>\n",
       "      <td>0.104208</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Xpreia 1 ll</th>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.210526</td>\n",
       "      <td>0.123930</td>\n",
       "      <td>0.130261</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>iphone 7</th>\n",
       "      <td>0.125000</td>\n",
       "      <td>0.052632</td>\n",
       "      <td>0.094488</td>\n",
       "      <td>0.094188</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>U Ultra</th>\n",
       "      <td>0.062500</td>\n",
       "      <td>0.105263</td>\n",
       "      <td>0.116398</td>\n",
       "      <td>0.114228</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Galaxy S5</th>\n",
       "      <td>0.015625</td>\n",
       "      <td>0.052632</td>\n",
       "      <td>0.099281</td>\n",
       "      <td>0.102204</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>iphone 11 pro max</th>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.105263</td>\n",
       "      <td>0.154742</td>\n",
       "      <td>0.130261</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Moto G5</th>\n",
       "      <td>0.015625</td>\n",
       "      <td>0.078947</td>\n",
       "      <td>0.098939</td>\n",
       "      <td>0.100200</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pixel</th>\n",
       "      <td>0.125000</td>\n",
       "      <td>0.105263</td>\n",
       "      <td>0.097912</td>\n",
       "      <td>0.100200</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Capacity       Ram    Weight      inch  OS_Android  OS_ios\n",
       "Galaxy S10         0.125000  0.210526  0.111606  0.124248    0.166667     0.0\n",
       "Lumia 950          0.031250  0.078947  0.102705  0.104208    0.000000     0.0\n",
       "Xpreia 1 ll        0.250000  0.210526  0.123930  0.130261    0.166667     0.0\n",
       "iphone 7           0.125000  0.052632  0.094488  0.094188    0.000000     0.5\n",
       "U Ultra            0.062500  0.105263  0.116398  0.114228    0.166667     0.0\n",
       "Galaxy S5          0.015625  0.052632  0.099281  0.102204    0.166667     0.0\n",
       "iphone 11 pro max  0.250000  0.105263  0.154742  0.130261    0.000000     0.5\n",
       "Moto G5            0.015625  0.078947  0.098939  0.100200    0.166667     0.0\n",
       "Pixel              0.125000  0.105263  0.097912  0.100200    0.166667     0.0"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "smartphones_normalize_l1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "id": "b694c945",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x72b6ef8f4530>,\n",
       " <matplotlib.lines.Line2D at 0x72b6ed710bf0>,\n",
       " <matplotlib.lines.Line2D at 0x72b6efb4d8e0>,\n",
       " <matplotlib.lines.Line2D at 0x72b6efb4d1f0>,\n",
       " <matplotlib.lines.Line2D at 0x72b6ed3135f0>,\n",
       " <matplotlib.lines.Line2D at 0x72b6efb4c620>]"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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wbcZamHp7oNUbEJfu+npqWURCIoIiImG32dBYxrpqIlJGd0szettboVJrkJSV67bzhERGIyIhcaiumvOqifwZk2oioglOfmKTlJMHtUbjtvMIgsAt4ESkOLnrd/yULGgNBreeKyVPehLOedVE/o1JNRHRBCc37El2Y22hLIXzqolIYc6t3x5Y85Kdax6TaiJ/xqSaiGgCEx0OGIuH6qk9kVQPnaOpohRWs8nt5yMiOpQoih5pUiaTz9FSUwVTH+uqifwVk2oiogmstbYapr5eaA0BiEuf4vbzhccnIjgyCnabDQ1lJW4/HxHRobqaG9HX3gaVWoPE7By3ny84IhIRicmAKDpvYBKR/2FSTUQ0gcmjXtxdTy07tK6aY2aIyNPkbdgJmVnQ6t1bTy1LyZsqnZtrHpHfYlJNRDSB1XqwtlAmj7CpZY0hEXmYfDPPnaO0fkg+F5NqIv/FpJqIaIISHQ7UF7t/PvUPDddVl8FqYl01EXmGNJ9aapLo0RuJQ+dqrTmIwb5ej52XiDyHSTUR0QTVWlsNU38fdAEBiJvs/npqWVhcPEKiYuCws66aiDynq6kBfZ0dUGs0SMhyfz21LCg8ApFJKayrJvJjTKqJiCYo+YlNUk4+VGq1x84r1VXLNYYcrUVEnjFcT50DrU7v0XPLT6s5TpDIPzGpJiKaoDw5VuaHOLuViDxNXvOSFVjz5BIbI9c8Ir/EpJqIaAJyOOwwFsn11J5r2CNLHTpnU2UZLKZBj5+fiCaWQ+dTp3qwh4TMWVddW43B3h6Pn5+I3ItJNRHRBNRafRDmgX7oAgIRm5bu8fOHxsQhJDoGDrsdDaXFHj8/EU0snY316O/sgFqrRUKm5+qpZYFh4YhKTgUA5w1NIvIfTKqJiCYg5zbIXM/WU8sEQXA+reaYGSJyN7nUJDEzBxqdTpEY5C3gXPOI/A+TaiKiCUiJsTI/lMzGPUTkIfI6o0Q9tYzNyoj8F5NqIqIJxuGww1hcCECZemqZc151ZTnrqonIbQ6tp05RoJ5aJif0bXU1GOjpViwOInI9JtVERBNMy8EqWAYHoA8MQkzaZMXiCIuNQ2hMHESHA/UlRYrFQUT+raPeiIHuLmi0OiRMyVYsjsDQMESnTAIAGLkFnMivMKkmIppg5Cc2Sbn5UKk8X099KOd2SF5gEpGbyOtLQpZy9dSyZK55RH6JSTUR0QQj1/OlKrj1W+Zs3MMaQyJyE2/Y+i1zNmjkvGoiv8KkmohoAnHY7agvkeqplWzYI5MvcpurKmAeGFA4GiLyN6IoOrdaK9mYUZaUmw8AaDfWYqC7S9lgiMhlmFQTEU0gLQcrYRkchCEoGLGTlKunloVGxyIsLh6iw4GGUtZVE5FrddTXSfXUOj3iFaynlgWGhiEmNQ0AUMd51UR+g0k1EdEEUju0zTopdyoElXf8CpCfHtVyCzgRuZi8riRm5UCj1SocjSSZZS9Efsc7rqiIiMgjvGkbpEyOhd1wicjVjIXeu+axWRmR/2BSTUQ0QdhtNhiHRld5Q8MemVzb3VxVCfNAv8LREJG/OHw+tfKNGWXJedMAQUBHfR36uzqVDoeIXIBJNRHRBNFysBJW0yAMwSHOmj5vEBodg/C4BIgi51UTkeu019VgsLcHGr0e8VMylQ7HKeCQNZhPq4n8A5NqIqIJQr54S/aiemqZc7QWLzCJyEXk9SQpOw9qjXfUU8vkJ+cseyHyD951VUVERG4jN8Xxpq3fMmeNIRv3EJGL1HlhPbVsuEEjk2oif8CkmohoArDbbM6t1d54gSl3w205WAVTf5/C0RCRrxMdDtQVSyOrkr1xzcudCggCOhuM6OvsUDocIhqnMSXVTz31FNLS0mAwGDB37lzs3LlzRO974403IAgCLr300rGcloiIxqi5qhxWswmGkFBEp0xSOpwjhERGIyIhcaiuulDpcIjIx7XV1cAk11NneE89tcwQHIzYSekAWPZC5A9GnVSvXbsWd9xxB+6//37s2bMHM2bMwJIlS9DS0nLc91VXV+POO+/EmWeeOeZgiYhobJzbIL2wnlqW7NwCzgtMIhqfw+upNQpHc3Qp+VMBDI/9IiLfNeorq8cffxw333wzVq1ahby8PDz99NMIDAzE888/f8z32O12XHvttfjTn/6E9PT0cQVMRESjNzxWxvu2Qcrkxj1MqolovJw3Er1olNYPOdc8Pqkm8nmjSqotFgt2796NhQsXDh9ApcLChQuxY8eOY77vgQceQGxsLG688cYRncdsNqOnp+ewDyIiGhu7zYr6Uu+tp5bJsbXUVMHUx7pqIhob0eGAcaie2pvXvKScfAiCCp2N9ejraFc6HCIah1El1W1tbbDb7YiLizvs63FxcWhqajrqe7788ks899xzWL169YjP89BDDyEsLMz5kZKSMpowiYjoEE2VFbCZzQgICUWUF9ZTy4IjIhGRmAyIIoysqyaiMWqtrYaprxdaQwDi0qcoHc4xGYKCETuZddVE/sCthXW9vb247rrrsHr1akRHR4/4fffeey+6u7udH3V1dW6MkojIvzlHaeVNgyAICkdzfCl5Uo0hR2sR0VjJW7+Tcry3nlqWzHGCRH5hVCtNdHQ01Go1mpubD/t6c3Mz4uPjj3h9ZWUlqqurcdFFFzm/5nA4pBNrNCgtLUVGRsYR79Pr9dDr9aMJjYiIjkF+ApLsxfXUspS8adi/eT2f2hDRmDl7SHjx1m9ZSt407P7oXa55RD5uVE+qdTodZs+ejS1btji/5nA4sGXLFsybN++I1+fk5ODAgQPYu3ev8+Piiy/GOeecg71793JbNxGRm9msVjSUFgMAUr24YY9MbtzTWnMQg329CkdDRL5GdDhQL9dT+8CNxORcqa66q6kRve1tSodDRGM06u3fd9xxB1avXo0XX3wRxcXFuOWWW9Df349Vq1YBAFauXIl7770XAGAwGDB16tTDPsLDwxESEoKpU6dCp9O59k9DRESHaaosg81iRmBYOCKTvP9GZlB4BCLluuqhC2MiopFqqTkIU38fdAEBiJvsvfXUMn1gEGInS7s2+bSayHeNOqlevnw5Hn30Udx3332YOXMm9u7di/Xr1zubl9XW1qKxsdHlgRIR0ejJ80+TfaCeWiY/rebsViIaLaM8nzonHyq1WuFoRkZ+os5xgkS+a0zdG2699VbceuutR/3etm3bjvveNWvWjOWUREQ0BnVFw03KfEVK/jTs2/QJG/cQ0ajVFvrmmvfdh+uc6zUR+R63dv8mIiLlSPXUJQB86wIzOVfqAN5aW43B3h6FoyEiX+Fw2FFfLI3j86U1Lyk7H4JKhe7mJvS0tSgdDhGNAZNqIiI/1VReCpvVMlRPnax0OCMWFB6BqORUAICxiHXVRDQyrdUHYR7ohy4g0Fmn7Av0gYHOedpc84h8E5NqIiI/dehYGV+pp5Y5Z7eycQ8RjZBcMpKc6zv11DL5yXoty16IfBKTaiIiPyVfYKb4wCitH0p1Nu7hBSYRjYwvzaf+IWeDRt5IJPJJTKqJiPyQzWJBQ/lQPbUPzGr9IflJdVtdDQZ6uhWOhoi8ncNuh1Gup/bBG4lJ2blSXXVLM3paWVdN5GuYVBMR+aHGilLYrVYERUQiIiFJ6XBGLTA0DNEpkwCA86qJ6IRaqqtgGRyAPjAIMWmTlQ5n1HQBgYjPyATAshciX8SkmojID9UdMlbG1+qpZc66am4BJ6ITkNeJpNx8qFS+VU8tS+GaR+SzmFQTEfkhX64tlKU466r51IaIjs8v1jw2aCTyWUyqiYj8jNViRmOZ79ZTy+R51e3GWgx0dykbDBF5LYfdjvoS362nliXm5EGlVqOntQXdLc1Kh0NEo8CkmojIzzSWlcJusyE4IhLh8YlKhzNmgaFhiE5NAwDUcXYrER1D88EKWAYHoQ8KQsykNKXDGTOdIQBxcl01t4AT+RQm1UREfsa5DTJ/us/WU8ucW8C5HZKIjkEuEUnOneaz9dSy1KEn7VzziHwLk2oiIj8jP+FI9uHaQhkb9xDRifhDPbVsuEHjAYiiqHA0RDRSTKqJiPyI1WxCU0UpAN+up5Yl504FBAEd9XXo7+pUOhwi8jJ2mw31JUUA/GPNS8rKhUqtQW97K+uqiXwIk2oiIj/SUFYi1VNHRSM8LkHpcMYtICQUMc66am6HJKLDtRyshNU0CENQsHOt8GVagwHxU7IAAHVF3KFD5CuYVBMR+RHjIdsgfb2eWiZv6TQyqSaiH6h1lrtMhaDyj8valDyOEyTyNf6x+hAREQCgtlBuUub72yBl8oicWl5gEtEPGA9pzOgvDm3QyLpqIt/ApJqIyE9YTSY0VZQBAFLy/OcCU66r7mwwoq+zQ+lwiMhLHFZP7QdNymSJWTlQqTXoa29DV3Oj0uEQ0QgwqSYi8hMNZSVw2G0IiY5BWGyc0uG4jCE4GLGT0gFwCzgRDWuuKofVbIIhJBTRKZOUDsdltHoDEjKzAXALOJGvYFJNROQn5KY2/lRPLUvJnwqAF5hENExeD1Jy/aeeWubcAs5xgkQ+wb9WICKiCcx5gelH2yBlyUPb2dkBnIhk8nqQ7Idr3qENGllXTeT9mFQTEfkBi2kQTZVD9dR+1LBHlpybL9VVN9ajr6Nd6XCISGF2mxX1pVI9daofNWaUJWTlQK3RoK+zA11NDUqHQ0QnwKSaiMgPNJQWw2G3IzQm1q/qqWWGoGDEpkl11XxaTURNFeWwmc0ICAlFVHKq0uG4nFanR0JmDgCWvRD5AibVRER+oM45n9r/nlLL5CfwTKqJaHjNm+Z39dSyQ0drEZF3889ViIhogpGb2fjTfOofkmsM2biHiOR1IHmCrHmsqybybkyqiYh8nFRPXQ7AP5uUyZJz8yEIKnQ1NaK3vU3pcIhIITarFQ1lJQD8e81LyMyBWqtFf1cnOhvrlQ6HiI6DSTURkY+rLymC6HAgLDYOoTGxSofjNvrAIMROzgDA7ZBEE1lTZRlsFjMCQsP8sp5aptHpkMi6aiKfwKSaiMjH+fNYmR8ant3KC0yiicpZ7pI3DYIgKByNeyWz7IXIJzCpJiLycfLFVqofjtL6oeHGPbzAJJqojHKTsgmw5qUe0qCRddVE3otJNRGRDzMPDKC5qgLAxHhSnZSdD0GlQndzE3raWpQOh4g8zGa1oqHU/+upZfGZ2dBodRjo7kJHvVHpcIjoGJhUExH5sIZSqZ46PC4BodExSofjdvrAQMSlTwEAGIsKFI6GiDytqbwUNqsFgWHhiExKVjoct9NotUjMHqqrZi8JIq/FpJqIyIfVymNlJsATG5n8dKqWNYZEE85h86n9vJ5a5qyrZlJN5LWYVBMR+bDh2sKJl1QbeYFJNOE4m5RN0DWPddVE3olJNRGRjzIP9KO5qhLAxKgtlCXl5El11S3N6GllXTXRRGGzWNBQPlRPPQGalMnip2RDo9MP1VXXKR0OER0Fk2oiIh9VX1IEUXQgPD4BIVHRSofjMbqAQMSnZwLgdkiiiaSxvAR2qxVB4RGISEhSOhyP0Wi1SMyS6qpZ9kLknZhUExH5qLoJNFbmhzivmmjiOXTNmyj11DJ5nTdyzSPySkyqiYh8lLO2cAJt/Zal5HFeNdFEI99Em9hr3gGIDofC0RDRDzGpJiLyQab+PrQcrAIwMS8wE3PyoFKr0dPagu6WZqXDISI3s1rMaByqp55I0w5k8VMyodHrMdjbg3ZjrdLhENEPMKkmIvJB9SWFEEUHIhKSEBwZpXQ4HqczBCAuY6iumjWGRH6vsawUdpsNwRGRiEhIVDocj1NrtEjKzgPAXhJE3ohJNRGRD5rI2yBlKZzdSjRhyKUeyRNoPvUPOdc81lUTeR0m1UREPsiZVE+gWa0/JDfuqSvk7FYifze85k28xowyZ4PG4gLWVRN5GSbVREQ+xtTXh5YaqZ56ItYWypKycqFSa9Db3sq6aiI/ZjWb0FheCmBi30iMS8+EVm+AqbcHbXU1SodDRIdgUk1E5GOMJYWAKCIyMRnBEZFKh6MYrcGA+ClZANgFnMifNZSVwGG3ITgqGuFxCUqHoxi1RoOkHNZVE3kjJtVERD7GOUprAj+xkbHGkMj/GYuGe0hM1HpqWTLXPCKvxKSaiMjHyE8oJvLWb9lhs1tZV03kl2rZmNFJ/hkYWVdN5FWYVBMR+ZDBvl601hwEwAtMAEjMzoFKrUFfexu6mhuVDoeIXMxqMqGpogzAxG5SJotLnwKtIQCmvl601lYrHQ4RDWFSTUTkQ4zFBVI9dVIKgsIjlA5HcVq9AQmZQ3XV3A5J5Hfqy4rhsNsQEhWDsNg4pcNR3GF11VzziLwGk2oiIh9i5FiZI8g/CyMb9xD5HWc9dT7rqWWHlr0QkXdgUk1E5EOcTcq49dtpuFnZftZVE/mZWq55R5CbVBqLD8DhsCscDREBTKqJiHzGYG+Ps4YuJW+qssF4kYSsHKg1GvR1dqCrqUHpcIjIRSymQTRXlgPgtINDxU2eAl1AAMz9/WitqVY6HCICk2oiIp9hLCoAAEQlpyIwLFzZYLyIVqdHQmYOANYYEvmThtJiOOx2hMbEIiw2XulwvIZKrUZSTj4Alr0QeQsm1UREPqLukNpCOlwyawyJ/A7LXY5N/pnI2+OJSFlMqomIfITzApNNyo6Qms+6aiJ/M3wjkWveD8k/k/riQtZVE3kBJtVERD5goKcbbXU1AIDkXNZT/1BCZg7UWi36uzrR2VivdDhENE6WwQE0DdVTc807UmxaOnQBgTAP9KO1+qDS4RBNeEyqiYh8gLFYqqeOTpmEwNAwhaPxPhqdDgmZ2QBYV03kD+pLiyE6HAiNieN86qNQqdVIzpXqquu4BZxIcUyqiYh8ALd+n1hKnvSz4QUmke9jD4kT47xqIu/BpJqIyAfIT1/ZsOfY5IvvuqIDrKsm8nFsUnZi8k1WY3EhHHbWVRMpiUk1EZGXG+juQruxFgCQzPnUx5QwJRsarQ4D3V3oqDcqHQ4RjZF5YADNVRUA+KT6eGLSJkMfGATL4ABaqquUDodoQmNSTUTk5eqG5lPHpKYhICRU4Wi8l0anQ0LW0Lxqbock8ln1pYUQHQ6ExcUjNDpW6XC8lkqlRhLrqom8ApNqIiIvx7EyI3foFnAi8k3D5S5c804kdej3Atc8ImUxqSYi8nLyE4hkboM8Ibn+0si6aiKfZWSTshFLHlrz6ktYV02kJCbVRERerL+rEx31dYAgcFbrCMQfVlddp3Q4RDRK5oF+NFdVAmCTspGImZQGfVAQLIODaD5YoXQ4RBMWk2oiIi8mb+mLmTQZAcEhCkfj/TRaLRKzcwEAtawxJPI59SVFEEUHwuMTEBIVrXQ4Xk+lUiM5d6jspZBbwImUwqSaiMiLObdB8onNiDm3gPMCk8jn1HKU1qhxXjWR8phUExF5sdpCNikbrZRDGveIDofC0RDRaBjZmHHU5Nrz+pIi2G02haMhmpiYVBMReam+zg50NhileuqcfKXD8RnxUzKh0ekx2NvjnO9NRN7P1N+HloPSvOXkPPaQGKmY1DQYgoJhNQ0653sTkWcxqSYi8lLyE5vYSekwBAcrHI3vUGuG66q5HZLId9SXFEIUHYhISERIJOupR0pQqZw3IbjmESmDSTURkZdyzmrlWJlRc85uZV01kc/gfOqxk7fLG5lUEymCSTURkZeq46zWMZNnt9YVF7CumshHyEl1Mte8UUvJY101kZKYVBMReaG+jnZ0NtZDEFRIYj31qMVnZEKj18PU24O2uhqlwyGiEzD19aGlRqqnZufv0YtOmQRDSCisZhOaq8qVDodowmFSTUTkheSn1LGT02EIYj31aKk1GiRl5wFgjSGRLzAWFwCiiIjEZARHRCodjs8RVCqk5A7VVbPshcjjmFQTEXmhOo6VGbcU1lUT+Qx5zUvl1u8xk0uFeCORyPOYVBMReaG6wv0AuA1yPOSfnZF11UReT04Ek7nmjZmzrrq0CHabVeFoiCYWJtVERF6mt70NXU2NQ/XUeUqH47Pi0qdAqzfA1NeL1tpqpcMhomMY7OtFa81BALyROB5RyakICAmFzWxGUwXrqok8aUxJ9VNPPYW0tDQYDAbMnTsXO3fuPOZr161bh5NPPhnh4eEICgrCzJkz8fLLL485YCIifyc/sYlLz4A+MEjhaHyXWqNBUq7U5I1bwIm8l1xPHZmUgqDwCKXD8VmCSuW8KcEt4ESeNeqkeu3atbjjjjtw//33Y8+ePZgxYwaWLFmClpaWo74+MjISv/vd77Bjxw7s378fq1atwqpVq7Bhw4ZxB09E5I+cY2X4xGbceIFJ5P1Y7uI68jgy+WdKRJ4x6qT68ccfx80334xVq1YhLy8PTz/9NAIDA/H8888f9fXz58/HZZddhtzcXGRkZOD222/H9OnT8eWXX447eCIif1RXJF0MpbJJ2bjJjXuMxQfgcNgVjoaIjsZYyMaMriL/3mgoK4HNyrpqIk8ZVVJtsViwe/duLFy4cPgAKhUWLlyIHTt2nPD9oihiy5YtKC0txVlnnXXM15nNZvT09Bz2QUQ0EfS0taC7uQmCSoXEbNZTj1fc5CnQGgJg7u9Ha0210uEQ0Q8M9HQ7ex6k5E1VNhg/EJmUgoDQMNgsZjRVlCodDtGEMaqkuq2tDXa7HXFxcYd9PS4uDk1NTcd8X3d3N4KDg6HT6XDBBRfgySefxKJFi475+oceeghhYWHOj5SUlNGESUTks4xFBQCkJlv6wECFo/F9KrUayUPN3ozcAk7kdeqLCwFITbYCw8KVDcYPCILAshciBXik+3dISAj27t2LXbt24S9/+QvuuOMObNu27Zivv/fee9Hd3e38qKur80SYRESKq5VrC7kN0mXkn2UtawyJvI6c+KVwPrXLyGsebyQSeY5mNC+Ojo6GWq1Gc3PzYV9vbm5GfHz8Md+nUqkwZcoUAMDMmTNRXFyMhx56CPPnzz/q6/V6PfR6/WhCIyLyC/JFEBv2uI5zdmtxIRwOO1QqtcIREZGMTcpcT/5ZNpRKddUarVbhiIj836ieVOt0OsyePRtbtmxxfs3hcGDLli2YN2/eiI/jcDhgNptHc2oiIr/X09qC7pZmCCrOp3al2MkZ0AUEwjzQj9bqg0qHQ0RDBnq60VZXA4DTDlwpMikZgWHhsFktaCpnXTWRJ4x6+/cdd9yB1atX48UXX0RxcTFuueUW9Pf3Y9WqVQCAlStX4t5773W+/qGHHsKmTZtQVVWF4uJiPPbYY3j55ZexYsUK1/0piIj8gLwNMj4jEzpDgMLR+A+VWo1k57xqbgEn8hbyzpzolEkIDA1TOBr/cWhdNcteiDxjVNu/AWD58uVobW3Ffffdh6amJsycORPr1693Ni+rra2FSjWcq/f39+PnP/85jEYjAgICkJOTg1deeQXLly933Z+CiMgP1HGsjNuk5E1D1Z5dqCs6gJMvulzpcIgIh9ZTc81ztZT86Sjd8QXrqok8ZNRJNQDceuutuPXWW4/6vR82IHvwwQfx4IMPjuU0REQTijyfmrWFruds3FNcCIfdDpWaddVESnPeSOSa53Jy47eG8hLYLBZodDqFIyLybx7p/k1ERMfX3dKMntYWqNRqJGbnKh2O34lJmwx9YBAsgwNoqa5SOhyiCW+guwvtxloAQNJQeQa5TkRCEoLCI2C3WtFYXqJ0OER+j0k1EZEXkGt94zOyWE/tBiqV2nnhzrpqIuXVFRUAAGJS01hP7QaCIDh36HBeNZH7MakmIvICnNXqfvIWU15gEilPvrmVzDXPbZxrXiHXPCJ3Y1JNRKQwURQPqS1kwx53kZ/a1JdIddVEpBw2KXM/+SZtY3kJrBaOsiVyJybVREQK625pRm97K1RqDRKzcpQOx2/FTEqDPigIlsFBNB+sUDocogmrv6sTHfV1gCAgOXeq0uH4rfD4RARHRMJus6GxjPOqidyJSTURkcLkrt/xU7KgNRgUjsZ/qVRq5wU8t0MSKUd+Sh2TmoaA4BCFo/FfgiAg2Vn2wl4SRO7EpJqISGFygpfK2kK3k7fXs66aSDlGbv32GGezMt5IJHIrJtVERAoSRdGZ4CVzVqvbyTWG9SVFsNtsCkdDNDHVcj61xwzXVZfCajYpHA2R/2JSTUSkoK7mRvS1t0GtYT21J8SkpsEQFAyraRDNVayrJvK0vs4OdDYYWU/tIeFxCQiOiobDbkNDGedVE7kLk2oiIgXJW/Lip2RDq2c9tbsJKhWS84bqqrkFnMjj5H93sZPSYQgOVjga/ycIAkdrEXkAk2oiIgWxttDz5J+1kUk1kccZ5a3f7CHhMfLPmjcSidyHSTURkUKk+dRSR1bWFnqO/LNmXTWR5w3Pp+aa5ylyg8amijJYTayrJnIHJtVERArpampAX2cH1BoNErKylQ5nwohOmQRDSCisZhOaq8qVDodowujraEdnYz0EQYWknHylw5kwwmLjEBIVA4fdhvqyYqXDIfJLTKqJiBQi17clZOVAq9MrHM3EIahUSOG8aiKPc9ZTT06HIYj11J4iCIJzZwDLXojcg0k1EZFCnNsgufXb4+TxZawxJPIcudyF4wM9T/49Uzv034CIXItJNRGRAg6dT80mZZ6XKs+rLi2C3WZVOBqiiUFe81K55nmc/HumubIcFtOgwtEQ+R8m1URECuhsrEd/ZwfUWi0SprCe2tOiklMREBIKm9mMpgrWVRO5W297G7qaGofqqfOUDmfCCYuNQ2hMLBx2OxpKWVdN5GpMqomIFCDX8iZm5kCj0ykczcTDedVEnjVcT50BfWCQwtFMTMPzqrkFnMjVmFQTESnAOUqL2yAVI//seYFJ5H51nE+tOOeaxxuJRC7HpJqIyMMOq6dmwx7FyD/7hrIS2KysqyZyp7oi+UYi1zylyGteU2U5LIMDCkdD5F+YVBMReVhHvRED3V3QaHWIz2Q9tVKiklMREBoGm8WMpopSpcMh8ls9bS3obm6CoFIhKZvzqZUSGhOLsNg4iA4H6llXTeRSTKqJiDxMfkqdmJ0DjVarcDQTlyAIwzWG3A5J5Dby1u+49CnQBwYqHM3Elsy6aiK3YFJNRORhw1u/WU+tNLnG0MikmshtOD7Qe6SyrprILZhUExF5kCiKzgQumbWFinPWVZeyrprIXYzsIeE15KkHzVUVMA+wrprIVZhUExF5UEd9nVRPrdMjPiNL6XAmvMikZASGhcNmtaCpnHXVRK7W09qC7pbmoXrqXKXDmfBCo2MRFhc/VFddqHQ4RH6DSTURkQfVDtWxJWbnsp7aCxxaV13LGkMil5O3GcdnZEIXwHpqbyCXHsm17kQ0fkyqiYg8yFjIbZDeRh7xw7pqIteTG2JxzfMe8prHpJrIdZhUExF5yGHzqdmwx2vI/y0ayktgs1gUjobIv3DN8z7yDY6Wg5UwD/QrHA2Rf2BSTUTkIe11NRjs7YFGr0d8xhSlw6EhEQlJCAqPgN1qRWN5idLhEPmN7pZm9LS2QKVWI5H11F4jJCoa4fEJEEUH6kuKlA6HyC8wqSYi8hD5iU1Sdh7UGtZTewtBEIZnt3ILOJHLyFu/4zIyoTMEKBwNHYq9JIhci0k1EZGH1LGe2ms5Z7eyxpDIZeSbVKnc+u115O347CVB5BpMqomIPEB0OFBXXABguEkMeQ/5SXVjeQmsFrPC0RD5PlEUnTepknkj0esM11VXwdTfp3A0RL6PSTURkQe01dXA1NsDrd6AuPRMpcOhH4hISERwRCTsNhsay1hXTTRe3S3N6G1vhUqtQVIW66m9TXBkFCISkobqqjmvmmi8mFQTEXmAs546Jw9qjUbhaOiHWFdN5Fp1RVKtbvyULGgNBoWjoaORn1az7IVo/JhUE5HX2VHZjl++8T0qW/1nS5qznpq1hV4rhXXVRC4j/ztK9aNyl/9uq8QfPyiEze5QOhSX4LxqItfh4xIi8io9JitufW0P2vst+KqyHa/fPBdTYkOUDmtcRIcDRrmemrWFXku+wGwsL4XVbIJWz6drRGMhiqJzx4e/1FPvrunEI+ul0pDchBAsPyVV4YjGT/5v01JTBVNfHwzBwQpHROS7+KSaiLzKk1vK0d5vAQC09prxo/99i/LmXoWjGp/W2mqY+nqhNQQgdnKG0uHQMYTHJSA4MgoOuw0NrKsmGrOu5kb0tbdBpdYgMStH6XDGTRRFPPLp8JrwxKZymKx2BSNyjeCISEQkJgOi6LzxS0Rjw6SaiLxGVWsfXviqGgDwj+UzkZsQirY+M65e/Q3KfDixdnbAZT21VxMEgVvAiVxA/veTkJntFzs+tpa2YGd1B/QaFeJDDWjqMWHN19VKh+US8vZ89pIgGh8m1UTkNf7ycTFsDhHnZMfg0pOS8NpNc5GXEIq2Pguu/t83KG3yzcTa37ZB+rMUNisjGre6QqlJmT+MD7Q7RPxtfSkA4PrT0nDnkmwAwH+2VqB7wKpkaC7hbNA49N+MiMaGSTUReYXtZa3YUtICjUrA7y/MAwBEBOnw2s1zkZ8YivZ+C65Z/Q1KmnoUjnR0RIcD9UPb6lLZpMzryU+qmyrKYDWZFI6GyPeIogjj0E2plDzfX/Pe+74eJU29CDVocMv8DFx2UhKy40LQY7LhP59XKB3euMk3EltrqzHY55s3rom8AZNqIlKc1e7Anz8qAgCsnJeGjJjhZinhgTq8etNcTE2SE+tvUdzoO4l1S81BmPr7oAtgPbUvCIuNQ0hUDBx2G+rLipUOh8jndDU1oK+zA2qNBglZ2UqHMy4mqx2PbyoDANwyfwrCA3VQqwTcvVT6c635qhqN3YNKhjhuQeERiExKYV010TgxqSYixb36TQ3KW/oQGaTD7Qsyj/h+eKAOr954KqYlhaFj6Il1UYNvJNZG53zqfKjUaoWjoRMRBAEpeVMBDP+3I6KRG66nzoFWp1c4mvF55Zsa1HcNIj7UgFWnpzm/fm5OLOakRcJsc+Afm8qVC9BFUrgFnGjcmFQTkaI6+y14YrN0UXLHoiyEBWqP+rqwQC1euWkuZiSHoXPAimuf/QaFDd2eDHVMap21hb6/DXKikP9b1fICk2jU5H4Evl5P3WOy4qmt0vbuXy7MhEE7fFNUEATcc57U1fyt3XWoaPHtbdPymmdkg0aiMWNSTUSK+sfmMnQPWpETH4IfnZJy3NeGBWjx0o1zMSMlfCix/hYF9d6bWDscdtQXFwLgfGpfIicDzZXlsJh8e2snkSeJojjcpMzH17zV26vQOWBFRkwQls1OPuL7sydFYFFeHBwi8PcNpQpE6Dry7pzW2moM9Hjv71Qib8akmogUU9bci1e+rQUA3HdhHjTqEy9JYQFavHzjHMxMCUeXlyfWrdUHYR7ohy4gELGT05UOh0YoLDYeoTGxcNjtaCgpUjocIp/R2ViP/q5OqLVaJGT67nzqlh4Tnv3iIADgriU5x/zddPeSbKgEYENhM3bXdHoyRJcKDAtHVHIqADhvBBPR6DCpJiJFiKKIP39UBLtDxJL8OJw2JXrE7w01aPHSjXNwUmo4ugelxPqA0fsSa/mJTXJuPlQq1lP7Eo7WIho9uZ46MTMHGp1O4WjG7l+flWPQasdJqeFYkh93zNdlxoU4n2I/8mkJRFH0VIgul8J51UTjwqSaiBSxpbgFX5S3QadW4Xfn5436/aEGLV66YQ5mORPrb7Df2OX6QMdhuLaQ9dS+Rv5vxgtMopGr84MeEgfb+vH6zjoAwD1LcyAIwnFf/8uFWdBrVNhZ3YGtpS2eCNEtnGsee0kQjQmTaiLyOIvNgb98Io0ruuGMyUiNChzTcUIMWrx4wxzMnhSBHpMNK579FvvqulwY6dg57HYYWU/ts5JzpRrDpspyWAYHFI6GyPuJojh8I9GH17xHN5bC7hBxTnYMTk2POuHrE8MDcP1paQCAv62X3uuL5DWvra6GddVEY8Ckmog87sWvq3GwrR/RwXrceu6UcR1LTqxPlhPr577FXi9IrFuqq2AZHIA+KAgxaZOVDodGKSw2DqExcRAdDtSXcl410Yl01Bsx0N0FjVaH+EzfnE+939iFj/c3QhCAu5eOvCb8lvkZCDVoUNLUi/e+r3djhO4TGBqG6JRJADhOkGgsmFQTkUe19Znxry3SCK27l2YjWK8Z9zGD9RqsuWEOTkmLQK/Jhuue/Rbf1yrbNGa4nnoq66l9lLPGkNshiU5IfkqdmJ0DjfbooxG93SPrSwAAl81MQm5C6IjfFx6owy3zpRvEj28qg9lmd0t87sayF6KxY1JNRB712MZS9JptmJYUhmWzjhxTMlbBeg3WrJqDOWmR6DXbsPK5ndijYGLtD9sgJzo2KyMaOeeNRB9d874ob8VXFe3QqVX41aKsUb//+tPSEBeqR33XIF75ptYNEbqfc83jvGqiUWNSTUQeU9jQjTd2SQ1g7rsoDyrV8RvAjFaQXoMXVp2COZOHE2slxpw47HbUlwzVU/tww56JzjmvuqoC5gHWVRMdy2H11D645jkcIh7+VHpKveLUSUiJHH2fjwCdGr9aKCXj//6sHD0mq0tj9ITkoXnV7cZaDHR3KRsMkY9hUk1EHiGKIh74sAiiCFw4PQGnpEW65TxBeg3WrDoFp6ZHos9sw4+f34ndNR1uOdexNB+sgGVwEIagYMSkpnn03OQ6odGxCIuLH6qr5uxWomPpqK/DYE83NDo94jNG/5RXaR8daERhQw+C9Zpx9flYNjsZGTFB6BywYvX2KhdG6BkBIaHO31l1RQXKBkPkY5hUE5FHrC9owrcHO6DXqHDv+bluPVegToPnrz8F89Kj0Df0xPq7as8l1vLWuaTcqRBUXGZ9GbdDEp1Y7dDW78Qs36unttgceHRDKQDgp2elIzJo7PO1NWoV7loiNTh79ouDaOkxuSRGT0pmLwmiMeHVHhG5nclqd47Q+unZGUgKD3D7OeXE+rSMKPRb7Fj5/E7s8lBiLW+DTM33zdpCGjY8u5VJNdGxGAt9d+v3G7tqUdsxgOhgPW48c/yTGpbkx+Gk1HAMWu3412flLojQs9isjGhsmFQTkds99+VBGDsHER9qwM/OTvfYeQN0ajz341Nw+pQoDFjs+PHzO7HzoHsTa7vNhvqSIgC+27CHhslPqlsOVsI80K9wNETeR3Q4fLYxY7/Z5pxGcfvCTATqxj+NQhAE3DM0juuNnXU42OZb60Zy7lRAENBRX4f+LmWnaBD5EibVRORWzT0mPLW1AgDwm/NyXHLRMhpyYn1mZjQGLHZc/8JOfFPV7rbzNVdVwGoahCE4hPXUfiAkKhrh8QkQRQeMxayrJvqhdmMtBnt7oNHrET8lU+lwRuXZLw6irc+CtKhA/OiUFJcd99T0KJyTHQObQ8SjG0tddlxPCAgOQcwk6Yk9n1YTjRyTaiJyq7+tL8WAxY5ZqeG4ZGaiIjEYtGqsXnmyM7Fe9cIu7Kh0T2ItX4Qks57ab3C0FtGxyf8ukrLzoNb4Tj11e58Z/9teCQD49eJsaNWuXa/vXpoDQQA+3t+I/cYulx7b3eQ1z8g1j2jEeMVHRG6zt64L7+wxAgDuvygfguDaEVqjISfWZ2XFYNBqxw1rduHryjaXn8fow2Nl6Ojk/5a8wCQ6ktxvwNe2fj/5WQX6LXZMSwrDBdMSXH783IRQXDYzCQDwyPoSlx/fneQ1r5a9JIhGjEk1EbmFNEJL2i57+awkzEgJVzYgSIn1/66bjbMPTawrXJdYH1pPncImZX5Dnt3acrAKpv4+haMh8h6iw4G6Ymn0ki+teXUdA3j12xoAwD1Lc6BSueeG768WZUGnVuGrinZ8Ud7qlnO4Q3JOPiAI6Gwwoq/TsyMpiXwVk2oicosP9jVgT20XAnVqZ9MWb2DQqvHMdbNxTnYMTFYHVq3Zha9clFg3V5XDajbBEBKK6ORUlxyTlBcSGY2IhESIogP1JayrJpK11dXA1NsDrd6AuHTfqad+fFMZrHYRZ2ZG44zMaLedJyUyECtOnQQAePjTEjgcotvO5UqG4GDETpKairLshWhkmFQTkcsNWGx4+FNpu9vP52cgLtSgcESHM2jVePq62Tg3JxZmmwM3rNmFL8vHn1gPb4NkPbW/ScmTR2txdiuRzFlPnZMHtcazTSjHqqihB+/trQcAj9zwvfXcKQjWa1DY0IOPDjS6/XyuIu88MHILONGI8KqPiFzu6c+r0NhtQnJEAG4603MjtEZDr1HjvytmYcFQYn3ji7uwvWx82/N8dawMnVjy0AVmXWGBwpEQeQ/5RqIvjQ/824YSiCJw4fQETE0Kc/v5IoN0+MlZ0u/BxzaWwmJzuP2criAn1XxSTTQyTKqJyKXquwbxzOdSR9Xfnp8Lg1atcETHpteo8Z8Vs7AwV0qsb3rpO3w+xsTabrOivlSup2aTMn/jnFddUwVTH+uqiUSHA8aheupUH1nzdlS2Y1tpKzQqAXcuzvbYeW88YzKig/WoaR/AG7tqPXbe8UjKyYcgqNDZWI++DveNoSTyF0yqicilHv60BGabA3MmR+K8qfFKh3NCeo0a/7l2NhblxcFic+Dml77DttKWUR+nqaIcNrMZASGhiGI9td8JjohERGIyIIrORIJoImutrYaprxdaQwBiJ2coHc4JiaKIh4e6cF89JxVp0UEeO3eQXoPbF0wBAPxrSzn6zTaPnXusDEHBiJ3MumqikWJSTUQu8111Bz7c1wBBAO67ME/REVqjodOo8NQ1s7B4KLH+yUu7sbVkdIn1oVu/feXPTaOTMtQFnBeYRMNbv32lnnp9QRP21UnNM28bSnA96UdzUpEWFYi2Pgue/eKgx88/FvK2fvaSIDoxJtWuJIrSB9EE5HCI+NOH0vbnH52S4pFaNVfSaVR46tpZWJIfB4vdgZ++vBuflTSP+P3yRQe3fvsv+b8tLzCJfKuHhM3uwN83lgIAbjpjMmJDPN88U6tW4ddDW87/t70S7X1mj8cwWvK2ft5IJDoxJtWuIorA5vuBTfcxsaYJ6e09Rhyo70aIXuO8cBizpgPA538HBjtdE9wIadUq/PuaWThvajwsdgd+9vIebCk+cWJts1rRUCZtK/SlWa00OnLy0FpbjcG+XoWjIVKOw2GHsXgoqfaBNe+t3UZUtfYjMkiHm89SrnnmBdMSMC0pDP0WO578rEKxOEYqKScPgqBCV1MjettdM3qSyF8xqXaVup3AV/8Evv4X8PkjSkdD5FF9Zhv+vkF6CnDbgimIDtaP/WD1u4HnzwO2Pgi8dT1g92ztmVatwr+uPgnnTxtKrF/Zjc1Fx0+smypKYbOYERgWjsikFA9FSp4WFB4h/fcVRRj55IYmsNaaapj7+6ELCEDcZM9vpR6NQYsdT2wqAwDces4UhBi0isWiUgnOMV6vfluDuo4BxWIZCX1gEOLSpXp5Pq0mOj4m1a6SOhdYOpRMb3sI+OpfysZD5EFPba1Aa68ZaVGBuP60yWM/UFMB8PLlgGXoKWDVNuCzB1wS42ho1Sr880cn4YJpCbDaRdzy6m5sLGw65uvli41k1lP7PflpNS8waSIzOudT50Ol9t4JDwDwwtcH0dJrRnJEAK49VfkmkmdkRuOMKdGw2kU8PpTse7PhumqueUTHw6TalU79GbDgPunzTX8Adj2rbDxEHlDbPoDnhpqu/P6CPOg0Y1xWWsuAly8FTF1A0snAxf+Wvv7VP4GCdS6JdTSkxHomLpwuJda/eG0PNhwjsTb6UG0hjY+81dXIC0yawGrlHhJevuZ1DVjw323SiMdfL86CXuMdNwDkp9Xv7a1HUUOPwtEc3/C8avaSIDoeJtWuduavpQ8A+PjXwN7XlI2HyM3+8kkRLHYHzsyMxoLc2LEdpLMaeOkSoL8ViJ8GrHgbmHUdcPrt0vff/wXQXOiymEdKo1bhH8tn4qIZiVJi/eoerC84PLG2Wa1oKGU99URxaF31QE+3wtEQeZ7DYUd9sbQee3tjxv9sq0SvyYac+BBcMiNJ6XCcpiWH4cLpCRBF4G8bSpQO57iSsvMhqFTobm5CT9vox00STRRMqt3h3D8Ac2+RPn//F4o8ZSPyhK8r27ChsBlqlYA/jHWEVnc98OLFQG8DEJ0NXPceEBAhfW/B/UD6OYB1AHjjGmCgw6Xxj4RGrcITV83AJTMTYXOIuPW1Pfj0QKPz+03lpbBZLVK9bWKyx+MjzwoMC3fOIZcTC6KJpLX6IMwD/dAFBCI2TbmmXyfS0DWINV9XAwDuOS8HKpV3lebcuTgbGpWAbaWt2FHZrnQ4x6QPDERculQ3zy3gRMc2pqT6qaeeQlpaGgwGA+bOnYudO3ce87WrV6/GmWeeiYiICERERGDhwoXHfb1fEARg6UPArB8DogNYdzNQ+qnSURG5lN0h4oGhEVrXzk1FVlzI6A/S1yI9oe6qASImAyvfB4Kih7+vUgPLngfCU6Wn2etuBhx21/wBRkGjVuGxK2fgUjmxfv17fDKUWMvbIFlPPXHIOxJqOVqLJiB5pFxyrnfXUz+xqQwWmwNzJ0diflaM0uEcIS06CFfPkW7QPby+BKIXT45J4WgtohMadVK9du1a3HHHHbj//vuxZ88ezJgxA0uWLEFLy9G3hGzbtg1XX301tm7dih07diAlJQWLFy9GfX39uIP3aoIAXPgEMO0qwGED3vwxULlV6aiIXOaNXbUoaepFWIAWv1qYNfoDDHQAL10KtJcDocnAjz8AQhOOfF1gJLD8VUATAFRsBrb+Zdyxj4VGrcJjV83E5Sclwe4Qcdvr3+Pj/Y2sp56A5P/W7ABOE5EvzKcua+7FO3uMAIDfnJfjtTc8b1swBYE6NfbVdR2zZ4c34JpHdGKjTqoff/xx3HzzzVi1ahXy8vLw9NNPIzAwEM8///xRX//qq6/i5z//OWbOnImcnBw8++yzcDgc2LJly7iD93oqNXDpf4GcCwG7Wdq+WrND6aiIxq170IrHNkpdS3+1MBMRQbrRHcDUA7xyBdBSCATFSgl1+HG6siZMBy5+Uvr8i8eAovfHGPn4qFUC/n7lDFw+S0qsf/XaLhhLiwF4f20huY7cDbetroZ11TShOOx2GH2gnvpv60vhEIGl+fE4KTVC6XCOKTbEgJvOkCZm/G1DKWx2h8IRHV1STp5UV93SjJ5W1lUTHc2okmqLxYLdu3dj4cKFwwdQqbBw4ULs2DGyZHFgYABWqxWRkZHHfI3ZbEZPT89hHz5LrZG2r05ZKNWFvnolUL9H6aiIxuVfW8rR0W/BlNhgXHvqpNG92dIPvHYV0LAHCIiUtnxHZZz4fdOvBObdKn3+7i1AS/HoA3cBtUrA35fNwBWzkhEz2ATRboM6OAwRCYmKxEOeFxgahugU6e89n9zQRNJSXQXL4AD0gUGISRvH+EQ3+q66A5uLm6ESgDuXZCsdzgndfFY6IoN0qGrtx1u7jUqHc1Q6QwDiMzIBcAs40bGMKqlua2uD3W5HXFzcYV+Pi4tDU9PItq3cc889SExMPCwx/6GHHnoIYWFhzo+UlJTRhOl9NHpg+StA2pnS/N1XLlekkzGRK1S29uHFoeYvf7gwD1r1KJYRqwl441qgdgegDwWuWwfE5Y38/Qv/BEw+C7D2S8cZ7BpV7K6iVgn427LpWBzRBwAoFmPwwb4GRWIhZSRzXjVNQHI9dVJuPlQq76unFkURD38qddNefkoKpsQGKxzRiYUYtLj1HKkR2BObyjBo8XzfkJFIcc6rZi8JoqPxaPfvhx9+GG+88QbeffddGAyGY77u3nvvRXd3t/Ojrq7Og1G6iTYAuPp1IPkUYLBTas7UVq50VESj9pePi2FziFiQE4uzR9P8xW4F3roeqNoKaIOAa98GEk8a3cnVGmDZC0BYCtBRCaz7CeBQZrucWiUgW2wGABgNSfjV2r1473s/7xVBTqly4x52w6UJRL6JlOqlW7+3FLfgu5pO6DUq3L5gDL0+FHLtqalIjghAS68ZL3x9UOlwjorNyoiOb1RJdXR0NNRqNZqbmw/7enNzM+Lj44/73kcffRQPP/wwNm7ciOnTj78Y6/V6hIaGHvbhF/QhUiIRP02ax/vixVJHYyIfsa20BZ+VtECrFvC7C3JH/kaHXUqAyz4F1HrpBlPq3LEFERQt7fzQGIDyDcDnD4/tOONktZjRVFEKAJg5ZzYcInDHm3vx7vfeuX2PXCspNx8A0G6sxUB3l7LBEHmAw25HfYm0yy7ZC5uU2R2ic+bzDWdMRnzYsR/eeBu9Ro1fL5ZuAvx3WyW6BiwKR3SkxOxcqNRq9LS2oLvFe5uqESllVEm1TqfD7NmzD2syJjcdmzdv3jHf97e//Q1//vOfsX79epx88sljj9YfBIRLc3hjcqS5vC9eDPRw2yh5P6vdgT9/JI3Q+vG8NKTHjHBbncMBfHAbULgOUGmlhDj97PEFkzgTuOif0uefPwKUfDy+441BY1kJ7DYbgiOj8MB183H1nJShxHof3vHSujhyncDQMMSkpgHgkxuaGJoPVsAyOAhDUDBiJ3lfPfW6PUaUNfchLECLn509gj4dXuaSGUnIiQ9Br8mG/2yrVDqcI0h11VLizx06REca9fbvO+64A6tXr8aLL76I4uJi3HLLLejv78eqVasAACtXrsS9997rfP0jjzyCP/zhD3j++eeRlpaGpqYmNDU1oa+vz3V/Cl8TFC01Z4qYLM3nfekSoK9V6aiIjuuVb2pQ2dqPyCAdbluQObI3iSLw6d3A3lcBQQVc8SyQtdg1Ac34ETD3Z9Ln634KtJa55rgjdOhYGbVahb9cOg3XzE2FKAJ3vr0PbzOx9nvJ+XKNIS8wyf/Jf8+TcqdCUHm0evCETFY7ntgk/Q74xTkZCAvQKhzR6KlUAu45LwcAsObrajR0DSoc0ZFS8tlLguhYRr0qLl++HI8++ijuu+8+zJw5E3v37sX69eudzctqa2vR2NjofP1///tfWCwWLFu2DAkJCc6PRx991HV/Cl8UEj80lzcZaCsDXr5UmttL5IU6+y34x2apB8Cdi7NHdsEiisDm+4FdqwEI0ni5/EtdG9jiB4FJp0sNAN+4RhrV5SHyBaa8DVKlEvDgJVNx7VBifdfb+/DWd37QD4KOKYXNymgC8eb51C/vqEFDtwkJYQasnJemdDhjNj8rBnMnR8JiczhvEngTZ4PGwgMQRVHhaIi8y5huNd56662oqamB2WzGt99+i7lzh2sjt23bhjVr1jj/f3V1NURRPOLjj3/843hj933hqVJiHRwHNBcAry7zaFJANFJPbC5D96AVuQmhWH7KCLvxf/434KuhLdoXPi49WXY1tRa4cg0QmgS0lwPv/swjjcusZhMay6V66kMb9qhUAh68dCquO3USRBG4+539eHMXE2t/lZw3DRAEdNTXob+rU+lwiNzGbrOhvkQq/5GfVnqLHpMVT22rAAD8amEWDFrv60o+UoIw/LT6nT1GlDX3KhzR4ZKycqFSa9Db3oruluYTv4FoAvGu/TsTUVSGtBU8IBKo3w28thywDCgdFZFTaVMvXvmmBgBw34V5UKuEE7/p6yeBbX+VPl/yV+DkG9wXYHAssPxlqQFa6cfAF+7fBdNQVgKH3YbgqGiExR3epFEQBDxwST5WzhtOrNfuqnV7TOR5AcEhrKumCaG5qgJW0yAMh/yd9xbPfF6JrgErMmODcfmsJKXDGbdZqRFYmh8Phwj8bX2p0uEcRmswIH6KXFfN0VpEh2JS7Q1ic4Hr3gX0YUDt18DaawGbWemoiCCKIv78UREcIrA0Px7zMqJO/KZdzwIbfy99fs7vgHm/cG+QAJA0W3oaDgBb/wqUrnfr6eSt36l50yAIR95kEAQBf7o4H9eflgYAuOedA3h9JxNrf+QcM8MLTPJj8k2jZC+rp27uMeG5L6URVHctyYZG7T2xjcedS7KhEoDNxc34rtq7SgNTWVdNdFT+sfr4g8SZwIq3pfm9lZ9J83ztVqWjogluc3ELvqxog06twm/PH8EIrb2vAx//Wvr89F8CZ93l1vgOc9IK4JSbAIjAupuBtgq3ncp5gXmcbZCCIOD+i/Kw6vQ0AMC96w7gtW+ZWPub4brqAoUjIXIfo1xP7WVbv/+5pRwmqwOzJ0VgUV6c0uG4zJTYYGep1cOflnhV/XLyIb0kvCkuIqUxqfYmKXOAa96Q5u+WfiLN9XXYlY6KJiizzY6/fCzV0N105mSkRgUe/w2F7wLv/1z6fM5PgYV/BI7yFNetljwEpJwKmHukHR9m19ejWU0mNFVIDWQOrac+GkEQcN+FebjhdGn8zG/fPeDcSk/+ITl3KiAI6Gwwoq/Tu54oEbnC4fXUx1/zPKmytQ9rh3pW/Oa8nKPuGvJlty/Igl6jwnc1ndhS3KJ0OE6JWTlQazToa29DV3Pjid9ANEEwqfY2k88CrnpZmudbuA744P880niJ6IfWfFWN6vYBxITo8fNzphz/xaXrgXduAkSH9MR46cOeT6gBQKMDrnoJCEkAWkuA926RupC7UH1ZMRx2G0KiYxAac+InI4Ig4A8X5uLGM6TE+vfvFeBlJtZ+wxAcjNhJ6QC4HZL8U3NVOaxmEwwhoYhOTlU6HKfHNpbC7hCxICcWp6RFKh2Oy8WHGbBq6Ibs3zaUwO7wjqfCWr0B8VOyAXCcINGhmFR7o6zFwLLnpLm+e18B1t/j8sSA6Hhae8148jNp+/TdS7IRrNcc+8WVW4E3VwIOGzD1CuCifwFK1tyFxA3fmCr+EPjycZce3njIWJmRPhkRBAG/vyAXN58pXSD94b0CvLSj2qVxkXJS8qcCAIy8wCQ/JCdOKV5UT723rgufHGiCIAB3L81ROhy3ueVsaeZ2WXMf1u0xKh2Ok3NeNXtJEDl5x+pIR8q7BLj0aQACsPN/wOY/MrEmj3lsYyn6zDZMTw7DFbOSj/3C2m+k+dB2M5B9AXDZM4DKC8aZpJwCXDDUBXzLn4HyzS47dO3QRcRot0EKgoDfnp+Ln54lPdW87/1CvPh1tcviIuU4m5UV8QKT/E+dl9VTi6KIRz4tAQBcflIysuNDFI7IfcICtfj5/AwAwBObymCyekdJYEqetOYZWVdN5MSk2pvNWA5c+IT0+Vf/ALb/XdFwaGIoqO/G2u+kOrX7L8qD6lgjtOr3AK9eCVgHgIxzgStfkOZGe4vZ10sfEIF3bgA6qsZ9SItpEM2V5QCGG1SNhiAI+M15Ofjp2VJiff8HhXjhq4PjjouUlZSTD0FQobOxAb0dbUqHQ+QydpsV9aVD9dRjWPPcYXt5G3ZUtUOnUeGOxVlKh+N2Pz4tDQlhBjR0m/DyDu8oHUrIypbqqjs70NXUoHQ4RF6BSbW3O3mV1HwJALb+Bfj638rGQ35NFEU88GERRBG4eEYiZk86Rp1acyHwyuVSQ7BJpwPLXwU0es8GOxLn/Q1IPgUwdQNvXAuY+8Z1uIaSIjjsdoTGxCEsdmydZgVBwG+W5uCWoacPf/qwCM9/ycTalxmCghE7WbpRwi3g5E+aKsphM5sREBKKqJRJSocDh0PEw0NPqVeeOglJ4QEKR+R+Bq0av1oo3Tx4alsFekzKT4bR6vRIyJK23bOumkjCpNoXzPs5cM7Q3N+NvwN2PadsPOS3PjnQhJ3VHTBoVfjNeceoU2urAF66FBjslOZDX7MW0J2gM7hSNHqpvjo4DmgpAj64dVxlFHWH1FOPhyAIuHtJNn5xjpRYP/BREZ79YvxP0kk5h46ZIfIXdWPoIeFOH+5vQHFjD0L0GvziRA00/cjls5KQGRuMrgErnvm8UulwABw6TpBrHhHApNp3nHUncMavpM8//jWw7w1l4yG/Y7La8ddPigEAPz0rA4lHewLQWQO8dDHQ3wLETQNWvAPovbyeLTRB6giu0khjv77+15gP5craQkEQcOfibNw6dGH44MfFTKx9GC8wyR/JjaiSvaCe2mJz4NGNpQCAn83PQESQTuGIPEejVuGuJVLH7ee+PIjmHpPCER2y5hXuZ101EZhU+w5BABbcD8z9GQBRGhVU+J7SUZEfefaLKtR3DSIhzICfnZ1x5At6GqSEuqceiM4CrnsXCIjwfKBjkXoqcN4j0ueb/whUfjbqQ1gGB9A0jnrqoxEEAb9enIX/O3c4sf7fdu94CkGjk5wr1VV3NTWit5111eT7bFYrGsqkrdapXjCf+rVva1DXMYiYED1WnZ6mdDgetygvDrMnRcBkdeCfW8qVDgcJmTlQa7Xo7+pEZ2O90uEQKY5JtS8RBKm++qTrpHnA79wIlG1QOiryA03dJjy1VUrmfnNeDgJ0P+jg3dcKvHQJ0FkNRKQBK98HgmM8Hue4nHyjNENbdABv3yD9WUahvrQYosOBsNg4hMbEuiwsQRBwx+Js3L4gEwDw109KvGZ7H42cPjAIsZOlm1F8Wk3+oKmiFDaLGYFh4YhMSlE0lj6zzTnm8ZcLMxGoO86YRz8lCALuGRoftnZXHSpbx9cjZLw0Oh0SM+W6ak4+IGJS7WtUKuCifwJTl0lzgddeB1R9rnRU5OP+tr4Eg1Y7Zk+KwMUzEg//5kAH8PJlQFsZEJoErPwACE08+oG8mSAA5z8GJM6S6sHfWAFYBkb89roxjtIaqV8tysIvF0qJ9UOfluC/25hY+xrObiV/It8cSvaCeurV26vQ3m/B5OggXHWysgm+kuZMjsSCnFjYHSIeG9oKryTnOEE2KyNiUu2TVGrgsqelucB2M/D61UDtt0pHRT7q+9pOrPte2rp134V5h188mXqAV5cBzQeAoFgpoY5QvgPsmGkNwPKXgaAY6c/04f+NuHGZq5qUHc8vF2Y5u7w+sr4ET22tcNu5yPWcSTWfVJMfMHpgzRuJ1l6zs9/EXUuyoVVP7EvXu5fmQBCkxqJ767oUjeXQXhKsq6aJbmKvTL5MrZXmAmcsAKz9UuLT8L3SUZGPEUURD3wkzSBdNjsZM1LCh79pGQBe/xFQv1uqnV75HhDtB91Ww5KBK1+UGpcdeAv45j8nfIt5YADNVVKCm+zmC8zbF2bi14ukxPrvG0qZWPuQpOx8CCoVupub0NPWonQ4RGNms1rRUCrVU7uiMeN4/PuzcvRb7JiRHIbzpsYrGos3yI4PweUnJQMAHvm0RNFkNj4zGxqtDgPdXeioNyoWB5E3YFLtyzR6YPkr0pxgc4+0Rbe5SOmoyIe8v7cB39d2IUinxt1DnUUBADYzsPZaoOYrQB8KrFgHxOUrF6irpZ0OLPmr9PnGP5ywhKK+tBCiw4HwuASERru/lvy2BZm4c/FwYv2kFzSloRPTBwYiLl268cTtkOTLmspLYbNapHrqxGTF4qhp78drO2sBAPecl6P4NnRvccfiLOg0Kuyoasf2cuUaI2q0WiRmD9VVc4cOTXBMqn2dLlCaE5x0slQn+tIl0hxhohMYsNjw8KfSk4ifnzMFsaEG6Rt2K/DWKqlDtjYQuPYtIGmWgpG6yZyfADOuBkQ78PYqoKv2mC+VEyR3P6U+1K3nZjpHqDy2qQz/3MzE2hdwtBb5g1q5h4TC9dSPbSyD1S7irKwYnJYRrVgc3iYpPAArT5VKsR7+tAQOh3JPq5Pz2EuCCGBS7R/0IcCKt6W5wf0t0tijzhqloyIv9/S2SjT1mJASGYAbz5gsfdFhB979KVD6MaDWA1e/Lo2j8keCAFz4BJAwAxhoB9auAKyDR32pnFSnengb5C/OmeLs9vrE5jL8Y3OZR89Po8fGPeQPnPXUCo7SKqjvxgf7GgDg8J1UBED6/RCi16C4sQcf7m9QLA7nmse6aprgmFS7kCiKsFnsypw8IEKaGxydJc0RfulioKdRmVjI6xk7B/DMdqnxy+/Oz4VBqwYcDqlxV8E7Ur3xVS8B6fOVDdTdtAFSCUVgFNC4D/joV0c0LjMP9KPloNSJO1mB2sJb5mfgN+dJifU/Npfj8U1lvHDxYknZuRBUKvS0NqO7pVnpcIhGzWaxoKFc+Xrqv22QultfMjMRU5PCFIvDW0UE6fCz+dIYv0c3lsJicygSR3xGFjQ6PQZ7utFRX6dIDETeYOIN+nMTy6ANW18tgdVkxwU/nw5BpcB2qeAYaX7wC+dJM3hfugRY9QkQxC1TdLiHPy2B2ebAqemRWJIfLyWS638DfP8KIKiAK54FspcqFl9dUQdKv23CnIsmIzQ6wL0nC08FrlwDvHQpsO91IPEkYO5Pnd82FhdCFB2ISEhESKQy/5Z+dnYGVII0w/pfW8oBUcSvFmWxvtAL6QICEZ+RicbyUtQVHUBYbJyi8Qz0WPDVO+XobTMpGgedWECIDvMuy0B4XKCicTSWl8ButSIoIhIRCUmKxPB1RRu2l7VCqxbw60V8Sn0sq05Pw5qvq1HXMYjXvq3B9adP9ngMUl11LmoP7EVt4X5EJad6PIZDmfqt+OrtcnS3HH3nGXmX3NMTkHuaD45pPQom1S7S22HCwX1tsFsd+H5TLWYtUWjsUGiiNPbohfOBtlIpUbj+Q+lJNhGAnQc78NH+RqgE4L4L8yEAwJY/ATufkV5wyVNA/mWKxdfR0I9Pnt4Pm8WB9oY+XHH3bGi0aveedPJZwOI/Axt+C6y/V2rKlnYGgMNntSrpJ2dlQCUIePDjYvzrswqIAO5gYu2VUvKmobG8FMaiA5g6f6FicTgcIjY+W4D6si7FYqDR6Wzqx5X3ngKt3s1r3nEcOj5QifVFFEU8vF56Un7t3ElIjVL2JoM3C9Rp8MuFmfjduwV48rMKLDs5BcF6z1/ap+RNQ+2BvTAWHsBJSy70+PllokPE5heKUFPQrlgMNDopeZFKh+AyTKpdJCopGGctz8LWV0rwzftViE8PQ2JmuDLBREwafmLdfAB4ZZk0Dkkfokw85DUcDhEPfFQIAFh+SiryEkOBz/8OfPmE9IILHgNmXqNYfFazHetXF8BmkbaxtdX14cu3KjD/Gg88qTj159JYugNvAW/+GPjp50BYstfMagWAm85MBwA8+HExnvysAg5RxJ2Ls5lYe5mUvGnY+f7bijcr2/XRQdSXdUGjV2P+NdnQ6Fjx5a1EB/Dlm2XobBrA56+VYsH1uYr9u5b7ASi15n1yoAn7jd0I0qlx67l+MMbRza46OQXPfnEQB9v6sXp7FX41NJLRkw6bV+1wQFAps9Z8v6kWNQXtUGtUOPuabOgClLs5RSMTER+kdAguw6TahXJPT0B9eSfKvm3GxmcLcNXv5iAwVKdMMNFTpMR6zflA/XfAa8uBa9+WuoXThPX2biMK6nsQotfg14uzgB1PAVsflL65+EHglJsUi00URXz+eik6G/sRGKrDvMszsGVNMQq31yMxMwxZp7h5PqkgABf9C2gpkW5Grb0OpqveRstBqfbcG5JqQEqsVYKABz4qwlNbK+EQpSY+TKy9R2JOHlRqNXpaW9Dd0oSwWM/P1q0tasd3n1YDAM65NhtZczjf19sFhmrx3uPfo/TbJiRmhSPvdM9vibRazGhUsJ7aanfg0Y1SLfVNZ6YjOljv8Rh8jVatwp2Ls/GL1/bg2S+qsOLUSYgJ8ezPLX5KJjR6PQZ7e9BurEV0appHzw8ADRVd+OZ96ff1mcszkXtagsdjoImNt61dSBAEnH11NiLiA9HfbcHmNUUQFRxzgLg8qXmZPlSaN7x2hTR/mCakXpPV2fjl9oWZiC55TdruDADzfwucdpuC0QHFXzei9JsmCAKw+KZ85JyagNlLpTKKba+UorOp3/1B6AKBH70ilUs07EH9q3cO1VMnITgyyv3nH6EbzpiM+y/KAwD8d1slHllfyuZlXkRnCEBcRiYAZbqA93Wasen5IkAE8s5MZELtIxIzIzD3Emk3yvY3ytBm7PN4DI1lJbDbbAiOiER4vOeT+je/q8PBtn5EBelw81npHj+/rzp/WjxmJIeh32LHvz/z/PhFtUaLpGzpd1KtAmveYK8FG1cXQHSIyJoTh7wz/KNGl3wLk2oX0xk0WHLzVGi0KtQVdWD3+mplA0o8SZozrA0EKrcAb98gzSGmCeffWyvQ1mdGenQQrg/+Vup0DQCn/R9w9t2KxtZm7MP2N6RxUXMuTkdSltQDYM5Fk5GYGQ6r2Y4Nqws8010/Ig1Y9jwgqFBX8D0AZTvgHsuq0yfjTxfnAwCe/rwSD39awsTai6Q6R2t5dnarw+7AxucKYOqzIjolGGdelenR89P4zFo8Can5UbBbHdiwugAWk82j5687ZJSWp3e/DFhs+MdmKSG87dwpitQG+ypBEJzjF1/bWYuadg/chP4BeTeX0cNlL6JDxKYXitDfbUFEfCDOvoY7t0gZTKrdICopGGcP1YDu/PAgjKWdygaUeqo0b1itB0o+At79mTSPmCaMmvZ+vPBlNQDgH9Nrofng5wBE4JSbgUUPSFufFWIx2bBhdQHsVgdS8yMx+5Amfyq1CotvykdAiBbt9f3YvtZDc5ozzgUW/hF1/eEAgJRYg2fOO0o/Pi0ND1wiJdbPbK/CXz8pZmLtJZKdNYYFHv1v8u0HVWis6IbWoB66wcuaQl8iqAQsXJWL4Ag9upoHsO0Vz94sk3dWKNGY8YWvqtHaa0ZKZACumatQs1cfdtqUaJyVFQOrXcRjGz30u/IQ8s3nuuICiA7Pjffavb4adUUd0GhVWHLzVOgMvBlDymBS7SY58xKQc1oCRBHY+Fwh+rsV3nadPh9Y/rI0f7jgbeDD26W5xDQh/OXjYljsDtyaXIVp39whdcWZeS1w3t8UTahFUcS2V0vR1TyAoHA9Fq7KO2IcXVCYHotuzAcEoPirRpR+45n566bpN6DFHAwASCl8FOhp8Mh5R2vlvDT8+dKpAIDVXxzEXz5mYu0NkrJyoVJr0Nve6rF51dUH2rBnQy0A4NzrchEeyx4aviggWIfFN02FSiWg/LsWFH7hmbXHajahsVwqEZJ3WnhKZ78FT2+rBADcuTgbOg0vT8fi7iXSA50P9jWgoL7bo+eOS8+EVm+AqbcHbXU1HjmnsbQTOz88CAA46+psRCUFe+S8REfDVcuNzvpRFiITgzDYY8Gm5wvhULK+GgCylkjzhwUV8P3LwIZ7pfnE5Ne+qmjDxqJmnKEuxB2df4bgsAL5lwMXPwko1KFTVvhFA8p3NUNQCVhyUz4Cgo/e2C8lJxKnnJ8GANj2Wik6Gty/tc1YInVJjwy0I8jSCLy50mt7Elx36iQ8OJRYP/vlQfz5IybWStMaDIifInXh9cQW8N4OEzavKQIATDs7CVNmx7r9nOQ+CRlhOPXSDADAl2+Wo7W21+3nbCgrgcNuQ3BUNMLiPFuH/9TWCvSabchLCMVF01kPO1ZTk8JwyUzp5yf3UPEUtUaDpByprtoTkw8GeizY9FwhRBHImRfPxmSkOCbVbqTVqbH0J1Oh0atRX9qFXR8fVDokaf7wJf+RPv/2aWDLA8rGQ25lszvwwIdFmCWU4Xn941DZzUDWecDl/wNUym4Lba3txZdvSvVzp16ajoQp4cd9/ckXTEZyTgRsFgfWry6A1ezeEgZnbeHsswBDGGDcBXyqbO358aw4dRL+epm0/e75rw7igY+KmFgr7NAxM+5kt0n1t+Z+G2JSQ3D6MtZR+4OZi1KQNj0adpu05pkH3VtffegoLU/WpBo7B/DSDunJ5j3n5UClYj3sePx6UTa0agHby1rxdUWbR8/tLHtx841Eh0PExucKMdBjQWRiEM662gNjN4lOgEm1m0XEB+Gca6V/7N99Uo3aIi8YSD/zauCCx6XPv3wc2P53ZeMht3l9Vx00Lfvxov4R6ByDQPo5wJVrALVW0bjMgzasX10Au82BtGlROGlh6gnfo1IJWHRDPgJDdehs7Mfnr7u347V8UZAy+wzgiucBCMDuNcB3L7jtnON1zdxUPHS5dFHzwlfV+NOHTKyV5KwxLNzv1v8OO96rRPPBHugCNFj6k6lQa/mr3R8IgoAFP85FSKQBPa2D2PqSe3egDDcp82w99RObymGxOzAvPQpnZUZ79NyjUdVdhYd3Pow/fv1H7Gvdp3Q4x5QaFYhr5ki/Ux9e79mafLlswFjk3rrqXR8fRH1pJzR66eGVVsfeEaQ8/ub1gKw58cg7MxEQgc0vFKG/ywu2kJ5yozSXGAA+exDY8R9l4yGX6x6w4v0Nm/Cy7iGEYBBInQf86FVAq2zTLVEUsfXlYvS0DiI4Uo8F1x9ZR30sgaE6LL4pH4IAlH7ThOKv3VNfPdjXi9baagBAcu5UIHMhsOAP0jc/uQuo2+mW87rC1XNS8cgV0yAIwJqvq3H/B4VMrBWSmJUDlVqDvo52dDW75+9q1d5W7NtcBwBYsDIXodEBbjkPKcMQpMWSm6dCpRZQ+X0rDmwzuuU8VpMJTRVSc6uUPM/VU5c09WDd99Kf6Tfn5Xhd12ZRFPF1w9e4ZfMtuOS9S/Bq8at4p/wdrPhkBVZ8sgIbqjfA5vBsh/aRuG1BJoJ0auw3duOTA00eO2/s5AxoDQEw9fc5f4e6Wl1RB777RDr2/GuyEREf5JbzEI0Wk2oPOfPKTEQlB2Ow14oNzxbAYfeCJmGn3SbNJwak+movfgJHo/fyx5/hv44HECn0QUycBVzzJqBT/pfPgW31qNzTCpVKwJKbpsIQNLqn5klZEZhz0fAs1/Z6189yNRYdAEQRUcmpCAqXxnvhjDuA3IsBhxVYex3Q67kLldFafkoqHrl8OgQBeGlHDe57n4m1ErR6AxIypZ1K7tgO2dM2iM9eKgYAzDg3Beknxbj8HKS8uMmhOO3yKQCAr96uQHN1j8vPUV9WDIfdhpDoGITFxrn8+Mfy9/WlEMWhOcsp4R4774mY7WasK1+Hyz+4HD/d9FN8Wf8lBAiYnzIfl2RcAq1Ki32t+3Dn53fignUX4MXCF9FrcX/d+0hFB+tx05nS78lHN5bC6qFrTrVGg2S5rtoN86r7u8zY9EIhIAJ5ZyQie65na/+JjodJtYdodGosvXkqtAY1Giu68e2HXlBfDUjziU+/Xfr8o18B+9YqGw+5RHVlCS4vuAUxQjf6w7MhrHgHMIQqHRaaq3vw1dtSHfVpV0xBfHrYmI4ze+kkpOZFwm51YP3/XD/LVd4GedhYGUEALv0PEJMD9DUBb/4YsFlcel5XuuqUFPztCimxfvmbGvzh/QLlmyVOQMNbwF17gSnPMTYP2BA3ORTzLs9w6fHJu0w/NxnpJ8XAYRexYXUBTP1Wlx5fni3syXrqnQc7sKWkBWqVgDsXe0dNbNtgG/79/b+x+O3FuP/r+1HRVYEATQCuzrkaH172IZ4890k8eMaD2LhsI34242eI0Eegob8Bj373KBa9vQiP7HwExl737CYYrZvPSkdUkA4H2/qxdledx86b7KZeEg67AxufK8RgrxVRycE48yr2jiDvwqTag8LjAnHOihwAwJ71Nag+4NkGEkclCMDCPwFzfgJABN67BSj6QOmoaDx6GhHw+mVIFNrRqE1B0E0fAYGRSkcFU78VG1YXwGEXkT4zBtPPTR7zsQSVgIU35CEofGiW66uura82HtKw5zD6EOBHrwH6MKDuG2DDb112Tne48uQU/H3ZDAgC8Mo3tfg9E2uPk/8OGYsOuPTv6FfrKtBS0wt9oAaLb8qHmiOI/JogCDh3ZS5Cow3obTfhMxfXV9fKPSQ8NJ9aFEU8/Km0y2L5KSlIj1F2FFJpRyl+9+XvsPjtxXhm/zPoMHUgISgBv579a2y+cjN+O/e3mBQ6PDs7OiAav5j5C2xcthF/nPdHZIRloN/aj1eKX8EF716AO7bdgb0texXdIRSs1+C2c6UdDv/cUo4Bi2e2qcs3Eo3FB+BwuK6h6LcfHkRDeRe0BukhlYZ11ORl+FvYwzJPjsO0s5MAAJvXFKG3w6RwRJAS66WPADNXAKIdePsGoGyj0lHRWPS3of/ZCxBna0CdGAvrte8CwcqP1hFFEZ+9VIzedhNCow04d+X4a+cCgnVYclM+BJWA8l3NLpvlOtDT7awFS8mbeuQLojKAK1YDEIBdq4HvX3HJed1l2exkPHallFi/9m0tfvfeASbWHpSQlQO1RoO+zg50Nrrm72jF7hYc2Co9DVt4fR5Co1hHPRHoAzRY+pNpUGkEHNzXhn1bXPP00WIaRHOltIMoxUPzqTcVNWNPbRcCtGr8coEyTxwdogPb6rbhxg03YtmHy/BB5QewOqyYHjMdfz/77/jk8k9w/dTrEao79i4vg8aAK7KuwLuXvIunFz6N0xJPg0N0YFPNJlz36XVY8ckKrD+4XrG662vmTkJKZABae8144atqj5wzbvIU6AICYO7vR2uNa85ZU9COPeulDvHnrMhBeFygS45L5EpMqhVw+rJMxKSGwNxvw8ZnC2D3hvpqlQq4+F/S/GKHFXjzOuDgdqWjotEY7IT40qUI6qlEoxiJD2Y+jdQ079getW9LHQ7ua4NKI2DJzVOhD3RN9/GEKeE49RKpbsxVs1zri6X51FHJqQgMCz/6i7KWAOcMPaX+6A6gfve4z+tOl89KxuNXzYBKAF7fWYffvsvE2lO0Oj0SMqUdSkYXbIfsbh3A1pelJ3wnLUpF2nTv7ZZMrheTGoIzhkam7VhXiaaq7nEfs6G0GA67HaExsR6pp7bZHfj70AzlG85IQ2yoZ5tnDlgH8Frxa7jo3Ytw22e3YWfTTqgFNZakLcEr57+CV89/FUvTlkKj0oz4mIIg4PSk0/HMomew7uJ1uDzzcuhUOuxv24+7tt+F89edjzUFa9BjcX09/PHoNCrn1vqnt1Wis9/9JUsqtRpJOfkAXLPm9XaYsPmFIgDA1LOTkHmy52r+iUaDSbUC1FoVltw8FboADZqqevDNe1VKhyRRqaX5xdnnAzYT8NqPvLrLMR3C3Au8sgxC8wG0iqH4hfp+XHfeWUpHBQBoqurGjnWVAIAzlmUidpJra7tPWpSKtGlRw7N6xznL1bkN8kRPbM68E8i+ALCbpcZlfa3jOq+7XXZSMh6/aiZUAvDGrjr8Zt1+JtYeIm+HrB1nszKb1T7UQ8COhIwwzL003RXhkY+ZenYSppwcC4djqL66b3z11c7xgR7q+r1uTz3KW/oQHqjFT8/2XC+Apv4mPP7d41j49kI8tPMh1PbWIkQbglX5q/Dp5Z/i0bMfxYyYGeM+T2ZEJv502p+wcdlG/HzGzxFpiERjfyMe2/0YFr21CA/vfBh1vZ6rcb5oeiLyEkLRa7bhqa0VHjmn/PtzvGue3e7AxmcLYeq3HnZDicgbMalWSFhMABaszAUA7N1Ui4P7vOSCXK0Flr0gzTO29gOvLAMa9iodFR2PZUC6AVL/HboRhOssv8WVS89FqEHZWdQAYOobqqN2iMiYFYupQ6UPriSoBCy4Pg/BkXp0tw5i68vjqzU0jnRWq0oFXPY0EJUJ9NQDb10P2F3bPMjVLj0pCU8slxLrN78z4p53mFh7gqvqqr98qwJtdX0wBGulOmo1f4VPRIIg4JxrcxAWG4C+TjM2rymCOI5/x56cT22y2vHEZml0163nTPHI76l9rftw1+d3Yek7S/FC4QvotfQiNSQV9865F5uv3Iw7Tr4DCcEJLj9vVEAUbpl5CzYu24gHTnsAU8KnYMA2gFeLX8UF6y7AL7f+Enua97i97lqlEnDPedJumZd21MDYOeDW8wHDa159ceG46qq/ea8KTVXd0AVosOTmqVBrueaR9+LfTgWlnxSDGeemAAC2vFiMnrZBhSMaojVIzZhSTwPM3cDLlwEtxUpHRUdjM0tb9Wu+hEkViOvMv4EQPxVXnZyidGQQHSI2v1iEvk4zwmICcO517ptBagjSYslNU6FSCajc04oD2+rHdJyBnm601Ul1W8m5R6mnPuLEodK/FV0IUPMlsPEPYzqvJ10yMwn/+NFJUAnAW7uNuPud/bAzsXarhMwcqLVa9Hd1orNxbH83y3Y1oXC79N6Fq/IQHKHsvHlSli5Ag6U/kZKMmoJ2fL+pdkzHsQwOoEmup/ZAk7IXv65GY7cJiWEGrDh10onfMEY2hw3rq9fj2k+uleqaq9fDLtoxJ34Onjz3SXx42Ye4JvcaBGrdX5urV+txWeZlWHfxOjyz6BmcnnQ6RIjYUrsFP17/Y1z98dX4pOoTWB3uuyl7VmY05qVHwWJ34IlN5W47jyx2cjp0AYEwD/SjtXps024O7m/D3qG/1wtW5iIshr0jyLsxqVbYvMszEJsWCvOADRueLYTd5gX11QCgCwSuWQskzgIGO4CXLgHaK5WOig5lt0lN5So2w6Ex4MemO7FfzMD9F+VBrfLMSJTj+X5TLWoOtEOtUWHJT6RyB3eKTw9zjhX66u1ytNSMvnZNfkodnZqGwNARjvuKyQIuf0b6/Nv/AvveGPV5Pe3iGYn4549Oglol4O3dRtz19j4m1m6k0emQOFRXPZZ51Z1N/dj2ilSDOnvpJEzKj3JpfOSbopNDnGOFvnm/Cg0VXaM+Rn1pMUSHA2GxcQiNcW9Ty+4BK/6zTbqOuGNxNgxa13dv7rH0YE3BGpy/7nzc9fld2N+6HxqVBhdnXIy3LnoLzy15DvNT5kMleP7yVxAEnJZ4Gp5e+DTeu+Q9XJF5BfRqPQrbC3HPF/fgvHfOw/MFz6PbPP46+aOd+zdDT6vXfW9ESZN7a7tVKjWSc6W66rGseT3tg9iyRqqjlsfJEXk7JtUKU2tUWHJzPvSBGrRU9+DrdZ6pdxkRQyiw4h0gbirQ1wy8eDHQNba74eRiDjvw3s+Ako8gqnV4OOw+fOvIwfnT4nFquvIX3A0VXfjmfalXwJnLMxGTEuKR885YkILJM6LhsItY/7/Rz3KtKzrGKK0TybkAOPse6fMPb/eJkomLZiTiX0OJ9bo99bjrLSbW7uSc3TrKedU2ix0bVhfAarYjMTMccy6a7I7wyEflnZGIrDlxEB0iNq4uwGDv6BpRyQlPsgeeUv/380p0D1qRHReCy05ybSlQbU8tHvr2ISx8ayEe2/0YGvsbEaGPwE+n/xSblm3CX874C3Iic1x6zvHICM/AH0/7IzYu24hfzPwFogxRaB5oxhO7n8Citxfhr9/+FbU9rr3empESjvOnxUMUgb+vL3XpsY8mZYzzqqX+KIUwD9gQmxaK0y6f4o7wiFyOSbUXCI0KwMLr8wAA+z8zonJPi8IRHSIwErjuvaG6UaP0xLq3SemoJjZRBD76JXDgLUClwfen/hP/q0+DTqPCveflKh0dBnst2Li6AKJDROYpccg7I9Fj5xYEAQt+PPZZrnLCM6bawrN/A2QtlZr8rV0B9LeP/hgedsH0BDx59VBi/X09fv3mXibWbpI61LinbpR11dvXlqG9vh8BIVIdtYp11HQIQRBw9jXZiIgPRH+3BZteGF19tZzwpLp5lFZTtwkvfCVtA757abZLdlOJoohdTbtw22e34cJ3L8RrJa9h0DaIKeFTnI3Cbj3pVkQHeG+H/EhDJH4242fYuGwj/nz6n5EZkYlB2yBeL3kdF757If7vs//Dd03fuazu+s7F0s9+S0kLdh7scMkxj0VuVmYsLoTDPvK66q/XVaClugf6QA2W3JQPtYZrHvkG/k31EmnTo3HSolQAwGcvFaO71f2NJEYsOAb48QdA+CSgo0pKrPvblI5qYhJFYP29wJ6XAEEF6yVP41d7pQYrN585GSmRys5uFB0iNr9QhP5uC8LjAjH/2my31VEfiz5QiyU3Tx31LNeB7i60G2sBQRhZPfUPqVTAZc8AkRlAdx3w9vXSFn0vd/60BPz76pOgUQl4b28D7nhzL2zeMObPz8RnZkOj1WGguwsd9cYRvaf0m0YUf9UICMCiG/MRFKZ3c5Tki3QGqYmTRqtCXVEHdq+vHtH7zAMDaK6Sdse5+0n1P7eUwWxz4JS0CJybM75t5la7FR9UfoDlHy3HDRtuwLa6bRAh4oykMw4baWXQ+E7fAZ1ah0unXIp3LnoHqxevxplJZ0KEiK11W7Fqwyos/2g5Pqr6CNZxNsNMjwnG8lOknisPfzq+pp4nEpM2GfqgIFgGB9BSPbIpN5Xft2D/Z9L6uOD6PIRGs46afAeTai8y99J0xKeHwWKyY8PqQtisY++Y6HKhiVJiHZIItJZIzcsGu5SOauL57M9S3S4AXPxvPNc1CzXtA4gN0ePn85XfIrV7fQ1qizqg0aqw9CdToTO4t476WGInheL0K0Y3y1V+YhOTmoaAkDGO/QoIH2pcFizNed98/9iO42HnTUvAv6+ZBY1KwPt7G/CrN/cxsXYxjVaLxOyR11V3NPRj22vSFs1Tzk9DSk6kW+Mj3xaVFIyzrpbmEe/88CCMpZ0nfE99aSFEhwPhcQkIjXZfzWpFSx/W7pJubv7mvLE3rOw0deKZfc9g8TuL8bsvf4fijmIY1AZcmXUl3r/kffx34X9xWuJpHr+R60qCIODUhFPxn4X/wfuXvo8rs66EXq1HcUcx7v3iXix9ZymePfDsuOqub1+QCYNWhT21XdhU1OzC6A8n1VVLN6hHsuZ1tw7is5dKAAAzF6Vi8nTv3WFAdDRMqr2IWi3VVxuCtGit7cVXb3tRfTUARKRJiXVQDNC0H3h1mTQfmTxj+6PAF49Jn5//KFqmXIF/fyb9HblnaQ6C9MoksLL60k7s/FC6G33W1VmISgpWNJ5p85OQMWtoluuzJ66vdm79Hu8Tm9gc4NKhGx87/g0ceHt8x/OQpVPj8dS1UmL94b4G/HItn1i7WvIIawytZjvWry6AzeJAck4ETr6AddR0YrmnJSBnnlQzu+m5QvR3m4/7ennNc/dT6kc3lMIhAovy4jB70uhvDlV0VuCPX/8Ri95ehH/v/TfaBtsQExCD/zvp/7Bp2SbcN+8+pIf738z29LB03DfvPmxatgm3nXQbogOi0TLYgn/u+ScWvb0ID37zIKq7q0d93LhQA248Q1pT/rah1K3r/Ejrqm1WqXeEZdCG+PQwnHqp//33JP/HpNrLBEcYsPAGqb664PN6lH/nvruIYxKdKdVYG8IB4y7g9asBq5eMAvNnO/4jPaUGgEUPAHNuxqMbStFntmFGSrjLm76M1kCPBRufK4QoAjmnxiP3NM/VUR+LIAg497ochMUEoK/DjC0nmOUq/9JPdsWs1ryLgTN/LX3+/q1A0+gatShlSX48/nPtLGjVAj7a34jb39gLKxNrlzn0AvNY2y5FUcTnr5eis7EfgaE6LLohHyov6OZPvuGsq7MRmRiEgR4LNj1feNw59OPqITFCe2o7sb6wCSoBuHtJ9ojfJ4oivqz/Ej/d9FNc9sFleKf8HZjtZuRF5eGhMx/Chis24ObpNyPcEO622L1FhCECP5n+E2y4YgP+csZfkB2RjUHbINaWrsXF712M27bchl1Nu0a1lfunZ2cgPFCLipY+rNsztjF/IyHfsKkvOX5d9VdvV6C1theGIKl3hJq9I8gH8W+tF5qUH4XZS6X5jVtfLkFXsxfVVwNA/FTgunXSbN7qL4C110nzksk9dq8BNtwrfX72b4DTb0dBfTfe2i3VHd13YZ6iF90Oh4iNzxVioMeCiIQg5xZEb6AL0GDJT6ZCrVGh+sCxZ7n2d3Wio75u7PXUR3PO74ApCwHbIPDGtcCAe5vCuMri/Hj899rZ0KoFfHygEbe/8T0TaxeJn5INjU6PwZ5uqX7/KIq/bkTpN00QBGDxTfkIDNV5OEryZVqdGkt/MhUavRr1pV3Y9fHRZwSbB/rRclAab+Wu+dSiKOLhT6XtvMtmJyMz7sRTIEw2E94qewuXvn8pbtl8C75u+BoCBCxIXYA1S9fgjQvewIXpF0Kr1rolZm+mU+uGR4Mtfg5nJ58NESK2Gbfhhg034KqPrsKHlR+OqO461KDFredIJWNPbC6DyU3lhrGTJsMQFAzL4CCaDx5992X5d80o+FxK7BeuykNIpO/UwhMdikm1l5pz0WQkZoZL2wD/VwCbxYvqqwEgaTZw7VuAJgCo2AS8c6NPNGXyOfvfBD78pfT5vFuB+b+BKIr404fSU+FLZiZi9qQIRUP87uODqC/thEanwtKbp0Krd/3s0fGISQnBGSeY5eqsp540GQHBLhr/pVIDVzwrlU101UgzxR1e9u/4GBbmxeHpFbOhU6vwyYEm/N/rTKxdQaPVIjFrqK76KNsh2+v7sP2NMgDAnIvSkZSl7L9t8k0R8UGYf410c/O7T6pRV3TkDb36kiKIogPh8QkIiXJP7eq20lbsPNgBnUaFXy7MOu5rWwZa8K89/8KitxfhgR0PoKq7CoGaQKzIXYGPL/8Y/zjnH5gdN9un66VdRRAEzEmYg38v+Dc+uPQDLM9eDoPagJKOEvz2y99iyTtLsHr/anSZuo57nBWnTkJimAGN3Sa8+HW1e2JVqZDkrKs+cs3rah7A1lekGy+zlk7CpKnKjwQlGism1V5KpVZh8Y35CAjRor2+D1+8Wa50SEeaNA+4+jVArQOKPwTeu8VnkgafUPwh8O7PAIjAyTcCix8EBOnp4a7qTgRo1fjNecrO3awr7sCuT6oBAPOvzUFkYpCi8RxL/pmJyDxlaJbrs4VHzHKVm6ikunobZECE1LhMGwhUbQW2PODa47vRgtw4PH3dLOjUKnxa0IRbX9sDi42J9XjJY2Z+2LjHYrJh/f8KYLc6kJoX6dytRDQW2XPjpXGGIrDphUL0dx2+m6x26O9fiptGaTkcIh5ZLyVL15+WhsTwo3dxLmovwr1f3CslggdWo8vchaTgJNx18l3YfOVm3DPnHqSEpLglRn8wOWwyfn/q77Fp2SbcPut2xAbEonWwFf/6XrpB8ecdf8bB7qPvVjBo1fjVIulmx3+2VaJ7YHydxY9F/r36wxuJNovUO8JqsiMxMxxzL2LvCPJtTKq9WFC4HotuyAcEoOjLBpR+64XzoTPOBa56CVBpgANvAh/9Shr7RONTvgl4axUg2oEZ1wDnPwoIAkxWOx76RLpQ+dnZGUgIU27cRH+XGZueLwREIO/0BGTPjXfZsR0DA7AYjS4b9yEIAuZfm43wuED0d5mx+QezXOuKCgAAyXluuMCMywcueUr6/Kt/AAXrXH8ONzk3Jw7PXCc9sd5Q2MzE2gXkrbbGogKIDulnKYoitr1aiq7mAQSF67HwhjwIrKOmcTrzqkxEJQdjsNeKDc8WwHHIbhNjkYsaMx7D+/vqUdLUixCDBj+fn3HY9+wOO7bUbsH16693joqyOWw4KfYkPD7/cXx02UdYmb8SIToX7RpyIVtnJ6zNLUqHcYRwQzhumnYT1l+xHn8946/IjcyFyW7Cm2Vv4uL3LsYvtvwC3zZ+e8Tv1MtnJSMrLhjdg1b89/NKt8Q2XFddBLtteEfjF2+Wo93Yh4AQLRbfmA8V66jJx/FvsJdLyY3EKeenAQC2vVqCjsZ+ZQM6muzzgMtXA4IK2POiNEeZifXYHfwCWLsCcFiBvEuBi5+UZiAD+N/2KtR3DSIxzICfnKVcd0yH3YGNzxVisNeKqKRgnLn8+Fv7jke0WDB4oACdr7+Oht/+DlUXXYzSk09B5cJFqDh7Php+/3v0bNgIe+/4Os3rDBqp1lCrQm1RB3avrwEA9HV2oLPBOFRPnT+ucxzT1MuB02+XPn//F0BzoXvO4wbn5MTifytnQ6dRYWNRM37BxHpc4qdkQqPXY7C3x1lXXfhFA8p3NUNQCVhyUz4CgllHTeOn0amlkhyDGo0V3fj2Q+mJpam/Dy0HpUkN7kiqzTY7Ht0glTHcMj8D4YHS3+d+az9eKXoFF757IX659ZfY3bwbGkGD8yafh9cveB0vnfcSFk1aBI1K2UkWMtFmg6m4WPrddM89qFiyBOXzTkPF2Wej4twFqL/7bnSufRPmykq3znseDa1ai4syLsLaC9fi+SXPY37KfAgQsN24HTdtvAnLPlyG9yveh8Uu7dZSqwTcvUTa8fbCVwfR1G1yeUwxqWkwBIfAahp0zkUv/bYJRV82AAKw6IZ8BIXrXX5eIk/zjpWLjuvkCyajsbIbxpJObFhdgGX3nOx1dauYejlgM0lbwL/9L6ALAhb8QemofE/dTuC15dLPMnOJdLNCLf0zbeo24b/bpDvJvzk/FwE65f4O7PzwIBrKu6DVDzXFGWEsot0OS1UVBg8UwFRwAIMHCmAuKYFoPcq2M40GtpYWdL/9DrrffgfQaBB40kkIOutMBJ91FvRZWaOur4tKCsaZP8rC1pdLsPPDKiRkhKGnTXpiE5uWDkOQG8eALbgfaNwvbQN/41rgJ1ul7eE+YH52LFavPBk3v/QdNhU14+ev7sF/rp0FnYb3ZUdLrdEiKTsPNfu/R23hAYiIwpdD5T2nXpKOhCnhygZIfiU8LhDnrMjBxmcLsWd9DRKnhMNmroAoOhCRkITgSNfXsL76TS3quwYRF6rHqtMmo76vHq8Vv4Z15evQZ+0DAITqQnFl1pX4Uc6PEB/kul1O42Hr7MTg3r0Y3LtP+t8DByAOHKVRrEoFa0MDrB80oOeDDwEA6shIBM6ehYDZsxE4+2QYcnMgaJS7xBYEAafEn4JT4k9BTU8NXil6Be9Xvo+yzjL8/qvf4x97/oEfZf8IV2VfhQW5sTglLQK7qjvxj81lePgK1+7YElQqJOdORcWuHagrOoCA0BRse60UAHDy+WlIyR39mDUibySI3nJ77Th6enoQFhaG7u5uhIaGKh2OIgZ6LFj74E4M9FiQMy8eC36cp3RIR7dzNfDJndLnC+4bHitEJ9a4D1hzEWDuBiafDVzzJqAd7oL5q7V78e739Th5UgTe+tk8xRq21BS046N/7wMgdSfOPDnuqK8TRRFWoxGmA1LybDpwAKaiIjiOcpGiDguDYdo0GKZNRcC0aTBMnQp1WBgGvvsO/du/QN/27bAcPLwuTBMXh+CzzkTQWWchaN48qINHlhCLoojPXixGyTdNCAzVIX7SbhRt34TZF1yK+StvGuVPY5QGOoD/nQ101QJTFgHXrJUamvmI7WWtuPml72C2ObAwNxZPXTsLeo3vxO8tvn33TXz5xktIn30q+nsXoLt1EGnTonD+LdO57Zvc4vPXS1HweT0MQVqkZBfiwJaPMH3BUiz6ya0uPU+vyYqz/74NHf1m/GKpBo3YiC21W+AQpd0taaFpWJG7AhdlXIRAbaBLzz0aot0Oc0UFBr//HoPf78Xg3r2w1NQc8TpVcDACpk9HwMyZCDhpJgKmT4eg0WBg714M7t6Nge92Y3DfPojmw2vWVYGB0ntOno3Ak09GwPTpUBmU7Wrdbe7G22Vv47WS19AyIG1h16v1uCjjIswKuwi3vdQIlQBs/NXZmBLr2hvMez79AFvX/A+p02bC5rgIHQ39SMqOwMW3z+TIQPJ6I81DmVS7kMlqh16jcluyU1/aiff/8T1EETh3ZY5XzAI+qq/+CWy6T/p86cPAqbcoG48vaCkB1pwPDLQDKacOjSwbbvq1p7YTl//nawgC8MEvzsC05DBFwuztMOHNv+yCqd+KqWcl4exrhsdnWVtaYCoowOCBAzAdKICpoAD2rq4jjiEEBiIgLw+GadMQMG0qDNOmQZucfMJ/NxajEX3bt6P/8+3o//ZbiKZDtqlpNAicNQvBZ5+FoDPPhD4z87jHs5rteOvh79DZ2A/74Iuwmtpx6d33IWP2nFH/TEatcT/w3GJp1NaZd/rcjo4vyltx04tSYn1uTiz+u4KJ9Wg1lBXj9T/cBbUmAJrgnyEkyoDlv5sDQ9DEGxNEnmG3OvDO33ejtbYXDtNrsAw24YL/uws5p5/t0vM8uqEQT+9+DyGxO2DTDiepcxPmYmXeSpyRdAZUgud3uNi7ujC4b5+UDO/dC9P+A3D0H1lOp0tPl5LhmTMQMHMm9BkZENTHX98cFgtMBYUY2P0dBr/bjYHvv4ejp+fwF2m1CJg6FYEnz5aeZs+aBbVC17NWhxUbqzfipaKXUNRe5Px6OKahoXYOFk46A8+sPNml52ytrcZLd90KlVoLbcjPERQWgKt+dwqCwrjteyIbtNgV3XU5UkyqPczhEHHPQ29CSEjEfdfOQ7DePdt+vvukGt9+UAWNVoVlvzkZUUlu3K46HlsfAj5/WPr8on8Bs3+sbDzerL0SeOF8oK8JSDwJWPk+YBhOmh0OEZf992vsq+vClbOT8fcrZygSpt3uwHuPfY+mqm5EJwZg6Xw7rEWFGCyQkmhbc/MR7xG0WuhzcqTkeaqUROvS0094kXIiDrMZAzt3oe+L7ejf/gUs1dWHfV+TkIDgM89E8FlnIvDUeVAHH9mVvKOhH2v/8hkG2p4BBAG3Pv8G9IEe6l6+/y1g3dBT8ateBvIu9sx5XeSrijbc+OIumKwOnJYRhT9enI+sEcygJYndZsOTP14Ou80MQ9hKXPGbJYhPV+ZGGU0c3a2DWPvgF+ht+hcA4KdPv4TgCNdsve02d2PNgTewet/LEDTdAACdSocL0i/AirwVyIoYe9+N0ZKeQlcObeUeegp98MgO2KrAQBhmSE+hA2fORMCMGVCHh4///A4HzOXlGPjuOwx8JyXattbWw18kCNBnZyNw9mxnoq2NjR33uUcVpyhiT8sevFT4ErbWbYUIKR2wm+Lxk5nX45aTl0Gvdk3SKzoceHLVNbCa+qAL+REuv+sCJGX7RvkTuceW4mbc885+vHD9HMUeFI0Uk2oP21PVCu0F86ERHegxhCAkewoicrKgz0iHLj0D+ox0aOLjx/0UW3SI+Ojf+1Bb1IGI+EAs+83J0Bm8sDReFIFNfwC+fhKAINUGT79S6ai8T1cd8MJ5QHcdEJsHXP8xEHj4Rc66PUbc8eY+BOnU2HrXfMSGeHYLmWNwEKaiIuz4qA4ljaHQOMw4eedfEWhqO/yFKhX0GRnDT6CnToM+Owsq3YmbLnWbu1HZVYmKrgpUdlWisqsS7aZ25EXl4aTYkzArdhYmh00+5r8fS20t+rZ/gb7tn2Pg252Hb8XTahE4ezaCzzoLwWedCV1GhvM4W154F3vXPwdBHYcr//B3z9Z2bfgdsOPfgC4YuGkLEKvseLTR+rqiDTcMJdaCAJw/NQG3LZiCnHjvXKO9SUtND1793W/hsFYjc94yXPzL65UOiSaIL9duwLfrnoSgisRl9z6OydPHN6O6ursarxS/gg8qP8CgbRAAoHaE4Cf/z95Zhkd1bm34Ho+7G3FHAiQUinuVFgrUaEsLde9pT+Wc72j91L3QQksLpa4UdychgRjxEHe30f392FESIAlx5r6uuZLM7Nn7ncnM3u9613qeFXE7y4OWY2/a/32H9dXVNJ4+3VrG3Xj6NIa6uk7bKb29m7PQYim3yt//khd4u4MgCGhzc2mIjmnNZndVaq7w8moOsidiNnECCi+vAZN55dbk8lXyV2w+8wN6xOunnYkdNwffzPKg5diZXNq1sTy/jg3P/R29Og3viGtZ8uz9fTFsI8OUDUey+ceviRgEuDHCnbeWjxvsIV0QY1A9wGgLC0lddgvS0s7ZuhYkZmaofHxQ+vmi8vVr/umL0ssLiaL7ZX+NtRo2v3iC+io1AZHOzLs7dND0tRdEEOCPpyD6M5DIYNkXEHLdYI9q6FBbJAbUFZlg7w93bQHLjvrkerWO2W/spbhGzV8XBvPAOa1J+hpBq6UpNbVZBy1moNXp6ZTZhnJ6tFjGH56wBqeyOBReXpiGh7cF0SEhSM0vnOmt09S1Bs7tf5Y2ll7weQA2KhvGOY1jvNN4IpwiCLMPQyHr/L0xNDXRcOIEdfv2U3dgP9qzOR0el7u5NgfY0zmcHEfC/t3IVBOwcp7L8r9FDVw5ml4HX90IWfvBzk80LjMZ2qu155JWXMtbO1PZEt/W7u+qcBcenRNAiOvQPFcPNk31Wr596QSV+fvRNR7EL/IKbvjL3wZ7WEYuE/Z8sYaTW35BphyDheNClj0fiZVDz1ozCoLAsaJjfJX0Ffvy9rXeb2hyRV1xJeuWrmaaf//I0wSDAU1GRmsZd2PcKTQZnVtBSczMMB09WtRBN2eh5bZDJzOqKy2loVmT3RATg/rMmU5dU2SODphNmNiazVYFBvb7IkBqaQmLvngDqc0hpIq2ioPr/K7j9pDb8bf17/E+NU06vn8lmtLsQ+ga9zBq9Dhu+tt/+3roRoYBBoPAK1vP8Ol+sfvA8ome/PfGcBRDvJ2aMageJMpLKnnz8+0Unk7Bs66YSEkNQZpydLm50K4/XwfkcpReXm1ZbV+f1p/nC1IK0qv4+c1YBIPAzNuCCJvm3o+v6hIwGMQWQqc2glQBt3wDAXMHe1SDT325qKEuPQPWXnD3n2Dt0WmzN7an8N7udLzszNjx5PQ+1a4KBgOarKzW4LkxIR518hkEjabDdo0qO05EPY9OZkqAQyVTFrpgGh52wTK5Bm1Dx8C5WvxZVH/+Xusu5i742fjhb+2Pn40ftia2xJfFE1sSS3xpPE36jq0+VDIV4Q7hrUH2WKexWCk7nx802dliFvvAARqOHevw+vYGe9GgUmBjfy1NhkDcAmxY9Pi4geuXWV8Gn84UKxUCF8LNm1rbpw0nzhTV8N6udLYkFLbOCxeGicF1qNvQPmcPJIIg8OfH8WSdKsPUvJzKvC8wMbfgwbUbkQzD/7uR4ceXzzxC6dks7L1vor7aCydvKxb/ZTyybjj5q/VqtmRu4avkr0itTG29f4bHDCoLr+BQgg0zg5xYv7LvvCn0NTU0njrdVsp9+jSGLtorKkZ5iSXczTdVQMCgum/3FH1tLY2xsWKQHR1NU3x8p64YUktLTMdHiIH2xAmYhId3qxKsp7y0JZlP96fh5ZmO26gTJJYntD52pduVrAhdwRS3Kd1K5giCwM51SaQeL0ZlVkN1/lrkKhUPf/4NMrnRQ+Jyokmr58lv41oX4f8yP5CHZvkPzaTgORiD6kFEEAQ+O5jFK3+eQWcQ8LIz4/1lownSV6POzESTkYk6M0P8mZXVdcuGZuSurs3Zbb/moNsXlZ8fMjs7YrfncOSnDGRyKUv+OgFHzyGqadTr4Id7IOlnkJvAbd+Dz7TBHtXg0VgFX1wHRafB0hVW/gl2Pp02y61oYM6b+9DoDHx8+wQWhve+7YggCGjzC5rbWDUbiSUmdmnUIrW2xjQsDJPRo1GGhbP9mDml+Y04jbJk8dMTOky+GnWNZFVnkV6V3qF0O78u/7xjcTJ1ws/GTwygbfzxt/XHz9oPC+X5/QG0ei3JFcnElsRysvgkcaVxVDRVdNhGggR/W//WIHu803hcLVw7bGNobKTh+HHq9u2n9OABttsqQRCYmlFHbMRf0ctNCDLPYdK1ozCfNAmp2QC40xbEwmcLQK+GGc/CrOf6/5j9RGpxLe/uSuOP+Lbgen6oM4/OCSDcfXhl4fuDuJ05HPo+Halcwo1PjuXbf92HtqmRFa++i5P34PWdN3J50FhXy4erbgVB4LZX1vD7e2moG3SMme3BtGXn1zyXNZbxXcp3fJPyTet511RuyvV+13N7yO3U1tpy3fsHkUjgj0em9XohrXWht50WWp2e0SmDKzE1FbPQrUH0WOR2I6stk0Gtpun06dZsdmNsbKfrtUSlEt+HiRMwmxiJ6bhxXfqH9JSqBg3TXttDbZOON5aOwdezjC8Tv2R37u5WF3d/G39uD7mda/2uvaDuOvFAPnu/TkEilbDoiXH88upDNNbWcPO/XsM9eIh2sTHS55TXqVn9ZTQnc6pQyqS8dtMYbogYosnALjAG1UOA2JxKHt4YS35VIwqZhOevDuGuKd4dVmUEgwFdcTHqjEw0mRnNPzNRZ2aiLy8/776l1tYofX2JcbiBYq0Dlhaw+H5fzH29hmbGQ6eBb1dA6lZQmItmXJ6Rgz2qgUddBxtuhLzjYOYgBtSOXU9mHvr6JH/EFzLZ156Nqyf1aDVPV1bWIQPdFJ+AvrKy03YSU1NMQkM7lHG313Ed/DaNU7tzUZrKiHzYgTxJVocMdF5tXqu5ybnYm9jjb+PfGkAH2Abga+2LterSgytBEDhbc1YMsktOElsSy9mazho1F3OX1gA7wikCfxt/ZM0trBL372brB2/iYG3LLL2KzEwtiUGiod7Y0x/gUJeOWWSk2LZr2nSUPt79t6Iatwl+btaY3bwJgq/un+MMEGnFtby3O53fThe0zofnhjjz+NzLN7guyqzmp/+dxGAQmLY8kDGzPPjh5X+QHRfDzDtWM+GaRYM9RCMjnLQTR/j1fy9i5+7Jyjc/Iut0GVs+PA3AwvvC8YvoaJSVWpnKhqQN/JH5B1qDmDV1NnPm1pBbWRKwpPVcfvvaYxxML+uxNlJfV0fjqVNtvaFPnersmg0oPD1bg2eziAixDHoYZaH7AkGno+lMCo0x0a0l4/qKjgvLSKWYhIS0OYxPmIDcvnea9g/3pvPa1hTcbUzZ/ZcZqOQy8mrz+Dr5a35M+5EGnZgMsjOxY3nQcpYFLcPBtKM+vyyvlu9fiUGvMzD5Rj/GLxjFb2++TOqxQ1y5fAVXLF7eq7EZGV5kldWzct1xsssbsDKR8+kdE7nCt/+9FvoSY1A9RKhu0PLMD6fYlihqreeHOvP6TWOxNrt42Yu+qkrMbGdmom6X3dbm57eu3Grl5hyf+CxqEzucSk4SnvEVKh8fUavt69taUq708e6XMqEeoW2CTcshc6+oHb3zd3AdM7hjGki0jfD1Usg+IL7+u/4Al9Fdbnoss5zlnx5FKoE/Hp12QX2qvqaGpsTE1l7QjQkJ6AoLO2+oUGASGNiuF/RoVH6+HSYnWr2W7JpsMqoySIstQrrdC4BtwWvJso3v8vg2KpvW4Ln9T1uTgdWvlTWWEVcSJwbZxbEkVySjF/QdtrFUWDLWaSzjncZjuvMsxcfiiLx+CdNvW4mhoYFdHxwlNQMU+gYij7+Iibqq9bkKT0/RUXzGdMyiopCa9kyHeFG2PAPHPwGlJazefd7FluFEeklzcH2qAENrcO3EY3MCh7zbZ1/SVKdl80vHqatQ4zfeiQWrw5BIJBz/5XsObFyP38RJ3PD08GqtZmT4sXv9J8T++Rtj51/D3HtEj4xDP6QTtyMHpamcZc9PxNLBhIP5B/ky6UuOFR5rfe5oh9GsCF3B3FFzUUjb5i8H08q4/bNjKGVSdj01A0+7rqt7BEFAk5XdMQudltY5C21igml4eEcttMOlmamNRFrez9Y2XtHR4tzwHJQ+Pq3GZ6YTJqJwd+vW4nCjRs/M/+2huEbN/10byt1T26rpajW1/Jj2I18nf01hvTjXUEgVXOt7LStCVxBgG4CmUce3L5+guqSRUeH2XPPgGCRSCXHb/mDX5x/hFT6WpX9/se/eECNDkujsClZ/GU1lgxYPW1PWr4zE32mIVtVeAGNQPYQQBIEvDmfz0pYzaPQG3G1Mef/WCCK8ehd0GJqa0GRno84Qg+zC9AoOqqcgSGQEpm7Go2B/5ydJpSg8PVD5+LYapbWUkw9or0RNPWxYDLlHwcxeNOcaZq7HvUKngc23Qdp20e35jl/Ao+s+kHqDwPXvHySxoIbbJnnx4o1tgbehsZGm5DPNZdxiEH1uOykAJBKUfr6Yho9uDaJVwcGtCytag5bcmtzWjHNaVRoZVRnk1OSgE3RYNtlz0+m/oNKbEee6m6Pev2CptCTAJqBD6bafjR/2JvZDUhPToG1o1WTHlsQSVxLXuroOsHivG1YNCtLnmOE/fhIRThGMsR3LrnczKMutw9ldxQy3NBoP7afhRHQHfZtEqcQsKqrNUdzb+9IHrNfCF9dDzmFwCBQdwU2G3/muKzJK63h/dzq/xOW3Btezg514bE4AYz1tBnVs/Y1gEPjjo9OcjS/H2tGUZc9HojQVF7IK01PY+MJTqMzNeXDtRqTSvvNM0Bq0ZFdnk16VTlZ1FuYKczwtPfG09MTD0gNTeR8vChkZ8nz59MOU5mRz7ePPEjR5KtCxVaLMScufYz4is140/pJKpMzxmsMdoXcw1nFsp/O8wSCw6INDxOdXs/JKb/5xXVjrY/q6epriRS10Q1wcTXGn0FdXdxqTwsOjXRn3OEyCAntk3GqkDW1RUQeHcXVaWqdt5C4urcZnZhMnit0wzlPduOl4Ds/9GI+duZJ9T8/E0qTj/0Vn0LEzZycbEjdwuux06/2TXSYzPeUWqpMELGxVLH8hChML8bnleTmsf+pB5EoVD33+DXLj/3rE8vvpAp789hQanYGxHtasvTMSR8vh2ZfcGFQPQeLzqnlo40lyKhqQSyX8dWEwq6adv01QT2jV6sngqllgWZWJOjMLTUYG6szMLo09WpA5OrQLspuN0vz8kDs59U+w1FQNXy4StaQWLrByC9j3r6v1oKLXwfcrIflXkJvC7T+A95Xn3fyb4zk8+2M8NkoJW693R5WR0hpEq9PSQK/v9ByFh4cYPDcH0SahYcgszNEb9OTW5raWbLfcsmuy0Rm6Ns6zlFmxKP4xLKodkLmoGX2PFQH2ATiaOg7J4Lm76Aw60irTOFlyklMZx3HekIFBIrBxXi46edtpMFQxjqlHViDVyvGbYcuCm8chNDRQf+wYdfv3U7d/P7qCjpUACi+v1gDbLCoKqUkv257VlcAnM6C2AIKvFXtYD0U5Ry/JLK3j/T3p/BzbFlzPDHLksTkBvV5kHOqc3Hb2vN4XBr2eD+65GU1jI7e/8g7OPj0/DxoEA4X1haRVppFelU5qZSpplWkX/I4DOJo64mHp0RpktwTcnpae2Kpsh/V33UhnGmqq+Wj1bQA88OlXmFnbAFBUX8Sm6O8xfDcKE505Cc4HiAvaypKAJdwScgvuFufXPf52qoBHNsVioZSx62ZfVKlJYhY6tjkLbTB02F6iUmESHt5axm06dixyR8d+e82XO7rKyjbzs5homhKTOhnmyqytW0vFzSZOwCQ0tHVRQ6c3MP/t/WSW1vPobH+enB903mPFlcTxZdKX7MrZRUjhFKZlLcUg0WO/vI7FU6/CRC5eEwVB4OP7VtBQXcXyf76CR0h4/70BRgYFQRD4ZH8mr/x5BoB5oc68e3MEpsr+b1/XXxiD6iFKTZOW536M54/T4qR8TrAT/1s6FlvzSyvNbu8qa+VgwrLnI1E1l5gLgoCutBRNZlabQVpmBprMLHTF528BJrWwEEvIzzFKU3p6XrqeqaEC1l8DJUlg7Slqi208L22fQxGDQdTKnt4MMqXofu4/p9NmgsGAJjubypNxbN6wDc+SswTWFSLTajptK3N06JCBNgkPR2pjTX5tvph5rm4OoCvFDJXG0HkfIBrN+Fn74W/r36FsO+XXahL25aMyl7P8hSgs7Qa2L/ZAkLhvF1s/fAs7Xx8cVs3nZMlJ4kriSK9KB8CnfCwLUu8G4ODoTbiGWbZqswNtAzFknW3ui72fhpgYaJ/FVqkwmxSFxfQZYhbby6tng8uLgXULQa+BWX+DGU/32eseKmSV1fP+7nR+jstH3xxdzwh05LG5AYwfQcF1+y4NM24NInx65wDlx1f+SVZsNDNW3MPEa2+84P4qmipIr0wnrSqNtMo00qrSSK9M71CBAWDWJOBeDr5VSkJrrfCokqGRQ4mphlxlPcUmaqosoMpCQqU51JkC7YJoc4U5HhYeHTLbLYG3q7krcunlpWcdCaQdO8yvb76EvYcXd73xIQllCXyZ9CU7snegE3R4VoZwzRnR12HGXf6EX3H+85ahvp7aU6f58IOfcc1LI6I+H2VdF1poN7cOfaFNgoKQDLYM7TLG0NBA4+nTrQ7jjadOITQ2dthGYmoqLno0O4wfVDhz33dJmCpk7HtmJk6WF54PJJ7JYM+7WUgMUg6P+onTbnuxVdmyNGgptwTfgoOpA7+9/SqpRw4wZdltTF5yS3++ZCMDjE5v4B+/JvL1MbGV6V1TvPn7taHIpMN7kdYYVA9hBEHg62M5/Pv3JDQ6A67WJrx3SwQTvS/NvbKl/2lteRO+4xxZeF/4RbMN+rq6Vs22JjOjNbutyc3tMiMKgEKBcpRXu17bzQG3t3fP3JLrSsQ+zeXpYOcrBtaWvXe4HnIIAvz+OMSsF/t0L98AwdeIixwFBWL5dksZd2Iihrq6TruQWllhGh6GSXMQrQoPo9TCQGZ1Zoc+z5lVmZ1aTrVgIjPBx9qHANuADmXbruauSCUds6Bp0cVsX5sIwDUPjcF79MjUsm396G0S9+4kctFNTL/1rtb7q9XVrbrsil0KXDJDaZLV8/3Y16lTiUZvpnJTxjiOaTU/Czfzg+jTrW27ztWzK0eNwnzGdCymTccsKhKpqhvlTyc3wK8PAxK4dTMELujDVz90yC6r54M96fwY2xZcTwtw4PG5AUwYNbzdfBtrNWx+8QT1VWoCIp2Zd3dol+fjE7/+wP6v1+E7IYobn/k/oGNLutTK1NbgubypnXmlIGBbBx5lAp4VUoJrLRlVIcO+uBFlVWdX/wuhl0mot1RQaS5QYqajyhwx6DaXUGUBlc0/q8wBhQJXC9e2gNuiY7bbTDEAjvlGeszudZ8Qu/U37KeMYXdgHrElsa2PTXCewIrQFZjGeBG7LReFiYxlz0Vi42wmdo7IyWkt426MO4U6JaVzFlqpbM5Ci4ZipuPGoXByOncYRoYQglZLU1JSq/FZY0xM5xJ9uZwcey+OW3riOGUSqx+88bztNNUN4hy0pqwJz9E2VM5IYFPKxtZuIAqpgqt8rmJ6qT8J3/yAV/gYlv79pX5+lUYGinq1joc3nmRPSikSCfz9mo5a/OGMMageYARB4I3oN7BSWWGttMZaZS3+rrLGRmWDtdIac4V5h0lVYkE1D2+MJausHplUwlPzA7l/uh/SS1jRKc6u4cfXYzDoBaYuDWDsnN5lfw0aDdqzZ8VgO6udUVpmFkJT18EbiCvTSj+/jkZpfn7Ibc+TfarOF7NyVTngGCKad5kPL1fALhEE2PYCHP0AXZOMxuCnaKq1bnPiPte1E0ClIsnClRQbT2ZeP4NR04PIsmwko10AnVGV0Skr1YJSqsTH2qfVadvPWgyg3SzcWh2vL0RVcQPfvnwCbZOe8QtGMfnGkVuSv+bhe6gpLWbJc//Ce9yELrfR6wz88Ho0pWfrkDg3kTh1C7FlJ6nVdJRSSCVSgmyDGO88ngjHcYyptUV5IoG6/QfELHa7cjuJiQnmkyZhPn0aFjNmoPTo3Ju8ld+fhOjPQGUN9+4Z0RKJnPIGPtiTzg8n89A1B9dT/R14bG4AkZe42DgYCAaB398/RU5SBTbOZix9biJKk87ZXa1By6nTB9n38hugknP2Dk/SqtPJr8tvddWXGgScK8G9XMw+B1ab4lUpw664CUVj11UoAHJnZ5S+PuLip7c3gk6HrqwUXWkp+rIydKWl6ErL0FdV9ei11ZlAZbuAu8ocKi0kzT8BO1ssXT1xcBqFp5VXh2z3UPVfGOnUamr57Kn70JfUsCeilLOuDcilcq7yvorbQ28n1F5sbWTQG/j5jRgKM2uxNlUztWkLmlMnu7xelZnZkmjrhc/0SUy/YTYmISHGLPQwRzAY0GRk0BDd5jCuKyrqtJ0qIEBs49WczVa4uCAIAls/SSAzrhRLe7Fa0sRcgc6gY3fObjYkbSCuNA4A6zo5N+53RyKX8/C6zSiVw1Nna6SN4pom7l5/gsSCGkwUUt65OYIFYSMnSWYMqgeYBm0DkzZOuuA2MolMDLaVYrBtrbLGTG5JQq6WjCIBQW9GsJMzD04bjaeNA9ZKMTC3VFp2yiheiNN78jiwORWpVMKNT4/HxafvXHYFgwFdYSHqzMxWozR1lth7u6uWTS3IbG3FINu32SitOfCWu7oiqTorZqxrC8F1LNzxK5ja9NmYBxJ9ba3oxP3zOzRFH6GxQoGuoYtSSbm81YlbF+RNgacZL6ankVKdia1NBRJlMXXazplrALlUjreVd2vGucU8zMPSo9dlmTqNnu9fi6E8rw5Xf2tueCICqWzkaHnbU11SzNpH7kEqk/HQ59+gNDm/YVNNWSPfvnQCdYOOsbM9mbLUj4yqjFbzs9iS2C57cntYeDDeeTwTLEIYnQ3m0SnUHzjQSW6h9PHBYvp0zKdPwywysqNDv04DX1wLucfAMRhW7QTV8HPN7Am5FWJw/X1MW3A9xc+ex+YEMGkYteCI3pLNsV8zkSuk3PTsROzczFt1zy2l2y0GYjqdllt2eqLUSYkOLsC6UY1HmYBPlRKvCil2pWqkekPXB5LJUHp6Ni9k+oieGC0GlBbn7/veHoNGg768XAyyy8rQlTT/LG3/sxR9aVkHs76LoZHRGnRXNQfd9ZYKJA52qJxcsHT1wt7NDxfPYDzsvHG1cO3gKm3k0smtzWVj8kb+SPyZRVvF78+Wq2tYNPombg6+GUdTR7R5ea066Ma4OKqzCjke8Ve0SkvcCg4SnLoJiUKBSVhYayn3N/XWvHSinFH2Zux8cgaKEXqtuNwRBAFtfgGNMdFs2fgn9hlJeNaVdtpO4e5OwejFxDcFI5XCjU9P6HLeebr0NBuSNrAjewdLdrlgppYTOxuun3kH1/ldZzRPHKakFNWyct1xCqqbsDdXsvbOiSPOI8UYVA8w9dp6Pjn1CdWaaqrVzbfm32vUNectze0OUokUS6Vla8a7JQPekhE/N1C3VloTv6mCnLgqLOyanRfN+3+yoqusFI3R2vXa1mRkoC0oOO9zJKamKH28Ubk7oirfjdK0BlVACMqHf0JiMbS/lIamJpqSkzv0gtZkZXXeUCJB6euLNCSASl8HzrorSLStJ61edOat0XTWooG4CDPKalRryXbLzdPKs88nn3u+PkPSgQJMLRUsez4KC9uRu3KcsHcn2z56G9fAYG79z/8uun1mXCl/fiy2E7vqvtH4RnQ01imqL2pr5VUSS0pFSqfe3TYqG8Y5juXKRg/C0jRYnEyj6WRsB4mFxNQU8yuuaOuL7eEOtUWicVldEYRcD8u+7KB9HankVjTw4d4Mvo/JRasX38vJvvY8NjdgyPe3TInPZeeHaSBA7ZRUEhwOkl6VTr1WLMm2bBAzzu5lAu7lAl4VUuplzlSamhJcUIZvaWeHZImJSWvWWeXni9JHrAJSjBo1YK0SBUHAUF3dMdguLesQgGtKitCWliGp7XpB8HzUmIqBd6OVCr2dJVIHe0ycXMXA28MfV88QLF09kVpaGjPdF0EQBE6WnGRD0gb25O7BIBgYVWjGrFhHFE42rLj9AQzxyTTExtF46hT6srJO+6j2nUyM122AhBmzzQm9IaL1c1Zep2bG63upU+t475YIrhvrNsCv0MhgkFRQwzXvHcCqqZavo0xwzE6mITqGpuRkasw9iYl4EkEqJyDtW7ybEjAbP15s4zVxIibBwUhkbdVyhXWFbHz9BThTQmxAFacCqrFWWbMscBk3B9+Mk5lRNjBcOJRexv0bYqhV6/B1NGf9XVF42Y88CZAxqB5iNOmaOgXa5wbgedVlxOQV0KSvRSJrQKlUoxN6F4wrdSYsOf0XrNWOFDukkzppN9Ym1q1BuY3KpkMQ3lqu3vx4XwZthoaG5hZgmR2N0s7mdDB46oBE1KIq/fx7nYHpSwStFnV6Oo3x8c1BdLMTt66zu67MXIfaUU9JeDBpYX7E2laR3HSWSnXXmXypRIpE50BTvSOjnQK5J2oKfjZ+eFt5o5D1/2JIyrEidq5LAglc98hYvEKHdtByqWz98C0S9+0i6oalTLvlzm4959D3acTtzG3u5RqJteP5V9RrNbWcLj3dGmTHl8Z3WlRTSpVMMA9mVokDQWfqMY9JwVDacXKr9PMT+2KHOGF68mmkEi3M+QdMe7LnL3qYklfZwEd7M/g2ui24nuRjx+NzA5nsN7if00Zdo9iOrp1hWE5JAbOP3YO51pps62MUKr/Co6KtdNurQop5fWevikxHa864OeDUqGGalVNb68P2FT3DyAXeoFa3lZg3/1SXFFNTcJaGkgJ0paVIK6pRVjUiM3R/CqKVS2iyNkVvZ4XMwR5TZ1esXEdh5ToKuaMDckcn8ae9/aWbaQ4ztHotW7O38lXyVySVJ4Eg4FgN1zYE4papIr+mllHlNYTlnZNpVCgwCQ3BrF1bK4WrK8d+yyT6j2zkKhlLn52Inas5AP/+LYnPD2UR7m7Frw9NvSS5mpHhxROb4/gpNp+p/g58tUqszKwvqeK7V09SXw+uuixCjr8Lmo6yFKm5OaYREWIbrwkTMBkzhvj9u9m59gMU3k5siSporfhqkSWsCF1BiH3IgL9GI93nu+hcnvsxHp1BIMrHjk9XTMDGbGRKQIxB9TClQaPj/35J5PuYPAAm+1vxj0U+KBRqqtXVVKmrxKBcU9MpI97+73ptPfb17twY/wRyQcERr1845b672+MwV5h3yIRfKDPeEqBbKa1Qyrr/hRJ0OjS5ue2M0jJRn4lHk5GOQXv+C7XcyanVIK31p68Pcse+afkkOnGf7dALuik5GUGt7rytnTW1fs7ke5iSZFnMUcsizlp1PZmTIMHD0qODWViATQCHkyX867c0bM0U7P3LLKzNBq4EsrKonm9fjkan1jPxam8mXe87YMceLNY8fDc1pSUsef7feI8d363niL1cT1KUWYOjlyVLnp6ATNG9IEer15JckdyhZLyiqaNGUSLA1CZPZuVZ4Zdcg+mZs9Cu5FeiUmDuUIOFqwaLB95GceWy7r/gEUB+VSMf7U3n2xN5aJrflygfOx6fE8Bkv/7V6eoMOnJqckitShWdt5tLt3Nrc5HqDbi2Bs0SnHUPISiDMG0oJCr6NWTncd4/13uixtyE7zd8itLUjIc+24RUNnxbj/QEwWBAX12NtqSE8vx0yvLSqSnIpqGkEH1pKdKKGkyqGrGs02Pe+fR7/v1KQLC2RObggImTK0qn5mDbwQG5oyOy5p9yR0ek5ubDOvtd1VTFd6nf8UP8JiyySgjMFwgukBBepMC0WlzM2x/kSZ2JkvHZRXiozDv2hQ4L7dI80WAQ+PWdOPJTKrFzM+emv06kqF7NnDf2odEb2HBPFNMCjO2wLidyKxqY/cZetHqBDfdEMdXfgS0fxZN9ugwrR1OWPR+JQmagKSGhrV92zMlOJqwShQLt6DC262uRyWTc++4aDteLpeEnS062bhfpEskdoXcw3WN6jySQRvoXQRB4a2ca7+4S+6AvGufGazeNQSUfudctY1A9zPkhJo+//ZxAo1aPg4WKd24ex5X+3Xdi1hq01KhriN+fR+JP5SABp5sbaHKsbAvMu8iWn2vC1FNM5aZdBuDnZsbPLWFv6WEIIGQdRLdmKZoKHWqz8WisJqPOykadmYG+tHOpWgtSS8tmzXZHfaHCw6ND6VF7BEFAV1jY2Ym7i77eenMTKrxtyXKVccqhnpP2NZRb0mU5rruFG37t2lT52fjha+3bSTNU1aBh1v/2Utmg5T+Lwlgx2bt7b3QfoNXo+f6VaCoK6nEPsuH6xyJGfNahuqSItY+sQiqT8fDnm1H0oJ90bUUTm188jrpex+gZ7ky/5fw9Oy+EIAicrTlLbElsazb7bM3ZDtuYNQlMK7BmWq45PsmVKCo7TkpUPqMwnzVH7Is9fvxlYxBUUNXIx/sy+OZ4bmtwHelty2NzArnS/9KCa0EQKKov6qB5TqtMI7M6E3mjBrdy0Wm7JevsXi7gXAWy5rWPTO+ryfa+BqleTWTMa5hry7vdJcFg0PPhPbeibqjnthffxMU/sNevo0vqSsHECuTDU9ZRq6klrzSDgtxkyvPSqC7MQV1ciL6sHHllLdZ1Ajb1ohu6dT3IejCrkZiatgbbrT8dO/4tc3BAbmc3ZLLfgiCQmXqMfX+uoebkCXxztfgUg/xc6b1cDiHBbJGKbZNW//dNLP0Duv09aajRsPm/x2mo0RA82YU/TDT8eE6m8rKjqQbUNWDlfllIcc7lX78lsu5QNuHuVvw70IsjP2Ugk0tZ8swEHL06e34Iej3q1NRW47OGmGjRnwHYHToKtULOpMxC3DxGYTZhAmWBTnxnGs8vlfvRC2JVzyirUdwecjvX+11v7C4wyGh0Bp794TQ/xoqVBQ/N8uOpeUG9mztqm6AoHqzdwWpoy0iMQfUIIL2kloe+jiWluBaJBB6Z5c9jcwN71O9NEAR2fJ5E2olizG1ULP9bJKYW55+A6w16ajW1YuDdLuC+WGa8Rl3TSUfaE1QylRhgN5eoW2s1WOccxVqnw8opHOuIO7E2scFaK8e6sA7T/HLkOUUYsnNRZ2agzc3r1OKjBYlSidLbu9UoTeHliTYvn6b4eBoTEtCXl3d6jl4po9jDnFRnA6cdGshwlVBkB8I5F1EXcxf85Fb458Tgp9Hg77cAv2vex0xp3q3X/c9fE1l/OJsgZ0v+eHQq8gE0fNn1ZTJnDhdiaqVk+QuRmFsPzwl3T0jYs4NtH7+DW2AIt/zn9R4/Pzu+jD8+OA3A/FVhBEx07pNxlTWWteqy40riSC5PRieI0gKJIDCqGK7IlnNFqhbXAj0Soe1zKDUzw2zKZCymT8di+nQULiPHcfN8FFY38vHeDDadyEWjE7/3E0bZ8vjcAKb6O1w0aKhWV5NamdoaOKdVppFemYa8qk4MmsvaSrY9ygTsLiARlpqbUxM0neNW1wASrowSCJnli9LTs0dB2E+v/ZvMmONMv20lkdcv6fbzzotOA8m/wvE1kHsUTO1g/B0w8W6wHXXp+x8iaPVaCuoLyK3NJbc2l7zqXMqKMqkpPIu6pBizajU29WBTJ2Db/NOmDmzqwez85umdkUqR2dl1DsAdHJA7dfxbat698393MajVNCUm0RAXS/6R3WhOx2NR3XnwMgcHzCLaZ6HDSIuN5ve3X8HRy5s7Xn+/x8fOT6nkl7djEQT400xDglLPbw9PZbRH3xmgDgsK4uDEGoj/HnRNYOYA7uPBfQK4jRd/Nx+ZLSjb06Kpt6o3cFu9CgSYcWsQ4dPdu/X8lhZtDdHR7PzjJ87WV+FfVEFgcUd5nNTLg7M+ZuywziPWTU2xDViprFkaKPa7djbvm2uvke5T3ajl/g0xHMksRyaV8OIN4dwcdf5+9h0w6KH0DOSfhPwYKDgJxYlg0MGCl2Hyg/07+EukX4PqDz74gNdff52ioiLGjh3Le++9R1RUVJfbJiYm8n//93/ExMRw9uxZ3nrrLR5//PEeHe9yDaoBmrR6/vVbIpuO5wKinvDdWyJwtup+hk3TpOO7l6OpKm7AK8yOax8ai6SPM5IGwUCtprZT9rslOK9R13QKyFsC9ZbVyN6gkCqwVlljL7HEu0aFR4UElzI9DsWNWBfWYVZYiVTTWffcHr1UQqGzgmRnLRmuEjJcJeQ6gqHde+Ro6tiacW756Wfjh2XOMdh0C+g1MOZmuOEj6Kb2Mb2klgVvH0BvEPjqnklMDRi4C3Ly4UJ2f5mMRALXPx6BR9DQNoXrK/58/w2SDuxh0o3LmXrzil7t48hPGZzcdrZDL9e+pkHbQEJZQmsmO64krrWVmnmjwJgsgfGZAhOyZVjUdvx8qwICsJgxHfNp0zEbH4FEMXIdlYtrmvhobwYbj+e0BtfjvWx4bG4g0wMcaNI3kVmd2Rw0p4va5/JUJIWleLTLOIumYVywxFjm6NCWbfZtrobx80OttObbl07QWKsl9EpXZq3onQ4w5o+f2fvlWnzGTWDxc//q1T4AsU1hzDqI+QLqS7rYQAKBCyFqFfjO7vb5ajgiCAJljWXk1eW1Bt2twXdtHvU15WLA3S7Ytq1vDrrrwK5Bgl29BIs6PdIezJSkZmbIWrPdjm3B9zkBuMzWtssqKm1RUbMjdywNcXE0JSV38h/RSaHCwwqbiZPwnjIf03ERKNzdOi0o7Vz7Iad2bGH8Vdcz6657e/U+Rm/J4tivWWgRyJ1gxRurI3u1n2GHTg2JP4vBdN6JtvslUhC6WMS38WoXZE8Qu5moBt4Dpr95d8sZ6n/Lw0qQ4jfBiQWrwnpVJXR611Z2fPo+bn6BXDNtnpjNjo5GnZoqtiVtR7WljAQPA2c8JaR6yQmNXMiK8DsIsw/rq5dl5ALkVjRw9/oTpJXUYa6U8eHtE5gReB75hyBA1dm2ADr/JBSegmazzg6YO8KUR+DKx/r3BVwi3Y1De1zLtHnzZp588kk+/vhjJk2axNtvv82CBQtISUnByamzY19DQwO+vr4sXbqUJ554oqeHu+wxUch4efEYrvC15/kf4zmWVcFV7xzgreXjzv+BPgeliZwFq8P5/tVochIriNl2lolXeffpOKUSaWt5tyfd740tCAJ12rrOJm4tf+efoDpjF9VSCdU27lSb2bY+pjPo0Bq0lDWWUUYZKXLAqfkmtt1EYhBwqJE1l202l2xWQrkVrQF0thNoFQZAhp2JHQE2AVzZHDS3BNDWqi5W5bMPwje3iQF1yPWw6IMeTVD/83syeoPAvFDnAQ2oy/Pr2L8pBYDIa30um4BaEARykxIA8Awd3ev9TLreh8KMKgrTq9m2NoElT09AruxbLZGZwowo1yiiXMXFSp1BR1plmhhkZ+0kVnGMI6EyJIKAd5GMiEyBiAwDAQWgTktDnZZG+drPkFpYYD55stgXe/p0FM4ja3Xf2cqEf14fxr3TR/HG3sP8cSaWhIZC7ttWhPXeEpyqSnErN+BeJuBRDjeWC7hWgPJ863hSKQpPjzajsPYGiV1cSA16AzvejqOxVou9uwXTlve+bNuj+TOZdyYJg17fM121IEDWfnHyf2YLtCxUWrjAxJUQsQIK4+D4p5C5F1L/FG92vhC5CsbdCqYj7zwgkUhwNHPE0cyRCKeITo/Xa+vJq+0YcGc3B9yF9YWtC74SgwyrBjH4tq0Tmn+Cm9oMlyYldvVSLGp0qKoakKo1GBoaMJzNQXs258IDlMmQ29m1BuAShYKmhMQuewNXmUGau4RMTwVuV8xiwcIHGe108c9bbpLYucAjrPfnPG2QJVlyPT46GaNztGiadF32XR8xVOVC9Odw8ktoaJacSRUQugiiVoPrOChOaAsY8mOgPA2qcsRb4k/icyRSsR2i+/i2QNs5DAbAgLS/EAwCXmmN5AtSKqQGLIPMei27abkOF5/NxPSfs7G66ioA9DU1NJw8SWNMDA3RMTQmJGBdq+XKZLgyWQA01H31K7Huv7E/xIPwOcuYPOt25D2QcxnpPqfzqrh7fTRldWpcrEz4/K5IQt3aXQ/rSsXMc/ssdEPnClCUFuAWId7cJ4g3a48RJaPocaZ60qRJREZG8v77YhmRwWDA09OTRx55hGefffaCz/X29ubxxx83Zqp7SWZpHQ9tjCW5UGzB9MBMP56aF9jtkuGkQwXs2XAGiQQWPRGBe+AwmUSd3AC/Piz+Pv0ZmP0CgiDQqGvsEIx3aeJ2Tna8Sl2F1qDFRmXToVVVy++2Jt18T/Ki4ctFoKmDgPmw/GuQd1/XuudMCSvXn0Ahk7DjiRl4O/RtueD50DTp+P6VaCqLGvAMseXaR8aNeB11C1XFRXz26CqkMjkPr/sGhar3F+C6SjXfvnRczE5Oc2PWbcF9ONKLIxz7lPwdzxJrYkrs6OuIbSomvSodiwaBMdkCERkC4zIFrBs6Pk8ZFIhlc5m46bhxwy6LLQgCxQ3FHTTPeQXJaDKycCrTdlg8c6yC850ZJSoVSh+fZg8G32bDMD+U3qO6NG06H0d/ziBm61kUKhnLnr+0qgXBYODDVbfSVF/HrS++gat/NzT7TTVw6hs4sRbKUtruHzVVzEQHX9t5Al+WJm4ft1HUhgLITWHMUohcDa5jev0aRhJag5aiuqKO2e12Ge9GXWOXz1NpRG23u9oMX50dnloLnBqVYsa7RouqugHKq9BXVHTKxrUik1LubkWMYy1n3ARS3SXI3F25LfR2bgy4EStl9+ZB9VWVfHzfCpBIeHDtRkwtet7nXhAEbvzwMClnq3hAbY5MbSAwypm5K0OHtcFbJwQBMvfA8bXiglNLJtrKHSashAl3gsUFWj01VokLV62BRSzU5HfeTqYSv2MtQbb7BHFxa5hUjMRszeboz5kgk7DerBHBWsm+p2dirur5IosgCHz64F3UVZSz9O8v4hU+tsvtDE1NNJ4+TUN0NI3RMdTFnkTS2LGjhkYhQR3khceV87CKugKzceP6XIZxObIzqZhHNsXSqNUT7GLJ+ttCcak/025B6SRUd7F4KFWAS3jHyg2HAJAOTzOzfslUazQaYmJieO6551rvk0qlzJ07lyNHjvR+tOegVqtRt3Narqnpuo/v5YavowU/PTiFF/9IZsPRs3y0N4MTWRW8e0sEbjbnb/HTQsgUVwrSqkg5WsT2zxJZ/kIUZlbDwOBo/ArQNsKfT8P+10BphmTqE5gpzDBTmOGKa7d3JQgCGoMGpVTZ+wlB4Wn4arEYUPtMF3sH9yCg1ugM/Of3JADuvtJnwAJqQRDYtymFyqIGzK2VzF0ZdtkE1AC5iaIW2jUg8JICagALWxVzV4by23unSDpQgHuADYFRA6dllkStxqPwFB5xX3Fd3Fa4dy/VZratuuzDJbGsKY3HM1/LuOYstn8BaFJSKU9JpXzNWgRzU8ymTMFm5izMp05D4Ty0eoNWq6vbNM8VqRRnJ6HOzMC2qAGPcgG3coHrysCm4fz7qFWakmPhRK6lMzqPUUyeNYHI6REo3d3Pa17YXc4mlhOzVTSYm3V78CXLACRSKe4h4WREHyU3Mf7CQXVJshgYn/pGPA8BKMxh7M1i5tk59PzPdQiAq16F2X+H+G/FIKIkUczKnfwSPCeJwXXooh6d10YaCqkCTytPPK06V14JgkB5U3lrlvvcbHeRspwiGomhi6AKsaWep5kfAYITPnpbPNTmODepMDQ08KcimS3KFNTKOkDCWMdxPBO6gjlec5BLexa4tGSpHUf59CqgBtiWWExcbhWmKhlzbw9l78eJpB4vxi3AhrBp3dPRDmmaqsUFphNroTy97X6f6eL3IOhqkHXjfTe1Ad+Z4q2FmsLOGbymarGUvH05ucoa3CPaBdrjh6RxU0FaJcd+yQRg2vJAvj2WytnyBj47mMWjcwJ6vD+JRIJn6GiSD+4lNyn+vEG11MQE86gozJtlpoJOR1PyGYqP7CF7/xbMks5i2SCgTDhLdcJaqj9ZCzIpUvORV3o/kGh0emy1OjahRyExIJcYqP7MQHWnLV3ERSGJXAyaW240Agebb+fH4d7V2K9a1S+vYaDp0Rm6rKwMvV6P8zklhM7Ozpw5c6bPBvXyyy/zr39dgqZsBGOikPGfG8K5wteeZ384TfTZSq5+9wBvLhvL7OALl3ZKJBJm3BJEydlaKgvr2fF5Itc9OkwylZPuFfUYO/8p3hRmMOm+Hu9GIpGgkl2CIVdpCmy4Qbwwek6CmzeB4uILGu358kg2mWX1OFgoeXi2f+/H0kOSDxWSeqwYiVTC/FVhw2NBpQ9pmWBeSul3e7xC7Zl4lTfRW7LZ83UKjl6W2LoM0Mq4RALXvCEGQwWxsPl2rO/ezgzPGczwnAGAWq8msSyRkyUn2VESx4fZMfik1DA+Q2BspoBVfSONO3bRuGMXABo/d6ynz8Rp7lWYjh07YE7HTTpR95xelU56aQqlaadpyszAsqC6WesscG05mJynpT2AxNkRU/+ADuXaKj8/KhTm7DyYxYYjZ2nU6vkgAUZX5vDoHBVzQ5x6vbBWW9HEzs/FhbHw6e4ERPZNWb1n6GgxqE6KJ2rRTR0f1GvhzB/i5D/7QNv9DoHi5H/szaLDd3dRWYimZRNWQs4R0dAs+VfIPSbetj0HE+4SH7ceAcFTHyKRSHAwdcDB1IFxTuM6Pd6gbWjNbJ8bcBfWFaIxaMioyyaD7LYnqZpvgEwiZ+GoeawIXcEYx95XDuRd4jlPpzfw2jZxbrdqmg+hY5xoWtTIkZ8yOLA5DSdvKxw9exesDzrFieJn/vS3bVpPpSWMu0VcmHLsXXeHDli5gtU1EHyN+LcgQEVmxyC78BSoq0VZRubetudaurYZoLlPEEtmTW0ufUy9pKFGw7a1iQgCBE1yYfQ0N56ylvDoplg+3Z/JbZO8sLfo+dzKoyWoTozv9nMkcjmmo8PxHh2O972PUK+pZ9u+z0jY9R3OaeUE5wk4VRswGBNyl4ScjkGiWLtxoYoKXfOtZxiaetAvcYgzJEUxzz33HE8++WTr3zU1NXh6dl+nezlwzRhXwt2teHhjLPH51dy9PprV03x4ekEwSvn5P/QKlYyFq8P57pUT5J2pJHpLNlHX+gzgyC+BqU+ApkHMVv/5jBhYj++d2VSvqMiEL64XtSKuY+G273psQlJep+ad5t5+Ty8IwtJkYMpvy/Jq2f9NKiBqgt0Chknpfx8hCEJrptozrO/KWyOvFfXV+SlVbP00gZuenYiij/XV50VhAsu/gk9miG0pfnsMFn/aqk9SyVSMdx7PeGexF7dBMJBZlcnJkpP8UnSSsthjeCQUMy5DwK8QlBn5NGZ8zdl1X6M2U9AUEYjdrHl4z78BpdOlB416g57c2lzSqtLILEykPCUedXo6qvwy3JqD53mVXbQFakaQSZF4uGEeEIiJX4AYPPv4ovL1OW+ZnxPw/NUh3DvdlzUHMtlw5Czx+dWs/jKaMDcrHpsTwLxQ5x4F13q9ge1rE2mq1+LgacGVS/tuYcyzWfean5yIXqdDJpdDbZFoOhazDmoLxQ0lUjGDFnWvmFG7lDJciQRGTRFv5x5r/+tw4E0IvloM3C/1WJcJZgozguyCCLLrHJjpDDqK6os6GKa1BOC1mlrmj5rPLcG34GrR/Qqs85HTHKj09pz3fUwemaX12JopuHe6LwAR87woSKvibEI52z5NYNnzkShNh+RUsjM6DZz5TazOyDncdr9jiCiXGLMcVP24SCCRgL2feBuzVLxPrxUrT/Jj2srGS5LE71/KH+KtBXv/jtlsl9E9XtTvDQaDwI7PE2mo1mDrYsaMW4OQSCRcO9qVT/dnkJBfw/t70vnHdT03DfNq/mwWpqWgVTf1qorMXGnO4nmPcsPch9mXu48vkjeQnnYck564+xsZNJaNUTMy8tQ91FRrNBrMzMz4/vvvueGGG1rvv/POO6mqquKXX3654PONmuq+R63T88qfZ1h3KBuAcZ42vHdLBJ52Fy5FTDlayM71ySCB6x8bh2ew3QCMtg8QBNj+NzjyPiCBJWth9E0XfdolU50Hn18lakccQ+CuP8Dcvse7eeGneL4+lkOYmxW/Pjy1R+3ReoumUce3L5+guqSRUeH2XPPgmD53fx/qVBYV8Plj9yKTy3lo3WYUyr5rH1ZfrWbziydorNEQMsWV2Xf0zv2512QfFBd7BH2PW1MU1RcRVxJHQvphGg8dwfV0AWMyDVh2lKtR4mmJJioMp9kLCZ56HSrV+c8vgiBQ0lBCemUaWdmnKE85hTojA2VuCS6letwrBBwukEDQmyiReHtg4R+EuX+QqHn28xNbVF2iBryiXsOaA5l8eTibeo1oQhXqasWjcwKYH+rcraqdwz+kE7sjB6WJjGUvRGLt2Hfu74LBwIerb6OprpZbHroTt+ItYvbY0Lz6b+4I4+8UzcesPfrsuJ1oyYofXwNn25XuOQSJWbyeZsWNDDh1lRV8cv8dIJHw0NpNmFj0bAG4UaNn1v/2UlTTxN+vDeWeqW2L7011Wja/eJy6SjX+E5yY30v35wGjpgBi1ou3umLxPokMQq4VF4u8pw6txSJNvSgza8lm58dAZXbn7aRycAptC7LdJ4jGaH2sWz3xRxbHf8tCrpSy9NlI7NzaFjEPpJWy4rPjKGVSdj0146Jzz3MRBIFPH1pJXXkZN/3tv4waPa5Pxpxbk0uluhKDYEBAwCAYOtwEQcBAu98FAwY6/m4wnHPfOftqv49u77d5H3pBf9H9tjzeuu05++3qOe332/ocvQZBXYtBXYtBU4egqcdg0GJAggEwSEAADBIpTShoEBSoUWBqZoqJStnhvTjfa+zyPe5mm92Hxj3E/WPv75P/e3/Rby21Jk2aRFRUFO+99x4gGpV5eXnx8MMPG43KBpFtiUU8/d0papp0WJnIeX3pWBaEXVjjuXtDMsmHCjG1VLD8b1HDp0+xIMAfT4runBIZLN/QVl7VH9QWw7qroCJDNBRZ+SdY9lw/m1xYwzXvHsAgwLf3TSbKp/8XMgRBYPtniaRHl2Bhq2LZCxfuUz5SaWnd4RESzvJ/vtLn+887U8Gv78QhCDDnzhCCJ196lqlHHP0Ytv5V/D7c8bOYUewFdZo6ThfFkX50K7pDx3A+XYBvYcfUcZ0J5ITYoY8ag+vcazC3d+Fs6gnKz5xGnZmBIrcExxI1HuVg0XSeAwFaazPw9sQyIBirwNDW0m25i0u/T9Ar6jWsPZDJF+2C62AXSx6bE8CCMJfzBtfZp8v440Ox4mHhveH4je9jHbq6jl/+8yTpaQVMdcxikkOeeH+rzvl6kA/webo4SSw5P725Tb+ttGjTbzsN8CKSkW6RfGgfW959HScfP1a88k6Pn//R3gxe3XoGdxtTdv9lBip5x0CtKLOan/53EoNBYPrNgYye2Y+LPL1BEMQFxxNrIPn3do74zs2yhruGpG75vNSXi1nsliA7PwbqSztvpzAXK+nc25WO24zq9aJB3pkKfnknDgSYc1cIwVd0vrbdvvYYB9PLWBzhzpvLx/X4GH3R6tJIM9pGcUGm9XNyUpy7notE1rwgI35O8sxCuOO3WjIr1FibKvh0xQQm+fY8cdQeQRBag+0LLTYoZUpM5f1fcXEp9FtLrSeffJI777yTiRMnEhUVxdtvv019fT0rV64E4I477sDd3Z2XX34ZELPbSUlJrb/n5+cTFxeHhYUF/v4Dpycd6SwIcyHU1YpHNsUSl1vFfRtiuGuKN89dHdzpYtjC9OWBlGTXUJ5fz/a1iSx6fBzSbjqJDyoSCVz9hlgKfvob+O4uuGUT+M/t+2M1VIga6ooMsPaEO37tVUAtCAL//i0JgwDXjHYdkIAaIHF/PunRJUilEuavCr8sA2qgVa/l0Ud66nPxCLYj8lofjv+Wxb6NKTiOssTebQBNUibdJ064Wr4P9+4Dm55LZiyUFkzxmsoUr6mwTHRAPpN2lLM7f0E4EoNrYjEWjQKhsRUQuxc+2YtGDsHnkVEJEmhyskIyyhOLgGDsg8eIpdu+PshsbC7lFV8SduZKnlkYzOppvnx2MIv1h7M5U1TLA1+fJNjFkkfnBLDwnOC6pryRnevFa9mYWR59G1CXpTc7cn+NZ40F6fiR22jHpPGzB9+R2zkUrn0T5v6z2Wl8DZSliuM9sRa8p4nBdfA1w7pV0EgjL7H3euqqBg0f7RVNu56aH9jlHMLF15rJi/049H06B79Pw9nHCqdRQyDpoa5t/px+BqXJbfd7TWl2xL9ueBrwmdtDwFzxBuKiQXVeu2z2SfEaoKkTS9vbl7eb2Z+jzx4PFhdvyVpfrWb750kgiEa3XQXUAH9dGMzB9w/yU1w+q6f7EuLas8+BR9hokg7safU9MdJN9DooPdOxoqE4qW0BqT22Pm1u8+7jwWUMKMWqgujsClZ9GU1VgxZPO1PW3RWFv9Olz18kEgkSJEglwyCu6CN6nKkGeP/993n99dcpKipi3LhxvPvuu0yaNAmAmTNn4u3tzfr16wHIzs7Gx6ezZnfGjBns3bu3W8czZqq7j0Zn4PVtZ1hzIAuA0e7WvH9rBKPsu9YcVhbV893L0WjVeiZcNYorFvkN5HAvDb0Ofrgbkn4RW8Lc/gN4X9l3+2+qFstqC+PEfq93N/d17QVbE4q4/6sYVHIpO5/seYlUbyg5W8MPr8dg0AlMWeJPxDyvfj/mUEQQBD594E7qKitY+veX8ArvnwDFYBD4/b04cpMrsXUx46ZnJw5sL1dtI3w2H4pOi31U797a53o7g1ZL1tEdFOz8HenRWOzOVgGgk0tocLVF4u2BZUAwTiHjMQ8IQuntjXQY9A6tatDw+cEs1h3KplYtrhAEOVvyyBx/rg53RTAI/Pi/k5Rk1+A0ypLFT09AdgHvim5h0EPqVrHEOnNP692lqkC+jHNGrlTy8LpvRV31UOJ8PbEtXdtaD/Vi4dFI3/L54/dSWVjADc/8Hb8Jk3r03Je3JPPJ/kyCXSz549Fp55UpCYLAnx/Hk3WqDCsHE5Y9H4nKbJAWVkpTmhemNoGmVrxPYSbqpCNXie19RjoGvdgyr302uygBDF24PFp7dcxmu47toCc36A38+k4c+alV2LmZX9Qv5KGNJ/njdCGzghxZtzKqR8Pu0O7y829QDINrxoAjCKIEoEV3nx8jmtxpu2h/Ye7ULoBudpU36zqR89upAp767hQanYGxnjZ8dudEHHphODfS6bfy78HAGFT3nF3JxTz13SmqGrRYquS8smQM14zpepUx9UQROz5LAglc9/BYvMIureRjQNFpYPPtkLZNLEm84xfwmHjp+1XXiW2zco+Jq7x3bQGn3vUiVuv0zHtzPzkVDTwy25+n5veBq+jFjtmo49sXj1NT1oT3GAeufmD00Na89SMVBfmse+I+ZAoFD3++Gbmy/7IUDTUavn3xOPXVGoImuTDnrpCBfd+rckTjssYKGHsL3PBRv2oFdeXlGOrrUfRBi6qhQHWDls8OZbHuUBa1TWJwHeBkwe0mVtSdrkRlJmfZ85FYOVzCYkV9GZz8AqLXQXVu850SCFwAUasRvGfy4f130FRbw83/fh33oCFcXl2dL5qaxaxvK0WVyiHkeohaDV6Th5ZW9TKhtqKMTx+4C4lEyoOfbcSkB62FCqoamfm/vWh0BtbdFcms4AtXZDTVa/n2pRPUljfhO86RhfeFD9w5T6+DlC3iAk/W/rb77f3FCo9xt4CJ9cCMZaiiU4uBdfty4LJU6KR3lYh67OZA+1hKCNEH1ShUMpY+N/GinS2yyuqZ9+Y+dAaBb+69git6UDosCAJrHr6b2rJSlrzwH7zHRPT8dY406ko6usTnnxSv6+eitOyiHZv7Rc+7giDw8b5MXt0quvvPD3XmnZsjMB0oo9VhRr+VfxsZHswJcWbLo9N4dFMs0WcreWjjSY5kevG3a0IxUXT80gRGulCQVk3i/nx2rEti+QuRWNgOk5VCuRKWfQEbl4kX1a8WiyZiLpdQ5qttgm9uEQNqE2tY8XOvA2qAzw9mk1PRgLOVivtn9H8lgCAI7P4ymZqyJiztTJhz5wAHdkOMlrYyrgFB/RpQA5hZKZm/Koyf34wl5VgRbgE2hE4dQN2ejRcsXS9KFk5tEi+0k+7tt8PJ7e3Bfhgtwl0EazMFT84L5J6pPqw7lMXnB7Mgr4G6BjEbazHNCXO7XpwbBQHyosXJf+JPoG+2pTW1EzsYTLwbbL0BkACeIeGkHT9MXlL80A6qrd1h9t9g+tOQ9Kv4+nKPQeKP4s05HCLvgdHLetwpwUjvyUtKAMDJx7dHATXA2ztT0egMRPnYMTPo4iXCJuYKFqwO58fXY8iMK+X07jzGzunnbi11JW0u9TXNvcAlUgi8Sizx9pkp9s01InoweEwQb6wW72uqESvwWoLs/JNQkyeWy5cmk3MsgejK/wOkzHT7HtuTv7eVjdv7d/ne+jiYc3OUJ18dzeGVP8/w04NTuj3vaOlXnbR/N3lJ8ZdfUN36/zjZloluXXBth0wpzm3bB9D2AT3+rOv0Bv7xayJfH8sB4O4rfXjhmpABMc4d6RiD6hGMm40pm+69grd2pPLh3gy+OprDybNVfHDbeHwcOq46Tl3qT3FWNWW5dWz/LJFFT0QgGw76ahBLXG/e1JZZ/vIGWLmld30mdRr49g4xQFdawG0/XJKesaSmifd3iy20/rowGHNV/3/lTu/JIzO2FKlMwoLV4ZiYX946x5yWVlqhA6NLdQuwZdIiX47+nMn+zak4eVvh4DGAAYXvDJj3H9j+gthr2Dmsb2URlwHWpgoenxvI0mAXfno1BhA4odKy91gGazKLeGR2ANeNdbv4JETbCPHfi8Fm4am2+93Gi5ncsBu7LNH3DBtN2vHD5CSeZtKNy/r2xfUHcpXYImjMUtEk58QaOP0dFCfA70/Ajn/AuNvEMlwHo5dKf9Pb9oFpxbV8HyOa4z17VXC3gyJnbyuuvMmfA5vTOPxjOs6+Vrj49HGGWBAg93jzwtTPbSXNZg4w/g5xYaoXPhKXJSZWoplle0PL2mIoOEldWjw7toYBUsJMtxHY+CUcb/dclRW4jWsLst0niIZvEgmPzgngh5h84nKr2JZYzMLw7stAPMPGkLR/d+v1esSiU4vnxZbFjIKTonShy8qBoLYe5e4TxGv5JRpV1ql1PLzxJHtTSpFI4P+uDWXllcOkre4wwBhUj3AUMinPLAxmkq89T2yOI6mwhmvfPcBLi0ezaJx763ZyhYwFq8P59qUTFKZXc+yXTKYsHkaTH5WF2Df6i+vEyeuXi8TAuicaaL0OflwllpLLTeDWzeAZeUnDen1bCvUaPeM8bbih3fvdXxRn1XD4B9FgZsoSf5x9Lm+5hCAIrZnqlh7AA8H4+aMoTK8We7muSWDpcwOsr578kLjanfA9fHenaFxm3f+fv5GEXmvg0JcpSLQCjt6WTJhgTezhbDJK63l8cxzv7krj4dn+XD/WDfm5C5AVWRD9GcR+BY2V4n0yFYQvFstSPSZc8Ngt5lIFKcnodVpk8mG0MOY6Bq5/D+b9G+I2ijrXikw49pF4850lLigELuzz1j9GRFoMn3pqUvb6thQMAiwIc2a8l22Pnjt6pgcFaVVknCxl25oElr8Q1TcLupoGiP9ODKaL2hlZeUSK36WwGwbeEX8kYumMwX8B239zoklXjYOHBVNXr4aSyLYMauEpUNeISYf25fYWLuA+Hif38fxntBP/PmnCa9vOMDfEqfO58Ty0fFaLM9LQNDWiNBnabtDdwmCA8rR2FQExYkCt76KBtrVnRyM5t3F93jO9uKaJu9efILGgBhOFlHdvjmD+RboEGekZRk31ZURxTROPborlWJaoy7g50pN/Xh/WoRw8PaaEbWvE0rFrHhyD9xiHQRlrr6kvh/XXiGVM1l6iuVh3+rkaDPDzA6J7slQBt3zT5rLZS+Lzqrn+g4MIAvz04BQiejhJ6SlN9Vq+ffEEtRVN+EU4suDeAdS2DVEqCvJY98T9yBVKHvr8m34v/25PY52Gb188QV2lmoCJTsy7Z4B7uWoaROOy4njxQn3XFlAME1nHEGD/phTi9+WjMpez/IUoLO1MqG3S8uWRs6w5kElVg5gp83Ew5+FZ/iwa64I8c7c4+U/bQWvmwdoLIu+GiDu63dteEAQ+Wn0bjbU13Pyv13APDu2nVzkAGAyQuVs0ZEvdRtv74in22x5/J5gPs+vMEKa2vIxPHxT11A99/g0qs+6ZYsacrWDJR0eQSmD7EzN65f6rbtTx7UsnqCltvHQvj/IM0cE77ivRNBTExe7RN4nBtNu43u3XyHk58lM6J7floDCRsez5SGyczvns6HXi3Kp92XhJ127TWQZnJB4T8B49rdkIbcxFjTPXPHwPNaXFLHnuX3iPu/DC45BDEEQpQvsAuiCuzTSvPaZ2HQNo9/Fg0cftGc/hTFENK9edoLC6CQcLJWvvjGScp02/HnMkYdRUG+mEs5UJX6+axLu703lvdxrfnMglNqeKD26LwN9JXBHzn+BEQboH8Xvy2PlFUutkcthgbi+albX0lf7i+ua+0s7nf44gwJanxIBaIoOl6y45oBYEgX/9loggwI0R7v0eUAuCwK4vkqmtaMLKwYRZd1zeOuoWWsogXQODBzSgBjC1UDJ/VTg/v3GStOgS3AJsCJ8xgL1clWZw81fw6UzxAr/lKbj+faNxVDdIiy4mfp+o1Zx7V2jrOdDSRMFDs/y5c4o3Xx7JZs3+TCrKijnz42au+H0X7kJx20785ogZ2YD5Pc7ItmgMU48dIjfx9PAOqqVSsd2h/1zRvTb6czi5QdQM7vo37H1FLIOPXC2aTBo/n5dES5ba2dev2wG1IAi8+mcKAMsmeva6nY7KVM7C1eH88FoM2afLiNuRS8T8HnSdMOghbbu4AJOxq+1+W2+YeA9E3H5eF2Mjl0Z2fBknt4ka29krQjoH1AAyuajpdRkt9vkGcfG26HRHU62KTHykxVCwRbyBOLdyDu1YNu4YLO6zGc/Q0STuKyY3KX7oB9UNFW0GYi2vvb6k83YKM7EbR4vTutt48fM8gOe5g2llPPBVDLVqHX6O5qxfGTUgHWguR4xB9WWGXCblyXmBTPKx47Fv4kgpruW69w7xnxvCuWmCOOG/crE/xZnVlJytZduaBG58avylt48ZSCyd4c5f4fPmwHrDDaJ5WVcXY0GA7X8TJ3pI4MZPIOS6Sx7Cb6cLiT5bialCxl8X9t7krLvE7cwl+3QZUrmEhfeORmVq/GpDW3/q3vRq7Qtc/ay54kY/Dv+QzoHv0nD2scbRq29Lui6IrTfc9Dl8tUQsRXYbLxpHGTkvVcUN7PlKdEQdv2AU3qM7Z1EtVHIeDKxjdcXPSBK+R25QgwDVghlbFXOxnHof86ZNQXEJvhQeYc1BdVI8Vyy5udf7GVLYeotl4TOfF43Mjq8RJ6anN4s317FicD36pj5vB3e50HLO8+jBOW/3mRKOZ1egkkt5fG7gJR3f0cuSqcsC2LcxhSM/Z+Dia4Wrv82Fn1RfDrEbRMlEVU7znRIImCd+HvznGo3H+pHaiiZ2rk8CxDJ+/wk9yJoqzcDrCvHWjLq2jGff/RL3hiRucinGu+mMGHAWxYu3mPXihgoz8TvfnK31HOVMIgy9ftWtCwftstCVWZ23k8hE3XP7LPQ5CwcDzbfRuTz/Yzw6g8AkHzs+XTER68Fqe3cZYJx5X6Zc6e/Alsem8sTmOA6ll/OX705xJKOc/9wQhplS3qqvLs6q4chPGUxdGjDYQ+4Z1h5w5y9iYF2SBBtuFAPtc9tr7H0Zjrwv/n7dO6LRziXSqNHzypZkAB6Y6YeLdf9m+gszqjnyUwYA05YGDGzQNoQRBKFNWziAeupzGTfXk4K0KrJPl7H103iWvRA1sIsefrNhzj9g5z/gz7+KF/12EyAjbeg0erauSUDbpMfV35pJ159j4KJTiyZJJ9ZA3glapiZ659EctLmB59OCyK+TwtZqPI/v5eFZ/iwe79Gr4Nqr2WSqICUZnVaLXDGCJkIKExh3q3jLj4HjayHhB1Gz+evDsOPvYlZy4j1gZzTR6Qm5SWJ1jlc3Tcr0BoHXtopZ6pVX+vTJ9SpsmhsFqZWkRZewbW0iy/8WialFF5VC7f/3erV4n4lNmyN+TzxRjPQKvc7AtjUJqOt1OI2y5Moll+6lo7J0YNpVy3jy21N8US7nwNMzsdGVntMiKlYsjc45It4AT60KiKIoPQXN1n+j9IkakNLoDuh14pyxtQVZ7HlL3LHza3Phdp8gZvCHyGKgIAi8tSOVd3eLHjuLxrnx2k1jUMmNPhb9iVFTfZmjNwh8sCedt3emYhDA38mCD24dT5CLJZlxpfz5sRiUXHX/aHzHXby9xpCj5AysvxoaysHzCljxIyibnc8Pvi0GGgALX4Ur7u+TQ76zM423dqbibmPKrqdmdGph1pe01+36T3Ri/kDrdocw5Xm5rH/qAVFPvW7zoAYlg653FwT47i5I+hksnEXjMquu+9Zfzuz5+gxJBwowtVSw7PkoLGybDZCqcpvLlr+EhjLxPqkCQheJJd6ek0AioUGj46ujZ/l0fyZldaIZjYetKQ/N8mfJeA+UPaj4EQSBj+9bQUN1Fcv/+QoeIeF9/XKHFsZs5SVTU1bCmofuRiKV8vDn36A0vXiJ5w8xeTz13SmsTOQceGZ2n2WxNE06vns5mqriBrzC7Lj2obFIpBKxZWX7KoUWXMdC1L0QvmTIBCaXAwe/T+PUzlyUpnKWvxCJlUPfvPd6g8A17x7gTFEt90335bmrz2kNaDBAeXq7IDsGiuJZmzKGaq0piz0T8LFoNnm09mxzwHYfL5ZTm/RBLCAIooliQWxbFrrwFOgaO29r4dJ8/Ig2R27T/pX19RaNzsCzP5zmx1hRwvTIbH+enBdonBteAt2NQ41BtREAjmaW89g3sRTXqFHJpfx7URjLJnpy6If0fjnhDiiFp+GLa0WzE58ZcOu34uT4z6fFx+f8H0x7qk8OVVDVyOw39tKkNfD+rRFcO6b/ehQLBoHfPzhNTmI51k6mLHsuEqWx7LuVuG1/sOvzj/AKH8vSv7842MOhOKuGH/8Xg0EvMHVZAGNnD3D7F3UdfDZPXHX3iIK7fje65rYj9XgROz5PAglc98hYvELsIHOPmElL/RMEg7ihlTtMWAkT7jxvBqVRo+frY2f5eF8mZXViBs7dxpQHZ/mxdIJnt4Pr395+ldQjB5iy9DYm33RLn7zOIY9RV9trEvftYuuHb+HqH8StL75x0e2btHrmvLGP/KpGnrsqmPtm+PXpeMry6vj+1Wj0WgNXLLBjgsWPop6+UTRLRaY06ukHkf5OnOw5U8LK9SdQyqXs/ctM3GwuMn/Uadj2zn9JOH6SyBBLpjtmXrjdVIvJl/t4cA6/+PWstrhjEJ9/EpqqOm+nsm5rG9aShbbqv7lcX1LdoOW+r6I5mlmBTCrhpRvDWR7ZA18DI11iNCoz0iOu8LVny6PTeOLbU+xPLeWvP8RzJKOcf18fRlFGNcVZNWxbk8Div0xAphhm2QLXMWK/6Q03QNY+WDtXdEQGmPaXPguoAV7deoYmrYEobzuuGd2/mcCT28+Sk1iOTCFl4b3hxoD6HHrbVqa/cPaxYsoSfw5+m8bhH9Jx8bEe2JZnKgtY/hWsmQV5x8VS8OveHrjjD2Eqi+rZ87VYAjtxniteNd/C+2vFTEoLPtPFyX/Q1RfVyJkqZaya5sttk0ax8XgOH+/LIL+qkRd+SuCD3ek8OMufpRM9LlqK5xk6mtQjB8hNimcyl0lQLZVB0FXirb0DdGW2WBa+50WjA/R5aDnneXRT7vLV0bPkVzXiam3CnVO8+3w8Dm5mTJ+lY892Kce2leJitxN3ZUWb83vEHWAxDCvgRgA1ZY3s/lKUqY2d49kvlYgzgxyJ8rHjeFYFb+9M5bWbxl74CXIlnpEzSTh+klytKzy0CdS1oot2+3Ls6hwoPSPeTm0UnytTioF1+2x2fWnHALomv/MxZSpxjthinuY+XizrHoZVMbkVDaxcf4L0kjosVHI+vG080wON36+BxJipNtIBg0Hg4/0ZvLE9Fb1BwNfBnP9dE8bJtcmo63WMnunB9Jsvzchk0Mg+KBo26ZrEvyc9AAtf7rPV8ZizlSz56DASCfz28FTC3a0v/qReUpBWyc9vxiIIMOv2YEKnDo9V1IFCEAQ+uvd2GmuqWf6vV/EIDhvsIQHiuLZ9mkBGbCmWdiYseyGyb3q59oS0HfD1UkCA694VM66XMVqNnu9fiaaioB53+1KuN30Sqa5OfFBpCeNugchVYmaklzRp9Ww8JgbXJbVi5trV2oQHZ/qxLNLzvMF1BwnDALeEG1IYexV3i5aWRIuf+xc+F3FPrmnSMv21PVQ1aHltyRiWRfZh5UxjZWuPcqE8k13Vj5LSNAszRR3L75JhFmHsUT6Y6LUGfvxfDCVna3H2sepXM9qTOZUs/vAwUglse3w6Ac4X9nypKStlzUMrkUilPPTZeVrC1bUPlpsD5pbqhwsiAaeQjhlupzCQD//z6um8Ku5eH01ZnRoXKxPWrYwkxNUYL/UVxky1kV4hlUp4cKY/Ud52PLIplsyyem7+OprnrhgFu4qI35uHW4BNz9whhwreU+Hmr+HXRyH0BljwYp8F1AaDwL9/SwRg2QTPfg2oG2o0bFsrtusKnORMyJVGbey5lOfl0FhTjVypwtV/6CwCSSQSZt0RQmluLTVlTez6IvnSern2hoB5MPtvsPs/sOUvonGZx8SBO/5QQqdh/0e7qChQYSqtZJ70GTGgdgyBqFUwZjmoLt34z0Qh4+6pPtw6yYtvjufw0b4MCqub+PsviXywJ4MHZvqxPNKzk/+CnbsHZtY2NFRXUZieMmSqLgYcpZm4+DP+Dsg9LgbXiT9D3gnxtu158bGJd4PNAMsqhgjVJcXUlBYjkUq71YLt031ir3V/JwsWj3fvm0EUnhb/N6e/a9WlSkysmTHRQMkJOZUlFuw4YMt1EVKGXx5w5HDox3RKztaiMhdNafuzu8t4L1sWhDmzLbGY17alsOaOC19rrBwcsXF2paq4kPyURHwjIjtvZOEIgQvEG4ja6Mrsji2uik6LMpH2Lbxcx4oVWyOMnUnFPLIplkatnhBXK9bdFdnvBrlGusZ4XjPSJRO97djy6DTmBDuh0Rn4V0wWZZ5iJmD3hmSqShoGeYS9xH8uPJEIC1/qU/3WT7H5nMqrxkIl5y8Lep/RuhgGg8DOdYk0VGuwdTFjxi1BRvOJLmgpg3QLCkEmH1quySpTOQvvHY1ULhF7ue7MHfhBTHtKbB2n18DmFaLW7HKipgD2vMSZf6/iTLIKCXrm27yD+egZcOfv8OARMTvdBwF1e0wUMu660od9T8/i34vCcLEyoaimiX/8msiM1/ew/lAWTdo2l9mWftXQ1irpskYiAa9JsGQtPJkkLg5ZuYvmcQffhHfGwKZbIWO3ONG+jMhLTgDAxS8ApcmFtaslNU2sPZgJwNMLgpBfQus3dBoxiP5sPnwyTfQr0TWKGcBr34anklFc9yIL75+AXCkl70wlMX9m9/54Ri6J9JgS4vfkATD3rlAs7fo/+Hp6QRBSCexIKibm7MUzyh49PedJJGKXgPAlYrLk7j/h+Xx4PB6WrocrHwXvK0dkQP3F4Wzu3RBNo1bPjEBHvrt/sjGgHkSMQbWR82JrrmTtnRP52zUhyKUS1tdUUWoC2iY929YkoGs3+RtW9HEQWq/W8epWsa/tw7P9cbTsvzLEmD+zyU2uRK6QsuDecJQmxmKTrshN7FlbmYHG0cuSac1t6o7+lEFRZvXADkAigRs+AocgqC2A7+4UJ8cjGUGArAPw7R3wVjjlOzexr0TUKUeGF+Dx7Dew7EvwmdbvhkkmChl3TPZm3zMz+c+iMFytTSiuUfPP35KY/toePj/YFlx7Nn+GW1olGWnGwgmmPw2PnYZlG0TNu2CAlD/EForvT4SjH4kGlZcBLec8z26c897ZlUaT1sB4Lxvmhzr37oDVebDrP/BWKPy4CnKPgVQOYYth5Z/wwCFRN93cbcPOzZwZt4oLzsd/zyL3THfKdY30JVUlDezZIOqoI+Z74T3aYUCO6+9kybKJYgXJK3+e4WKqU68w40LixTAYBP77exL/+DURgwC3RHmy9s6JWKiMc8LBxBhUG7kgEomEVdN8+e7+ybjZmvK9spEGiUBZbh0Hvk0b7OENCT7cm05JrZpR9masvNK7346Td6aC479nATDj1iDs3UbeqmtfIBgM5CWJWRuPIVwuGzbdHf+JThgMAtvWJNBUpx3YAags4eaNoLIS+4Ruf2Fgjz9QqGtFJ+kPJ4tdAJJ+QaNXsK3+/9ChwjPYhgkP3jEo7q4quYwVk73Z+/RM/ntDOG7WJpTUqvn370lMe20Paw9k4hgolvIWpqWg04zwhY/eIJND6PVw52/w0HGxLZPSUjSZ2/osvBECvz0OxYmDPdJ+pbvGjJmldXxzQqyOefaqkJ5VOgkCZO6Fb26Dt8fAgf+JZlCWrjDzebEKbOk6GDWly4Wp4CtcCZniCgLs+CyR+mp1949t5JLQacVkiKZJj6ufNZMWDWwP8MfnBqKSSzmRXcnuMyUX3Lblul2SlYG6oX4ghjesaNLqefDrk6w9KM4Hn1kYxEs3jkZxKRUnRvoE43/ASLeI8LJly6PTmBLuzB9mGgQEkg4UEHewCzfFy4jcigbWHBBPbC9cHXJRN9/eUl+tZvvnSSBA8BRXgicbddTnozwvh8baGuQqFS5+/oM9nPMikUiYdVsw1k6m1FWq2bk+CcEwwCWrDv6weI34+/FPIfbrgT1+f1KaAlueFoOqLX+B0mRQmCGMX8k+lx+pbLLH3FrJ3LvDkUoHV0Khksu4/YpR7H16Fi/dOBp3G1NKa9X8949krv8yFYmZFXqtlsK0M4M6ziGPYxBc/To8lQzXvCFq47X1ELMOPpoCn18FCT+MuKqM6pIiakpLkMpkuAWFXHDbFhPSOcFORPl0szVZUw0c+xQ+iIIvF8GZ30HQg/c0WPqFWGY7869g6XLRXU27ORB7d3Maa7Xs+CwRg97QvTEYuSQOfpdOWW4dJhYK5q8KQzbAAZiLtQkrr/QB4LWtKegvcK2ztHfAxsUVQTCQlzyyF8N6SlmdmlvWHGVrYhFKmZR3b4ngwZn+RhngEMEYVBvpNtZmCj5ZMYE7Fwdz3FQsTdz7dQqHYwsHeWSDx8t/JqPRGbjS3555vS2juwgGg8COzxNprNFg52Y+fN3XB4ic5pIx96DQIaenPhelqZyF94YjU0g5m1DOye1nB34QQQvFLBPA70+IJi/DFb0Okn6FL64TA4Djn4KmFuz9YeGr8NQZku2eIfW0GokE5q8Kw8xq6Di/KuVSbp3kxZ6/zOTlxaPxsDWlrF7DGYloDPnzln00aHSDPMphgMpS1MQ/eATu+kM0ppTIIOcwfH83vB0Oe14StfUjgJYyWRe/wAvqqU/lVvFHfCESCTy9sBveHyXJ8PuT8GYI/Pk0lKWC0qL5vT0q9roPuwFk3T/PKpQyFqwOR6GSkZ9axYk/srv9XCO9I/VEEYn780EC81aGYmE7OJrbB2b4YWUiJ6W4lp9iL5yQaZO9GEvAW8gsrWPxh4eJzanC2lTBV6smcf1YY+eXoYQxqDbSIyQSCSuv9OGpJyIpNgGFANvXJrJmT/pFdTIjjaOZ5WyJL0Iqgb9fG9pvK4Unfs8iP6UKuUrGwnvDUSiNrUguRN4Q6099MRw8LJm+XFwoOfZrFgVpVQM/iOlPi/2X9WrRuKyudODHcCnUlcC+10Wjqm9XQNZ+kEgh6BpY8RM8dAKuuJ+yMin7N6cCMGmRL24BtoM88K5RyqXcEiUG168uGU2DvTcAWQmnmfbqHj7el0G92hhcXxSJROz6sOwLsTR5xrNg4Qx1xbDvVXgrXNTYZx0Y1sZmraXfF+hPLQhCq/fHjRHuBLucpy2MXguJP8G6a+DDKyD6M9DUif4LV/8PnmyuAnC6cEb8Qti6mDPzdjGoj/4zm5zE8l7vy8iFqSyqZ+9XKQBMWDgKrzD7QRuLtZmCB2eJ1WNv7UjtYMp4Li3X7zxjUA3A8awKFn90mJyKBrzszPjxwSndrzQxMmAYg2ojvWKsly0PPjcJnUKCo15K9E+ZrPoimsr6kVVWdz70BoF//ZYEwK2TvM4/QblEcpLKiW52Sp11WxC2Lub9cpyRgmAwtJtgDk2Tsq4IudKVwEnOCAaB7WsTaKgZ4O+RVAo3fgz2AVCTB9/dJU6uhzKCADlH4ft74M1Q2PNfqMkHMweY+qRoYHXLRvCbDVIpmkYdWz9NQK81MCrcnvHzRw32K7goCpmU5ZFevPXYTQC4qEuoqm3glT/PMO21PXy01xhcdxsrV5j1HDyeADd9Dl5TxBLmpF9Erf2Hk0Xtvbp2sEfaIwRBaM1Ue4ae/5x3IK2MwxnlKGVSnpzXRbVTbRHsfRXeHi1+/88eFLP7IdfBHb/CQ8cgajWY9M21LjDShbBpbqK+el0SdZVNfbJfI21oNaKOWqvW4xZgQ9S1PoM9JO6a4o2LlQn5VY18dfT8lVmerbrqTJrq6wZqeEOS304VcPvaY1Q1aBnnacOPD07Bz9HoqTMUMQbVRnqNi7M5ix8UL+JjNHKKTpdzzbsHutUyYbjzbXQuyYU1WJnIeXJe/7TQqqtUs6NZRx06zY3AqIvr1S53ynLP0lRXi0JlgrPv0NVTn4tEImHGLUHYuphRX61h5/okDAOtrzaxFo3LlJbihHrH/w3s8buLph5i1ovtez5fAAnfg0ELHpFw46diq6W5/+jQr1gQBPZ8fYbqkkYsbFXMuSsEySDrqHuCg7s7FrZ2yAQ9/5xkxih7MyrqNby69QxTX93NB3vSqTMG191DrhRb79z9JzxwGCasBIWZqLnf8pdmDf7ToiZ/GFBdUkxteSlSmRy3oOAutzEY2rLUKyaPwsPWTHxAEODsYfhuJbwVBntfgtpCMG92Vn88HpZ/Bb4z+sURf+qyABw8LWiq07LdqK/ucw5sTqU8vx5TS1FHLR0CRlYmChlPzBM7X7y/J52apq4Xby3s7LF1dUcQDOSfuTx11YIg8NHeDB7ZFItGb2BBmDObVl+Bg0X/dZgxcmkM/jfMyLDGM8SeqOvE1c/5jUrUFWqWfXKUj/ZmDHxQMEDUNGn53zZxwvX43EDszPtek2nQG9j+megIbe9h0dp+yciFaWkr4x4Shkw+vFpLKE3kLFgdjlwhJTepYnB6uToGihlrgKMfwqnNAz+G81GeAVufF/Wdvz0GRfEgN4Fxt8O9e2HVThi7HOSdJxyJ+/NJjy5BKpUwf1U4phZDR0fdHSQSSWvlha+uiF1PzuCNpWPxcTCnskHL69tSmPrqbt7fnUbteSapRrrAOQyuexueOiNq7u39RQ3+8WZTri+uEzX6+qG7YNFyznMNCESh6lor+9vpAhILarBUyXlolj+o6yD6c/joSlh3FST+CAYdeF4BSz4TS+Vn/w2s3ft17HJFs77aREZhejXHfs3s1+NdTqQcLST5UKGoo74nDHProROILRnvgb+TBVUNWj7dd/7/uWdra63Lr52gTm/ghZ8TWhfD7pnqw4e3TcDUKP8b0hiDaiOXzISrvPEItkUuwG16M6R6cVV85foTlNeNvJYZ7+9Op7xeg5+jOSsm908J6bFfsyhMr0ZhImPh6nDkxhNpt+huW5mhir27RWsv1xO/Z5GXUjnwgwi5FqY/I/7+26NQeGrgx9CCQQ8pf8KGxfDeeDj6gdh32NYb5v1H1Hfe8AG4RZx3F6U5tRz4Tmz/d8WNfrj6WQ/Q4PuWljYzuUnxyGVSlkzwYMcT03lr+Vh8HcypatDyv+2pTH11D+/tSjtvBshIF5hYwxX3i9r7FT+JWnyJVNTmf7tC1Orve13U7g8xLnbO0+gMvLFd9BH4a6QUu/1/Fxemfn8CShJBbgrj74T7DsA922D0TWI2f4CwcTJj9gpRn31yWw7Z8WUDduyRSkVBPXs3igv/kdf44Bk8tLS3cpmUpxeI17m1BzMpqem69L/lM52bmDBgYxsK1Kl13PNFNBuP5SCVwD+vC+Xv14YiG0bVVZcrxqDayCUjlUqYd3cYZtZKVA0GnnFwQiWTsi+1lKvfPcCxzJFjQpJVVs+6Q2ILrb9dG9ovfQGz48s4uU3UGs1eEYKNs1mfH2Mk0r4/9XANqgGCJ7sSPMUVQYDtg9XLdeZzEDAfdE3wze1QP8Df4fpyOPgWvDsONt0MGbsAiTimW7+DR2LhykfB7MKTRXWjjq2fxmPQCXiPcWDcXM8Lbj+UacnaFKalolWLk1C5TMqNER7seHIGby8fh6+jOdWNWt7YkcrUV3bzzs40qhuNwXW3kUpFDf4tG0VN/tQnRY1+Tb6o2X8zFH5YBTnHhoSxmSAIrUG1x3nOed8czSS4ah+bTV/h9uib4NjHoK4BO19Y8JLYfuz6d8F18Dwo/Cc4MXqmBwA71ydRW2HUV/cWrVrP1jUJ6DQGPIJtmXi192APqUvmhzoz3suGJq2Bd3aldblNa7/qs5k01V0euuqi6iaWfXyEfamlmCikfLJiInddOfhaeCPdwxhUG+kTzKyUzL8nDIkENBm1fHRlIH6O5hTXiD313tuVdsG+hMOFF/9IQqsXmBXkyKwgpz7ff21FEzvXiwZoo2e44z+h748xUinNyaapvg6Fiemw0lN3xfSbA7FzM6exRsOOzxMHXkohlYr9q+18oToHvl85MCWweTHw0/1iJm3nP6EqB0xsYMoj8OhJuO07CJwvju8iCILA7i+TqSlrwtLOhDl3hgzrXp42zq5Y2Dtg0OsoSOnYr1omlXBDhDs7npjBOzePw9/JgpomHW/tTGXqq7t5a0fqZWMi2WfYeIra/CeTRK2+R6So3Y//Dj6fL2r6Y74ATcOgDbGquJC68jJkcjlugefoqevLUO95nXk7F/Cp8i0mCacBCQReBbf/AA/HwOSHwHRoOOBfucQfp1GWqOt1bFuTgF5n1Ff3FEEQ2LcphcrCesyslMy7OwzpEM1uSiQSnr1KrFD45kQumaWdg2YLWzvs3DxAEMhLHvnZ6jNFNdz44SGSCmtwsFCx+d7J/daq1Uj/YAyqjfQZ7oG2TFrkC0Dqn7l8sXgcS8Z7YBDgjR2p3PH5MUprh285+IG0UnYmlyCXSvjbtaF9vn+93sD2tQmo63U4elly5U1GHXVPaHHA9QgORSob3uXyCqXYPk2ukpGfUsWJP7IGfhCmNqJxmcIcsvbBrn/2z3G0TRC3ET6dCWtnw6lNYmsv17Gw6ANR7zr/v2KA3wNO78kjM7YUqUzCgtXhmJgP7Z7lF0MikbSVQ56nzYxMKmHROHe2PT6d926JIMDJgtomHe/sSiPqpZ3cvyGGnUnFaI2GUN1HrhK1+qt2wr37IOJ2UctfFC/KI94MFrX+5RkDPrTW/tT+QaKeWhAg9wT8eC+8GYJq339xpYxqLNFPeQweOwW3fgP+c7u1MDWQyBRSFqwOR2kqpzirhiM/D/z7OdxJPlxIytEiJBKYvyoMM6uh7R0R5WPH7GAn9AahVaJwLq266hHeWutAWik3fXSEwuom/J0s+OnBKYz1tBnsYRnpIUPrrGpk2DN+vtgHUa81sG/9GV6+Poz/LR2LqULGofRyrnrnAIfSh59mSqc38O/mFlp3TPbul3YGR3/KoCizBqWpaFglUxi/nj0hN0k0MxlOrbQuhK2LObNua+7luiWbnKRBkFE4hcCNH4m/H34P4r/vu31XZosO42+GwM8PQEEsyJQwZjnc0y6AUZj2eNfFWTUc/iEdgClL/HH26Z+WdwNNd417ZFIJ1411Y9vj03n/1gjC3a3Q6gW2Jhax6stoJr+8i//8nkRSQc1ADHvk4DZOXOh5Mllc6LH1FjX+Rz8QNf8bFoseAIbz99/tS1o+B54hIXByA3w6Az6bC6c3g15DvODHk5r7ObzoILL5/wbbod1GzsrBlDl3itnLUztzyYwrHeQRDR/K8urY/40YmEZd74t74NCoQLgYzywMQiKBP+ILOZVb1enxluv5SDYr+/ZELivXnaBOrWOSjx0/3D8FTzuj7G84Ypy1G+lTJFIJc1eGYGGroqq4gb1fnWHJeHd+ffhKgpwtKatTc/tnx3hzR+qwKgf/+lgOaSV12JopeGxO32eQM+NKiduZC8CcO0Kwdux5IHE5YzDoW8vDhrOe+lwCo1wIbe7lunNdEvVVg1DpEbpI1JYC/PKwmKHrLQYDpO2EjcvhnXFw6B1orABrT5jzf/BEEiz+FDwje93Cp6ley7Y1CRj0An4RjoyZ5dH78Q4xWvoQF2Wkom26uO5UKpVw7Rg3fn9kGn8+No1VU31wsFBSVqfhs4NZXP3uAa565wCfHcyibASaSvYbZnaiJOGRWFHjHzAfkIja/003i14AB9/uVy8CQRDIS4gDwDP+Nfj1YdFUUKaCsbfyadBarlP/h3S361g4bmgH0+3xHefI2Dmi98GuL5KpKWsc5BENfTRNzSXzWgNeYXZMWDB8/t/BLlbcGCG6zL+69QzCOV4FHiHhgCjvaqwbXj3kL4YgCLyxPYVnfjiNziBwY4Q7X94ThbXZ8K6qupwxBtVG+hxTi2Z9tVRCWnQJiQcKCHC25OeHruTmSE8EAd7dlcZta49SfB7Xx6FEVYOGt3aKK8BPzg/q8xNeTVkju79MBmDsbE98Ixz7dP+XA6Vns1HX16M0NcXJx2+wh9OnTFsagL2HBY21WratTRicXq6z/wZ+c0DXCN/cBg097EXfWAmH34f3J8DXSyB1KyCA7yyxxPyxUzDtKbC4tM++IAjs+iKZ2oomrBxMmHXH8NZRn4u1kzOW9o4Y9HryU5N79NwQVyv+dm0oR56bw2d3TuTq0S4oZVKSC2v4z+9JXPHSLlZ9Ec3WhEI0Rj1r95BKRY3/bd/Bo7FioG1qK3oB7PyHWIXx0wOQH9N3xzQYIHUblZ8soa66GpnEgKusEGy8YO6/4Mlkcqa/wesJ5gA8uzB42H0HJt/oh7OPFZrGtmDRSNcIgsDer1OoKm7A3EbF3JWhSIaojvp8PDkvEKVMyuGMcg6kdaxkNLexxc7dU9RVj6AScLVOzxOb43hvt1hR9chsf95cNhaVfHhL1y53jEG1kX7B1d+GyTeIwc3Bb9MozanFVCnjlSVjeOfmcZgrZRzNrODqdw6wL3Vol3i9vTONqgYtwS6W3BLZt+7Bep2BbWsSUDfocPK2YvLikRUQDhQtpWEeIeHDXk99LnKl2FattZfrb4Ogr5bKYMlasdy16iz8cE/3SlwLT4nZ7TdCYPsLUJEJKmuY9AA8HA13/AzB14j77wPiduaSfboMqVzCwntHozIdXr3KL4bYr/rSercqZFLmhDjz4W0TOP7CHP6zKIyxnjboDAI7k4u5/6uTRL20k3/8ksDpvKpOmSMj58HORywJfzJZLBF3HSd6A5zaCGtmw6ezRO8AbS8Xkhsq4NC78F4EbFxGXnIiAK52MhS3bYJH42Dq42Buz5s7UtDqBaYFODDF36GvXuGAIZOL+mqVGaQ7TgAAUbpJREFUuZySs7Uc+jF9sIc0ZEk8UEDaiWIkUgkLVoVhajG0ddRd4WFr1tqe9JU/z3Qy5mwtAR8hQXV1g5Y7Pz/Oz3EFyKUSXlsyhqfmBw27xS8jnTEG1Ub6jXHzPPEe44BeZ2DrmgTUjaJ78KJx7vz2yFRCXK0or9dw5+fHeW3rGXRD0DwnrbiWDUfF9lb/d20o8j5uoXX4h3RKztaiMpOzYHUYMrnxK9kbLtZWZrhj42zGrNtFd9+TW88OTi9XMztY/jUozCBjN+z+T9fb6TRw+jv4bD58Mh1iN4gZbqcwuPZtsYXPVa+AQ9/KKAozqjn6k2huNG1pAI5eln26/6HCxczKeoKNmZIVk7355aEr2fnkdO6f4YezlYqqBi1fHDnL9e8fYv5b+/lkX8Z5e8kaOQeFqegFcO9eWLULxtwsegUUnBS9A94MEb0EKs92b38FsfDzQ83P+7voRWBiTa5ZJACes26GoIWtC1OJBdX8HFcAwF8XBp9vr0MeSzsT5t4lGoLG78kjPWbo9QgfbEpzajn4rdiO6oobfHH1txncAV0CD83yx1IlJ6mwht9OF3R4rOWcl5c4/IPq3IoGFn90iKOZFVio5KxbGcmyPk7WGBk8jDN4I/2GRCJhzp0hWNqZUFPayJ4vk1uzHr6Oorvh7Vd4AfDh3gxuWXOUwuqho58SBIF//56E3iAwP9S5z1f802NKOL0nD4C5d4ViZW/UUfcGg0FPfnPWxmuEmJR1RcBEZ0bPELVng9bL1SUcFr0v/n7wLUj8qe2x6jzY9R94KxR+XAW5x0Aqh7DFsPJPeOAQTFwJSvM+H1ZjnYbtaxMwGAT8JzoRNt29z48xVGjJ2hRnpKFp6rvzpb+TJc9eFczhZ+fwxd1RXD/WDZVcSlpJHS//eYYrXt7FnZ8f57dTBTRpB8aIa1gjkYDHRFj8iZi9nvMP0TugsUL0EnhnrOgtkLZTLOluj7YJTn0Da+aIrvhxX4k9411Gw3XvIjyRTG6FmNU615jxta0pAFw/1o1wd+uBeKX9hvdoByLmi3OE3RuSqSoZvPZlQw11o46tza3HvEfbEzHXa7CHdEnYmSu5b4bY4eGN7akdJCieoW266oaa6kEZX19wKreKGz88REZpPa7WJnz/wGSmBRjlfiMJY1BtpF8xMVcwf3UYUpmEjNhS4vfmtT2mkPHfG0bz/q0RWKjknMiu5Op3DrD7TPEgjriN3WdKOJBWhlIm5YVrQvp039WlDezZIGoiI+Z54T1m+JXoDRVKs7NQN9SjMjPH0dtnsIfTr1x5k5iBVdfr2L42Af1gVHeEL4Epj4q///yQWNL6zW3w9hg48D+oLwVLV5j5PDyRCEvXwagpvTYeuxiCQWDnumTqKtVYO5ky67bhpyHtCdZOzlg5OmHQ6yk4k9Tn+5dJJcwIdOTdWyI48be5vLx4NBNG2WIQYF9qKY9siiXyxZ08/1M8MWcrjeXh3cHcAaY9KXoH3LxR9BJAEL0Fvl4ieg0cfh+KE8X+7G+Fwk/3QX40SBUweincvR3uOwAT7qSyvJL6qkpkCgWu/kGthzmcUca+1FLkUglPzQ8ctJfbl0xa5IurnzXaJj3b1iSgMy7oIAgCezYkU1PaiIWdijl3DT8ddVfcPdUHR0sVORUNbDqe03q/mbUN9h7iokHLAvpwY3tiEcs/PUJZnYZQVyt+fuhKgl1GRlcKI20Yg2oj/Y6LjzVTFvsDcOj7dIqzO7ZxuXaMG388OpXR7tZUNmi5e300L21JHtReqhqdgf/+IQa9d0/1YZR932XXdFo9Wz9NQNOkx8XXmkk39Kz/rpGO5DRrS91DwpD2kTZ3qNK+l2tRZg1Hf84cnIHM+Qf4zgRtvVjSeuZ3EPTgPQ2WfgGPx8PMv4KlS78P5eT2s+QkliNTSFl4r/jejHRaXMBz+lljaGWi4JYoL354YAp7/jKTR2b742ZtQm2Tjo3Hcljy0WHmvLGPD/akU1A1dKqMhixSmeghcMfP8HCM6C2gsha9Bra/AB9NEStAGsrByl00CHwySfQz8JrUujDVoqd3CwxBrhQ1tIIg8Gpzlvq2SV59es0aTGQyKfNXhWFioaAst46D3xn11fF788k4WYpUJmHB6nBMzEeGW7SZUt7aXeXdXWnUqXWtj7VUZOQMw9Za6w9lcd9XMTRpDcwMcuTb+yfjbGUy2MMy0g8Yg2ojA8KY2R74Rjhi0AtsW5NAU722w+Oj7M35/oHJ3DXFG4BP92ey9OMj5FUOTrnXl0eyySqrx8FCxcOz/ft034e+S6cstw4Tc4Woo+5jnfblRosj6EhqpXUhrB1NmXOHWDkRtyOHrFODYPQnk8NN68A+AJQWELkKHjwKd/0OYTeAbGAmeQVplRz7VTRum748EAePkamjPpcWs7KB1Bj6OJjz1PwgDv51NhtXTWJxhDumChmZZfW8vi2FK1/dze1rj/FTbB4NGt3Fd3i54+Avegs8lSx6DTiLJa74zIDlX8Fjp2H602Dh1OmpuYmdz3lbE4o4lVuFmVLGw7P7vu3jYGJha8K8laEggcT9+aSeKBrsIQ0axdk1HPpe1FFPWeyPi8/wLvE/l+WRnvg4mFNer2HtgbZF49Zz3jAyK9MbBP7zexL//C0JQYBbJ3mx9o6JWKhG/sLv5YpxNm9kQJBIJMxeEYyVgwm15U3sbqevbkEll/HP68P4+PYJWJnIicut4up3DrAtcWAvoGV1at7ZJV60nlkQ1KcnwNQTRSTszwdg7t2hWNgaVysvBYNe3+qCe7kE1QC+EY6MnT3IvVzN7OCBw/BMFlzzBjj1rUTiYjTUaNi2NhHBIBA4yZmQK10H9PiDSctnvSgzDU3jwC48SqUSpvg78ObycZz421xev2kMk3zsEAQ4mF7GE5tPEfnfnTzz/SmOZZYby8MvhtJc9Bq4/yD8rRTu/BVCrhMXrrpAEIRWk7qWz4FOb+D1bWKWevU0XxwtVQMz9gHEK8yeCQtFh+i9X6VQWVQ/yCMaeJrqtWxbk4BBL+A7zpExsz0Ge0h9jkIm5S/zRUnDmv2ZlNWpgbZ+1WW5Z4eFrrpRo+fBr2P47KC46PvsVcG8eEN4n5vdGhlaGP+7RgYMlZmCBavDkcolZJ0q49Su3C63Wxjuwh+PTmOspw01TTru2xDDv35LHLDeqW9sT6W2SUe4uxU3Tei7i1ZlUT17vxInPhMWjmJUmH2f7ftypSQrA01jAyrzka+nPpfJi/1w8rZC3aBj29pE9IPRW1iuFG8DjMEgsHNdIg3VGmxdzJhxy+XVjsTK0QlrJ2cEg4H8ftBVdxcLlZylEz3ZfN9kDjwziyfmBuJlZ0a9Rs+30Xks//Qo01/fw9s7U8mtMJpMXRCJpFvfpYr8PBqqq5ArlLgEiMHHt9F5ZJbVY2+uZPX0kSsnirrWB7cAG7RqUV+t1Vw++mpBENj9ZTK15U1YOZgw+46R6x1x9WgXxnhYU6/R835zH2czK2scvLyBoZ+tLqtTc8uao2xLLEYpk/LeLRHcP8NvxP6/jLRhDKqNDChOo6yYepNYmnbkxwyKMrtecfS0M+O7+yazepoYKK07lM1NHx8mp7x/J2ZJBTVsPiEaZPzjujCkfWT+odPo2bYmEa1aj1uADVHXXV4BYH/R2korJHzE66nPRezlGobKTE5Jdg2HL6NerjF/ZpObXIm8RWNucvmV03n0YWutvsDTzozH5gaw7+mZfHvfZJZN9MBCJSe3opG3d6Yx7bU9LP/kCN9G53bQShrpGS3/b7egYOQKBY0aPW/vTAXgkdn+I7q0VNqsrza1VFCeX8+BzamDPaQB49SuXLJOlSGVizpqldnI0FF3hUQi4dnmdnBfHzvbOu/ry3aC/UVGaR2LPzxMXG4VNmYKvl49ievGug32sIwMEMag2siAEz7DHf+JThgMzfrqOm2X2ynlUl64JpS1d0zExkzB6bxqrnn3AFviC/tlXGILrUQMAlw7xpVIb7s+2/eBzamU59dhaqlg/j1hSI0lQH1Ci2FPi3HT5YaVvWlrL9fTu/PIODnye7nmnangxO9iSd2MW4Owd7cY5BENDi3t43KHmHGPRCIhyseO124ay4kX5vL28nFMC3BAIoFjWRU88/1pIv+7kyc3x3EovQyDwVge3hPOPed9fiiLklo1nnam3Dpp1GAObUAwt1Yx754wkEDyoULOHO2f+cBQoiizmiM/ZgAw9aYAnEaNfNfoKf4OTAtwQKsXeGOHWOHXoqvOHaL9qo9nVbD4w8PkVDTgZWfGjw9M6dN5pJGhj3Fmb2TAkUgkzLotGGsnU+oq1excn4RwgYnV3FBntjw6jQmjbKlV63jw65P8/eeEPu+Vui2xiKOZFajkUp67uu/0oSlHC0k6VAgSmHdPGOY2I0/vNhgY9HrymktfWy62lyPeYxyImNfcy/XLZKpLR26ZbX21mu2fi6YvwVNcCZ58+eioz8WjuXdrcWYG6oah+T83Vcq4IcKdDfdM4tBfZ/P0giB8Hcxp1Or5MTaf29YeY9pre/jfthSyyi4/jWxPaa+n9ggbTWW9ho/3icHWU/OCUMovjymdZ7AdkdeI1V77NqZQUTByPztNdc06aoOA33gnwme4D/aQBoy/Nmerf4krILGgulVXXZ6XQ0N11SCOrDO/nirg9rXHqG7UEuFlw08PTsHX8fJc8L2cuTzOwEaGHEpTOQtWhyOTSzmbUE7sjpwLbu9mY8o3917BAzP9ANhw9CyLPzzcZxOxJq2+tYXWfdN9cbcx7ZP9VhTUs3ejuMoaebU3nsHGVcu+ojgrHW1TIybmFjg2a60uVybd4IuLrzWaJlFmMBJ7uRoMAjs+T6SxRoOdmznTbx4ZfXh7i5WDE9bOLgiCgfyUod+71c3GlIdm+bPrqRn8+OAUbpvkhaWJnPyqRt7fk86s/+1l8YeH2Hgsh+rGrquXLnfK83JorKlGrlTh4hfIh3vTqW3SEeJqxfWXWYnpxKu98Qi2RacxsHVNAlr1yDvnCQaBnV8kUVepxtrRlNkrRq6OuivC3a1bP9evbU3B1NKq9Vo/VErABUHgw73pPLopFo3ewFXhLmxafQX2FsbkyeWIMag2Mmg4eloybbmorz76SyYF6VUX3F4hk/LXhcGsXxmJnbmSpMIarn33AL/E5V/yWD47mEVeZSMuVibc3xy4XypatZ6taxLQaQx4BNsy8RqjjrovaSkB8wgNRyK9vE9lMpmorzYxV1CaU8uh70eevvrE71nkp1QhV8lYeG84CuXlpaHvipYS4KFaDtkVEomE8V62vHjjaE68MJf3b41gZpAjUgmczKni+Z/iiXxxJ49simVvSgl6Y3l4K2166hCK63V8ceQsAH9dGNRn/h/DBalUwry7wzCzVlJZWM++TSkjzmk+dkcOZ+PLRf+Me8NRmo5cvfz5eGp+IHKphH2ppRzOKGvtVz0Uznk6vYHnf4rnta0tzvs+fHDreEwUxmvT5crlPRM1MuiETnUjMMoZwSCwfU0CjbWaiz5nZpATWx6dRpSPHfUaPY99E8dzP57udTl4SU0TH+wRg5BnrwrGTHnpFy5BENi3KYXKwnrMrJTMu7vvTM+MiJzbVuZyx8LWhLl3i/rqhH35pEUXD/KI+o6cpHKi/8wGYNZtQdi6mA/ugIYIQ11jeDFMFDKuHePG+pVRHH1uDs9fHUygswUanYHfThVw17oTTH55Fy//mUxace1gD3fQyWvXn/qtHalodAYm+9ozI9BxkEc2OJhZKZl/TxgSCaQcLSL58MjRVxekV3H0F7FP87TlATh6Wg7yiAaHUfbm3DZJlDe9+ueZVtnLYGeq69Q67vkimk3Hc5FK4N+LwnjhmlDjPO8yxxhUGxlUJBIJM24NwtbFjPpqDTvWXVhf3YKLtQkbV03i0dn+SCSw6Xgui94/RHpJXY/H8Nq2FBo0eiK8bFg0rm9K6JIPF5JytAiJBOavCsPMauDbDo1k9Dod+S39qcMuT5OyrhjVrpfrng1nqCoemlrbnlBXqWbH50kgQOg0NwKjXAZ7SEOGlgWlkqwM1A3DW1fqZGXCvdP92Pb4dH57eCp3Th6FjZmCklo1n+zLZN5b+1n0/kG+PJJNZf3FF19HGoLB0BZIuPrz48k8AP561eVVEnwu7oG2RF0vthHb/00qZXk9nwMMNRprNWxfk4BgEAiIdCZ06uVV2n8uD88OwEwp41ReNWdwAomEivxc6qsqB2U8RdVNLP34CPtSSzFVyPh0xUTumOw9KGMxMrQwBtVGBh2liaivliuk5CZVELM1u1vPk8ukPDk/iA13T8LBQkVKcS3XvXeQH2Lyun3sU7lVfN+8/T+uC+uTyUl5fh37vxFbfURd54t7oO0l79NIR4oz09GqmzCxtMLBc+Q73vaEqOvaerlu/TQB3TDu5WrQG9j+mdghwN7DgmlLAwZ7SEMKS3sHbFxcEQQDeclDX1fdHSQSCaM9rPnXonCOPz+Xj2////buPDzGc/0D+Hdmklky2fdVEhKRxRJbiDWVSFCttj/VUmtQ1NKjlkO1lra0lrbOoZQS2lLqKK1d7BWREBKyiIgQkZVE9mWW5/fHyNTIvs6M3J/rcl0y8y73zDvv8rzvcz93D/i7W0GHy0FMWj4+/zMOvVefwcxfo3AmPgsSmRrqs6vB07RUlBYWQEcgwE93JJAzYJiXNbo5GKs7NLXrEeiIdp6mkEnkOLU9FhVl2luyjckZzoTEozi/AsZWehg8zq1N3zQBAAsDAaYOUNw4+e5SmrJetTqeVidkFGDU5jAkZBTAXF+A/R/2gb+HVavHQTQTNaqJRjCz08fA990AAJFHUpCWWP87kP1dzXF8Xn/4djBDqUSGTw7EYMGBGJRU1H5iVZTQUowe/XZ3u2a5OKkok+LktljIJHK08zBVPjUkzUvZ9dud8qlfxuVxMTS4spZrEf7+PUndITVaxF8pyLiXD10hD0HTvKBDedRVKHMMNWTgnubE1+EiyMsaP03siatLh+Cz1z3gYWMIiYzhRGwmpv58HX3XnMUXR+MRn16g7nBbVOrzrt8G7VxxJvEpeFwOFgS6qTkqzcDhcuA/2QNiYwGeZZXgwh7tza+OOvkQqfG50NHlImi6F/jCtpdHXZ1pA5xhJubj/pNilJgpxqdp7XKCl+7mYPTWcGQWlMHFUh+HZvmii71xq8ZANBtdjRKN4e5rg059rcEYELojDsX55fWe19JAiF+CfTA/oCO4HOB/UWl4c1MY7taSh/dXTDqiHuZBj89Tlm5oCsYYLuxJxLOsEoiNBfCf7AEO5de0iMqTqT3lU1dLbCxAwBRFLdf4y+lIjMhUd0gN9uD2E9w4pRiIye+DTjC20lNzRJqpsgu4ptWrbm7m+gIE93fG8XkDcHzuAAT3d4a5Ph9Piiqw43IKhv/nbwzf+Dd2XE7Bk6L6nzu0RdrzmybRUnMAwLs9HdCBSvYoifT5CJzqCQ6Xg6RrWYj7O13dITXY48Q8RB5R5FEPfL8jzOxo+1YyEOpi9msuAIBTTxVjajyKj2219f9+7REm77qGonIp+rQ3xcEZvnAwpXMSUUWNaqJRBr7vBlNbMUoKKhC6Mw7yBoz8yuNyMHeIK/ZM7QNLAwGSsovwxqbL2H8ttcpd69IKGb4+cQcAMGtwB1gZCpsce9zf6Ui6lgUOl4PAqZ4QGVAedUuQSaV4nEj1qevi4G6KXsOdAAAX9txBbob25NwW5pbhzC7FNu48yA6uPal7XU2UedUP7qOsWPvzSevDw9YQn73ugfAlQ/DThJ4Y5mUNPo+L+IwCfHE0Hn1Wn8XU3ddxMjYDFVLt7x7O5HI8SlA0ICLLTCDU5eJjf0qFeJmNizH6jFJ0E778exJyUrVncLuSggqc3hEHxoBOfazh7tu286irM9anHRxMRUhgFgA4yEtPQ1FebouukzGG9acSsejgLcjkDG972+HnKT4w0tNt0fUS7USNaqJRdPk8RX61gIfHic9w7VhKg5fRt4MZjs8bgAGu5iiTyLH44G38a380isr/6Q6+9WIyMvLLYGcsUubqNEVOaiEuP+9m2+fN9rBxMW7yMkn1MpOTIC0vh8jAEOb27dQdjkbrOcJZWcv1lJbUcpXJ5Dj9UyzKi6WwaGeAfv9HjYfa6JuawcTGDmDslcmrri9dHhf+HlbY8kEPRCwdglVveqKrvRGkcoYzCVmY8esN+Kw+g+V/xuJW2jOt7RL85NFDlBUWQMrVRbbAAlP6OTfLjeBXkbd/Ozh1NoNMqqhfXV6q+fnVcjnD6R1xKCmogImNWJkKR1QJdHj4JMAN5TwBnggUPTZaMu2lXCrDx/ujsel5dZi5Q1yx4d2u4OtQ04lUj34ZROOY2ogxeKzipHL9+AM8im/4nUhzfQF2T+6NRUFu4HE5OBydjjf+exnx6QVIf1aKHy8lAwA+HeHe5JqCFaVSnNoeC5lUDsfOZvAOoIZeS6rsBkn1qeumrOVqyEduejEu7UtUd0h1unooGZn3C8AXKQYw5OnSNq5L5dPqtPhXuwt4bUzEfEzo64Q/Z/dH6L8G4sNB7WFlKEBeiQS7wx/ijU1hCPz+En68mIzsgjJ1h9sglQ2HxwJrGIqF+HBQBzVHpLk4XA6GTPKAvqkABTmlOP9LgsbfTLl+LAWPE/Ogw1fkUesKaOyImrzR1RbuNoZ4JFA8yU9roXKC+SUSTNgRiT+j06HD5WDt/3XB/ICObX7QOFI7ulohGsnNx1pRRoIBoSFxKH7W8Bw5LpeDWYNdsG96H9gYCXH/STFG/RCGaT9fR5lEjt7Ophjm1bTyPIwxnPvlDvJzSqFvKoD/RMqjbmmpz3NHqZRW/bxYy/VOeCYSrmhuruH96BxEn3kEABgywR1GFiI1R6QdKtMgUrW0XnVzc7UywJJh7rjy7yHYPaU3Rna1hUCHi7tZRVhz4g76rDmLSSGROBKTjjKJ5vfeeHA7BgCQJrTDR4NdYCSirqe1EYp1ETjNC1weB8k3cnD7wmN1h1SjRwm5uHb8AQBg8LhOMLURqzcgDcflcrA4yA1pIkWjOuX5vtGcHuWW4O0tYYhIyYWBQAe7JvfGuz0dmn095NVDjWqisQa86woze32UFkpw6qdYyBtZOqWXkymOzR2A1zpZokIqR1x6ATgc4PPXPZp81/H2hcdIvpENLpeDwKleEOrTxU5LkkklSE9MAPDP0zlSNzs3E/Qe+byW62938fSx5uXeFjwpxbmfFdu262sOaO9toeaItEflgH05D1NQWqQ9eaQtjcflYFBHC/z3fW9EfuqP1W91Rg9HE8gZcCExB3N+u4neX53B0kO3EfUwTyOfaDK5HCmxipsl5eZOGN+XKkrUh7WzEXzfVgxsFfa/JGQ90LzR4YuflSN0ZxzAAI9+NnDzadpN/rZiUEcL2Ll5QA4OCrMzUJj7pNmWHf3oGd76IQzJOcWwNRLiwMy+6O9q3mzLJ682alQTjaXDV5TR0RXwkHEvHxFHGp5fXclUzMdPE3ri0+HuEPN5mDmoA7zsjJoUX/bDAoT9T5FH3fftDrBu37Tlkbpl3kuCtKIcIkMjmFE+dYP0CHJEOw9TSDWwlqtMqoipvEQKSydD9H2burc2hL6JKUxs7QHG8LiN5VXXl5FIF2N92uHgTF+c+2QQZvu5wNZIiIIyKfZGpOKdLVcwZMNFbD5/D+nPStUdrlJK0j2gvAQVHF1MHNm/yelKbUmX1+zRvpsF5DKGU9tjUVYsUXdISnKZHKd3xKG0UAIzO30MGNNR3SFpDQ6Hg4VveCOHr2jsXrtyvVmWezouE+9tC8eTogp42hri0Ef90MnasFmWTdoGalQTjWZspQe/8YpyVzdOPsTD2KeNXhaXy8G0ge1xe0UgFjWxhFZ5iQSntsdCLmNw7mqOrkOoa1BrqCwb5ODRmXKbGujFWq55mSW4uFdzarleOXgP2Q8LIdDTQeA0T/BoIJgGa+fZNkprNYf2FvpYEOiGy4tfw56pPnjb2w4iXR7uPynGulOJ6PfNOXzwUwQO3UxDaYV6u4cfPHoBAJBvZI93etFT6obgcDh4bUInGJoLUfi0DOd+1pz86sgjKUhPegZdAQ9B072gw6ebJQ3RzcEYfAfFIJbnLoQ1eXkhYSn48NcolEnk8HOzwO8f9qXBAEmD0ZUL0XiuPa3gNcgOAHAmJB6FuU0bZIbbxJxnxhjO7k5AwZMyGJoL8doEd2rgtZLKAXuo63fjiAz4GPq8luvdyCzEX1Z/fnXyjWzcOp8GAPCf5AFDM8qjbozKLuAtORruq4bL5aCfizm+HdMN15b5Y+3/dYGPsykYAy7fe4J/7Y9Br6/OYNH/YhBx/2mrN8iyCsqUefJdevYAj8braDCB3vP8ah0OUmKeIObsI3WHhIexTxF18iEAwG98JxhbUb3jxhgW0B8AwMm4j2sPGldaSyZnWHkkDiuPxIMxYJxPO2yf0BNigU5zhkraCGpUE63Q//9cYdHOAGXFEpz+KRayRuZXN4eYs4+QEvMEXB0OAqd5QSimPOrWIJW8kE9Ng5Q1mq2LMfq8qciv/nt/EnIeqS8HNz+nRJlH7R3QDk5dKHetsRxezKsu1Lz8UU2nL9DBuz0dsP/Dvvh7kR8+9neFg6kIReVS/H49DWO2XcWgdRfw/Zm7eJRb0ioxfX86EdalikG2/Pz6tso6X0WWjobo/7w0X/gfyci8n6+2WApzy3AmJB4A4DXQDq49rdQWi7br6dMTjMOBsTQf3x6KbPBNr9IKGWb+GoWQsAcAgH8P64QvR3lBh0dNI9I49MshWoGny0XgNE/whTxk3i/A1cP31RJH5v18hP+hKMfV7x1XWDpSvk1rybyXCKmkAnpGxjC1s1d3OFrNO6AdHJ/Xcj21LRYVaqjlKpXIcHJbLCrKZLBubwSfUU2vF9+WiY1NYGqnSENJS4hVczTazcFUDx/7d8TFBX7YP70P3u1pDzGfh9TcEnx/JgkD1p7HmB/D8fv1Rygqb5l9JzmnCOfCoyGUV4AnEMLK2aVF1tNWeA2yQ4fulpDLn+dXF7V+frVMJsfpn+JQViyBuYM++o2mbdoUAj09mDsqxt/ITU7AmYTses/7pKgc722/itPxWeDrcLFprDdmDOpAvQ5Jk1CjmmgNIws9vDbBHQAQHZqKlFvNN+JjfZQVPx+FXM7QobslOg+2a9X1t3Uvdv2mE1/TcLgc+E/0gL6JAPk5pTj/651W79oaduAenjwqel7+xhM8ejrQZJU9OKgLePPgcjnwaW+Gtf/XFdeW+eO7MV3R38UcHA4QkZKLRf+7hV5fnsH8/dG4cu8J5PLm24fWn0qETYniKbWjZ2dweZRz2xQcDgevje8EIwsRivLKcWZ3PFgzbq/6iDh8H5n388EXPs+jpkHnmqx9l64AALvSx1h78g5k9dim97KL8NYPYYh59AwmerrYO9UHr3exbelQSRtAVzFEq3TobokurymeUp7dFY+CJ60zSiuTM5zdFY+i3HIYWYjgN74TNexa2aPnuYWVNXlJ0wj1n+cacjm4F5WN2IutV8s16VoWYi8p1uc/xQP6JjQgTHOo7AL+iOpVNzs9vg7e8rbHr1N9ELb4NSwMdEN7czFKJTL8cfMxxv4UgQFrz2P9qUSkPClu0rpupubhRGwm7MsU+4g9jSHRLPgiHQRO9wJPh4uHt5/iZmhqq6075dYT5fpem+AOIwvKo24Olce8duXpSMouwsEbabVOH3H/Kd7ZcgWPckvhaKaHP2b1Q08n09YIlbQB1KgmWsf3bRdYOhmivESKUz/FQSZt+fzqm6GpeHD7KXg6XARO94JARINYtCZpRQUy7t4BQBeYzcm6vZGyfNXl/yUh+2HL5+LmZRbj/K+KbdkjyBGOnmYtvs62wsHDCwDwJPUBSgrUlzf6qrM1FuEjPxec/WQQDs70xVifdjAQ6uDxs1JsOn8Pfusv4J0tV7A3IhX5pQ3rZswYwzcn74DD5HCUZAGggRmbk4WDAQaMUeRXX/3zPtLvPWvxdRY8LcXZXYo86i5+9ujQ3bLF19lW2HXyAIfLhYGkAPrSQnwXehdlkupH7P8z+jHG74hEfqkE3dsZ44+ZvnA2F7dyxORVRo1qonV4OlwETvWEQE8H2Q8KcOWPey26vvR7z3D1T0UOd/93XWHhYNCi6yNVZTzPpxYbm8DUlvKpm1PXIQ5w7moOuZQ9rxXdcrmG0goZTm2Pg6RcBltXY/Qe6dxi62qL9IyMlfXbKa+65XE4HPRwNMHqtzrj2qf++O/73hjsZgEuB4h6mIelh26j91dnMOe3m7iQmF2vrqkX7+bg6v1c2MhywZWUgS/Sg6UzjTfQnDz628K1lxWYnOH09liUFla02LpkUjlObY9DeYkUlo4G8H2H8qibE1+kB+v2ipskXpwcZOSX4efwByrTMMaw+fw9zNsXjQqZHMM7W2PvtD4w0xeoIWLyKqNGNdFKhuYiDJmoyK++dS4NyTfrP0BFQ5QWVuD0T3FgcgbXXlbwHEB5N+pQ2Z3VnvKpm52ilqs7DMyEKHhShnM/t1x+9d/77+Lp4yKIDHQxNNgTXMqjbnYOntQFXB2EujyM7GqLXZN7I3zJECwZ1gkdrfRRLpXjSEw6JoVcg+/XZ7HmRAKSsqofcV8uZ/jmZCIA4E0rRWqTvbsnuFzKvW1OHA4Hg8e5wdhKD8X5FTgT0nL51eF/JCP7QQEEejoInKboek6al/3zY95gQ0VPq83nk5U9RCQyOZb8cRvrTin2q+kD22PT+90hpHx20gJo7yZay7mrBboFKJ7KnPv5DvJzmrfMCZMznAmJR/Gzchhb6WHwODdq0KnJo/hbAIB2VEqrRQjFugiarqjlej86B7fO1Z6X1hiJVzMQH5YBcICAKZ4QG9NTgpagHKws7paaI2m7rAyF+HBQB5z6eCD+mt0PE/s6wlhPF1kF5fjx4n0EfHcJb266jJ/DH+BZyT9PSf+KSUdCRgEMhDpwlmQAoPKBLYUv1Hk+WBgXqfG5yrrRzen+zRzEnFPUxR4y0R2G5qJmXwcB2j1Pj+BlJaOjlT7ySyXYejEZhWUSBO++jn3XHoHLAb540xNLh7uDS/XeSQuhRjXRan1GtYd1eyNUlEpxanscpDXk0jRG1MmHSI3PhY4uF0HTvcAXUh61OkgrKpCRpLjLTPnULcfS0RD93lF0o7ty8B4yU5ovJzc3vRgX9iq2Ya/hTnBwp4FhWoq9uyKv+mlaKkryn6k3mDaOw+Ggi70xVr7phYilQ7D1g+7wd7eCDpeDmLR8fP5nHHp/dRYzf43C6bhMbAhV7CMzBjoh666ifjvlU7ccMzt9DHy/IwAg8sh9pCXmNduy83NKcfZnxTbs5u8A564WzbZsosr2eV51QU42PvYxBwCEhKVg9NZwXLqbA5EuD9sn9MT4vk7qDZS88qhRTbQaj8fF0KmeEIp1kZNaiLD/NU9+9ePEPEQeUeRRD3ivI8zs9JtluaThMpLuQCaRQGxiChMb6n7fkjoPfqmWa3HT86sl5TKc3B4LaYUc9p1M0HME5VG3JD1DI5g7OAKgvGpNItDhIcjLBj9N7ImrS4fgs9c94GFjiAqZHCdiMzH9lyg8yi2FlaEAr9syVJSWQCAWw8KJ9peW5O5ri059rMEYELojDiUFTc+vlknkOLU9FhWlUli3N0Sftzo0Q6SkJnyhCNYdFDeE21VkoKejCcokctzJLISFgQC/f9gXQ9yt1BwlaQuoUU20noGpEP6TPQAAsRcfI+l6VpOWV1JQgdM74sAY4NbHGu6+Ns0RJmmk1DiqT91aOBwO/MZ3gqGFCEW55Ti7O6HJ+dWXfktEXkYx9Az5CJjiSV3vWkFll+FUyqvWSOb6AgT3d8bxeQNwfO4ABPd3hpmYDwBYHNQJ2XfjACh6HVA+dcsb+L4bTGzEynN/U+uNh/0vCTmphRCIdTB0qhd4NHZEi6s85qXF38bSEe4Q6nLR0Uofh2b5orO9kZqjI20F7enkleDoZYbuQYqnM+d/vYNnWY3Lr5bLGUJ3Ku5Wm9iIMeh9yqNWt7R4qk/dmgQiHQQ9H1Dnwa0niA591OhlJVxJx52rmeBwgKHBntAz5DdjpKQmlV2GK/cdork8bA3x2eseuLp0CMKXvIa3u9vjUfw/NxJJy9MV8BT51XwuHifm4fqxlEYvK+l6Fm5fVNQX95/kAQNTYXOFSWpRua88ir+N7u1MEP7vITg5byDsTageOGk91Kgmrwyfkc6wdTWGpEyGk9tiIa1oeH719WMpSLuTBx0+F0HTvKAroKcE6iSpKEdGkqKmMQ3Y03os2hmg/7uK7nThh5OR0Yhark8fF+Hib3cBAL1Htoedm0lzhkhqYe/xT1518bPmyxMlLUeXx4WNkQhymQyP7yieVNMxr/WY2ogxeFwnAMC14w/wKD63wct4llWC878qzlfdAx3h1Nm8WWMkNbNz8wCXx0NBTjbyszNhIuZTryjS6qhRTV4ZXB4XQ4M9ITLQxdPHRfj796QGzf8oIRfXjj8AAAwe6wZTW3ELREkaIuPuHcikUuibmsHYirrhtybPAf/Ucj31UxxKi+qfa1hRJsXJbbGQSeRo52GKHs97kZDWITIwhEU7JwCUV61tslLuoaK0FEKxvnIbktbh5mMNj342AANCQ+JQ/Ky83vNKKxRjR0jKZLBxMYLPG5QL35p0hUJYd1AMOkflBIm6UKOavFLExgIETPYEOED85XQkRmTWa77i/HKE7owDGODezwZufagBpwmU3SA9u1A3/FamUsv1WXm9a7kyxnBhTyKeZZVAbCyA/2QPcOiJQav7p7QWXWBqk8rtZe/hBQ6XLtFa24AxioFJSwslivxqmbxe8/19IAlP04ogMtDF0GAvcCmPutVROUGibrTXk1eOg4cpeg53AgBc2JuI3IziWqeXy+Q4/VMcSgslihIbYzq2QpSkPipPjpRbqB6VtVx5ulykxuXixum6a7nGX05H0rUscLgcDJ3qCZEB5VGrg/3zMQjoAlO7UD61eunwFfnVugIe0pOeIfJI3fnViRGZiP87HeAA/pM9oG8iaIVIycv+yauObfIAm4Q0BjWqySup1whn2LmZQFouw6ntsZCU15xfHXkkBelJz14YrITyqDWBpLwMGUmKnFy6wFQfMzt9DHxPcaMp4s/7eHy35hzdnNRC/L1fkXbR5832sHUxbo0QSTXs3b0ADge56WmUV60lZFIpHt+JBwDY0zFPbYyt9OA3XpFfHXXyIR7GPq1x2rzMYlzYq6gv3nOYE9p5mLVKjKQqW7dO4PJ0UPg0B/nZTasCQ0hjUKOavJK4XA4CpnhAZMhHbnoxLu1LrHa6h3FPEXVS8fTN74NOMLaikSI1RXriHchlUhiYWcDIylrd4bRp7r42cHtey/V0DbVcK0qlOLU9FjKpHI6dzeAd0E4NkZJKIn0DWDgq8jrpabV2yLp/D5KyUgj1DSifWs1ce1rBa6AdAOBMSDwKc8uqTCOpeD4oarkMdm7G6PU65VGrk65ACBvXyrxqOuaR1keNavLKEhsJMDTYExwOcCc8EwlXMlTeL8orw5mdiqcCngPt4NrLSh1hkhr80w3Si/Kp1YzD4WBQZS3X/AqE7lSt5coYw7lf7iA/pxT6JgL4T6Q8ak3wYpkZovkqt5O9O+VTa4J+o11g7qCPsmIJTv8UB9lL+dWX9t1FbnoxRIZ8BEzxpNGmNQAd84g6NeqovXnzZjg5OUEoFMLHxweRkZG1Tn/gwAF06tQJQqEQnTt3xvHjxxsVLCENZe9mgt4jFXePL/2WiKePiwAAsud51GXFEpg76KP/aBd1hkmqocynprIyGkFXwEPQNEUt17Q7eYg68UD5XuzFx0i+kQ0ul4PAaV4Q6uuqL1CiRIOVaRc65mkWHV1FShhfyEPm/XxEHL6vfC/hSgbuXMkAhwMMDfaE2IjyqDXBi4OVUV41aW0NblTv378f8+fPx/Lly3Hjxg107doVgYGByM7Ornb6K1eu4P3330dwcDBu3ryJUaNGYdSoUYiNpTIfpHX0CHKCg4cppBI5Tm2PRUWZFBGH7yMjOR984fM8al3Ko9YkkrIyZCY/z6f2pNxCTWFqK8bgsW4AgMijKXh0JxfZDwtw+YAij7rv2x1g3d5InSGSF9h38gQ4HORlPEZRbs15oUT9ZFIpHicqek7RMU9zGFno4bUJ7gCAm6GpSLn1BE8fF+HSb4qUsl6vO8PezUSdIZIX2Li6gaejg6Lcp3iWlVH3DIQ0owY3qr/99ltMmzYNkydPhoeHB7Zu3Qo9PT3s3Lmz2uk3btyIoKAgLFy4EO7u7vjiiy/QvXt3bNq0qcnBE1IfHC4HAZM9IDbiIy+zBH9tjMbN0FQAwGsT3GFkQXnUmubx3QTIZTIYmFvA0IK65WsStz42cK+s5bojDie3xUIuY3Duao6uQxzUHR55gVBfH5aO7QEAj6hetUbLup8EaXk5hAaGMLen8Qg0SYfulujiZw8AOLsrXpFHLZHDwd0EPYY5qTc4okJXIIS1i+LGL/XQIa1NpyETV1RUICoqCkuWLFG+xuVy4e/vj/Dw8GrnCQ8Px/z581VeCwwMxOHDh2tcT3l5OcrLy5V/FxQUNCRMtZCUl+F/X32u7jBILThyKSoKi5Aao/hbbCxA5OGTiDys1rBINYrzFE/V2lF9ao00cExHZD8owNPHxQAkMDAT4rUJ7rStNJCDZ2dkP0jGpT0hiD51TN3hkBqU5CtGaHeg+tQayfcdF2Tez0f2w0KUl0ghNuLDfzLlUWsiB88ueHwnDuEH9iDu4ll1h0Pq0NkvAF5+AeoOo1k0qFH95MkTyGQyWFmpPjmysrLCnTt3qp0nMzOz2ukzMzNrXM+aNWuwcuXKhoSmdowxpD/vukW0Q9FTxT+iuZy9e6k7BFINHT4PgdO8cODr65BLmSKPWkx51JqofffeiDp2GEVPn6Do6RN1h0Pq0J6OeRqJp8NF4DQv/L7mGiSlMgyd6gk9Q766wyLVaN+9J64e/A1FebkoystVdzikDk5dvNUdQrNpUKO6tSxZskTl6XZBQQEcHDS7W6GOLh9vzF+q7jBIPRQ8LYW+sQBcHj0N0GRCfX2q1arBTKzFGLeyD+QyBgNTobrDITVo59UF761ahxKqVa3xBGKxcvRionkMzUUYt7IPJGUyGJqL1B0OqYGNixvGfrmBxpHQEqZ2mt2+a4gGNarNzc3B4/GQlaVaVD0rKwvW1tXXkbW2tm7Q9AAgEAggEGjXSIpcHg+uPr7qDoMQQloNjXirHezc3NUdAiGvBJE+HyJ9dUdB6mLj6qbuEEgb1KBHdXw+Hz169MDZs//kKMjlcpw9exZ9+/atdp6+ffuqTA8AoaGhNU5PCCGEEEIIIYRoiwZ3/54/fz4mTpyInj17onfv3vj+++9RXFyMyZMnAwAmTJgAOzs7rFmzBgAwb948DBo0CBs2bMCIESOwb98+XL9+Hdu2bWveT0IIIYQQQgghhLSyBjeqx4wZg5ycHHz++efIzMxEt27dcPLkSeVgZKmpqeC+MHKlr68v9u7di2XLlmHp0qVwdXXF4cOH4eXl1XyfghBCCCGEEEIIUQMOY4ypO4i6FBQUwMjICPn5+TA0NFR3OIQQQgghhBBCXnH1bYfS8MeEEEIIIYQQQkgjUaOaEEIIIYQQQghpJGpUE0IIIYQQQgghjUSNakIIIYQQQgghpJGoUU0IIYQQQgghhDQSNaoJIYQQQgghhJBGokY1IYQQQgghhBDSSNSoJoQQQgghhBBCGoka1YQQQgghhBBCSCNRo5oQQgghhBBCCGkkalQTQgghhBBCCCGNRI1qQgghhBBCCCGkkahRTQghhBBCCCGENJKOugOoD8YYAKCgoEDNkRBCCCGEEEIIaQsq25+V7dGaaEWjurCwEADg4OCg5kgIIYQQQgghhLQlhYWFMDIyqvF9Dqur2a0B5HI50tPTYWBgAA6Ho+5walRQUAAHBwc8evQIhoaG6g6H1IC2k+ajbaQdaDtpB9pOmo+2kXag7aQdaDtpPm3aRowxFBYWwtbWFlxuzZnTWvGkmsvlwt7eXt1h1JuhoaHG/0AIbSdtQNtIO9B20g60nTQfbSPtQNtJO9B20nzaso1qe0JdiQYqI4QQQgghhBBCGoka1YQQQgghhBBCSCNRo7oZCQQCLF++HAKBQN2hkFrQdtJ8tI20A20n7UDbSfPRNtIOtJ20A20nzfcqbiOtGKiMEEIIIYQQQgjRRPSkmhBCCCGEEEIIaSRqVBNCCCGEEEIIIY1EjWpCCCGEEEIIIaSRqFFNCCGEEEIIIYQ0UptvVO/atQvGxsbqDoO0EA6Hg8OHD6s7DNIA6tonL1y4AA6Hg2fPnqk1jsYYPHgwPv744wbNQ/tG2/PgwQNwOBxER0erO5RXljYdNxqDjjWEkPqYNGkSRo0a1WzL04Zjq9Y2qjMzMzFv3jy4uLhAKBTCysoK/fr1w5YtW1BSUqLu8BosJiYGb7zxBiwtLSEUCuHk5IQxY8YgOztbOc3cuXPRo0cPCAQCdOvWrdrl3Lp1CwMGDIBQKISDgwPWrl3bSp+gds29c9VXRkYGhg0b1qRl3LhxAwEBATA2NoaZmRmmT5+OoqIilWk4HE6Vf/v27VOZ5sKFC+jevTsEAgFcXFywa9euJsXVEmQyGXx9ffH222+rvJ6fnw8HBwd8+umnLR7DmDFjcPfu3SYtIyMjA2PHjkXHjh3B5XIbfBGobf744w988cUX6g6jWVTe3Kju37Vr19QdXouqqcFS18XEyzeEXuTk5ITvv/++wfO1NW3xmqI+562XvUrHGgD46quv4OvrCz09vRr3sfpce5G6TZo0CRwOBzNmzKjy3kcffQQOh4NJkybVe3nNffy6d+8epkyZgnbt2kEgEMDOzg5DhgzBnj17IJVKldM5OTlV2W++/vrrZolBm1RuTw6HAz6fDxcXF6xatUr5XW3cuFEjr3NbklY2qu/fvw9vb2+cPn0aq1evxs2bNxEeHo5Fixbh6NGjOHPmjLpDbJCcnBwMGTIEpqamOHXqFBISEhASEgJbW1sUFxerTDtlyhSMGTOm2uUUFBRg6NChcHR0RFRUFNatW4cVK1Zg27ZtrfExNJK1tXWTauClp6fD398fLi4uiIiIwMmTJxEXF1ftgT8kJAQZGRnKfy/eREhJScGIESPg5+eH6OhofPzxx5g6dSpOnTrV6NhaAo/Hw65du3Dy5Ens2bNH+fqcOXNgamqK5cuXN3rZMpkMcrm8zulEIhEsLS0bvR4AKC8vh4WFBZYtW4auXbs2aVnawNTUFAYGBuoOo1n4+vqq7EcZGRmYOnUqnJ2d0bNnT3WH1yZVVFSoO4QW1ZavKWo7b1XnVTrWAIrf9ujRozFz5sxap6vt2qu5SCSSFl2+JnBwcMC+fftQWlqqfK2srAx79+5Fu3bt1BZXZGQkunfvjoSEBGzevBmxsbG4cOECpk6dii1btiAuLk5l+lWrVqnsN3PmzFFT5OoVFBSEjIwMJCUl4ZNPPsGKFSuwbt06AICRkZHGP1ludkwLBQYGMnt7e1ZUVFTt+3K5XPn/DRs2MC8vL6anp8fs7e3ZzJkzWWFhofL9kJAQZmRkpPz73r177I033mCWlpZMLBaznj17stDQUOX7CQkJTCQSsT179ihf279/PxMKhSwuLo5dvHiR6ejosIyMDJWY5s2bx/r3719tvIcOHWI6OjpMIpHU6/MvX76cde3atcrrP/zwAzMxMWHl5eXK1xYvXszc3NzqtdyWNHHiRPbmm29W+97L24AxxXfy4s+z8jPv2LGDOTg4MLFYzGbOnMmkUin75ptvmJWVFbOwsGBffvmlynIAsEOHDin/XrRoEXN1dWUikYg5OzuzZcuWsYqKihrj/vHHH5mlpSWTyWTK127dusUAsKSkpBrX87JFixYxT09PldfGjBnDAgMDa5xHnTZu3MhMTExYeno6O3z4MNPV1WXR0dHK98+fP88AsKNHj7LOnTszgUDAfHx82O3bt5XTVG7XP//8k7m7uzMej8dSUlJYWVkZ++STT5itrS3T09NjvXv3ZufPn68yX6W69sm6DBo0iM2bN6/O6So/U15eXrVxaLKXP6OjoyNbtWoVe++995ienh6ztbVlmzZtUpkHANu+fTsbNWoUE4lEzMXFhf35558q01y4cIH16tWL8fl8Zm1tzRYvXqxynBo0aBCbM2cOW7hwITMxMWFWVlZs+fLlKsvIy8tjwcHBzNzcnBkYGDA/Pz+V31JdKioqmIWFBVu1alX9vxAtVdNvta7f4su/3Rc5Ojqy7777jjHGWEpKCgPAbt68qfz/i/8mTpyojOOjjz5i8+bNY2ZmZmzw4MGMsbrPp9qqrV5T1HXeqs6reqypz/G+pmuv6gBgP/zwAwsKCmJCoZA5OzuzAwcOKN+v3P/27dvHBg4cyAQCAQsJCWEymYytXLmS2dnZMT6fz7p27cpOnDhR67oGDRrEZs+ezebNm8eMjY2ZpaUl27ZtGysqKmKTJk1i+vr6rEOHDuz48ePKeaRSKZsyZQpzcnJiQqGQdezYkX3//ffK90tLS5mHhwebNm2a8rV79+4xfX19tmPHjnp9By+rvA708vJiv/76q/L1PXv2sC5durA333xTeQxijLGysjI2Z84cZmFhwQQCAevXrx+LjIxU+f6qO37VNl915HI5c3d3Zz169FC51nt5mkovHlPbsuqu6wMCAlifPn2qvJ+dnc2srKzYV199pZw2LCyM6erqsjNnzjDGWIOvDTWR1j2pfvr0KU6fPo2PPvoIYrG42mk4HI7y/1wuF//5z38QFxeH3bt349y5c1i0aFGNyy8qKsLw4cNx9uxZ3Lx5E0FBQRg5ciRSU1MBAJ06dcL69esxa9YspKamIi0tDTNmzMA333wDDw8PDBw4EO3bt8cvv/yiXKZEIsGePXswZcqUatdpbW0NqVSKQ4cOgTHWmK8FABAeHo6BAweCz+crXwsMDERiYiLy8vIavVxNkZycjBMnTuDkyZP47bffsGPHDowYMQJpaWm4ePEivvnmGyxbtgwRERE1LsPAwAC7du1CfHw8Nm7ciO3bt+O7776rcfry8nLw+Xxwuf/sKiKRCABw+fJllWk/+ugjmJubo3fv3ti5c6fKtgwPD4e/v7/K9IGBgQgPD2/Qd9Ba5syZg65du2L8+PGYPn06Pv/882qf+C5cuBAbNmzAtWvXYGFhgZEjR6rcbS8pKcE333yDn376CXFxcbC0tMTs2bMRHh6Offv24datWxg9ejSCgoKQlJRUbSx17ZOkeuvWrUPXrl1x8+ZN/Pvf/8a8efMQGhqqMs3KlSvx7rvv4tatWxg+fDjGjRuH3NxcAMDjx48xfPhw9OrVCzExMdiyZQt27NiBL7/8UmUZu3fvhlgsRkREBNauXYtVq1aprGf06NHIzs7GiRMnEBUVhe7du2PIkCHK9dTlr7/+wtOnTzF58uQmfiPkRQ4ODjh48CAAIDExERkZGdi4caPy/d27d4PP5yMsLAxbt24F0PDzqTZo69cUtZ236utVOdY0t88++wzvvPMOYmJiMG7cOLz33ntISEhQmaby+0pISEBgYCA2btyIDRs2YP369bh16xYCAwPxxhtv1Hh+rLR7926Ym5sjMjISc+bMwcyZMzF69Gj4+vrixo0bGDp0KMaPH69MZZDL5bC3t8eBAwcQHx+Pzz//HEuXLsXvv/8OABAKhdizZw92796NP//8EzKZDB988AECAgJq/N3V15QpUxASEqL8e+fOndUe3xctWoSDBw9i9+7duHHjBlxcXBAYGIjc3Nxaj1+1zVed6OhoJCQkYMGCBSrXei968RgAAF9//TXMzMzg7e2NdevWqXQPb8tEIlG1PZssLCywc+dOrFixAtevX0dhYSHGjx+P2bNnY8iQIQDQ4GtDjaTeNn3DXb16lQFgf/zxh8rrZmZmTCwWM7FYzBYtWlTj/AcOHGBmZmbKv+tz58PT05P997//VXltxIgRbMCAAWzIkCFs6NChKnexvvnmG+bu7q78++DBg0xfX7/Gu+CMMbZ06VKmo6PDTE1NWVBQEFu7di3LzMysdtqa7pYGBASw6dOnq7wWFxfHALD4+PhaP2NLa44n1Xp6eqygoED5WmBgIHNyclK5s+jm5sbWrFmj/Bt13Ilft24d69GjR43vx8bGMh0dHbZ27VpWXl7OcnNz2TvvvMMAsNWrVyunW7VqFbt8+TK7ceMG+/rrr5lAIGAbN25Uvu/q6qoyPWOMHTt2jAFgJSUlNa5fnRISEhgA1rlz5ypPPCqfjO3bt0/52tOnT5lIJGL79+9njCm2KwCVJwUPHz5kPB6PPX78WGV5Q4YMYUuWLFHO15h9siZt9Ul1UFCQyjRjxoxhw4YNU/4NgC1btkz5d1FREQOgfCqydOlS5ubmpnJs27x5M9PX11fuc4MGDarytKxXr15s8eLFjDHG/v77b2ZoaMjKyspUpunQoQP78ccf6/XZhg0bphL3q6w1n1TXNt+gQYOYt7d3nfG+fD7VRm35mqKu81Z1XtVjTUs8qZ4xY4bKaz4+PmzmzJmMsX/2xRefDjPGmK2trcrTPMYUn3PWrFk1ruvl70YqlTKxWMzGjx+vfC0jI4MBYOHh4TUu56OPPmLvvPOOymtr165l5ubmbPbs2czGxoY9efKkxvnrUnkdmJ2dzQQCAXvw4AF78OABEwqFLCcnR+VJdVFREdPV1VXpwVFRUcFsbW3Z2rVrGWPVH7/qM9/L9u3bxwCwGzduKF/LyspS7v9isZht3rxZ+d6GDRvY+fPnWUxMDNuyZQszNjZm//rXvxr9vWirF6/r5XI5Cw0NZQKBgC1YsKDK+5VmzZrFOnbsyMaOHcs6d+6s3F+b69pQ3XRasf3eoiIjIyGXyzFu3DiUl5crXz9z5gzWrFmDO3fuoKCgAFKpFGVlZSgpKYGenl6V5RQVFWHFihU4duwYMjIyIJVKUVpaWuWp2M6dO5WDIMXFxancxZo0aRKWLVuGq1evok+fPti1axfefffdGu+CA4rBMubPn49z584hIiICW7duxerVq3Hp0iV07ty5Gb4h7efk5KSSy2VlZQUej6dyZ9HKykplIJaX7d+/H//5z3+QnJyMoqIiSKVSGBoa1ji9p6cndu/ejfnz52PJkiXg8XiYO3curKysVNb72WefKf/v7e2N4uJirFu3DnPnzm3sx1W7nTt3Qk9PDykpKUhLS4OTk1OVafr27av8v6mpKdzc3FTuxPP5fHTp0kX59+3btyGTydCxY0eV5ZSXl8PMzKzaOOq7TxJVL26byr9fHrDqxW0jFothaGio3H8SEhLQt29flWNbv379UFRUhLS0NGX+24vLAAAbGxvlMmJiYlBUVFRl25aWliI5ObnOz5CWloZTp04pn56Q1tOjR48qrzX0fKrN2sI1RXOdt16FY01LqO57eXnk/RfHiSgoKEB6ejr69eunMk2/fv0QExNT67pe/G54PB7MzMxUrh2trKwAQOX6aPPmzdi5cydSU1NRWlqKioqKKgOxffLJJzh8+DA2bdqEEydO1HiebggLCwuMGDECu3btAmMMI0aMgLm5uco0ycnJkEgkKt+Frq4uevfuXeVpf3PM9zIzMzPltho8eLDK09f58+cr/9+lSxfw+Xx8+OGHWLNmTZPG8NFGR48ehb6+PiQSCeRyOcaOHYsVK1bUOP369evh5eWFAwcOICoqSvl9NebaUBNpXaPaxcUFHA4HiYmJKq+3b98ewD9dcwFF+ZDXX38dM2fOxFdffQVTU1NcvnwZwcHBqKioqPYEuGDBAoSGhmL9+vVwcXGBSCTC//3f/1XpzhATE4Pi4mJwuVxkZGTAxsZG+Z6lpSVGjhyJkJAQODs748SJE7hw4UKdn83MzAyjR4/G6NGjsXr1anh7e2P9+vXYvXt3vb4ba2trZGVlqbxW+be1tXW9lqEOXC63Spez6gbs0NXVVfmbw+FU+1pNg2GFh4dj3LhxWLlyJQIDA2FkZIR9+/Zhw4YNtcY3duxYjB07FllZWRCLxeBwOPj222+Vv7nq+Pj44IsvvkB5eTkEAkGN28bQ0FDlN6sprly5gu+++w6nT5/Gl19+ieDgYJw5c6ZKF6i6iEQilXmKiorA4/EQFRUFHo+nMq2+vn61y6jvPkkariH7T2OWUVRUBBsbm2qPf/UZwCQkJARmZmZ44403GhSTtjI0NER+fn6V1589ewYjI6Na5wMUo/S//L3WNW9NXm6wNeZ8qg3omuIfL5+3mpOmH2vUpbYbIw1R1/VR5Xm48vvat28fFixYgA0bNqBv374wMDDAunXrqqTPZWdn4+7du+DxeEhKSkJQUFCzxDtlyhTMnj0bgKJxr06urq4AFN3Ivb29AShuTLi4uAAAdHRqbyr5+PhAKpXiwYMHcHNza9lgNYyfnx+2bNkCPp8PW1vbOr+r5ORkpKenQy6X48GDB8obP425NtREWteoNjMzQ0BAADZt2oQ5c+bUekCKioqCXC7Hhg0blE8V63riERYWhkmTJuGtt94CoNjQDx48UJkmNzcXkyZNwqeffoqMjAyMGzcON27cUDn5Tp06Fe+//z7s7e3RoUOHKnce68Ln89GhQ4cqI3XWpm/fvvj0008hkUiUB9PQ0FC4ubnBxMSkQetvTRYWFigsLERxcbFye7ZEHdUrV67A0dFRpSzUw4cP6z1/5Z3enTt3QigUIiAgoMZpo6OjYWJiorww6du3L44fP64yTWhoaJU72ZqgpKQEkyZNwsyZM+Hn5wdnZ2d07twZW7durTJC6tWrV5VPEfLy8nD37l24u7vXuGxvb2/IZDJkZ2djwIAB9YqnPvskqerq1atV/q5t27zM3d0dBw8eBGNMeUEWFhYGAwMD2Nvb12sZ3bt3R2ZmJnR0dKrt6VAbxhhCQkIwYcKEKheMryo3NzecPn26yus3btyocgf/Ra6uruByuYiKioKjo6Py9fv37yM/P7/GeSvH35DJZHXG1pjzqTaga4p/vHzeqi9tP9a0lKtXr2LChAkqf1c22qpjaGgIW1tbhIWFYdCgQcrXw8LC0Lt372aNLSwsDL6+vpg1a5byteqe6E+ZMgWdO3dGcHAwpk2bBn9//wZt25oEBQWhoqICHA4HgYGBVd7v0KGDckyHymOaRCLBtWvXlGUHqzt+1We+l3l7eyvHNnj33XdrzKuuSXR0NLhcbpOrlmgjsVisvPlQl4qKCnzwwQcYM2YM3NzcMHXqVNy+fRuWlpaNujbURFrXqAaAH374Af369UPPnj2xYsUKdOnSBVwuF9euXcOdO3eU3dZcXFwgkUjw3//+FyNHjlQZcKUmrq6u+OOPPzBy5EhwOBx89tlnVe6mzpgxAw4ODli2bBnKy8vh7e2NBQsWqNxtCwwMhKGhIb788kusWrWq1nUePXoU+/btw3vvvYeOHTuCMYYjR47g+PHjKoM53Lt3D0VFRcjMzERpaamy4enh4QE+n4+xY8di5cqVCA4OxuLFixEbG4uNGzfWOhBXa8rPz6/SWDYzM4OPjw/09PSwdOlSzJ07FxERES1S287V1RWpqanYt28fevXqhWPHjuHQoUN1zrdp0yb4+vpCX18foaGhWLhwIb7++mvl3e8jR44gKysLffr0gVAoRGhoKFavXo0FCxYolzFjxgxs2rQJixYtwpQpU3Du3Dn8/vvvOHbsWLN/zqZasmQJGGPKuotOTk5Yv349FixYgGHDhqlcsKxatQpmZmawsrLCp59+CnNz81pLsnTs2BHjxo3DhAkTsGHDBnh7eyMnJwdnz55Fly5dMGLEiCrz1GefrE7lb62oqAg5OTmIjo4Gn8+Hh4dHg74PbRUWFoa1a9di1KhRCA0NxYEDBxr0e5s1axa+//57zJkzB7Nnz0ZiYiKWL1+O+fPn1/uiw9/fH3379sWoUaOwdu1adOzYEenp6Th27BjeeuutWktknTt3DikpKZg6dWq9Y9Z2M2fOxKZNmzB37lxMnToVAoEAx44dw2+//YYjR47UOJ+BgQGmTp2KTz75BDo6OujcuTMePXqExYsXo0+fPvD19a12PkdHR3A4HBw9ehTDhw+HSCSq8alAY86n2qItXlPU57xVX9p8rElNTUVubi5SU1Mhk8mU5w0XFxflvlDXtVdNDhw4gJ49e6J///7Ys2cPIiMjsWPHjlo/x8KFC7F8+XJ06NAB3bp1Q0hICKKjo1VKXDYHV1dX/Pzzzzh16hScnZ3xyy+/4Nq1a3B2dlZOs3nzZoSHh+PWrVtwcHDAsWPHMG7cOFy9erXWz10fPB5P2R375SeTgKKxNnPmTCxcuBCmpqZo164d1q5di5KSEgQHBwOo+fhV13wv43A4CAkJQUBAAPr164clS5bA3d0dEokEly5dQk5OjjLG8PBwREREwM/PDwYGBggPD8e//vUvfPDBBxr98EoTfPrpp8jPz8d//vMf6Ovr4/jx45gyZQqOHj3aqGtDjaS+dO6mSU9PZ7Nnz2bOzs5MV1eX6evrs969e7N169ax4uJi5XTffvsts7GxYSKRiAUGBrKff/651sGIUlJSmJ+fHxOJRMzBwYFt2rRJZWCO3bt3M7FYzO7evaucJyIigunq6qqUK2CMsc8++4zxeDyWnp5e62dJTk5m06ZNYx07dmQikYgZGxuzXr16sZCQEJXpBg0aVKWEAACWkpKinCYmJob179+fCQQCZmdnx77++uv6f6ktaOLEidXGHhwczBhTDEzm4uLCRCIRe/3119m2bduqLan18jJfHgTh5UFU8NJAZQsXLmRmZmZMX1+fjRkzhn333Xd1Dnwwfvx4Zmpqyvh8PuvSpQv7+eefVd4/ceIE69atG9PX12disZh17dqVbd26tUpphvPnz7Nu3boxPp/P2rdvX2X7aoILFy4wHo/H/v777yrvDR06lL322mtMLpcrBwg5cuQI8/T0ZHw+n/Xu3ZvFxMQop69pUImKigr2+eefMycnJ6arq8tsbGzYW2+9xW7dulXtfHXtkzWp7vfm6OhY4/Sv2kBlK1euZKNHj2Z6enrM2tq6ygBEL+8bjDFmZGSk8rusT5mbl7fDy2VRCgoK2Jw5c5itrS3T1dVlDg4ObNy4cSw1NbXWz/T+++8zX1/fen3+V0lkZCQLCAhgFhYWzMjIiPn4+NSr7FFpaSlbvnw569Spk7Jk4PTp01lOTo5ympcHKmNMMViVtbU143A4KiW1qtu/6jqfarO2dk1R3/PWy161Y01N1yYvlvKpz7XXywCwzZs3s4CAACYQCJiTk5NyEE/Gqt8XGWNMJpOxFStWMDs7O6arq1vvklovfzfVlX16cTuUlZWxSZMmMSMjI2ZsbMxmzpzJ/v3vfyuvsypLve3du1c5f15eHnNwcKh14L7a1DZgLWNVt2dpaSmbM2cOMzc3r7E0VnXHr/rMV53ExEQ2ceJEZm9vz3R0dJiRkREbOHAg+/HHH5W/xaioKObj48OMjIyYUChk7u7ubPXq1VUGyGsL6tqeL75//vx5pqOjo3JtmZKSwgwNDdkPP/zAGGv4taEm4jDWhBpOpFbBwcHIycnBX3/9pe5QCGlWFy5cgJ+fH/Ly8jQ6X62tcnJywscff1xjdzdCiPbRxGsKOtZUj8Ph4NChQ7X23CKEvFq0svu3psvPz8ft27exd+9ejTr5EUIIIUS70DUFIYRoPmpUt4A333wTkZGRmDFjRq2DWRFCCCGE1IauKQghRPNR929CCCGEEEIIIaSRGjZuPCGEEEIIIYQQQpSoUU0IIYQQQgghhDQSNaoJIYQQQgghhJBGokY1IYQQQgghhBDSSNSoJoQQQgghhBBCGoka1YQQQgghhBBCSCNRo5oQQgghhBBCCGkkalQTQgghhBBCCCGN9P+gNUT0pg5IngAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,6))\n",
    "plt.plot(smartphones_normalize_l1,c=smartphones_normalize_l1.in)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "id": "bd73a755",
   "metadata": {},
   "outputs": [],
   "source": [
    "outlier_data=pd.DataFrame(np.array([-8,5,-0,80,12,2,3,1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "id": "90f0414d",
   "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>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-8</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>80</td>\n",
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       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
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       "1   5\n",
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       "3  80\n",
       "4  12\n",
       "5   2\n",
       "6   3\n",
       "7   1"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "outlier_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "id": "40bbd323",
   "metadata": {},
   "outputs": [],
   "source": [
    "outlier_data.sort_values(by=0,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "id": "01a48f57",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    0\n",
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       "2   0\n",
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       "5   2\n",
       "6   3\n",
       "1   5\n",
       "4  12\n",
       "3  80"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
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   "source": [
    "outlier_data"
   ]
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  {
   "cell_type": "code",
   "execution_count": 128,
   "id": "1f26483e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
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     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "outlier_data.boxplot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "id": "4692d611",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    80\n",
       "dtype: int64"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "outlier_data.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "id": "2538e4b9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    2.5\n",
       "dtype: float64"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "outlier_data.median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "id": "03a7a38f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    2.5\n",
       "Name: 0.5, dtype: float64"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "outlier_data.quantile(.50000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f95cc5b8",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Machine_learning_Puyan_Rajiyan",
   "language": "python",
   "name": "machine_learning_puyan_rajiyan"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}