Prediction Errors Using CART.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "suspected-fitting",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "all loaded\n"
     ]
    }
   ],
   "source": [
    "try:\n",
    "    import pandas as pd\n",
    "    import numpy as np \n",
    "    import seaborn as sns\n",
    "    import matplotlib.pyplot as plt\n",
    "    %matplotlib inline\n",
    "    sns.set(color_codes=True)\n",
    "    \n",
    "    print(\"all loaded\")\n",
    "except:\n",
    "    print(\"error\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "suburban-essence",
   "metadata": {},
   "source": [
    "#### Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "psychological-hygiene",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RangeIndex(start=0, stop=10000, step=1)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv(\"random10.csv\")\n",
    "\n",
    "data.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "pleased-cursor",
   "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>seic</th>\n",
       "      <th>seic_benefit</th>\n",
       "      <th>health</th>\n",
       "      <th>health_benefit</th>\n",
       "      <th>house</th>\n",
       "      <th>house_benefit</th>\n",
       "      <th>nutrition</th>\n",
       "      <th>nutrition_benefit</th>\n",
       "      <th>livelihood</th>\n",
       "      <th>livelihood_benefit</th>\n",
       "      <th>education</th>\n",
       "      <th>education_benefit</th>\n",
       "      <th>scheme1</th>\n",
       "      <th>scheme2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>10000.000000</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>10000.00000</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>10000.00000</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>10000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>58.646900</td>\n",
       "      <td>50262.961200</td>\n",
       "      <td>59.787400</td>\n",
       "      <td>40247.36220</td>\n",
       "      <td>65.027200</td>\n",
       "      <td>39992.306400</td>\n",
       "      <td>54.839200</td>\n",
       "      <td>39814.456000</td>\n",
       "      <td>50.09870</td>\n",
       "      <td>49714.019400</td>\n",
       "      <td>65.483600</td>\n",
       "      <td>39895.112500</td>\n",
       "      <td>59.962000</td>\n",
       "      <td>54.772300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>10.849746</td>\n",
       "      <td>28940.959236</td>\n",
       "      <td>23.634002</td>\n",
       "      <td>23320.27922</td>\n",
       "      <td>20.495025</td>\n",
       "      <td>22985.635879</td>\n",
       "      <td>26.191925</td>\n",
       "      <td>22882.348412</td>\n",
       "      <td>29.17101</td>\n",
       "      <td>28768.363387</td>\n",
       "      <td>20.618134</td>\n",
       "      <td>23091.447788</td>\n",
       "      <td>23.495216</td>\n",
       "      <td>26.182487</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>22.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>12.00000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>10.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>51.000000</td>\n",
       "      <td>25175.500000</td>\n",
       "      <td>39.000000</td>\n",
       "      <td>19706.25000</td>\n",
       "      <td>47.000000</td>\n",
       "      <td>20307.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>20124.250000</td>\n",
       "      <td>25.00000</td>\n",
       "      <td>25008.750000</td>\n",
       "      <td>47.000000</td>\n",
       "      <td>19929.000000</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>32.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>59.000000</td>\n",
       "      <td>50334.000000</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>40792.50000</td>\n",
       "      <td>65.000000</td>\n",
       "      <td>39675.000000</td>\n",
       "      <td>55.000000</td>\n",
       "      <td>39974.000000</td>\n",
       "      <td>50.00000</td>\n",
       "      <td>49817.500000</td>\n",
       "      <td>66.000000</td>\n",
       "      <td>39623.000000</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>55.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>66.000000</td>\n",
       "      <td>75413.250000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>60396.00000</td>\n",
       "      <td>83.000000</td>\n",
       "      <td>60144.000000</td>\n",
       "      <td>77.000000</td>\n",
       "      <td>59045.500000</td>\n",
       "      <td>75.00000</td>\n",
       "      <td>74367.250000</td>\n",
       "      <td>83.000000</td>\n",
       "      <td>60055.500000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>77.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>93.000000</td>\n",
       "      <td>99997.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>79996.00000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>79996.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>79998.000000</td>\n",
       "      <td>100.00000</td>\n",
       "      <td>99998.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>79996.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               seic  seic_benefit        health  health_benefit         house  \\\n",
       "count  10000.000000  10000.000000  10000.000000     10000.00000  10000.000000   \n",
       "mean      58.646900  50262.961200     59.787400     40247.36220     65.027200   \n",
       "std       10.849746  28940.959236     23.634002     23320.27922     20.495025   \n",
       "min       22.000000      3.000000     20.000000        12.00000     30.000000   \n",
       "25%       51.000000  25175.500000     39.000000     19706.25000     47.000000   \n",
       "50%       59.000000  50334.000000     60.000000     40792.50000     65.000000   \n",
       "75%       66.000000  75413.250000     80.000000     60396.00000     83.000000   \n",
       "max       93.000000  99997.000000    100.000000     79996.00000    100.000000   \n",
       "\n",
       "       house_benefit     nutrition  nutrition_benefit   livelihood  \\\n",
       "count   10000.000000  10000.000000       10000.000000  10000.00000   \n",
       "mean    39992.306400     54.839200       39814.456000     50.09870   \n",
       "std     22985.635879     26.191925       22882.348412     29.17101   \n",
       "min         5.000000     10.000000           7.000000      0.00000   \n",
       "25%     20307.000000     32.000000       20124.250000     25.00000   \n",
       "50%     39675.000000     55.000000       39974.000000     50.00000   \n",
       "75%     60144.000000     77.000000       59045.500000     75.00000   \n",
       "max     79996.000000    100.000000       79998.000000    100.00000   \n",
       "\n",
       "       livelihood_benefit     education  education_benefit       scheme1  \\\n",
       "count        10000.000000  10000.000000       10000.000000  10000.000000   \n",
       "mean         49714.019400     65.483600       39895.112500     59.962000   \n",
       "std          28768.363387     20.618134       23091.447788     23.495216   \n",
       "min             11.000000     30.000000           5.000000     20.000000   \n",
       "25%          25008.750000     47.000000       19929.000000     40.000000   \n",
       "50%          49817.500000     66.000000       39623.000000     60.000000   \n",
       "75%          74367.250000     83.000000       60055.500000     80.000000   \n",
       "max          99998.000000    100.000000       79996.000000    100.000000   \n",
       "\n",
       "            scheme2  \n",
       "count  10000.000000  \n",
       "mean      54.772300  \n",
       "std       26.182487  \n",
       "min       10.000000  \n",
       "25%       32.000000  \n",
       "50%       55.000000  \n",
       "75%       77.000000  \n",
       "max      100.000000  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "possible-attendance",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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>seic</th>\n",
       "      <th>health</th>\n",
       "      <th>house</th>\n",
       "      <th>nutrition</th>\n",
       "      <th>livelihood</th>\n",
       "      <th>education</th>\n",
       "      <th>scheme1</th>\n",
       "      <th>scheme2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>54</td>\n",
       "      <td>77</td>\n",
       "      <td>48</td>\n",
       "      <td>48</td>\n",
       "      <td>37</td>\n",
       "      <td>62</td>\n",
       "      <td>89</td>\n",
       "      <td>94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>65</td>\n",
       "      <td>51</td>\n",
       "      <td>66</td>\n",
       "      <td>82</td>\n",
       "      <td>71</td>\n",
       "      <td>56</td>\n",
       "      <td>20</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>79</td>\n",
       "      <td>98</td>\n",
       "      <td>97</td>\n",
       "      <td>89</td>\n",
       "      <td>32</td>\n",
       "      <td>81</td>\n",
       "      <td>80</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>63</td>\n",
       "      <td>41</td>\n",
       "      <td>70</td>\n",
       "      <td>87</td>\n",
       "      <td>45</td>\n",
       "      <td>72</td>\n",
       "      <td>47</td>\n",
       "      <td>66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>36</td>\n",
       "      <td>53</td>\n",
       "      <td>54</td>\n",
       "      <td>34</td>\n",
       "      <td>10</td>\n",
       "      <td>30</td>\n",
       "      <td>44</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9995</th>\n",
       "      <td>61</td>\n",
       "      <td>75</td>\n",
       "      <td>85</td>\n",
       "      <td>17</td>\n",
       "      <td>79</td>\n",
       "      <td>53</td>\n",
       "      <td>48</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9996</th>\n",
       "      <td>72</td>\n",
       "      <td>65</td>\n",
       "      <td>33</td>\n",
       "      <td>82</td>\n",
       "      <td>95</td>\n",
       "      <td>89</td>\n",
       "      <td>89</td>\n",
       "      <td>61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9997</th>\n",
       "      <td>52</td>\n",
       "      <td>43</td>\n",
       "      <td>68</td>\n",
       "      <td>53</td>\n",
       "      <td>33</td>\n",
       "      <td>64</td>\n",
       "      <td>43</td>\n",
       "      <td>56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9998</th>\n",
       "      <td>68</td>\n",
       "      <td>32</td>\n",
       "      <td>100</td>\n",
       "      <td>60</td>\n",
       "      <td>61</td>\n",
       "      <td>88</td>\n",
       "      <td>85</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9999</th>\n",
       "      <td>46</td>\n",
       "      <td>31</td>\n",
       "      <td>33</td>\n",
       "      <td>71</td>\n",
       "      <td>14</td>\n",
       "      <td>81</td>\n",
       "      <td>73</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10000 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      seic  health  house  nutrition  livelihood  education  scheme1  scheme2\n",
       "0       54      77     48         48          37         62       89       94\n",
       "1       65      51     66         82          71         56       20       23\n",
       "2       79      98     97         89          32         81       80       29\n",
       "3       63      41     70         87          45         72       47       66\n",
       "4       36      53     54         34          10         30       44       10\n",
       "...    ...     ...    ...        ...         ...        ...      ...      ...\n",
       "9995    61      75     85         17          79         53       48       30\n",
       "9996    72      65     33         82          95         89       89       61\n",
       "9997    52      43     68         53          33         64       43       56\n",
       "9998    68      32    100         60          61         88       85       35\n",
       "9999    46      31     33         71          14         81       73       70\n",
       "\n",
       "[10000 rows x 8 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Differentiating Scores and Benefit data for processing\n",
    "scores_data = data[[\"seic\",\"health\",\"house\",\"nutrition\",\"livelihood\",\"education\",\"scheme1\",\"scheme2\"]]\n",
    "scores_data.head()\n",
    "scores_data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "subsequent-retirement",
   "metadata": {},
   "source": [
    "#### Outliers if any:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "personal-honor",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function matplotlib.pyplot.show(close=None, block=None)>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib.pyplot import figure\n",
    "figure(figsize= (20,7))\n",
    "scores_data1 = scores_data[[\"seic\",\"health\",\"house\",\"nutrition\",\"livelihood\",\"education\",\"scheme1\",\"scheme2\"]]\n",
    "sns.boxplot(data=scores_data1)\n",
    "plt.show"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "african-cleaner",
   "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>seic_benefit</th>\n",
       "      <th>health_benefit</th>\n",
       "      <th>house_benefit</th>\n",
       "      <th>nutrition_benefit</th>\n",
       "      <th>livelihood_benefit</th>\n",
       "      <th>education_benefit</th>\n",
       "      <th>scheme1_benefit</th>\n",
       "      <th>scheme2_benefit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>97930</td>\n",
       "      <td>76494</td>\n",
       "      <td>48429</td>\n",
       "      <td>6586</td>\n",
       "      <td>7219</td>\n",
       "      <td>74548</td>\n",
       "      <td>Non-Eligible</td>\n",
       "      <td>Eligible</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1377</td>\n",
       "      <td>26442</td>\n",
       "      <td>66576</td>\n",
       "      <td>43498</td>\n",
       "      <td>91686</td>\n",
       "      <td>24616</td>\n",
       "      <td>Non-Eligible</td>\n",
       "      <td>Non-Eligible</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>88404</td>\n",
       "      <td>74235</td>\n",
       "      <td>61040</td>\n",
       "      <td>55503</td>\n",
       "      <td>60424</td>\n",
       "      <td>56585</td>\n",
       "      <td>Eligible</td>\n",
       "      <td>Eligible</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7618</td>\n",
       "      <td>74392</td>\n",
       "      <td>42888</td>\n",
       "      <td>24656</td>\n",
       "      <td>46427</td>\n",
       "      <td>45466</td>\n",
       "      <td>Non-Eligible</td>\n",
       "      <td>Non-Eligible</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>11917</td>\n",
       "      <td>27091</td>\n",
       "      <td>755</td>\n",
       "      <td>2402</td>\n",
       "      <td>3016</td>\n",
       "      <td>9648</td>\n",
       "      <td>Non-Eligible</td>\n",
       "      <td>Non-Eligible</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   seic_benefit  health_benefit  house_benefit  nutrition_benefit  \\\n",
       "0         97930           76494          48429               6586   \n",
       "1          1377           26442          66576              43498   \n",
       "2         88404           74235          61040              55503   \n",
       "3          7618           74392          42888              24656   \n",
       "4         11917           27091            755               2402   \n",
       "\n",
       "   livelihood_benefit  education_benefit scheme1_benefit scheme2_benefit  \n",
       "0                7219              74548    Non-Eligible        Eligible  \n",
       "1               91686              24616    Non-Eligible    Non-Eligible  \n",
       "2               60424              56585        Eligible        Eligible  \n",
       "3               46427              45466    Non-Eligible    Non-Eligible  \n",
       "4                3016               9648    Non-Eligible    Non-Eligible  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "benefit_amount_data = data[[\"seic_benefit\",\"health_benefit\",\"house_benefit\",\"nutrition_benefit\",\n",
    "                            \"livelihood_benefit\",\"education_benefit\",\"scheme1_benefit\",\"scheme2_benefit\"]]\n",
    "benefit_amount_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "destroyed-induction",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function matplotlib.pyplot.show(close=None, block=None)>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "figure(figsize= (10,7))\n",
    "sns.boxplot(data=benefit_amount_data)\n",
    "plt.show"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bigger-complex",
   "metadata": {},
   "source": [
    "#### Train test split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "cognitive-lyric",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((7000, 24), (3000, 24))"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "train,test = train_test_split(data,test_size=0.3,random_state= 3)\n",
    "train.shape,test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "hundred-contributor",
   "metadata": {},
   "source": [
    "#### Label Encoding Categorical Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "sunrise-removal",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Differentiating Numerical and Categorical Data for further processing\n",
    "\n",
    "#Categorical\n",
    "train_categorical = train.select_dtypes(exclude = ['int64'])\n",
    "test_categorical = test.select_dtypes(exclude = ['int64'])\n",
    "\n",
    "#Numerical\n",
    "train_numerical = train.select_dtypes(include = ['int64'])\n",
    "test_numerical = test.select_dtypes(include = ['int64'])\n",
    "\n",
    "#Encoding Categorical data\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "train_categorical_en = train_categorical.apply(LabelEncoder().fit_transform)\n",
    "test_categorical_en = test_categorical.apply(LabelEncoder().fit_transform)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "intermediate-question",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      SEIC ERROR  Health ERROR  House ERROR  Nutrition ERROR  \\\n",
      "5795           2             2            1                2   \n",
      "\n",
      "      livelihood ERROR  Education ERROR  scheme1_benefit  Scheme-1 ERROR  \\\n",
      "5795                 2                1                0               2   \n",
      "\n",
      "      scheme2_benefit  Scheme-2 ERROR  \n",
      "5795                1               2  \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'No Error'"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(train_categorical_en[train_categorical_en['SEIC ERROR'] > 1].head(1))\n",
    "train_categorical[\"SEIC ERROR\"].loc[5795]\n",
    "\n",
    "#Hence, 2 of categorical encoded defines : No error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "wicked-possession",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      SEIC ERROR  Health ERROR  House ERROR  Nutrition ERROR  \\\n",
      "8317           0             2            1                2   \n",
      "\n",
      "      livelihood ERROR  Education ERROR  scheme1_benefit  Scheme-1 ERROR  \\\n",
      "8317                 2                1                0               2   \n",
      "\n",
      "      scheme2_benefit  Scheme-2 ERROR  \n",
      "8317                0               2  \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'EOE'"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(train_categorical_en[train_categorical_en['SEIC ERROR'] == 0].head(1))\n",
    "train_categorical[\"SEIC ERROR\"].loc[8317]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "metallic-czech",
   "metadata": {},
   "source": [
    "\n",
    "Hence Categorical Encoded Indicators:\n",
    "\n",
    "- 0 of categorical encoded defines : EOE\n",
    "- 1 of categorical encoded defines : EOI\n",
    "- 2 of categorical encoded defines : No error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "backed-trailer",
   "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>SEIC ERROR</th>\n",
       "      <th>Health ERROR</th>\n",
       "      <th>House ERROR</th>\n",
       "      <th>Nutrition ERROR</th>\n",
       "      <th>livelihood ERROR</th>\n",
       "      <th>Education ERROR</th>\n",
       "      <th>scheme1_benefit</th>\n",
       "      <th>Scheme-1 ERROR</th>\n",
       "      <th>scheme2_benefit</th>\n",
       "      <th>Scheme-2 ERROR</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5795</th>\n",
       "      <td>No Error</td>\n",
       "      <td>No Error</td>\n",
       "      <td>No Error</td>\n",
       "      <td>No Error</td>\n",
       "      <td>No Error</td>\n",
       "      <td>No Error</td>\n",
       "      <td>Eligible</td>\n",
       "      <td>No Error</td>\n",
       "      <td>Non-Eligible</td>\n",
       "      <td>No Error</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1490</th>\n",
       "      <td>No Error</td>\n",
       "      <td>EOI</td>\n",
       "      <td>No Error</td>\n",
       "      <td>No Error</td>\n",
       "      <td>No Error</td>\n",
       "      <td>No Error</td>\n",
       "      <td>Non-Eligible</td>\n",
       "      <td>No Error</td>\n",
       "      <td>Eligible</td>\n",
       "      <td>No Error</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3807</th>\n",
       "      <td>No Error</td>\n",
       "      <td>No Error</td>\n",
       "      <td>No Error</td>\n",
       "      <td>No Error</td>\n",
       "      <td>No Error</td>\n",
       "      <td>No Error</td>\n",
       "      <td>Eligible</td>\n",
       "      <td>No Error</td>\n",
       "      <td>Non-Eligible</td>\n",
       "      <td>No Error</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3042</th>\n",
       "      <td>No Error</td>\n",
       "      <td>EOE</td>\n",
       "      <td>No Error</td>\n",
       "      <td>No Error</td>\n",
       "      <td>No Error</td>\n",
       "      <td>No Error</td>\n",
       "      <td>Eligible</td>\n",
       "      <td>No Error</td>\n",
       "      <td>Eligible</td>\n",
       "      <td>No Error</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4064</th>\n",
       "      <td>No Error</td>\n",
       "      <td>No Error</td>\n",
       "      <td>No Error</td>\n",
       "      <td>EOE</td>\n",
       "      <td>No Error</td>\n",
       "      <td>No Error</td>\n",
       "      <td>Eligible</td>\n",
       "      <td>No Error</td>\n",
       "      <td>Eligible</td>\n",
       "      <td>No Error</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     SEIC ERROR Health ERROR House ERROR Nutrition ERROR livelihood ERROR  \\\n",
       "5795   No Error     No Error    No Error        No Error         No Error   \n",
       "1490   No Error          EOI    No Error        No Error         No Error   \n",
       "3807   No Error     No Error    No Error        No Error         No Error   \n",
       "3042   No Error          EOE    No Error        No Error         No Error   \n",
       "4064   No Error     No Error    No Error             EOE         No Error   \n",
       "\n",
       "     Education ERROR scheme1_benefit Scheme-1 ERROR scheme2_benefit  \\\n",
       "5795        No Error        Eligible       No Error    Non-Eligible   \n",
       "1490        No Error    Non-Eligible       No Error        Eligible   \n",
       "3807        No Error        Eligible       No Error    Non-Eligible   \n",
       "3042        No Error        Eligible       No Error        Eligible   \n",
       "4064        No Error        Eligible       No Error        Eligible   \n",
       "\n",
       "     Scheme-2 ERROR  \n",
       "5795       No Error  \n",
       "1490       No Error  \n",
       "3807       No Error  \n",
       "3042       No Error  \n",
       "4064       No Error  "
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Combining Numerical and Categorical\n",
    "train_new = pd.concat([train_categorical_en,train_numerical],axis=1)\n",
    "test_new = pd.concat([test_categorical_en,test_numerical],axis=1)\n",
    "\n",
    "train_categorical.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "light-lease",
   "metadata": {},
   "source": [
    "#### Defining Target and Independent Variable:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "loving-vacation",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((7000, 2), (3000, 2))"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#defining independent variable:\n",
    "\n",
    "train_factors_seic = train_new[[\"seic\",\"seic_benefit\",\"SEIC ERROR\"]]\n",
    "test_factors_seic = test_new[[\"seic\",\"seic_benefit\",\"SEIC ERROR\"]]\n",
    "\n",
    "X_train_seic = train_factors_seic.drop([\"SEIC ERROR\"],axis=1)\n",
    "y_train_seic = train_factors_seic[\"SEIC ERROR\"]\n",
    "\n",
    "\n",
    "X_test_seic = test_factors_seic.drop([\"SEIC ERROR\"],axis=1)\n",
    "y_test_seic = test_factors_seic[\"SEIC ERROR\"]\n",
    "\n",
    "X_train_seic.shape,X_test_seic.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "valuable-maintenance",
   "metadata": {},
   "source": [
    "#### Model1 - KNN Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "funny-words",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy Score:  0.9613333333333334\n"
     ]
    }
   ],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "modelc = KNeighborsClassifier()\n",
    "\n",
    "clf = modelc.fit(X_train_seic,y_train_seic)\n",
    "prediction_c = modelc.predict(X_test_seic)\n",
    "\n",
    "\n",
    "scoreC_test = modelc.score(X_test_seic, y_test_seic)\n",
    "print('Accuracy Score: ', scoreC_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "genuine-magic",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#No of unique values in array:\n",
    "len(set(prediction_c))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "caroline-tourist",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{0, 2}"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(set(prediction_c))   #KNN didnt consider EOE Cases"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "caroline-fishing",
   "metadata": {},
   "source": [
    "#### Model 2 : Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "headed-means",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy Score:  0.9646666666666667\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "model_logr = LogisticRegression()\n",
    "logr = model_logr.fit(X_train_seic,y_train_seic)\n",
    "prediction_logr = model_logr.predict(X_test_seic)\n",
    "\n",
    "\n",
    "score_logr_test = model_logr.score(X_test_seic, y_test_seic)\n",
    "print('Accuracy Score: ', score_logr_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "developed-albany",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(set(prediction_logr))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "circular-green",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{1, 2}"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "set(prediction_logr)    #Logistic Regression Didnt consider EOI cases"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "foreign-malpractice",
   "metadata": {},
   "source": [
    "#### Model 3 : Random Forest Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "impressed-lease",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy Score:  0.9993333333333333\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "model_rfc = RandomForestClassifier(n_estimators=100)\n",
    "rfc = model_rfc.fit(X_train_seic,y_train_seic)\n",
    "prediction_rfc = model_rfc.predict(X_test_seic)\n",
    "\n",
    "\n",
    "score_rfc_test = model_rfc.score(X_test_seic, y_test_seic)\n",
    "print('Accuracy Score: ', score_rfc_test)\n",
    "\n",
    "len(set(prediction_rfc))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "overhead-north",
   "metadata": {},
   "source": [
    "#### Model 4: Decision Tree Classifier(CART): Best performer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "spanish-villa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy Score:  0.9996666666666667\n"
     ]
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn import tree\n",
    "\n",
    "model_dtc = DecisionTreeClassifier(random_state =0 , max_depth = 10)\n",
    "dtc = model_dtc.fit(X_train_seic,y_train_seic)\n",
    "prediction_dtc = model_dtc.predict(X_test_seic)\n",
    "\n",
    "\n",
    "score_dtc_test = model_dtc.score(X_test_seic, y_test_seic)\n",
    "print('Accuracy Score: ', score_dtc_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "pressing-kidney",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tree.plot_tree(dtc);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "solar-romance",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(set(prediction_dtc))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "official-blocking",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{0, 1, 2}"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(set(prediction_dtc))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "current-holly",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{0, 1, 2}"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "set(y_test_seic)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "nervous-backup",
   "metadata": {},
   "source": [
    "- 0 : eoe\n",
    "- 1 : eoi\n",
    "- 2 : No error"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "elder-store",
   "metadata": {},
   "source": [
    "#### Plotting Test Dataset:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "inclusive-documentary",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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KULRaMsoXH3+cUVgFo8LQ1lWLLLQwcEihS1tVqMvuavpBoaxgSZUiKiDe3fXNn0OeJQQSBw1dusHKWgBNTFKy2TZu6+9SSrJUqTCJFiHgoxYpOVHBleq5agYLFRFubXvKT3gV7XVyobUfceFiUKNAVpa9d0+prtbnf1yQKIs1xIYLXwdCUP7STcqcz6SZOv93yR37/MhjGp5ATksV8LJI0SDX110zKCwvRTUVpO+lu6wX9WBP9BhFsYemPQJBizQN8sFcW+kVWtEVWJcWORa8F1JXnEp+ABBJVqpg6KAZKK70xIAQNEnhoFNkLvg8an695jpOiOPu60Trfqa0aVCgQT6oq+kcv1dKay9kKeELv6j0Wn8Vr0uXJqkg6Gx5WUpqSSADGpDO6zA8ZRLXLeQnE4BKKMhKtSBpkFdBc8LFdZUJnBGu+ptksUgH1ky8Wo+1R6Is1hDC0Jn+i7PZcP7v4C5V0DZMIPT+Qt8ivcrZQZIiCy18UZK0rGNg9ww2d6I1BRBAk3VSNFhiIyBwMNFkvACm6QX2DdldUCcEZGWF7AAsqcJbsjnoGIaJYTVI0TvuEVUnMK6wMbFJYdDAkP1rYqJW2jUKXlWPjuZZF61FklEKI+p+LQtlGexvoyMkkVxgKdn0Fibxb76/KLExcTDQW+5BHKUmBGRkNaibqDBJXi4GowvwLInRu4dkCxfXuCJRFusAkTLRN02t9zQCmDTbiAXB8z1LGwMrlu/UtRpdldJ+QDRNlYaXdWTSRPYJTsdZvXe6lOK6hwzhgO2gxTjP3qIkwBfWPvWJSZGFgV1mvnJutShaUzrjziMObDI0yDFJOPcVKMdld4GcKuZLye5iQBudPAu46NQoYNAkJZfjH3ETGPzzOpgstblVzR4Fe/s+EmWRQDXMiXiJdOxYykI2wguH1CqxHrjTSkyRpYIu/QCi27Z2HMb3HqZswmIU4+pCGkUcRFUnqyLGrBcXGPR6V3rvewXGO2FSDxhgB0WNAhouhmwGMTYJmEFhm6oCr1KkxEY1N1xV3CotfILTeNcqxn7Fv1ZIlEUC3B7pim5PN4AkRR2TOm6P9741BdDF8IJ3CjoWObm0olz1MLQKrnGq1o7CoHPs9PvbGEiUFReXlmU1KtrjxjR891OvJKnouJzwAtmOV1jXaI9ref/nZJlFL+4h0agwhYbDhskM9YXHQl11sud5928kyiJBT3UQnTqoCP2CoHDEblLipcKGw8GkxDQaLhKHiZbmQq1jtM0ppiB00BBSRmbHjAuGtXgcNFypY2OSoUY+JP257xhSeLk/8e5RHAUTxDcQOGgYHRTkcVJa6zH4llx0XHSylCLum2rP25qt5KKjmRlqFCky3xVvqQ4YJ9mfkCiLfQQqMXW4bIpegiJqlExH8xjoDnJKL6Dci8dK4HptLRveX6BFZMv0Qpjg0r35xa1piFNnMAqMakWve+LS9Kg3hhmnTh6LbGTswC9+U+sBlSWnUliXI96dgt6H8Dz/rXOLqqRe3i5okEbH8uYkPLuhQNTT2Pv5DbdYlUt0mjRVdGnheg2BV5IGu68jURb7ANJUA5pm3+VTZqpnV7pWWKQjC4OsiHhFL5oCyXLhYOdKTXHtVDCwVLGWX6vqZWFpHcphGNdM53HDBGfH3W0Fo5mj6dHE94KFifR6DSqHjotAYmGSoRpUaIcq9pixIttzTwqkRxmy/EykZR0dhwpTocfKoDoh7LNol5IKhI9HY6G9AYmy2Muh1u2VZeHqvTQFuRCkrPaDg+GtGBttL32DTA+FE72a8xlATWlRYMFrUynQcCiyAPh9oeVIA88NMjTJdtUHjBNGdb2jUmb9uL8AUlioJUX7gsLosGjiZpSFJh8gmWBPELDuvEeGtDyXUvfz2CRNRkZR7kt0rK6WsINBej/7dywjURZ7OaIsAhG8JHFywoVHWSBJeRkmKp+km6DNR5NsW2V5MFLHS65LB5M6NimPWbR33+6VwMbEjflCr2XQexyoPcIQK410FV1xtFgEywHv8CwliUCLUBYNcqRodteEICiw5P2u4hGDUGgIXLKUAoWqKM2L+62rav9WlfsAonoe9OO16dy7wCIpmmrFLyQpGuRZIsqCaJDDpV0QRvmjc5SZYA+pIRhFY83e86vbpHvnbsn1EdxCtP+sFYZhpl0rNEhTYSLItuvndhQ9WcA0SmygRh5Lmh7fk0/zrn40ofpGDEL7nWdR9erwvjddOBRYQPRx2+2rSJTFXo4oym7hFRHFQVR7UwMrsm+GjhWrsM1fwY9aUHZes+25GXoxda6GsF4PgRxX6a30WlfzeizSuGgqZtUjo8r/3cHoQ+MhaJKjwlTPlX/c2g7dI9kMm1uUNR8FvydGjkVSVOnlwh1nJG6ovRwWaRyqAelbOxNmvLWAqrwOf4AN7FBXVjaEEA4G98mPgoxPCMVrpGEh0amT7fJhx5nXsFlKa71q97/nQbigVnqPV4LO++N67qF+FNz+cU3S3vMcb0LDZEd1IorwUrlW4zceMwIL3X9O1dWU2bDX1XMkymIfQOsrFKR9DmBuu+i4ktDWlFExABHC6eNjGEK7UUAFz8PXbasl+Ncra2o94y29lHAUbAxcdG/Frp6dfm7SxSBBY7CL7WVR25hI12U5ITgKEdlVMn5NCqiulp2LFE26pKl66cB7D/Yu1ZagCylqobxOg7RltPwq1w7TX/agOpDokYJ1EHfPKIWef15tBe6m9YgtdGK9YitRkBA8SWH3xfWC1b3mrDoS5kOJA7vOJ6FJCiWeVBA8RY0ie5hgjgwl79mWaDh0CnbHi22EuQgLLGLvuZ8iezB6uKSiVv1C9GM1WIaGE6pYhIiXhTZuSCyLvRx+AK4TEoGO5dE894ZPhZBjCc2rtnXQe1azSkTfVMnO4rZeFb+DuH/6kQ8Oi84x1osmZNxqPLSW76YTQoArBeamJ7E48xuyHRlyfvKBSwodOzLxonPs1ky8LKW25Ii0rHs0+MEsqJMN2GJ9QdzrWdSFS14uUaOAwPG6cS/34Y6yqNU8Y2bc9VAq49YFLw4SZdEC61eP0vzRL9A3byD9vKchtPE3vNyItoytXeDiwPGYOk0veGeR7dmDzkVHxqTBdv1irR77hP3uo7Oyeq3QKmDiKo69gYdqEMT5bnwmVpWW2t0b3k9XddAjFUWngslRDricOrPour8PSUZWkWg0yUbyjIXNvzX2JilRYgMSA73HGCJmS2NVv95Ole5fXy8KnHFFoiwA6brMvfufqHz9ewhDPfj69AQH/NPFGAduXOfZ9UaTbFfTeSmVEonfG1i2rd6khBRNGmSoR1S4Rp03dHXOyvI/VqsuIy7qMoNOEyMGxfWgCmVvUy5h862RI4tyfXbCD+oq2m+tK/kgyko0pAUet1MYwo7JyDJparGfta77LlF8Uej0ilkMUmdRYYICi2jSj5OoVqp7I5Pt+C+d1wDlr9xM9T+/D00LWa0jq3XsR2aZ+cuPrffU+sLBpEoBKYX3o1b9lQGaFunYbas332efph6ZFeJgUqOw7F/3Dg7zE680jrDeUGnI3S/3Sqwc/75Juu9Z57jDnmc10nrDvkN/Fd4rHlFgniJzHe6j3s/EJLPkvEyiWHND1UIYQrlSB71+FXRXY+jCjTxuEKtAolNiA2UmqTLBEtMexcje9zIklgVQ+o//bOuLDYDj0vzpA9iPzWNs2bA+E4sJiyyLZAKfsHIfxX8Ye+WemzRpRDwmTbI0SWNgMzmRZ26hQYaKog3xVlHjoiDCXB2diJprWjSWq4JHZAkEY8RI6V3J+VypDo6fwTMYhICUrGKXZ5Eh9C0+WoVvnJiUP3br33Hm0vq7lIpVF4jNPBw2hn9+10vLzlIO2Gzr5GJwsKkukXt7KV+iLABZCxeWQhPdSmRsIXqaxwI3yJxyMGmSwVcovQNx/aBhk0IzM4BFnSJ1ighcJlr6W68EoxDQYW4LKf1AqksmijZFtO8/CpfYWrrVVktJ+JBSWRayvqSWKDGSHvph0OSFXs9HlUlcNCbY0//EYWMjVGW4tyjKexT6KgW2gUmDcp9CwH0FiRsKyP32CyHVrTe1iTzGIVvWYUajhY7FBHvIUCUtGmQpU2RPkFrbi0+pV5C7FxQ3afexYavEwCUTkTLqC2q3xz7DQpEo5mLt2yv43vn7OGAUacCtFlXUd9OqUNcK/vPgyt4iTLIcaA5z7/W9PqQXw9CCvhmd7trMAL3g92YkygKY+P2TMQ6YRmQ9v7SpIzIpNr73zYhx8aOsADlK0EKrIARouMFD3svX3I++uheqFIM4CkQriqbHE1TrI7S1VRBKWcrkWeyKHcTFegff46Kfout33LhdmxCqwG6JjT3rHvxPKkwuKwyheRlJGVUsKFWTpk74iSKqyE9GZlkZEZQ4+xoSNxSgT+Q56D/+msqu26h//8cYB2+heMaxGI/bvN5TWzGElxcVZtqbskGNopcGG5LJ5B09LFRrHhFQkasxCfz0y3QOGRxSCAyyIRk1rXMbtdDSW4Khoxh7b8tuGgRxr0t2fL+rcT/8RA7vDESz1fr/a5TZgIbFVCHF/JIdpPz6MGiSk0uB687x6o/65fPtbbQdwyJRFh60bJri6a+gePor1nsqI0XvxaN6u5qkyVBuC7ZKT6qvJMUvx1JXNz1CBH9ellhi2qvGDZnlEMImKqAdlbI5KoG2NymK1ZyrLQ18B1A/hH1XoSnYHduaXlZSSzJeF5Y3S7KUSVHHLUEBjRqFtiw3v+JaesdpnjXheAkjDbKkQzjH6nthzcQw2D9U4n4AgYtJA4OWlpcAnhkd5q9tBO1ORdcqC5RlUWSOSWbJsxiZRhsO6REUdswzLADqWz8jDsa2xkFcoEqBhkyDsffluA+KlabbrjQ2VGZDZBe6znGjkg9a95cenYiKVQiP3lxHwwl9vn34tUaqjqgenEsXLnmWAlZlDZss5bY0b5/W3H+f6uRpkgnm4BfXNRNl0R/XXXcd27dvZ/v27Xzwgx8E4N577+X000/n+OOP5x3veAe2rQTMww8/zNlnn80JJ5zAeeedR6Wi/OVLS0uce+65nHjiiZx99tnMzKhewM1mk4suuogTTzyR17zmNfziF79YyVT3aSiP/xw5lsizxARzbcVMVYpBpbf/Y2N69AjK/A7jl9KQ6F4/AIMmRRYiV/+dGFzwy9htYKF/YLkzuKuh+i7XmEBL7/utNFcSjLelgRNB5zIIHM8N2XneuOO6UuBKjTo5ltjIEtOU2MAS0xg0mWSWIntI90j99vmkonqp+IWEqY76Dx8C6S3A1F81iiyxkTJTLLKxZ2/wfQ1DK4tarcZll13G1VdfzXXXXcedd97J7bffzkUXXcQ73/lObrzxRqSUfOELXwDg0ksv5ayzzmL37t0861nP4sorrwTgIx/5CFu3buWGG27gzDPP5LLLLgPg6quvJpvNcsMNN3DxxRezc+fOEVzuvgedpiINDISj3+hlAX9FpAqDpqkwSY0CZabaivbCLAAIc9NI0iExhTAMvigVXoxDi1WUJlFCzZb9s7X8e5OlBICWzrO/vOCdsKSOJcOVsqpOFpTZQN0ruBwUvhUHKjmis1/FIApIKYiNNPC/L9WvJEOFtGcl9Mv40oWLJqKpO3xrWXS6S1v363ialXPKYH9zzAx9tY7j4LoutVoN27axbRvDMKjX6xx55JEAnHbaaezevRvLsrjjjjs4/vjj27YD3HzzzezYsQOAk08+mVtuuQXLsrj55ps55ZRTAHjBC17A/Pw8Dz/88EqudZ9EZ0WsD7+tausWmxRNsl054X6Aux+EILIZUvf541Okqzlo+K9hPNcVlJmizIau+UddS+A/Fxp2RyrlaqS9xh1zLVNubVJYZLAirr/KFEBsCzIMvoW48iwhr7ATlzwLTDDHBHsCRdEP/ZRUK3WHHdFETH2279dQxMHQAe5CocAFF1zAiSeeSCaT4YUvfCGmabJ583IG0ebNm3n00UeZn5+nUChgGEbbdoDHHnssOMYwDAqFAnv27Gnb7h/zm9/8hsc97nGx5rdxYzRX/ObNe48bot9c7cUKYb1YhCaYKmbRUv1rCKSbx56vQgyCNEO4TDELuoGem/ZW6d1zlVJiz833HQ/Uizw1YNGUEIJNG4sIIXCbJs7SI23jRZ1n44SB2yhjCrvrs1Ej7phrFRRX1lVL8aGZAUstNoRmoE8cwLSmI50mTtVhoBBVyzlMHDZvKuLUXNzKcDUIQsDmTZPqOVp4CJzRp6cKAZnCJPlMASkLOEtNpNVg2S4W6PkpNuWmgmOkdJGNCtK1EXoKkcqtenr9uMiroZXFT37yE770pS/xrW99i2KxyF/8xV9w2223de0nhEprC9seBS2C7TVqexjm5sq4bvd5N28uMjNTij3OeiLOXE10RczcmT3iSuYWLSDetWpMkmcpWFH6VNLdWSrePXUs7KVHqVLAxWDDhgKz8+2+4yzpWH23W8eNSwdhYbIwq+pHJtijnBRxjlv8DZoIp6WIe+72jLHBhH3UeV3PxaL3cIeMGrJZZ8ljWUVKsguzpGi00YgPMxdXCmZmShg0lQNpyGy26syvANWtMc4Y/QLnYfs3SgtUSv6BBUzM4B40yOBUDKiod0jgUGShzWp20SkztWrps2sprzRN9FxkD32Ft956K0cffTQbN24klUpx2mmn8b3vfY/Z2dlgn5mZGbZs2cL09DTlchnHcdq2A2zZsiU4xrZtyuUyU1NTbNmyJQh2dx6TYBmqreqyS8EPYMdrq+p6vbQdXAxKTAd+4iU2Bpkf/k+YeyhHmQIL2AsPUWRPm/uiRpFGxxhhiOMu6ITpBTgnmetSamHw569FtI9t3W/QufY7Jur4VmhIjAjfun+OsN9XCj/Am6YWKHZNyJ5xgF5ozbJbSUEngCnsLguwFzrjF3GSINoz/AQWGSoe6V9nO+Gc13Sp9Twajko73w8wtLJ4+tOfzu233061WkVKyU033cQLX/hC0uk0d911FwDXXnstxxxzDKZpsnXrVnbt2tW2HWDbtm1ce+21AOzatYutW7dimibbtm3juuuuA+DOO+8knU7HdkHtXxCUmaJKkaZM0SRDmam+6XxpKkwyR55FiuyhwHxAIy29DmU1iiyyyWtvGXH24OVUXctaA+sgqHtjLLGRGrkVV0m3nnMQgRYnDtI5dlxUybHEZPwDIs7p/x4V4B8knTVOkgAQVCWnqQytHFrn53r1CBCfKiZsriutgelU5FHnjc/pFJ0K3knVv69CyDAfUUx88pOf5Mtf/jKmafLsZz+bd7/73dx3331ccsklVCoVnvnMZ/L+97+fVCrFQw89xM6dO5mbm+Oggw7iwx/+MJOTkywsLLBz504efPBBisUil19+OY9//ONpNBq8613v4p577iGVSvG+972Pww8/PPbc9hc31DBQSbNLXS4mG9PLkupGkbmAObQXpBSUmQx9CdOUyVAb6uVfDQzqtogawyKFjUl2SIEb5xyDuMcGOUZ1mCswyczQ19953gZZL6VUMtFh+cWx2EYFdX0ZmuRJUyVNrWseZSYR+GwDvZSbq6zYCMt3kdVhexgnN9SKlMU4I1EW0SgwjxFi3kup0hXD/K+KjLkUSwBVKQZ9vVtRZE9ArzEO6Iw/DCssgaDqF9YmYD2quTteJUFU+vQwcKVgiU2ActPkWArqfhwMqhRVHAKXNNW+rsFhISVUKGKTAa+Pd5oaupBYUkeiYdIMYjRqsTRJeFq1ZJLZSGXRIOt16suMNH4xTsoiofvYDxGd1ioCV1QnLDLUkGRkpS3vPOzlUcSFpaCPt59KuZ59hzuD0aNwdbSOIVq2rQVWOncfOhIhrFjzjquUWndRAeANLFdfqGer6T0Tcep24iQjRM1tmc5D0CRHkxybNxWpzjwaWIL+82xIixwlr/d891U5aBgd745/3zKihpSKgbbCZNBKdl/C/lVVkgBQbpOoQrdeprhFmgZZLMyeVRS+D17Hoeh1SJtkRimiEQnTQQK+w2QtRY0zSAB8nNCryt3/vR/iZiRZoYJSI0zcNEPqG/z76/a4z7D8uSNFG2uwf3y1R3V1OsQdqtJ+W1Nn21FjoitG4x/n/6+SPpYix9ibkVgW+yEa5FRQTsq2gGot9OVSH2q4XhB8+ZjQYrmOFa+Uy13SdE9ZjCJWACo+otw/3S+mlP7MBSADevOVnNO/nhpZdNz9JrDZC60CU91z1SwoLurkMbHQ5LJ7UnrfWa+vSAiwZMqzAtT+KaqY0sJFo0muZw/6XnQ0ikyw++wOJktMK1eWtNGw0UNcaMLrHb6vNURKlMV+CJ/+I00Vw3u5GuQ6Hu5llk71F221DCvJQrKkTpMcBnVSQ1T5qlVjHoscJlVyIc1nhABH6pSZZoKZkFHCx41zXdmIrnpxxhwmvjBsTKIVnQp+JXGOtkwjrx4hl9KoNpS7UnrV+N6Z+4yoUWaSDGUMbFy0rrqKUCtY+lZwECmiSZ64fS1tTEzZ7LoHriIo8c8SxDkEEosUdfJe8F7F/qIqF9fT5bpaSJTFfgqJFjz0YcixhMnyyzTKR1/HwSLjre76KwvfSlBZKxo18kh0MpQjaSl8QaagVp5h+/Ta1s9y8vcPE2ytFlvr/OPCn/+og7/KZaMppl/Zvz6lF4QApMQigzGxgeZMCbUMWP5eVGZUNBeX8FyVvsWqhbAIRFmyK2F7bZBVwW3Z/l2ptF91IsVUu1xUmpINTJqUmEai0SCLLruTPqK6RO7tSJRFgg5Ir5t396or1tEDrF4t0mQ7CprCAtGwLGo0XHKU+7LatgoABx0hu6uAwwR/nSwaLhoWhnT7WgV9s8O8mQ9Dv15hgiJLiBCFMayVUKWAQwaBQ4FFNOmEVutHIWyfNBVgAxo2BRbb9knLGgKXWmjQWHUqbD13rzksZ55pNEiTZyHoN1GngI2JjuNVw/cW1mnPOuw8r5qvOlEn+4CvHFPUaZDDIo1FE1MuuyMlij59tMur8UCiLBIAKsUxS2nF5G9xsnSWjXwNixSm7HYe9IxrxFgRS7lccNZrLl3HoVOjQJoSRgdJ46DC1Bcu/VwqUSiw2PNzV8ZXRMuKU2UHKVfkBnQvhTXPUvyJtUAIMKRyxYRVMguhVuT1iCy7QRYlNXLYZDBotNW0GDjk5aJ3XctpsFUmItNYo1KFVbvh6Awtdb1Nj95fUGUCHQsDy1tidKeM7ytIlEUCQLYFr2F1U0D91baOFSos+q2U4wptIf3gfPyLUcVb1VD222EwqCUC/dN8fVQo4qAzyXyseSgFauMGsSkRxKl8pT3MNWu4WLO/xCT6+jTsLvqMQdH0XEQ+JX8rlt2lrWmwS5FFpn4QvRNxFiGdgXMHc58LZochSZ3dT6Bhk2OJAvNkKCNafP0pGl1uiFY3DoxaeXgvdEQ4chRCejkAqnopx52/LtxYleqridaUVv/vMAwjoDIskaHURTVfoxB0o4Pe6cGtaKs1GcZt2WPc1p8qeXxxFceSEsKzHiJiWk2yoSm7YfMIO3Z/RKIs9gOoLnfzqu2qsElTo8h8EITUerB6+u0j4yKOgll2DayeuS6E4inqdZbOuY6q0G2toFyGgwVSTVzS1Cmw0OY2kuhUKLZ1thtV7YUaf3BR06o0Uy0Li/iZRqLNFdkK1Q417dVz9H7GfYXlSJW5tS8Gr+MgcUPt85BkO2g6fF96hjJVJnEwcGV45k2VCWxSZFkk1eGmiMoE6iWEpVxemTVJkwlJe+17RTECuiqbSL3UUS6lvUEh9EKOpYHUbef1pmWNJhlcDAwaQdxipem1rVAWXnR2UNTwnc+rIS3PhWZQJ0dWVno+i97WHrUWghoT1HHQcRA45CLYYxfZgAhSavfyh2YFSCyLfRzCS+Tr2i6WO5lZpNtcELDsxvG7hJkh1kdQiBVkqYhge+eLHFTlepksPq1IlXysyujO87Yi6hjLo8p2YnQC3BsVhyZWPm/TW7H7C4pRuCLbvk9ED76l+FC9PlQgvUmWGvnAInCl1lbB7c+h1iNld3lcPege2EnJ71eBq9rzITpB7WNILIt9HL1M9tY6hDIbyFAmJZte9UO67WXr1SbVwiSVSiEalVBh07pNky4TXjC9FYMG1lv3szEwpN1WBlYnH/j0B23xOo7orAcYlcvMv2dRSQDDnscihYtOnRw6DhmvCZdFBgfTY1qK/70IZMAca5GmScqzV2xsDCwypKhjykZQZLrMCxXvDDWKNMl4sTSBjUGOEhpukGVVpYA1VMxCtlSG74UrExJlsR9AYJHGlI2u1X6j5aGXaNSYiKxNdjAwIlZXKWFBMzzlNswaaVUUK4kVlJnARcfAxqDU9gqqwKYqhYvbaW3c0ZAmAj9wOxqkqfVMFR0WvsWS7nii0tRxPadOL3TWkQAYHmOxLu3Afem7qDLUcNG8GhkXh6ZH5jfYnVrObPI7MLptz2xOlilhBuSY/dHJhKCKSn2rd29Coiz2A1QpksfFkFawQmqQGSiro0aBglwARrvCXYkQL7TUBnT74+u4EeR1YRgkqNt6TNxU2Kh7NghqFNFxMVkY+NioufqZX77bpX8coD/6jRG3cjyqmj7KHdqaxZaWdTRkBINsf+hYgaLoRJoaNeL1xc5Rwmwp7hO45GSJMvpel26bKIv9AoIKU57pr1qoDpqd4mBSYgMZKujSVrGQVepD0A9RgfVOZFtWzHFqN1oFZtT+/dxkvQTuSq2bCfYE6/GwlfegtSlhLsMoAT0sVpJYsMQGDGyP8n6w8wiBsqYjigH7QfNcRmHu0jBKktA54bYpilYoKvOpgee1nkiUxX4EiY6zgrQ/F4Oq1z40x1KXayvyvH0EcBz0EtJRY/ZSKP2oO+KMGTbOMEV4caG1KIfVEPT9rm2tIcGzDofHsMWANmYkm3E4BXs3REuso227oI1ld29Bkg2VYCg0Y/pcVcWrjiWHN7l9odX60w/9hPYoheAgmVzDop8ia1OMjGYucbPOVgsCDQcTGVJUGXcuw3ByAR7RZnvPeD9DMO6z76JHKhyB6rxXZA9mB63MuCJRFnshdCzyLDDBrGqRGpuYuROyJRCs/tawvTTB3i+ZSjc021NnIwRmlYmBTO7OMcc9OD2IElsttCrUzt4do0LU9fX7/vulRkcXb4LK1JvC9dKfpZ8u6/3dr6CuXyBaw8ak7lW0tw/UJIuN0ZYG3KuhUjdEqMIB0IRECIkuHHKUSA1Ae79eSNxQewF0LFLUAobNNPXgxdWwycvFll7D4dBwSFFDx8HGwEVTZGzeC6IYO+3gbxctKNiLmpMZQsYWGkTF6vnShr3s/fz8nT77UQjqYQK8/Vw3owoa98NqWk694FuOhmEgrWZkHKtOFhcdDYeMrMX67gS+K0qRHmpex3D1TArVfAgHgUtWlrvus4Peo9paBjT80juXagE7FfTjyLOo3okgOC0pyEWWmO7LauujQQ4XTV2zlyrceY+EgIyseBbL+K6MEmUx5jCpBZWlfppgmGDIygqlCGWhY1HwMmjUGM3gdx+d42pSdcazMdC8xi8NckGwMBMSdIxyj5iy7uXX6+jSGYlgk4ArdYgoOhwVBs1kao3N9FKCeys6lbS/+sdR1miU8M94K2cbgxo50l7wuZebaII5QLk8axQ6FhzSO1ZGxjV6dcpLUw1ILP3palIxL1eZRMduUxTtx9a8d0HQX7gLLLJBbcYEsxF7tdZhjCcSZTHGkNLr3RAj8KoEpr9GahtF0UIM4PP2/5YSTKFqKzRZI0U9aPyi0y30w69BVX9PMhu8CCu1Cvwgo8pYYWTKotd96cyW8ufR75h9GcJb+hvCBqmevH6JAYZUQniJTYAgRTWUuqP1mJSso2NTZoPajhus+sMCyP6xvajRW63z1mMUXX70AkQIRZOSpoZEUCc/UAq6qgWJaNg1xooCkpjFWEPazdgPUNR+UXQfcdCpYPwq2mHGUb502TVuXLT6xUERy5lCucKGRT8qi/Vy7YwzVppB5q/ksyyRohppV3Qeo1b66rvOUgpW/cr3H35u6YXIwxFt0Sy7ukKOku3Pc5Yy5gDxhrpHb9M5Zt3rjzHOSJTFGEMILXZjm9Z2kG2fjfABFGKZT6rh8ejEOWaQv8MQVqvQ+v+wQjyOoBvFMQm6kaJJlgo5KrGfCR216o/bMEkgI2MWFqnQ59dFx2+L2rlPVLr1IBXwNmmqFILAvK8oVDOl8Ubihhpn6KYKCnb4+Tsf8t4PmwhtbNNp6of9HRa89U16xbtT7/q86+wxlUGvY5IV/mjQ7z6vJYY5t+fEjLWvlIrVOKogr05BWaVe61x1bwTVlsrsKhOkqZKS9SCmEIZBLXcVw8jsdVxRibIYYwihGDvzLKBJ6UUkZKAclqtTez9sNYpoLKC3FAJZHtWAIe2AeE3gkvL6CQucUFqGuuefTdGgE/5L1yCLiyBLjbgv976COAJ5NTK54sxLOe0yKiNpRHxQo8ggiwtVUBp9cOu9V4H0aEoOicYSG0hRx5A2jlc/0Z7lJGiQp0EeWriiuuc1jBgVYx+j6ESiLMYcKm1wOuiV7BcpAbHT9wRu4Lv1BbqOQ5kNXSuvpmehBEFEaeO/oA0yZKihUwLC+X0kylUlvTPqa5BKGscqGnScYccQAlypVq7ZiP4IAJbUkBhIaOsTMkrl0So8LdItq2ZJmlrXSrnz+n0Mkg3mj7Ma16MYZntXT7cG0nMsUu1Z36PRJBezSklQI09Olrqe3zr5WCPs7UiUxV6B5V7Jw8BvktPq59ekS5oK9YjVl0SjzIYWKmlJgcXgeIjw4UIQfIzy8Y66EjiOmyquklK9L7QgNjMsmmR7KguVCqootC0apGU1YEwdJVxvVItUUFcAajUspMp0CzK9vEoGXbq4aNQoKOJCWUfDCnXohN175Y9XqVJxK6jjUJWIln9DP++IJ5nSAlxGFZq1yFBBkJEVdK/mqUZ+xb3F9xYkymIfh8ANTXNdTi3szZ7p80kVmI8U/FHKI+qllwiW2EiehVDa89VwzcRdHTfJ0CTHBDMqHXRAq8j3lUN4IrMPEwvp1a/YpIPeC5PMxLqeuPEHXbjouBiy1LafLpfvuz+Wi/SsVdcT88K7HxkKMecFysGiCj8lqRhNg9TqPEODAhnKpGV3Wiv491XgYCgyyxjPSIoaTc+NlEKlvFqzs+QxQmo3+sMmTXmgPhn7DhJlsY9jVIt4vcdLb0sNQ9fB6b8alxKv+56ylqJ6ZKxWMLbfOBkqZFt6JXQeG5b22Jkk0KAAqNV7VIc1v89DFgIKiUGtikFiA/0UPYAupaLmFiCkTZEFquQAHRHUU8eDKaxoGo8Od5eL8AS6qltI0YCWeJn/zCjBLqlSVEWm3j69FhcGTS+E7LbVVhhYFJmnxIaBFcb+ihXZZzfddBOnnXYaJ5xwAu973/sAuP3229mxYwfHHXccV1xxRbDvvffey+mnn87xxx/PO97xDmxbvUQPP/wwZ599NieccALnnXcelYp6UZeWljj33HM58cQTOfvss5mZib+ySdCK8LfI57rpDSU8DBo9aJ4FZabR89PR52mrkRCBj7cRs5hpJfGH1r/jWD1+q9Je5ywzRYlpSkwGpH3++epkSVHzXH9uZIaYJpbPlaNMjtJA/chHoTzDFEhnSnKOqtctLr47KU5as4uGJU1q5CmxseX5EgGra+t9FbhMMssks+RYokKROnkaMq3qwCOmZ2KTodpVhOf/vhqNn3QsMpTIUArSffcFDK0sHnzwQd797ndz5ZVX8tWvfpUf//jHfPvb3+biiy/myiuvZNeuXdxzzz18+9vfBuCiiy7ine98JzfeeCNSSr7whS8AcOmll3LWWWexe/dunvWsZ3HllVcC8JGPfIStW7dyww03cOaZZ3LZZZeN4HL3P0T5jIUgNLNj+TiHIvMUWCBHKVTwKfeBqu8QqVyk8rExcKROk3TbSk6iU6W4TNQm21edo6qhCPs7zjFh1+t6FpGLRoHFIBbk/2S8znMp0UCPaJ4Tdt6VXu9qYdC5xZ2/wMXwSGRarVZ/W+d5fVeqEKpjXoESTdLUmKDsBbHDaiJ6zV2Nu7LYVCfSVCiwQJo6aeoUWOwZu9qbMLSy+MY3vsFJJ53EgQceiGmaXHHFFWSzWQ499FAOOeQQDMNgx44d7N69m4ceeoh6vc6RRx4JwGmnncbu3buxLIs77riD448/vm07wM0338yOHTsAOPnkk7nllluwrNF+sfsDelkPinrADlUaBRZVuFMsV8lCu1D3+aIA/DRfV4rgR0rlYqmwgRLT1Lw2qK2wyLDIRupkaXjcVuMiMMMUjoZkkhkmmQ2lt+hcnY8T+lWsryW0QPDbFFggRZU0FdIR1lX3vZSBK09iUGJa9f32nr04UMp/dC4ozbNiOpVUKmC13bsx9J164IEHME2TN77xjczMzHDsscfylKc8hc2bNwf7bNmyhUcffZTHHnusbfvmzZt59NFHmZ+fp1AoYBhG23ag7RjDMCgUCuzZs4cDDjhg2CnvpxA0yJJuYfoEP33WpcCCF5BMUaEIngLRIoLirTBlE5N6QJLmYLLERi+TSHqxid7rEYMGeZYiOX7iYi1rFzpjFOuBYa4xjhtuNRAn/pQdwAXnj9HaQMhFDxpzpaiGjhd2z+ojrJw2eyThGjT3ujaqnRhaWTiOw5133snVV19NLpfjj/7oj8hmu33QQghkyFvVa3sUNC2+IbRxYyHys82b4/XPHRZSyp7XMQhGMVcpCziVPcj6En6OjvB5mjwBbdJkg1nDmDwQ16rjLC7Qz9cqBORFDWN681BzlY6NPT/bNo9hsVaCcL0Ebq95jDu03DTCTONadajNd30+3LUIMvk8+dzyMycdC2nVkeRwy1U6n9+282gGRmET06nRKQunJnEr4efN5zJM5IZ7l1dbXsXF0Mpi06ZNHH300UxPq8DmK1/5Snbv3o2uL7sZHnvsMbZs2cIBBxzA7OwyNe/MzAxbtmxhenqacrmM4zjouh5sB2WVzM7OcuCBB2LbNuVymampqdjzm5sr47rdAmjz5iIzM/17+g6D5s9+xZ73/yuNu3+OyKQonHIMU3/6WrTMcHnYo51rCtiIQJJjMWCTbYXbrDI7s4hEMEm8l1i6DrOzJTZvnog1Vw2HNJWgjkELKe4LKw7rNRflToASmxA4TISk+UYdF2Yl9MuwWQmi0m070S9Fd73QOlcHI4g3tH5XDdIYKGaAOnncqo7KZ7IUXV7M76YTba5QJJWKjVZ5FAcdkwYpGv5SKJTsu+YxxG7aWGB2rgyLDrRR7av0WjWOoEnWC7bHu/kCyUTE8zxfAVkZ/F1eTXnVCU0TPRfZQ8csjj32WG699VaWlpZwHIfvfOc7nHDCCdx333088MADOI7D9ddfzzHHHMPBBx9MOp3mrrvuAuDaa6/lmGOOwTRNtm7dyq5du9q2A2zbto1rr70WgF27drF161ZMc3zNOPvRPfzmD95L439/BlIiaw1KX7mZmb/46HpPrQWKgzY6RVN48QtB1WPHbM9kikI8q0DgUGBeBTWFiy6iA+wOOrY0g4yp1nmEuTU0YJI5JuheufaCIzVsaQTjjDreEBa8D4sd1MgHZJDDBOfXEnUyLLKZMhtYYpo6WYSZoU6OJTZSZ4Iy01SZwm1xvUQ3Ioo6T5Yaee8cOVypIaXwqGq0IIMsz1IQFFexEKUoXAR1stTJssQGj51AILQwihxJgQWyVDCEjSksciyRGSA4LdFCEzaqFGKzLYwzhrYsjjjiCN70pjdx1llnYVkWL3nJS3jd617Hk5/8ZP7kT/6ERqPBtm3bOOGEEwC4/PLLueSSS6hUKjzzmc/knHPOAeDd7343O3fu5OMf/zgHHXQQH/7whwG44IIL2LlzJ9u3b6dYLHL55ZeP4HJXD6XPfR3Z7FitNy0ad96L9cAjmIcetD4TC4FNCi2i8Ml/oS2ylDFUhz7p9qlojpZmBg0yVNFwAm6rOK6cKhNB8NEirWippRojqm2o71rrzKrqZZ2UmULzYjdh8Ff1K6H/CBuv9XM/jblOgRT1vYoxSKLToMDEZJFGnxXwoD77ZkuzrWWOJsizgOiTaaZiGjJoupSmQYWJyDmYXlZW53eTlnWv0VE8YW+RYYlU0OrYJtUj7XzvgpBhgYN9AGvthnr0vA9S/697uraLfJZNl72F3LbnDTzmas3VT4ttFdyq8MlAxwEETdJePYTaocC8anLTAUcKSmwMdUP5Xf76BZ473UFN0kGzexc9SD2M65byx2k9Juq8JTYALkUWI/dxvTabYS6zUcAvOqsw5fV5aKyLGyruPVPNTE2v8lwVWPrPqu9mNLE8gkrFsOqjyBxajJRiKWGRjXQ7P6TKRBvifkgJS2xk0+bJrmc1xxIp0U2O6UpBjULbNawl9gk3VIJ2pJ7xJEiFGGq2jfmkg9d+Qj0gvZ7GDTI4UseSqm7AwEYTEk24pKl5q20lQTobz4NfZxHVtEWqHt8xA8KWNGjKVFDBm6FKRtTIesqm1T00SO2C/3vUkkjg9sxiASixkYqXabNa6aeKg8tWPn60ocbudHfF2b9rHjHumYHjLSUWyeEnTbS7GTXhYgibHKUgHVbzeMbi12KMHqmIRkW97vm+YhmsFMldGBGKr/1thGm2P+Fpk8xRz8J8wvil+0p06hQpMU2TbNdLrAqW7MD9FPWSpVHtVt1GmdbYRS/+/zBUmQj6B6xG7KBT+CnXj6onyURcmx84h+W0yDAXUuv+w0LHpcg8E+zxeJIyWC2xlH4YheKKEyvprCEwaQYulzTVUDejqpJ2vZ9BvtCwmJbAxgxduMRBlDu1GWI5+GPae3nK66iQKIsRwdgyzYH/+i4yW58Juo7IZyme+dts/ps/We+p9YXPBxT+mXI9mSH7KIXikqGEU5phgrmg+Ch2O1hPIOdZUmSFQ6bQxhXaUgJCPfZah+DrhU5/to9BU2kjLZyWeeQ8egqfNyuOIOwU4lHnbv0ZhSIWQikJt77Us4PdJHMUWyzVOJCdKahe/XyNQkCzAoNdixsh8lwMKkwEjLm++1FVh+9NUaTVQ8KgNUKkDns8B3zy7es9jUj4fS1Us5blF8BFD0/p9Dhre44p/JEkmoC8XGSJjagVoIHRwQ4adh4NECK+YIxC7GNlPMI+FSSFCWYD4TSsr7ynK6yPEgobYxTB9lFASjCwccpzaMjIe9SW9hojNgJ4ylLioJOjFFgFvn0yzPU0yBLllbdJs0QKHbvl2U8UhY9EWewXkEEvA79SerkVqwpmZ6iAbA9400rqRrzXRiAxsLA9Rtl+ro21TBMddmytI8uqX8A+6ryxffUh90RKxe4rItfG3YhTOT0KqHFlqEKMyv7qtYDwx8j57L9+TMTbp3P50suSakWDTFsqb8TV7PWV1quFRFnsB8hSClwE/ouXkVWvKU4WPHM7Rynoc+BgUGUCX0XUyZOVlXjCUfGADj3ffkVqkedtU3Tx9h1kv/Wuf2iSwaQRdD3sB4lqwmTQ9LLcVo5+CqiXBRV2TC8rZKVV/QBNUh53cjpRAitEoiz2eciuVEzwAo+yFvA6uRheZzzXOyq83WpGVrtWeuFnFSvie3LQcdGxSKHhkpHLVNKjiB3sjcgOSKctWO6bMUqohk1mXz6nuNboasIiEzSW6gfV86KKSQMQNMjQ9AolEyTKYp9HL2EdxjbbK01Q9SvOotJiS229o1vhZ0jZGJgyOnjeC6o/n4NJs2uW40R/0Q/DzDXsmJXGKEaZ5uui0ySnspykDD2PH8taqyquqPsc35qQFJhvywrMygoGtmdhJ0iUxT4O6VF8dCoGvxBscAjUqitPqqM+wRcMRo9gdRzh2RUAj/BpDzJmnHmMYpwwH/2gGMW8+o25EpjUlbUaUqS4Vq669mdAQEdwXUqwMEl71lg/N1SKemj6uCkbaNhJNz0SZbHmkFLSvPd+7IdnST3jUMyDt6zyGQU18uRaejD7wWu/Y90wcDGoUiQnl6uroT+VxyiE56hX3a3w6y9En/06j1nJXPph3KwoXUh84bxaCMsg67wPS2yAgO/MIUsJQ6pMJhsDEwu8DKo0NRpkqUfkQhkR6eMSgZ4oCyBRFmsKZ6HEY3/0Iaz7HwFNQ9o2+Ve+gI3veTNCX72SF4sMZXTF0SQdbK+LxKDEbmHjLpJGx2YyJ7ArCyMJSq4VOleiDoIKUx65B119nmH8BPdaYxSxobjZUP45opSSjo1AJWOoOokNgGpCVAxhHk7LWmjxHSxXcHctRJAjqeAWXvV6Z9r63oREWawh5t71SZo/fxDs5cyU6k13Yj7jSUy+/oRVPbeDGVBWjBYq1VAY+lCKYj3jD52CT5cSHScIiJaYJkUNQ1roWD0IDNdgsivEWvJL9XKjtRbSQXQQPI5SylMKkijs4PkWPelbVPC6Gw2yXckAfmHeSiq4BS45lrwaEeUuq5MPOkzuTUgquNcIbqVG7b/uaVMUALLepPwf31inWfWC4vYvsocJZsl5bVajIMysio/0oWHorCIeN+RZYpIZ8swjcLHIBDTpYRhnRbEe97nVEvDP7Xirdj9OtsQ0i2xmkc0wJCW7EOrHb/lrYKlaIfqxB4R/JtEpM+nRoPvzNlZcwe0rCjVfNdcMFYwIpTXOSCyLNYJsRK923Gp9DWcSDxkqpFluxapaqM6zxIZQumYhBGWmKLAI0g1WfBYmpvQpQOiqvI0TBI/rthiF4PbHMKRyZah5i73UcdAbq2Vt+NXuPu26Ymz1v+j2E47KbSkEpGTdO18qUr1bPdJoHVIsMY2GGySGrGhOuKGxECEgLauxU3rHBYmyWCNoGyYwDpzGfvCx9g90jewxR67LnKKg8s1rXa4AKSVpqtQJb/PoYrDEtMcnJb3sE2V6CyR5FjBEu3UySLFd+1x67zMMoiqNfYE2zim7cRTqMNXnw8BfEEgpyVGiglgTwehfjkSnSoGcLLepqBqFGHG6/hQ38efjE5N0K8ToBmTji8QNtUYQQrDxr85FZNNgqodRpFNoU0Wmzjuj97G4pKiRoewxfK6uX0E1KuqWJL653xsqhuG0taP003fjYVwE8npXbA8CX7lFkQT6bpvWv1dzLq3nbKUxBxU3KDDPBHNDnyPMvWm1xBYssl7XvgJ1Ciyx0asRWjtEKR011+FaLa8nEstiDZF53tM46Jq/pvQf/4l9/yOkn/c0Cqcdiz4RncKq01SuHQ9pam3BvNWAixbqHlABv+FXXRYpNFkbuRUwjrGPXhjUKopjBYyz1SNQzMYOKVJU2/qchCm3uLU4rem1EtWkqBUSbc0VRDtUe+LW9HJ/rntjgDtRFmsM8+AtTP/ZWbH2lVKSZ6nrxTGkRYr6yF8ElfehqA5sTIyQ6uuVPeSrI9WHEZKjKsobxXn7YRhX3bhB4EBEQ6zAIhqAHkYFyw2k1Lw6/8xYNilS7Yl1MtTQpINFymvTOn5z7YdEWYwxpN2ITCtMydEqizQVr0nNMlwEWssyuEZ+RWRsvfodrDbCirvCUjpXG2t5/Z2FbeupUHRctAieKiHAlSqmkKKOKbpdnWHfVZWJ2L2x1xMOKSp7odupE4myGGMIER4cg37pgYNBwybjdahr295BWZ5FZXAM74qKuJYBA69x94H+wrJXhfCgcxlHV9BKlOAor6dzIdIJgSry1HBDLdq2fb3v1O/M52B4xXZ732p9b0Jyd8cZeipUKUgZ1gZSomF75v5giCpU6g6IyoBrZziES4B+gdfWWoEoAdYqFP3fHTTV+Sxk3M4gbL95RSHO3NcL/rUNIvBHXZsRZJT1mYv/nCt3kuj6PsOOS1MnLRpkqTDBnqGe/QTxkVgWYwwhBBUmKchFWlflFqm2fHGDBjlKgb/XwaDC5Mj9oqrKuV82VDsMmqSpoAc9mIc7b+vvUdk+nfvoHjFclOBbi+ygKAWy1tbIIOdYYiNpKqTprv+JM/dhFI0M/teoUSBHqec4Xd+dVPGQtWaI1bDJUsbAQqLRIOsVcY6ZiTkCJMpizOFgssg0KZoIXGzMtriBht0VBNelTYEFSh7RWj9YpPu6CcDPhgp7ZCQy5M02qSsltsLMl04Ms+IfxqUU91yDIo41sh7uLGWxppBoPfuyx1EGg8aC/IWOhtP2zAxyPlNGF76uBjScoK+4qsdxycgKGg61iFqkvRmJstgroEUSoKVCgoZCgCYddOxYAWkXg5rXCS9srFa05ofrWGQpKT6luVlypKlSAI+KL0s5MvMFliu61wKdCiNKILtSbbQxEKjMs9Z5rpUV0E9hrJZC8eNRUUVjg8aT4s9R7ZgKWbTE/e6kR1Ej0bzndPQ3SEVHKi3UN91My6qSPL9XZjz1QqIs9nLoOBEvjghtbhSFJjls0l78wiVDeD2ESQOLDAKHAosI0Vls5VBhIrK1qp/50iCPTtOjke6Y+xCCMO4xdZklm8/iVuahI4AvUTUmmsc06kIXR2g/oTUKy6kTcdw9cc7Rr75DCNU9MTNAd73ORIJh05j9grooqvB+lqKU6nvKUkYiyAJlpkZKLd5pKUdBItBwcPYxZbFvXc1+CJtUqJnuZ4kMAhedhqc0wiCEWlllKXVV5fqf69hMsMczz8MhvfP0IibsF2gNC2j3PQaoU8DITVFmCgcj2N/1+J8M4aAJlxQN0l472tbAbL9Vc69YyiAIU0Ktv7te3/RyRH+GTrQK9X6ZRv7PIO6mlULHIcdi7AVOq5XaeW2a8Oll2mN9K4MkQ9we9BJ3HxStiWWxl6NBRlEry+UuX3621KA56MIrb4rKKvFXb2lR77mK7LWyllLNOWzfsHF6CSxHap5/3Y61wm91obkeo6ju0acUOlaMcRTBWiFMAWnSVf02VjBGnGPiuH9aFemwMShdOhjCCY1rhcG3BKPcg/490nBGZl3E4XPyn7O9of5jUCTKYiispbe9HzRKbFCN5mUjyMjoxa4ZhhQ1sihagij5PGhWUtjfoKyTJjks0ugh9B9xfd4lpjFotmWC9ZqHKZsYzGHNL5LCUK62Fa48BxWOo6wcXyvl1SmYe6H1uRgkSaDTcuuVbOD/HdVfpG3ckbIGCMLejtYFTZNMF+3IvoJEWcSG9Gi76+C5eGoUVlTRPLqZaQFh2jBQLSmXg9FxZVDclWfnMT5leYMcGWpDFeX56YnCY7SNNV9AEy44TbIjqCZvdX8F5+gTWxgV1iJW4mORjYBgktmB5tYrm2qQGIuFiUQjJZfrgWIdjxjYFRsNQZ0sGVntus81Ci0JKOOyiBw9EmUREzmW2ugqDGwKUqWnrnd/XkUpXsWk07KI9+CaIfn0EF8RDC+YlFWUZxFN9jbxleBQK7smmaB/uD0AjcJwWTrd8xh01TzIvPqdt/P3qDFGa3WIGGzDg6H1+4x8rrylgE0Km1RAcBlW/xE2voobjO5GNMip80s/AUBQJ+cpin1XSfhIlEUMBL78kOchTW2dc6pd1W8YP2bhossSDazIvhODQMUF1Eutt8RFQL2QigF3ik0b81iz9w8xe4MSG4PAZp4FdOl0nafCBK7HMOSn5ho0PbbR5bTfUQryXkJ5kLE7r2Ul81kPFNkTOGBGJRJrZHFIY9Ag0+GKXHYzSUB63213Wncv+HEQgTPC+IHwLPg8AtdLjd33lYSPFYfsP/jBD7Jz504A7r33Xk4//XSOP/543vGOd2DbNgAPP/wwZ599NieccALnnXcelYr64peWljj33HM58cQTOfvss5mZmQGg2Wxy0UUXceKJJ/Ka17yGX/ziFyud5orQq7+DavSzfkhTb1EUCkL42+PRH/RqTFNhijLToTnjQhBkNAmhUWGiLUMlLDOpM8AdbEcFqytM4qKrlaEUXrBeFQ0WWaDIAllKTDJLniUKLHgNmbK4Hr1HgzR1Mn2zo/phNWIC6xUkXwl04aIJd8RiUbHFNsirgHDL9w3dVmDnzyBnGT2Ep4DCJ6LhkKZKmiraOsuHUWJFyuK73/0uX/nKV4K/L7roIt75zndy4403IqXkC1/4AgCXXnopZ511Frt37+ZZz3oWV155JQAf+chH2Lp1KzfccANnnnkml112GQBXX3012WyWG264gYsvvjhQRusFFz2yv8PofKLDwYiweCQitiJzMGiQ7RLydXJBkZYRknEEfoaIujfLK39/Dt7/Lb59lZ673L9D4JKlxASzTDBLmiolJimzgSoTVJggRQND2Agh0YVDironNKSnsCQZap5Qk6RpkPFcFXujcA7DMIKyFZ1KM44CDSuwayuqXIGls/w+CapMUmKDFwM0Rvqd9SLcFDhkKFNgnixLIxHsKaoU2UOGChkqFJknPaBVNK4YWlksLCxwxRVX8Ja3vAWAhx56iHq9zpFHHgnAaaedxu7du7EsizvuuIPjjz++bTvAzTffzI4dOwA4+eSTueWWW7Asi5tvvplTTjkFgBe84AXMz8/z8MMPD32RK4VExyId+nKsdxMTx1uFd0J4hWXtkJ67p/uAOgXKTAXcNiU2tAn1fiy3UkoKLKJ5Vo7Kd2/93J+v1jKWpMACKepoQnqCvk6RRVT9eSo0tz3s76iVaNj+nXPq/H0Q9LKiBhljEMQVpmF1CIOMEb4IUdXtllxZYkd3Yojwngt34PvRa/+o7umKqmOeNDUMYZOiQZF5rxPlcBA4Qb+O1p/MPmJhDL0sfte73sWFF17II488AsBjjz3G5s2bg883b97Mo48+yvz8PIVCAcMw2rZ3HmMYBoVCgT179oSO9Zvf/IbHPe5xsee3cWN0ZtDmzYP78qUs4FbncetLqqbBSGMUNrHRWN3ewv3mKu009sJDdCoAYZhsmNqAEAIpJU51D7LmFdIJDS2/ET0T/z44VRu3utB1Hi07webCBG6z6vmY29EpcFLYZHIOem4DbqOMU3K79tdx2ThhoKVyWLPxMnCGwSgC3msV4B6XcTWh+MiUmTHcGMIwKZqArCGMLNKqIps+zccAgwoNhFAKwXW6jhWazobpKUTIjZhM15HN5f39XQpaBWPDdOgx/eDUFnFDjAghYCoLen64GOIw8mo1MJSyuOaaazjooIM4+uij+fKXvwyolWUnfEEVtj0KmhZu7ERtj8LcXBnX7T735s1FZmZKA421DBPYqH61gPkmrGAl0g9x52pQ7GadtYvIWVU3kaFMupW+Q7o4pRmWSo2e8Yp26GRJk6IedDSzSFGtpaBWYmMRXClj5L5LrMoieyoGGcpkQhSMlJLyYokGDkU0dLE21NOdj+q+4sKC0dV3BMcMsPzvqpmwLaS9iBDg1pdbjg4yVymhJCe9TERVrd2ZsVWWRRzvHWjF5s1FnEYt9FmVjs3c7NJQvE4p6iqhO2TulWqDRnVwubMyeTUYNE30XGQPpSx27drFzMwMp556KouLi1SrVYQQzLasAmdmZtiyZQvT09OUy2Ucx0HX9WA7wJYtW5idneXAAw/Etm3K5TJTU1Ns2bKFmZkZDj300LaxEoTDJs0SqSAQ3579IdsVhQchICMrlD1lIbxaC9NTfhZpahRaXhpBjSJ18mjYKgjdch5hZuLXZ7QotVDBgAhiJQ3SZGV3Y6aVol81ctg+o6xdWGv0u9Zhx4lbWxN1fNS8OhVMa9dGieqSt5yyrqj8DSwMmri+27iHwPcz/KI/GxwWabIR8YlBi2THEUPFLK666iquv/56rrvuOs4//3xe8YpX8P73v590Os1dd90FwLXXXssxxxyDaZps3bqVXbt2tW0H2LZtG9deey2gFNDWrVsxTZNt27Zx3XXXAXDnnXeSTqcHckHtnxC4GF1pgr0K1pbpCyRFFoL0YCF8UsAFOl8o6R3ZmQkidK8Pcp84QCvthnqhuxvd4Pmu01RJjdBy6xdXiCqoiwrqrlcq6zhhNZWnLTVsaVBhghKbWGKaEhtYYhM2aXQsTOpePEDVY9Qp0CTb1zLwEzpa4WfeDZsOK9HVAqsrUSS/7rVYo8BIr+Dyyy/nkksuoVKp8MxnPpNzzjkHgHe/+93s3LmTj3/84xx00EF8+MMfBuCCCy5g586dbN++nWKxyOWXXw7AG97wBt71rnexfft2UqkUH/rQh0Y5zf0KvVZJfuaSSSM0/VaTLgZNz1UlPXfWckFUk7RXY6IOrFHAxgyKlmwM0nI5K0nRRoigoA4EZTaQpYThVXU7GGjY5Fh2H/QTSHHdFlUKuOgUjTpY4d0BW9G6wh02SJxgePiZdnlKlBE4pIK8uwILbdl+Fimv8dFy8oRabKjvuUnaS0YR3v5pMlRUCK/l+22ssK99kywWqeC81oraEI8XhAwLKuwDWJ2YxdpiNHOVTDIbKtgcKSixyYsddNNS+6uiBjnSVLr6dAfuASRC06i7aTQsTO8ldtCpkieFhYaDjUkTkxxlDGxcBHWKgTICyQTzipKj31W1rvBRIiCq+E1VARuYqN4UmghXMKNCLwUzqvPGra7vdYyPTrfPSs8bdp6ov+NCShHQjig2hUbXfa2T8zL4JHkW2tK9/TT3MlNs3jxBeebhIAW7dQy1z4bBJreK2LSpwGxI3GU1sCoxiwR7D3pRPmt9YgfqM7Uq6lQU4FsfHl2DdBX7Lcvj6NKhyBJLTCPR0bCZZD7YR0hJniWvKqKIhhOL58kv7KtTQOAwwXzo3KsUsUl5lR2K+2q1jQGlQEFKTUVeYvjoV0NxRX6fUrkQm2S8FXAdTbqRfdg7EdeyUr3PRZBK3XpMpzswlpICz8pNdSkKf4y0rNEg78Uu7K7z6tIOguBRY+jSJkW1pYHSelCNL1vx9twMeUxqFNbdlZUoiwRe7KBCJ825i9aXe6lf8FJKyLKIRY4M7Zkv/v9pWQ+aDkUGHT0Xlp+FpZTEXOT+QqiOZRaZ0EZOYUHUUaXBahIG7TfuymVFNmgNxCCWQZmptrhWkwwa8ZVFP7QqgV6U3k3SOJiqMC6EebgTcQgj/c97FaMuZ0xFB7gzVILvosLkQPxjo4Cf2eXfEwOLIgsssWFdqc8TZbGPo3dGyHKmU4kplQ0l/WyoVFs8YlgIAaZ0MCh1uYpake3RA1xZEikscrgIJlqsE//zsGN8q0rv02RplBhW6VikcDFIUVu21gZAnCwjUKtziywCV/FwrZAOY9mttJwmryw42fZ5J5RPv4GLoEmKlGwGi4Go+dteq1QHA122Ww6tiRO9Gg/5nzVJkw7hpOq0PvNykUU2sfo2qYKmSh5DFgOSFPW2Qtm1RqIs9nkILFKYstn1YtRbgnkSnSqTLK+22l8OCxNTWqEvV98Z9HH/xEm9TMmmRyFodx0TlX7pSrUKaxVCvc47Cuti2ONV1ld45lern39Yn78Pn57GVxSjsKZsNDLFTdSX5jA7amKixg9clUg02aRKHg0XB4MU9eBZ86+3NY27RoECi0hPqfpWp99HQlnKZWhRuv4+TS+Fte65q3TZu7JaIjBprlnqa5TyFkL1gx+N/TccEmWxH6BGEY1FdGkHgrNJmmZb5oefPVIH76VqzR5RvuL2oqe1zAYSQEo0BwrESgQ5FlXL1Jir9UF96aNCr3OF+fkHhX8/XAwE9sgUhfLzu2jpAhozkefuPKYTOSpeooLARaNCEVNanoDPtPnrHUyW2ECKOrq0cbxmv62WcpmpNup7NeYEdOyjY6NjY1LHFN2KQ/Rwja4GnAg3k5SK4Xk9kSiL/QA+m2uKKjo2NilPUSynGfqpiP6LnJFVDCwqTAIitLBvLdEa5+iXKusLp5QXxBzEYrBIY2MGXQP7YZjsoLguo6h94iqN1v1UEmr8BkbDIToO0MtlGHy3ag806ZCi4Vm64ZAdhJSdSFFvi5louKSpU2sTuKr5gIOJi4Yhl3q4v9YGLgY2JkaLFe/XHjVD2JrXEvteV/EEXdCwPSbMGilhkaXi9ShQL5NBs2ulKYQKrOmeNRGn//BaIcrt1Pq7/xO1fxQaZDssrv5wpPCyjMIRN4MoLgYZw99XQ3Futd6XUUDFk9JI1yHK2Tjo9QsBqsSzRJrKwDTjGnawuGn9SVEPnueOq8AmFZCFtv60sxisDSpM0iCDlL5Vb1LqSExYDySWxX6AHEsqm6RFeGrSJU2FOkUvoBa+IjSwcUjhYGCMiDmz30p7PWk1NJyBW+X2yuJaT/RSlqO8vxka2Ht+pZiOR/jd+QWgGapUKWC1KHGDJmmq6Dg4GNTJBd+b2aPqv5VV1qBOlgq6cHGlapvaZMLbp9v9tZZQFo+NoQsc21hzhRWGRFnsBTC8WmmBi0WKJhlMmujYOOieeRr+MCmWpW7/tAoaN6hTDJoNhb3kbhBUzFOQi0MFuFuxTJshvAK56FTZ1rmOAnFcRhmv+HAQ9HKxrBZGIZRHG9CPQyIZbx5h88rJMkse35NBnTylZWUomxRoUmYKB7MPt5P6zKDZNoYmJFlZpU5uJB0mVwK/xkIIwIE0FiYNSkyzVllZYVh/dZWgJ1JUvbzrJoawyVBlgj1kKZMWdTJUmGBPpKneLzMdojmaJCLIAonTgzkqhbWTX2mRaRbZRJ18LAE7qn0AbKmDFu2D1oUbsNzGGTOs3qHXvEalUNbanbUSdD5XYfNYXkREz8ukgYZF1iuwbD1eCII4U6/MJT8bKqpPSoYa62klCpxlRdEyLw2XFN0sC2uJRFmMMaR025qpQPf/mlAZG1miaEE0bMxQoeW3N+1sZ6oK8nTKTOErlCjm2jhZOooAxKRG3stZV77XJplQJdXpax40ANwLhnDADXdThPnWw5TdIPPw71Fry9D9DXFiFi6CpjRVc6yI+5SlTJGFgHmgE34xnkSjSrHru6tSDPz+0XGQ/sV/qwkDO9Qy8uM464nEDTXGkHZ7oZKPMIGkiPhU8mEnqhQpsOBRPKuxbMy2Ln8OJiU2BEHvQdhrK0wwkTdwyntCP3fRW0x7VVykqKRVimSWat9891FgmECrK5eVWqZjZTeIG0cpY5hgYVUqx/cmhLmdNI/Bq9dz1u8+tRbjWWRYJNVCud9O3eF69DNdcws69g0H1Sa4HFTEq+LWQuzgtIsWeg8Ub1US4E4QASHCH5xBIdEpMY1BMyh8UsVZnS9FZy+MZXSm8/lQ7U/TIKOLiZYVgeutDJ1gxZ2mRoXJgG22wOKKrnWUUFXgkBH1nimfcaCCr+7Ax+0vEGJ5QRJmxfVLglCWcmeLYw0rIt20Tp58SAyu3lJbNDgkBebb+LBM2cRgniWPBLEfHAylyGR3nHHQLL1RI1EWYwxhpCKpDTr/tjwqhB6jDdAVrxtN0l1xC3Xe/mP6s0pTCxQFLF9DXqrCObxV3TAKMqzOYpjge1QNRFg8YhBkKIe6PlYjeL/WcKXqZOm4dBEHhqFfnYl/r2vk0TwfftgYy9+Hn8kUvw7BJkWFSbKy7DUN06iTW1Etg0EjlDgRKTGpt2VzRUNQZpI8S957L3A9F1pCJJigJypMBA+OL0hdtKAyFZRJXQvJ4BA4Xhuh7mZF6nMXHRsXre+DGBWzSMu66gGghweNVeWpgYbTRQndCl0oF1ngY26ZcVwh6DPS2+iYLYK5V7Fcr/P0+3sQZERtpDGLMOUY9fkoztFrnzo5JjceyOLMAkXm6aTZGHYeFhmv5123svCfqxoTkc93P9ikvAyj0SAVQcbhZx7GUxbKE1BmAwKHjVM5ZucVq8J6I1EWYw7/wdE8f66DgU6TrFesJNGokW/LwxY4SsF4PlnXC/g5QSWqovbIUA0UkINBhcnIfO5IzhpcJpnD9eLrYcIhTT30hQ/GCMkoUvQNAolGVpbbPosSPsLbx5TdBYZRqFFQribKQ72OceIPveodosZs3b+Xsus8R+vfK42NRJ2zLfiPCNwjyt25gTQVj5By+OoA14tiRE1ZrdjV8ydWGGcYFXrNYZh1gkRHGClYV0aoZSTZUHsJXAwcTAwsCixhCAdNqFTPPEuYgTBup+4Q3j4FFhGewDdpBP0pNKFWgTo2OZZ6nD8aYf7kVr/zIFlNwRgIGuRpkqXEBizSOFLHJTyrKCxjLA5cNJqkRyJqRuUG6rxXUQoo7n0ddRZW63fbWeNjeqxjgsGES2vqrHJBKcbjXlxJhhfjmmDOo79f33SzKJeslKzIvTUuSCyLvQyZjhxz8PLDZQWLDAZWpN/YpziOcikZ0kLghAa5RQTvT5hVUGYCiUaRhZEIUBfDa5mpVpJF5tSqcgSuDg17Rb7gfoHX9ULrPEY5ny6rUdZaanGaQQ2Ev1tcRSUBRxo46DTIxfpO1FxkMA+l+JeD3Ir5qYEgqr2p62XmWR4ZYWZFlBo2KRx09BbLVqWhayuKF44LEmWxlyHKHeT3K9ZwQxNohQDNy1jq1T1PxTC64xyDBJ018KIl/TGocJVoLX277Yhk4fjjuhiR92wYxMnaad1vtZTLWiqsDBXcuhFZ6BYHvru1E70ouzv/zshqoCxS1NrIIDNUqJEPPhc4FJkPaHCkbJKhRpnJgeleWmZBmQ2KvVnWEajEkJVlWI0PEmWxl8FFQw8R9r6/1MaIFJ5+zMIijRbRnSzvuaLcoFBv8EfEQe/ZgCbealNlkNiYXas9k0bAU9XrFYxDGaLUq7Zqr3JYFlWYNdYv2201hH+r4oobzI6ySJ3yHHrEgqJf7KSXm2aQ2oKgcRJOV5U3QFZWAgZZ1Q2vnS8NJDlZWmHQW7lP17NJ0WohiVnsZWiQi6jGVpTjLgYWqa6qaBc9oDpokEO2VMr6fuJWH7iGS4EFhvcDa6HVuGquaZaYptSDgjrjrQwn2EOGUjAP5SyodvnsVZW0+lEZOnkW2UiJaSyvx3gYlGUxvK87rpulX2yhM87TT9FF7TvoPAdxVfUOeMtQxdi6n5/B1Dl3B8N7frvhU3Z3Ps9RSFElQyXyc79Iz6QZej2qD/z4MCyPExLLYi+DWoFJMrLqraRUjnlrQVKVCVLUSMs6ILE6GhlJNEpsIEUtyFrpNPeX88MbkYVNUdBxcDGosEEVKbWk+doYXjW3iEw19M8vAn90HcejkE71yKpSSlDHCqwRlY4bnVOD9/nKAqP9BHuczKzONNhBM6eiPuslwEdhrfRzO3VejyFtbEwsTDQpsT0qGJNGkMbdGTSvMEmGiufa6f1dZXsoio6Z9fhk73cZrQYSZbHXQdAkR5OsooT2itnC9+msaF2GRAvM5Qxl9AiSMvUCN2mQIS3b6ySiXBe+60AppWkv6O54leOt/uDwFzZMAKWCQGp0xzsHwwskKq6sVsUSdYyOs+7CIW6ab9Qx/fYZVimMwv0V5baqk8P2HFfFlqpnVypK8jKTXkqF8OhiCtS9tqkZSn2fxSgFaXsir/dljSqCtW8hURZ7LUaXW25jko5QFmlqqpgupBgQuv3RNkZHnEO0dRrTPdZQHXug9XxABUG0EMtRAkq4iKDRTz/IFVoW45D5FBeD1l2sZqZXJ62LP64mQEpJkYXgs874WZ0CApeUbHYdH/W3DwMLKXu7mTTcnjG3/RWJskjgpfy104q01Uh4wlSTLjXy+KuusCCicmeFr8wU99NyOq2gtxvFRyutSJSvGZZ7Y8Tttw147qrBOrHtregXWA9Dv++nM0jeeZ44c+n3mSZV/MznV9Jwgn7wvdxsYchQxZ67v+c+q6MoXM8TMFy1+TggUZ8JUCl/U6rbmNSxvRahYas1E4smWYyQeIOvWPQIKuWwwGOc4K2L7lWlL/Su6O0xZjTEigLco8Coi+aGOXevOfQLzjtoiHSBGrk2yvnW77FzvEHgP1d+l7sspa5MpkESDaKOUYsSk9GKRUmWJSaZY4I9TDDXUkC7dyGxLBJ4WE7507HIsxjqnvEzRcK67/lQHfy6i5D8qvI4sDBBajjopKmSbmGqHaU7RHXFW73sl2HcPYOOO+z9GCTwHrWPECAkGMUtNOslLLKkqWJIv4p7dFabgYVNCiPkOWq1lKIsnKiFiQ8Lk2qPDL1hkGOpzRoWXnpuBa3NPbs3IFEW+ynaYweCBrkg/dbBCFUUKiZhYtDsw4MTnhvvYKDJbjdS5wrPxqDKFAAF5gPOJxidkvAx6u5jnYJ7tWIaa3GOuJAtz4pib1WB6BTVAbKT+iOOe0hZospa7KdEhQBHCkps9LeMZqLBaG6o21QISMvqXqcsEjfUfgg/dmAItULThPR6T/svtlANW0Jy+dPUyLGksogiTP8cJSaZIcdi28qyu99AuPugdb9BrJFBEVarMUjtQtSY/dBZE7I3Q80/7KJlT66ksN/7QaVwi646otZxhPAZjOPFY5yA2n/0D1mveo1RWlxrhURZ7IeIih2ojCj1gCuWn0ksUtjSWE45FMvkgxD+4vvC16TpWQZqTAeTChNBi9HO/f2fPEtoWOgx+n5HYRRCeDUFeY28RwOxdhhWSPeCED7VjIKORYE9TDLLBPOq5qVFMfrV2rY0cGX/Kn//mFZm5RpFRasf8hy1zitqzNbfV/M76OaiWj63PTSlyPphRcriYx/7GNu3b2f79u186EMfAuD2229nx44dHHfccVxxxRXBvvfeey+nn346xx9/PO94xzuwbUXX8PDDD3P22WdzwgkncN5551GpKEG2tLTEueeey4knnsjZZ5/NzMzMSqaaoAXRq3XR9uI7pKgySZkNoTGKzsBn6N8ebYcaXbWcbA1ORqHIgldBPhyG9eF3urs6K6tXCilVrphFbgUcROHj9vob4rmuVnK9yxar491LiYmFRYo6BWoUWGIjNYqU2cASG4OK7q55AA2ZpuGxDrfWDPn1O1UmqFFQdC0xv2/Xs4RsTMpMrXJDIUGdfIiSEnslHcjQyuL222/n1ltv5Stf+QrXXnstP/rRj7j++uu5+OKLufLKK9m1axf33HMP3/72twG46KKLeOc738mNN96IlJIvfOELAFx66aWcddZZ7N69m2c961lceeWVAHzkIx9h69at3HDDDZx55plcdtllI7jcBKAoFMKFgYz0Cw9biyCEopIWuKSptjHi9sqy6XQPrQfC3FT90E9oKwUKaUoYEVkxncI6VJjKjp+ObSsJmg96vU3P954NoQn3LUyLtNejpP35qjIRzL31uipMUWOCOoUIga7qd5pkcXrQuXSiQR5z05OoMDVSZR19vhxVithSx5XKhVZiKtLqGGcMrSw2b97Mzp07SaVSmKbJYYcdxv3338+hhx7KIYccgmEY7Nixg927d/PQQw9Rr9c58sgjATjttNPYvXs3lmVxxx13cPzxx7dtB7j55pvZsWMHACeffDK33HILljW8WyLBMsJMb989EPVIdPLzxIWUivhvgrlQavQw9EqnHdUqf5gxOgVo1Oo9jtDOUCfTo7MagC11QESO0SBNlQIVJgKP+0riO3EL2zrhLzCE20CEHGTbNr956Ff89Gf/h+y4aaph0kbq5LCk6VkS0wMJ8rixMCDgR1tLWGQoM80Sm6gOSc45DhhaWTzlKU8JhP/999/Prl27EEKwefPmYJ8tW7bw6KOP8thjj7Vt37x5M48++ijz8/MUCgUMw2jbDrQdYxgGhUKBPXv2DDvdBC1QsYNJHKl7Qk1QJ0fNy2IJg/qsdw591Iq4dbU6DDpX+MOin6IZlRLqN9e41+MXOEbNy8TyCBeXulxoa2WNKctBuZTv/el9gXu5FY7j8omP/xMnbT+TFxz1Sv73f3/Y9rlPPVNhyrMkBlt129Lg6i/fzOzcAtVqnUajyV13/4Rqrc5SqcLSUoVarcF9jzVW1K9if8eKVdzPf/5z3vzmN/O2t70NwzC477772j4XQnStJvptj4KmxddtGzdGC77Nm7v7VY8rVneuG4PvIBVDCkungFNbRNoNND2FMFK41flAMgnDQOgmsllVf69B2uiwx0XVDHi/0elOGXyMlSHOecKo6kc9jziYefQxvnj1Lm69+Wau+tg7g8UfQKVa44vXfZNv3Pw9qtUa1WqN33nt73PfL77P1JSqaXBdl2/dfBt33/0jnvykQzniyMN55zs/yK4bvkkmk+ZNbzybd1x8Aen0slWwsLDIR//un/mf//khGzdu4JovfpXz6g0OOfgAFhZLLC6VyWRSvPwlz0fTBDff+t/k8jl++X/fBxIZMAxWpCzuuusuzj//fC6++GK2b9/O97//fWZnZ4PPH3vsMbZs2cIBBxzQtn1mZoYtW7YwPT1NuVzGcRx0XQ+2g7JKZmdnOfDAA7Ftm3K5zNTUVOy5zc2Vcd3uF37z5iIzM6XhL3oNMZ5zTasfS2W+5EB1SxbQtAU1O4skh8Blgj19s1J8dGZXDSrsOovUosbvN0bdY67NUlr3+oVRYxSFfN1jSiqVCpe88wNsmp7gxDP/lL997wU89zlPY3GpzD986ot89B8/T7W2HJ9xHIdPX3UNb3j971KpVDj9zP+P//u/X9JsWqRMk1pd7eu6LktLJT58xT/yX9/7Hz5z9ScBuOeeH3Pi9t8JtWIeePCR4Pd6vcnub353+e9Gk09fdQ1//NZzxvC9CsdaygBNEz0X2UO7oR555BHe+ta3cvnll7N9+3YAjjjiCO677z4eeOABHMfh+uuv55hjjuHggw8mnU5z1113AXDttddyzDHHYJomW7duZdeuXW3bAbZt28a1114LwK5du9i6dSumufelm40LdJrkWaDIHDmW0Oh+0dqhmFsnmWGSGYrMttF4aDiq17dYDlibNMmziAqHa5GFe7bXJrXMFEtM0yQduMOamAFlRFSMItT9hTpeSrwg4gYqFD0uq/iwSI1lv+RRpL2uRiGfEIJiIYdlWTzy6Bx3/+jnHHPymyk+4eU8/lkn88GP/huW1f6s1Wo1ZmbU4vHyv/0YP/nJz6hUqliWRaVaxXVdXHfZaqrXG9x++/f5yU9+BsDrzv7DUEXRD67rcutt3+2/Y4JQDG1ZfOpTn6LRaPCBD3wg2Pba176WD3zgA/zJn/wJjUaDbdu2ccIJJwBw+eWXc8kll1CpVHjmM5/JOeecA8C73/1udu7cycc//nEOOuggPvzhDwNwwQUXsHPnTrZv306xWOTyyy9fyXXu1zCpk2tZKWuygUmDEhsig215FtpoFTQpKbBI2csiCat8FgJ0aXt0HwZ18mRlO9mgWr0X2gKYNSaC0RQh9Xxb1zVJuxNeBv8ouGiUmWrxR0vS1EhTHTiLq7iCdF3oXrGPagW/GoJ+VHOdm18Mfm82VRKKoeu4UnYJfv88uVyO6792I5//j6/QaIRzibXC0HV+fO9POeSQxzM7Ozf4JP35NZIkmWEhZFjgYB9A4obyIZlgLmBkDbZ6K/AwLhyBE+pCUhWvOmWmybOAKbpfPClVOuQyS2yDDBU0XGyvL0GvTJcCe7pqOnz3kECR1mU7WmKqa0lTZQKANJWgm17rGP0E46gE+6Bodb2t1xxa59IaIO9fBS05f+fl/PPV/2+g86RSJqlUmnK53H9nwDRNtmzZhG3ZPPrYcDVXQggue98l/OVFb9nPZEA89HND7Z05XAliQ0T0axBCNaEJgxHBGisEaNJBx8LGwJBWqDBxgsdKYtDwqA2E1yBTJXwaWB6PUM5TLIp6OoqgMEUdmxQpml1FfUKAKRtMMON1xesu+ouTFrqSOMlK4QKuNDGGrFof5VzqMoNEpfd2nsOHECr2cPeP/49/+dzX2vYTQpDNZKjV66FJLKAsEN8KiQPbtnnooUf679gDuVyW17z65BWNMc4olcp861vfwXEdXr7tpWzYMDXS8RNlsY+jN+FfeMiqF02BgLbK6s6gqUU6OD5LmRQN73MlNNJSOZzUNoecLGFR9yIc4dk9Skm5pEW9p1DUvPPsjbayBmgroDcZmWsKqFMkHUEJ01oAeNMtd/KHF/41tt3Zkldw0zf/H7//xrdy770/i3VeTdNwXZdsNkO93uiuxxjyS02ZJoZpMjk5wT994iNBBpaPBx/8NT++92c84ZCDecYznjbUOcYBN+z+T976x3+Brqt3z7ZtPvTB93DmGaeO7ByJstjnIWiSUf2LQ1w7YXBIBXGCXivy1sCypuvUnbTHXAsgVYe9GCt8s8VCiZIJrTUEvT7vtc+oMcrzrJf7q/P7dIEJZnrOSfO2H/akx4emuruuy6GHHsIZZ5zKe9/7N7HmcsABWzj9tB08/PBv+Or1N3QFxcOwadNG8rkcj/zmN5FWyj998qM84QmP52lPe0rbXB3H4YI/3cn1X/s6qZSJbdscfvgz+MzVn2RiYjxSVeNidm4Pf/TWv6Beb7cE//Iv38WLjno+hxzy+JGcJyES3A9QI4+DX4C3HK+IYgX1j4F2GoYwQS+AJaYxp5/gVdKqnQYJLK+FoB8na6Mz02u9YhRh59VRykDQPwX5/J1/y8zMfOg+37zpFo56wfN71k35SKdTnPW6M3jHxX/OwsJiLEUBcPDBB/GMZzyViYmJ0M+FEBz6xCfw9Kc/tWsen/jkVey64Rs0Gg1KpTK1Wp0f/OBH/PlfXBLr3OOEXbu+HnqfHdfl2ut2jew8iWWxHyBDtSsWYMpmwNnTDamCyLGFeLdUkV60pFNprJVw9K0e/1SrqYSGqV3oZ0mtB8K+76jrazSafPv2/8Zxu12HUsJ5f/RnNJvNSPeRX5Sby2Y54IAt/OGbzqFcLvOzn/1f7Pn+8Ic/5u6774lUSLlctmu17ePTV32GWq39s2azyde/fhO1Wp1sdvzSp6NQrzdwnG7Kc8dxIq9/GCTKYp+HDOVkEgLSshKqLHTsAdn9w/ZWPTFystRTMA6jPAY9xk+1HZciu35KeLXSb4eF+t5U4oAPXdd42YuO5Fu33hV6TKnUO8vp4IMP4vBnPp1jj30Zum7wsm0nMT+/MFD9hJ+SG6WQdF3n8Gc+PfSzSqUaul0iaTQae5WyeOUrjuH9H/hw1/Z0Os2rXnXsyM6TuKH2cfRyB2mRdBHxJJNKpVWh2TD4WVFRRXWjKC7rtY8mlv/vxKhW9KtT6Nb777XGIhtZZGNbf23DMPjSv36Qz3ziPbFcTZ14xtOfyr9cdSUHHLCFd737MmZmZocqtOuFqampNuqRVhz78pei693P7aGHPqErCD7uOOywJ3HuH/4e2WwGIQRCCHK5LGecfgpHHvHskZ0nsSz2cfRyBwFMMoOLRoMszaCtqh66Gu8UsC5Q9tqfhiHHEtBNcNcvWL0aGDcBvFKsBnVHNLx05I6U5Gw2zXGvOIoTf/todn3j9oFGPOf/ex0AH/qbj3a5g0aFxx6b4Ze/vJ8nP/mJXZ/95V/+KV/72tdxnOUFk6YJLv2rt6/KXFYbb995Ia/67ZfzpS//P2zb4TWv3s7RR79wpOdIlMU+D9FVSe0Lfb9QT8clK1Whm2rKouqo9RBKkAoFBKonhrIcwqWUGiG8YdLejDhCurMeYZgx+mEts79UqnS4KVbI5/idV79qIGWhaYJv33wr9/zwXu6771exjtm0aSOLi0tomkDXDZrNZl9LxDB0lpbCC9q+9rUb0XSN1mxlXdf57Gev4RXHviz2tYwTtm59Llu3PnfVxk+UxX4Av+lMWlYC11OnpSEEZGSVhkcCGNVNL0UztOq7HXLIVkne0WNU0dyJOPEGB80rIKyHR3PWIc132NgQgCHstr9bMb+wxI03Dca35LqSf/23z7Wt6juRTqfYtGkT+VyWP/7jcznj9FO8rKUamUyGs17/h/zkJz9D03RqtRrSoxZphRAisnbi01d9hnq9vZ+IZdl8/Rvf2usC3GuFRFnsJ7BIB8HsYgj9h4KqohbIUNeVX8EdBdU2tYTZUgHeL1jbFc9Ape3quGjSaRurF8JYZwel+xgGYePouOgRXfBGcY64c19pXKafVfjzX/yKbSe/hcVSdwFfP/RKj81k0rz30ot5/et/t237xEQxqIH42lf/gx/88Efcf9+vePwhB/Pmt/wpjzzyaJAVZJomp+w4kTN+5/9DSpffPfM0Xvva0wIy0mo1PMDNXhjgXiskymI/hIuBJpshAkd6rLHhgXEpFWNs6/4mTQwaWKU6BY8DqlNo9xNaikBEKTJV1Ldcy5xnHkNG9QxfHr9ztS6l6rcclTq7miv6USilVoXnAtqAgl9xdBWwyJCi7rU8XTlar+cP//SvmV8cPW+RlJJnPeuZbdv+9+4f8tnPXsPiYontJx3HSSe9iuc8+3Ce8+zDmZmZpVart1kWtm3zhS98BctzVf34xz/jazd8nc995p8BOPbYl/H//t+uLutmLQLcDz/8Gz7+j5/ijjv+m8MOezJ/dN4bOfzw8KytcUJCJDjGGH6uEgOLFDUEEouM105SveU6FgUWQlf4/VbnSgBl8RWLQXv1dadg7Hy6olw3/m4CqDCB7SmPHIuYNHtaI73GHUdX1qCIM//2eyKokaMZVOi7TLIHEWpNDnfeWq3B5qccF1pnMQo84+lP5aZvKnLCT3/633nfX19Oo9HEdV1yuRzPfe6z+fxnP4VhGFz63g/xyU9eFfq+tyKfy/Fv//pxTj31t/nf//0Zx51wGpVKlUajgWkamKbJ5z/7KV7wguetyjUB3HffA5xw0hnUajUsy0bTNNLpFJ/657/n2Jd3x0oSIsEEq4oMlbbaCkMqTlbpdZlwMKiRJy1rbemz/XzpQkBKNrDIYtJoUxS9jukHvxLcR14utbnBwqyCMLdT1LlHpTDWS+nEvYe+rFQxk1ZmX4GDhi6dnop9EGiaQGie2bMK+MlPf85Tn76VVCrFwsJCmwVQrVb5n//5AV/72tc59dSTuP763X0VBUC1VuP2736fU0/9bQ4++CBu+fYurrrq37n11v/iGc94Gm958+9z6KGHdB23sLBIrVbnwAO3DJUm3IrL/vpvKZcrgRXkui61Wp2/fNu7+f5/fXPF468mkjqLfQwCp6sITwjVrMgQLpqQmMIiS4UqEyyyiQaZ+JXHngA3A4LA1YEmunmp2uYR4nbqhWHnutKakLWCT/KoCTBwKLJAnnkmmGWCua7MtLgKKArpdIpXHvOCVRNuUkpKpTJzc3tCA+HVao2vXr8bgHI5Kv7QNSpTk5PB+J/9zDV8/B8/zT0/upfPff6L/N3ff4JmczlGNju3h9e+7o0c8dyXcvRLjuOoo3+b22//3oqu67bbv9cViAeV5rtnfmFFY682EmWxj0FRf3cj7J3OUEGFpY1YwlBKPHcW0FKg1blP2O9h+/jcSL3mGkcRdB4zSqxFTUice9YPQnTP1cBGEzIimWHluHTnuWhatAgxTRPTNFfl3gkhKBaVy2TDVDg3VCekhIXFRb7+jW/zr//6Wa746MepVmueK6rJl79yPZe+50PevpLffe3vc9vt36PZtGg0Gjz44EO8/py38MADDw497+h4iCCXzUZ8Nh5IlMU+Bul1dOgHIQjqKCzSXQqmlejO/9vB8Ar3oBHRejTO6tVFoylTirtphSvctcRKrJPO+xk17iirwEcxbi9F9q73fwIpw/1Qf/imc/ij896IJsIXFStFJpPmDV621LZtL4193Ef/7hO87qw3c/El76NWa+/2WK/X+eznvkiz2eTuu+/h/vsf7KrlsG2LT1/170PP+83n/h7ZDqWQTqfYcfLxY5+BlSiLfQy2Ty8eA6739av9u19qicpOaso0VYpetbYa2yFFnZyXdRR+fBiUu0TVcUjW1r3TbwW/WnNxUVlkq+TeX1VEKZxGo8lN37kzMlbwlKccxmMzMyuqt+lENpuhUMiTTqf4y4su4HnPOwKAX/7yvthj2LbN4mIpkk/KdR3K5Qq/fuhh9BCrybJs7rvvgeEuAPj/znkdrz/7TNLpNBPFIul0mpe+9EV86IOXDj3mWiEJcO9zEFSYJC8XW1RGd6BYSqh7NOQpGqHd55DgYFIPXE8Sg6bXY1unQY4mGQwsChmBrC31n503ribcnm6qwbJ/WsbucfygtQn9MqzijCml39hoeN6juNlfawnXlZECF+Ddf/V+HMdtiwEMC9M0eepTD+Pit/8ZlXKVF7/kKDZObwg+v/sHP1rxOXxs2LCBDRumOOI5h9O0untkZDIZXvzi4Wk0hBC859KLueCC8/j5z3/BwQcfxCGPP3glU14zJMpiH4SDyRIbPfZYiY1BhmrQpU56FCB+eqoWUa2tPvOL8FwKLKAHfwtcBGU2YJFBM3XcGMoCun3rnTLHRsOQ4VlavTCIZRA1ZpxzNaWBRCMVUjDopwELlOXWWncSd45Riihu/CZqzLC05mGVTjab5qjnP4v/uvOHodbFSviehBBs2jTNnj0LaJrgVb/9ci7/m/dGtgntFWQ3DB1l9cpQGu9WZLMZ3veedyCE4JBDHs+pp5zEV6+/IbgWwzCYnJzgrNedOeylBdg4vYGNR21d8ThriURZ7JOQmDQ8ugmJRZo6eerkg+rs1mRVBxMpu2nM1WfqEclS6ciokWhSkqWk6D96VHbHmzHgdfXzLR6ASWZD9x+l4IsaMwpmHyvBQafCBgrMxyoG7Fd93vr3KC2KlY710ff/GS8+4U247mBWk67rXHD+Wzj25S/l+9+/iw986KNYLat4TdM45mUv5oMf+CsMwySdTvUc76ijtnLjjd8MOY/G//z3d5icKPLsI17C4uJSx+c6T3ziE2g0mjzxiYdw4QXn8dSn/hZfufZ6stksf33ZOzniOYfzqav+nUq5wvHHv5K/+PM/3us66Y0KibLYB6F6Xy+3NNWljUmDMlOhfbctUmoVLNurr1VkQeXrp0JSZYVQTZRAIozw4Fxcnie/f3Za1tCxqXmKLS7Wwi3Tj6rEn4chnUgl12vs1jE6x1wpVuP+/MM/f3Ho1NlUymTr1ufylj/6szZFAappz1evv5Fzznkdk5MTPPUph/U8z9vf9qfcdNO32yhEhBCcespJbNo4DcDW5x/JN2+6pes8f/s37+Uob4X/yX/6V856/bkYhuElCAiu/rdPcNt3dg91jfsaEmWxj0HzQs+d8QdfYVihWUzKnZShgikVuVrTs0aWLZDeglsYKSxSmC00Ip2r5DiuHyVsLYosxA7UrxXiCvFhBfNK3ExrDdu2+dyXb4zsfd0LjuPw0pe8CIhuktRsNjnr7DfhupLHHXQAf/7nf8JNN93C4uIiJ598Aq8+9SRSKWVxlMsVhGhfBAkhWFhYBGBpqcStt/1X1zmEEHz6qn/nqKO28oMf/ogPfPAKGo0GjcYyweAbznkzP/jf28hkolsQ7y9IsqH2Meg96iyMHqR8Eo0aRZbYxBKbqFNk0MejygR1cjhSC6qJVyI4+xXmDYJha0LWAmE1EuOOeqNJs9mPIjx8LWoYBv/3f78E4KUveVGk1VCpVKnVavzil/fzR2/9c774pev4xn/ezIV/djE7TnltYJF87Mp/7rJOXNflttu/zyOPPMqDDz6EaXa7sqSU3POjnwDwuc99iUYjPAb17Vtu63md+wsSZbGPIarOQgVeV/vrFjTIU2IjJTYOP8oAWUtRtQud+7T+7/9uYeJIgbMCRTEuCme1EHV9KdOMzIbSdY03vOF3Ofro8Apv27Z56OFHAPij89448Jxc1+UHP/wxn/v8lwB44IFfhc7FNA1u+tYtPPbYDM1mo+tzIQRPe9pTAKhUq6GV1SB7MNTuX0iUxV4L6aVjtj/gfp1F2HvsF9TFhUGDPPMUmcNFC1+dI5hgD9b8g6So4burermQehWo9Tqm9dg6ORoygxXhSe2uGdGok8WWBiKVp8wUVaYoBVbU8HDkMi/TuGIlleFhv9u2gxbWr1btyYc+cCl/dN4byeW6n7l8PscRz3kWAP/27/8xdNzjU59WxXEvOuoFoVZMuVzh3X/1fs59y58CglTKbPs8k0nzpxe8BYDtJx0XOlfLsnjZS48ean77GhJlsddBkqHMJLMUWWCSObKUaOVtLTMVCHcpBa4UVJjARY99ljQV8ixhChtduKH9ulWBnUQTLjgWWcqxqLD9IjULM3YqqYOGI3UsUpSZokGeGkUsspFut1bXjl8IaJFCmNm2e9FOujc4NOgZXYljBfXDuFkwuVyGt51/DqbZLaT9e3HMy17M0576W6TTy/7+dDrNYYc9iSOPfDb33/8rvva1GyNW9P3xy1/ez2899Xl8/467SKXMUOqRSqVKuVyh2WziupJ8Pqcsiqf+Flf/6z/ynGcfDsBLXnJU6H192tOewqZNw1vJ+xKSAPdehhS1FqJA9XSnZN3ro61STl0MSkwHjYx6tT9dhiRNDdNr2hOHeC4s4JuSderkep6vs0itM3U07FxNMjRbUmp92Jix6U1MaWFi4VZqTABlJpCe0ugMzg+CqHqRzs+HDWDHITPslzq8GrGQt1/4e7xy2ws54czzse3l1OlMNstPf/pzpqc3cM01/8rHPvZPXHPNtUgkp+w4ifvuu5/nbX05hqFTrdZ6nKE3bNvGtm1+9KOfkE6nefGLX8jPfvZ/aJrOnj3zXQWBhmHw9p1/xkV/cS579rS7lq67bhdhSRw//en/8Ytf3Mdhhz1p6HnuK0gsi70MGbrrIYRQ29sfduH1yTaJoygKLJChgiEcDLGymgkDW7nCQh4vX6h1che5UsOV4cmyQhBaAAf9r6xzHF/JCiEpsEiRPRTZg46NjT6wi2xQ/qVBCgxbXVut96xT6cStMRm1dWKaBkc867d4zfaXt20vl8ts3/FaXnDUK3jTm87nzef+Hnd8/ybu/P63+MUvfslN37qFZrPZV1EcfvjTecmLj+LxBz+u71wajQZLSyXu/p9beeMfvD60AK/RaLCwsICuL1uVjuMwMzPLd75ze2ghoWHo3PXfd/esRG80mtxzz708/PBvIvd54IEHueOO/6ZcHk0TqvVAoiz2MohIhqHh334Dq6vn9kpWoq7nmKmR7yIjjEo/rZOmQTZS+Gs4TDBHngV0ljNf9JhtV6PgC1/NU61himy90CAHIvoVXYlbK2q8QVHI53j1Sdu6tlcqFRqNJt+59XZ+/w/eyjVfvI5PfepqbvrWd2Kn237xC//CF6/5V775n9fx6lNPCuofovCTn/wcUBlW6VR39lM2m+ElXsouwL//+3/wrOe8mBcc9Uq+tusboTGYRqPJn//FJTzpsCM54aQz+OE9P277/LOf+yLPes7RvOb01/Pilx7P6Weew54988Hn8/MLvPq01/PyV5zM2W84l2cf8VL+7u8/Gev6xw1Jp7wxRthcC8xjiO6URUfqlJju2Cq9n95rggzlrh4YYQgrSuv8WwJLbAIEJnVylPq6mAJlwrKlEFb81jqO300vTZUMlbEQ7qOEfy+jYsidxY7+74OeI8yF1u/7aoVtO1z12a9y/s6/7blfNptFSkm9Ho8GRAjB//38v9tou23bptFo8ltPDe9kZ5omv7r/hwC8+bwL+c//vDmwXnK5LC976dFc9el/YMuWCa76ly9y/gVvG5iWpFDIc8vNuzjooAO4/bvf5/VvOLdtDNM0eN7zjuTaL6vg++/87u/zX9+7sy21N5fL8g8fu5wTjn9l3/ONU6e8sbYsvvrVr3LSSSfxqle9is985jPrPZ2xQI1C12pdSrXdh8AlzyKTzDLJHAXPzRKFqMB3mNvClVrkilYFkn3rR5Kl3GWtRB4nlGD093db3EGdK30hCALp7pCFe2sZaB7mGP9+9Pq8szaj9Z4Nen1hFlUc5dNsWnz6M1/tu1+tVhtIUWw75iVd/R1aK6vDsXzRH/+Hv+Xyv3kvL3vZ0bz0JS/igx+4lE/9898Hx/7t334sVFEIIchkMpimEXqeZtPiqn9RsugfP3FV1xiWZXP33T/kV7/6NY8++hjfv+OurhqQarXGxz/+6b73YdwwtgHuRx99lCuuuIIvf/nLpFIpXvva13LUUUfxW7/1W+s9tXWFg0nJq7bWpY2LoQrhgoweFX/QWgLUunQosMAS06FxBKdHlpQrlQKwMahT9ILlqv+2KcLdCQY2NkYoXUcn7UfU6tUig4MRmV2leQrJoTdvUCuGXYF3Iig4jHnOUbn3ekEIRRVfJ48Ech33rZcLcFg0mxZvu/Rj/O8Pf7aygTyk0ykM3WDzlk185Iq/Dt0nm80yNTXJfEhXucMOe3Lwu6ZpvObVJ/OaV58cOs7Dj4THF0zT5Ctfupp7fnQv73nPhyh1xBiazSb33vtTAB7pMcbMzCz5fA7DMEOL/WZmB6ODGQeMrWVx++2386IXvYipqSlyuRzHH388u3cnHC2gsp2qTFJiIxUmWxSFquDuZDr1g7qqDqIbBtE+5AZ5FtlMhQ0BqSAIHC8YHD6//o9VZwC3ewydJtm+Y6n90rFX73XyYK6sI9kSm2hGNH9qPf8oyA0Hg6BJFtU0dxJH6kNZGXFRbzT50U9+6TG7Do9MJs0Ff/JmLv2ri/mnf/oot95yAwccsCV0XyEEf3nR+V0NhLLZDO94+5/FPufhhz89dHuxWOA5zzmcFx21Fcvufi/S6XTQR+Pl214aUI60wrJsnv70p/DkJz8xNA5imgavfMUxsec6LhjbmMUnPvEJqtUqF154IQDXXHMNP/jBD3jve9+7zjMbb7j1Ek55lrCAt0gXMIrdL6FTncetzndtB4GW34ie7W5bKZ0m9vxD3efRUxhTip/fnrsvfJJCx9x4KNJuYi+EjIHA2PB4hG7i1BZxK3s69hGI7ARGXuW/SymRjTJObRGkCz1YUM1NT8apLuBW90TuEw2BSOcxiltwmlXcpYjsF81AaAbSsVgpG+9As8tOBvfER/B6O03spUfBdVhJMkQnbEfy+rdexg/vuZcnPfEJ3Hb7HdTr3dXSnfAL4KQrOe207Vz16Y/0bNHaiU9f9Tne894P8/DDv+Gww57IB99/Caeccnzs47/3/f/mt1/1O23d8nLZLH//95fx/52jOvC95rTf5z+/eUvgatI0janJCX50zy1s3ryRxx6b5cjnvZKFhcUgaJ/LZbn0ry7iwj99MwBX//sXeesf76RWqyOlJJVKMTU1wV13fJ2DDjog9nzHAWOrLP7xH/+RWq3Wpix++MMf8p73vCfW8ftqgLsfdOxQamwV18jTJNd1jIZDkT2hxyyxMZImxKCpAtieJWNJkyoTwf5ZlrrYaqWEKgUsr5rcD663ok4uqBlR9R9VMiznxTfJeDGa8GW7itE4XedtkqbGBJums1h7fhV6vWGBdUWhIrExqTDpnVcywVxX0yh1zzYgMbwK+KWeSQH+NuUKFChbqbsHRq+5diYWREMG/UnyLIb22ghLWPA3KSrKtErfRtIkozK2EMGzetW/fIb3XXY5uqbTtCw2bpxuq3lIp9Oc84bf5SUvPorHZmZ5wQuex1OfcliPOY8e/lz/+7/v5q8/cAU/+tG9PP7gx/Hnf/7HbUHnRqPJ3374Y3zms9dQq9U45mUv5q/evZMnPvEJwT4zM7Nc+fFP8c2bbmHLlk2c9+Y/4JWvbM8O+/4d/83H//HTPPTQI2w75sW8+dzfi13oN04B7rFVFl/5yle48847ueyyywD4h3/4B6SU/PEf/3Gs4/dXZQGQZwEDq0OYaCwxTZQwSVEjSzkQjABVihEsta2QaLhMbywyO9fJoaP6XaRYXmkqRdBetKdjYeKz3WZwQ0NpStC5XklfL2g4SmG2rKBddEpMARqbNxdZmJkhR/u99a/d/7/udQLUsT0R3j4vgUuORQwvecBFUGWiLY6SokqWCr6CCY5t+26E991oCFwKzLcJ8rBsMAtDKRUkTVI0KAzI/eVSYLEt8cFGa2v+amNQYartiqPQ+qzWanV++rOfs2nTRh5/8OP4n//5AV+57mtIKXn1KSfx/OcfOcA8R4/9QQYMg37KYmwD3C9+8Yv5+7//e/bs2UM2m+XrX/964oKKiQqTpKmSlnVAYpGi3mMlDni+7jQmTSQ+x1Qc4aOINIQW5rcW1JigjovA9bKuuufgYLbFXaLPE+9xddFZYiMmDTQcHAxsUm3ntsiwRCpg4rW8z1V3QVc1hPKu347KFkOjwoYg+0uGEH80ydEk6ykcDYkgR8nrA6LOW21h+JVolJjGpIEubfLFPHMllxQNTNlEenEJe4DAfjg0yt7c278bFx0HF31o4slsNsORRzw7+Pu5z30Oz33uc1Y43wTrjbFVFgcccAAXXngh55xzDpZlccYZZ/Cc5yQPXDwo9tdGCD1GL0i0noHbYSHR1oDxthOir1Uk0br26a+0wsfpN5fWcatM9t3fIoMFFDNFKJU8pdPtQlwpur8bDWd8814SrCPGVlkA7Nixgx07dqz3NBIkSJBgv0eyhEiQIEGCBH2RKIsECRIkSNAXibJIkCBBggR9MdYxi5UguotX78/GDclcVwfJXEePvWWekMx1mPOMbZ1FggQJEiQYHyRuqAQJEiRI0BeJskiQIEGCBH2RKIsECRIkSNAXibJIkCBBggR9kSiLBAkSJEjQF4mySJAgQYIEfZEoiwQJEiRI0BeJskiQIEGCBH2RKIsECRIkSNAX+7Sy+NjHPsb27dvZvn07H/rQhwC4/fbb2bFjB8cddxxXXHHFOs9wGR/96Ec56aST2L59O1dddRUwvnP18cEPfpCdO3cCcO+993L66adz/PHH8453vAPbju6DvZY455xz2L59O6eeeiqnnnoqd999N1/96lc56aSTeNWrXsVnPvOZ9Z5igJtuuonTTjuNE044gfe9733AeD4D11xzTXA/Tz31VJ7//Ofznve8ZyznCnDdddcFcuCDH/wgMJ7P6yc/+UmOP/54duzYwcc//nFgzOYp91Hcdttt8nd/93dlo9GQzWZTnnPOOfKrX/2q3LZtm/zVr34lLcuSf/AHfyBvvvnm9Z6q/N73vidf+9rXSsuyZK1Wk8cee6y89957x3KuPm6//XZ51FFHybe97W1SSim3b98u/+d//kdKKeXb3/52+ZnPfGYdZ6fguq58yUteIi3LCrb95je/kccee6ycn5+XlUpF7tixQ/785z9fx1kq/OpXv5IvfelL5SOPPCKbzaZ83eteJ2+++eaxfgaklPJnP/uZfNWrXiUffvjhsZxrtVqVL3jBC+Tc3Jy0LEueccYZ8rbbbhu75/W2226TJ598siyVStK2bfnmN79Z3njjjWM1z33Wsti8eTM7d+4klUphmiaHHXYY999/P4ceeiiHHHIIhmGwY8cOdu/evd5T5YUvfCH/9m//hmEYzM3N4TgOS0tLYzlXgIWFBa644gre8pa3APDQQw9Rr9c58sgjATjttNPGYq6//OUvEULwh3/4h5xyyin8+7//O7fffjsvetGLmJqaIpfLcfzxx4/FXL/xjW9w0kknceCBB2KaJldccQXZbHZsnwEff/VXf8WFF17Igw8+OJZzdRwH13Wp1WrYto1t2xiGMXbP649//GNe+tKXUigU0HWdl73sZVx99dVjNc99Vlk85SlPCW7y/fffz65duxBCsHnz5mCfLVu28Oijj67TDNthmiZ/93d/x/bt2zn66KN57LHHxnau73rXu7jwwguZmJgA6Jrr5s2bx2KuS0tLHH300fzDP/wD//Iv/8LnP/95Hn744bG8rw888ACO4/DGN76RU045hc9+9rNj/QyAcpHV63VOPPHEsZ1roVDgggsu4MQTT+SYY47h4IMPxjTNsXteDz/8cG699VYWFhZoNBrcdNNNGIYxVvPcZ5WFj5///Of8wR/8AW9729t4whOe0PW5EONDVXz++efz3e9+l0ceeYT777+/6/NxmOs111zDQQcdxNFHHx1skyHExeMw1+c+97l86EMfIpfLMT09zRlnnMHf/d3fde03DnN1HIfvfve7/M3f/A1f+MIX+OEPf8ivf/3rrv3GYa4+Pv/5z/P7v//7wPg+Az/5yU/40pe+xLe+9S1uvfVWNE3jtttu69pvved69NFHc9ppp/GGN7yBN73pTTz/+c/HcZyu/dZznvtsPwuAu+66i/PPP5+LL76Y7du38/3vf5/Z2dng88cee4wtW7as4wwVfvGLX9BsNnnGM55BNpvluOOOY/fu3ei6HuwzLnPdtWsXMzMznHrqqSwuLlKtVhFCtN3XmZmZsZjrnXfeiWVZgWKTUnLwwQeP5TOwadMmjj76aKanpwF45StfObbPAECz2eSOO+7gAx/4AAAHHHDAWN7XW2+9laOPPpqNGzcCypXzqU99auye13K5zKte9apA+V511VUcfPDB3HnnncE+6z3PfdayeOSRR3jrW9/K5Zdfzvbt2wE44ogjuO+++wKT//rrr+eYY45Z55nCr3/9ay655BKazSbNZpNvfvObvPa1rx3LuV511VVcf/31XHfddZx//vm84hWv4P3vfz/pdJq77roLgGuvvXYs5loqlfjQhz5Eo9GgXC7zla98hb/5m7/hu9/9Lnv27KFWq/H1r399LOZ67LHHcuutt7K0tITjOHznO9/hhBNOGMtnAOCnP/0pT3ziE8nlcsD4vltPf/rTuf3226lWq0gpuemmm3jhC184ds/rr3/9a9761rdi2zalUolrrrmGM844Y6zmuc9aFp/61KdoNBrBygfgta99LR/4wAf4kz/5ExqNBtu2beOEE05Yx1kqbNu2jbvvvptXv/rV6LrOcccdx/bt25menh67uUbh8ssv55JLLqFSqfDMZz6Tc845Z72nxLHHHhvcV9d1Oeuss3j+85/PhRdeyDnnnINlWZxxxhk85znPWe+pcsQRR/CmN72Js846C8uyeMlLXsLrXvc6nvzkJ4/lM/Dggw9y4IEHBn+n0+mxfLde+tKX8uMf/5jTTjsN0zR59rOfzbnnnsurXvWqsXpen/70p3Pcccdxyimn4DgOv/d7v8fzn//8sXqvkk55CRIkSJCgL/ZZN1SCBAkSJBgdEmWRIEGCBAn6IlEWCRIkSJCgLxJlkSBBggQJ+iJRFgkSJEiQoC8SZZEgQYIECfoiURYJEiRIkKAvEmWRIEGCBAn64v8HofndfbTGnK0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = test[\"seic\"]\n",
    "y =  test[\"seic_benefit\"]\n",
    "\n",
    "group = y_test_seic\n",
    "\n",
    "plt.scatter(x, y, c = group,label =group )\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "embedded-fruit",
   "metadata": {},
   "source": [
    "#### Plotting Prediction Results for Decision Tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "later-confusion",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = test[\"seic\"]\n",
    "y =  test[\"seic_benefit\"]\n",
    "\n",
    "plt.scatter(x, y, c = prediction_dtc,label = prediction_dtc)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "varied-repair",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "french-nirvana",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}