{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting pandas\n",
      "  Using cached pandas-2.2.3-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (89 kB)\n",
      "Requirement already satisfied: numpy>=1.26.0 in /home/br0kenpixel/Documents/ui-cviko1/lib64/python3.13/site-packages (from pandas) (2.2.4)\n",
      "Requirement already satisfied: python-dateutil>=2.8.2 in /home/br0kenpixel/Documents/ui-cviko1/lib64/python3.13/site-packages (from pandas) (2.9.0.post0)\n",
      "Collecting pytz>=2020.1 (from pandas)\n",
      "  Downloading pytz-2025.2-py2.py3-none-any.whl.metadata (22 kB)\n",
      "Collecting tzdata>=2022.7 (from pandas)\n",
      "  Downloading tzdata-2025.2-py2.py3-none-any.whl.metadata (1.4 kB)\n",
      "Requirement already satisfied: six>=1.5 in /home/br0kenpixel/Documents/ui-cviko1/lib64/python3.13/site-packages (from python-dateutil>=2.8.2->pandas) (1.17.0)\n",
      "Using cached pandas-2.2.3-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (12.7 MB)\n",
      "Downloading pytz-2025.2-py2.py3-none-any.whl (509 kB)\n",
      "Downloading tzdata-2025.2-py2.py3-none-any.whl (347 kB)\n",
      "Installing collected packages: pytz, tzdata, pandas\n",
      "Successfully installed pandas-2.2.3 pytz-2025.2 tzdata-2025.2\n",
      "\n",
      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m24.2\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m25.0.1\u001b[0m\n",
      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "%pip install pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "loans = pandas.read_csv('loan_historical_data.csv', sep=\";\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Income</th>\n",
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       "      <th>6</th>\n",
       "      <td>K7</td>\n",
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       "      <td>Poor</td>\n",
       "      <td>Man</td>\n",
       "      <td>No</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>K8</td>\n",
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       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
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       "      <th>10</th>\n",
       "      <td>K11</td>\n",
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       "      <td>Fair</td>\n",
       "      <td>Female</td>\n",
       "      <td>Yes</td>\n",
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       "      <td>Yes</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Client Income     Credit  Gender Unemployed Safe\n",
       "0      K1   High  Excellent  Female         No  Yes\n",
       "1      K2   High  Excellent     Man         No  Yes\n",
       "2      K3    Low       Poor     Man         No   No\n",
       "3      K4    Low  Excellent  Female        Yes  Yes\n",
       "4      K5    Low  Excellent     Man        Yes  Yes\n",
       "5      K6    Low       Poor  Female        Yes   No\n",
       "6      K7   High       Poor     Man         No  Yes\n",
       "7      K8   High       Poor  Female        Yes  Yes\n",
       "8      K9    Low       Fair     Man        Yes   No\n",
       "9     K10   High       Fair  Female         No  Yes\n",
       "10    K11    Low       Fair  Female        Yes   No\n",
       "11    K12    Low       Fair     Man         No  Yes"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loans"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting scikit-learn\n",
      "  Downloading scikit_learn-1.6.1-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (18 kB)\n",
      "Requirement already satisfied: numpy>=1.19.5 in /home/br0kenpixel/Documents/ui-cviko1/lib64/python3.13/site-packages (from scikit-learn) (2.2.4)\n",
      "Requirement already satisfied: scipy>=1.6.0 in /home/br0kenpixel/Documents/ui-cviko1/lib64/python3.13/site-packages (from scikit-learn) (1.15.2)\n",
      "Collecting joblib>=1.2.0 (from scikit-learn)\n",
      "  Downloading joblib-1.4.2-py3-none-any.whl.metadata (5.4 kB)\n",
      "Collecting threadpoolctl>=3.1.0 (from scikit-learn)\n",
      "  Downloading threadpoolctl-3.6.0-py3-none-any.whl.metadata (13 kB)\n",
      "Downloading scikit_learn-1.6.1-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (13.2 MB)\n",
      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m13.2/13.2 MB\u001b[0m \u001b[31m8.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0ma \u001b[36m0:00:01\u001b[0m\n",
      "\u001b[?25hDownloading joblib-1.4.2-py3-none-any.whl (301 kB)\n",
      "Downloading threadpoolctl-3.6.0-py3-none-any.whl (18 kB)\n",
      "Installing collected packages: threadpoolctl, joblib, scikit-learn\n",
      "Successfully installed joblib-1.4.2 scikit-learn-1.6.1 threadpoolctl-3.6.0\n",
      "\n",
      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m24.2\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m25.0.1\u001b[0m\n",
      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "%pip install scikit-learn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier # Import Decision Tree Classifier\n",
    "from sklearn.model_selection import train_test_split # Import train_test_split function\n",
    "from sklearn import metrics #Import scikit-learn metrics module for accuracy calculation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "arr1 = [1,1,2,2,2,4,4,2,3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{1, 2, 3, 4}"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "set(arr1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 2, 3, 4]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(set(arr1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "arr2 = [\"High\", \"Low\", \"Low\", \"Low\", \"High\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['High', 'Low']"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(set(arr2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "def cat2int(col):\n",
    "    vals = list(set(col))\n",
    "    for i, string in enumerate(col):\n",
    "        col[i] = vals.index(string)\n",
    "    return col"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     0\n",
       "1     1\n",
       "2     1\n",
       "3     0\n",
       "4     1\n",
       "5     0\n",
       "6     1\n",
       "7     0\n",
       "8     1\n",
       "9     0\n",
       "10    0\n",
       "11    1\n",
       "Name: Gender, dtype: object"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cat2int(loans[\"Gender\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>High</td>\n",
       "      <td>Fair</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>K11</td>\n",
       "      <td>Low</td>\n",
       "      <td>Fair</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>K12</td>\n",
       "      <td>Low</td>\n",
       "      <td>Fair</td>\n",
       "      <td>1</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Client Income     Credit Gender Unemployed Safe\n",
       "0      K1   High  Excellent      0         No  Yes\n",
       "1      K2   High  Excellent      1         No  Yes\n",
       "2      K3    Low       Poor      1         No   No\n",
       "3      K4    Low  Excellent      0        Yes  Yes\n",
       "4      K5    Low  Excellent      1        Yes  Yes\n",
       "5      K6    Low       Poor      0        Yes   No\n",
       "6      K7   High       Poor      1         No  Yes\n",
       "7      K8   High       Poor      0        Yes  Yes\n",
       "8      K9    Low       Fair      1        Yes   No\n",
       "9     K10   High       Fair      0         No  Yes\n",
       "10    K11    Low       Fair      0        Yes   No\n",
       "11    K12    Low       Fair      1         No  Yes"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loans"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     0\n",
       "1     0\n",
       "2     0\n",
       "3     1\n",
       "4     1\n",
       "5     1\n",
       "6     0\n",
       "7     1\n",
       "8     1\n",
       "9     0\n",
       "10    1\n",
       "11    0\n",
       "Name: Unemployed, dtype: object"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cat2int(loans[\"Income\"])\n",
    "cat2int(loans[\"Credit\"])\n",
    "cat2int(loans[\"Unemployed\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Client</th>\n",
       "      <th>Income</th>\n",
       "      <th>Credit</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Unemployed</th>\n",
       "      <th>Safe</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>K1</td>\n",
       "      <td>0</td>\n",
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       "      <td>Yes</td>\n",
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       "      <td>Yes</td>\n",
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       "      <td>K3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>K4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>K5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>K6</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>K7</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>K8</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>K9</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>K10</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>K11</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>No</td>\n",
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       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>K12</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Client Income Credit Gender Unemployed Safe\n",
       "0      K1      0      0      0          0  Yes\n",
       "1      K2      0      0      1          0  Yes\n",
       "2      K3      1      1      1          0   No\n",
       "3      K4      1      0      0          1  Yes\n",
       "4      K5      1      0      1          1  Yes\n",
       "5      K6      1      1      0          1   No\n",
       "6      K7      0      1      1          0  Yes\n",
       "7      K8      0      1      0          1  Yes\n",
       "8      K9      1      2      1          1   No\n",
       "9     K10      0      2      0          0  Yes\n",
       "10    K11      1      2      0          1   No\n",
       "11    K12      1      2      1          0  Yes"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loans"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "#split dataset in features and target variable\n",
    "\n",
    "X = loans[['Income','Credit','Gender','Unemployed']] # Features\n",
    "y = loans.Safe # Target variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      ],
      "text/plain": [
       "   Income Credit Gender Unemployed\n",
       "0       0      0      0          0\n",
       "1       0      0      1          0\n",
       "2       1      1      1          0\n",
       "3       1      0      0          1\n",
       "4       1      0      1          1\n",
       "5       1      1      0          1\n",
       "6       0      1      1          0\n",
       "7       0      1      0          1\n",
       "8       1      2      1          1\n",
       "9       0      2      0          0\n",
       "10      1      2      0          1\n",
       "11      1      2      1          0"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     Yes\n",
       "1     Yes\n",
       "2      No\n",
       "3     Yes\n",
       "4     Yes\n",
       "5      No\n",
       "6     Yes\n",
       "7     Yes\n",
       "8      No\n",
       "9     Yes\n",
       "10     No\n",
       "11    Yes\n",
       "Name: Safe, dtype: object"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
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   "source": [
    "y"
   ]
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  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=15) # 70% training and 30% test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</table>\n",
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      ],
      "text/plain": [
       "   Income Credit Gender Unemployed\n",
       "9       0      2      0          0\n",
       "3       1      0      0          1\n",
       "4       1      0      1          1\n",
       "0       0      0      0          0\n",
       "7       0      1      0          1\n",
       "10      1      2      0          1\n",
       "5       1      1      0          1\n",
       "8       1      2      1          1"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9     Yes\n",
       "3     Yes\n",
       "4     Yes\n",
       "0     Yes\n",
       "7     Yes\n",
       "10     No\n",
       "5      No\n",
       "8      No\n",
       "Name: Safe, dtype: object"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
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    "y_train"
   ]
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  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "   Income Credit Gender Unemployed\n",
       "11      1      2      1          0\n",
       "6       0      1      1          0\n",
       "2       1      1      1          0\n",
       "1       0      0      1          0"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "11    Yes\n",
       "6     Yes\n",
       "2      No\n",
       "1     Yes\n",
       "Name: Safe, dtype: object"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create Decision Tree classifer object\n",
    "clf = DecisionTreeClassifier()\n",
    "\n",
    "# Train Decision Tree Classifer\n",
    "# Training the model on the data, storing the information learned from the data\n",
    "# Model is learning the relationship between x (features: Income, Credit,\tGender,\tUnemployed) and y (Safe)\n",
    "clf = clf.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = clf.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "   Income Credit Gender Unemployed\n",
       "11      1      2      1          0\n",
       "6       0      1      1          0\n",
       "2       1      1      1          0\n",
       "1       0      0      1          0"
      ]
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     "execution_count": 40,
     "metadata": {},
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    "X_test"
   ]
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  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['No', 'Yes', 'No', 'Yes'], dtype=object)"
      ]
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     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "11    Yes\n",
       "6     Yes\n",
       "2      No\n",
       "1     Yes\n",
       "Name: Safe, dtype: object"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.75\n"
     ]
    }
   ],
   "source": [
    "# Model Accuracy, how often is the classifier correct?\n",
    "print(\"Accuracy:\",metrics.accuracy_score(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "clf = DecisionTreeClassifier()\n",
    "clf = clf.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = clf.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.5\n"
     ]
    }
   ],
   "source": [
    "print(\"Accuracy:\",metrics.accuracy_score(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting pydotplus\n",
      "  Downloading pydotplus-2.0.2.tar.gz (278 kB)\n",
      "  Installing build dependencies ... \u001b[?25ldone\n",
      "\u001b[?25h  Getting requirements to build wheel ... \u001b[?25ldone\n",
      "\u001b[?25h  Preparing metadata (pyproject.toml) ... \u001b[?25ldone\n",
      "\u001b[?25hRequirement already satisfied: pyparsing>=2.0.1 in /home/br0kenpixel/Documents/ui-cviko1/lib64/python3.13/site-packages (from pydotplus) (3.2.3)\n",
      "Building wheels for collected packages: pydotplus\n",
      "  Building wheel for pydotplus (pyproject.toml) ... \u001b[?25ldone\n",
      "\u001b[?25h  Created wheel for pydotplus: filename=pydotplus-2.0.2-py3-none-any.whl size=24687 sha256=c477e8981a47f023f93b6f4aa926f3899a8d5302c81197821877eb34780c3280\n",
      "  Stored in directory: /home/br0kenpixel/.cache/pip/wheels/4a/c0/ed/a9eeeb08c3c53bb90d3822cf76557c8fdcbc349ee11a011169\n",
      "Successfully built pydotplus\n",
      "Installing collected packages: pydotplus\n",
      "Successfully installed pydotplus-2.0.2\n",
      "\n",
      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m24.2\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m25.0.1\u001b[0m\n",
      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "%pip install pydotplus"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.tree import export_graphviz\n",
    "from six import StringIO\n",
    "from IPython.display import Image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pydotplus"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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taW4cZzqTqTcAMtyAutNfRk8yT0v4HHDjjN2GfcgDoBC/O8ztord55WnzHZ7eOn/lyGbHy758QqK1leUXD6zHsbHPXLIOeQjlc9mNX1hsqt1KrXGTzz39RN+2IDt904zR1mt2Wi5eh6iMpILK3ZMHJOSVFdRHkUnt/7k7VleUWixei7ygeOXIluyUhNUHT2FxbLlpSXdOHpRX14F13SHdA3Wnv1jZb4p6/cTrwrG3Afd5+AVLf+Y4OJ48u3slC0PiExLddfbumV0rVhmr8AmLVpeX4nBsO07dRraXoVAp3p6uChqjp9r9PUgMBrvf0+fomrk7rSfwCYk21tW0tbZMsJiHvJ4zeqLZ9IWrgx/d+PjSl5Obt7z4p5Si6n5P757sTQoZyUDd6S8Ebp7TjyNiQp+X/MzhFRAeazoTSZFgsTgAgIbehAWbHHmFRAAAyqPGLtjkKCj+e69ODgLngk2OSlp6DJ79R8/Y4mpI8pcPQVVlxbyCIvrGFsgrxQAANAq9YJMjv7BYpw15+AQWbHJU1vq9KFRCTuniiy9fw18X5WSysbOr6RrSz+zWHTlnbrv8R9yn1qYmaSW10RPNkI2bIZBugLrTXyhkcnV5if6UGez4Xw+qUr9FAwCQ9d8a+hM19CcidhWdsSo6Y+nb4glcCzb92jyP3pMp8PAJTJm7pKMdhUbTOu20VcejbOx4A/PZXTVRUB/FsEIdAukeOB7uLzHvXqwxVX9w1hl5ebe+psrvijsGi9Mzms7q0CCQfxQ43ukv401nmVovfX7XM+KVP5+QSFFOJonUvvGYZw/r3UAgIxCoO/0FhUZvdr262fUqqwOBQIYMcJ4FgUAGG6g7EAhksIG6M+TxveIe/sKX1VFAIL0A5neGPH5X3HUMTWjrGFgFlUJJ+PShMDuNSqWKyyqNMpyCxmBYGxLknwXqDoQJFOVmeGxdkp+ZKqWgikKhfmb+EJGS2332npyaNqtDg/yLQN2BMIHTO1cU52Wf8vuILBDNz0zdt8Dk1I5ll4LiWR0a5F8E6k6/yM9MTYuLqa2u4OHlV9ebKCmvTDtEJrXHRYQW52UBAMRllUZPNKPNO3yvuMur6YyZZB77IaggK41XUNjAfDaBm6e5sSEm9Hl1eYm4rOLYKZbIKicqlepzyU1OTVvf2CIuIjQ/I4UNjx890UxcVqnTkKgUSkL0+9y0JCqFIiGvPHqiGVI3/q8B95m6qoqs5DiDqXNoq9KlFdX0TWaEPfcpK8wTlpDpfxeQYQbUnb5z1Xlb8KMbgmKSfEKiFcUF1eUls+w3Oex3BwCU5Gcfsp9RWVYkrahGJpMKstIkFVRc7r9Btsd6cvPMOLNZr31upX+PxXNwlhbkPr526uA1f+dVc6hUamtLU21luZ6xxcGr/ki9G78r7uPMZgU/upGR+JVXQKg4L5sKqBuPXeq4H1ZlSeGxdfPy0pOlFdXQGEx+RqqgqMShawGSCirdB0zPz8wfkcGPO71kPIFrzsqtDEY2Dg5a9WgaiGjS1o5AIPRA3ekjuWlJwY9u2KzZtWSHMzIqeXTxuO/lE8ZWCxQ0Rl913lZdUXraPwJJcHz5EHx83TwfT9fVh04jv8nPb1/MWLzuwBU/AMCDM0f8r5/at8hsg/PF8eZWFDL5+HqbLx+CELVCqvx9fvdymt3Kg9ceYzDY0oLcA0un3Ti2Exkl0Ud1eteKkrxs90fvlHX0kbI7+xebndqx7NyzmO4Dpj9JeVH+24AHnV41kV+wo+7gOTjHTJ4aFxlKG93UVpbHRYRq6E+kLUaFQOiButNHqstLAADs/y/Eh0Kh5m/YN3PJeqTEp8nsRZMsbWlZVT3j6Tx8ApnJcbTmGCxuwSZH5LPhdGv/66dklTXGm1shKjPezOpb+Jvi/CxEd5DV7ct2H8NgsAAAEUlZyyXr7548kPDpPf1yzbz05JQvkXNWbkVEBwAgqaAyy36T1/mjGYlfG2qruwmYnjGTp954l9qru7HtxI1z+1ZvtRqroT8Jg8Umx0YoaelucbvWy5sKGSlA3ekjGnoTpBRVH55zTv0WrTt5qvZ4EylFVdpv2MhqQW1lefSbpxXFBa2tLRQSiUKhNNPVPBaXVaLVJ0Squ0sp/t55pmO9dxllDfo5i4ySOgCgJC+bPqSslHgAQHFelrenC82IFCrMSf1ubLWgm4D7SU1VOYVEwnNwIrMtNnZ8Y31tSX42n5AoU84PGWZA3ekjbHgOD5+wlw+vRgUH3HTZTaVSxaQVFm09NMnSFgDge/mEzyU3dg4OGRUtTm4iBosltbXRN2fn+L3TC1LzuKOFHob5FDsHAQDQ2tJEb2xurEeyM9UVpfR2ZR19dg5C9wH3h4a6moPLpotJK1wJSUT21Whva3XbON/JYfaloDhBMcl+nh8y/IC603c4OLls1uyyWbOrrqri28eQgBunz+xaISgmicXivC4c0zO22HPuPhv+l5qsMlHpT18NdTX0X1uaGpEsL72Rk4sIAJjjsK2rXQC7Cljtzz1Ce5tXToh8W1dVsXLvCdpmPjg29ql2K799DPkSFjx9wao+XTFkOAN1p4/UVVWU/MxBMik8/ILGsxcqj9LfME0n7mMIv4g4AMBs3jKa6BTlZlSWFBIUVPvcXWFOeltLM+2EP7N+IJUA6X3kNUYBANLiY+h1Jzctqa6qQnPspIba6q4CZtCd3uaVW1tbOg6+kD02yKT2Pl8yZBgDdaePBPvcenTx+MEr/rpG0xDLj7gYAAC/iLigqAQAIP17LJInLi3IPb9vLb+IeGN9bZ+7a21qeuTpsmznMRQKVVVWHOR1nZOHqDXeiN5HWlFNTdcw7LmP4bS5SFSVpUVndi5vbmy4/Dqhm4AZ+uptXll7nBEWxxZ456Ke0XSk6lBddWXgnYtoDGbUhO62MISMWKDu9BEr+41JMWHH1loLiUvzCgrXVVWUFuTqGVuYWi9Fo9FKWroBN858eR+EweGKc7M2u11N/Rr94v5l51WzF2093IfudAxN0hNiHYyUBMUk89NT2tvbtrnfoN+kFGHHqdvH1847ttZaREqOi4c3PyOFi4fX8Yofjo29m4D7eSsExSS3ud+4fnznWnNNSXllDAZbkJ1OBdQNzhcZRmQQCALUnT6CJ3AdvfMqLeFzTkpCQ30tkU9QSVtPVkUTOXri0duYty9K8nO4iLy6k6cJiEroGU+XVFBpa2nmExSZt3oXfVl1Tm7eBZsc1cYY0ixSCmoLNjlKKajRLGgM9uidl3EfQ/MzUibPtNM1miYmrYAcsl2/V1RKDvksJCZ19kn095iw3B+JVCpVVFpOd/I0Nnb8XwPuJxNn2OhPmREf+a6sILe1tUVYXHrUBFPkJUkIpCMoZNtsCAIKhdp99t4Ei3msDuQPbLX5dQxNDl7tPNc7DPDYukScG+Pn59fP89ja2vr7+zMpqH8LGxub/t+ffwdYfwcCgQw2UHcgEMhgA/M7QwD6DA4EMgyAujMEsFu/l9UhQCDMBM6zIBDIYAPHO4NH6teohOj3U+1WdrU3+eCQlRIf++4l8nmc6awBKkX65UNQZtI35HM32yJDRiZQdwaPlK/R3p6uesYWrNWd7OR4b09XtTEGnDxEdd0JiLG8+GdKbGRlaZGAiLjqmPEikrI9OVV1Relr75sd7SazF5UX/cxM+lZakPsz8wfUHQgDUHdGKMv3uqrojEU++15x9718gkIhc/Py11VVoDHYuQ7blmx3+utJSvKyvD1dOTi5aDU9ELTGGVksWmOxaM3zu5633GByCsII1J2RTkzoc6/zRydZ2q13Ok/g5qmtLPfYtsT/2knV0eP0jC26b9tYXwcA2OZxa5zpzMGKFzIcgHnl3vHlQ5C3p0ttZTm9saayzNvT5cuHYORrcX5W6ON7j6+ffvfkQenPnE7P8/1TmLenS111Jc1SW1nu7emS9PkjzVJRXBDqf9f/2slg75tFuRkDdEUxb1+wseM3HruIlPghCggt23UMABAX8favbZsa6gAAnNzEAYoNMlyB453e0d7W6u3pykXkm7l0A80YFujt7em6/5IPUiz58c0zRD5BAVGJ0oLc5saGFXtd6Z0REj998LvqMXGGLW0RU21Vuben64JNQHPsJADAs/8u3Dt9mJObR0xGsaww75rztjkO2+x3He8YUpDX9dqq8o52AICesYWi5pjur2iL27XNrlfpK40hdXywONxf70ZjXQ0AgIOL+6+eEAg9UHd6h77JDC4e3ohXj+mlJPyFLw+/oJ7RtLT4GP/rpyZZ2m33uInGYNpamp1Xz/nP3XGCxTw+QZGe9/I9+sN/7vuNZs3f5HIFx8ZOIZNvn9j35OZZFZ2xSG0NemLevkCKmXZETEbxr7rTsbzha59biGb9tWFjQx1S1/3Lh6CC7HQMBqM6eryStt5fG0JGOFB3egcWxzbBYt4b39sVxQVIBc+ivMys5LhZ9pswWBwnD9/qg6dGTfi1VRYbnmPCdOvk2Ii8tKRe6U6Q13U0BrP28Dlk6ys0BmO/+3io/90Q/zsddcfpViBTLu3ZfxcqSgpTv0aV5GevOXRGa9zkvzZpqq8FABxeYVlVVswrIFxTWUalUMZOsdx19h6yCB4C6RSoO73GZM6i1z63IoMDZq/YAgAIf+4LADCduwQAICmvLCGrmJ745Y3vfw211WQyKTs5npYH6TlZKfF4Amfg3Yv0Rg5OrpzU78y+mt+89rld8jObSqGMM5slJC7VkyZcPHwKGqMnTLe2XLqOHU9oqq+76bLr3dOHD846rdx3YuBChQx1oO70GtXR48WkFSKCHiO68/Glr5yatqyqFrJd3/H1Nsi+V4Kikmzs+Kqy4j500dxYT2pv//YxhN4oJCE9oIOIy8HxVAqlpCDn8bVTLuttV+x1Qy6wG6xX77BevYP2lcDNs/H4pbjIt2HPvaHuQLoB6k5fMLJa4O3pUlqQ21BXU5iT4eDogdivH99ZlJt5/F4wkhsGAIQ+vud5YH1Pztnc1ED7TOAm4tjYPXw+9KRhP/PKNZVlbS3NyH57KDRaTFphk8uVxM8fn9+79Ffd6QgGixOVkvsRH9PbhkxBSkpKXl6eJV0PNFJSPRqBDhWg7vQFk9kLvT1dPoUE1laVY7A4o5nzEXt2cry0ohpNdAAA36Pfd3oGJHHTRFdxOSvp965+Cuqjot88ZdhcPPyFr4b+RIEO5ZD7mVfeO9+kraX5VlgGbft2AAAahW5orO++IbLxcW1F2d6LXjRLW0tzQXaaqCRrVs+fOXPmzJkzLOka0ivg+zt9QURKTm2Mwdew4E9vAvWMp9N2vxMQlSgrzKupLAMAUCmU5/cuZSXHAQAa6xgruiOFhyODA5Cv+RkpwXQLDqYvWEWlUq86b2tubED2FH5x//KZXSti37/qGIzTrcAb71I7/Wc0a/5fr8VkzuLqitIrTluaGuqRFwV8LrkV52eNmTQVcfC55Bbkdb3TtngOQnTIs7snD7S1tiCb7Vw8sL6+pqqrjXQgEAQ43ukjJnMWXXHaSqVQ7Pf83pzTZu2uU9vtN0zXkZJXLS3IlZBXPnQtYJOl7qOLx4vzszi5eWme+iYz5NS0n/13ITI4gI0NX1tdvs39pst6WwqFAgDQNjC233X8wTnnFZMUJOWVayrLKooLptqtmGrL/N+z3fq9ZYV5oY/vhT6+R+QTrK+tJpPaNcdOWuV4EnHwuewmKa9isWhNx7ZLtjuVFuQ9uXXu5YOrXES+6opSQKVaLFrTca8bCIQeqDt9ZKKFTVVZMQqF1vv/tjAAAIOpc84Hfv7+6UNrS7OsiqaOgQkag3F58Dr5c4SYrAIvv9CCTY7IolA2PMcpv/DP716W5OcQuHn0TWYQ+QUXbHJU/f9WVnNXbZ9kaRsf+bamsoyLyKepP4m2VzpzQWMwW9yu2azd9SP+c3VZCZFfUEZFU0lLl+Zgar00Ny2p07ZYHNveCw9z05LSE2Lrqit4+AS0xxuLSg/PDAuEiUDd6SMEbp5Ol1lLKqgwCISKzk+71TYAACAASURBVFjaCkw13d+bRmCwOIOpc+g9GU4oKCZpZmPP7MA7R1xWSVy28z1nJOVVGDYmZUBWRZNZ+1JARggwvwP5C2g0Gs6bIMwFjndGKHfcHTl5iFb2m7UNjLv3nGW/qQ/nD/K6/jX8dWlBbl8DhAxnoO6MOMxtl5vbLh/oXpD6OwPdC2SIAudZEAhksIG6A4FABhuoOwNO6tcob0+Xvi3UgkCGJVB3BhyknDvUHQiEBtQdCAQy2EDdgUAggw18js5MivIyv0e9b2qoF5GS1Te2YMNzdOpWnJ+VHBtZW1nOJySioTdBhG7v87bWlm/hb8oK86hUqqiU3JjJU5GV690f6jOx719lJcdZLFxDFBCiGRvqal7cuySnpoPsElFRXEBbrqE93oj+teaBCAkyEoC6wzS8PV18Lp9gZ+fgFxYrLczj4RM4cvNZxwUE3RR+z89IObR8RlN9naSCCpVK/ZmRyiso7Hz7haSCSjeHGM7fq3I8VCrV29OVHU+Yu2o7zRgV/MTb03XP+Qfdl5fveUgQCANQd5jDt48h3p6u5rbL1zmdx2CwpQW5+xaanty25FJQPL1b94XfvS4cI7e3X3+XihRjLi/K37fQ9M7JAwev+ndziCGSXpXj0TOaLiAq8TbgHr3ufHzpy8MnMHaKZffl5XseEgTCANQd5vDG9zYGi1u5zx2DwQIARCRlNx2/XPIzu6W5kd6t+8Lv1WUlKDSaHU9AnIXEpc89i+Hg5AIAdHOIgV6VeUdjMGbzlvlccvsR90l19Hhk6+Gk2IiZSzdgcWzdl5fveUgQCANQd5hDdkqCiKQs/a9Ol64+Bo3uC79PW7jq4v61m2aMnjjDRnu8kYb+JG5efqRhN4f6ibnNcr+rHqH+dxHdiXjlT6VQzG3s/1pefuBCggx7oO4wh+bGegFRib+6dV/4fcqcxWJScq+8rr8LuB945yIWx2Y4bc4qx5M8/ILdHOpn5IJikmMmmUcEBaw6eArPwfnxpZ+Stp60kvpfy8sPXEiQYQ/UHeZA4OKpq6r4q9tfC7+r6Rqq6RpSyOSs5Liw5z6vvK7VVVU43X7e/SF6+lDmfdp8hy8fgj+9CVTXM0xPiN1w9NcA56/l5XsYEgTCANQd5iCvpvPp7fPy4p9CYr/q/ifGhOf++G4yZzG9WzeF3ylkcn5mKpFfkE9IFI3BKGnrKWnr1VVXfHzl39bSXJSX1ekhUnsbFsdG30UfyrzrTp4mICoREeRfUVLAjidMnGGL2LspL88nKNJVtP24i5CRAtQd5mBuuzw65Nlt173bT91mY8dXlhReOrSxqaFuxuK19G4CohLFeZk1lWW8AsJUCuXFgyu0wu9tbS2OS8yVtfT2XnyE5ImaGxvyM1J4+YUoVEpXhxhEp2/bh6IxGHMbe7+rJwuy0w2nzyH8f7/z6QtWRb1+ctV52+6z9zk4uahU6ssHV2667F7ndN549sKuQurfjYSMCKDuMIcxk6cu2OToc/nEknGSgqKSpQW5BC7ufRcfYbA4erfuC79vdrlydreD/QRZSXllAEBhdgaOjW2bxy08B2dXh5gVv9k8e98r7iX52ZtdrtCM3ZSXR2MwAx0SZBiDolKprI7hHwKFQu0+e2+Cxby+NS/Ky0yIfNfUUC8sIaVnPAMZCKR+jUqIfj/VbiVS0b0gK42h8Htawmek8LuB+eyGupq4jyGVpUUoFEpYQnrM5Km0B9XdHGIK22aPa2ttvRwcz2Cnf1+Zobw8s0Ly2LpEnBvj5+fX74uADA2g7vxBP3Vn6JKT+n37XINVB04ib04PMlB3RhpwXSgE1FaWn9+/RkBUYiD254JAOgLzOyOaqrJi51WzS/Jz0Bj04etPulrICoEwF6g7Ixp+YbHzgZ9ZHQVkxAHnWRAIZLCBugOBQAYbqDsQCGSwgfkdRgLveEYGP2F1FCOLtITP4hMNWB0FZPCAuvMHNjY2A3r+L1++EAgEdXX1Ae2FuZDJ5PDwcFVVVTExsQHqQnyigYEB1J0RBHxvcPBISkrS0dHx8vKaP38+q2PpHdbW1hkZGQkJCWg0nJhDmADUncHDysoqLy8vLi5uyP1609LSNDU1//vvvyVLlrA6FshwAOrOIBETE2NgYPDq1avp06ezOpa+sHLlyvfv3//48YOdHe4YAekvUHcGCRMTEyRRwupA+kh+fr6ysvLp06c3btzI6lggQx6oO4PBq1evLC0tIyMjDQ0NWR1L39m+fbuXl1dmZiY3NzerY4EMbaDuDDhUKlVXV1daWvrp06esjqVfVFRUKCgo7Nmz58CBA6yOBTK0GWIJzqGIt7d3QkKCs7MzqwPpL4KCgtu3bz958mRlZSWrY4EMbeB4Z2Bpb29XV1c3NDS8e/cuq2NhAg0NDQoKCsuXL3d3d2d1LJAhDBzvDCw3b97Mz88/cuQIqwNhDlxcXPv27btw4cLPnz8H4vwnTpxAoVBfvnwZiJND/h2g7gwgzc3Nrq6u69atk5eXZ3UsTGPjxo1iYmLHjx/v9Gh8fDyqa0pKSgY9Xsi/CFwnMYCcP3++urra0dGR1YEwEzY2toMHD65Zs2b79u2qqqqd+kyYMGHOnDkd7Tw8PAMfIGQIAHVnoKipqTl58uSOHTtERERYHQuTsbe3P336tJOTk7e3d6cOo0aN2rVr16DHBRkywHnWQOHu7k6lUrdv387qQJgPBoM5duyYr6/vt2/f+tA8KyvL3t5eWloaj8fLyso6ODgUFRV1dKNSqWfPntXR0SESidzc3KNGjfL09KQdLSsrW79+vZSUFBsbm6io6JIlS3Jzc/t3WZDBA453BoTi4uKLFy86Ozvz8fGxOpYBwdraety4cQcOHAgKCupVw+rqamNj49bW1sOHD8vKyiYnJx87duzbt2+xsbFY7B//G93c3A4cOLBly5YpU6ZQqdRXr15t3ry5qalpz549tbW1hoaGVVVVBw4c0NLSyszMdHFxGT9+fHx8vKioKLOvFTIAUCEDwPr168XFxRsbG1kdyADy4cMHAMDbt2/pjXFxcUjuuatWT5484efnv3HjBs2yb98+AMDXr1+pVKqbmxsAIDY2lkqlGhoaysvL07d1c3O7ePEilUo9fPgwACAkJIR2CHkEtnPnTmZfJWRAgLrDfLKzs9nY2Oh/WsMVc3PzsWPHUigUmgXRnU6RkZHp9CR37twBADx+/JhBd+bPn49CoU6dOlVVVcXQRE9PT0BAgMGooKCgra09AFcJYT5wnsV8Dh48KCsru3z5clYHMuC4ubnp6+sHBgbOnj2b3m5gYGBlZcXgTCQSkQ/e3t537txJTEysqqoikUgkEgkAQKFQGPzPnz9fWVm5a9euPXv2jB49eurUqcuWLUOeoOXn51dWVqJQKIYmvLy8A3CVEOYDdYfJJCYment7e3t7M2QrhiW6urrz5s3bt2+fpaUl/fWOGTMGmT115MyZMzt37jQzMzt79qyUlBQejw8ODu70VQMREZGQkJC0tLSXL1+GhoaeOXPG3d399u3b9vb2KBRKXFz81i3GvdgxGAyzLxEyMLB6wDXcmDFjhq6uLv3UY3iTlpaGxWLv3r2LfP1rfkdRUVFUVLS9vZ1mOXHiBADAz8+PYZ7FQHFxsaqqKj8/P5VKHTt2LIFAIJFIA3NNkAFnyP9NLi8vT05O7sZBSUlJQkJicIKJiIh49erVmzdvOk4BhivKysr29vaHDx+eP39+T0qCtbW1EYlE2uCoqqrqypUrHedZtbW1K1asmDt37tKlSxGLqKiovr7+w4cP29raLCwsPn/+7O3tvXjxYuRoWVnZxIkT9+/fv2IF3Gp5KMBq4esvfn5+3V8g8gRkcDAyMpo8efKgdfePUFBQwMHBcf78+Z6Md+zt7QEA+/btCw4O9vT0VFRURJZcrFy5Micnh368M23aNDwef/DgwWfPnj179uzQoUM4HM7GxoZKpVZXV8vJybGzsx85cuTFixc3b95UUVEhEolpaWmDe+mQPjLkdaeqqiqWDgUFBS4uLnpLWVnZ4ETy/PlzAEBUVNTgdPdPsXPnTiEhobq6ur/qTkVFxaJFiwQFBQkEwvjx44ODg8lkspWVFR6PNzc3p9edhoaGvXv3qqiocHJycnNza2pqurq6NjU1IecpKSlZu3atpKQkDocTERGxs7NLTk4exCuG9IvhVgdDU1OzoKCgpqaG3pifn5+dnT1hwoSWlpbU1FQNDY2ysrK8vDwkTYD4NDY2xsbGysrKysrK0hpmZWWVlpby8/OrqKh0P3WiUCi6urpycnIBAQEDdnH/LpWVlfLy8rt27Tp06BCrY4EMBVgtfExGQ0ODSCQyGJG/ouHh4YKCgsj7ZkjFvNTUVJpPYmIiAODIkSPI148fP9IvepSSkgoMDOym3/v372MwmKSkpIG5rCGAs7MzNzd3aWkpqwOBDAFGxPosHA4HADhw4MDixYsDAgI0NTW7909PT582bRoHB8eXL19aW1uTk5Pl5eWtra27eimuvb3dyclp2bJlGhoaA3MFQ4AdO3YQCAQPDw9WBwIZAowI3UFe65CQkDh37tzcuXP/uoTn9OnTTU1NPj4+urq6bGxs6urqyMLrs2fPdup//fr1nz9/jvApBhcXl6Oj46VLlwaoJBhkODHkn6P3nI5v0HZFeHi4gIBAYWFhYWEhzSghIREVFdXRubGx8fjx4xs2bJCTk2NesEOSdevWnT9//ujRozdu3GB1LJB/mhGkOz3f3ruioqKystLExITB3un+LefOnWtoaOjq9dwRBRsb2+HDhx0cHHbs2KGmpsbqcCD/LiNinoXQ/cKFtrY22mekLkxzB8rLyxla1dTUnD59eufOncOvuFffWLp0qaam5rCpJw0ZIEaQ7tCDaFBLSwvNkpGRQfusqqpaWFjY3t6Op6O8vLzj+7hubm4YDGbHjh2DGPs/DRqNdnJy8vf3j4mJobcPs9c1IP1khOoOsnLi06dPyNfGxkb6nPH8+fPb29vpS5fHxMTIyckxbN5SVFTk6enp6OgIywbTM2fOHAMDA/ose2ho6NixY+mTZZARzgjK79Aza9YsHh6eHTt2REVF4fH40NBQBweHmJgY5M/yypUrAwMDPTw8YmJi9PX1S0pK/P39tbS01q5dS38SpJzgunXrWHcd/ygnTpyYPHny27dvubm59+7di1QIS0tLG7SFcpB/nOGmO/r6+vQvHCNISkoaGRnR6r8giwy/fft27ty5rKwsIpF44cIFMzOzkJAQ5IeBRqOfPn3q6+v78uXLhIQEfn7+M2fOLFu2jJOTk3aGjIyM//777+rVqxwcHIN4fUODSZMmTZw40dbWtqamBpnSYrHY9PT0KVOmsDo0yD/BcFsnMWgsWLAgPj4+KSlpJNTZ6RUFBQVHjx69desWCoUik8mIkY2NbcOGDV29AAUZacDfTF/4/v27n5+fn58fFB16KioqXF1dL1261LGuRVtbW0pKCutCg/xbwPFOX7CwsKioqPj8+fPIqbPTE44ePdrNE3RJSUn4KjMEAf657jUfP34MDg4ODQ2FosPA4cOHiUTi9u3bO/1jVlRU1NLSgsfjWREa5N8Cjnd6zYQJEzg5Od+8ecPqQP5R7t27t2LFCmTZMcOh5ORkdXV1FsUF+YeA453e8ezZs+jo6OjoaFYH8u+ybNkyIpFoa2tLJpPpszwoFCotLQ3qDmTkvjfYNygUypEjR+bNmzdu3DhWx/JPM3v27NevX+PxePq8Ow6HS0tLY2lckH8FqDtdQqFQvn79Sm958OBBUlLS0aNHWRfUkMHExCQsLIyLi4smPRQKBeoOBAHqTpfk5+fr6+vb2Nggv5a2tjZnZ+fly5fDldY9RE9PLyoqSkBAAKm7RiKRkKKOEAjMK3fJ69evp0+fjsViKRTK8uXLZWRk3Nzc0tLSpKWlWR3aUCIvL8/ExKSgoKC9vZ2bm7uuro7VEUFYD8wrd0laWhoOh2tvbwcA3L9/v729XUtLCz4G7i0yMjLR0dGmpqbJycn19fUVFRVIlWvISAbOs7okPT2d9hlRnx8/fsjKyu7btw/+0e4VIiIikZGRBgYGiJqzOhwI64G60yXJycmI3NBob29vbm4+ffq0nJxcx825Id1AJBLfv38/d+5cejWHjFhgfqdLxMTESkpKOtoxGAwHB0dQUNDEiRNZEVeXFBQUbN++ndVRdAeFQqmpqeHn52d1IMMQOzs7W1tbVkfRU2B+p3MaGxtLS0s72rFYLA8Pz9u3b0eNGsWKuLqjtrbW39/fzFCPm4vA6li6hIgF5LoyVkcx3AiN/KKhoQF1Z8iTkZHRcSSIw+GEhYXfv3+vpKTEorj+jvve9WqKjBWIIMObMVYOrA6hd8D8TuekpaUxLPvE4XAyMjKfPn36l0UHAhkSQN3pHOQhOu0rFovV1NSMjo6WlJRkaVwQyHAA6k7npKWl0WrlYbFYAwODsLAw+OIJBMIUoO50TlJSEqI7GAzGwsLizZs3nW7aB4FA+gDUnc7JzMxESjesWLHiyZMn8DVlCISJQN3phOLi4qamJgDAnj17rl+/jsFgWB0RBDKsgM/ROwF5mOXh4bFr1y5WxwKBDEP+0J26ujpYeRsAEB4efuTIEQsLi+Tk5EHrVEpKCu472imnbj46fPZmhO/lMRoqg9ZpVn6hlsWynQ4Lju1YPWidjhz+0J3g4OD58+ezLph/Cycnp8HszsfHx87ObhA6ComInb123+HNK/atW8JwKDw2YfryHQc2LDuw0X4QIhkhNDQ1u16+F/A6rKS8UpCfd+aUCU5bVvDydPKY4lty2kS7DR3tL26enGIwZlCCHSQ6mWfpHHvHikhGNAmH4EaawxMKhTp33f5P8cmr7GbpqCml5+Rfefgk9nvqB6+LuA6br9XWNwIAls+z0FZVpLeryEsNbtQDTie6QxAfvNEsBDIMqG9sehz04e6ToMeXXPh5/5gsB7wOi/yaeHLfxo1LrRGLprL8qv0n7j8JXmk7k+E8tfUNAIC5U43MJ+oPYvgsAD7PgnTO2ds+BA3T/KLSoxf/U5qyQEB3hq7VyofPfu/eU15Vs+XoOaUpC4g602Qn26zc65pXWELfNvtn0Zaj5yQM5wjpWVqt3ltUWpFfVDp3vaOw/kzZyTabnM42tbQi/i6X7xE0TFMzc3e4XJSdbMM7aprenFUBr8M6DexncdkaR3d5YzuizjTpSfMWbnVKycgBABz3vEPQMH0X/Y3eOSY+haBheuTcre4DBgC8jfo6wW493+jpUhOtNxw+VVff2JO7FPUtae0BD3kj2+0uFyRFhTnw7AwOT96EseGwK2wtaZb5lqY83JyPgzu5OmS8w8PF2ZOuhzTweRakc7BYDABg+/ELAICj21eRyOSzt7xXO7orykiOG6VeV99osmhzVW393rWLNZTlsvMK3a8/NFq4KSbguoggP9J269FzkqLC5w5u/Zacdv6O3/rDp4rKKmZNmbBolvnTkPDbfi/EhPiRRBIbDgsAWHPAQ0ZC9OS+jdW1dadveS/deYyPyG0y/o+8RllltdHCTQ2NTfvWLdVSkc8tKHa9ct940eZIv6vLbSxPXHtwN+AVfSrEL+g9AGCZ9fTuA05Oz5m3wVFYgO/Cke2CfMTg8Jg1Bz26uTkl5ZUPA0PuBQRl5BZoqSg4b3NYOMucj9hJyub7jywVeRkCnR5hMGgdVcW4lIyOznUNiO78u+UEmAXUHUjnoAAKAECmUJ5edUMskqJClg67Q6O+jBulfuGuX/bPot/5TkM9fW21CXbrz/3n67Z7HdKWn5fnyrFdAADbGSYRX76HRMSe2LN+i70NAGDmFMPX4THhsQkHkL5QKACAAB/xwZnDSF8mBro6lvaX7gcw6M65/3xLyiufXTtBm4kYjNEcO3f1yesPr7vunTpp3PO3kTV19UjWlkKhPg7+MElfR0Fa4rjnnW4CvvTgcVs7yffisVHqSgAAC6PxW4+dT07P6XhbaurqV+13f/0xhotAsLOc8p+HY/dP2YrLKgx1tRmMwgJ8NXX1La1teHY2enttXQMA4FXYp1WO7unZ+RgsZvwojQMblulrD7etBOA8C9Idi62m0j4ryUoBAKpq6gAAweGf+Xl56EcWozWU5aXE30b93vlnttkk2md5aXEAwGyzX5XSOPDsYsKCVbV/lIudb2lK15ekoozEl8QfDPG8jfrKy8NtNkGPZtFUlpeTFAv7HA8AcLC1bGlt837xFjkU9jmutKLK3trirwF/iksW4udFRAdhjvkk0BmVNXWvPkSbTxyb+ubB+UNb//pov6WtjZ0Nx2BELK1tbQz22oZGAMCl+wFTxo9x37t+ubVF1NdEs6XbIr58776XIQcc70C6Q1zk91JYHBaD1AwEAPwsLq2qqSNomDL4E7m5aJ9FBH/XFWTH4ZC/8zQLGxuOQvmjwpGMhCj9VzFhweyfjL+3wtJySVEhhhIlEqJC0XFJAIDpk8dLiAjdDQhat2gOMsni4SLMmTr5rwGXVFRLiQl3deH0CPIRZ5lOCPrwSW3qkgUzTZfPm6GjptipJwIHO3trWzuDsaW1DQDA0WHxzXLrGVMMdA1Ga9Cesi+YaWa0cNPBMzc+eF3sppchB9SdEQeBAw8AqK6t73iopLySQTsYfuH0djFhgavHdjPY0Wg0vU/HVt0ExlBojUqldurf1s74M6ZSqcjMDoNB21tPd71yPzEtS1VB5umbj7YzpiC5lZ4ETA+ZRO7UTuTm8rlwtKyy+uGzN/cCgq89eqajprhi3oz5M03p7xsNMWEB5K7SU1xWKcBHRLJa9Ggoy2koy9FbRqkraakoxCUPt6LULNad8ij/tpoSiRmbmOgJ6R55KXEUChWf2kle83NCKgBAVeHvG4RJi4ukZORMMdDFYJg2VS8qraD/WlJeST8+QpASE87KKySTKfT95heVSooJIZ+X21i6X3/o+/KdwRjNmrr65fNm9CRgIX4igzrkFnZSWpuGsADf9pXzt6+cHx2XdPdx0IHT1/edvDrHfNLlo7sYUjZjNFSevAlvbG7h5Pg1umlta//+I9NQV6vjaX9k5zc2Netq/jF363SmNtRhcX6nPMqv8JUncz17SHNxRsm7O4WvPCs/B5JbGpjo/I8jJixgaqD7MTaBlgdB+BibcMM7UFZS1GS87l9PMm3S2KaWVuRpEUJ5VY32jGX3ngT3OTD6B+c5BcVZ+UXjR2l07LehqfnVh2iaJfZ76s/iMrMJv9LMkqJC5hPH+gd/8Hn5VkNZjvYb7j5gPS3V8qqaeLpnTN4vQnsSs8FozavHd2eH+Z1x3Jz9s7ipuYXBwXaGSTuJdNPnOc3yn//LxuaWBTPNOp5ti/NZ82XbcgqKaZbouKQfWbmTx/5zxbz7CYvHO8obrlNJjMPmfnr2hDzfY0VvrqFxeCwnsa2mlI1HSHXrXU4ZxucOfXAeElw+tmvK4s0r97p6PXujq6WCQaMTfmS9+hDNy8P18MyRngxhNi2b9zDwzfpDJ7PyCnQ1VUoqqs/951NWVWMwWrPPUaXn5K87eHKW6YT6xmbXy3fRaNRmexsGny32tl6BIasdTzhusFdXlEnP+ely+Z4AH3HPmsU0n1V2M202HvxZXOq+5/eag+4DXrtwts/Lt9YbDuxevZDIzfXyfdTP4s6Lz9fVN77++LmjnZPAsXGJdceK+pYmhuYT9Q+euZ5bUDxKXSkxLfv6o2eT9HWQJDqJTObRnjpaQznS9woA4MiWlbNW75myeMsqu5niIoI/svJv+T7n4iQc276qz3f134TFuoMlEJnu+VcqYp4Wvb4qYrxUdoEzGsfeXJyRcmZR2qXVo10jUFjGAW2vnIcKkqJCsU9vnr3t8+J9dPS9xwAASVHhrcttty63pU8GdwORm+uDl+cxzzt3Hgd5XPfiI3JP0td5dN5ZSbbvdWDPHtrqH/R+w+HTtfUNSnJSj845dRzv8PPyvPe6ePTCf2dv+1RW1/Lz8kyfPO7gpuWSokI0n2mTxkmICJVXVS+c9XtM0X3AY3XUH51zPuZ5Z//Jq1ycBEtjg4dnj6iYLWztkEsqKCmz3328q0sonvSsY5bH+7zziasPfF6+ve33UkSQb9OyeQc22qPRnaSuJuhqhdw7d/KG123/lxVVtQJ8xDlTJ+9fv1RBWqKX9/Jf54/9s3x9fefPn29wq5C5fZBbGqrj37RWFbILSPKPnt5WU1oe/VhwrBWHmBJ91qY8+jG5uV50yvLm4oya5HBAoRCk1Ihqv568MjG/k+Rq1VKer3vqCwrzS3bLo/0zb25VXndVQH9Wf5z7TLSDRP/XhSYnJ2tqan59dmvI7SfB3BXnzS2tKuaLzAx1b7s7MiO6IcAYK4f5S+wHeSVzfxjw8U5LeV6y+7y2mhK8sBwA4OeTkyImywoCz3DJjeYQUyqP8mvITUDUpDL2edPPFBQWl+t1iF1Ipr2ugtRQJTjeWmn1RSS/Q/Okh9LWXBh0uavexUxXYLn4//Rvqc+OExw7m6YjAABeTRMAQG1qBIOU9MoZ8o9w4a5/ZXXt5mWMczTIv8OA606ez9G26mLlDdcFdC0BAKVhD3K9nQAAqA7jTBQa3V5fUR7lP8YjBscjRKWQUs8srvgUIGW1Ay8i18XpAYXUVh7p29VRIQMbBt1pKc8FVApe5I8RAY5bAIPnainNZmjeK2cIa2lqaQ2J+PwpLtnz/uMVNpajNZRZHRGkSwZWd6ik9urEt9yKeojoAABEjJZUfHpSl/6pU39Ke6uMzQEcjxAAAIXGCujNrE2NaC7J6kZ3sATiGI+YnodEbm4AAGDwjEtpMBxcpKa6/jhDWEt1bd2qfSewWOy6RXNcdq5ldTiQ7hhY3WmtLKCS2glS6vRGXk2jrnQHAMAp8/u9BgyBBxEjpgbVxX7wVCro5C21XjlD+siuVQt3rVrYz5NIiAiVf3nJpIggA8vAvr9Dbm0ELmIdsQAAIABJREFUAGDY/1jXj+UW6MofhcagcYyVBJgL8lyM3Mw4WiE113d8ZNYrZwgE0kMGdryDiAil7Y+XqUiNNUzsord5ZXZhWRQa01L6x1LjtuoSSmsTh6gCQ/NeOUMgkB4ysLrDLiAJUOjm4j9eya9N+cjELnqbV0Zj2biVxtamRlBIbWjsr1faqxNCAABEDSOG5r1yhvQEbm1zCyMD34tHmegJGXIM8HiHjYNHeVxdWnTtjyiiqiHy8ktDTjwTu+htXhkAID51zY+LK3IeHpBbdByNY2/MS/z59CSHmBKfjhny4L88yp9bUY9Xw+ivzpDe4mA3S1Opy6cEffPsCR9i4k5cuR+XkkGhUDRV5HevXjTD2OCvrZLSsyfYrufmIhREPqEZn4aEX7jjn5aTT6UCJVnJtQtnL5xl1v2SVwgDA/4cXXb+keSTtqmnF3CIKwMKBQAgabklz99loPvtBr5RUyWtdhQ8P1fx6QmWk9hWXcIuKK2y8SYKjQEAtJbnFwSeEZ+2FtGd7p0hveXcwS1M9/wrIRGx1hscVeRlDm9egcNiHjx7Y7vp0MOzh+eYT+6mVTuJtNrRvZ1Eoje6Xb1/7OKdOeaTt66wRQHUkzfhq/afSEzLctu9jlnRjgQGXHc4ZbRGu0ZUxb8mNVTjReT4dMzLI3wAACgMDgAgZGhLVP9VYElg7GxO6T8W6RIkVCStdhDElRg8+4/U7J1C4+fWJIeRWxrxInJ8WqZotl/LhdmFpCWtdnAr6vXEGTIk2HPispiQwPuHF7g5CQAA+3kzDOat2XPi8qwpE7tZjOZx3SunoNhk/JiEH5mIpbWt/fSNR1oqCg/PHkYGOFZmE7PyCy8/eOK4YRlyckhPGHDdobS3kpvrRCb/XrbXXJIFAGDjEwMACBn+fqlUcOxshrYECVWChCrymd6TKeBF5EVF5DuxC8lIzd7ZQ2dIRxqamg+eufH0TXhdQ6O2quKx7atevo+6cNe/5FMgDzcnfdZGUHeG3UzTNfOt9rhf/paUxkngmDx21Ml9G0SFBJiY30nNzE3Lyd+3bglNF9jZcMusLRxPXYtJSDEc0/lC1qT0bI9rD087bgqP/Z0WqK1vaGpp1VZVoJ9Vaasqfkn8UV5VA3Wn5wz8+8p+x0ve3lbZeJN/jAUAoKkgtSzCm0NcGT4PGq6scfR4GhK+ePbU2WYTfxaXLdt1XE5SDCkwyOCJxWLSs/MXbXdaaTtzw5K5MfEp5+/41dY3Bl4/0c35n7wJX7zdudNDwgJ8ueH+DMaEH1kAgNHqf7y+PEZTBQAQn5Leqe6QyOTVju4GozUc7GbS644QP68gP29qZh69c2buTx4ugqSocMfzQLpiwHVHympHfWZs2qVVeBF5NAbXXJKF5eZXWnVhoPuFsIT8otKnIeHTJo+74boXsagryVmsYBw//gKFivqW9MHr4lgddQDAHPPJsd9Tw2LiSGQyFtNl+kxXU+Way55OD3Gwd/LyV3FZBQBARPCPEmJIRbHiMsZKgAge170ycn7GPrvFkC1GoVAuO9esO3hy+/ELy+ZOx2Ixj4M/hMcmnDu4pWPxQEg3DPjNwnLxaR96VZsa0VSYRmlvwQvL8Wmbotk4BrpfCEuI/Z4KALCke1RkNHaUmoJMalZep/7KclKI6CAoyUpFfUuqb2jqdE8YBGlxkaVzpvU8pJa2NgAAO9sfZQCRCn4tHSqrIzMs96sPXHetRYZpDCydM626tv7g6evXHj1DKqs6rl+6eoFVz+OBDFb9HRSaqD6ZqN7dswPI8KC0oqpjUXTVrnVHXPgPTyxd6XhmgQyCGDZvQCqrEzpUVkdmWHpaqusXz+30bFuPnb8XEOS01cHUUBeNRn2MTXA6f/t7WrbPBWf4KL3nwMEhhJkg1ZzQf/4Cu6qd/tdK70xBTFgAAFBSXkVvRGZYHTeNOHnDKyuvMPrxtU7rcsV+T73hHei4fukOh/mIRUtFgUSm7PO48jo8ZrrR+IG8jmHFkNedturi0nAvXg0j+iffEFaB7A5eWllNb0zLzmdiF73NKyO1xL4mpVn9f/cuAMDnhBQAwJgOtTKevvnY0NSsZbGMwU7QMDUYrYnM71QV/qiLoiwrCQBIy/kJdafnDHndaa0uLgg8g8Fzslx3yK2NNYkfWivyMQQeTikNLrnhVou7JyBVb95Ff0V2ywMAfIpPTkpnZq2i3uaVlWQlddQUHwa+2blqIbIFcENT853HrxRlJPS0VBmcPfZtYNgZ/fwd38S07Jtu+/h5eZpbWwEAkV+/21gY0xw+xScjWScmXd+IYMjrzj9C9fd3Wbe3tddX4rgFSE21VDKJT8dMae1lhrX4wx5VeekJulp+r94L8vFOmzQ2v6j0zG3vCbpakV8TmdVFb/PKAIBT+zfNWLnLZPFmB1tLNBp92+9lYWn5k6tuyCzP+cJt92sPH192sTAab9Rh5wa/oHc/svNnmU4AAJDJlPGjNG74BFIBmDJ+DBaLifqW6HnvsbaqoqWJIbMucCQAdYcJtFUXp19dixeS1nQMxAvLUknteX7HikNvFTw7LWN3mNXRDTZe55x2uXk+DHxzLyBIV1Pl/unDV72eRn5NZGHadYKuVvCd08cu3jly7hby8s6Lmyc7SsxfwWDQz2963PAO9H7x1vtFaEtLq7S4yIYl1nvXLobP0XtF/24WlVqX/qmpKJ3c0sBGFOHVMsHR1dYhNdXWJL5vqyrCcHBzSmtyyY9G7EhGRkB/JjufeNW3oLbaMg4xRT4dcxQa01pVWB0fQiW1cymM4Vb4tYtTa0V+WaQf/xgLvJA0Uh8exyPEP2oqw0JzGuSWhprE9y3leWgcO5fcKG5F/R4G3GdqksOo7W2y84/ghWUBACgsTmb+4bKP3jXJYTL9P/tQQ4if9+7Jg/SWsspqNhwWeXRd/z2EZi/5FMjQ9uKR7RePbEc+03v2H4PRmq9un+r00JEtK49sWdlVw3unDtF/5eTAb1tht21FvyrwQ/quO+TWxpRT8xty4jlEFTHshObSbGp7q+LqiwJ6MwEAVXHBGTc2owAKLyrfXl/ZVlUkMNZKec1lgEK111UUBJ5Bs+HLwh5SqVRySwOpoUpw3FzhifPTLq1i4xVpqy4htzbK2B0Sn7YOANBWU1oQeAZQyOXRj6lUCgqNba3Iz+PkVd/tx/lnJUMAQO2PqPQrayltzQQJFVJTXYu3E1F9ssqmmxh2zu4Dpqf6+7uGnLhOr5ogoSqgZ8lgFJ64QHjiAnoLCo1Fs3OgMGxg5OF0/lZbO8l1169Ko7X1DTEJKbDaMYSevutO2Ufvhuw4tR1eyLptSltzmqdD9v19/KOmUQE189Y2Nl5RrYMvsQQeZLVEUfCVSr2ZArqWAIUGABS98lRae5lX04RKJqWcXlDx+Wnjz2StQ684RBVIDdXxh6cUBV9BdAegMQCAwuDLiivPCY6bAwCoSfrw4/yynAeOmvuf0odEaqhKv7SKjV9MbccjNqIwskdF+tV1PwPcZRce7SZghp2wGnK+lUf6dXrVfDrmHXWnI1XfgtrrKkSMl/b59g5dmlvaLt7zr6iqsTKbWFvf6Hn/cV194+7Vi1gdF+Qfou+6015bBgDAsP9aC4dm41DecB2gUCgsjtxUK2N7EC8kjYgOAEBogm1R8JXG3ERagXdupXHIhjAoDJZ/1LS6tGjBsbORRVtYLj6i6oSKmCfklgYM/tcuaNwKeojoAAB4NY15NY2qv78jNfzxvLYi5impqVZx9UVEdAAAAvqziGEPyiJ8ZOYf6SZghkuTmr1LavauPtyTqm+vGrLjmwp/1KZ8FDFaImHJtEoOQwi33euEBfkePn39+HUYGoXSUVMMuOwCnzFD6Om77ggaWJe8u5NyeqGA3kyi+iSi+iTarx1LIIoYLWkuySqP9m+vq6CS2pDapuSWelpz2kJzWv12gsTvoTgiWFTS73dMaekeBA4J1erv71rK/3gLtiEvEQBQmxxOX1qM0tpEbmloLc/vJmBmUf39XXm0P5XUziU/miCpiur6fblhDBqN2umwYKfDgh74QkYofdcdgriKjnNIccjNqrjX5VF+AAAe5fGyC5w4ZbQobS0Z1zdUxb1m4xPlEFVAsxGoFDJDczT77yVayJOOPxZtdXj2geH4Y8EOho2AzJVQdHXgKS2NAIC6jM+oP1cV/o+9s4yLqmnD+GwHGyws3Q0CSqmgGIBdCA+Khd0tduNjB8aj2IEtiC2KrYhigYiIdHc3bL8fju+6LLASiys4/58fODOzc+6DcHHOfWaum6JvLeBzJQTc6m+CGAZT9xlM3cepKCoMv5F21afsW6jponPSmhwC6TS06X0WgamtO/5f3fH/1uWnlkQ9yg4+/P3AROudb/JfXiz5/EjbbbXG8EWIgtQVpn9e06YFDtyactFDpFIFmlBP0RBtMp5ztKl6W00FLCZqLc0rc2sqOOUFJDVD5BBHY6oPnlObE18QFlBXmE5U+gtfakEgkmj9g0BdYXptTgLyNVFFT33wXG2PdZzK4qq0L1UZXwEAKs5ThLct5d/D2hhoTcY30cPanASAQpPqe3Ehdy6VyRGijWUxL6szYyUHLHauqtTIwjeBjf6riA9vGFt6gE/Uhn41OfH1WtEYAACvrrrheIgU2Xf6KtncJfJbfDPGQv4UWn+/k3J+VW1esvmqIGTRChDwq1I+AwDw8qoEhhoAoCol8v+lxN8UhF4BKFRbamyWx70p/fIUcVOvSHhXFvOKbuYodp/C7OGaeWt35h1fqoEtcstTmfgh4ehs+W4uxnOOSQhY7FwtzSszHTwK3gQmn1tuPOcogakNACiNelz07iaeoUbWMGn1JUM6CpHf4h3Hzm/Yfv/0XmcHG1lE9KfTet3Rm7g9dv/4z+v6kNQMMQQyqyiTW1Ou5bqcpGao6jy1ICwg7r+pctoW3OoyPofVxftKwon5JZ/uJ3DZ6oNbU0NWpd+klItrMIFkNJ5YkxWPpcjrTRB3wMRSGMbzTyUcnxu1sT9JzUjA59XmJFD0rfUmbJMccKu/CQh00156E7alB22PXO2AoyryWDV8di1RWcdw1hFo//43UF5ZDQCY+s/Qrqb1fpZM9LVkF9QfTet1h6RmaLMrvOxbaF1BCp/Dwsur0M364OVVkDSK9Y7XJVFPOBWFBEVNhtVADEHOdMn54o/30HgSnqGmOcqbZtxTOBVZy1xzlDdR+WdSRt7SBUtREM00E5jaVltflEQ9Zpdkq7pMV7AZilTsJDDUNEd5Uw1+bAqlmzna7H73Y70yniinZU4ztkce9yQE3HZUnacye7iWfw9jFWeh8SSisi69Sx8oOn8J5ZVVAAC3Qf0GOnZvxnBI2/LKKAyW0dUZAOdG5qUoKDt6irYQFDR+rAMEQMw4XU7bXE7bXLSF0dWZ0VVsWgGGRFVy+EfsRHiGmthsGCJFsfvIlgbcdrAURlPn7ZQIBIIjF29cvPUoIyePzxfoaalP8xg2d8KPNVYpmTk7j1189f5zYUmZClPByd5m06JpiBXOgbMB631PxoRcPHgu8EbISzab42BtcXzbSi6Pt2TroTefoskk4gjn3nvWzCcTCQCA7UcvbPc7H3HnzKmAezcfvSqrqDTU1Vo3z8t9cCOlEwtLyrYe8X/48l1BcSmDTnV2sNm8eLqOhuovA24LyP0OjfJ37QFuC3AzG6SV7D111efQmfmT3Pv3tBIIQEjoe+/th2tqWd4zPMsqKgdP8WZxOOvmeeloqMYmpSE188ICj2IxGMRUcMm/BzVVlQ9uWBL5Lf6Q//V5m/blFBSNdO49YeTA209Cz16/r6aksH7BFAAAsuVy9vo9Ohqqe9csKC2v8D1zzWv5Vgad6mRfL3tSUVntNGFRSXnl6jkTzY31UtKzd5+83G/8wvc3T6owFSQELHZptq4zvielNXrV/21aOtNT/K9LRRWiO7CeRHOBugNpJSGv3ulpqu1buwA5HOnSW09LjUwiAABef4yuqavb7j17qscwAMDQfvblFVX7Tl+NiU+x6mKEAijEIezY1hUAgDHDnMI+RT8J+7hr1bzFUzwAACOcez0KfR/68ct6AITLuxQZ9Ev7f2zud3Kw7TZ8it/Fm2K689/56ymZOT+zub3sunc16z123sFzgTtXzpUQsBhblkwvrahq9Kp7dhPfEggAKK+oAgA8ePVu5rrdCSkZGCzG3sp8/fzJ3buate173GnpALojlsGB/CFoqim9/xJ7yP/6FPch8jQqAGDFzPFI10iX3ohhjRBjfW0AQHpOnlUXI6TFdcDPKoz62uoRMfGu//cDJBEJasrMkvJ6bz89h7sIvzbS1TTU0fj0NU4spJDQDwryNNFXSNbmxvpa6s/eRkgOWIwRzr0bbW+K8qpqAIDfxZsTRw2cMWZ4Qmrm2evBA7yWBp/Z62jXtUVT/SV0AN1pmMGB/AnsW7uwpKxi7d7j631PdDMzGtDLboLrQBM9baT3+oMXF28/+paYUlpeyeXyuDweAIDPFwg/rsL8aWNCwOGEtWUQ8Hic6GAAAJKjEaKmzEzJjBYLKTM3v6SsgmzuItZOp1J+GXBbmOo+zNnB1sHaHJEzAMC4EQP6jV+4Yf+pl1cOt33+zkcH0B3In4myIuP+6b0JqZkhoe+fh0f8d/6675mrx7etmuQ66L/zQWv2HHN2sNmzer6mqjKRgH8c9gHx3BLS0AZMsjGYQCAQO2x0BjVlxeNbV4q1I8byEgJu1TfgJ+bGeubG9ZbIW3UxsjQx+PwtoY0zd1ag7kDahLGelrGe1uIpHvlFJUOmLl+9++gk10Gnrt1VYSrcPrFLWH7v6dtPbTxRTn6R6GFeYbHo/RGCtrpKbGKqs4OthKrnjQYsNqaleeW4lIzqmlpbi3prROvYbEKDEqkQhN+qO98PTKxI/NDzaOLvPGlDEk8tKnp/m2HpBAAwmLYfR2MCAGpzE8u/v+HVVRGZ2vJdnYX+G82hOv1rVXo0r7aSoKglb94XWUWdcnENuySnKi1awOd2PxTTnhckAyoqq2dv2DPKxXHCqIFIiwpTwdbS5Nr9p2wOl83h0KlyQtEpLa88de1uGwtj3Xz0aswwJ+Tr1Kzc5Iyc0QP7iI0Z3KfHp69x1x++GDfix6NWYUmZy6TFK2ZNGD2gj4SAxVxKW5pXXrzlwKevcRF3zwpL/YV/jolLThvSF7p/NM5fer+DxmBNl1wQHqYHbs15fAKNI2Ll6OyyfDxNyXTJeTmdX2cE+Vx24on5JZEP0TgCmkDmVpViKQz9ybsVbYfre+0CAMQfmV6R+KGdr0YG0KhyNbWshT77k9KzkL/zkTEJ1x+8cB3QB4/D9uthdenO400HTvft0S05I+fIhRvTPIZv+e/s0zefGpZwaCYJqRlzN+wd6dK7srp2x9HzaDRq0RQPsTELJ/9z+e7jeRv3Jqdn2VqY5BWVHjwXUFBS5mBtITlgsXlamlfevHj6yFmrnCcunjl2hLoKMy4540zgPYoceeuyma272E7PX6o7ohS9v53z6LhKfy/dcVvQOEJtbmLs/gnxfrOsd4Q1tAQTI+P6tpLIhzpjN6kNnIlCY6ozYr4f9Eo6vZhmbC8V5+Y/mauHfHYdu3jz0avD54PQaLSWusqGhVMWev0DANi1ah6Hyzt348HRSzctTAwObFjk7GD36WtcQPCzrLyCwX16NmN6cQ5sXBL08MX8Tb7llVVGelpXD/rYW5mLjaFTKS+vHNl6xN//xsM9J68w6NQ+3btdPbTFSFdTcsBtpLet5ZMLB/eeunI2KLiopFyRQR89qO/aeV4G2hptn7xT0mLdyXt+js+qVR9abxdcVcrn0q/PFbuPIKubIDvCazK+8eqqCIqa8pZOYrs3EQrCrnGry4QrmAEAValRpdHPlHuPJTB/7GqpzoipTPzAY1XjGeoMS6emjNzbSN6zsziakt6EbSgMFgBAUjPSdl+ddHpJyeeQX64/Lv3ylGpoJ9xxJqdtoTZgRsaNnZWJ7xVshrVHtH8OciTiVu9ZW71nNexSkKed27NOrPH6ka3CrxdOrvfbfnLH6pM7Vou2RNypl4QGAFDIxP82Lf1v01Kx9hUzx4u+DldWZAid4ZsfcNuxtTC5dqjxaoKQhrRYd2pzk/Oen5O3dCJr/lwTlXX/UFnMC1XnqXx2XdzhqeWxr4nKOhgitTY3EY0nmS46RzXqITZPwetrdQVp9XXnc9bd/XTTXgSmloDLSTq7tOj9bQJTG0dTrM1NSuHzDGccFNqkCuGza7MfHm0qWjWXaZLVis+uq0z5zOzhiogOwv+30Yf9Unesd70F9d+zYEk0AMDf6egOgTSTFuuOUi+PvOfnij7c1f6/7nBrysu+vWR0HYCjKmY/OFwe+1pv0g5VpykAAFZJdrTPoJSLa7v9+6xFZ8m6d6Do/W29idtVnacip4g7NCXp9BKqvi2eUW8dB5/LLnwT2GS0Dh6SdaeuMA0I+ESVepVncVRFDJFSl9+8Kpcib3P5HFbB20AsmSbz4qUQyJ9Mi3WHomdFUjMs/nhX2/3HjXHxp/sCLkep91gAAEXfVmfsRmFRF4KCBt2sd3HEQz67Do0nNvccAkHey4sU3W6I6CCGzToe62J2uRW9v6U+ZF69CyDTbfa8b+lVCOHVVgEAMETxJ0EMidJ8tyBudVnu0zOcisLS6GdoLM5kkT9WTr7VIUEgnZ7W5JWVHP7JuLm7Ov0r4u9X9O4WjqrI6OqCONFQ9W0q4sPrCtJ4dZUCPr+uMAMI+Hx2TfN1h1WSza0qETBUM+/4ChsRj/fq+q6D0kDQRLOgocdzU3BrynMeHeezazF4MqPnaKwcXZoB/vWIZXAgnYDW6A7T4Z+MW3uKPt6V07Fkl+VXJLxXGzADyY+URj9LOr2YV1clp2WOpSqisTikkkSL4NVVAQDY5YVlMS9E2yn61ji6UisClgBi4sOrFb+14dZWktSMmjkJUUmn59FEAZdTlfE1/ZpP9JbBFmtuC+ujQiAQMVqjOwQFDZqxffHHezoe64s/3AECPvKQxeeyk04twpBp3Xye4BXUkcFJp5cUFmU2Z1qhFTGSmqWbORrNPvLLT7Uxr0xQ1kWhMXX5qaKN7NI8PqsGKeYlAQGfyykrQGHxyMpDFBZH1bcxmnsscmWPvOf+hlB3IJAmaOX6HaVeY5LPeVenfy36eFdO2xypF8wqzODWlCv18hCKjoDHrUhsPPmCxuF5dVWijzPV6T+2+eEV1HFUxarUSCDgI8VFkWeZ8m+h8pZOYiuJ25hXRmPxVKMe5d/D+Fw2GvvjJVTplycAALp5I7ZSorDL8iNX9lC0G24876SwEYXCCG/ZIGIwrIc4OdjePLpdtmEYOnsiuy601VXinlxpj1PkF5Xo9RuDfD16YN8rBze3x1k6Lq3UHUW74amX1+W/ulSVGqU7zgdpxMurABQK2RmAQmN5rOqU86tRGBxS6UXs95+oalD+/U3plycMq0EAgOJPweVxP0s1qPSblHX/UOYdXy3XFQCF4rPrUi+tK/pwt9u/T5ElQj8voG15ZQCA+qDZcYenpV5erzdhGxpHqE7/mnl7L0nNCPGQZ5VkF7y+RtGzbuB/CAgKGjQTh+KIB/kvLyr3nYBCYziVxamX1wMA5C2d2hISpL3padXFd91CAu7HX5qbj14duXAjLiUDMdlY4OU+ZqiT5H2qQp69jfA9ffVrQgqHy9XXUp/lOXKy21BFBj0s8CgAYMhUaKXQCK3UHQyRomA9JD/0CgqNYfZ0+9FIoqoNmJn75FTkKns8Q7U2O0G53wQdj3XxfjMTjs1WGzhbdAZV56nFH+7GHZlOUjVA6hFru69OubBawOcDADRGLq3NT8m6dzD/1SWComZdfiqPXWMwebeY6EgFhtUgzVHeWfcOFr27hZWjs0vzCExtkwWnEXdkdnF21t39qs7TGuoOAMB47rG4w9NSLq5Ju7oJQ6ZzKotQKLSK02TlPjAP+kdDlZOzMf/xs7Tv9NVNB0472nVdN8+Lw+UGBD+funJ7Ymom4nYomav3ns5Ys9PMUNd7uicWi7kR8mrB5v3fEtP2rV2AzI9ueofq30zr90moD11AVNEnKKiLbgjQHeejYD24Ki0ahcHQjO3ltC0AAMbzT7IK0klqhkx7d6GdO1ndxHrH69LoZ+yyfIKipoLVIHZZvuYob4KCOvL4Yzz3ePWwRZWJ73msary8qrylc/vtPNByXa5k71b27RWvrpqoosewdBG+fcMratDNeotWNxUFR1OyXH+/MulTTXYct6acwFCjGtohpWwgHYLK6pptR/z7du/28JwvcoMzd8JoqxFTD54LXDVnIg4r6ReEx+Ov3OVnqKPxOuAoYgU9d8Joe/fZp67d3bJ0hhyp2QtH/j5arztyWl2QtI4YNBMHmomDaItwkTFFz6reuSkKSr3GCA+JKnq/9HtvP4gq+qr1qwAiEBQ00HiSZLdDqqHd37NQkGk7zNrc+MmFg6KNzhMXxySkpIYGyZGIZ6/fP3M9ODkjGwBgpKO5aIrH2GHit4pcHo/WdZDH0P4X9m0UNk5YuuX2k9Cabz+WmN5+EnrI/3pMfApfIDDW05o9btQ0D/HV6m2nuqZ246JpvWwshE9VJCKht63ltfvPSsoqRM3JGlJZXb3Ay93cUA8RHQAADou1t7b4npxeWFwmpylelw0iBO4L/QXcmnKqoZ2CVVutoToNowY4BgQ/z8kvUldhIi0ZOfnvv8ROch0kRyIitSLcB/dbPXsil8c7E3h/6srtOCzWbVDfFp3l+JXb3tsPD+1nf3rXGgwafetx6ILN+7PzCjcsnCo28tbj0InLGt8YpazISAsNknwiVSXF5TPGiTUmpWfTKGQlBXF/HzHkadS1c73EGhNSM6hyZKRyBqQp/lLd4fO4cYcmi/rvNAWWTNcYtqgVpxD677QhzD+RCa6Drt1/dvPRK+HezsDxZXY5AAAgAElEQVQHzwUCwaTRgwEA95+/NdDWuLBvIxqNAgAMdOyu1dvt8p3HLdKdyuqaTQdO97KxCPLbhtyGjHDuXVZR5Xvm2vxJ7gryNNHBthYmJ7avanQeEqERz3YJ8Hj8jNz8g+cCI2Li969fhFxCi7hwK+RNxNf18ydDxy/J/I26o2g79EepYgBQ2PbawEnWMMHRlOR0u2HwjeeGOihOPW3UlBWDQl4KdScg+JmupipiYP7s0iHRwVQ5spoyMz0nr0WnePMpuqqm1qP+GyWPoU4PX70L+xQ96v/27wja6ipeowe37ZoAAMA/6MH8zb6IkfP1I1uH9XdoxofqcfXe08VbDgzr77CmwU0QRIy/UXcUbIb9BpMKVedp7X0KmYDBoD2Huxw8F5iZW6Clpvw9Ke1bQur6BVMQjcgtKN53+uqrD1FZeQV1dSwuj8fnC8wMdZsx8U8ycwsAAN7bD3tvFzdFR7rag+5dzbZ5zy4qLXv2NmLisi2bFk1bNl28rlZTCASCfw+f233i8mS3IYd9lklwWYUg/I26A2kjE0cNQkp9Lp02NiD4GQqFmjhqEGJ+2nfcgoqqau8Z43p0M5OnUrBYzD/z1zdzWqFzOyJh3jM8+/UQX/NtpNteFcdFvdnnrN+z3vekvbW5g7XFLz/I5nAnr9ga/OLt7tXzFk0Wt0CENArUHUiLMTfWszQxQHTn+oMXfey66mqqIoXrsvMLD2xYPGe8KzKSw+WWV1ZTGxTwRaPQAAAut57dcnZ+IfKFtroKkrVtTrnxNuaVX77/fPFWyLyJbqIGrK4D+1y8/ehjdNwvdYfD5U5ctuV5eMQNv+2D+oibTEGa4nfozh9i5w6RIhNdB63de/zhq3epWblr509GGtkcDgBAgf4z73v00q3K6hpBAzt3NBpFo8oh79oRvielfY79UfWll60lhUy6cufx0mljhebwPofOvI2MuXV8p9i6mDbmlclEwtV7T2vr2KJbGV6+/wwA0FCR9MIBYd/pqyGh724e3dEciYQIgfc7kNbgOdx5ve+JlTv9KGSS8F1VLxtLLAaz4+gFLBaDw2LuP3+bnp03oJddWET0hy+xYlmeAb3sbj56tWzbf/16WmXkFJy8eseppw1S7oZCJvksmb5ip9/gKd4zxo5g0KlPwj6euHpnxtgRDRfjtTGv3KNbl/EjB1y993TY9BVD+9tjMZjXH6PvPH1tbqyHuLvvOn7p38PnLh/Y3PCVXHp23t6TV4z1tJMzspOvZIt2ufSyQ0ydIY0CdQfSGlSYCs4Otk/CPnqNHizUAkMdjfP7NmzzOz9jzU55GtV1gON1v23vor5FxyePmr368gEf0RkObliMx2FvPQ69cCvE0sTg9K41tx+HPn37icXmEPC4+ZPcVZUUj1y4seTfgwKBQE9bffeqeQu83NvjWo5vW2lvZX7+ZsiOoxf5fL6Wusq6eV6LJnuIvgvHoBtJFccmpdWx2N+T0hrmv8/sWgt1RwJS1p3KpI9VKZ8BAGRNM7qZY1PWWRKM3zkVRWXfXrLL8jEEMlnTjGbUUziJhK5Wk/fsLJ9dJ2ZTX50RU/L5kYLNUGRBtgR7+fYIqaNw58Suho1ug/qK3RcM6GUnTLKUfg4RtjMV5M/uruf9bm9lvmvVTzNJ98H93Af/whJAKuCw2FnjRs0aN6rR3jVzJz189U69sWeuof3shaurIS1CarrDZ9clHJtdGv0MR1VEE+RYRRlUA1uzZZfFiklINn4vDA9K8V+JxpOIyrrc2oq6/FSasb3pkvMYIkVCV/35W2bHU1eUkfv4FM20l+gejpyQ48Uf76n095JsL9/MkCAdmvDPMTV1dWK1QCFtRGq6k3nXtzT6meH0A4gHWGn087j/Jqdf36Y/ebfosNynpyQYv6dd3UwxsO2y/BriXlgeGxq7f0L+q8vqg+dI6BKdv6V2PCp9J+U+PlXw+qpQd/jsutKox/JdnfF05cxbeyTYyzczJMgfSGV1deS3eAIOL1bXvCEO1hYfb51u6fxcHi86LgkAwOe1vkRqJ0ZKuiMQFLy+StW3QUQHAMDo6qzvtQtDIIsNlGD8jvxio7F4xIACAEDv0tfONxJHY/LZdU11iV9PC+14SGqGNGP7og93dMdtQfagl355wmNVKzuOk2wvr+o8rZkhQf5A3kfFOo6d336+X8Wl5Y5j5zdj4F+KdHSHVZLNrSol1y9updJvUsOREozfsRQF5d5jC8ICov8drGAzjG7Wm6JvjaMrAwDQeGJTXW1Hpf+kxJMLiyPuKzl4IOVDcTQlRjcXyfby7RoSpF1Jeh7Q3qdQYSrA1I8EpKM7iK0nGvdrwxHJxu8GU31ppr0K3wZl3T+YeXsvVk5e1Xmq5ihvFBojoauNwSvYDsdSNha8vqbk4MGrrSz7+lx14EwUGvtLe/n2CwkC6dxIR3eQ5DGnsljysF8bv6NQSg4eSg4efHZtRXx43nP/rHsH0TiCxvDFkrpET9Fym3c0Fq/Ua0zuk1OskuyK72/4XDbyGPhre/nmhQSBQMSQzgY2AkMdS2FUpX4WLdpb+PZ6QWi9h2fE+F3BalCjxu98dm1l0icBlwMAQONJ8pbOpov98Qy10q8vJHSJRYLklZv6x62pbDR+lX6TgEBQ/PFe4btbVKMeSCWJevby/4dbU1788R6vrqr5IUFEYVgPcW/2ji1IZ0VKeWUUSrnP+JyHR3MeHUfqeVbEhyefX8mwdFHuO0E4SrLxOyc3KWaXm8aIxVquK5BHldq8FG51KdWoe3X616a6xK+nVTbvJFUDmolD/qtLrMIM/Sl7he0S7OV51eXNDAkCgYghtffoWqOW12bHp1/flv3gCIZEYxVlkDVM9b12io75pfG7+uC52cGH819eIipp81m1NbmJJFV9bfc1RCWdprqkFb9Kv4mJJxdiiBRm95HCRsn28u0dEgTSWZGa7qDxRNMlF5D1ygKBgKxuJG/RH6l+JWrnLtn4XamXh4rT5Ir4cE5FEYZIIakZ0k17IyuAdcZubKpLKshbOAEAmD1c0SLv/iXby7d3SBBIZ0XK+ySoht2phuIPGkoO/4geSjB+R2r+EpV0Gp1cQlfbKQwPAgCoujRi1iXBXr5dQ+rQXLz9yO/iTcRsuJeN5eYl0031GymzIcEEPjYxdcexi++ivhWVlCnI0x3tLDctmm6ooyG5q40wbYeNHeEy23PUqt1HI2Pi5cikvj2s9q6Zr6qkKLyuE1fuxCWn8QUCfW2NiaMGLpk6thWOqH85cF8oAABUpUZl3tqraDucrGkm61g6A/vPBGzYf3K4U68FXu7VNbWH/K8PnLw0LOCojka9EgsSTODLK6uGTl+hQKdtWDBVXVkxIyf/wNkAl0mLY0Iu8vn8prqocvXWqbbCmgeLxSSkZExY5jN9zIj5k9zeR8Ue8r9eXll99+QuAMDO4xe3HvYf6Nj9+LZVRALu1uPQ9b4nk9Kz/bZ4S/tb2Mn523Wn5POjjJu76vKSCUo6epN2yDqczkBFZfWOo+ddetleP7IVaenZrcuAyUsDHzxfOWuC6EgJJvCfvsYXlpTtXDl3wqiByODedl1PXL2TV1iSkZPfVJeY7rTGmgeFehsZ8/LK4R7duiAlhj9Gf3/1/jOXx6usqtlz4nJPqy63ju1EAh7h3Lu6ts7/xoMVs8braapJ4Xv31/C36w5JzVDJ4R8cjanYfSSGIG6LB2kFbyK/1tSxRjr3FrZYmxsXRzxoOFKCCbyGqhIKhTp66ZaxnpathQkKhTIz0Dm4YTEAgMfnN9UlRuuseYz1tBDRQTDS1XobGVNZVRP2KZrF5vwzuL/oU9UoF8e7T8Nef/wCdadF/PW6o2qgMWyhrKPoVGTlFgAA1JR/vU9Nggm8qb62n4/32n0n+o5boMig9+thNdyp1z9D+uNxWAldUolfvX7kWCwGAMDn8xEbVsSDVYiGqhIAICe/SCqn/nv423UH0k7UsliSB/zSBH6qxzDPES7PwyOevvn0JOzjzUevDp4LfH75PzkSUUJX2yNHSXwjyWJzRA8RL3rJH4E0BOoORMpoqSkDALJyCyUPa44JPIlIGO7Ua7hTLwDA0Us3V+z0u3Dz4byJbpK7hLTR8r3R6xIrpJORkw8A0FRVatFUkEZ0R3T7NQTSUnrZdiUTCQHBz5ZMHYNUkkrNyrUdNX3OeNedK+cKh0k2gb9wK+T2k9fXDm0RPj0N7e+wYqdfQXGphC6xSKRYShQA0MeuG5lIuP7w+dJpP1+c33z0CoNB97cXr7cDkUw93aHT6UYmZoK4RlKAkHbFyMSMTqfLOgrpQKOQ182fsmH/yVGzV48d7sxisY9cvIFBo4WvnxAkm8CrKDKehn0cOm35VI9hKoqMwpKyk9fu4rDY0QP7VtXUNtUlFom0Son+uC6qHHJdHgs3THYbDAAIDH7x+PUH7xmeGirwfqdl1NOdwYMHJ8TFyi4YSCfBe4YnU4F+9NKtpVsPUeTI3buand+7wdLEQHTML03g75zcdeBs4Abfk+WVVUwFeTtL071r5nczMwQASOhq7+tSYTKOXr41Y80ugUBgaqBzxGfZ9DEj2vu8nQ+UQGQHOaRD8+3bNwsLi4g7Z1paFxjS0bEZNcNz0hQfH59mjP0jgIWcIRDI7wbqDgQC+d1A3YFAIL8bqDsQCOR3A3UHAoH8bqDuQCCQ3w3UHQgE8ruB+7M6G7auM2QdAgTyC6DudB60tLQCAtq9Eqa0OHToUGlp6R+71O3Ro0cBAQF+fn4kEknWsTQLc/PGrXj/TOB6ZYgMSE1NNTY2vnDhwvjx42UdS+OUl5fr6Ohs2LBhxYoVso6lEwJ1ByID5s+fHxISkpCQgMX+uXfcy5cvDwgISElJwePxso6lswHzypDfTUFBgb+///Lly/9k0QEAeHt7FxYWXrp0SdaBdEKg7kB+N4cPHyaTyVOnTpV1IL9AQ0NjwoQJe/bs4fP5zRgOaQFQdyC/lerq6mPHji1evFhOrgO46K9evToxMfHevXuyDqSzAfM7kN/KgQMHNmzYkJ6ezmT+2vj9T2DUqFH5+fnv37+XdSCdCqg7kN8Hh8MxNDR0c3M7ePCgrGNpLu/fv7e3tw8NDe3Tp4+sY+k8QN2B/D7Onz8/a9asxMREHZ2OVNzZ0dGRwWDApy0pAnUH8psQCARdu3a1tra+cOGCrGNpGXfv3h09enR0dLSFhYWsY+kkQN2B/Cbu378/cuTIz58/W1lZyTqWliEQCCwtLW1tbc+fPy/rWDoJUHcgv4k+ffrQaLTg4GBZB9Ia/P39Z8+enZSUpK2tLetYOgPwPTrkd/Dhw4ewsLDVq1fLOpBWMnHiRDU1tQ6UDv/Dgfc7kN/B6NGjc3JyPnz4IOtAWo+vr+/mzZvT09MVFRVlHUuHB97vQNqd+Pj4e/furV27VtaBtIm5c+cSicRjx47JOpDOALzfgbQ706dPf/v2bWxsLBrdsf/OrV+//vTp02lpaR3FHOOPpWP/HED+fLKzsy9fvrxixYqOLjoAgCVLllRWVvr7+8s6kA5Ph/9RgPzhHDx4kMFgTJo0SdaBSAFlZeXJkyfv3buXy+XKOpaODdQdSDtSUVFx6tQpb29vIpEo61ikw6pVqzIyMm7cuCHrQDo2UHcg7Yifn59AIJg9e7asA5Ea+vr67u7uO3fuhInRtgB1B9JesFisw4cPz5s3T15eXtaxSJNVq1Z9+fLl2bNnsg6kAwPfZ0Hai5MnTy5evDglJUVdXV3WsUgZFxcXNBr95MkTWQfSUYG6A2kX+Hy+qalpv379Tp06JetYpM/jx48HDx786dMnW1tbWcfSIYG6A2kXgoKCPD09Y2JizMzMZB1Lu2Bra2tkZHTt2jVZB9IhgboDaRccHBzU1dU78Xufq1evenl5xcXFGRoayjqWjgfUHYj0efHihbOz89u3bx0cHGQdS3vB4/FMTEwGDRp09OhRWcfS8YC6A5E+Q4YMqaure/nypawDaV/8/PxWrFiRmpqqqqoq61g6GPA9OkTKREdHP378uONaXjSfadOmUalUPz8/WQfS8YD3OxApM3HixK9fv3758gWFQsk6lnbn33//PXjwYHp6OpVKlXUsHQl4vwORJmlpaYGBgatWrfobRAcAsHDhQg6Hc/r0aVkH0sGAugNpPRwO59GjR6Itvr6+ampqnp6esgvqt6KgoDBjxoz9+/ez2WxZx9KRgLoDaT35+flDhgyxsLC4cuUKl8stKSk5d+7c8uXLcTicrEP7fSxfvjw/Px8u5GkRML8DaT0fP37s0aMHGo0WCATq6updu3Z99+5dRkYGhUKRdWi/FS8vr4iIiJiYmE7gMfR7gN8mSOvJyclBtkQIBILs7OyQkJDa2tpt27bl5eXJOrTfyqpVq+Li4h48eCDrQDoMUHcgrScvLw+LxQoPBQJBXV2dr6+vrq7uokWL0tLSZBrd78PS0nLo0KF79uyRdSAdBqg7kNaTm5uLwWDEGrlcLovFOnLkiKenJ4fDkVFov5tVq1a9fv367du3sg6kYwB1B9J6cnJyeDxew3YcDmdqahocHPz3JJj79evn4OAAb3maCdQdSOvJzs5u6DSMw+E0NTWfP3/OZDJlFJdsWLly5d27d2NjY2UdSAcA6g6k9WRmZoq14HA4dXX1sLAwNTU1GQUlM0aPHm1qaurr6yvrQDoAUHcgrSc3N1f0EIvFMpnMly9fdj6DweaAQqG8vb0vXrzYUI4hYkDdgbQSPp9fWloqPMRisYqKimFhYbq6ujKNS5Z4eXkpKSn9999/sg7kTwfqDqSVFBYWCpPKWCyWRqO9fPlSX19f1nHJEgKBsHjx4hMnTpSVlSEtOTk5q1evhibwYkDdgbQS4UMWFoulUqmvX782NTWVdVCyZ968eRgM5vjx43FxcTNmzNDV1d2zZ09iYqKs4/qzwDZjDATSCMiiZDQaTSaTX7161aVLF1lH9EdAo9FGjBjh4+Ozbt06HA7H4XDwePzftoD7l8D7HUgrQTZJkEikZ8+eWVpayjqcP4KwsLChQ4deunSJy+UKBAJkk7pAIBBLwEOg7kBaSV5eHoVCef78uZ2dnaxjkT2BgYFmZmZ9+vR5+vQp4r4s7OJwONnZ2TKN7o8D6g6klZSVlQUHB/fo0UPWgfwRyMvLJycno1CohgspAQAZGRmyCOoPRgBpNgEBAbL+7/p7CQgIkPX//y8ICQnB4XCNGi0qKSnJOro/C5hXbjFzdvvLOoS/jhOrp8o6hF8zePDgwMBADw8PxBhEtKukpITP50N3HiFQd1pM94Husg7hr6ND6A6yVeLatWuenp5iusPj8YqKipSVlWUX2p8FFGAIRJp4eHicO3eu4dMWfKUlCtQdCETKTJ48+cyZM2LSA3VHFKg7EIj0mTZt2smTJ4XSg8FgoO6IAnUHAmkXZs6ceeDAAeRrLBYLdUcUqDsQSHuxZMmSHTt2AADYbDbUHVGg7kAg7cjatWs3b94sEAiQbSUQBPgeHQJpX3x8fOrq6l6/fi3rQP4goO78boqy0ytKCggkOQU1LZIcVbqTrxxiylBWX3fheWJU+O7pgz1X7Bo4Yb50TyEkM+FrXXWlkXUvCWOSot7xuOIlJRTVtJkaOu0UlWR8fHxkcl4ikUin02V19j8Bc3PzMWPGCA+h7vwmuGxWyIVDLwJPlxf9sERAoVBd7J1GzV5r0K2n1E+npms8e+dZHVMr5LAwO43HYavqGktlcj6fd8tva4j/ATyR5PdGksPDvrkjuWyWWOPwGSvdFmyUSiQtZcuWLWryZApRNj/2SZ/fyOS8Mie3rHbICFeoO78bVm3N/nmjkqM/GFk5uC/cpKxtUFdVmRD55uX103tnD5+144yti6t0z0iRV+wx2EN4GLh/HYlCm77lePNn4HE57LpaEoUm1l5VVnxy7fSclDhlLf2yQkm5Ui6bxWWz7IeNHTrVW7SdpqDU/DCkzuaBmiMtFGUYwF/I7IAEsRaoO7+DAN81ydEfBk6Y77lil7DR0nGQ/TDPPbOGXti22NSuLxqLzfgepW5gJkdnZCbEyDNV6ExVZGRFcUFRTjoOT1DTM8HiCaIzczns7KRYgEKp65vg8ERhe21VRUbcF2UtfSKFlvE9KvFzuI6ZVfyn10qaegqqmpKjzU2ND7tz8e39q5PW+NoOGC3We2X3SlZt9cYroWc3zZWsO7VVFQAARTUdDUNoCQapB9Sddqe8KC/sziU1PZMx3tvFujQMu4xfuac0P1sABLkpcXtnD5/mc/TVjXMpXz8iDyOlBTn+W+Z/C3+OjCeQyMOmLx8+YyVy+CX04bnN86rKSwAAJArNa/1BFOrHC8qspG97Zw/3XLHLsGvPAwvduWxW7PsX8RFhbvM3Dp68uNE466qrPj6+EXbnYnL0BwVVzX7/TDe2dWw4zG7g6K59hmBx+F9eeE1lOQCARJFyDgvSCYC60+7EfXzN53Hth41Fo8VL+gIA7Id5Il8UYzMAAGF3LsnR5OfuPq+qa8TlsA8scKsoyl+4/2oXe+faqvL7p/fd8ttKlKO5jJtTXpx/cu00irzi4sNBWsYWmQkxF7ctqa4slVdSFZ1fz8L20PO0BY5qDsPHNfWclRgVHnb7wqcnt3g8nlW/Ycv8bnXp6YRqYvO0jfOoZl44cr9DotA47LqCjBRWXY2arnHDBzfIXwjUnXanKCcNAKCubyZ5GGKSUJSTvvv+VzQGCwD4EBKUk/x9ysbDVv2HAwDwRNKE1XsTI988On/IZdycT49vsWprpmw6om9hBwDQt7CbtO7AzqkDWhrezqkDkqM/aBpZuC3YbD/ck0JXaMO11qOmqhwAEPH0TtChTYgGoTHY3qMmjl+5B08kSesskI4I1J12h8flAgDE8jJNYdl7ECI6AICEyDcAAByBGP/p59IPpqZu1Mvg0vzsjPhoAIC+5U+7P30LO9EUTzMpyctS0TF0nbuuW98hwlNLhdrKcgBASX6217qDWiaWVeUlTy8ffX3rPKu2evaOs1I8EaTDAXWn3aEzVQAApflZzRks+pRUWVYMADi9YVbDYRWlRdUVpQAAOTpD2IhCo8lUekvDG79qz8ugM0dXTqIymL1GTHB09VLVNWrpJI1i7TTCLywXRyQKHzANu9nvmjbwQ0jQmKXbGMp/Y01RCALUnXZH19wWAPD1zZO+7tMa9gr4/OrKMuHTDRrzMweEwxMAANtvf1ZQ1RD7FBZHwGBxyNtu0XZ2XU1Lw7NxHmXjPKokLyvszsU3dy+FnD9oZOXgOHqy3UA3Aonc0tlEQWOwBHK9HzAUCmXavV9y9Iei7DSoO38zcH9Wu6Nj2k3dwCzq1YPEqPCGvQ/O7V89rEtS1LuGXWp6xgCAwqxUHJ4o/FddXorB4lAolIKyOgCgKCtNOL40P7u2urJ1QSqoao6as3bXva/eR+/IK6tf3LF0+SDDxM9vWzcbQuy7F3eO72DV1pPC0oJsZHlRW2buKAR9KfQ49y2tpE6KIzsHUHd+B1M3+6FQ6MNLxkY8vS10wGTV1tw5vuP20a3aplYGXRupymDrMhqFRj88t5/D/vHjWFVWvHPqgP8WjwEAmNj1AQCE3jqPdAkEguCzvo2eHY3FIKf7ZZwoNLqLvdOcXed8HyW4zd/UinRPRtyX+E+v+XweAKAgM/neyV33Tu4U9qZ8/fjh0Q01PRM1PZOWztwRqWXzC6o4XL5AiiObybOE0qW3kiZc+L7oRuL9b8VSHCwV4HPW70Dfwm6Z381T62ceWzWZrqiibmDGZtXmJMXWVlfaurhO33K80ZfWqrpG/yzyCTq0acs4R3MHZz6PF/nsLpfLdlu4GQDQre9QfcvuoTfP5abGq+ubpsdF4XAEZS19ARD/2cXhiUx17S+hD8/5zNPtYu00drZob+CB9aya6kbDNrbtLdbCYdedXv8j35SZ8JXDYh1b6YUcTlizj66ocmXPyqSod35huQSynOPoyV9CH4acPxQd9ljbxLKsMD8hIoxMpc/cdqoN38uOhFd3Fa/uKtId2Rx8QtJOheeaKpPN1eSSCmvnBCaMs1H2dTVo+2BpAXXnN2HWo/+ue18jnt1JiHxTVphLotCMrOy7D/bQMe2GDCCQKSa2jopq2qKfGjJlqZGVQ/iDgLy0RDyR1M9jeh+3KUhmBIVGLz929+nVo0lf3pcWZFv1GzZw4sIA37UCPg9ZNWNi66jw/xzK7F3+If4HaysrCGTxVXwFGcnIS+6G1NVUibUIBIKqsh9/D9V0jYEuEB7yeTwAgLZJVwwGi8KgAQBYHH7xoeuRL+59DXtcWpBDolDdFmx0dPWiynSfRKfnY0blqfDccTbK+0YZIH6HWx+nH3+TM8Jc0clQvi2DpQhKzPgeIoHAwEBPT8/TkY3/lkLaj5k2tICAgLFjx7ZxHhQKdXyMsXT3Z+VXsk+F58bl11CJGLeuSv0M6BMvfh/eRXFaT9WgL4XXIgv2uRroKhDvfC26+Cn/lKfJ19zqoC+FpTUcLQZxRk9VAyYJye8IR7YxnlV3U65E5keusFOm4JCWShav655Pg0wYJ8aKbwxu0eBWMzsggdTF6fr168IWeL8DgbSegirOkBNfi6s5I8wVVai4jQ9Sh5ophKdVWGtSAADZZazwtIpqNg8ZGZ5WceZ9bsDngn4G8opyuKCowsDPBS8WWmnJE0RHirHsdlJmqfiefoTZvdQHmTDEGiOyKvUUiEIdAQBQCZguKuSIzEbeObRosBSBugOBtJ5T4TkFlexD7oYe3ZQAAEv7ag47+RUAgAKNVA0FADyILXk+34pKxAAA+hoULbqRGPyteG5vSUsKVKn4pp5JKPhG0oLZZaxuGhSxRnUaPiq7issXYNGoVg+WIlB3IJDWE5ZSTiVg3CyZyCGdhJ3TS33t/ZSmxk/vqYqIDgDATosKAMitYEs+xWoXbckDxKjh8Ek4cT0i4TEAgKxPG/sAACAASURBVBo2j1bfe6hFg6UIfI8OgbSe7HK2FoOIEbkvsNUSv30QRV/xZ/qGiEMDAKT47hwBg0bxGsyJnAWHEf99b9FgKQJ1BwJpPWwuH4+p9zBCwjXiOiAE256/zAhMOWxJDVessbiaQ8ajG97atGiwFIHPWRBI66EQMBV19X5vcysazwG3mpbmlU2VyR8yKnl8gehdWHxBjamyXMMZWjRYisD7HRnjO3fUor6/MABs6chmIuDzc5K/p3z9WF6c3/xP5aTExX963dTq57b0dkSMlcjppayy2p/Sc0/a631VqXhNeUKj/xrNKw80Uahi8Z4mlApbXiSVFVZxhpiJK1RLB0sReL8jY0bMXInsLJfiyObw+cX9y7uWI0alKBTK0nHQ9C3Hf7ltqignY/tkJ1ZN9ZbAdw3dS9vS20EZaaH4Krls2a2kbcP1FMm4gM8F79KkvLyrpXnlsVZKx9/mrLqbwuULLNTkYvNq1gWnqtHwk7urAgB4fIHn+VgDJmn3SP1fDm4/oO7IGGSblXRH/pKUmE/HVk3W7WI9e+dZeSW12PcvAvatPbZq8sqTwRI+JRAI/Lcs4HMbWWPSxt6Oy1grpc9ZVVci8x/HlwIAumtT97kauJ6OkWFIRBz6qpfZoptJQjd1Kw3KIXdDKgEDABAAILpQSPLg9gPqzm+iND+7JD9bQVWToaxeU1GWmfBV3cCMymBmJnytq6kysnIAAGQlxnDZbF1zGz6fl5P8nc/jKWsZEOV+vB8RHdlGHpzZh8XhFh0MoDKYAABlLf3q8tJbfv/GR4SZNOapjPAq6Ezi57fOnrOfXPaTbm/HBYNG7Rmlv8JZK6O0TkkOp6NA/JZXLXxX9U83pR46NGQJ8ghzRQs1OTPln9YiDBI2aJq5KhUvNrLt6CgQ7860yCxj5VeylSg4HcbPaTEoVNA0czk8pjmD2w+oO+0Oq7bm1PoZUS+DkScau4Hu3foOOb1h1uJDgV37DAncvz4tNvJwaBYA4OaRf3NSvs/eee74yskl+VkAADyBNNZ7e/8xMwEAoiNF4XE5jdpoIOhZ2ImZivL5vNh3L8x69kdEB6HH4H9u+f37JfRhU7pTlJNx/dDGETNXNrq7qi29HZqMUta7tIo+BnRkMQ4AICq7Svi+HMnCIO1qNLwarZ4ZPg6DctD94TYtOlJaaMkTtBrMiUIB4Ul/Obj9gLrT7tw84hP1MnjAhHlDpy4DAAQd2nTL71/EFktsJBqDqS4vvbh96bhVu42sHMoKco6vnnJlzypbF1cJv7G1VRV7Zw9vqnfbzU9i5foKs9LYrFp1fVPRRqaGLhZPyEn+3ugkAoHg/L8LlTX1h01f/vr2BSn2dnRKajjed5J66dJ3jNBToeIjMiv3Pc9UouAGNnjNBBEF6k77IhAIwoOvqegYei7fhUKhAADTthzb6G7X1PjaqooRM1bYOI0EAFAZzH4eMwL3r8uI/2ru4NzUR+RojF33m0woMJTVxFqqy0sAAHL1/dtRKJQcVV64uVyMV0Fn4iPCNlx8gZgcSrG3o2OlQdnvarj9SXq/w1FIi6ky+aC7oeiDDKQhUHfal5K8rJqKMuv+IxDRAQCg0RjbAaMfNOHRBQDoYv9TYuiKKgCAuhpJm/RQaDRTvQWvPHg8rpih6o/AsFger5G8b3Fu5vVDG4dOXab9f8sOafV2DsZaK7l1ZaaV1JXVcpWpvyk/0tGButO+1FSUAgCojHrvpyXIBBqNEa0whUJWc0nVq4RIkmvUiZlVW81U1xFrRN5DKapqjZy1quFUbentTOAwKCMlWJmnBUDdaV9QGIywlI0Qdp00bXRbmldmaugidyKijbVVFTUVZUqaumIff3P30vcPLz2X70yO/oC05KcnAQDSYiOryooLslJb3Wts6yi8B4T8bUDdaV8YSuoAgOKcDNHGtNgIKZ6ipXllEoWmbmCGFOcSEvcxFABgZN1L7ONZiTEAgADftWLt53zmAwAQy9TW9Z78WIKSarmuzsr4C7Fd1SlrB7Rs9eAfDvyPb1/k6Aw1PZPvH16WF+XRmapI5fLIZ/ekeYoW5pUBAH3cpgTsWxN256KjqxcAgFVTfe/Ubjk6w27AaAAAsrxIQVVTSVPPfZHPqDnrRD/75u6lAN+1a/2fqumZYPF4twUbW9cr3RqBnZh36ZXtujW8mZTUcC98zIvNq+HyBToKhIm2KobM1j9awv/7dmfUnDUn1kzb5uVk4zySz+NHvbzfx33Ks6uNlypvBS3NKwMAnMfOig4NOf/vwncPAuWVVOI/hVWUFM7Z7Y+kltK+f94/z3WQ16Kxy7bjCSQ8od6PF45ABAAQyRSkRmBbeiEdhZjcag//byyuwFGPjkWDqxEFZ97l7RtlMNa6lQuyoO60O90H/UOmyr+9fyUvLVFF22D58XtIXSqkiqaWsQWO8GO9loZBl7rqelbqNAUlE1tHCoMpNrKNYLC4ZX4339y9HPP2aWVpsbXTiL7/TNM0NEd65WgME1tHZU29Rj8rr6RmYutIIDW+X7ktvZA/lgVBiWgU6uk8S8QNOq+CPfh49JZHae7dmK3zJIS68zswd3Axd3ARHn54FAQAoMgrAADGeu8Qtos9lQAATLv3M+3eD/ladGTbQWOwfdym9HGb0rBLx8xq5akHTX3Qqt8wq37D2qP3T6OGzb8eVfAps7K4mssgY+11aGOslIj/d6VJKKy9FlmAVNrTVyRO7q6qzfjxV2Hixe99DeijLZnH3uQkFtYyKbjpPVW7qVOic6rPf8wrqGTrK5IW9NFAXI0FAjDG/1t/Q/mxVkonw3PjC2qIWLSzMcPTWrnR3+isMtalT/mx+TUCgcCASRpvo2zy/70XkgNuNSU1XBUq3r0r0+D/D1aqNLyzsXzg58LcCnbrVjlD3Wl37p7Y+erGuVWnH6poGyDJlPDgaxR5xU6zJ7tTwuEJxvh/SyisGWqmaK5GzipjbQpJC/pSeHO6OQaNep5YNu1KnIY8YYAxg8sXBH0pPPs+7/4siy6qcgCAiMxKIhZ9JaLATJWsQsXf+1YU/K34kLvhxgdp/QzpJBzmzPvcJ/GlrxdbYdAoFAp8yqzEoFGXI/JNlMka8oT3aRUPvpfE5FZvHy5+y/kuvWLypTgqETPMTAGDRj2JLz33Ie/kWOPBpgqSAxadJDytwvdFJmgMeRL29DjxkooKZGzgVPGf1co6HhaNkie1UkCg7rQ7Ns4jn1z22znFxcbFFYPFfX3zqDg3c8a/Jzrl+t1OQ1R2VVR21e6R+pPsfpTTexRXsutpRnxBTRdVuV1PM0h4dMgcS8SBeE4vNcdDn/3Ccvw8jAAAAAUexZecGWcy2FQBAGCvQ1t2O2nJzaSn87shOz/XB6f6f8iLzqlGyk6gUKiwlPIDow2RdAmby/fwjz3/MW9eb3XRTVtcvmDJzSQGGftkXlfkvGsHaA878XXl3RQnQ/kvOdUSAha9NDIe3dResGYaKkdlVz1NKB1hrtjqbetQd9odTSMLn4Dw0JvnspO/c1i1lr0H9R45UdfcRtZxQX7Nu/QKT2tlHAYFABhsqoDoCADgzHgTUdtzHQZRmYpPKqoVflCDThAO7qohBwBwNmIIt5t3U6cAAPKrfjq6K8rhxlj9yNHiseixVkoRmZXv0io8rH4mbj9lVmaVsdYM0Bael4BFT+6usi449V16JWJL2lTAonRTpxx0M2z19yShsHb61Xh1OmHbsMYzgM0B6s7vQFFNy23BJllHAWkBdlpUj25KQV8KnyWUOerRHPXpA0wYGvT/vwGgE0LiSi5+ys8pZ9dx+Fy+oKyWSxe5WRDNeiB7tURvMeQIaMSCS9hixCSJLqLUUSACAHLql5pILqoFANz4UvgqqUzYiFgdphTXTumuKiFgafEiqWze9QQNOuHyJDMGufXqAXUHAmkEFAoccjec3F3l3rfi0OTyB99LNjxInWinsnO4Pl8gmHjx++uU8j76dBstKp2IwaBR3/Pr1ZjHNMgJS37vQ8DWy/4ig8VKTXB5AgCAviJJbOGMsxHDVIUsIWBpLQs/FZ679XH6AGPGf+6GlLYZg0Hd6eTUVldmfI9S0tJTUJGmN/Nfgq0W1VaLCgDILmftfJpx8WO+gw5NjoB5nVI+r7f6hkE/t7MdDs1uy4mKqjmih8hdjFjWlknBAQAc9enTezZpQtpowK6WTNExLc0rIxx5nb3zacaiPhqrXbTbLmSyXwcJaVdyU+L2zh7+8dFNWQcCAABVZcVp3yLzM5J5XE4zhsuSd+kVx9/kCA816IR1A3QAAN/za/Iq2MhvuLD3eWKZmHC0lPjCmsKqnzN8zqoCAJirkkXH2GlRMWjUk/gS0cbg2OI9zzNrOXwJAYudC8krN/pPvYnnsuDY4p1PM1Y6a60ZIAXRgfc7kN9EYVbq5V3LY94+RQ7JVPqImasGeS2SdVxNklXK2vo4vaiaM6WHqpIcrriG4xeWAwCw0aIqkLEAgIDPBT11aDg06lFcyal3ueaqclnlLBaXL/bE1EwoeMzim4nbh+ur0/ChKeXnP+bpKxLtRKQNAKBCxY+xUroWWbD9Sfq0Hmp0EuZlUtnyO8mmyuRVzloSAhY7V0vzypUs3sYHaaYq5CFmCnEF9VRMg0YQlj9tEVB3IO1OXXXVgQVuVeUlUzYeNujWs6K44M7xHYEH1uMIBGTv6B+Ih5VSZhnrRHjOsf/fRDDlcJsG6yD1qmY5qJ1+l2u5+yNi9HVsrHFoctnmh2lWeyN+vEpvId21qWYqZCe/KCSJo6NAPOlp0jBJtHO4HgGLPhWeezQsBwCARoGBJgq+rga/DLgtfEivyK9k51eyXfy+iHX95274T7fWbJWAutO+sGprinMz6mqq5JmqCqriGZbyorySvGwShcpU18Hif9ziIhkZxPW9KCejojhfSVMP8UIWCAS5KXFcDltV11jobvHDJV7flKqghLjH0xSVlTTEHS1EqSguKMpJx+EJanomwvM2J+DW8SX0YUFmyvQtx3uNnAAAUNc31TS2WD7QKDw44I/VHQDAsv6aCxzV00tZFXVcRTmcJp2A/X9pUJ8hugv7aGSWsugkLGKlbKxE6m8oX8fhGzJJFyaaiVa2UqbggqaZq9N/mivb69CCppkbi1j2CARgtYv23F7qKcV1RBzaRJks1Jyrk83k//+mDI9F7xiut2GgTmJRjUAAtBlEBZGXShICbgs2mtSgaeaNdrV6ayjUnXYk6NCm5wEn2HU/lnVom3abtf20mp4J4kRzdvNcoTENmUp3W7DJaewsAEBBRvLe2cOnbDz89c2TyOd3kT0NY5Zts+43/PAyz+ykWAAAmSY/Z6c/Yn6anRy7d/bw8av2xn96jYwHABjb9J7ve5lCF1++UVqQ479l/rfw58ghgUQeNn358BkrfxmwKPkZyWUFOaAxqApKYs7NAADTHn1Xn32kbfLTcpBCV6ApKNXVVDWY4M8Cj0U3ZejFlMMx5eqt/BT+EvbQrvdoQ8CixazUFeVwDvU/i7y4opOwyEpCUex1xG3YyXg0sgKoRQG3GgYZ26gVfFuAutNefAgJCjl/cNSctU5jZuJJchnxX85tnue3fMK2mxECgeC/JWMqS4u8j93Rt+heWVp4aaf3ld0rtE27GXTtgUajAQAP/Q/YDhi950FsdXnJ0ZVeN//z+RgSNGD8vK59h2QlfPNbPv7q3lXbbn5CNnkCAILP7LVxGrnzXjQGg30ZdObBWd8ru1fM3nFWNCQuh31ggVtFUf7C/Ve72DvXVpXfP73vlt9WohzNZdwcCQGLXdrTK8deBJ5s9Kq7D/pnzq5zYo10RRXEsFVIbmp8WWGuqKMr5K8C6k57kREfDQBwGDEeKQVhZOWwwPdKUU46j8upLi/VMbPWt+zepacTAIAoRxk9b8O38Gff378w6NoD+bgcnfHPIh8AgIKqpqPrpFt+W3W62CDbOOkOKuYOLp9f3Oew63D4H0tgKXSFiWv3Iw5+7gs3x75/EfnsrvDOBSHy2d2c5O9TNh626j8cAIAnkias3psY+ebR+UMu4+ZICFhsS8foeesHT17c6FUTSORG2xFyU+OLczOzk2IfXzqibmDWcB8s5C8B6k57YWLrGHL+4JFl4wZNWtSlZ3+GioaGYRdkLyhNUXn2zrMAgJL8rIriQh6HjdQLrigpFH5ctD4fTVEFAGBkZS9sQW4fWLU1Qt3pYu8kahtq2K1n2rfI/PRE0ZAQj0EcgRj/6bWwkampG/UyuDQ/W0LAYsjRGXL01mQrg8/sffcgEDE29Fy+U16pEU+yvxDRDM5fwt91tb8TS8dBc3efv3967zmfeQAAFR3DHoM9Bk1aiHhrPblyNMT/YHlRHhqNwZNIiBePqH07mSYv/BrpFW1BoTGg/gdoCsqiZydT5QEANZXliNUWQmVZMQDg9IZZDaOtKC2SHLBUGD1vo9OYWSUF2e+CA7Z79XdfuHnoNG9pTd5xaZjB6fRA3WlH7Aa62Q10K8rJiH33POpV8L2Tu6JDH6678Dzi6Z2AfWusnUZ4LP5XRccQKXezalibbDHEHqmQhXli76pweAIAYPvtzwqqGmIfx+IIEgIWe85qaV5ZCFNDh6mhYwBA94HuBxa43fT713H0ZNGypZC/BKg77Qifx0VjsEx17b7uU/u6T713cted4ztSYyK+vH4IAJi0dj/iuAwAyEr81sZzFWSliB4WZqch+1FL8n7WNVbTM0aW8CFOQAhlhbk0RWXkGa2pgA1FHvFakVcO8F37PuS6z7W3NMWfN2Vqesbfwp+V5GV1Gt25FV10OSJ/90h9gzYYD7edZwmlwiU8C/poOBnK/+oTreHk29zH/1853dRbdglA3WkvDiwYXVNZvvp0iPCmA3nkQWMwRDIVAFBdUYboTmVp0f1TuwEAXA6r1aeLehlcnJupqKaFLM+JeftUXd9UXklNVHdsXUbfPrb94bn9JnaOSGKoqqx459QBanomS4/clBCw2LlamlfWNLJ4ctnv1tGtXusPIs+M5UV5kc/u4YkkVZ3WrLL7M8kuZ4WnVVSxG6l9+DvJr+SEp1VM76mqQSeo/78i+/PEskdxJTnlLEU5XHdtqqe1cvP9SZ8llN77VlxQyVGUww42VRhhrggAMFMl8wWC9+kVj+NLWxEk1J32wmXcXL/lEzePte9i70wgkQsyU7+EPuxi76Rnbsths17dOHt4yVjbAa7VFWXRoSGT1h24tNM7+vWjF4GndM1tW3E6sx79d0x2tuo/HE8gRjy7W1tVMXOr+C2Jqq7RP4t8gg5t2jLO0dzBmc/jRT67y+Wy3RZulhyw2DwtzSv3Gjkh5s2T17fOx314pdPFmlVbHf/pNZfNnrRuP4EMvZbbBbeuTBvNH8uIltxKCooqNGSSzFTJ3/NrrkcV+n/Ivz3DvDnFlH1C0k6F55oqk83V5JIKa+cEJoyzUfZ1NeijT++jT8egUVB3/iy69hmy8Urou+Br+RnJHFadvJLarO2nbZxHotBoE1vHFSfuvb13JTMhRlFNe9GhQN0u1jgC8XnAifiIMGPb3ia2jopqP0tE0JkqJraOcrSfv+oq2gYmto6iaRcTW8fhM1e+vumfnfLdxM6xj9sUY5veAACiHNXE1lGY0BkyZamRlUP4g4C8tEQ8kdTPY3oftykMZXXJAbfxW4FCoebs9u/tOinq1YPi3Aw8kewyfl5ftylKTVjHQ6TIna9FQVGFU3uobhumh7zw3P0s47/Q7MufCmb3+sX7xI8ZlafCc8fZKO8bZYB8duvj9ONvckaYK7bx8Q3qTjuiaWjusWRro12ihu0IFr0GWPQagHwtZqsu2oUwYMK8ARPmibYIBAJ9Czt9CzuxE6nrm4rNZtCtp0G3ni0NuO00vIo/kAsf8+/GFO0YoS+6iSG+oGZ9cKqrBdOruwqby7/1tehtakVRNUeJgnPUp7t3VWr4yHI1suDGl8JD7oZC562MUpb37aSxVsqImSlfAO5/K36aUFpYxZYnYfvoy4+xUsJJY1uDGAVVHGtNind/TeEqi4m2Kv+FZsfWNwxqlOtRhSgUEDW+WNpP8+z7vGuRBVB3IBCpYaZCXnu/4lZ04WqXn/ebN74UhadVrB+ow+UJJlz8/iGjcoipgokyOaGgZsnNpOeJZUcb7AXNKK0LT6uo5fCFLTUcXnhaBbLhgC8AM6/FP4orGWDM6KZOSS2pW30v+XpUQeBU84bSs+x2UmZp44m/2b3Uf7ntc5aD2iyHevc1lSweAECJ8mt774isSj0ForLISCoB00WFHJFZ+cvPSgbqDgTyk+7aVB0F4r1vxULdEQjAnZgiQybJWpPyKK4kPK3Cu7/mcictpHfpraTrUYUrnLSQ3aHN5Gpk/qO4Ep8hukJFuBldtOhGov+HPDGNAACoUvECQWOzACC6+7SZCATA90UmFo0aY6X8y8HZZaxuGuIbwdRp+Kjsqv+1d94BTV1vHz/ZO4GEsPdGQJaiFlFcde9ZV6e+ta0d+uuwtXV0D2erXXa4q+LCvQAVHAxF2XuFTQZkz/v+cTXGAJERSJDz+Suec3LuEyRfzn3uOd9Ho0O6VzkLBepOv8cogwPpIXMH221N5uXUSUOcaACA9GoxT6T8dII7ACDGi3XtrTAn5pOT5dHuzGNZTSXN8i7pzqlsPoWANbQNnDPYbuPFirO5/La6Y7jy6iEIAjZdqriQL9g4ydO/E8dHZWodpU35LQoRBwAwtLXvBlB3+j1tMziQnjAvjLs1mZeQw0d151R2Mw6LmRfGBQDQSTgaCbc/o6GgUSaSaTQ6pF6sAgCotbpOTPyE0mY5AsDCvXmGjSqNrowv7/hNPUWm0r13ovji0+ss0+CwGK3OeK2Fuj73sGQ71B0I5Ck82OQhbowzuc2fTnDX6JCzufxR3iwHBhEAcL1U9MqhQhYZNzmIHWBPJeOxOXXS4qYui4VGh1AIWP1zbpRIV0Zv5JVR6lpVyw8WVAsV/ywOHO/f2T0QdjS8QKYxauRL1VQitu06qEtA3YFAjJkfzv34TFlWjUQo1/Cl6gURj1Ih312tIuIwV1aF6ZOy+9Ib4h80tZ0B3f9tmJcRGBgw29EIDWIVeu/2THqYV0bLmc/9JxdBkDMrQrvkzhNoT02rEmt1iKHzYWGjLNC+p7uuoK+7FbHt7dlvx3RYKqDP2LP+jTcimW/HOL4d42i43VkuaS3MuClo4Jl8dzvodNqmmoqK3Hut/EZ945eLY9+OcVw5xPb9MaasES3C9GAOEY+9VCA4m8tnkvETAx99t+tbVW42JL3o6BDQrugAAKgErFGViBulLfrX0R4MkVyTYfBUSKNDPkooSy1vaTMTcGQQO7Jh70xeWaNDlh8s0GiRE6+FdNUSbEIAW6LUXi16sjMwqUTUJFFPCuqpdypc70DaAU8k7UqtN2zhleTuXruksbps/vtfdXRIol3SLsYf2/G5sOFRmRfv0KFL1211Dwz7/NBNAMAva14quX/b3OH3FBYFP8Hf9nKhsFGsmhnK0Vu1BzpQb1W0JpeIXvBklgsUPyVWc2kEAEClwHg9Eu5CR2u/OLOIZDz2UoHQUHdWDHc6ltX07omSLTN9wpzpTRLVN1erzuby0SMIRvQwr7zndl1uvfS7ad4tCk2L4slNEwmH9eKQAQAL/s1zsSFum9WO0/uCcO5vt2o/SijT6JAQJ1pevezTc+VOTOLyoT396wh1B/Js7l44tvfLd7xChjRWl3Vi+BOyks/9+dnr3qFDX92428bOsej+rWPbPtv61qxvzzyk0IzrHFgVc8PsXjtcCABYaPC8+YuJnq8eKliyPx91FH3zBad3Yl1G/5z17dXK66WiWG+WfmSMF+uVaMe96fUvbL8PABjsTNs+23fsrgdoUtbHjnJgadAnZ8rm/fPoPLADg/jzXL9RPqz2YukRCTl8AMAnZ43/4zzY5FvvRaAVe/xk7a+DyATs4WVBq0+UrDxShLaEu9B3zPHtdll0PVB3zEzpwzQMBuMdOtSwsYXfUF9e5Ojlj/p1qVWK5ppKhVTCcXIzPKJtSFXBA7VSYbixWNhY21hV6hYwmMp49Nup02pqywuVMqmtg3PvleWrryj+Z+OqxR//5OoX/M3L47r03ov7dpCotPd/OYGa+Dj7BIkFTad/+yY/LTlyzPReCtgsjPOzjX81GIMBhobHQQ7UlHcjSvlyuVrnY0dBv37J74QXN8ntGQS0qJYPh4KWG/16qteaONdqkZJBwvnYUXQIiH81WH9Qc4Qn8/rq8EqBokmqtqHgPdnknmyHMcF3072l7R1VJT9exH3+osfFfEHbASgebHLCGyHVImWDWMWlEzxsu7BdwARQd8zMlYO7Mq+d/vFCvqGZ3qldX948te+rE5ksjsO5v368+O92ufTRvX3g0NGvb/7N1sF4982hHz5srC7beqVE33I/6eyh7//34R/nAobEAgCuH//75C+bJS2PfmO8Q4a8tvl3R0/jjbNajbok605H0XqFDNHXpegIIoXyv9/P+oYPL8tO7/SP4RGL/ve9RqU0dA6zd/NBK9t0dao+Bo/DtGtmjsdhAuyfOnNPJ+H02mRUj5xDI3Ae+7djMaDthB5sMloKvfcY7PyMHHClUDHhWclpNxuSYcX3ngN1x8yMmLoo48rJjCun9OenNGpVZmKCd+hQR0+/O+ePntz1ZcyMJbNWrSfTmbm3rv7x6ev/bn7ng10nu3SVO+eP7v/6/ehJ8+au3sTkcEuy7vy9YdXWt2Z+eTzDyIlCLmn9ceXUjub56kSGo6e/6WuxHVy7vZjyHBRh1JJ3NwmDwfh2cEAM0vdMD+Z0VJ2i94C6Y2ZCYyYwbO3SLx/X605O6hVZqyhmxhIAgFIuGRw7ccEH36A+EkMmzE5NOJCfdh013Or8Vc78+Z2di8frm39Hj6QHRcctWvvtrx8tT798fOTMZYYjaUzb787mdDSPwfy71gAAIABJREFUrX2fOhynXz6emnBgzIKV9m7efXndAcj8f/OwGPD9dJ85g59hqzasWy6rGy5UHLrXoNZ2cILjWUDdMTNYHD560rzE/34X1PPQund3L8YTiOShL84FAIye+9roua+pFPLa0ny5VKzTaQEGo1GrZJLWtrWuOkIsaGqoLAmKjjO8gUJlq/TBXSPdwWCxds5m22jfE26c+PfAt2vCRk1euPYbS8fyPLM4yn5x1LPPXvWQTZM9N03u/gYIqDvmZ8TUl64d/i3jyskXl61WyqQPbpyPGDMNTQY31VQc/HZN3p0knU5LJFFwBIJapQRGO8yehVjYDADIT0vOT0s26jKsSGE96HTao1s/vXro1zELVrz00Q+o5SBkIAN1x/x4Dopw8gpIu3z8xWWr7yefVSnk6E0WgiA7Vs8TNtas+GbP4NjJaCLmn41vpSYc6My0GrUKfYHaj74w7aVl63cYjWn7le55XrmHaNSqXz9clnPr6vL1O0fNeaVXrwXpL0Dd6RVGTF104pdN/LrqtEvHbe2d0fp8jdVl9RVFo+a8it5zodSU5rU7Aw6HVysVhi2NVaXoC46TG5FEqa8o1hfPQp+pi0V8o7KcZskr9wQEQX7/5JX8u8nv7YwfNHxM713ISrASa3frB+pOrzB86sKTu79Mu3gs707ihKXvoFahZBodACATi/TDko7+WVdeCAB4dLdlgI29U2FmSn1FEaoLTbzytEvxaBcWh48cN/3O+aPZqVdCYyagjVcO7o7f8fm6f68abR3q7byyXNJaVfDAxt7ZsEaFnpsn995POvvWTwcGguhYj7W79QN1p1dgO7gGRMWe/3uLRq2Kmb4EbWRxHAKHjsq8euqPda9xnNzKstM1GvXc1ZsOff+/079+HTtrueEMwycvvHvh2E//Nz189BSFTJyfdmPMghXn/voJQRAAwPz3vyp9mP7z+wsjx063c/bgFefk3Lr6wvTFRqJjlrzyrbOHH1w/DwCQtgoBALfPHkY38vhHxox76U1eSe6PK6eOWbByySc/Gb1Rq1Ef/3kDkUS5e+HY3QvHDLuChsXFzXu9J1FB+jVQd3qLcYv+7+qh3XYunoZ7+d7eevjKgV8q8u7XVxaHx02Nm/86okPqK4qaeOXNtVVu/iH6WsOhI1/83+9nbp/7r6mmguPk/sHukyq5rCTrDolCAwCw7Bw3HE69eXpf8b1b1UUPbbhO72w9HDZ6Sm98EJVcKhHx0dcBUSPR6jcAAIVMDACg0JlcVy88oR3TTK1W4+obrB9viPXvG4T0KlB3eouIMdMixkwzaqTQGDP+b51R4+KPH60Uhk2eb9je1vvd0N+LTKNPWPzWhMVvmTtwY+LmvxE3/42Oel19g4OiRzt5BbTtIpIoz6shmUaHxGc1JZeKJAqtO5u8ONIeNQkzwrQJ/MNaafyDpmqhQocATzZ5fjhXP4mJrp5wOrt5f0bDnwsDsuuk8Q+ahDK1my359WGO+myURouceNh8vVQkkKmZZPwwD+aiCHtq191UnwnUHUiPkLWKWpobhk1eYOlA+g6NDlm6Pz+lvGW0j40Li3SrvGVfev0vc/1mhT61Q8+0Cfz5fMHKI4UhjrThnkwEATdKRf+m1e9ZFDAhwNZEl1EkXbXmaZSob1e0/nW37sj9xtE+NhwaIT6r6ej9xqR3wt1sSGot8tK+vNsVrWP9bEKd6FUixcaLFQcyGk6/HsIgm3nrA9QdSDvotNpL+3YCAEbNfdX0wXEq02b19iPduERqwgGJSNDVA+7WwKHMxptlLT/M8F4S5YDqy5Q/stefL5866CkXi2vFQhMm8PvS6p2YxHMrQ1FLLY0WeflQQWKxcEKArYkuo0i6Z/l+Pk+Q+FY4KiWjfJpXHy8+l8t/M8b537T62xWt61/0WBXjjI5MyOGvOlb0S0rNuvFm3noKdQdijJNXgF/48Ic3L6IP5nrJsKIg/Yagnsew4dj3t+p9Z3L5NCJu4WMTQjwOs2dRgEylNTpO/kwT+GapJqdeip6NwuMwB5cF6Uea6DKke9Y8rw1z1K9fhrgxUBdUAMD5PD4Jj33dwG1+ejBn/XnC5QIB1B1IrzP19Q+nvv5hb1/l9TZllPsLpc1yNxuSoW2Fu207Z7VNm8B/PM592cH8Kb9nhzjRYrxYo31ZMV4sdE4TXWbBsPQFmYDVW7WXCxQetiQi/slCCYMBXmzyw7pnV/jrKlB3IJCu0cnqEaZN4CNc6bffizyd03y1SHgos+H3W7UuLNIfC/3DXegmuswSP76DUhBKDYLFGKsbAYfRdLFaRqdiMPuMEMjzDYdGaJSonjnsmSbwDDJu6RCHpUMcNDrkSqFw7enSd0+U3FgdbrrLkJ5bvhtiR8O3/Vx8qZpNfXZl0a4Cfd0tj5XYuUM6SbQ7UyDTZNU82YL0b1r9a4cLmyRqw2EmTOClKu1fd+ru8x7NgMdiJgexJwexS5vlQpmmoy6Vxnjd0UPLdyOGeTAFMs3D2id3VfWtqpJm+VB38yf44HoHAukarw93PJrV+P7Jkm+neftwyHcqxd9cqfLmkI0qjpswgacQcH/drf/rTv0PM7zRQsAPayWJRcIQJxqLgu+oyzDzgmLGUqJoJfX4B00fnCrZPts3gEsp5Ss+TCjFYDD6x1tmBOoOBNI1Auyp/y4O/NjAlT3Wm7VjjnE9BtMm8PuXBL53skRfMhSDAaO8bb6Z5oXFgI66+uBz7V0S+MmZskm/PURbnFnE3xf4R7nB9U4/R6NS1pQVAAAc3HzQY6LtYtr4XdDAEzXVk8hUrquXkYuFia7u0a5NvVjYXFua7+Dhi3pIm7aXN3tI1kCcr83dDyLbtr8T6/JO7COr7CAH6p02YzLWRulfn10R2u7kPnaUjrp6yIoRTkYViu3phJpNI/T/HO1jc/v9dj6X2YG603dc2rcz4Y9vlTIpAIBAJE9Y+vbst7/AtHmCYML4vargwT+b3q4ufPTniESlTVjyzqxVn5nuMqSrdjzXDv+Wfvn4d2dzOE5u+sbzf/105dDujUdv23CdTNjLdzIkyAAE6k4fcfPUvmPb18fMWDpx2WoyjZF45I/zf2+hMliTXn7fcJhp4/ff172K6HTrD1x38gqQS1qSjv559s/vnb0CoifNM9FlOH9X7XhGzXkl7VJ8asIB/bEyRKdLv3LSMzjS1TfYtL18J0OCDECg7vQRF/7Zau/m/cqGXegCZ957m0VNtahjqSEmjN81GnVDZcmIqYvQIg0kCnX221+4+oe4+YWolPKOuozm76odT+DQUQ4evqkJB6av/ASNvOheqqipbvrKj03by0dPmtfJkCADEKg7fYFY0NRYXfbC9MWGd1VvfLWn7UjTxu9eIVF3L8ZTmTZDxs/yChmCJxCHTpiDvtFElyHdsOMZNfuVY9vX56clo66Jdy/GE0mU6Inznmkv38mQIAMQqDt9AbquoTFsnjnStPH7uzuOHd+5IeXUvmuHfyOSKUHRceMXrwqKjjPd1UNiZiw5uWtzyun9g4aN0Wk1966djho/k0Jn1pbmm7aX772QIP0dqDt9AZ5IAgDIJC2mhz3T+J1ha/fKhl1L120tzrqdezvxzvkjW1fNfPOHfVHjZproMrxEN2ze6TacyLEz7ieelUtaS7LuSFoEI2ct74y9fCdDggxAoO70BWxHVzyRVFdeZNhYW1YAAHD2DtS3PNP4XafTYrE4PJEUFB0XFB035bW1n0wPvR7/d9S4mSa6DC/aPZt3NLt8P/lc3p1Eezdv/8iYztjLdzIkyAAE6k5fgCcQI0ZPTb9youheKvqlFQuavn99ooO776d7r+mHmTZ+L0i/vmP1/JXf/q23McQTiFgsDoPFmugyiqR7Nu/67HJF3r2pr/0PzVKZtpdXKWSdDKmP2XSFt+VmvWVjGGjUieSTBj3VAnWnj5i/5qvSh2nb3podGjOBwmBlp15WKxQL135rOMa08fsL0xd7Bkf+9vHLYbGT7N29lXJ53p1rCql40svv+UfGdNRlFEa3bd7R7DIWi3th+uInH6pje3mdVtPJkPqSDRs2WPDqA5ng4GDDf2KQrlSqHOAcPXp04cKFe+61du/t0hZhcvyesuwMBEGcvQPHLFiBbsaL3/E5rzj3/V9OAADkUjFq/I7D4/0jY1Dj9/gdnzfxyodPWRQ5bnraxfiC9ButgiYyjeHk6T9y1nJUR9QqRUddZkEsaPpgvM/g2Inv7niqMoRCKkHt5ZVyiQ3XKSJuWtjoKeiCyIwhvRHJPHLkyIIFA8hN9fkG6k4X6KHu9GvuJ53dtXbxez8f199S9SVQd54zoA8G5NnIxC3xOz53DwwLeWG8pWOBPA/A/A7EFGU5GbfPHn5w44JEyP/470ttT5NBIN0A6g7EFGqlorG6LCBq5IvLVrv598ohacgABOoOxBQBUSPRGqEQiBmB+R0IBNLXQN2BQCB9DbzP6jLpV05YOgQIpH8DdafL/P7xK5YOAQLp38B9g1bHoUOHli9fnpubGxAQYOlYnkFJSUlQUNC+ffteeuklS8cC6U9A3bEudDpdWFhYeHj4/v37LR1Lp1i6dOm9e/dycnKwlj7wCelHwN8V6+LIkSP5+fmffdZvzM83bNhQVFR09OhRSwcC6U/A9Y4VgS52Bg8efPDgQUvH0gUWL16clZUFlzyQzgN/UayIY8eO5eXlrVu3ztKBdI3PP/+8sLAwPj7e0oFA+g1wvWMtIAgSFhYWHBx8+PBhS8fSZRYtWpSdnZ2dnQ2XPJDOAH9LrIX4+Pjc3NxPP/3U0oF0hy+++KKgoODECbizCdIp4HrHKkAQJDw8PDAw8MiRI5aOpZssWLAgLy/v4cOHcMkDeSbwV8QqOHHiRHZ2dj96jNWWDRs25Ofnnzp1ytKBQPoBcL1jeRAEiYiI8PPzO3bsWCeGWy/z5s0rLCx88OABXPJATAN/PyzPqVOnHj58uH79eksH0lM2bdqUl5eXkJBg6UAg1g5c71gYBEGio6Pd3d2PHz9u6VjMwJw5c8rLy+/duwedCSEmgOsdC5OQkJCZmfnFF19YOhDzsHnz5ocPH545c8bSgUCsGrjesTDR0dGurq7P0xPo2bNnV1ZWZmZmwiUPpCPgeseSnDlzJiMjo5/u2emIDRs2ZGVlnTt3ztKBQKwXuN6xJNHR0c7Ozs/fs+eZM2fyeLyMjAy45IG0C1zvWIxz586lp6f36z07HbFx48b79+9fuHDB0oFArBS43rEYw4YNc3BweF6fOk+fPr2uri49PR0ueSBtgesdy3DhwoW0tLTPP//c0oH0Fps2bbp3796lS5csHQjEGoHrHcswcuRIGxubs2fPWjqQXmTq1KkCgeD27duWDgRidUDdsQCXLl2aNGnS3bt3o6OjLR1LL5KZmTl06NCLFy+++OKLlo4FYl1A3bEAsbGxDAbj/Pnzlg6k15k8eXJLS8utW7csHQjEuoD5nb7mypUrKSkpz+VjrLZs2LDh9u3bV69etXQgEOsCrnf6mlGjRlGp1IsXL1o6kD5i4sSJEokkNTXV0oFArAi43ulTrl27dvPmzefg6Hnn2bhx461btxITEy0dCMSKgOudPmX06NEkEuny5cuWDqRPmTBhglwuT0lJsXQgEGsB6k7fkZSUNHbs2Bs3bsTGxlo6lj7l1q1bMTExSUlJcXFxlo4FYhVA3ek74uLi8Hj8wEyyjhs3TqfTJSUlWToQiFUAdaePSE1NHTly5PXr10eNGmXpWCwA+vGTk5NHjx5t6VgglgfqTh8xduxYAMBATq+OGTMGi8Veu3bN0oFALA98ntUr7NmzJz8/X//P1NTUpKSkDRs2WDQoC7Nhw4bExMQbN27oW/Lz8/fs2WPRoCCWAa53eoWIiIgHDx7Mnz//iy++CA4OHjdunFKphA909Bmu3NzczZs3Hzt2LCws7P79+5aOC9LXQN3pFWxtbUUiEYFA0Gg0o0ePTk5OTkxMHDNmjKXjsjCJiYnjxo2Li4u7fv06gUBQqVQ2NjZCodDScUH6GnifZX6kUqlIJAIAqNVqBEHQZc6PP/547949S4dmSXJzc//++28MBpOSkoIgiEqlAgCIRCKpVGrp0CB9DdQd81NRUWH4T41Ggx7LGjJkyJQpUzIzMy0XmmXIyclZunTp4MGDjx49iiAI+gPRU1lZabnQIJYBb+kAnkPa/SKhX7YLFy7cvn37wYMH7u7ulgjNAlRVVcXGxqILQJ1O13ZARUXFoEGDLBEaxGLA9Y75qaioIBAIbdtxOByDwbhy5crAER0AgLu7+5UrV5hMJh7fzh85AoFgtDyEDASg7pifioqKtgXC8Xg8lUpNTEwcMmSIheKyGEOGDLl+/TqDwWgrPVgsFt5nDUCg7pif8vJytVpt2ILH4xkMRmpq6gAUHZTw8PDU1FQbGxsj6VGr1eXl5ZaLC2IZoO6Yn+LiYsNEBh6PZzKZN27cCA0NtWhcFiYoKCglJYXNZhtKj06nKy4utmhcEAsAdcf8VFVV6V8TCAQbG5ubN2+GhIRYNCirICAgICUlhcPhGOa/4H3WAATuGzQzMpmMTqejP1UCgWBnZ3fjxg1fX19Lx2VFVFZWxsbG1tfX6+9GJRIJjUazdFyQvgOud8xMRUUFKjp4PJ7L5aakpEDRMcLDwyMlJcXJyUl/w2W4QoQMBKDumBn0rgGPx7u6ut65c8fb29vSEVkj7u7uKSkprq6uqPTAR+kDDag7Zgb9Cnl4eKSmprq5uVk6HOvFzc0tNTXVw8MD6s4ABOqOmamoqAgKCkpNTXV2drZ0LNaOs7NzampqUFAQ1J2BhpnzykePHjXjbP2RkydPjhs3jslk9vF13dzcRowY0cNJbt++XV1dbaaIOotUKk1MTJw+fXofX9faWLBggaVD6EMQs2LpTzNwmTdvXs//++bNm2fpzzFwMcf3r99g/nOhv833nx7CMfu0EBOsPFJkrqk4Q6b6r/rDXLNBOgM//UzRb29aOoo+BeZ3IBBIXwN1BwKB9DVQdyAQSF8DdQcCgfQ1UHcgEEhfA3UHAoH0NVB3IBBIX9O/dedOZeuWpOpGidqMIyF9CS9hW3PaafOOhFg//buexN2K1q3JvElBbHt6Oz7q3RvZSZql6sQiUYNExaESxvjZODGJ5ho8oOCd2WYbNt4ueqYZR3YKBGktuiOtzEYQHdU1iBU0EoPFmWcwpBP0b91ZMcL5pSgHNvXZn6LzIzvDmRz+mtMlKg3CpROapWoMBvPdNK+FEfY9HzzQiNqSicGTzDvymWiV0sKdr7QU3MJTWRgcXi3m070jgj44iKeyejgY0kn6t+5QiVgqsVO3ip0f+UyqRcrVJ4pDneh/vRRgTye0yDVvHiv6MKEswpXhz6X0ZPAAhMDkmn3kM6k8srml8Lbva9u4L8wHGIzg/sWi3f9Xvn+d3//t7uFgSCfpB/kdHQISi4W7Umr2pzc0ilUaHbIlqTqxWGSUtUmrEm9JqpapdHyp+vC9xt0ptWdz+SrNI391M+Z3DmY0qLXIjzO80Vs2FgX/4wwfHYL8c7euh4OfV1SC2obk/TUXdolykgGCtOTdrD69RadSGGVteGd3CB9eAwC0FNyqvfhrQ9I+WU2BfhJz5Xe0Cklj6hF2xGRuzAKAwQAA2BGTuC/Ma05PULc09mQwpPNY+3pHpdEtOVBwq7zFnkF0oBO+vlL59VSvrcm8VTHOY/1sDLM2WTzJ1mRegD31k7NlTDJeq0N4IqUfl3JmRSiDhDOR39mbVt8sbV+PxgfYhjnTjRpTy1sdGMRAB6q+xdWG5GdHSSlraTtDlwY/lwizLhf99iaC6KjO/nVX9tDcgkls54YbB10mv2WUtam9sJsdMYmfniC4d5HEcVU0VSAatdfSbxxGLzWR35HXFjWnn2n30jgyzXmi8XlLcUkGolHbhMYZNtqEjmlM+a+l4JbdsFndHgzpPNauO/syGm6Vt7w+3GnTJE8MBmTVSF49XAgAwGIwRiPRhm+uVB1cFoSKxU9J1duSeQk5zUuiHExc4lKBsJQvb7fLi0NpqzvlAnkAl2rU6MEmJxWLdAjAYro/+PkD0WpK936IozCCPzpOcfJFtJrSf//XdOcEAABg2qy1MRhB1iXOkGlDd+ZicHh1a/ODL8byEraiutMRSj6vKbV91ycCg9NWdxSN5QAAsr2XYSOZ6wEAUDQYV/Lq0mBI57F23bmYLyDgMJ+Mc0dlJdyFviTKflsyr6PxiyLt9UoxK9RuWzKvtFlh+hKHlgd1KSSxUksnGT/OYJBwGh0iVWkZT3d1afDzh7gkXd3a7DL1XYqTLwAAg8N7Lf5SkHm+o/EYDMZr8ZcYHB4AQGDaMYNi+GkJWqUUR+qw2oRN6NjIH+52PiStXAwAwFOe+nOCozAAABq58SK0S4Mhncfa8ztlfIUnm2yYEo7ztTExfrDzk19QJhkPAFBqdCbGmwvU8ayTy5cuDe7XyBvKAAA0t0H6FhyFQfeJ6mg8xdEXS3ySbsdTmAAARK00Z0ztmtMhHfyfdGkwpNNYu+7IVFoa8alFAYdqagMOldjrKwgWGS9Wao0aWxVaPA5Da3P1Lg1+/tApZAAA7NOrFQKjQ1s4LMn4ntTs4GgsAIBGJjZs1MhbAQB4mvGftC4NhnQea7/PIuGxCvVTCxaRXGPeS3Q1r+zNIZe3yQeVNsu92eQ2SaeuDX7+wBJIAACd+qmfgEYqMuMluppXpjh4o4kbVlCMvlFRXwoAoDj69GQwpPNYu+6425LzGqRqLULAPfqa3jT3k6Cu5pVjvVnpVeKcOmmI06M/48VN8gqBYsUIp7YzdGnw8weJ6w4AkNeV6Ft0Krm4NMOMl+hqXpnhOwRLpAgfXjNMVwseXMFg8azAmJ4MhnQea9edMX4293jiXSk17492BQDkN8gOZTaY9xJdzSsvG+Lw263aj8+U/fNSgD2D2CxVrz1dSsRjXx3miO422pZc7cAgLh3i8MzBzz1Mv2gcidZ445B9zAKirROi01b8t9G8l+hqXhlLpDjELa27sqfx5mH7kYsABtN0O56ffsYhdjGebgsAaMlPbS26wx0+m+zg/czBkO5h7bqzYrjTuVz+j4nVR+43sqmEKqFi02Sv1ceLLRiSPYP463z/d+KLh269Z88gNErURBzml7m+HrZkAIAOQbYm8wY701DdMT34uQdLonos2lC275P7n8ZSXQJUonqGXzQ7YmLTrXgLRuU+Z52ivrT03/9Vxn8NAEYjEbAGxXoueiSILQUpNWd30j3DyA7ezxwM6R7WrjsMMu7C/4VeLBBWCBRcOmFiIFsgUwMA8DgMAGCYJ3NNnCuXTgQARLnR18S5urCeHLmkEbFr4lwjXOlGI3vOeH/bW+9HXi0U1otV9nTC+ABbO9qjbDcWg1kT5+rAIHZm8EDAYdQSpu9QUe51RKele4YxA0YU/b4KAIDB4QAArtM/IDs+KuXsMuUdos1Ty0DbsAkElj36hMtwZA/BEkiB7+1vLborKb8PAKB7hjH9h4PH+TZW4EgMFk95fC3TgyHdw8x1+zAYjHnr2Gh1SG2ryo5GoBAePXq7VCB47XDh99O90QUFBK1jQxk05tixYz2cZ/78+UkVcvPWsdHKxRpZC4njqm95uOlFVUvTkK33zXiVfg1ax2ZAlZ+z9ufolwqFw7fd+/5alQ4BAAChTLPjeg0ehxnnD++u+wc53816uHmSfndv061j0qpcduQkS8cFsSTWfp81KZC9MML+z9t1p3P49nRCGV+u1iI/TveBFjb9BZ9XtuRvX5q1Po7qGqCRiZXNVXSvcPc56ywdF8SSWLvuYDFg6yyfN4Y7ZlRLRHKNPZ0wxs/GMHsCsXLoXuFRP6QJH15TNlcDLJbmHsIKjIH5kQGOtesOyiBH2iDHDk/oQKwcLInKGTrd0lFArAhrz+9AIJDnD6g7YMd13smHzZaOAtJNxMVp1ae3qERm3k0K6VWg7oDtN2pOZluL7pzL429Jqm4QqywdSL+htTiNl7BVJaq3dCBA3drcnHa69sLuptSj8vpSS4dj1fSP/M5AQKNFNlys+DetHj2PCnPn/YvGG4cq/tuoVckIDI5GIkQQxDFumdeSr2EGvV2g7lgFDWLViiNFTRLVnMF2J+BNX39DXJJRtv8Thu8Qv5W7ibaOGqmoZM/q+qS9DP9hZiu883xhYd0pbJRlVkv4MrUtBT/ck+lr98TzSaNFkktF5XwFAMDHjjzaxwb32BZ0x3VeiBMtztfmapGwuEnOpROmBHEYZJxEqb1YIGgUq73tyC8GsNHhCAK2XecFO1LH+9teLxUVNMjJBGycr403p/0TUjoE3CwT5dfLdAjw5VLifFhE/JO7URMB94TzeQIyHnt+5eCD5j712qvo1EpRdqKymYcgOrK9h03oWCz+yTJN0VjRWnRH3dpMZNkz/Ieh9qBoRkaUd9Nl0iq1uFn44KpOo2L4DmX4RAEAJOVZrUV3cSQqa9Aosv2j8S0Ft1oLbzu/uFIrFwsfXtPIRGQHb3b4RNSWsC0qQa0o97q6tQlPs2UFxaDHrDoTcLfhZ5xFdFrvl38k2jqi1jwe8z8XPkxsyU+ButMultSddWfL9qU3OLOI9nRibauqUaxaMcJp4yRPAEClQDH/37x6scqfS9HqkOJmuZ8d5fhrIWgBrN2ptZMC2fszGu7zJFQitkqo/Plmzb4lQUv25yEIkKt1zVL1eH/bvUsCUd/lHdd5EwPZ+9IbsmokdjRChUCxEYAfZ3i3LWJV16pafrCgoEHqb0/FYTCFTTJnJnHfkiA/LsV0wIYUNcnP5LS/ZqERcW/GOLdtH+tns2yoA75f+S3LagryflqolbVSnP0Agshqiggsu0Fr/6M4+QEAqo5/V3NhF4HBIdo6KZurtAqJx4IvnMa/jooLL2ErmetefuhzIsteKazTKWXeL/+gaeXzzu0ksZ0VTZUYHD4Jl3N5AAAgAElEQVTog0NM/2EAAElJBi9hK4FuW3X8O3Q1oRbzae4hwR/Fo5ajhtRe+r3q+Dc4CpPi4KVsri47sM550iqPeZ89M2BD6pP2qlvb/x+0DRtP9wwzavRctNF97jrUbAgFPVPWkTJCLPZzyW+Q7UtvWB3r8sl4d3RV8lNS9fbrvDmDuYOdaevOljdKVBf+LzTYkQYAuFYkXH6wYGty9VdTvFApuVQgeHWY47+LAwEA312t+vlmzay/cr6f7j05iK3VIa8cKkCXQqheYLGYy4WCpUMc9i4JxGMxVULl3H9y1p8vR1dJhlG9HV9cIZAnvBGKniYtbpLP/jtn1bGiq2+FmQ7YcBKeSHk0q6ndT82hEtrVHQ92/zueXn3qJ0SjjvzhLoFlj/rg5Hw7s/LYV4Hv7hWXZtac/9lu2CzfN3ZisDidSpG/bUnl0c12Q6cTWPZoyqPmwu7BGy6RuR7q1qasz0ZXxX9DdQ0asi0LR6aLSzJyvp1Zn/gPqjsAiwUA1Jz/JeSzBKpLIACg+uQPvLM7ai7udp/9sWFILfkplUc32w2f4/PqFiyeiOi0lUc21V7YzfCOZEdONhGw0UcT3r/UUWKY4uDVVnf0Dmd6Gm4eQovemOEH/TxiMd1BH9nobUkxGPDBaNfXhjlyaAQAwNwwu5mhnODHewXH+duyqfiHtVL92/E4zJo4N/T1tGDOzzdrAu2pk4PYAAAcFjMpiJ1YLKoQKPwe18Yj4DCfTfBAFxTutqTXhjl9dbnyZnnLlCC2fs6CBtndytY3Y5xR0QEA+HEpK4Y7/ZBYnVUjQX0OOwrYkLF+Nnc/iOy1n5y1oBY1ACxWb4dM4riGbbyKI9MBAHgqy2vxl6zg0Wg9XyyRzBk6vbXojpSXb8N6tMZ0iH0JvfMiMLkM/2HCrMtusz5E387wHUK0cVA0VhhezmH0UlR0AACuM9fUXftbmHXZSHfqE/disDjvpd+gd08YLM59/vqGm4cbbx5iR042EbARQWsO9eQnI7h3vubcL3bDZtsEj+7JPM8xFtOd4R5Mfy7l+2tV6VWtY/xsR3qz/LkU/Xd4bhi3Wao+l8evbVEpNDqtDtEhQGJgVOzNoegdCFH/9gD7J6kWFhkPAFBpnxikBtrT9CfaAQAB9lQAQAX/qVIT2XVSAEA5X7ElqVrfWMpXAABy66VzB3NNBDwAcRizvOTvD7LWj+ZEz2AFjmQGDNdbYVGcfCmO3uLyrIYbBzVSEdBqJZXZAACtXKJ/O8UlQP8aT2UCAKgGLTgqC1E/tZmA7v1EyjFYPMXRx7CqH4q0KhtLotZefuo8PY5Ml1blmg7YjNRd3VN5ZDM7YpLva9vMPvlzg8V0h0zAnlkR+k9a/dlc/hcXyhEEeLLJH411mxlqBwDYfp23LZlHIWCDHKhMMh6PxRiKCADAUETQJ5UUAs6oxRDm0/dTaIEKufopx3VU14qaZI2Sp37jI1zpFALOdMADEO4L88lcj/rEf5tSjtZd/hODJ3Cipnm+tInA4CgaKwt2viyvK6Y4+ZHYzhgCSd1mXx+OaJiSx+hzIo//bfxfiKMyn/oniapTKcDT3hFahQTRqkU5SYaNJI4LlkA2HXDPfxoAAESjLjv4aeONQ86T3/KYu66dAmGQx1gy70Un4VbHuqyOdeFL1Uklot0ptW8fL3ZmkQg4zI+J1eP9bX9f4E9+rC9Dt2b25FpGbvAylRbN8ho2ormeVTHOHdX56yjgoe5PZTe7kVfupzD8ohl+0YhOK63Mbr5zoj7xX7WYP2jt4fJD6xUNZcEfxTMDRqAjG1P+k/yztifX0j7tBq9VyrAkqtFfGDyFqcMTQz8729WAjYZ1Na+MVigs3P1GS+4NvxU/2w2f08UPN+CwmO7wpeoqoRLNpHBohHlh3ChXxsid95NKRE4MIlqBTy86ZXxFXauKwe1+tKV8uUKt009Y1CgHAPg8/RQ81IkGAMislhjqTn6DjC9Vj/BkiuSajgI20p1u5JX7HYhOK68twjM4RJY9Bouje4XTvcLVYkFz2mlEo5ZWZlOc/fWiAwBoyUvp4RWl1bk2oWMfX10jry9tW9GB5hHCzzyvbK4m2bnpG5vvnmT6jyCwuCYCxuCful/uRl655K/3W/JuBr6/nxU0soefdCBgMd05kNHwU1L13iWBY/0e3WNnVIsBAE4MohOLCAC4z5OgeeIqofL9kyWODGKLovsVbOQq3Zbk6k/He2AwoEGs2ptezyTjY7xYhmMC7KnR7owTD5umBbPRqOpbVW/HF0uU2pR3w00EbHStgZBXRtTKnO9m070iAt7+E03NahUSWU0BgWmHwROItk6KxnJ1axOByQWIru7a39LKhwAAraz7tUAakg9woqaRHbwAALUXf9XKxZyoKUZjHOKW8zPOlR341P/NX3FkOkCQumt/Vxz+wnvZd9wRc00EbDRPV/PKwofXmu+e9H75Byg6ncRiuvPGCKfU8tZlBwpcbUh2NIJApq4SKsf72y6M4GIxmHAX+q6UmqtFQjwWUy6Qb5vlm1Yl/utO3ZL9+R+OdevE9MbEerPuVUuitmQ6s4iFjTKVFtk529ewDCnKL/P8lh8sWHagwMOWzKLgChrlNmTcP4sDiXisiYB7/tNYuDcvt14KAECLhS3cm4c+ets0yXNumBnmNztYEtXn1S0lf67O+CCM4ugLAJDXl2LxRN8VPwMAXKa+U/zbW/c/jaU6+SmaqymOPoHv7c/6fHT16Z8UjRUEVnc+ETdmwcMvJ5O5HlqFRNFYwfCOdJqwwmgMK2ikx7zPqk5+n7kmkuLkq2ptUglqHUYtsR+1GIPFmQi4h9Sc+xkAUBX/TdXxbw3byVyP0PXnej7/84fFdIdGxB19ZdA9njinTtai0HCohAhXepDDo3KRp18PuVQoqBAoWGT8OH9bJyZxnL+tnx1FodHZ04nvjHRxNFhlsMi4NXGu0R5P8o5+XOqaOFc/7pPikzgs5sgrg66XiAoaZbND7cb523o+3jLz/igX98fVHVxYpMtvDk4tb82rlyIArGaTx/nZkPDYZwbcQ6YHc6LdjbfAAQD87Xu9fma34URNZQWNFOUkq4R1GAyWyHGxDR2L5oY5UVOpm6+25KfqVHKqaxBrUCwGiwv++ERr4W2KgxfBxtF1xhoix0U/FTtyEsnODXV6R3EY+7LR5djhE+xHLhQ9TNTIWyiOPrbhL2KweLRUjuuMNXpDeOfJb9kNm6Xfr8wMGK7fFmgi4B7CfWEea1Bs23ZYU7QjrN3X3Sx4fXk31pu1b0mgpQPpLazZ173n1Jz/per4t6Gfn283sfIcAH3dIRAIpNeBugOBQPqaAXFuzTCDA+l3GGVwIM8BA0J33hvt2olRECsF3exn6Sgg5gTeZ0EgkL6mP6130qrEN0tFS4c4WNYDNLtOerlAgL6eFMQO7p0CO1eLhA9qHp2iXDumO1uWrBDe2R1krrvdsNmWDaPu8h8auRg9j9p2E5BZ0CqltRd/Q19Tnf1hGR8j+pXuVLZuTeZZ3Hs4u1a6NZk31J3BJOOHGWwa4kvVBzIa3G3Jswd37aRos1SdWCRqkKg4VMIYPxu0FGqNSPmgVlotVBQ1yZ8b3ak5s501KNbiulN7+XdEo6Z7hROYj3cwIkhr0R1pZTaC6KiuQaygkaiDRydpLbor4+UjWjXZ3tMmOA6DJyBarbTiAQBAlHOdHTER6o4R/Ul3rIovJnpEuj7Z6XefJ1lxpLCuVTXO37ZLunMmh7/mdIlKg3DphGapGoPBfDfNa2GE/cvRji9HO/55u27jxYpOTAPpGjSP0MD39qGvtUpp4c5XWgpu4aksDA6vFvPp3hFBHxzEU1nPmgZoJMKCn18Vl6RjSVQMFqeVi0l27v5v/kr3CkfnT3vnud011hNgfscMHMhomPNP7qxQO0Mn5s5QLVKuPlEcaE9LXxuVsTbqwYdDhnswPkwoK2qS91qwEGMqj2xuKbzt+9q2oTtzh2x/GPDOX9KK7PL9nargXvznanFppu/r24ftKor+pSDw3b0qYW3Jnnd7P+r+TV/rztUi4Zak6map2rCxSaLeklR9rUiI/rNCoPjvXuMvN2uO3m+qFCranSe1vGVLUrVA9uSkaLNUvSWp+nZFq76ltkV1+F7jzhs1+9Mbyvjtz2MWdtzgbZvls/5Fj66+8WBGg1qL/DjD255OAACwKPgfZ/joEOSfu3W9E6kZqD69hZ9+xqiRn5ZQfXqLVilFHSGED67WXfmz7sqfwoeJiE7bziyIrvr0Fn7GU2eX+Olnqk9vMRwjyr1ee+m32gu7hVmXdZpeqSmmVUgaU4+wIyZzYxagrhrsiEncF+Y1pyeoWxpNv1cjEbTk37QbOp37wnz0vbZh49kRk+T1pSqh5et5WTN9fZ+l1Oi2JvNsKPjXhzvpG08+bN6azPv7pQDULHlXSg2HRnBiEquESolK+8WLHoaDUVLKWnbeqJkZaoc6vaO6szWZBwAY4ckEAPx+q/abq1VMEs6LQ6kWKdedK1sV4/zZhHakYW9avZEO6hkfYBvm3I4PphH/LR/k063CEqnlrQ4MYqDBIS9XG5KfHSWlrPvntnsbwb3zDUl72VFT9BkQRKspO/gpie3sNnOtorEy78f5KlE9xdkf0WlR66+Qj4/j6WzDSRBEx0vYyomewRkyVd/ITz/DzzznNnMtAEAlrCvYsVzKK6C6+GOwOFlNIdHWOej9fW092OW1Rc1tdBAFR6Y5T3zT9McRl2QgGrVNaJxho03omMaU/1oKbtkNm2XivXg6e/jvFTrtU788jx3du5AeGoD0te68GMBmUfAJOfyndCe7iUMjjPOzzawW/3yzZlao3c45vjgsRqHWLdmfv/ly5fQQO3RF0ElSylo2X6qcM9huy0wfIh6r1SGbLlXuTqmNdGVMDmIbDb5UICzlt39f48WhdEZ3uic6AIBygTyAa3zy04NNTioW6RBgndUluCPmVR77qrUglTVoFNoiyknWSISu098HAJQfWKdqbQz9/ALNPRg1iCjYsbz69FavJV916SrFf7wtb6wI/TSB7h0BAJDXFed8N7vot1Vhm64ajVTyeU2pR9udhMDgPFN3FI3lAACyvZdhI2r8rGgof3agGIxhJRx1a7PwwRWGT9STjDWkPfpadwg4zPRgzsHMhtoWlTOLiPoZP6yVrhjhhMdhWBT8l1O8Rvuw0FJZZAJ2egjnTmVrfoPUnt6Fo7170+pxWMw3U73RhAsOi1k/wf3wvYZDmY1tdefQ8iBzf8rOIlZq6STjP4wMEk6jQ6QqLaNNlzVgN3xOZfw3zeln9LrTfPcUBodHn1LZjZjLiZ6Jig4AwHbwODydjZrvdB4Zr6C16K7zxDdR0QEAUJz8nCasqD75g6Q8i+4VbjjYJnRs5A93u/1xtOgDdcpTf13Q8jgaeddWnVqltHDXGzq10mvZd92OZ4BggedZ88O5BzIazubyV77gBABAa5MvCOcCAHztKN4cSlaN+GBmg0iu0epAdq1E73zcebLrpFQi9o/btYaNdCIO9bixctBTyVa51gEAAKKNAytopCDzvPfSbzE4vE4lF2Rdsh08HnUp5o6Yq25t5meeUwlqdWoFotUCRGdo594ZpFXZ6ErEMN2jqC9FXQeNdKentHsIHOnyf4JKWFew82VFY2Xgu3tpboPMFt5zigV0Z4gbw5NNPp3TjOrOqezmYEfaIEcaWq7v5UMFaN0rZyaJhMc0iNvPvJhGotKqtUhSyVOOvC42JHIXnzf1NiwyXtxGUlsVWjwOY+T9bFVwX5jXknejJe+GTehYQdZlnVLGjVmAdvHObOclbMOSKFTXIDyVicHiu5EP1iokAABZbZHq6cwu3TsCZw67HENwNBYAQCMTGzZq5K1dcs8Rl2YW7noDSyCHfHpaX2wHYgLL7N+ZG8bdklRdJVS2KDSlzfJNkx+V3Fx/vryMr4h/NRjNDQMA/rvXuPZ0p/5aolbtKEwSnojTnV0R2pk39jyv3G28OeTyNqml0ma5N5vctiSG9cCJnFxOojWnJdiEjm2+c5LA4NgOHocWAq0+9aNt2Hj/N3/HEh8dxM38cGgnp0Ufh+lvc5wnrXIYteSZ7+phXpni4I2urVhBMfpGdG3V1r+5XVryUwt2Lqd7RQS8vQcafXUSS+mO3Zak6ov5gmapGo/DzH5cCia7TurPpehFBwCQUt7+PTbqAShWPNEaw6p+IU608/n8apHSzeZJFceTD5tHeDIdmcZ7nXueV+42sd6s9CpxTp00xOnRYYviJnmFQLFihPHzO6sCS6KyoyYLsy5rJEJRbrLjmFfQgrzSymwAgP3IRXrRUTSUqYR1eCdjK0UMFo/B4rQGqwxEq5Hx8tHXNI9QAICkNNNQd2S8fLWYzwwYYbSTuId5ZYbvECyRInx4zWH0Un2j4MEVDBbPCowx+VYAAJBW5xXsfJkVNDLgrT1tfZohHWEZ3fGwJQ91Z1wtEta2Ksf72eqr3zkxieV8RZNEzaUTdAj4+24dqiYtcuObER8OBQCQkNuMFngoaJTtT39SoWn5UIdzefxPz5b9Ot+fTsIhCPj7bt0XFyq+m+a9bKhxjZrezitvS+axqfiXo9uxcVg2xOG3W7Ufnyn756UAewaxWapee7qUiMe+OszaPR+4I+Y13YqvOvk9olHrb7KItk4AAEnZfXbkZACAsrmq5K/3iTaO7SZoyQ7e4tIMlbCOaOuE6LSV8V/p1Eq0i+ocwPCLbrpzgh01zXbwWACASlhf/PvbWoUk/NsUI93pYV4ZS6Q4xC2tu7Kn8eZh+5GLAAbTdDuen37GIXYxWtWvtehOS36q3dDpFGd/4zcjurK9HxJtHf3f/A2KTpew2DmJeWHcdWfLdAj43GC73TuxLm8dK479+b6fHbVapPDhUPYvCRz9S9ZPSdUVAoVh7b0JAbbBjrQ/btWdzeWT8Fi+VLNzju8rhwp0CAAAjPRmfTbB4/vEqsifMn3tKE1SVW2LakmUw+Ioe7N/kOulorfji9HXKo3ueqko5Pt0AIADg3jtrTAAwLbrPD87Sru6Y88g/jrf/5344qFb79kzCI0SNRGH+WWur4fVuwWxgmKItk4N1w/S3IP1aVSbkDi6V3jNhV3CB1cxeLy8odz3tW3i4rS6q3/lb1viNutDwxlcpr5T+vfa++tiyPZeKmEtKyjWbtjs+sR/AKIDGKzfyl8Kdiwv2LGMzPXAUVnymgIczSZw9T+GD63NhfucdYr60tJ//1cZ/zUAGI1EwBoU67loI9rbWnSXl7CV6hrYVnda8lMl5VlYAqntvaT3su8NtyZBjLCY7swMsWsQq7AYzDi/J7Vipw7iXH2LmlreIlfrghyosd4sHBZz4rXg2xWtXhyKHY2wJs4VdXQnE7DnV4ZeKhRUCpRMMm5CgC2HSlgT5zrU/dE92lsjnWeF2l0vFTVJ1LYU/HBPpr5WunlxsyG/2p6mMEiPfrYLI7h59bKO3j7e3/bW+5FXC4X1YpU9nTA+wNauX9Q+xmC9lnwtrcph+g970obDh6w7Lbh/SdFUgaeybAePI9o62Q4eR3Hy06kURJa9y/T3yVx3dDB3xDy6Z3hr4W2tXEx1G2QTPLol/yaebovodBgclsR2GbzhcmtBqrQ6DyAI2X61Teg4LIHUcUDdB0sgBb63v7XorqT8PgCA7hnG9B+urwjI9B9G5nq0m88mcVxcZ6xpd06Ko3dvhPrcYDHdYZBx7R6z9uNSjAQi0pWhP4FpWHQBj8NMHfSUgbzRhM4s4kuR5l/gGOHNIZs+L+5rRzH9cIpNxS8wRzGcPoYdMZEdMdGoEYPDG/2dxxIpDnHL0Neu094z7KI4+VKcfPX/ZA0apd8TBADAYHFGLb0K03+YoYbqoXuG67Rqmsfgtl1kB290dzWkq1jXc+XnEiwG8+YLz0ON0IGJvL7EY+46ArNr3iYQ00AfjG6y+VIlk4xfOcJppPcz3BK693Bqb1r9tWJRdQfHYiE9RFqZXbBjOYHJ9Xl1i+mRNPcQmntIV+fXyFpL/nwHAKBTQmuBdoC602UWR9n3Rn7aCNR/p7evMjCJ+imzty+BpzL1/j6QtsD7LAgE0tdA3YFAIH1NP9OdtCrxlqTqBnGvWEBBegne2R3Nd09aOgqIFdHfdKeydWsyrx7qTr+i5sz25jtQdyBP6Ge6A4FAngOg7kAgkL7Gep+jl/MVN8taxEqNhy15vL8tmdC+RFYIFHcqWpulans6cZgnw/Bkk1KjSywW8URKHYJ4sMljfW309R5MdPUEtLLgqhgXuVp7uVAolGncbUkvBtgaTl4pVNwsbRHINCwybpgH09Bcub+DaNTC7ER5fQmezGD4DaW6tn/gFtFqRDnJjwxGHXxsQkYbHvWU1RZKSjLVEj6eZsv0H264odlEV/fQKiS1l35vW1dPcO+CtDrP+cWVOAoDIDpR3k0ZLx/odBQnX1ZInOEZMbOHNECwUt3ZklS9/TqPTMA6MIjVIiWbSji0LCiozVfUhAl8QaNs4b95rUqtnx0FAaCoUWZHJ/y3fJAfl2Kiy2j+rlrzZPEkW5N5AfbUT86WMcl4rQ7hiZR+XMqZFaGoaen316p+uVnDIOO9OeQakbJRop4fzt06y9c6rZS7hKKxMn/7EkVDOYnjqlPJ1WK+49hXvJZ83XaYCeP3sgPrGpL2EdnORJa9SlCraml0mrACPaJposuQLtnx4Mh04YMr9Vf/so2Y+ERNEKT80Od4CtNt5lrT9vKdDAnSFmvUnaQS0dZk3uIo+2+neeOxmCqhcuZfOf93tOjG6qcMLk2bwP+UWK3WIXc/iEQN4Xki5cy/cr66XLl3SaCJLqNIumrNg54l/OZK1cFlQWjvT0nV25J5CTnNS6IcLhYIdt6omRHC2THbl4jHIgjYdKniz9t1Ua6Mtu4c/Y7iP9/WSEWDv7hA8xgMEF3ViR9qzv/M8Iu2i55pOMyE8buMl9+QtM9lymr3uZ8AAACCVJ/+iXdmO3fEHAyO0FGX0cmprtrxOIxeWrbvY8G9C/o4W4vvqoR1zi/+n2l7eRPRtnuYC2KINerOwYwGPA6zYaInHosBALjbkrbM9K4QKGUqneEw0ybwDWI1FgMoj+/OXG1IV1eFoSbqJrqM6J41z6JIe70kzQq125bMK21WAAAOZTbisJivH7vNYzDg0/HuBzMbjmY19XfdkfHyJWX3Xaa88+grh8G6TFuNpzKJLOON3SaM31WiBgAAjvR4VYvBuE7/wHHcawQGR5ST3FGX0fxdteOxGzar8sjmxpv/6XWnOe00Boe3GzHHtL28RirqZEiQtlij7uTUSd1tSIZCMNbAK0OPaRP45UMdPjhVMvrnrBkhnJHerOEeTNvHlbZMdJmFwc40/WsmGY+mkwAAefVSVxsS2+BaRDzWz45a2NihS0Z/QVqVAwAwTOjgSDTnyW+1HWnC+J3pP5zi7F918vvWknTb0DGsoJEUZ3/0a2yiq4fgyHS7YbMabh5W8nkkjiui0wgyzrHDJxIYHFF2ogl7ee7wub0U0kDAGnVHotI6MZ/ttGLaBH5+ONeDTf43rf5oVtOft+sIOMy0YM6mSZ4cGsFEl1nip3bgeiFRad2oxpegErEyddeqZVghqBO73t7UBCaM37FEcuhnZ+oT/+Gnny0//AVAELK9p9vsj+yiZ5ro6nnw9qOXNNw42JR61HXGmpa8m2ox3z520TPt5Xs1pOcea9QdBgnH7yCba8gzTeCj3RnR7gytDsmuk5542PxvWj1fqj68fJDpLkPMa/nOJOEFMuPZJEot3YpLR3QSHJkBAFCL+aaHPdP4HUemu0xZ7TJltVrMF+Uk1Z7fXfzH2yS2M8N3qIkuw0t0w+ad7hlG8whtuhXvOmNN893TRFsnVvDoztjLdzIkSFusUXdCHGkXCwQ1LUoX1qNVz63yltx62fzwp8yxTJjAa3VIUZOcQ8XbM4g4LCbchR7uQhfI1KezmxVqXblA0W6XWosQcE89WDKv5XuIE+1KoUBfsBAAIFfripvl4b1pHd83oE4RkrL7+q8ootPUXvqd5jbIJmSMfphp43e1mK9sqkKTKQQGhztiHsM76v6nI0XZSWQH7466jL7k3bN5R7PL4uI0wf2LTuNeQ5/rm7aX10hFnQwJ0hZr1J2XohzO5ws2Xqj4ZZ4fCY+ta1X9L6FMotQa2YmaMIFXapDZf+dEuND/XBiA5okkSm1Bg8yORtAhoKMuI9Exu+X70iEOlwoEX16u+HmOHx6H0eqQjRcrFGpdH7hq9DZU10C6d0Tz3VPcEXOZASMAgtRe/K3q+LeeL20y1B3Txu8N1w9Un/op8N29qJc7AEBcmoG+y0SXUSTds3lHs8ulez/SKiTckQsffSiT9vKdDwnSFmvUnbF+NmviXLdf5w36Lt2ZSawSKRkk3J5FAfindcG0CfyWmT6rj5eE/Zjha0dBi1IR8dif5/hSidiOuvrgc60d47b9Ou9mWYYXm1IhkAvlmhUjnOaG9T+T07b4rdiVv31J7g/ziGxnRKNWtzZxomc4jn3VcIxp43fnyW+zAkcU7FhG4rgSmHZqsUDZXGUbNp47ciGiVbcWpLbbZZbgH2WXbxxkBb6AFkd/9KE6tpd3mvBGr4b0fINBkHYLtXZ3Ogzmt/n+00NgVr9PWXmkiDJozLFjx3o4z/z585Mq5P6r/uje2x/tV64rxpHpdO8IumcY2s47u4PMdUcLqCNajZHxu04lb7oVr1MpOEOmEtnO4rJ7ssocjbyFQOfQvSMMn5GZ6Oo5gnsXCne94bfyFzTOJx9KpzWwl/cwspc3S0j89DNFv71p3m+ilWON6x1IPwWDJ7AjJoI2Zu+Gdu6mjd8BAAzvSIZ3ZLvzm+jqOU234glMLidqmlG7aXv5Xg3pOQaeC4VAQMP1A4L7F12nvwfL7/UNcL0DGdBUn/yh6c5JZXMVO9zjMckAAACcSURBVHKK45iXLR3OQAHqDmRA4zb7I7fZH1k6igEHvM+CQCB9DdQdCATS10DdgUAgfY358zt/3K47k/uMczoQ83KPJ44xPlvWTcSl94p+XWmeuSCdQyWos3QIfY2ZdWfevHnmnRDSGWIGgREjRvR8HrNMAukynt4g0tvSQfQpZt6vDIFAIM8E5ncgEEhfA3UHAoH0NVB3IBBIX/P/KaXZtsN6onMAAAAASUVORK5CYII=",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dot_data = StringIO()\n",
    "export_graphviz(clf, out_file=dot_data,\n",
    "                filled=True, rounded=False,\n",
    "                special_characters=True,feature_names = ['Income','Credit','Gender','Unemployed'],class_names=['no','yes'])\n",
    "graph = pydotplus.graph_from_dot_data(dot_data.getvalue())\n",
    "graph.write_png('graf.png')\n",
    "Image(graph.create_png())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Titanic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "titanic = pandas.read_csv('titanic_full.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  \n",
       "0      0         A/5 21171   7.2500   NaN        S  \n",
       "1      0          PC 17599  71.2833   C85        C  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3      0            113803  53.1000  C123        S  \n",
       "4      0            373450   8.0500   NaN        S  "
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>886</th>\n",
       "      <td>887</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Montvila, Rev. Juozas</td>\n",
       "      <td>male</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>211536</td>\n",
       "      <td>13.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887</th>\n",
       "      <td>888</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Graham, Miss. Margaret Edith</td>\n",
       "      <td>female</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>112053</td>\n",
       "      <td>30.00</td>\n",
       "      <td>B42</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>888</th>\n",
       "      <td>889</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Johnston, Miss. Catherine Helen \"Carrie\"</td>\n",
       "      <td>female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>W./C. 6607</td>\n",
       "      <td>23.45</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>890</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Behr, Mr. Karl Howell</td>\n",
       "      <td>male</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>111369</td>\n",
       "      <td>30.00</td>\n",
       "      <td>C148</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>891</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Dooley, Mr. Patrick</td>\n",
       "      <td>male</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>370376</td>\n",
       "      <td>7.75</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId  Survived  Pclass                                      Name  \\\n",
       "886          887         0       2                     Montvila, Rev. Juozas   \n",
       "887          888         1       1              Graham, Miss. Margaret Edith   \n",
       "888          889         0       3  Johnston, Miss. Catherine Helen \"Carrie\"   \n",
       "889          890         1       1                     Behr, Mr. Karl Howell   \n",
       "890          891         0       3                       Dooley, Mr. Patrick   \n",
       "\n",
       "        Sex   Age  SibSp  Parch      Ticket   Fare Cabin Embarked  \n",
       "886    male  27.0      0      0      211536  13.00   NaN        S  \n",
       "887  female  19.0      0      0      112053  30.00   B42        S  \n",
       "888  female   NaN      1      2  W./C. 6607  23.45   NaN        S  \n",
       "889    male  26.0      0      0      111369  30.00  C148        C  \n",
       "890    male  32.0      0      0      370376   7.75   NaN        Q  "
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>714.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>446.000000</td>\n",
       "      <td>0.383838</td>\n",
       "      <td>2.308642</td>\n",
       "      <td>29.699118</td>\n",
       "      <td>0.523008</td>\n",
       "      <td>0.381594</td>\n",
       "      <td>32.204208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>257.353842</td>\n",
       "      <td>0.486592</td>\n",
       "      <td>0.836071</td>\n",
       "      <td>14.526497</td>\n",
       "      <td>1.102743</td>\n",
       "      <td>0.806057</td>\n",
       "      <td>49.693429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.420000</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>223.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>20.125000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.910400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>446.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>14.454200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>668.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>31.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>891.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>512.329200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       PassengerId    Survived      Pclass         Age       SibSp  \\\n",
       "count   891.000000  891.000000  891.000000  714.000000  891.000000   \n",
       "mean    446.000000    0.383838    2.308642   29.699118    0.523008   \n",
       "std     257.353842    0.486592    0.836071   14.526497    1.102743   \n",
       "min       1.000000    0.000000    1.000000    0.420000    0.000000   \n",
       "25%     223.500000    0.000000    2.000000   20.125000    0.000000   \n",
       "50%     446.000000    0.000000    3.000000   28.000000    0.000000   \n",
       "75%     668.500000    1.000000    3.000000   38.000000    1.000000   \n",
       "max     891.000000    1.000000    3.000000   80.000000    8.000000   \n",
       "\n",
       "            Parch        Fare  \n",
       "count  891.000000  891.000000  \n",
       "mean     0.381594   32.204208  \n",
       "std      0.806057   49.693429  \n",
       "min      0.000000    0.000000  \n",
       "25%      0.000000    7.910400  \n",
       "50%      0.000000   14.454200  \n",
       "75%      0.000000   31.000000  \n",
       "max      6.000000  512.329200  "
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Survived\n",
       "0    549\n",
       "1    342\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.Survived.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sex\n",
       "male      577\n",
       "female    314\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.Sex.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Cabin\n",
       "G6             4\n",
       "C23 C25 C27    4\n",
       "B96 B98        4\n",
       "F2             3\n",
       "D              3\n",
       "              ..\n",
       "E17            1\n",
       "A24            1\n",
       "C50            1\n",
       "B42            1\n",
       "C148           1\n",
       "Name: count, Length: 147, dtype: int64"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.Cabin.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Embarked\n",
       "S    644\n",
       "C    168\n",
       "Q     77\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.Embarked.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_4882/3535274200.py:1: FutureWarning: Downcasting behavior in `replace` is deprecated and will be removed in a future version. To retain the old behavior, explicitly call `result.infer_objects(copy=False)`. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n",
      "  titanic['Sex'] = titanic['Sex'].replace({'male': 0, 'female': 1})\n"
     ]
    }
   ],
   "source": [
    "titanic['Sex'] = titanic['Sex'].replace({'male': 0, 'female': 1})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = titanic[titanic.columns.difference(['Survived','PassengerId','','Name','Ticket','Cabin','Embarked'])] # Všetko okrem\n",
    "y = titanic.Survived"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "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>Age</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>SibSp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>22.0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>38.0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>26.0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>35.0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>35.0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>886</th>\n",
       "      <td>27.0</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887</th>\n",
       "      <td>19.0</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>888</th>\n",
       "      <td>NaN</td>\n",
       "      <td>23.4500</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>26.0</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>32.0</td>\n",
       "      <td>7.7500</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>891 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Age     Fare  Parch  Pclass  Sex  SibSp\n",
       "0    22.0   7.2500      0       3    0      1\n",
       "1    38.0  71.2833      0       1    1      1\n",
       "2    26.0   7.9250      0       3    1      0\n",
       "3    35.0  53.1000      0       1    1      1\n",
       "4    35.0   8.0500      0       3    0      0\n",
       "..    ...      ...    ...     ...  ...    ...\n",
       "886  27.0  13.0000      0       2    0      0\n",
       "887  19.0  30.0000      0       1    1      0\n",
       "888   NaN  23.4500      2       3    1      1\n",
       "889  26.0  30.0000      0       1    0      0\n",
       "890  32.0   7.7500      0       3    0      0\n",
       "\n",
       "[891 rows x 6 columns]"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      0\n",
       "1      1\n",
       "2      1\n",
       "3      1\n",
       "4      0\n",
       "      ..\n",
       "886    0\n",
       "887    1\n",
       "888    0\n",
       "889    1\n",
       "890    0\n",
       "Name: Survived, Length: 891, dtype: int64"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "clf = DecisionTreeClassifier(max_depth=4)\n",
    "\n",
    "# Train Decision Tree Classifer\n",
    "clf = clf.fit(X_train,y_train)\n",
    "\n",
    "#Predict the response for test dataset\n",
    "y_pred = clf.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.7821229050279329\n"
     ]
    }
   ],
   "source": [
    "# Model Accuracy, how often is the classifier correct?\n",
    "print(\"Accuracy:\",metrics.accuracy_score(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "person = np.array([ # 23 ročný muž bez detí\n",
    "    23,\n",
    "    100,\n",
    "    0,\n",
    "    1,\n",
    "    1,\n",
    "    0\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 23, 100,   0,   1,   1,   0])"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "person"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 23, 100,   0,   1,   1,   0]])"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "person.reshape(1, -1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Prediction:  [1]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/br0kenpixel/Documents/ui-cviko1/lib64/python3.13/site-packages/sklearn/utils/validation.py:2739: UserWarning: X does not have valid feature names, but DecisionTreeClassifier was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "print('Prediction: ', clf.predict(person.reshape(1,-1))) # Prežije?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "titanic_columns = X_train.columns.to_list()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from sklearn import tree\n",
    "fig = plt.figure(figsize=(20,6))\n",
    "_ = tree.plot_tree(clf,\n",
    "                   feature_names = titanic_columns,\n",
    "                   class_names=['0','1'],\n",
    "                   filled=True)"
   ]
  },
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