Decision Trees - Diabetes.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "e93a70e2",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from math import sqrt\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.svm import LinearSVC\n",
    "from sklearn.metrics import classification_report\n",
    "from sklearn.tree import export_graphviz\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn import metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "55a6440c",
   "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>Pregnant</th>\n",
       "      <th>Glucose</th>\n",
       "      <th>BP</th>\n",
       "      <th>Skin</th>\n",
       "      <th>Insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>Pedigree</th>\n",
       "      <th>Age</th>\n",
       "      <th>Label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6</td>\n",
       "      <td>148</td>\n",
       "      <td>72</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>33.6</td>\n",
       "      <td>0.627</td>\n",
       "      <td>50</td>\n",
       "      <td>tested_positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>85</td>\n",
       "      <td>66</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>26.6</td>\n",
       "      <td>0.351</td>\n",
       "      <td>31</td>\n",
       "      <td>tested_negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8</td>\n",
       "      <td>183</td>\n",
       "      <td>64</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>23.3</td>\n",
       "      <td>0.672</td>\n",
       "      <td>32</td>\n",
       "      <td>tested_positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>89</td>\n",
       "      <td>66</td>\n",
       "      <td>23</td>\n",
       "      <td>94</td>\n",
       "      <td>28.1</td>\n",
       "      <td>0.167</td>\n",
       "      <td>21</td>\n",
       "      <td>tested_negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>137</td>\n",
       "      <td>40</td>\n",
       "      <td>35</td>\n",
       "      <td>168</td>\n",
       "      <td>43.1</td>\n",
       "      <td>2.288</td>\n",
       "      <td>33</td>\n",
       "      <td>tested_positive</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Pregnant  Glucose  BP  Skin  Insulin   BMI  Pedigree  Age            Label\n",
       "0         6      148  72    35        0  33.6     0.627   50  tested_positive\n",
       "1         1       85  66    29        0  26.6     0.351   31  tested_negative\n",
       "2         8      183  64     0        0  23.3     0.672   32  tested_positive\n",
       "3         1       89  66    23       94  28.1     0.167   21  tested_negative\n",
       "4         0      137  40    35      168  43.1     2.288   33  tested_positive"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "col_names = ['Pregnant', 'Glucose', 'BP', 'Skin', 'Insulin', 'BMI', 'Pedigree', 'Age', 'Label']\n",
    "diab = pd.read_csv(\"C:/Users/user/Desktop/Sneha's Program/ML Datasets/diabetes.csv\", header = None, names= col_names)\n",
    "diab.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "38344b1c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bound method DataFrame.info of      Pregnant  Glucose  BP  Skin  Insulin   BMI  Pedigree  Age  \\\n",
       "0           6      148  72    35        0  33.6     0.627   50   \n",
       "1           1       85  66    29        0  26.6     0.351   31   \n",
       "2           8      183  64     0        0  23.3     0.672   32   \n",
       "3           1       89  66    23       94  28.1     0.167   21   \n",
       "4           0      137  40    35      168  43.1     2.288   33   \n",
       "..        ...      ...  ..   ...      ...   ...       ...  ...   \n",
       "763        10      101  76    48      180  32.9     0.171   63   \n",
       "764         2      122  70    27        0  36.8     0.340   27   \n",
       "765         5      121  72    23      112  26.2     0.245   30   \n",
       "766         1      126  60     0        0  30.1     0.349   47   \n",
       "767         1       93  70    31        0  30.4     0.315   23   \n",
       "\n",
       "               Label  \n",
       "0    tested_positive  \n",
       "1    tested_negative  \n",
       "2    tested_positive  \n",
       "3    tested_negative  \n",
       "4    tested_positive  \n",
       "..               ...  \n",
       "763  tested_negative  \n",
       "764  tested_negative  \n",
       "765  tested_negative  \n",
       "766  tested_positive  \n",
       "767  tested_negative  \n",
       "\n",
       "[768 rows x 9 columns]>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diab.info"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "5f98e9b4",
   "metadata": {},
   "outputs": [],
   "source": [
    "feat_cols = ['Pregnant', 'Glucose', 'BP', 'Insulin', 'BMI', 'Pedigree', 'Age']\n",
    "x = diab[feat_cols]\n",
    "y = diab.Label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "c8f4b0a8",
   "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>Pregnant</th>\n",
       "      <th>Glucose</th>\n",
       "      <th>BP</th>\n",
       "      <th>Insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>Pedigree</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6</td>\n",
       "      <td>148</td>\n",
       "      <td>72</td>\n",
       "      <td>0</td>\n",
       "      <td>33.6</td>\n",
       "      <td>0.627</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>85</td>\n",
       "      <td>66</td>\n",
       "      <td>0</td>\n",
       "      <td>26.6</td>\n",
       "      <td>0.351</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8</td>\n",
       "      <td>183</td>\n",
       "      <td>64</td>\n",
       "      <td>0</td>\n",
       "      <td>23.3</td>\n",
       "      <td>0.672</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>89</td>\n",
       "      <td>66</td>\n",
       "      <td>94</td>\n",
       "      <td>28.1</td>\n",
       "      <td>0.167</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>137</td>\n",
       "      <td>40</td>\n",
       "      <td>168</td>\n",
       "      <td>43.1</td>\n",
       "      <td>2.288</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Pregnant  Glucose  BP  Insulin   BMI  Pedigree  Age\n",
       "0         6      148  72        0  33.6     0.627   50\n",
       "1         1       85  66        0  26.6     0.351   31\n",
       "2         8      183  64        0  23.3     0.672   32\n",
       "3         1       89  66       94  28.1     0.167   21\n",
       "4         0      137  40      168  43.1     2.288   33"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "80579ad6",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size = 0.3, random_state = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "8a565f82",
   "metadata": {},
   "outputs": [],
   "source": [
    "dt1 = DecisionTreeClassifier()\n",
    "dt1.fit(x_train, y_train)\n",
    "\n",
    "y_pred = dt1.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "ff9749a0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy  :   0.6623376623376623\n"
     ]
    }
   ],
   "source": [
    "print(\"Accuracy  :  \", metrics.accuracy_score(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "cec369ab",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0.4158088235294118, 0.96875, 'x[1] <= 129.5\\ngini = 0.449\\nsamples = 537\\nvalue = [354, 183]'),\n",
       " Text(0.15551470588235294, 0.90625, 'x[4] <= 26.3\\ngini = 0.329\\nsamples = 357\\nvalue = [283, 74]'),\n",
       " Text(0.047058823529411764, 0.84375, 'x[4] <= 9.1\\ngini = 0.06\\nsamples = 97\\nvalue = [94, 3]'),\n",
       " Text(0.023529411764705882, 0.78125, 'x[0] <= 7.5\\ngini = 0.444\\nsamples = 6\\nvalue = [4, 2]'),\n",
       " Text(0.011764705882352941, 0.71875, 'gini = 0.0\\nsamples = 4\\nvalue = [4, 0]'),\n",
       " Text(0.03529411764705882, 0.71875, 'gini = 0.0\\nsamples = 2\\nvalue = [0, 2]'),\n",
       " Text(0.07058823529411765, 0.78125, 'x[5] <= 0.669\\ngini = 0.022\\nsamples = 91\\nvalue = [90, 1]'),\n",
       " Text(0.058823529411764705, 0.71875, 'gini = 0.0\\nsamples = 76\\nvalue = [76, 0]'),\n",
       " Text(0.08235294117647059, 0.71875, 'x[5] <= 0.705\\ngini = 0.124\\nsamples = 15\\nvalue = [14, 1]'),\n",
       " Text(0.07058823529411765, 0.65625, 'gini = 0.0\\nsamples = 1\\nvalue = [0, 1]'),\n",
       " Text(0.09411764705882353, 0.65625, 'gini = 0.0\\nsamples = 14\\nvalue = [14, 0]'),\n",
       " Text(0.2639705882352941, 0.84375, 'x[6] <= 27.5\\ngini = 0.397\\nsamples = 260\\nvalue = [189, 71]'),\n",
       " Text(0.15294117647058825, 0.78125, 'x[4] <= 45.4\\ngini = 0.243\\nsamples = 120\\nvalue = [103, 17]'),\n",
       " Text(0.12941176470588237, 0.71875, 'x[2] <= 12.0\\ngini = 0.212\\nsamples = 116\\nvalue = [102, 14]'),\n",
       " Text(0.11764705882352941, 0.65625, 'gini = 0.0\\nsamples = 1\\nvalue = [0, 1]'),\n",
       " Text(0.1411764705882353, 0.65625, 'x[0] <= 7.0\\ngini = 0.201\\nsamples = 115\\nvalue = [102, 13]'),\n",
       " Text(0.12941176470588237, 0.59375, 'x[5] <= 1.272\\ngini = 0.188\\nsamples = 114\\nvalue = [102, 12]'),\n",
       " Text(0.10588235294117647, 0.53125, 'x[4] <= 30.95\\ngini = 0.165\\nsamples = 110\\nvalue = [100, 10]'),\n",
       " Text(0.09411764705882353, 0.46875, 'gini = 0.0\\nsamples = 43\\nvalue = [43, 0]'),\n",
       " Text(0.11764705882352941, 0.46875, 'x[2] <= 53.0\\ngini = 0.254\\nsamples = 67\\nvalue = [57, 10]'),\n",
       " Text(0.08823529411764706, 0.40625, 'x[0] <= 2.5\\ngini = 0.5\\nsamples = 6\\nvalue = [3, 3]'),\n",
       " Text(0.07647058823529412, 0.34375, 'x[5] <= 0.566\\ngini = 0.375\\nsamples = 4\\nvalue = [3, 1]'),\n",
       " Text(0.06470588235294118, 0.28125, 'gini = 0.0\\nsamples = 3\\nvalue = [3, 0]'),\n",
       " Text(0.08823529411764706, 0.28125, 'gini = 0.0\\nsamples = 1\\nvalue = [0, 1]'),\n",
       " Text(0.1, 0.34375, 'gini = 0.0\\nsamples = 2\\nvalue = [0, 2]'),\n",
       " Text(0.14705882352941177, 0.40625, 'x[5] <= 0.652\\ngini = 0.203\\nsamples = 61\\nvalue = [54, 7]'),\n",
       " Text(0.12352941176470589, 0.34375, 'x[3] <= 36.5\\ngini = 0.15\\nsamples = 49\\nvalue = [45, 4]'),\n",
       " Text(0.11176470588235295, 0.28125, 'x[3] <= 34.0\\ngini = 0.32\\nsamples = 20\\nvalue = [16, 4]'),\n",
       " Text(0.1, 0.21875, 'x[4] <= 33.65\\ngini = 0.266\\nsamples = 19\\nvalue = [16, 3]'),\n",
       " Text(0.07647058823529412, 0.15625, 'x[2] <= 71.0\\ngini = 0.444\\nsamples = 6\\nvalue = [4, 2]'),\n",
       " Text(0.06470588235294118, 0.09375, 'gini = 0.0\\nsamples = 3\\nvalue = [3, 0]'),\n",
       " Text(0.08823529411764706, 0.09375, 'x[5] <= 0.44\\ngini = 0.444\\nsamples = 3\\nvalue = [1, 2]'),\n",
       " Text(0.07647058823529412, 0.03125, 'gini = 0.0\\nsamples = 2\\nvalue = [0, 2]'),\n",
       " Text(0.1, 0.03125, 'gini = 0.0\\nsamples = 1\\nvalue = [1, 0]'),\n",
       " Text(0.12352941176470589, 0.15625, 'x[4] <= 37.95\\ngini = 0.142\\nsamples = 13\\nvalue = [12, 1]'),\n",
       " Text(0.11176470588235295, 0.09375, 'gini = 0.0\\nsamples = 11\\nvalue = [11, 0]'),\n",
       " Text(0.13529411764705881, 0.09375, 'x[6] <= 24.0\\ngini = 0.5\\nsamples = 2\\nvalue = [1, 1]'),\n",
       " Text(0.12352941176470589, 0.03125, 'gini = 0.0\\nsamples = 1\\nvalue = [1, 0]'),\n",
       " Text(0.14705882352941177, 0.03125, 'gini = 0.0\\nsamples = 1\\nvalue = [0, 1]'),\n",
       " Text(0.12352941176470589, 0.21875, 'gini = 0.0\\nsamples = 1\\nvalue = [0, 1]'),\n",
       " Text(0.13529411764705881, 0.28125, 'gini = 0.0\\nsamples = 29\\nvalue = [29, 0]'),\n",
       " Text(0.17058823529411765, 0.34375, 'x[3] <= 65.5\\ngini = 0.375\\nsamples = 12\\nvalue = [9, 3]'),\n",
       " Text(0.1588235294117647, 0.28125, 'gini = 0.0\\nsamples = 7\\nvalue = [7, 0]'),\n",
       " Text(0.18235294117647058, 0.28125, 'x[4] <= 38.55\\ngini = 0.48\\nsamples = 5\\nvalue = [2, 3]'),\n",
       " Text(0.17058823529411765, 0.21875, 'gini = 0.0\\nsamples = 3\\nvalue = [0, 3]'),\n",
       " Text(0.19411764705882353, 0.21875, 'gini = 0.0\\nsamples = 2\\nvalue = [2, 0]'),\n",
       " Text(0.15294117647058825, 0.53125, 'x[2] <= 63.0\\ngini = 0.5\\nsamples = 4\\nvalue = [2, 2]'),\n",
       " Text(0.1411764705882353, 0.46875, 'gini = 0.0\\nsamples = 2\\nvalue = [2, 0]'),\n",
       " Text(0.16470588235294117, 0.46875, 'gini = 0.0\\nsamples = 2\\nvalue = [0, 2]'),\n",
       " Text(0.15294117647058825, 0.59375, 'gini = 0.0\\nsamples = 1\\nvalue = [0, 1]'),\n",
       " Text(0.17647058823529413, 0.71875, 'x[6] <= 22.5\\ngini = 0.375\\nsamples = 4\\nvalue = [1, 3]'),\n",
       " Text(0.16470588235294117, 0.65625, 'gini = 0.0\\nsamples = 1\\nvalue = [1, 0]'),\n",
       " Text(0.18823529411764706, 0.65625, 'gini = 0.0\\nsamples = 3\\nvalue = [0, 3]'),\n",
       " Text(0.375, 0.78125, 'x[5] <= 0.563\\ngini = 0.474\\nsamples = 140\\nvalue = [86, 54]'),\n",
       " Text(0.27941176470588236, 0.71875, 'x[1] <= 101.5\\ngini = 0.408\\nsamples = 98\\nvalue = [70, 28]'),\n",
       " Text(0.21176470588235294, 0.65625, 'x[2] <= 27.0\\ngini = 0.208\\nsamples = 34\\nvalue = [30, 4]'),\n",
       " Text(0.2, 0.59375, 'gini = 0.0\\nsamples = 1\\nvalue = [0, 1]'),\n",
       " Text(0.2235294117647059, 0.59375, 'x[6] <= 42.5\\ngini = 0.165\\nsamples = 33\\nvalue = [30, 3]'),\n",
       " Text(0.21176470588235294, 0.53125, 'gini = 0.0\\nsamples = 23\\nvalue = [23, 0]'),\n",
       " Text(0.23529411764705882, 0.53125, 'x[1] <= 97.5\\ngini = 0.42\\nsamples = 10\\nvalue = [7, 3]'),\n",
       " Text(0.2235294117647059, 0.46875, 'x[4] <= 35.25\\ngini = 0.5\\nsamples = 6\\nvalue = [3, 3]'),\n",
       " Text(0.21176470588235294, 0.40625, 'gini = 0.0\\nsamples = 2\\nvalue = [0, 2]'),\n",
       " Text(0.23529411764705882, 0.40625, 'x[1] <= 96.0\\ngini = 0.375\\nsamples = 4\\nvalue = [3, 1]'),\n",
       " Text(0.2235294117647059, 0.34375, 'gini = 0.0\\nsamples = 3\\nvalue = [3, 0]'),\n",
       " Text(0.24705882352941178, 0.34375, 'gini = 0.0\\nsamples = 1\\nvalue = [0, 1]'),\n",
       " Text(0.24705882352941178, 0.46875, 'gini = 0.0\\nsamples = 4\\nvalue = [4, 0]'),\n",
       " Text(0.34705882352941175, 0.65625, 'x[2] <= 67.0\\ngini = 0.469\\nsamples = 64\\nvalue = [40, 24]'),\n",
       " Text(0.3058823529411765, 0.59375, 'x[2] <= 58.0\\ngini = 0.465\\nsamples = 19\\nvalue = [7, 12]'),\n",
       " Text(0.2823529411764706, 0.53125, 'x[4] <= 30.05\\ngini = 0.245\\nsamples = 7\\nvalue = [6, 1]'),\n",
       " Text(0.27058823529411763, 0.46875, 'x[5] <= 0.182\\ngini = 0.5\\nsamples = 2\\nvalue = [1, 1]'),\n",
       " Text(0.25882352941176473, 0.40625, 'gini = 0.0\\nsamples = 1\\nvalue = [1, 0]'),\n",
       " Text(0.2823529411764706, 0.40625, 'gini = 0.0\\nsamples = 1\\nvalue = [0, 1]'),\n",
       " Text(0.29411764705882354, 0.46875, 'gini = 0.0\\nsamples = 5\\nvalue = [5, 0]'),\n",
       " Text(0.32941176470588235, 0.53125, 'x[5] <= 0.425\\ngini = 0.153\\nsamples = 12\\nvalue = [1, 11]'),\n",
       " Text(0.3176470588235294, 0.46875, 'gini = 0.0\\nsamples = 11\\nvalue = [0, 11]'),\n",
       " Text(0.3411764705882353, 0.46875, 'gini = 0.0\\nsamples = 1\\nvalue = [1, 0]'),\n",
       " Text(0.38823529411764707, 0.59375, 'x[4] <= 43.1\\ngini = 0.391\\nsamples = 45\\nvalue = [33, 12]'),\n",
       " Text(0.3764705882352941, 0.53125, 'x[1] <= 102.5\\ngini = 0.337\\nsamples = 42\\nvalue = [33, 9]'),\n",
       " Text(0.36470588235294116, 0.46875, 'gini = 0.0\\nsamples = 2\\nvalue = [0, 2]'),\n",
       " Text(0.38823529411764707, 0.46875, 'x[4] <= 34.6\\ngini = 0.289\\nsamples = 40\\nvalue = [33, 7]'),\n",
       " Text(0.3764705882352941, 0.40625, 'x[6] <= 35.0\\ngini = 0.434\\nsamples = 22\\nvalue = [15, 7]'),\n",
       " Text(0.36470588235294116, 0.34375, 'gini = 0.0\\nsamples = 6\\nvalue = [6, 0]'),\n",
       " Text(0.38823529411764707, 0.34375, 'x[1] <= 123.5\\ngini = 0.492\\nsamples = 16\\nvalue = [9, 7]'),\n",
       " Text(0.3764705882352941, 0.28125, 'x[5] <= 0.375\\ngini = 0.426\\nsamples = 13\\nvalue = [9, 4]'),\n",
       " Text(0.36470588235294116, 0.21875, 'x[0] <= 6.5\\ngini = 0.298\\nsamples = 11\\nvalue = [9, 2]'),\n",
       " Text(0.35294117647058826, 0.15625, 'x[2] <= 85.0\\ngini = 0.444\\nsamples = 6\\nvalue = [4, 2]'),\n",
       " Text(0.3411764705882353, 0.09375, 'x[2] <= 75.0\\ngini = 0.444\\nsamples = 3\\nvalue = [1, 2]'),\n",
       " Text(0.32941176470588235, 0.03125, 'gini = 0.0\\nsamples = 1\\nvalue = [1, 0]'),\n",
       " Text(0.35294117647058826, 0.03125, 'gini = 0.0\\nsamples = 2\\nvalue = [0, 2]'),\n",
       " Text(0.36470588235294116, 0.09375, 'gini = 0.0\\nsamples = 3\\nvalue = [3, 0]'),\n",
       " Text(0.3764705882352941, 0.15625, 'gini = 0.0\\nsamples = 5\\nvalue = [5, 0]'),\n",
       " Text(0.38823529411764707, 0.21875, 'gini = 0.0\\nsamples = 2\\nvalue = [0, 2]'),\n",
       " Text(0.4, 0.28125, 'gini = 0.0\\nsamples = 3\\nvalue = [0, 3]'),\n",
       " Text(0.4, 0.40625, 'gini = 0.0\\nsamples = 18\\nvalue = [18, 0]'),\n",
       " Text(0.4, 0.53125, 'gini = 0.0\\nsamples = 3\\nvalue = [0, 3]'),\n",
       " Text(0.47058823529411764, 0.71875, 'x[0] <= 8.5\\ngini = 0.472\\nsamples = 42\\nvalue = [16, 26]'),\n",
       " Text(0.4588235294117647, 0.65625, 'x[2] <= 87.0\\ngini = 0.5\\nsamples = 33\\nvalue = [16, 17]'),\n",
       " Text(0.4470588235294118, 0.59375, 'x[1] <= 97.0\\ngini = 0.477\\nsamples = 28\\nvalue = [11, 17]'),\n",
       " Text(0.43529411764705883, 0.53125, 'gini = 0.0\\nsamples = 4\\nvalue = [4, 0]'),\n",
       " Text(0.4588235294117647, 0.53125, 'x[1] <= 116.5\\ngini = 0.413\\nsamples = 24\\nvalue = [7, 17]'),\n",
       " Text(0.43529411764705883, 0.46875, 'x[5] <= 1.395\\ngini = 0.165\\nsamples = 11\\nvalue = [1, 10]'),\n",
       " Text(0.4235294117647059, 0.40625, 'gini = 0.0\\nsamples = 10\\nvalue = [0, 10]'),\n",
       " Text(0.4470588235294118, 0.40625, 'gini = 0.0\\nsamples = 1\\nvalue = [1, 0]'),\n",
       " Text(0.4823529411764706, 0.46875, 'x[3] <= 78.0\\ngini = 0.497\\nsamples = 13\\nvalue = [6, 7]'),\n",
       " Text(0.47058823529411764, 0.40625, 'gini = 0.0\\nsamples = 3\\nvalue = [3, 0]'),\n",
       " Text(0.49411764705882355, 0.40625, 'x[4] <= 33.45\\ngini = 0.42\\nsamples = 10\\nvalue = [3, 7]'),\n",
       " Text(0.4823529411764706, 0.34375, 'gini = 0.0\\nsamples = 4\\nvalue = [0, 4]'),\n",
       " Text(0.5058823529411764, 0.34375, 'x[4] <= 41.1\\ngini = 0.5\\nsamples = 6\\nvalue = [3, 3]'),\n",
       " Text(0.49411764705882355, 0.28125, 'x[5] <= 0.712\\ngini = 0.375\\nsamples = 4\\nvalue = [3, 1]'),\n",
       " Text(0.4823529411764706, 0.21875, 'gini = 0.0\\nsamples = 1\\nvalue = [0, 1]'),\n",
       " Text(0.5058823529411764, 0.21875, 'gini = 0.0\\nsamples = 3\\nvalue = [3, 0]'),\n",
       " Text(0.5176470588235295, 0.28125, 'gini = 0.0\\nsamples = 2\\nvalue = [0, 2]'),\n",
       " Text(0.47058823529411764, 0.59375, 'gini = 0.0\\nsamples = 5\\nvalue = [5, 0]'),\n",
       " Text(0.4823529411764706, 0.65625, 'gini = 0.0\\nsamples = 9\\nvalue = [0, 9]'),\n",
       " Text(0.6761029411764706, 0.90625, 'x[4] <= 27.85\\ngini = 0.478\\nsamples = 180\\nvalue = [71, 109]'),\n",
       " Text(0.5352941176470588, 0.84375, 'x[1] <= 145.5\\ngini = 0.375\\nsamples = 36\\nvalue = [27, 9]'),\n",
       " Text(0.5117647058823529, 0.78125, 'x[6] <= 59.5\\ngini = 0.1\\nsamples = 19\\nvalue = [18, 1]'),\n",
       " Text(0.5, 0.71875, 'gini = 0.0\\nsamples = 16\\nvalue = [16, 0]'),\n",
       " Text(0.5235294117647059, 0.71875, 'x[6] <= 61.0\\ngini = 0.444\\nsamples = 3\\nvalue = [2, 1]'),\n",
       " Text(0.5117647058823529, 0.65625, 'gini = 0.0\\nsamples = 1\\nvalue = [0, 1]'),\n",
       " Text(0.5352941176470588, 0.65625, 'gini = 0.0\\nsamples = 2\\nvalue = [2, 0]'),\n",
       " Text(0.5588235294117647, 0.78125, 'x[4] <= 23.1\\ngini = 0.498\\nsamples = 17\\nvalue = [9, 8]'),\n",
       " Text(0.5470588235294118, 0.71875, 'gini = 0.0\\nsamples = 3\\nvalue = [3, 0]'),\n",
       " Text(0.5705882352941176, 0.71875, 'x[4] <= 25.55\\ngini = 0.49\\nsamples = 14\\nvalue = [6, 8]'),\n",
       " Text(0.5588235294117647, 0.65625, 'gini = 0.0\\nsamples = 6\\nvalue = [0, 6]'),\n",
       " Text(0.5823529411764706, 0.65625, 'x[1] <= 156.0\\ngini = 0.375\\nsamples = 8\\nvalue = [6, 2]'),\n",
       " Text(0.5705882352941176, 0.59375, 'gini = 0.0\\nsamples = 2\\nvalue = [0, 2]'),\n",
       " Text(0.5941176470588235, 0.59375, 'gini = 0.0\\nsamples = 6\\nvalue = [6, 0]'),\n",
       " Text(0.8169117647058823, 0.84375, 'x[1] <= 158.5\\ngini = 0.424\\nsamples = 144\\nvalue = [44, 100]'),\n",
       " Text(0.6867647058823529, 0.78125, 'x[6] <= 30.5\\ngini = 0.487\\nsamples = 88\\nvalue = [37, 51]'),\n",
       " Text(0.6176470588235294, 0.71875, 'x[2] <= 23.0\\ngini = 0.49\\nsamples = 42\\nvalue = [24, 18]'),\n",
       " Text(0.6058823529411764, 0.65625, 'gini = 0.0\\nsamples = 4\\nvalue = [0, 4]'),\n",
       " Text(0.6294117647058823, 0.65625, 'x[2] <= 88.0\\ngini = 0.465\\nsamples = 38\\nvalue = [24, 14]'),\n",
       " Text(0.6176470588235294, 0.59375, 'x[2] <= 72.0\\ngini = 0.444\\nsamples = 36\\nvalue = [24, 12]'),\n",
       " Text(0.5764705882352941, 0.53125, 'x[4] <= 33.75\\ngini = 0.5\\nsamples = 20\\nvalue = [10, 10]'),\n",
       " Text(0.5529411764705883, 0.46875, 'x[4] <= 31.05\\ngini = 0.397\\nsamples = 11\\nvalue = [8, 3]'),\n",
       " Text(0.5411764705882353, 0.40625, 'x[0] <= 1.5\\ngini = 0.5\\nsamples = 6\\nvalue = [3, 3]'),\n",
       " Text(0.5294117647058824, 0.34375, 'gini = 0.0\\nsamples = 2\\nvalue = [2, 0]'),\n",
       " Text(0.5529411764705883, 0.34375, 'x[0] <= 4.5\\ngini = 0.375\\nsamples = 4\\nvalue = [1, 3]'),\n",
       " Text(0.5411764705882353, 0.28125, 'gini = 0.0\\nsamples = 3\\nvalue = [0, 3]'),\n",
       " Text(0.5647058823529412, 0.28125, 'gini = 0.0\\nsamples = 1\\nvalue = [1, 0]'),\n",
       " Text(0.5647058823529412, 0.40625, 'gini = 0.0\\nsamples = 5\\nvalue = [5, 0]'),\n",
       " Text(0.6, 0.46875, 'x[5] <= 0.535\\ngini = 0.346\\nsamples = 9\\nvalue = [2, 7]'),\n",
       " Text(0.5882352941176471, 0.40625, 'gini = 0.0\\nsamples = 6\\nvalue = [0, 6]'),\n",
       " Text(0.611764705882353, 0.40625, 'x[6] <= 28.5\\ngini = 0.444\\nsamples = 3\\nvalue = [2, 1]'),\n",
       " Text(0.6, 0.34375, 'gini = 0.0\\nsamples = 2\\nvalue = [2, 0]'),\n",
       " Text(0.6235294117647059, 0.34375, 'gini = 0.0\\nsamples = 1\\nvalue = [0, 1]'),\n",
       " Text(0.6588235294117647, 0.53125, 'x[1] <= 157.5\\ngini = 0.219\\nsamples = 16\\nvalue = [14, 2]'),\n",
       " Text(0.6470588235294118, 0.46875, 'x[4] <= 43.4\\ngini = 0.124\\nsamples = 15\\nvalue = [14, 1]'),\n",
       " Text(0.6352941176470588, 0.40625, 'gini = 0.0\\nsamples = 13\\nvalue = [13, 0]'),\n",
       " Text(0.6588235294117647, 0.40625, 'x[2] <= 82.0\\ngini = 0.5\\nsamples = 2\\nvalue = [1, 1]'),\n",
       " Text(0.6470588235294118, 0.34375, 'gini = 0.0\\nsamples = 1\\nvalue = [1, 0]'),\n",
       " Text(0.6705882352941176, 0.34375, 'gini = 0.0\\nsamples = 1\\nvalue = [0, 1]'),\n",
       " Text(0.6705882352941176, 0.46875, 'gini = 0.0\\nsamples = 1\\nvalue = [0, 1]'),\n",
       " Text(0.6411764705882353, 0.59375, 'gini = 0.0\\nsamples = 2\\nvalue = [0, 2]'),\n",
       " Text(0.7558823529411764, 0.71875, 'x[4] <= 34.05\\ngini = 0.405\\nsamples = 46\\nvalue = [13, 33]'),\n",
       " Text(0.7058823529411765, 0.65625, 'x[2] <= 75.0\\ngini = 0.49\\nsamples = 21\\nvalue = [9, 12]'),\n",
       " Text(0.6941176470588235, 0.59375, 'gini = 0.0\\nsamples = 4\\nvalue = [4, 0]'),\n",
       " Text(0.7176470588235294, 0.59375, 'x[5] <= 0.436\\ngini = 0.415\\nsamples = 17\\nvalue = [5, 12]'),\n",
       " Text(0.7058823529411765, 0.53125, 'x[5] <= 0.371\\ngini = 0.496\\nsamples = 11\\nvalue = [5, 6]'),\n",
       " Text(0.6941176470588235, 0.46875, 'x[4] <= 29.7\\ngini = 0.444\\nsamples = 9\\nvalue = [3, 6]'),\n",
       " Text(0.6823529411764706, 0.40625, 'gini = 0.0\\nsamples = 3\\nvalue = [0, 3]'),\n",
       " Text(0.7058823529411765, 0.40625, 'x[4] <= 32.45\\ngini = 0.5\\nsamples = 6\\nvalue = [3, 3]'),\n",
       " Text(0.6941176470588235, 0.34375, 'x[5] <= 0.164\\ngini = 0.375\\nsamples = 4\\nvalue = [3, 1]'),\n",
       " Text(0.6823529411764706, 0.28125, 'gini = 0.0\\nsamples = 1\\nvalue = [0, 1]'),\n",
       " Text(0.7058823529411765, 0.28125, 'gini = 0.0\\nsamples = 3\\nvalue = [3, 0]'),\n",
       " Text(0.7176470588235294, 0.34375, 'gini = 0.0\\nsamples = 2\\nvalue = [0, 2]'),\n",
       " Text(0.7176470588235294, 0.46875, 'gini = 0.0\\nsamples = 2\\nvalue = [2, 0]'),\n",
       " Text(0.7294117647058823, 0.53125, 'gini = 0.0\\nsamples = 6\\nvalue = [0, 6]'),\n",
       " Text(0.8058823529411765, 0.65625, 'x[5] <= 1.088\\ngini = 0.269\\nsamples = 25\\nvalue = [4, 21]'),\n",
       " Text(0.7764705882352941, 0.59375, 'x[3] <= 306.5\\ngini = 0.172\\nsamples = 21\\nvalue = [2, 19]'),\n",
       " Text(0.7529411764705882, 0.53125, 'x[5] <= 0.222\\ngini = 0.1\\nsamples = 19\\nvalue = [1, 18]'),\n",
       " Text(0.7411764705882353, 0.46875, 'x[2] <= 74.0\\ngini = 0.444\\nsamples = 3\\nvalue = [1, 2]'),\n",
       " Text(0.7294117647058823, 0.40625, 'gini = 0.0\\nsamples = 2\\nvalue = [0, 2]'),\n",
       " Text(0.7529411764705882, 0.40625, 'gini = 0.0\\nsamples = 1\\nvalue = [1, 0]'),\n",
       " Text(0.7647058823529411, 0.46875, 'gini = 0.0\\nsamples = 16\\nvalue = [0, 16]'),\n",
       " Text(0.8, 0.53125, 'x[3] <= 356.0\\ngini = 0.5\\nsamples = 2\\nvalue = [1, 1]'),\n",
       " Text(0.788235294117647, 0.46875, 'gini = 0.0\\nsamples = 1\\nvalue = [1, 0]'),\n",
       " Text(0.8117647058823529, 0.46875, 'gini = 0.0\\nsamples = 1\\nvalue = [0, 1]'),\n",
       " Text(0.8352941176470589, 0.59375, 'x[3] <= 147.5\\ngini = 0.5\\nsamples = 4\\nvalue = [2, 2]'),\n",
       " Text(0.8235294117647058, 0.53125, 'gini = 0.0\\nsamples = 2\\nvalue = [2, 0]'),\n",
       " Text(0.8470588235294118, 0.53125, 'gini = 0.0\\nsamples = 2\\nvalue = [0, 2]'),\n",
       " Text(0.9470588235294117, 0.78125, 'x[5] <= 1.157\\ngini = 0.219\\nsamples = 56\\nvalue = [7, 49]'),\n",
       " Text(0.9176470588235294, 0.71875, 'x[5] <= 0.343\\ngini = 0.147\\nsamples = 50\\nvalue = [4, 46]'),\n",
       " Text(0.9058823529411765, 0.65625, 'x[6] <= 48.5\\ngini = 0.332\\nsamples = 19\\nvalue = [4, 15]'),\n",
       " Text(0.8823529411764706, 0.59375, 'x[1] <= 177.0\\ngini = 0.219\\nsamples = 16\\nvalue = [2, 14]'),\n",
       " Text(0.8705882352941177, 0.53125, 'gini = 0.0\\nsamples = 9\\nvalue = [0, 9]'),\n",
       " Text(0.8941176470588236, 0.53125, 'x[0] <= 8.0\\ngini = 0.408\\nsamples = 7\\nvalue = [2, 5]'),\n",
       " Text(0.8823529411764706, 0.46875, 'x[1] <= 187.5\\ngini = 0.278\\nsamples = 6\\nvalue = [1, 5]'),\n",
       " Text(0.8705882352941177, 0.40625, 'gini = 0.0\\nsamples = 4\\nvalue = [0, 4]'),\n",
       " Text(0.8941176470588236, 0.40625, 'x[0] <= 4.5\\ngini = 0.5\\nsamples = 2\\nvalue = [1, 1]'),\n",
       " Text(0.8823529411764706, 0.34375, 'gini = 0.0\\nsamples = 1\\nvalue = [1, 0]'),\n",
       " Text(0.9058823529411765, 0.34375, 'gini = 0.0\\nsamples = 1\\nvalue = [0, 1]'),\n",
       " Text(0.9058823529411765, 0.46875, 'gini = 0.0\\nsamples = 1\\nvalue = [1, 0]'),\n",
       " Text(0.9294117647058824, 0.59375, 'x[6] <= 63.0\\ngini = 0.444\\nsamples = 3\\nvalue = [2, 1]'),\n",
       " Text(0.9176470588235294, 0.53125, 'gini = 0.0\\nsamples = 2\\nvalue = [2, 0]'),\n",
       " Text(0.9411764705882353, 0.53125, 'gini = 0.0\\nsamples = 1\\nvalue = [0, 1]'),\n",
       " Text(0.9294117647058824, 0.65625, 'gini = 0.0\\nsamples = 31\\nvalue = [0, 31]'),\n",
       " Text(0.9764705882352941, 0.71875, 'x[1] <= 182.0\\ngini = 0.5\\nsamples = 6\\nvalue = [3, 3]'),\n",
       " Text(0.9647058823529412, 0.65625, 'x[0] <= 0.5\\ngini = 0.375\\nsamples = 4\\nvalue = [1, 3]'),\n",
       " Text(0.9529411764705882, 0.59375, 'gini = 0.0\\nsamples = 1\\nvalue = [1, 0]'),\n",
       " Text(0.9764705882352941, 0.59375, 'gini = 0.0\\nsamples = 3\\nvalue = [0, 3]'),\n",
       " Text(0.9882352941176471, 0.65625, 'gini = 0.0\\nsamples = 2\\nvalue = [2, 0]')]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn import tree\n",
    "tree.plot_tree(dt1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "aa304828",
   "metadata": {},
   "outputs": [],
   "source": [
    "dt2 = DecisionTreeClassifier(max_depth = 3, random_state = 0)\n",
    "dt2=dt2.fit(x_train, y_train)\n",
    "\n",
    "y_pred = dt2.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "c0a2f001",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0.5, 0.875, 'x[1] <= 129.5\\ngini = 0.449\\nsamples = 537\\nvalue = [354, 183]'),\n",
       " Text(0.25, 0.625, 'x[4] <= 26.3\\ngini = 0.329\\nsamples = 357\\nvalue = [283, 74]'),\n",
       " Text(0.125, 0.375, 'x[4] <= 9.1\\ngini = 0.06\\nsamples = 97\\nvalue = [94, 3]'),\n",
       " Text(0.0625, 0.125, 'gini = 0.444\\nsamples = 6\\nvalue = [4, 2]'),\n",
       " Text(0.1875, 0.125, 'gini = 0.022\\nsamples = 91\\nvalue = [90, 1]'),\n",
       " Text(0.375, 0.375, 'x[6] <= 27.5\\ngini = 0.397\\nsamples = 260\\nvalue = [189, 71]'),\n",
       " Text(0.3125, 0.125, 'gini = 0.243\\nsamples = 120\\nvalue = [103, 17]'),\n",
       " Text(0.4375, 0.125, 'gini = 0.474\\nsamples = 140\\nvalue = [86, 54]'),\n",
       " Text(0.75, 0.625, 'x[4] <= 27.85\\ngini = 0.478\\nsamples = 180\\nvalue = [71, 109]'),\n",
       " Text(0.625, 0.375, 'x[1] <= 145.5\\ngini = 0.375\\nsamples = 36\\nvalue = [27, 9]'),\n",
       " Text(0.5625, 0.125, 'gini = 0.1\\nsamples = 19\\nvalue = [18, 1]'),\n",
       " Text(0.6875, 0.125, 'gini = 0.498\\nsamples = 17\\nvalue = [9, 8]'),\n",
       " Text(0.875, 0.375, 'x[1] <= 158.5\\ngini = 0.424\\nsamples = 144\\nvalue = [44, 100]'),\n",
       " Text(0.8125, 0.125, 'gini = 0.487\\nsamples = 88\\nvalue = [37, 51]'),\n",
       " Text(0.9375, 0.125, 'gini = 0.219\\nsamples = 56\\nvalue = [7, 49]')]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAgMAAAGFCAYAAABg2vAPAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAABaIUlEQVR4nO3deVhUZfsH8C+IgIiKSm6BS1ohKLkvaUoiuJaQMrhF5pJbKriQuKSWuGTooIkpoERqOVgRlguEuYsoDqgIuaVSqUQsKpvAzO8Pf8wrKAIyM2dmzvdzXe/1XsLMOfc99zynm+c85xwjpVKpBBEREYmWsdABEBERkbDYDBAREYkcmwEiIiKRYzNAREQkcmwGiIiIRI7NABERkcixGSAiIhI5NgNEREQix2aAiIhI5NgMEBERiRybASIiIpFjM0BERCRybAaIiIhEjs0AERGRyLEZICIiEjk2A0RERCLHZoCIiEjk2AwQERGJHJsBIiIikWMzQEREJHJsBoiIiESOzQAREZHIsRkgIiISOTYDREREIsdmgIiISOTYDBAREYkcmwEiIiKRYzNAREQkcmwGiIiIRI7NABERkcixGSAiIhI5NgNEREQix2aAiIhI5NgMEBERiRybASIiIpFjM0BERCRybAaIiIhEjs0AERGRyLEZICIiEjk2A0RERCJnInQARPTY7du3kZGRIXQYWmdtbY2WLVsKHQaRqLEZINIBt2/fRvv27ZGXlyd0KFpnYWGBlJQUNgREAmIzQKQDMjIykJeXh507d6J9+/ZCh6M1KSkpGD9+PDIyMtgMEAmIzQCRDmnfvj26dOkidBhEJDJcQEhERCRybAaI9Jirqyvu3LmD+Ph4uLm5ITExEQAQGxsLb2/vCt9XXFz83O2W396uXbuwatUqzJ07FwCwdOlSBAYGYsOGDar3ZGdnY9iwYZBKpUhLS6tRXkSkXTxNQKRHIiMjUVhYiJMnT8Lf3x/29vZo3rw5mjdvDjc3N9XrnJ2dsW/fvjLvvXTpEmJjY1FQUIARI0YAAA4ePKj6vYuLCxwcHAAAPXr0KLO9cePGAQAmTpwIALh79y4+//xzuLi4wMfHBwBgbGyMpk2b4sGDB6hdu7bacycizeHMAJEecXNzQ2hoKAYMGIB69epV+X3nzp3DkiVL0KJFC8yePRt2dnbV2q9CocCyZcswa9YsAI8bh82bN8PMzEz1mvr162P79u2YOXMmgoKCqrV9IhIWZwaI9EhwcDAWL16MyMhI9O3bV/XzK1euIDo6GsnJyWjVqhUaNmxY5n3dunVDZGQkrl27htDQUDg5OaFDhw4VNgXlt7d06VIolUocPXoUjo6OUCqVyMvLw4QJEwAAmzdvxtChQyGTyXDnzh1IJBKNfQZEpH5GSqVSKXQQRGJ3/vx5dO3aFQkJCdW6mmDevHmYP38+mjdvXubnsbGxuHnzJiZNmqTuUNXqRfMmIvXiaQIiPRYQEFCmEYiKikJ+fj6cnZ3LNAJ79uyp0vb2798Pf39/+Pr64sm/EwoKCjBu3DhERkYCAK5du4aePXsiOzsbBw4cwIoVKzB79mxR3jSJyBCwGSDSYzExMVi+fDk+/vhjREZG4vz58ygsLMS7776L8PBw+Pn5AQBOnz6tek9qaiqkUqnqf8nJyWW2t3jxYnTo0AFJSUmqn0ulUowdOxYAkJOTg59++glDhgwB8HgR4oIFC/D6668jJiZGG2kTkZpxzQCRHouKisL69eufunKgTZs28PLyeu7lhVWVmpqKrKwsHDhwAAqFAo8ePYJCoUBcXByio6Mxa9YsBAUF4datW2jTpk2N90dE2sdmgEiPDR8+HKtWrUJ6errqL3UAMDIyqvA9dnZ2FS4cdHFxwapVq5CTk4P3338fmzdvxsyZM7F27VocOXIE2dnZqksO8/Pz4erqir///hsA0KRJE7i6uqovOSLSGi4gJNIBL7qQ7t69e4iKikJKSgr8/Pzw0ksvaTBK9eMCQiLdwJkBIj3WtGlTTJkyRegwiEjPsRkgEhFvb29IpdIabWPevHmwtbXFyy+/DA8PD+zduxc3btxASUkJJk+ejF27duGvv/5C/fr18emnn6oncCLSKDYDRHoiKCgIRUVFaNeuHRwcHLBv3z6kpqbC398f06ZNg5OTE+Lj49GrVy+cOHEC4eHhGDRoECZMmIDU1FSsWLECwOMbCgUHB8PKygpvv/02fv/9dzRo0ACOjo7o168fgMfPJjh16pRq3yNHjoStrS2Ax7MRSqUSjx49AvD41sfnzp2Dubk5XnrpJXh7e2PZsmWYPHmylj8hInpRvLSQSE907doVRUVFyMjIQF5eHoyNjWFqaoqLFy+icePGmDZtGszNzTFlyhS0bt0a9+/fR7NmzTBmzBgUFhaq/uMdFRWFpk2bokWLFrh27Rq6d++OwsJCZGZmVikOX19f+Pj44Ny5cygqKkLDhg2xZs0a1a2JCwsL8d9//6FFixYa+yyISL04M0CkJzIzM1GnTh2kpqaibt26MDc3R0lJCUpKSlQPBjI1NYWRkRGMjY2hUChw7949bN26FQqFAqampgAeX4Gwe/du1K1bF71798aVK1dgZmaG1NRU1b569OiBHj16PDOO8PBwpKWlwdTUFLVr18a6detQUlKC/Px8AI9vcOTp6anhT4OI1IlXExDpAE2tqlfHGgFN4tUERLqBpwmIDJguNwJEpDvYDBDpmSNHjqieEVATYWFhCAgIAACEhoZizZo12LJlC7KzszFs2DBIpVKkpaUBAM6cOYM+ffo8czsymQxOTk6qf3/00UcICAjAihUrkJ2djcDAQCxatAhHjx5FcnIyRo8eXePYiUi9uGaASActXLgQK1asQFhYGPr27YtTp07h3r17sLe3R6NGjQD87xSAVCqFm5sbZDIZTE1NkZ2djeXLlwMAHj58iJCQENV227dvj0GDBqn+7ezsDAAYOHAgAgICYGVlBWNjYzRt2hQPHjxA7dq1cfv2bSQlJaF79+7PjFUikZS58qCwsBA5OTmwsbGBlZUVunXrhk2bNqFOnTpwcHBAs2bN1P1xEVENcWaASAeNGjUKERERSElJgZ2dHQoKCmBjY4OjR48+9drSZT+xsbGwsrJCQUGB6sqBqmrVqhU2btyIrKws1K9fH9u3b8fMmTMRFBSEX375Bbm5uZDL5Th69CgePXqEkpKSZ24nPz8fb775Jj777DNcvXoVANCnTx98/fXXOH/+fDU/BSLSFs4MEOmgbt26YeXKlRg9ejTy8/ORlpaGjh07ori4WPUaR0dHBAUFIT4+Hu7u7nBxcUFubi7atm2runLA0tKy0ocVPXr0CGvXroWRkREsLS3x559/QiaT4c6dO5BIJHjzzTcBALdu3UL//v3x5ZdfwtPTU3XfgcOHD0MulyM4OBgTJkzAuXPnkJeXh+bNmyM1NRWRkZHIyMgo8+wEItItvJqASAcIsar+8OHDuHHjRrVvDnT37t0XnupPTk5GZGQkFi9eDIBXExDpCs4MEOmQlJQUre3LysoKXbp0eaHp+3/++eeF9ztkyBDVPrWZLxFVjM0AkQ6wtraGhYUFxo8fL3QoWmdhYQFra2uhwyASNZ4mINIRt2/fRkZGxnNfEx8fr5piX7lyJXr27KmN0KrszJkzWLJkCQDA39+/wrsYPsna2hotW7bUdGhE9BxsBoj0QElJCT777DN8/vnnGDBgAHbu3Kmzl+jdvXsX48aNw++//46lS5fi008/Ra1atYQOi4ieg80AkY77559/MHbsWBw/fhzLly/HokWLdP4/riUlJfD398eKFSvQr18/7Nq1iw8uItJhbAaIdNihQ4fw/vvvw8TEBN999x369+8vdEjVcuTIEYwdOxbFxcXYuXMnXF1dhQ6JiJ6BNx0i0kHFxcXw8/PD4MGD0aVLFyQmJupdIwAATk5OSExMROfOnTFo0CAsWrSozL0SiEg3cGaASMekpaVhzJgxiIuLw8qVK+Hr6wtjY/3u2xUKBb744gssWbIEvXv3xnfffQcbGxuhwyKi/8dmgEiH/Prrr/Dy8oKFhQW+//77Ch8OpK9OnDiBMWPGID8/H+Hh4Rg6dKjQIREReJqASCcUFRVhwYIFGD58ON58800kJiYaXCMAAH379oVcLkevXr0wbNgw+Pr6oqioSOiwiESPMwNEArt16xY8PT2RkJCANWvWYO7cuTAyMhI6LI1SKBRYv349/Pz80K1bN3z//fdo1aqV0GERiRZnBogEFBkZiU6dOuHu3bs4fvw45s2bZ/CNAAAYGxtj/vz5OH78OO7cuYPOnTvj559/FjosItFiM0AkgEePHsHb2xvu7u5wcnJSTZ2LTa9evSCXy9GvXz+4ubnB29u72o9fJqKa42kCIi27ceMGPD09kZSUhC+//BKzZs0SxWzA8yiVSmzcuBELFizAG2+8gT179uCVV14ROiwi0eDMAJEW7d27F507d0ZmZiZOnTqF2bNni74RAAAjIyPMmTMHJ0+exH///YfOnTvjhx9+EDosItFgM0CkBQUFBZg5cyY8PDzg6uqK8+fPo1u3bkKHpXO6d+8OuVwOV1dXjBo1Ch9//DEKCgqEDovI4PE0AZGGXb16FRKJBCkpKdiwYQOmTZvG2YBKKJVKbNmyBT4+PnBwcMCePXvw6quvCh0WkcHizACRBn333Xfo0qULcnNzERcXh+nTp7MRqAIjIyPMmDEDcXFxePDgAbp27Yrvv/9e6LCIDBabASINyM/Px0cffYSxY8fi3XffRUJCAjp16iR0WHqnc+fOOH/+PIYPH44xY8Zg6tSpyM/PFzosIoPD0wREapaamgqJRIKrV69i06ZNmDRpEmcDakipVCIkJASzZ8/Gq6++CplMBjs7O6HDIjIYnBkgUqPw8HB07doVRUVFiI+Px+TJk9kIqIGRkRGmTJmC+Ph4FBUVoVu3bvj222+FDovIYLAZIFKD3NxcfPjhh/jggw/g4eGBc+fOoWPHjkKHZXA6duyIs2fPYuTIkfDy8sLEiRORm5srdFhEeo+nCYhqKDk5GRKJBDdv3kRQUBA++OADoUMShbCwMMycOROtW7eGTCaDg4OD0CER6S3ODBC9IKVSie3bt6N79+4wMjLC2bNn2Qho0YQJE3D27FkYGRmhe/fu2LFjB/i3DdGLYTNA9AIePnyI999/H5MmTcK4ceMQHx8Pe3t7ocMSHXt7e8THx2Ps2LGYOHEivLy88PDhQ6HDItI7PE1AVE1JSUmQSCT4559/sHXrVowdO1bokAjArl27MHXqVNjY2EAmk8HR0VHokIj0BmcGiKpIqVRi69at6NmzJ+rUqYOEhAQ2Ajpk3LhxSEhIgJmZGXr06IGtW7fytAFRFbEZIKqC+/fvY/To0Zg2bRomTpyI06dP47XXXhM6LCrn9ddfR1xcHD788ENMmzYNY8aMwf3794UOi0jn8TQBUSXOnz8PiUSC9PR0hISEQCKRCB0SVYFMJsPkyZPRtGlT7NmzB126dBE6JCKdxZkBogoolUp89dVX6N27N6ysrCCXy9kI6BGJRILz58+jfv366N27N7766iueNiCqAJsBomfIzs7GqFGjMGvWLEydOhUnT55E27ZthQ6Lqqldu3Y4deoUpk6dilmzZsHDwwPZ2dlCh0Wkc3iagKic+Ph4eHp6Ijs7G9u3b4e7u7vQIZEa/Pjjj5g4cSIaNWqEPXv2oHv37kKHRKQzODNA9P+USiU2bNiAvn37okmTJpDL5WwEDMh7770HuVwOa2tr9OnTB1KplKcNiP4fmwEiAJmZmRgxYgTmzp2L2bNn4/jx42jdurXQYZGatWnTBidOnMCsWbPg4+MDNzc3ZGZmCh0WkeB4moBE79SpUxg9ejRyc3MRFhaGd955R+iQSAv27duHDz74AJaWltizZw969+4tdEhEguHMAImWQqHAF198gX79+sHW1haJiYlsBETknXfeQWJiImxtbfHWW2/hiy++gEKhEDosIkGwGSBR+vfffzF8+HB88sknWLBgAY4cOQJbW1uhwyIta9myJY4cOYL58+fjk08+wfDhw5GRkSF0WERax9MEJDrHjh3DmDFj8OjRI3z77bcYPHiw0CGRDjhw4AC8vLxgZmaG7777Dm+99ZbQIRFpDWcGSDQUCgX8/f3x9ttvo127dkhMTGQjQCpDhgxBYmIiXnnlFTg5OcHf35+nDUg02AyQKNy7dw+DBw/G0qVLsWjRIsTGxuLll18WOizSMS+//DIOHz6MRYsWYenSpRg8eDDu3bsndFhEGsfTBGTwDh8+jHHjxkGpVGLnzp0YOHCg0CGRHoiJicH48eNhbGyMXbt2YcCAAUKHRKQxnBkgg1VSUoJly5Zh4MCBsLe3R2JiIhsBqjIXFxckJibC3t4eAwcOxPLly1FSUiJ0WEQawZkBMkj//PMPxo0bh2PHjmH58uVYtGgRatWqJXRYpIdKSkrg7++PFStWoH///ti1axeaN28udFhEasVmgAxOdHQ0xo8fDxMTE+zevRtOTk5Ch0QG4MiRIxg7dixKSkqwc+dOuLi4CB0SkdrwNAEZjOLiYixevBiDBw9G586dkZiYyEaA1MbJyQmJiYno1KkTBg0ahCVLlqC4uFjosIjUgjMDZBD++usvjBkzBqdPn8bKlSvh6+sLY2P2uqR+CoUCa9euxdKlS/Hmm29i9+7dsLGxETosohphM0B6b//+/fDy8kKdOnXw3XffoW/fvkKHRCJw4sQJjB49GgUFBQgPD8fQoUOFDonohfFPJ9JbRUVF8PX1xbBhw9CrVy/I5XI2AqQ1ffv2RWJiInr27Ilhw4bB19cXRUVFQodF9EI4M0B66datWxg9ejTOnTuHNWvWwMfHh6cFSBAKhQLr16+Hn58funfvju+//x4tW7YUOiyiauHRk/TOzz//jM6dO+POnTs4fvw45s2bx0aABGNsbIz58+fj+PHj+Pvvv9GpUydERUUJHRZRtfAISnrj0aNH8Pb2hpubG/r37w+5XI5evXoJHRYRAKhOVfXr1w8jRoyAj48PHj16JHRYRFXC0wSkF27cuAFPT08kJSXhyy+/xKxZs2BkZCR0WERPUSqV2LhxIxYsWIBOnTphz549aNOmjdBhET0XZwZI5/3www/o3LkzMjMzcerUKcyePZuNAOksIyMjzJkzBydPnkRGRgY6d+6MH3/8UeiwiJ6LzQDprIKCAnz88ccYNWoUXF1dcf78eXTr1k3osIiqpHv37jh//jwGDhyIkSNHYtasWSgoKBA6LKJn4mkC0klXr16Fp6cnLl++jA0bNmDatGmcDSC9pFQqsWXLFvj4+MDBwQEymQzt2rUTOiyiMjgzQDrn+++/R5cuXfDw4UPExcVh+vTpbARIbxkZGWHGjBmIi4vDgwcP0KVLF3z//fdCh0VUBpsB0hn5+fmYOnUqxowZg3feeQcJCQno1KmT0GERqUXnzp1x/vx5DB8+HGPGjMHUqVORn58vdFhEAHiagHREamoqJBIJrl69ik2bNmHSpEmcDSCDpFQqERISgtmzZ+O1116DTCbD66+/LnRYJHKcGSDBffvtt+jWrRuKiooQHx+PyZMnsxEgg2VkZIQpU6bgzJkzKCwsRNeuXbFz506hwyKRYzNAgsnNzcXEiRPh5eWFkSNH4uzZs+jYsaPQYRFphaOjI86dO4f33nsP77//PiZOnIi8vDyhwyKRYjNAWpWbmwsPDw8cOHAAPXr0wJ49e7Bjxw588803sLS0FDo8Iq2ytLREeHg4duzYge+//x7du3fHgQMH4OHhwcaAtIprBkir1q1bBz8/P5iYmKBdu3aQyWSwt7cXOiwiwV2+fBkSiQTXrl1DcXEx1qxZg/nz5wsdFokEZwZIa3JycrB06VKUlJSgXr16eOedd9gIEP0/e3t7DB8+HPXq1UNJSQmWLFmCnJwcocMikWAzQFpz+fJlFBcXo3Xr1hg2bBhcXFyEDolIp7i6umLYsGFo3bo1iouLkZKSInRIJBI8TUBERCRyJkIHQJW7ffs2MjIyhA5D46ytrdGyZUuhwyDSKo5v0gVsBnTc7du30b59e1GsLLawsEBKSgoPGCQaHN+kK9gM6LiMjAzk5eVh586daN++vdDhaExKSgrGjx+PjIwMHixINDi+SVewGdAT7du3R5cuXYQOg4g0gOObhMarCYiIiESOMwMGytXVFd988w2aN2+O8PBw/P7779ixYwdCQ0Px4MEDeHt7P/N9xcXFMDGp+Guxa9cu3Lp1CxkZGVi/fj327NmD27dvo3Xr1vDw8AAAZGdn45tvvsG9e/cwaNAg9O/fXxMpEomOtsb1li1bUFBQgN9++w2//vorAODhw4eYM2cO7OzsYGZmhn79+iEgIABdu3bFhx9+iAYNGmgiZdISNgMGIjIyEoWFhTh58iT8/f1hb2+P5s2bIyYmBq1atVINVGdnZ0RGRqrep1QqcfbsWRw/fhzFxcXw8vJCWloaTp06pXrNyJEjYWtrCwAYN24cAGDixIkAHj9kaNiwYWVisbKyQrdu3bBp0ybUqVNHk2kTGTShxvX06dNx6NAh2NjYqF5vYmKCBw8eID09Hf369YOpqSkaNWqE3Nxc1KpVS9MfBWkYTxMYCDc3N4SGhmLAgAGoV6+e6uexsbGQy+WQy+W4fv36U+87ePAg1q1bBwcHB8ydOxfNmzd/7n4UCgWWLVuGWbNmAQAKCwsxffp0xMTElHldnz598PXXX+P8+fNqyI5InIQa18DjRsTd3V317/T0dIwePRrr1q3DsWPHYG9vj8DAQAwZMgQymUwN2ZKQODNgIIKDg7F48WJERkaib9++qp+vWbMGAHDz5k20bdsWN2/eLPO+IUOGYMiQIUhKSsJXX32FkSNHokePHujRo8cz9zN79mwolUocPXoUjo6OGDx4MAIDA1UHm82bN6v+SsnIyMCQIUM0kzCRCAg1rtPS0mBra6s6tbB582Z4enri4MGDuH79OhwcHCCXyxETE4ObN29i7ty5mvkASGt4B0Idd/78eXTt2hUJCQnVWm08b948zJ8//6m/CL777jtYWlrinXfeUXeoNfKieRLps+p+7/VtXJfi+NZ9nBkwUAEBAQCAqKgouLi4qM7djxkzBgCwZ88eeHp6Vrqd/fv3Qy6XIycnB2vXroWRkREAIDQ0FP/++y8aNGiA4cOHY+fOnbh79y4kEgksLCywZ88eFBYWYv78+Xj55Zc1lCWRuJSO61Kl47t0XJeq6fguKCjApEmT4OHhgY4dO2Lfvn24cOECnJycYG9vj2+//RampqZwc3NDnz591JcgCYZrBgxMTEwMli9fjo8//hiRkZE4f/48CgsL8e677yI8PBx+fn4AgNOnT6vek5qaCqlUqvpfcnJyme0tXrwYHTp0QFJSkurnAwcOxD///AMjIyPY2trCz88P7u7uuH79OmJjY/Hhhx9i4MCB2Lt3r/aSJzJw2hrfUqkUY8eOBQC0bdsW3t7eqFu3LiQSCUxNTZGdnY2srKwyCwxJv7EZMDBRUVFYvHgxBgwYUObnbdq0gZeXF/Lz89Wyn1atWmHjxo3IysoCAMjlchw+fBjjx4+Hl5cXoqKicPbsWdSuXVst+yMi7Yzv1NRUZGVl4cCBA4iOjgYA3L17F1ZWVjA3N8fVq1fh7++PpUuXYt++fTXeH+kGniYwMMOHD8eqVauQnp5eZvFe6fTfs9jZ2cHOzu6Zv3NxccGqVauQk5OD999/H5s3b8aUKVNUU4qWlpa4fv06Jk2aBC8vL5w+fRqtWrVSvb/0kiUiqjltjO+ZM2di7dq1OHLkCLKzswE8Pi04adIkAMBLL72EwMBAmJiYYNSoUepLjgTFBYQ6rroLb+7du4eoqCikpKTAz88PL730khairDkuMCIx4vgmXcGZAQPTtGlTTJkyRegwiEgDOL5JU7hmQOQqun1pdURHR2PdunX45JNPAACenp6QSqU4ffo0/v33X0ilUsyfPx+fffZZjfdFRFWnrvHt5OSkOmWwYMECBAQEwMfHBwqFAvPnz4e/vz8OHTpU432RcDgzoMeCgoJQVFSEdu3awcHBAfv27UNqair8/f0xbdo0ODk5IT4+Hr169cKJEycQHh6OQYMGYcKECUhNTcWKFSsAAFeuXEFwcDCsrKzw9ttv4/fff0eDBg3g6OiIfv36AQDi4+MrvJWps7MzTp8+jfv37wMAmjVrhvz8fBgZGeGll16Ct7c3li1bhsmTJ2v5EyLSX7oyvl1dXcv8rri4GA8fPkTjxo2RlJQER0dHeHl5wcfHB4MGDdLiJ0TqxJkBPda1a1cUFRWpnolubGwMU1NTXLx4EY0bN8a0adNgbm6OKVOmoHXr1rh//z6aNWuGMWPGoLCwEI8ePQLweIVy06ZN0aJFC1y7dg3du3dHYWEhMjMzqxRHrVq1sGzZMrzyyisoKSlBYGAg/Pz8sHPnTgCPb1n833//oUWLFhr7LIgMja6M7/JatWqFZcuWIScnB8D/Fi8+bxEj6T7ODOixzMxM1KlTB6mpqahbty7Mzc1RUlKCkpIS1SV9pqamMDIygrGxMRQKBe7du4etW7dCoVDA1NQUwOMVyrt370bdunXRu3dvXLlyBWZmZkhNTVXt63m3Mt22bRuys7ORlpaGWrVqwd/fH4WFhXjjjTcAVP0GKET0P7oyvs+dO4e4uDhs2bIFvr6+uHHjBgIDA2Fubo433ngDu3fvxurVq+Hq6qr5D4U0hlcT6Dh1r8L19vaGVCqteWBqxtXGJEYc36QreJpAZHTxQEFE6sHxTS+KpwkMSOlNQtzc3Gq0nbCwMPz333+YN28eVq9ejYKCAjRt2hReXl6YM2cO7OzsYGZmBnd3d/zwww+4ffs2pk6ditdff73MdrZs2YKCggL89ttv+PXXX8vc7/ytt97CpEmTEBYWBisrqxrFSyQG6h7fH3/8MbZs2YLc3Fxcv34d27dvxxdffIHLly8jLCzsqfdduXIFq1atgpubG9zc3J56rsGnn34Ka2trKBQKzJgxAzNnzsTHH3+MTp061She0g42A3pm4cKFWLFiBcLCwtC3b1+cOnUK9+7dg729PRo1agTgf1OFUqkUbm5ukMlkqvuJL1++HADw8OFDhISEqLbbvn37MiuBnZ2dAQB+fn7Izs7GZ599BhMTEzx48ADp6eno168fbG1t8dprr+HYsWOq85NPmj59Og4dOqS6f3np/c6LiorQuHFjHiSIytHm+DYzM4O3tze2bdumOt/v6+tb4eWIr732GiZMmKC6xDAmJgYbNmxAeHg4kpKScPfuXXz++edwcXGBj49PmUcuk+7jaQI9M2rUKERERCAlJQV2dnYoKCiAjY0Njh49+tRrS5eDxMbGwsrKCgUFBaoVxlWVmZmJ5cuXY8mSJUhPT8fo0aOxbt06HDt2DAAwdOhQLF++HCkpKXj06BFKSkrKvD8yMhLu7u7PvN85EZWl7fENAGfPnkX37t2f+bvqPOvAxcUFmzdvhpmZWbVjIOFxZkDPdOvWDStXrsTo0aORn5+PtLQ0dOzYEcXFxarXODo6IigoCPHx8XB3d4eLiwtyc3PRtm1b1V/wlpaWVbohyeDBgzFixAjExMTA2dkZBw8exPXr1+Hg4IC4uDgcPXoUaWlpmDZtGjZu3AhPT0/V9ck3b96Era0tTExMYGdn99T9zomoLG2P72PHjqnuNQAA4eHhkMvlOHDgADp16oRvvvkGCxcuBPD4YUV79+5Ffn4+Onfu/NRzDf744w/k5eVhwoQJav1MSEuUpNMSEhKUAJQJCQla22dsbKwyODi42u+7c+dOlV+bkZGh/OSTT5QPHz5UKpXC5EkkNF0e39nZ2cq8vLwX2kdBQYFyxYoVyhs3biiVSo5vfcCZAXrKgAEDnnpEalU0a9asyq9t3Lgx1qxZU+19EFHNVHV8N2jQ4IX3YWZmhk8//fSF30/ax2ZAT6SkpAgdgkYZen5Ez2Po339Dz88QsBnQcdbW1rCwsMD48eOFDkXjLCwsYG1tLXQYRFrD8U26gncg1AO3b99GRkZGjbaRkJCAjz76CPPmzcPYsWPVEteuXbuwfv16bNu2DV27dq3x9qytrdGyZUs1REakPzi+SRewGRCBhw8fwtHRETY2Njhy5AiMjdVzRalCoYCTkxP+/vtvJCUlwdLSUi3bJaKq4/gmdeB9BkTgk08+wb1797Bjxw61HSgAwNjYGNu3b8edO3dUlx8RkXZxfJM6sBkwcIcPH0ZQUBDWrl2Ltm3bqn377dq1w9q1a7F582YcPnxY7dsnoopxfJO68DSBAXvw4AE6duyINm3aIDY2Vq1/NTxJoVBgwIABuHXrFi5cuIB69eppZD9E9D8c36ROnBkwYAsWLEBGRga2b9+usQMF8L/pxH///Re+vr4a2w8R/Q/HN6kTmwEDFR0dja1bt2LdunVo06aNxvf3yiuv4IsvvsDXX3+NmJgYje+PSMw4vkndeJrAAOXk5KBjx4547bXXEB0drdG/Gp6kUCjg4uKCq1ev4tKlS6hfv75W9kskJhzfpAmcGTBA8+fPR1ZWFkJDQ7V2oAAeTyeGhoYiKysL8+fP19p+icSE45s0gc2AgTl48CBCQkKwfv16tGrVSuv7b926NQICAhAcHIxDhw5pff9EhozjmzSFpwkMSHZ2Njp06AAHBwccPHgQRkZGgsShVCoxaNAgXL58GZcuXYKVlZUgcRAZEo5v0iTODBgQHx8fPHjwACEhIYIdKADAyMgIISEhuH//PubOnStYHESGhOObNInNgIH49ddfERYWhg0bNsDW1lbocNCyZUts2LABO3bswP79+4UOh0ivcXyTpvE0gQHIysqCg4MDOnXqhF9//VXQvxqepFQqMXToUFy4cAGXLl1Cw4YNhQ6JSO9wfJM2cGbAAMyZMwd5eXkIDg7WmQMF8Hg6MTg4GLm5ufD29hY6HCK9xPFN2sBmQM9FRUXh22+/RWBgIF5++WWhw3mKjY0NpFIpwsPDsW/fPqHDIdIrHN+kLTxNoMf+++8/dOjQAd26dUNUVJRO/dXwJKVSiXfeeQcJCQlITk5Go0aNhA6JSOdxfJM2cWZAj82ePRuFhYXYunWrzh4ogMfTidu2bUNBQQFmz54tdDhEeoHjm7SJzYCe+vHHH7F7925s2rQJLVq0EDqcSrVo0QKbNm3Crl278NNPPwkdDpFO4/gmbeNpAj2UkZEBBwcHvPnmm/jxxx91+q+GJymVSri7u+P06dNITk6GtbW10CER6RyObxICZwb00Mcff4zi4mJs2bJFbw4UwOPpxK+//hrFxcWYNWuW0OEQ6SSObxICmwE9ExERgT179mDz5s1o1qyZ0OFUW7NmzfDVV1/h+++/x969e4UOh0incHyTUHiaQI+kp6fDwcEB/fv3R0REhF791fAkpVKJUaNG4dixY0hOTkaTJk2EDolIcBzfJCTODOgJpVKJGTNmAACCgoL09kABPJ5O3LJlCwBg5syZAkdDJDyObxIamwE9IZPJ8MMPPyAoKMggOu0mTZpg8+bN2Lt3L2QymdDhEAmK45uExtMEeuDu3btwcHDAwIEDsWfPHqHDUSuJRILDhw8jOTkZTZs2FTocIq3j+CZdwGZAxxn65Tr//vsvHBwc0LdvX/zwww96PT1KVF0c36QreJpAx+3evRs///wzvv76a4M7UADASy+9hC1btuCnn37Cd999J3Q4RFrF8U26gjMDOuzOnTtwcHDAkCFDsGvXLqHD0aixY8fi4MGDSE5ORvPmzYUOh0jjOL5Jl7AZ0FFKpRIjRoxAfHw8kpOT0bhxY6FD0qj//vsPDg4O6NGjB37++WdOJ5JB4/jm+NY1PE2go7799lvs27cPW7duNfgDBQA0btwYW7duxb59+7Bz506hwyHSKI5v0jWcGdBBf//9NxwcHPDuu+8iPDxc6HC06v3338cvv/yCS5cu6eTz24lqiuOb41sXsRnQMUqlEsOGDUNiYiKSk5PRsGFDoUPSqszMTHTo0AGdO3fGL7/8wulEMigc3xzfuoqnCXTMjh07cODAAQQHB4vuQAEAjRo1wrZt27B//36EhYUJHQ6RWnF8c3zrKs4M6JC0tDR06NAB7733Hnbs2CF0OIKaMGECfvrpJyQnJ8PGxkbocIhqjOP7fzi+dQ+bAR2hVCoxePBgJCcn49KlS7CyshI6JEFlZ2fDwcEBHTt2xIEDBzidSHqN47ssjm/dw9MEOiIkJATR0dEICQkR/YECAKysrBASEoJDhw4hNDRU6HCIaoTjuyyOb93DmQEdcOvWLXTo0AGjR49GcHCw0OHolMmTJ0Mmk+HixYto1aqV0OEQVRvHd8U4vnUHmwGBKZVKuLi44MqVK7h06RLq168vdEg6JScnBx06dICdnR2io6M5nUh6heP7+Ti+dQdPEwhs69atiI2NRWhoKA8Uz9CgQQOEhobit99+w7Zt24QOh6haOL6fj+Nbd3BmQEB//vknOnbsiPHjx+Prr78WOhydNnXqVOzatQsXL15EmzZthA6HqFIc31XH8S08NgMCUSgUcHZ2xp9//omLFy+iXr16Qoek0x48eICOHTvilVdewW+//QZjY05qke7i+K4ejm/h8RMXSFBQEI4cOYLt27fzQFEF9erVQ2hoKH7//Xds2bJF6HCInovju3o4voXHmQEBXL9+HY6OjpgwYQI2b94sdDh6ZcaMGfjmm29w4cIFtG3bVuhwiJ7C8f3iOL6Fw2ZAyxQKBZycnPDXX3/hwoULsLS0FDokvfLw4UN07NgRLVu2xO+//87pRNIpHN81w/EtHH7SWrZp0yYcP34cO3bs4IHiBVhaWmLHjh04duwYvvrqK6HDISqD47tmOL6Fw5kBLbpy5Qo6deqEKVOmIDAwUOhw9Nrs2bMREhKCpKQkvPrqq0KHQ8TxrUYc39rHZkBLSkpK0K9fP6SnpyMxMRF169YVOiS9lpubizfeeAPNmjXD0aNHUatWLaFDIhHj+FYvjm/t42kCLQkMDMTp06exY8cOHijUoG7dutixYwdOnTqFjRs3Ch0OiRzHt3pxfGsfZwa0IDU1FZ07d8b06dOxfv16ocMxKD4+Pvj666+RmJiI119/XehwSIQ4vjWH41t72AxoWElJCfr06YOsrCzI5XJYWFgIHZJBycvLQ6dOndC4cWOcOHGC04mkVRzfmsXxrT08TaBhAQEBOHv2LMLCwnig0AALCwuEhYXhzJkz/KuMtI7jW7M4vrWHMwMadPnyZXTu3Blz5szBF198IXQ4Bm3BggXYtGkT5HI52rdvL3Q4JAIc39rD8a15bAY0pLi4GG+++SYePHgAuVwOc3NzoUMyaPn5+ejcuTMaNGiAkydPwsTEROiQyIBxfGsXx7fm8TSBhqxbtw4JCQkICwvjgUIL6tSpg7CwMJw7dw5ffvml0OGQgeP41i6Ob83jzIAGXLx4EV27dsW8efOwevVqocMRlYULF2LDhg1ISEhAhw4dhA6HDBDHt3A4vjWHzYAaXbhwAb/++iv27t2LwsJCJCQkwMzMTOiwRKWgoABdu3ZFnTp18N5772H48OFwdHQUOiwyABzfwuP41hyeeFGjXbt2ITg4GDk5Odi1axcPFAIwNzfHkiVLMH78eNy4cQP379/nwYLUguNbeBzfmsM1A2okl8uRlZUFExMTSKVSocMRLalUChMTE9W130TqwPGtGzi+NYPNgBolJiYCAD766CPs379f2GBE7MCBA5gyZQoA8GBBasPxrRs4vjWDawbU6MCBA2jcuDF69OghdCgE4MyZM8jMzMSQIUOEDoUMAMe3buH4Vi82A0RERCLH0wREREQip9dXE9y+fRsZGRlCh1Fj1tbWaNmypdBh6BxDqW9lWP9nM5T6s77PZij1rYy+1F9vm4Hbt2+jffv2yMvLEzqUGrOwsEBKSopefGG0xZDqWxnW/2mGVH/W92mGVN/K6Ev99bYZyMjIQF5eHnbu3KnXD65ISUnB+PHjkZGRofNfFm0ylPpWhvV/NkOpP+v7bIZS38roU/31thko1b59e3Tp0kXoMEhDWF9xY/0NG+urO0SzgNDV1RV37twBAISHh+PDDz8EAISGhj73BiLFxcXP3W5AQAACAwOxbNmyMj//4osvMGHChBrFTNVXWudz587B399f9Qz0mtZ5165dWLVqFebOnQsA2LJlCzZs2IBhw4aVed2QIUMglUqRnJxcs0TouUrrHB8fDzc3N9U9AGJjY+Ht7V3h+yqrc/ntAcCmTZtU2/T09IRUKsXp06dVv8/OzsawYcMglUqRlpb2oinRc2ir3vPmzYNUKkVERASAx49ODggIgI+Pj+o9hlpvvZ8ZqEhkZCQKCwtx8uRJ+Pv7w97eHs2bN0dMTAxatWqFBg0aAACcnZ0RGRmpep9SqcTZs2dx/PhxFBcXw8vLC2lpaTh16pTqNSNHjoStrS0AIDU1FcHBwfDw8EB2djasrKwAAL6+vs/9kpJ6VFTnzz//HPb29lAoFFAqlTWu87hx4wAAEydOBABMnz4dhw4dgo2NTZl4mjVrhocPH/IRq2pWUZ2bN28ONzc31eucnZ2xb9++Mu+9dOkSYmNjUVBQgBEjRgAADh48qPq9i4sLHBwcAAA9evQos73du3fD2dkZ169fB/C4vvn5+TAyMlK9xtjYGE2bNsWDBw9Qu3ZtdacuSkLVu2nTplAqlXj06BGAx83Ew4cP0bhxY9VrDLXeBjsz4ObmhtDQUAwYMAD16tVT/Tw2NhZyuRxyuVw1wJ908OBBrFu3Dg4ODpg7dy6aN2/+3P2MGjUKmzdvRm5uLmrVqqX2POj5Kqrz3bt3MWHCBJiZmeH8+fNPva+6dVYoFFi2bBlmzZql+llkZCTc3d3LvG7Hjh3w8/NDUFBQDTOjJ1VU58qcO3cOS5YsQYsWLTB79mzY2dlVa78nTpxAdHQ05HI5/v33XwQGBsLPzw87d+5UvaZ+/frYvn07Zs6cybqriVD19vX1hY+PD86dO4eioiK0atUKy5YtQ05Ojuo1hlpvg/3zJTg4GIsXL0ZkZCT69u2r+vmaNWsAADdv3kTbtm1x8+bNMu8bMmQIhgwZgqSkJHz11VcYOXIkevToUeFdxxQKBQoKCuDu7o569eph8+bNmDlzJsLDwyGXy3HgwAHeIUuDKqrz1KlTERAQgKysLIwbN+6pS5iqW+fZs2dDqVTi6NGjcHR0RFpaGmxtbVUzAJs3b8bYsWPx9ddf4/79+3ByctJYzmJUUZ2vXLmC6OhoJCcno1WrVmjYsGGZ93Xr1g2RkZG4du0aQkND4eTkhA4dOlT4H4ny2ys92N+8eRMvvfQS/P39UVhYiDfeeAPA47oPHToUMpkMd+7cgUQi0dAnIC5C1Xvfvn1IS0uDqakpateujRs3biAwMBDm5uYADLzeSj2VkJCgBKBMSEio0uvnzp2r/Oeff576+e7du5VRUVHqDq/KqpuHWLzo56Krda4I6/9slX0uFdX5t99+U4aEhGg6vCpjfZ9NXcdvXat3efpUf4OdGSgvICDgmT8fM2aMliMhTWKdxaGiOjs7O2s5EtIG1lvzRNMMlIqKioKLiwvq1KlT5ud79uyBp6dnpe/fv38/5HI5cnJysHbtWtVCouXLl6NevXpo0qQJRo8ejVWrVsHS0hJDhgyBvb29RnKhimmqzqGhofj333/RoEEDvPPOOwgICICpqSnGjRuneqRqZGQkwsLC0Lp1a02kRs+gjXq7urpi3759uHDhApycnODo6IiAgAB07doVH374oWpRMmmepupdUFCASZMmwcPDA127dsXOnTtx9+5dSCQS9OnTB2fOnMHcuXNx8uRJjeQlJINvBmJiYnDy5ElkZGRg4MCBSExMRL9+/eDp6YlRo0YhJSUFq1evxunTp1VfotTU1ApXn8bExGDDhg0IDw9HUlISOnXqhKysLBgbG2PevHnw8fFBo0aNkJWVBUtLS5iZmQmSt9hoo84AMHDgQAQEBMDKygpxcXEYPHgwbGxsEBISgg0bNqBv3774448/2AhomBD1btu2Lby9vTFr1ixIJBLcuHEDjRo14uJhLdBWvaVSKcaOHYuioiLY2trCz88PR44cwfXr12Fra4ukpCR0795d6/lrg8FeTVAqKioKixcvxoABA8r8vE2bNvDy8kJ+fr5a9lPaWRoZGaGoqAivvvoqpk+fjk2bNqll+/R82qpzq1atsHHjRmRlZWHo0KG4ePEiYmJiVAuMIiMj8e6776plX1QxIeoNPL5KxcrKCubm5rC3t0dgYCCGDBkCmUymlv3Rs2mj3qmpqcjKysKBAwcQHR0NAJDL5Th8+DDGjx+PX375Bbm5uZDL5Th69GiN96drDH5mYPjw4Vi1ahXS09PLrOp/8jrh8uzs7Cpcferi4oJVq1YhJycH77//vurqgZKSEqxfvx5dunTBwIEDsWjRIgQEBPBKAi3RRp2nTJmimlK0tLRESUkJFAoFcnJyMG3aNADAoUOHsHXrVvUmR08Rot7A49MGkyZNAvD4PxQxMTG4efOm6mZUpBnaOo6vXbsWR44cQXZ2Nq5fv45JkybBy8sLp0+fxowZMwAAt27dQv/+/dWboC4QegXji6rqKs27d+8qt23bpvTx8VGmp6drKbqq06fVptpU3c9F1+tcEdb/2Sr7XPSl3qzvs3F86x6Dnxlo2rQppkyZUuXXe3t7P/e2tVUREBAAExMTZGZmYtGiRdiyZQtyc3Nx/fp1bN++vUbbpmcTos67du3CrVu3kJGRgfXr12PPnj24ffs2WrdujZEjR8LX1xcNGzZEt27dMGjQoBrti8qqbr1LqaPu0dHRSEpKQkZGBtauXYutW7fi4cOH6NChA+usIUKM7+joaKxatQqRkZGwsrKCUqmEr68vXn75ZXh7eyMjIwPjxo3D2rVrVWsO9JlBNQNBQUEoKipCu3bt4ODggH379iE1NRX+/v6YNm0anJycEB8fj169euHEiRMIDw/HoEGDMGHCBKSmpmLFihUAHt+IIjg4GFZWVnj77bfx+++/o0GDBnB0dES/fv0APL6ndVVuUZyfnw9vb29s27YNrq6u2v9QDJCu1Ln8LYq//fZb1bMKkpKS4OjoCC8vL/j4+PA/EmqgK3V3dnbG6dOncf/+fTx48ABRUVEYNmwYjI0NfgmWVuhKnV1dXcv8bvPmzRg5ciTi4uJQVFSErVu3GtRNhwzq29u1a1cUFRWpHo9pbGwMU1NTXLx4EY0bN8a0adNgbm6OKVOmoHXr1rh//z6aNWuGMWPGoLCwUHU/6qioKDRt2hQtWrTAtWvX0L17dxQWFiIzM7NKcTzrFsVnz5412FWo2qYrdS5/i+LCwkJMnz4dMTExAMouKqWa05W616pVC8uWLcMrr7yCR48eoW7dupgxY4bq4TZUM7pS5ydlZmbi6tWr2L9/P44ePYpTp06hqKgIsbGxqsWG+s6gZgYyMzNRp04dpKamom7dujA3N0dJSQlKSkpUD5QwNTWFkZERjI2NoVAocO/ePWzduhUKhQKmpqYAHi9W2b17N+rWrYvevXvjypUrMDMzQ2pqqmpf1blF8bFjx1SdKNWcrtS5/C2KBw8ejMDAQDRv3hxvvPEGdu/ejdWrV3NGSE10pe7btm1DdnY20tLS0LhxY7z++usIDAxE+/btNf8hiICu1PncuXOIi4vDli1b4Ovri8DAQNy8eRORkZHo378/+vfvj7CwMIM4RQDA8BcQVmbOnDnqCegF6dMCE21S9+cidJ0rwvo/G8e3YeP41j0GdZrgRdR0kQnpB9ZZnFh3cWCda070zQAREZHYiaIZOHLkCCIjI2u8nbCwMAQEBEChUMDHxwfr1q1DeHg4AODatWvo2bMnsrOzn3pfdHQ01q1bh08++QQAsHTpUrXEQ4+pu77A4/PCbm5uAICHDx9i0qRJWLduHTZu3Ai5XI6FCxfCx8cHf//991Pb2bJlCzZs2KC6suDJbRUWFmLy5MlITEyscbykmdqvXr0ay5YtQ1BQEP79919IpVLMnz8fn3322VPvO3DgAFasWIHZs2cjLy8PsbGx8Pb2rnE89D+aqHFBQQHGjRun2m5lx28nJyfV79avXw+pVIrAwEAoFArMnz8f/v7+OHToEG7duoXhw4fXOFYhGMwCwoULF2LFihUICwtD3759cerUKdy7dw/29vZo1KgRgP9deyqVSuHm5gaZTAZTU1NkZ2dj+fLlAB4f+ENCQlTbbd++fZnLwpydnZGZmQlzc3MsWLAArq6uGDFiBH766acK7zb45KVIpf9+1peOKqbN+gLARx99hMuXLwMATExM8ODBA6Snp6Nfv36IjY3Fhx9+iGvXrmHv3r2YM2dOmVinT5+OQ4cOwcbG5qltmZmZlXk+O1VO27X38/NDdnY2PvvsM7z00kvw9vbGsmXLMHny5KdiO3jwIFavXo0dO3YgJiYGI0aMwL59+zT4aRgmbdf4yWcQ5OTkPPf4Xf4Sw7S0NGzYsAE+Pj7PvIS4Xbt26v54tMJgZgZGjRqFiIgIpKSkwM7ODgUFBbCxsXnmPaSVSiUAIDY2FlZWVigoKFBdjlIV1tbWaNu2LTZt2oQmTZrg0KFDUCgUiIuLQ3R0NIqLi1FUVKR6/ZOXIpWUlNQ8WRHSZn3LS09Px+jRo7Fu3TocO3YMXl5eiIqKwtmzZ1G7dm08evToqbpGRkbC3d39hfdJ/6Pt2mdmZmL58uVYsmQJgMezOf/99x9atGgBAGXugz9r1iwEBQUhNTVVtdKdqk+bNS7/DILKjt8VKX/psL5fQmwwMwPdunXDypUrMXr0aOTn5yMtLQ0dO3ZEcXGx6jWOjo4ICgpCfHw83N3d4eLigtzcXLRt21Z1OYqlpWWVpvkUCgXy8/MxYcIEDBw4EMDjg4SrqysiIiLQpk0b9OrVC0DZS5H4dLMXo+36RkZGQi6XY8+ePXB2dsbBgwdx/fp1ODg4lDnwjBs3Dhs3boSnp6fqZiU3b96Era0tTExMntpWVR6vSmVpu/aDBw/GiBEjEBMTA09PzzJ1u3PnDr755hssXLgQwONGAQCaNGnCS0hrQJs1trOzK/MMgtJTeBUdv8tfYtiyZUtIpVK0bt3asC4hFvZihhcnxCUbsbGxyuDg4Epfd+fOnef+XiqVKuPi4pRKpX5deqJNulzf8iqr95MKCgqUK1asUN64cUOpVLL+FdH251LV2mdnZyvz8vIq/P1vv/2mDAkJUf2b9X02XR7f1RnP5d28eVO5cOFC1b/1qf56PzOQkpKitX1ZWVmhS5cuOH/+fKWv/eeffyr83VtvvQUAOH/+vFbj10e6Wt/ynlfv8oYPH46srCxkZWWx/pXQ1udTk9o/qWHDhmjYsKFqO6zv8+nq+K7OeC7Pw8NDL+uvt82AtbU1LCwsMH78eKFDqTELCwtYW1sLHYZOMaT6Vob1f5oh1Z/1fZoh1bcy+lJ/I6Xy/1dj6KHbt28jIyNDLdsaN24cbGxssHbt2ue+ztfXF//88w927typlv0CjwdGy5Yt1bY9Q6HO+pYnZL3LY/2fjePbsHF86xihz1PogqtXryoBKCMiIip9rUwmUwJQXrt2TQuRkSaw3uLCeosL6/1iDObSwpqIiIiAhYUFhg4dWulrhw4dCgsLCz6hTI+x3uLCeosL6/1i2AwAkMlkeOedd2BhYVHpa+vWrYvhw4dDJpNpITLSBNZbXFhvcWG9X4zom4ErV64gMTEREomkyu+RSCSQy+W4evWqBiMjTWC9xYX1FhfW+8WJvhmIiIhA3bp1K7wV5bMMGTIEdevW5dSSHmK9xYX1FhfW+8WJvhkonVKqU6dOld9jYWHBqSU9xXqLC+stLqz3ixN1M/DHH3/gwoUL1ZpSKiWRSJCUlIQrV65oIDLSBNZbXFhvcWG9a0bUzUBERAQsLS0xePDgar+XU0v6h/UWF9ZbXFjvmhF1MyCTyfDuu+9Wa0qpVJ06dfDuu++KfmpJn7De4sJ6iwvrXTOibQZSUlJw8eLFF5pSKiWRSHDhwgWkpqaqMTLSBNZbXFhvcWG9a060zUBERATq1auHQYMGvfA2Bg8eDEtLS1FPLekL1ltcWG9xYb1rTrTNgEwmw4gRI2Bubv7C2zA3N8eIESNEPbWkL1hvcWG9xYX1rjlRNgPJyclITk6u0ZRSKYlEgkuXLuHy5ctqiIw0gfUWF9ZbXFhv9RBlMxAREYH69evD1dW1xttydXVF/fr1RTu1pA9Yb3FhvcWF9VYP0TYDI0aMgJmZWY23ZW5ujnfffVeUXx59wXqLC+stLqy3eoiuGUhOTsbly5fVMqVUSiKRqKaqSLew3uLCeosL660+omsGZDIZGjRoABcXF7VtU8xTS7qO9RYX1ltcWG/1EVUzoFQqIZPJ4ObmppYppVJmZmZwc3ODTCaDUqlU23apZlhvcWG9xYX1Vi9RNQOXLl1CamqqWqeUSkkkEqSkpIhuakmXsd7iwnqLC+utXqJqBmQyGaysrDBw4EC1b9vFxQUNGjQQ7TWquoj1FhfWW1xYb/USTTNQOqXk7u4OU1NTtW/f1NQU7u7uopta0lWst7iw3uLCequfaJqBCxcu4MqVKxqZUiolkUjwxx9/4OLFixrbB1UN6y0urLe4sN7qJ5pmQCaToWHDhnB2dtbYPpydnWFlZSWqqSVdxXqLC+stLqy3+omiGVAqlYiIiIC7uztq166tsf2UTi1FRESIZmpJF7He4sJ6iwvrrRmiaAaSkpJw9epVeHh4aHxfEokEV65cQVJSksb3Rc/GeosL6y0urLdmiKIZkMlkaNSokUanlEo5OzujYcOGorthhS5hvcWF9RYX1lszDL4ZKF11+t5772l0SqlU7dq18d5774lqFaouYb3FhfUWF9Zbcwy+GZDL5bh+/bpGV52WJ5FIcO3aNSQmJmptn/QY6y0urLe4sN6aY/DNgEwmQ+PGjfH2229rbZ9vv/02GjduLJpVqLqE9RYX1ltcWG/NMehmoHRKaeTIkTAxMdHafsU0taRLWG9xYb3FhfXWLINuBhISEvDnn39qdUqplEQiwY0bN3D+/Hmt71usWG9xYb3FhfXWLINuBmQyGaytrdG/f3+t79vJyQnW1tYGP7WkS1hvcWG9xYX11iyDbQZKb0yh7SmlUiYmJnjvvfdEc8MKobHe4sJ6iwvrrXkG2wycO3cON2/eFGRKqZREIsGff/6JhIQEwWIQC9ZbXFhvcWG9Nc9gmwGZTIYmTZqgX79+gsXQv39/vPTSSwY9taQrWG9xYb3FhfXWPINsBoRadVqeiYkJRo4cafCrUIXGeosL6y0urLd2GGQzEB8fj9u3bws6pVRKIpHg1q1bOHv2rNChGCzWW1xYb3FhvbXDIJsBmUyGpk2b4q233hI6FPTr1w9NmjQx2KklXcB6iwvrLS6st3YYXDOgUCgQERGBUaNGoVatWkKHg1q1amHUqFEGO7UkNNZbXFhvcWG9tcfgmoEzZ84gLS1NJ6aUSkkkEqSlpeHMmTNCh2JwWG9xYb3FhfXWHoNrBmQyGZo1a4Y+ffoIHYpK37590bRpU4OcWhIa6y0urLe4sN7aY1DNgEKhwN69e3VmSqlU6dTS3r17oVAohA7HYLDe4sJ6iwvrrV0G1QzExcXhr7/+0qkppVKGOrUkJNZbXFhvcWG9tcugmgGZTIbmzZvr1JRSqT59+qB58+YGN7UkJNZbXFhvcWG9tctgmoHSVaceHh4wNta9tEqnliIiIgxqakkorLe4sN7iwnprn+59yi/o1KlT+Oeff3RySqmURCLB33//jdOnTwsdit5jvcWF9RYX1lv7DKYZkMlkePnll9G7d2+hQ6nQm2++iRYtWhjU1JJQWG9xYb3FhfXWPoNoBkpKSrB3716dnVIqZWxsDA8PD4OaWhIC6y0urLe4sN7C0N1PuhpOnjyJO3fuwMPDQ+hQKuXh4YE7d+7g5MmTQoeit1hvcWG9xYX1FoZBNAMymQw2Njbo1auX0KFUqnfv3nj55ZcNZmpJCKy3uLDe4sJ6C0PvmwF9mVIqVTq1tHfvXpSUlAgdjt5hvcWF9RYX1ls4uv9pV+LEiRO4d++eTq86LU8ikeDu3bsGMbWkbay3uLDe4sJ6C0fvmwGZTIaWLVuiZ8+eQodSZT179oStra1BTC1pG+stLqy3uLDewtHrZuDJKSUjIyOhw6kyQ5pa0ibWW1xYb3FhvYWl183AsWPHkJ6erldTSqUkEgnu3buH48ePCx2K3mC9xYX1FhfWW1h63QzIZDK0atUK3bt3FzqUauvRowdatmyp91NL2sR6iwvrLS6st7D0thkoLi7GDz/8AIlEoldTSqWMjIwgkUjwww8/oLi4WOhwdB7rLS6st7iw3sLT22bg6NGj+Pfff/XixhQV8fDwQHp6Oo4dOyZ0KDqP9RYX1ltcWG/hGSmVSqXQQVTX6NGjcf/+fVy+fBkpKSmoU6eO0CG9kPz8fNjZ2cHBwQH169fH999/L3RIOon1FhfWW1xYb92glzMDFy5cwOHDh1FQUICPP/5Y6HBe2MyZM1FYWIjDhw/j4sWLQoejs1hvcWG9xYX11g162Qw0aNAAhYWFKCgowJw5c4QO54XNmTMHBQUFKCwsRIMGDYQOR2ex3uLCeosL660b9LYZqFWrFo4ePQpHR0ehw3lhb7zxBo4ePYpatWrp5ZdHW1hvcWG9xYX11g16uWagsLAQubm5aNSokdChqEVmZibq1q0LMzMzoUPRSay3uLDe4sJ66wa9bAaIiIhIffTyNAERERGpj4m6NnT79m1kZGSoa3NaY21tjZYtW1b7ffqab3mV5W8oeVamos/BUPOv6vfeUPIX2zivbr76mmdl+D2vBqUa3Lp1S2lhYaEEoHf/s7CwUN66dUs0+VYnf0PK80U+B0POvyrfe0PKX2zjvDr56nOe6vgcDCn/F/mel1LLzEBGRgby8vKwc+dOtG/fXh2b1IqUlBSMHz8eGRkZ1eqm9DXf8irL31DyrExFn4Oh5l/V772h5C+2cV7dfPU1z8rwe149ajtNAADt27dHly5d1LlJnSaWfMWSZ0WYP/MXQ/5iybMiYs9fqwsIo6KikJ+f/9TP9+zZU6X379+/H/7+/vD19YXyiYsgCgoKMG7cOERGRgIArl27hp49eyI7OxsA8Ouvv+Ldd9+tcfwvQlM5L1++HAEBAfj222+RlpaG1atXY86cOTh58iQuXryINWvWYOrUqbh69aracqkuTeW+evVqLFu2DEFBQQAed/aDBg1CYmIi0tLSIJVKMXfuXPzxxx/qSaSatPE9v379OqRSKSZOnIjw8HAAwJkzZ9CnTx/1JFFDmvoMtm3bBjc3N3WFqRbaqPdff/0FHx8ffPLJJ7hw4QL27duHRYsWYcaMGbh+/bracqkObeR9+/ZtTJ06FStWrEBERASOHj0KqVQKJycn3Lx5U12pVJm2jmlffPEFJkyYAABP1V5T1DozUF5MTAxOnjyJjIwMDBw4EImJiejXrx88PT0xatQopKSkYPXq1Th9+jQ8PT0BAKmpqTh48KBqGy4uLnBwcFBtb8OGDQgPD0dSUhI6deoEAJBKpRg7diyKioqQk5ODn376CUOGDAEAyOVyFBQU4JVXXtFkqlrNOSsrC8bGxpg3bx58fHzw/vvvw8/PD0eOHMH169fh5eWFjh07IiwsDHfu3MGrr75qMLkDgJ+fH7Kzs/HZZ5+hqKgIW7duVT0D3dbWFq+99hqOHTsGU1NTg8r7ye9527Zt4e3tjVmzZkEikeD27dtISkoS7PGv2voMPvroI1y+fFnr+T1JiHrHxcVh8ODBsLGxQUhICIYNG4bMzEwUFBSgSZMmBpt37dq1cf/+fZiYmGDYsGHo1q0b+vbtiz/++AOtW7c2mJyfPKYBgK+vL7y9vQHgqdpv2LBBI7lqdGYgKioKixcvxoABA8r8vE2bNvDy8npmh1VdqampyMrKwoEDBxAdHY1Dhw5BoVAgLi4O0dHROHDgANLS0iCXyyGXy2u8v8poI2cAqsd8lv6/XC7H4cOHMX78eACPv3TZ2dno16+fWvZXFdrKPTMzE8uXL8eSJUtw6tQpFBUVITY2FtHR0QCAoUOHYvny5UhJSVHL/iojxPccAO7evQsrKyuYm5vjl19+QW5uLuRyOY4ePVrj/VWXtmqvC4So99ChQ3Hx4kXExMTA3Nwcf/zxB7766it8+OGHWqu3EHnfunULc+bMgVQqxY8//ggAiIyM1NpMrxDHtPLK115TNDozMHz4cKxatQrp6emqv9QBPPd51XZ2drCzs3vm71xcXLBq1Srk5OTg/fffx+bNmzFz5kysXbsWR44cQXZ2tmoKMT8/H66urqq/GG/evInOnTurL7kKaCvnkpISrF+/Hl26dMH169cxadIkeHl54fTp0zAyMsLy5cvh4eGBixcvomPHjmrP81m0lfvgwYMxYsQIxMTEwNPTE/3790dYWBg6deqEuLg4HD16FGlpaZg2bZrac3wWIb7nABAaGopJkyYBAGbMmAEAuHXrFvr376+mzKpOW59BZGQk5HI59uzZo/pLTNuEqHdJSQkUCgVycnIwbdo0xMXFYeXKlcjJyYGPj4/ac3wWIfJu2LAhNm7ciJiYGNUpsEOHDmHr1q3qTa4CQh3TwsPDIZfLceDAAfTt27dM7TXmha5BKCchIUEJQJmQkFDm53fv3lVu27ZN6ePjo0xPT1fHrtSqorhr8j5dz/lJleVf3c9Hn3J/UkV5VjV/fcu7qnlVp/66/Bmoe5zrcq5KZfXz5fdcHMfzymh0ZqBp06aYMmWKJnehc8SYcymx5i7WvJ8kps9ATLk+SYx5iylnnb4dcekCipqIjo7G2rVrERISUvOANEwd+QYEBCAwMBDLli0DoJurr0upq75OTk6qqfOgoCB8/vnnWLlyJYCyV13oCk3krVQqsWDBAkilUigUCsyfPx/+/v44dOhQjfelbprIf+nSpQgMDNTY4ip10ETeW7ZswYYNGzBs2LAab1tT1JG3v78/vvzyS9VpsY8++ggBAQFYsWJFjbetKerIe968eZBKpYiIiADw+KqFdevWqf6tThqbGQgKCkJRURHatWsHBwcH7Nu3D6mpqfD398e0adPg5OSE+Ph49OrVCydOnEB4eDgGDRqECRMmIDU1VVXkK1euIDg4GFZWVnj77bfx+++/o0GDBnB0dFQtjouPj8epU6dU+x45ciRsbW0BAOHh4ejVqxeMjTXb9+hKvqmpqQgODoaHhweys7M1tvpaV/J1dXUt87sZM2bg0aNHmD179jOvujDUvDdv3oyRI0ciLi4OSUlJcHR0hJeXF3x8fDBo0KAa563r+d+9exeff/45XFxcNHIOXVfznj59Og4dOgQbGxu156xLedeqVQs5OTmqRwMXFhYiJyfH4PNu2rQplEolHj16BAD49ttvNdb4aey/kF27dkVRUZHq7k7GxsYwNTXFxYsX0bhxY0ybNg3m5uaYMmUKWrdujfv376NZs2YYM2YMCgsLVclHRUWhadOmaNGiBa5du4bu3bujsLAQmZmZVYojPT0dH3/8Ma5evYr//vtPU+nqTL6jRo3C5s2bkZubi1q1ahl8vuUVFBTAz88Pfn5+AJ6+6sIQ887MzMTVq1exf/9+HD16FI8ePVJ73rqcP/B4YdbmzZs19thYXc0beLy63t3dXV2plqEreZubm+Pzzz9Ho0aNkJOTgzfffBOfffaZxu6joit5+/r6wsfHB+fOnUNRUREKCwsxffp0xMTEqD1njc0MZGZmok6dOkhNTUXdunVhbm6OkpISlJSUoHbt2gAAU1NTGBkZwdjYGAqFAvfu3cPWrVuhUChU14gPHz4cu3fvRt26ddG7d29cuXIFZmZmSE1NVe2rR48e6NGjxzPjGDt2LAICAvDo0SM0bNhQU+nqTL4KhQIFBQVwd3dHvXr1NLb6WlfyPXfuHOLi4rBlyxb4+vrC09MTDg4OiI6OxpQpU8pcdWHIeQcGBuLmzZuIjIxE9+7dsXfvXqxevRqurq5qyVvX81cqlcjLy1PdqEXddDXvtLQ02NrawsREM4dyXck7KysLGzZswH///Qdzc3OcO3cOeXl5aN68uUHnHR4ejrS0NJiamqJ27doYPHgwAgMDNZO3LqxiLDVnzhx1hFNlmriaoDq0nW956r6aoDJC51uRml5NUBldy1sTVxM8j9D5CzXOhcpbU1cTVJXQ9S7F73n16NQCQqlUKnQIWsV8xUGseZcSa/7MW1z0PW+tNgNHjhxRPT+gJsLCwhAQEADg6ecSPOn48eNYu3YtJk+ejMzMTJw5cwaTJ0+u8f6rSt35KhQK+Pj4YN26dQgPD8eDBw/g4+ODJUuWIDEx8an3lV95HBoaqrEvrCZq++SVEOVXyFf2DAKZTAYnJycAjxcbSaVS+Pv7Y+LEiSgsLMTkyZOf+Zm9KE3k/+SK6aKiIqxYsQIBAQHPXBBaftWx8omrCzSR75M0XfuHDx9i0qRJWLduHTZu3PjU++Lj4+Hm5qbKLzY2Vi0ruatK0+O8vPLPJdBWvpqoc2hoKNasWYMtW7Y883kbT5JKpVixYgVCQ0MBaPf4Bqg///LHpWd58nkjO3bswJdffom5c+dCqVSqPV+1n2hauHAhVqxYgbCwMPTt2xenTp3CvXv3YG9vj0aNGgF4fMmFVCqFVCqFm5sbZDIZTE1NkZ2djeXLlwN4fAB48nLA9u3bl1kV7ezsDKDsfazLe+utt/DWW29h5cqVyM7ORs+ePfHdd9/pbb6ZmZkwNzfHggUL4Orqinr16sHd3R1vvvkmFi1apLrPdanyK4+dnZ1r9GXWdm2fvBLiWSvkn/cMAolEosrdzMwM3t7e2LZtG1xdXWFmZoa+ffvqfP5PrpiOjo5GVlYWLC0tn7lIrvyq4yevLnjRfIXM/cnam5iY4MGDB0hPT3/m7bV79OhR5vJZZ2dn7Nu3r0b5Cpl/+XHu5eVVJpY6deqUeS5B27Zt1Zavtus8cOBABAQEwMrK6qnnbZTXsGFD5Ofno6CgAID6j2/azr/8cam88s8bOX36NLZt24YFCxbgwoULasn3SWqfGRg1ahQiIiKQkpICOzs7FBQUwMbG5pn3z1b+/1ObYmNjYWVlhYKCAtXBrCqedb/28veK3r17N9q0aaOxBxVpM19ra2u0bdsWmzZtUj2cxMjISLVivLi4+JlNkbpoM9dnKb9C/slnEDx69AglJSXPff/Zs2dr9CAfbeafn59fZsV0UVERXn31VUyfPh2bNm16qtZPrjq+d+9emasLCgsLXzhnIXIvLz09HaNHj8a6detw7NgxKBQKteRUHUKP8yePa5p8LoG269yqVSts3LgRWVlZAMo+b6N8nT/44AP4+fmhsLAQN27cqEGWFRPie/7kcSkvL0/18/LPG5kyZQqkUin+/vtv1SJGdVL7zEC3bt2wcuVKjB49Gvn5+UhLS0PHjh1RXFyseo2joyOCgoIQHx8Pd3d3uLi4IDc3F23btlX9lWdpaVnp1JednV2Z+1jfuXMH33zzDRYuXAjg8VRxeHg4Bg8ejFu3bqFVq1bqTler+QKPp8vz8/MxYcIE9OzZE8uWLUN0dDTGjh2LiIgItGnTBr169QLw9Mpjfcv1ySshPDw8sHv3btUK+fLPINi4cSM8PT1V1+cePnwYcrkcwcHBmDJlCo4dO1bjhzZpM38TE5MyK6YHDhyIRYsWISAgAEOGDHmq1k+uOm7atGmZqwvUcbmdkLV3dnbGwYMHcf36dTg4OODMmTO4desWRo8eDeDxtdzR0dFITk5Gq1atNHLVkJDjvPxxzdraWmPPJdBmno8ePcLatWthZGQES0tLAGWft1G+zj///DMuX76Mv//+GzY2Nmo/vmk7fwBljkslJSVYunSp6hRK+eeNnDp1CiUlJejSpQvs7e3V/whnXVjFWF2xsbHK4ODgp36enZ2tzMvLq/B9cXFxSqlUqvq30FcTVFVF+ZZ3586d5/5+9+7dyqioKNW/tX01QVVUNdfyKsv9SQUFBcoVK1Yob9y4oVQqNX81QXWoq9ZPqmq+5enq9/zevXvKkpKSCn//22+/KUNCQlT/1vdxXtlxrab56kqe5VVW5/Kqe3yr7uvUpaL8CwoKlJmZmVXezovmWxGNPptAUwYMGPDUIyUBqO5OVZGePXuiZ8+emgpLYyrKt7xmzZo99/djxoxRV0gaU9Vcy6ss9yeZmZnh008/rfY+tEFdtX6SLuf7pKrmXjp1XpHS89H65kWPa/qWr7rqXJ4+HN+AivM3MzOr1iyeuvNVazOgrefHq0tN49W3fMuravz6nmdlKsvP0PKvbj76nr/YxvmLxqtveVaG3/NqeqH5hHJu3bqltLCwUALQu/9ZWFgob926JZp8q5O/IeX5Ip+DIedfle+9IeUvtnFenXz1OU91fA6GlP+LfM9LGSmV/78ksoZu376NjIwMdWxKq6ytrdGyZctqv09f8y2vsvwNJc/KVPQ5GGr+Vf3eG0r+Yhvn1c1XX/OsDL/nVae2ZoCIiIj0k07djpiIiIi0j80AERGRyLEZICIiEjk2A0RERCLHZoCIiEjk2AwQERGJHJsBIiIikWMzQEREJHJsBoiIiESOzQAREZHIsRkgIiISOTYDREREIsdmgIiISOTYDBAREYkcmwEiIiKRYzNAREQkcmwGiIiIRI7NABERkcixGSAiIhI5NgNEREQix2aAiIhI5NgMEBERiRybASIiIpFjM0BERCRybAaIiIhEjs0AERGRyLEZICIiEjk2A0RERCLHZoCIiEjk2AwQERGJHJsBIiIikWMzQEREJHJsBoiIiESOzQAREZHIsRkgIiISOTYDREREIsdmgIiISOTYDBAREYkcmwEiIiKRYzNAREQkcmwGiIiIRI7NABERkcj9H/7KfrFjAXBlAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tree.plot_tree(dt2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3ce748d1",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}