in-vehicle coupon recommendation Data Set_DT_06.05.2022.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "871a7843",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(12529, 59) (12529,)\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from numpy.random import RandomState\n",
    "import matplotlib.pyplot as plt\n",
    "from tqdm import tqdm\n",
    "\n",
    "seed = 42\n",
    "rng = RandomState(seed)\n",
    "\n",
    "df = pd.read_csv('Model.csv')\n",
    "\n",
    "X, y = df.iloc[:, 1:-1], df.iloc[:, -1]\n",
    "print(X.shape, y.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "7a9d50a4",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>destination_Home</th>\n",
       "      <th>destination_No Urgent Place</th>\n",
       "      <th>destination_Work</th>\n",
       "      <th>passanger_Alone</th>\n",
       "      <th>passanger_Friend(s)</th>\n",
       "      <th>passanger_Kid(s)</th>\n",
       "      <th>passanger_Partner</th>\n",
       "      <th>weather_Rainy</th>\n",
       "      <th>weather_Snowy</th>\n",
       "      <th>...</th>\n",
       "      <th>income</th>\n",
       "      <th>education</th>\n",
       "      <th>age</th>\n",
       "      <th>time</th>\n",
       "      <th>expiration</th>\n",
       "      <th>driving_distance</th>\n",
       "      <th>gender</th>\n",
       "      <th>has_children</th>\n",
       "      <th>direction_opp</th>\n",
       "      <th>Y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 61 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0  destination_Home  destination_No Urgent Place  \\\n",
       "0           0                 0                            1   \n",
       "1           1                 0                            1   \n",
       "2           2                 0                            1   \n",
       "3           3                 0                            1   \n",
       "4           4                 0                            1   \n",
       "\n",
       "   destination_Work  passanger_Alone  passanger_Friend(s)  passanger_Kid(s)  \\\n",
       "0                 0                1                    0                 0   \n",
       "1                 0                0                    1                 0   \n",
       "2                 0                0                    1                 0   \n",
       "3                 0                0                    1                 0   \n",
       "4                 0                0                    1                 0   \n",
       "\n",
       "   passanger_Partner  weather_Rainy  weather_Snowy  ...  income  education  \\\n",
       "0                  0              0              0  ...       3          2   \n",
       "1                  0              0              0  ...       3          2   \n",
       "2                  0              0              0  ...       3          2   \n",
       "3                  0              0              0  ...       3          2   \n",
       "4                  0              0              0  ...       3          2   \n",
       "\n",
       "   age  time  expiration  driving_distance  gender  has_children  \\\n",
       "0    1     1           1                 0       0             1   \n",
       "1    1     0           0                 0       0             1   \n",
       "2    1     0           0                 1       0             1   \n",
       "3    1     1           0                 1       0             1   \n",
       "4    1     1           1                 1       0             1   \n",
       "\n",
       "   direction_opp  Y  \n",
       "0              1  1  \n",
       "1              1  0  \n",
       "2              1  1  \n",
       "3              1  0  \n",
       "4              1  0  \n",
       "\n",
       "[5 rows x 61 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "fa4a3954",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train set:  (10023, 59) (10023,)\n",
      "Test set:  (2506, 59) (2506,)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split, cross_val_score\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y,\n",
    "                                                    test_size=0.2, \n",
    "                                                    random_state=seed)\n",
    "print('Train set: ', X_train.shape, y_train.shape)\n",
    "print('Test set: ', X_test.shape, y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "ac434dba",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Depth:  34\n"
     ]
    }
   ],
   "source": [
    "# Grow a full tree to get maximum depth \n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "\n",
    "tree = DecisionTreeClassifier(criterion='entropy', max_depth=None,\n",
    "                              random_state=seed)\n",
    "tree.fit(X_train, y_train)\n",
    "print('Depth: ', tree.get_depth())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "0cba850e",
   "metadata": {},
   "outputs": [],
   "source": [
    "def cross_validation_score(estimator):\n",
    "    scores = cross_val_score(\n",
    "        estimator, X_train, y_train, cv=10, n_jobs=-1,\n",
    "        scoring='roc_auc'\n",
    "    )\n",
    "    \n",
    "    return scores.mean(), scores.std()\n",
    "\n",
    "\n",
    "def plot_cross_validation(title, X, Y, error, xlabel, ylabel='ROC AUC'):\n",
    "    plt.title(title)\n",
    "    plt.grid()\n",
    "    plt.xlabel(xlabel)\n",
    "    plt.ylabel(ylabel)\n",
    "    \n",
    "    plt.errorbar(X, Y, error, linestyle='None', marker='o')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "72368c62",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████| 11/11 [00:19<00:00,  1.74s/it]\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "title = 'Change max_depth, criterion=entropy'\n",
    "xlabel = 'Max depth'\n",
    "max_depths = [3, 5, 7, 10, 15, 20, 25, 30, 35, 40, 45]\n",
    "means = []\n",
    "stddevs = []\n",
    "\n",
    "for n in tqdm(max_depths):\n",
    "    clf = DecisionTreeClassifier(criterion='entropy', max_depth=n, \n",
    "                                 random_state=rng)\n",
    "    mean, std = cross_validation_score(clf)\n",
    "    means.append(mean)\n",
    "    stddevs.append(std)\n",
    "\n",
    "plot_cross_validation(title, max_depths, means, stddevs, xlabel)\n",
    "plt.savefig('DT_entropy_change_depth.png', bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "dab1f094",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████| 11/11 [00:07<00:00,  1.44it/s]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "title = 'Change max_depth, criterion=gini'\n",
    "xlabel = 'Max depth'\n",
    "max_depths = [3, 5, 7, 10, 15, 20, 25, 30, 35, 40, 45]\n",
    "means = []\n",
    "stddevs = []\n",
    "\n",
    "for n in tqdm(max_depths):\n",
    "    clf = DecisionTreeClassifier(criterion='gini', max_depth=n, \n",
    "                                 random_state=rng)\n",
    "    mean, std = cross_validation_score(clf)\n",
    "    means.append(mean)\n",
    "    stddevs.append(std)\n",
    "\n",
    "plot_cross_validation(title, max_depths, means, stddevs, xlabel)\n",
    "plt.savefig('DT_gini_change_depth.png', bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "e775b347",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy on test set:  0.6887470071827614\n",
      "ROC AUC on test set:  0.6664097685976084\n"
     ]
    }
   ],
   "source": [
    "# Best params\n",
    "from sklearn.metrics import roc_auc_score, accuracy_score\n",
    "\n",
    "tree = DecisionTreeClassifier(criterion='entropy', max_depth=5, \n",
    "                              random_state=rng)\n",
    "tree.fit(X_train, y_train)\n",
    "y_pred = tree.predict(X_test)\n",
    "\n",
    "print('Accuracy on test set: ', accuracy_score(y_test, y_pred))\n",
    "print('ROC AUC on test set: ', roc_auc_score(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "712c8f4d",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Preetam\\anaconda3\\lib\\site-packages\\sklearn\\utils\\deprecation.py:87: FutureWarning: Function plot_confusion_matrix is deprecated; Function `plot_confusion_matrix` is deprecated in 1.0 and will be removed in 1.2. Use one of the class methods: ConfusionMatrixDisplay.from_predictions or ConfusionMatrixDisplay.from_estimator.\n",
      "  warnings.warn(msg, category=FutureWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import plot_confusion_matrix\n",
    "\n",
    "plot_confusion_matrix(tree, X_test, y_test)\n",
    "plt.savefig('DT_confmat.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "982fd614",
   "metadata": {},
   "outputs": [],
   "source": [
    "#feature_importances = tree.feature_importances_\n",
    "\n",
    "#k = 10\n",
    "#topk = feature_importances.argsort()\n",
    "\n",
    "#for i in topk:\n",
    "    #print('%-30s: %.6f' % (X.columns[i], feature_importances[i]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "36d474fd",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "coupon_Restaurant(<20)        : 0.199249\n",
      "coupon_Carry out & Take away  : 0.148621\n",
      "CoffeeHouse                   : 0.143513\n",
      "driving_distance              : 0.101344\n",
      "expiration                    : 0.096575\n",
      "coupon_Coffee House           : 0.088707\n",
      "Bar                           : 0.078694\n",
      "time                          : 0.023662\n",
      "destination_No Urgent Place   : 0.018722\n",
      "income                        : 0.017290\n"
     ]
    }
   ],
   "source": [
    "feature_importances = tree.feature_importances_\n",
    "\n",
    "k = 10\n",
    "topk = feature_importances.argsort()[-k:][::-1]  #taking last 10 rows and among them sorted by descending order\n",
    "\n",
    "for i in topk:\n",
    "    print('%-30s: %.6f' % (X.columns[i], feature_importances[i]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "132dd62b",
   "metadata": {},
   "outputs": [],
   "source": [
    "#feature_importances = tree.feature_importances_\n",
    "\n",
    "#k = 10\n",
    "#topk = feature_importances.argsort()[-k:]\n",
    "\n",
    "#for i in topk:\n",
    "    #print('%-30s: %.6f' % (X.columns[i], feature_importances[i]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "6d629581",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import plot_confusion_matrix\n",
    "\n",
    "plot_confusion_matrix(tree, X_test, y_test)\n",
    "plt.savefig('DT_confmat.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "cf52ae7d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "|--- coupon_Carry out & Take away <= 0.50\n",
      "|   |--- coupon_Restaurant(<20) <= 0.50\n",
      "|   |   |--- CoffeeHouse <= 0.50\n",
      "|   |   |   |--- coupon_Coffee House <= 0.50\n",
      "|   |   |   |   |--- Bar <= 0.50\n",
      "|   |   |   |   |   |--- class: 0\n",
      "|   |   |   |   |--- Bar >  0.50\n",
      "|   |   |   |   |   |--- class: 1\n",
      "|   |   |   |--- coupon_Coffee House >  0.50\n",
      "|   |   |   |   |--- income <= 4.50\n",
      "|   |   |   |   |   |--- class: 0\n",
      "|   |   |   |   |--- income >  4.50\n",
      "|   |   |   |   |   |--- class: 0\n",
      "|   |   |--- CoffeeHouse >  0.50\n",
      "|   |   |   |--- coupon_Coffee House <= 0.50\n",
      "|   |   |   |   |--- Bar <= 1.50\n",
      "|   |   |   |   |   |--- class: 0\n",
      "|   |   |   |   |--- Bar >  1.50\n",
      "|   |   |   |   |   |--- class: 1\n",
      "|   |   |   |--- coupon_Coffee House >  0.50\n",
      "|   |   |   |   |--- expiration <= 0.50\n",
      "|   |   |   |   |   |--- class: 1\n",
      "|   |   |   |   |--- expiration >  0.50\n",
      "|   |   |   |   |   |--- class: 1\n",
      "|   |--- coupon_Restaurant(<20) >  0.50\n",
      "|   |   |--- driving_distance <= 1.50\n",
      "|   |   |   |--- expiration <= 0.50\n",
      "|   |   |   |   |--- direction_opp <= 0.50\n",
      "|   |   |   |   |   |--- class: 1\n",
      "|   |   |   |   |--- direction_opp >  0.50\n",
      "|   |   |   |   |   |--- class: 1\n",
      "|   |   |   |--- expiration >  0.50\n",
      "|   |   |   |   |--- destination_No Urgent Place <= 0.50\n",
      "|   |   |   |   |   |--- class: 1\n",
      "|   |   |   |   |--- destination_No Urgent Place >  0.50\n",
      "|   |   |   |   |   |--- class: 1\n",
      "|   |   |--- driving_distance >  1.50\n",
      "|   |   |   |--- passanger_Alone <= 0.50\n",
      "|   |   |   |   |--- age <= 3.50\n",
      "|   |   |   |   |   |--- class: 1\n",
      "|   |   |   |   |--- age >  3.50\n",
      "|   |   |   |   |   |--- class: 1\n",
      "|   |   |   |--- passanger_Alone >  0.50\n",
      "|   |   |   |   |--- CoffeeHouse <= 3.50\n",
      "|   |   |   |   |   |--- class: 0\n",
      "|   |   |   |   |--- CoffeeHouse >  3.50\n",
      "|   |   |   |   |   |--- class: 0\n",
      "|--- coupon_Carry out & Take away >  0.50\n",
      "|   |--- time <= 0.50\n",
      "|   |   |--- CoffeeHouse <= 3.50\n",
      "|   |   |   |--- weather_Rainy <= 0.50\n",
      "|   |   |   |   |--- education <= 3.50\n",
      "|   |   |   |   |   |--- class: 1\n",
      "|   |   |   |   |--- education >  3.50\n",
      "|   |   |   |   |   |--- class: 1\n",
      "|   |   |   |--- weather_Rainy >  0.50\n",
      "|   |   |   |   |--- driving_distance <= 0.50\n",
      "|   |   |   |   |   |--- class: 1\n",
      "|   |   |   |   |--- driving_distance >  0.50\n",
      "|   |   |   |   |   |--- class: 0\n",
      "|   |   |--- CoffeeHouse >  3.50\n",
      "|   |   |   |--- occupation_Healthcare Practitioners & Technical <= 0.50\n",
      "|   |   |   |   |--- expiration <= 0.50\n",
      "|   |   |   |   |   |--- class: 0\n",
      "|   |   |   |   |--- expiration >  0.50\n",
      "|   |   |   |   |   |--- class: 1\n",
      "|   |   |   |--- occupation_Healthcare Practitioners & Technical >  0.50\n",
      "|   |   |   |   |--- class: 1\n",
      "|   |--- time >  0.50\n",
      "|   |   |--- passanger_Kid(s) <= 0.50\n",
      "|   |   |   |--- education <= 3.50\n",
      "|   |   |   |   |--- CarryAway <= 3.50\n",
      "|   |   |   |   |   |--- class: 1\n",
      "|   |   |   |   |--- CarryAway >  3.50\n",
      "|   |   |   |   |   |--- class: 1\n",
      "|   |   |   |--- education >  3.50\n",
      "|   |   |   |   |--- occupation_Personal Care & Service <= 0.50\n",
      "|   |   |   |   |   |--- class: 1\n",
      "|   |   |   |   |--- occupation_Personal Care & Service >  0.50\n",
      "|   |   |   |   |   |--- class: 0\n",
      "|   |   |--- passanger_Kid(s) >  0.50\n",
      "|   |   |   |--- destination_No Urgent Place <= 0.50\n",
      "|   |   |   |   |--- Restaurant20To50 <= 0.50\n",
      "|   |   |   |   |   |--- class: 1\n",
      "|   |   |   |   |--- Restaurant20To50 >  0.50\n",
      "|   |   |   |   |   |--- class: 0\n",
      "|   |   |   |--- destination_No Urgent Place >  0.50\n",
      "|   |   |   |   |--- income <= 5.50\n",
      "|   |   |   |   |   |--- class: 1\n",
      "|   |   |   |   |--- income >  5.50\n",
      "|   |   |   |   |   |--- class: 1\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.tree import export_text\n",
    "\n",
    "text = export_text(tree, feature_names=list(X.columns))\n",
    "print(text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "e7689256",
   "metadata": {},
   "outputs": [
    {
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       "<text text-anchor=\"middle\" x=\"862.5\" y=\"-52.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">entropy = 1.0</text>\r\n",
       "<text text-anchor=\"middle\" x=\"862.5\" y=\"-37.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 1322</text>\r\n",
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       "<text text-anchor=\"middle\" x=\"1501.5\" y=\"-290.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">expiration &lt;= 0.5</text>\r\n",
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       "<text text-anchor=\"middle\" x=\"1501.5\" y=\"-156.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">entropy = 0.656</text>\r\n",
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       "<title>22&#45;&gt;24</title>\r\n",
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       "<text text-anchor=\"middle\" x=\"1685.5\" y=\"-171.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">age &lt;= 3.5</text>\r\n",
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       "<text text-anchor=\"middle\" x=\"1685.5\" y=\"-141.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 23</text>\r\n",
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       "<title>25&#45;&gt;26</title>\r\n",
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       "<text text-anchor=\"middle\" x=\"1902.5\" y=\"-171.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">CoffeeHouse &lt;= 3.5</text>\r\n",
       "<text text-anchor=\"middle\" x=\"1902.5\" y=\"-156.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">entropy = 0.86</text>\r\n",
       "<text text-anchor=\"middle\" x=\"1902.5\" y=\"-141.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 233</text>\r\n",
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       "<title>25&#45;&gt;29</title>\r\n",
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       "<title>27</title>\r\n",
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       "<text text-anchor=\"middle\" x=\"1655.5\" y=\"-52.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">entropy = 0.0</text>\r\n",
       "<text text-anchor=\"middle\" x=\"1655.5\" y=\"-37.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 14</text>\r\n",
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       "<text text-anchor=\"middle\" x=\"1655.5\" y=\"-7.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">class = Yes</text>\r\n",
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       "<!-- 26&#45;&gt;27 -->\r\n",
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       "<title>26&#45;&gt;27</title>\r\n",
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       "<text text-anchor=\"middle\" x=\"1775.5\" y=\"-52.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">entropy = 0.918</text>\r\n",
       "<text text-anchor=\"middle\" x=\"1775.5\" y=\"-37.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 9</text>\r\n",
       "<text text-anchor=\"middle\" x=\"1775.5\" y=\"-22.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [3, 6]</text>\r\n",
       "<text text-anchor=\"middle\" x=\"1775.5\" y=\"-7.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">class = Yes</text>\r\n",
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       "<!-- 26&#45;&gt;28 -->\r\n",
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       "<title>26&#45;&gt;28</title>\r\n",
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       "<title>30</title>\r\n",
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       "<text text-anchor=\"middle\" x=\"1902.5\" y=\"-52.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">entropy = 0.89</text>\r\n",
       "<text text-anchor=\"middle\" x=\"1902.5\" y=\"-37.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 215</text>\r\n",
       "<text text-anchor=\"middle\" x=\"1902.5\" y=\"-22.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [149, 66]</text>\r\n",
       "<text text-anchor=\"middle\" x=\"1902.5\" y=\"-7.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">class = No</text>\r\n",
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       "<!-- 29&#45;&gt;30 -->\r\n",
       "<g id=\"edge30\" class=\"edge\">\r\n",
       "<title>29&#45;&gt;30</title>\r\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M1902.5,-103.73C1902.5,-95.52 1902.5,-86.86 1902.5,-78.56\"/>\r\n",
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       "<polygon fill=\"#e58139\" stroke=\"black\" points=\"2074.5,-68 1976.5,-68 1976.5,0 2074.5,0 2074.5,-68\"/>\r\n",
       "<text text-anchor=\"middle\" x=\"2025.5\" y=\"-52.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">entropy = 0.0</text>\r\n",
       "<text text-anchor=\"middle\" x=\"2025.5\" y=\"-37.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 18</text>\r\n",
       "<text text-anchor=\"middle\" x=\"2025.5\" y=\"-22.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [18, 0]</text>\r\n",
       "<text text-anchor=\"middle\" x=\"2025.5\" y=\"-7.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">class = No</text>\r\n",
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       "<!-- 29&#45;&gt;31 -->\r\n",
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       "<title>29&#45;&gt;31</title>\r\n",
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       "<title>33</title>\r\n",
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       "<text text-anchor=\"middle\" x=\"2543.5\" y=\"-409.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">CoffeeHouse &lt;= 3.5</text>\r\n",
       "<text text-anchor=\"middle\" x=\"2543.5\" y=\"-394.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">entropy = 0.91</text>\r\n",
       "<text text-anchor=\"middle\" x=\"2543.5\" y=\"-379.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 974</text>\r\n",
       "<text text-anchor=\"middle\" x=\"2543.5\" y=\"-364.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [317, 657]</text>\r\n",
       "<text text-anchor=\"middle\" x=\"2543.5\" y=\"-349.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">class = Yes</text>\r\n",
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       "<!-- 32&#45;&gt;33 -->\r\n",
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       "<title>32&#45;&gt;33</title>\r\n",
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       "<title>46</title>\r\n",
       "<polygon fill=\"#67b4eb\" stroke=\"black\" points=\"3158.5,-425 3002.5,-425 3002.5,-342 3158.5,-342 3158.5,-425\"/>\r\n",
       "<text text-anchor=\"middle\" x=\"3080.5\" y=\"-409.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">passanger_Kid(s) &lt;= 0.5</text>\r\n",
       "<text text-anchor=\"middle\" x=\"3080.5\" y=\"-394.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">entropy = 0.702</text>\r\n",
       "<text text-anchor=\"middle\" x=\"3080.5\" y=\"-379.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 889</text>\r\n",
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       "<text text-anchor=\"middle\" x=\"3080.5\" y=\"-349.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">class = Yes</text>\r\n",
       "</g>\r\n",
       "<!-- 32&#45;&gt;46 -->\r\n",
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       "<title>32&#45;&gt;46</title>\r\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M2606.15,-487.85C2701.53,-467.07 2883.52,-427.42 2992.24,-403.73\"/>\r\n",
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       "<title>34</title>\r\n",
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       "<text text-anchor=\"middle\" x=\"2416.5\" y=\"-290.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">weather_Rainy &lt;= 0.5</text>\r\n",
       "<text text-anchor=\"middle\" x=\"2416.5\" y=\"-275.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">entropy = 0.889</text>\r\n",
       "<text text-anchor=\"middle\" x=\"2416.5\" y=\"-260.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 885</text>\r\n",
       "<text text-anchor=\"middle\" x=\"2416.5\" y=\"-245.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [271, 614]</text>\r\n",
       "<text text-anchor=\"middle\" x=\"2416.5\" y=\"-230.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">class = Yes</text>\r\n",
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       "<!-- 33&#45;&gt;34 -->\r\n",
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       "<title>33&#45;&gt;34</title>\r\n",
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       "<polygon fill=\"#fdf7f2\" stroke=\"black\" points=\"2836.5,-306 2504.5,-306 2504.5,-223 2836.5,-223 2836.5,-306\"/>\r\n",
       "<text text-anchor=\"middle\" x=\"2670.5\" y=\"-290.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">occupation_Healthcare Practitioners &amp; Technical &lt;= 0.5</text>\r\n",
       "<text text-anchor=\"middle\" x=\"2670.5\" y=\"-275.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">entropy = 0.999</text>\r\n",
       "<text text-anchor=\"middle\" x=\"2670.5\" y=\"-260.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 89</text>\r\n",
       "<text text-anchor=\"middle\" x=\"2670.5\" y=\"-245.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [46, 43]</text>\r\n",
       "<text text-anchor=\"middle\" x=\"2670.5\" y=\"-230.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">class = No</text>\r\n",
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       "<!-- 33&#45;&gt;41 -->\r\n",
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       "<title>33&#45;&gt;41</title>\r\n",
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       "<text text-anchor=\"middle\" x=\"2278.5\" y=\"-171.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">education &lt;= 3.5</text>\r\n",
       "<text text-anchor=\"middle\" x=\"2278.5\" y=\"-156.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">entropy = 0.851</text>\r\n",
       "<text text-anchor=\"middle\" x=\"2278.5\" y=\"-141.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 738</text>\r\n",
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       "<title>34&#45;&gt;35</title>\r\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M2368.62,-222.91C2357.49,-213.47 2345.55,-203.34 2334.11,-193.65\"/>\r\n",
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       "<text text-anchor=\"middle\" x=\"2430.5\" y=\"-171.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">driving_distance &lt;= 0.5</text>\r\n",
       "<text text-anchor=\"middle\" x=\"2430.5\" y=\"-156.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">entropy = 0.994</text>\r\n",
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       "<title>34&#45;&gt;38</title>\r\n",
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       "<text text-anchor=\"middle\" x=\"2148.5\" y=\"-52.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">entropy = 0.769</text>\r\n",
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       "<text text-anchor=\"middle\" x=\"2148.5\" y=\"-7.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">class = Yes</text>\r\n",
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       "<title>37</title>\r\n",
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       "<text text-anchor=\"middle\" x=\"2281.5\" y=\"-37.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 356</text>\r\n",
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       "<text text-anchor=\"middle\" x=\"2411.5\" y=\"-52.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">entropy = 0.863</text>\r\n",
       "<text text-anchor=\"middle\" x=\"2411.5\" y=\"-37.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 84</text>\r\n",
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       "<!-- 38&#45;&gt;39 -->\r\n",
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       "<title>38&#45;&gt;39</title>\r\n",
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       "<text text-anchor=\"middle\" x=\"2535.5\" y=\"-37.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 63</text>\r\n",
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       "<text text-anchor=\"middle\" x=\"2535.5\" y=\"-7.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">class = No</text>\r\n",
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       "<text text-anchor=\"middle\" x=\"2666.5\" y=\"-171.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">expiration &lt;= 0.5</text>\r\n",
       "<text text-anchor=\"middle\" x=\"2666.5\" y=\"-156.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">entropy = 0.995</text>\r\n",
       "<text text-anchor=\"middle\" x=\"2666.5\" y=\"-141.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 85</text>\r\n",
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       "<text text-anchor=\"middle\" x=\"2787.5\" y=\"-164.3\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">entropy = 0.0</text>\r\n",
       "<text text-anchor=\"middle\" x=\"2787.5\" y=\"-149.3\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 4</text>\r\n",
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       "<text text-anchor=\"middle\" x=\"2659.5\" y=\"-52.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">entropy = 0.926</text>\r\n",
       "<text text-anchor=\"middle\" x=\"2659.5\" y=\"-37.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 44</text>\r\n",
       "<text text-anchor=\"middle\" x=\"2659.5\" y=\"-22.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [29, 15]</text>\r\n",
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       "<!-- 42&#45;&gt;43 -->\r\n",
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       "<text text-anchor=\"middle\" x=\"2783.5\" y=\"-52.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">entropy = 0.979</text>\r\n",
       "<text text-anchor=\"middle\" x=\"2783.5\" y=\"-37.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 41</text>\r\n",
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       "<text text-anchor=\"middle\" x=\"2783.5\" y=\"-7.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">class = Yes</text>\r\n",
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       "<!-- 42&#45;&gt;44 -->\r\n",
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       "<title>42&#45;&gt;44</title>\r\n",
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       "<text text-anchor=\"middle\" x=\"3080.5\" y=\"-290.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">education &lt;= 3.5</text>\r\n",
       "<text text-anchor=\"middle\" x=\"3080.5\" y=\"-275.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">entropy = 0.672</text>\r\n",
       "<text text-anchor=\"middle\" x=\"3080.5\" y=\"-260.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 839</text>\r\n",
       "<text text-anchor=\"middle\" x=\"3080.5\" y=\"-245.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [148, 691]</text>\r\n",
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       "<!-- 46&#45;&gt;47 -->\r\n",
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       "<title>46&#45;&gt;47</title>\r\n",
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       "<text text-anchor=\"middle\" x=\"3461.5\" y=\"-290.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">destination_No Urgent Place &lt;= 0.5</text>\r\n",
       "<text text-anchor=\"middle\" x=\"3461.5\" y=\"-275.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">entropy = 0.981</text>\r\n",
       "<text text-anchor=\"middle\" x=\"3461.5\" y=\"-260.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 50</text>\r\n",
       "<text text-anchor=\"middle\" x=\"3461.5\" y=\"-245.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [21, 29]</text>\r\n",
       "<text text-anchor=\"middle\" x=\"3461.5\" y=\"-230.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">class = Yes</text>\r\n",
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       "<!-- 46&#45;&gt;54 -->\r\n",
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       "<title>46&#45;&gt;54</title>\r\n",
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       "<text text-anchor=\"middle\" x=\"2974.5\" y=\"-171.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">CarryAway &lt;= 3.5</text>\r\n",
       "<text text-anchor=\"middle\" x=\"2974.5\" y=\"-156.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">entropy = 0.563</text>\r\n",
       "<text text-anchor=\"middle\" x=\"2974.5\" y=\"-141.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 432</text>\r\n",
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       "<!-- 47&#45;&gt;48 -->\r\n",
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       "<title>47&#45;&gt;48</title>\r\n",
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       "<text text-anchor=\"middle\" x=\"3186.5\" y=\"-171.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">occupation_Personal Care &amp; Service &lt;= 0.5</text>\r\n",
       "<text text-anchor=\"middle\" x=\"3186.5\" y=\"-156.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">entropy = 0.767</text>\r\n",
       "<text text-anchor=\"middle\" x=\"3186.5\" y=\"-141.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 407</text>\r\n",
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       "<!-- 47&#45;&gt;51 -->\r\n",
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       "<title>47&#45;&gt;51</title>\r\n",
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       "<text text-anchor=\"middle\" x=\"2910.5\" y=\"-52.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">entropy = 0.492</text>\r\n",
       "<text text-anchor=\"middle\" x=\"2910.5\" y=\"-37.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 363</text>\r\n",
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       "<!-- 48&#45;&gt;49 -->\r\n",
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       "<title>48&#45;&gt;49</title>\r\n",
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       "<text text-anchor=\"middle\" x=\"3037.5\" y=\"-52.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">entropy = 0.828</text>\r\n",
       "<text text-anchor=\"middle\" x=\"3037.5\" y=\"-37.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 69</text>\r\n",
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       "<!-- 48&#45;&gt;50 -->\r\n",
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       "<title>48&#45;&gt;50</title>\r\n",
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       "<text text-anchor=\"middle\" x=\"3164.5\" y=\"-37.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 402</text>\r\n",
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       "<text text-anchor=\"middle\" x=\"3284.5\" y=\"-37.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 5</text>\r\n",
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       "<text text-anchor=\"middle\" x=\"3461.5\" y=\"-171.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">Restaurant20To50 &lt;= 0.5</text>\r\n",
       "<text text-anchor=\"middle\" x=\"3461.5\" y=\"-156.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">entropy = 0.881</text>\r\n",
       "<text text-anchor=\"middle\" x=\"3461.5\" y=\"-141.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 20</text>\r\n",
       "<text text-anchor=\"middle\" x=\"3461.5\" y=\"-126.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [14, 6]</text>\r\n",
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       "<!-- 54&#45;&gt;55 -->\r\n",
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       "<title>54&#45;&gt;55</title>\r\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M3461.5,-222.91C3461.5,-214.65 3461.5,-205.86 3461.5,-197.3\"/>\r\n",
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       "<text text-anchor=\"middle\" x=\"3635.5\" y=\"-171.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">income &lt;= 5.5</text>\r\n",
       "<text text-anchor=\"middle\" x=\"3635.5\" y=\"-156.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">entropy = 0.784</text>\r\n",
       "<text text-anchor=\"middle\" x=\"3635.5\" y=\"-141.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 30</text>\r\n",
       "<text text-anchor=\"middle\" x=\"3635.5\" y=\"-126.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [7, 23]</text>\r\n",
       "<text text-anchor=\"middle\" x=\"3635.5\" y=\"-111.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">class = Yes</text>\r\n",
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       "<!-- 54&#45;&gt;58 -->\r\n",
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       "<title>54&#45;&gt;58</title>\r\n",
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       "<text text-anchor=\"middle\" x=\"3394.5\" y=\"-52.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">entropy = 0.0</text>\r\n",
       "<text text-anchor=\"middle\" x=\"3394.5\" y=\"-37.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 2</text>\r\n",
       "<text text-anchor=\"middle\" x=\"3394.5\" y=\"-22.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [0, 2]</text>\r\n",
       "<text text-anchor=\"middle\" x=\"3394.5\" y=\"-7.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">class = Yes</text>\r\n",
       "</g>\r\n",
       "<!-- 55&#45;&gt;56 -->\r\n",
       "<g id=\"edge56\" class=\"edge\">\r\n",
       "<title>55&#45;&gt;56</title>\r\n",
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       "<text text-anchor=\"middle\" x=\"3511.5\" y=\"-37.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 18</text>\r\n",
       "<text text-anchor=\"middle\" x=\"3511.5\" y=\"-22.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [14, 4]</text>\r\n",
       "<text text-anchor=\"middle\" x=\"3511.5\" y=\"-7.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">class = No</text>\r\n",
       "</g>\r\n",
       "<!-- 55&#45;&gt;57 -->\r\n",
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       "<title>55&#45;&gt;57</title>\r\n",
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       "<text text-anchor=\"middle\" x=\"3635.5\" y=\"-52.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">entropy = 0.934</text>\r\n",
       "<text text-anchor=\"middle\" x=\"3635.5\" y=\"-37.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 20</text>\r\n",
       "<text text-anchor=\"middle\" x=\"3635.5\" y=\"-22.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [7, 13]</text>\r\n",
       "<text text-anchor=\"middle\" x=\"3635.5\" y=\"-7.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">class = Yes</text>\r\n",
       "</g>\r\n",
       "<!-- 58&#45;&gt;59 -->\r\n",
       "<g id=\"edge59\" class=\"edge\">\r\n",
       "<title>58&#45;&gt;59</title>\r\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M3635.5,-103.73C3635.5,-95.52 3635.5,-86.86 3635.5,-78.56\"/>\r\n",
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       "<polygon fill=\"#399de5\" stroke=\"black\" points=\"3804.5,-68 3706.5,-68 3706.5,0 3804.5,0 3804.5,-68\"/>\r\n",
       "<text text-anchor=\"middle\" x=\"3755.5\" y=\"-52.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">entropy = 0.0</text>\r\n",
       "<text text-anchor=\"middle\" x=\"3755.5\" y=\"-37.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 10</text>\r\n",
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       "<!-- 58&#45;&gt;60 -->\r\n",
       "<g id=\"edge60\" class=\"edge\">\r\n",
       "<title>58&#45;&gt;60</title>\r\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M3680.18,-103.73C3690.38,-94.42 3701.21,-84.54 3711.38,-75.26\"/>\r\n",
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       "</svg>\r\n"
      ],
      "text/plain": [
       "<graphviz.files.Source at 0x1dd087f1bb0>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.tree import plot_tree, export_graphviz\n",
    "import graphviz\n",
    "\n",
    "dot_data = export_graphviz(tree, out_file=None, \n",
    "                           feature_names=list(X.columns),\n",
    "                           class_names=['No', 'Yes'],\n",
    "                           filled=True)\n",
    "\n",
    "graph = graphviz.Source(dot_data, format='png')\n",
    "graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5e22959a",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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