in-vehicle coupon recommendation Data Set_DT_RF_SVM_06.05.2022.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "68a10b3e",
   "metadata": {},
   "outputs": [],
   "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)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "511bdb3d",
   "metadata": {},
   "outputs": [
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      "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": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"Model.csv\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "33a776ba",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(12529, 59) (12529,)\n"
     ]
    }
   ],
   "source": [
    "X, y = df.iloc[:, 1:-1], df.iloc[:, -1]\n",
    "print(X.shape, y.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "4099c29b",
   "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": 5,
   "id": "cffc2105",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(C=15.0)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Train decision tree, random forest & SVM with their best params\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.svm import SVC\n",
    "\n",
    "dt = DecisionTreeClassifier(criterion='entropy', max_depth=5, \n",
    "                            random_state=rng)\n",
    "rf = RandomForestClassifier(criterion='entropy', n_estimators=100, \n",
    "                            random_state=rng)\n",
    "sv = SVC(kernel='rbf', C=15.0)\n",
    "\n",
    "dt.fit(X_train, y_train)\n",
    "rf.fit(X_train, y_train)\n",
    "sv.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "59021085",
   "metadata": {},
   "outputs": [],
   "source": [
    "dt_pred = dt.predict(X_test)\n",
    "rf_pred = rf.predict(X_test)\n",
    "sv_pred = sv.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "7755a993",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Decision tree acc: 0.688747\n",
      "Random forest acc: 0.754589\n",
      "SVM acc: 0.752993\n",
      "\n",
      "Decision tree AUC: 0.666410\n",
      "Random forest AUC: 0.745442\n",
      "SVM AUC: 0.744329\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score, roc_auc_score, plot_confusion_matrix\n",
    "\n",
    "dt_acc = accuracy_score(y_test, dt_pred)\n",
    "rf_acc = accuracy_score(y_test, rf_pred)\n",
    "sv_acc = accuracy_score(y_test, sv_pred)\n",
    "\n",
    "dt_auc = roc_auc_score(y_test, dt_pred)\n",
    "rf_auc = roc_auc_score(y_test, rf_pred)\n",
    "sv_auc = roc_auc_score(y_test, sv_pred)\n",
    "\n",
    "print('Decision tree acc: %.6f' % dt_acc)\n",
    "print('Random forest acc: %.6f' % rf_acc)\n",
    "print('SVM acc: %.6f' % sv_acc)\n",
    "print()\n",
    "print('Decision tree AUC: %.6f' % dt_auc)\n",
    "print('Random forest AUC: %.6f' % rf_auc)\n",
    "print('SVM AUC: %.6f' % sv_auc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "8183e9da",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x1a4d2fb4b80>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "plot_confusion_matrix(dt, X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "7568e24d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x1a4cd1ebc40>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAT8AAAEGCAYAAAAT05LOAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8QVMy6AAAACXBIWXMAAAsTAAALEwEAmpwYAAAcSElEQVR4nO3deZRcZZ3/8fcn3Ul3VrJDIAQSCSCggGBEPDKIKIu7I79BceT4Y0RkBBQVYUZlxJ8jLrihICEgOAqKEhgVZBFExMOSEAKBBEwgkISQhHQWkpC16/v7495OKqGXut1VXdV1P69z7qHqqVv3PpUcvnme+yxfRQRmZnnTr9oVMDOrBgc/M8slBz8zyyUHPzPLJQc/M8ulxmpXoFjDkMHROGpEtathGTSvaK12FSyDjVvXsmXbq+rJNU54x+BoWVXa3/ujT2y+MyJO7Mn9KqWmgl/jqBHs8Z/nVbsalsHrf7Cq2lWwDB58/roeX6NlVSuP3DmhpHMbxs0f3eMbVkhNBT8zq30BFChUuxo95uBnZpkEwdbo+487HPzMLDO3/Mwsd4KgtQ6WxTr4mVlmBRz8zCxnAmh18DOzPHLLz8xyJ4CtdfDMz8vbzCyTIGgt8eiKpGslrZD0ZFHZKZKeklSQdOQu518kaYGkZySdUFR+hKQ56Wc/ltTlKhYHPzPLJqC1xKME1wG7Ln97EvgwcH9xoaSDgFOBg9PvXCGpIf34SuBMYHJ6dLmkzsHPzDJJVniUdnR5rYj7gVW7lM2LiGfaOf0DwK8jYnNELAQWAFMkjQOGRcSDkWxN/wvgg13d28/8zCwj0UrJeyOMljSz6P3UiJjazRvvBTxU9H5JWrY1fb1reacc/Mwsk2TAo+TgtzIijuz6tJK0d9PopLxTDn5mlkkyz69Hu2J11xJg76L344Glafn4dso75Wd+ZpZZIVTSUWa/B06V1CRpIsnAxiMR8RKwTtJR6SjvJ4D/7epibvmZWSblbPlJuhE4luTZ4BLgYpIBkMuBMcBtkmZHxAkR8ZSkm4C5wDbg3yO2by/zGZKR44HAn9KjUw5+ZpZJIFrL1GmMiI928NEtHZz/TeCb7ZTPBA7Jcm8HPzPLrAJd2l7n4GdmmQRiSzR0fWKNc/Azs0ySSc59f6zUwc/MMqvSVJeycvAzs0wiRGu45WdmOVRwy8/M8iYZ8Oj7oaPv/wIz61Ue8DCz3Gr1PD8zy5tyrvCoJgc/M8us4NFeM8ubZGMDBz8zy5lAbPXyNjPLmwg8ydnM8kh1Mcm574dvM+tVQdLyK+XoSgd5e0dKulvS/PS/I4o+c95eM6ueVvqVdJTgOl6bY/dC4J6ImAzck7533l4zq66gtPwdpWx42l7eXpL8vNenr69nRw5e5+01s+pJUldWNHTsniYlIiJekjQ2LXfeXjOrpqolLXfeXjOrniDTCo/uJC1fLmlc2uobB6xIy52318yqqzVt/XV1dNPvgdPT16ezIwev8/aaWfVEqGxrezvI23spcJOkM4BFwCnJfZ2318yqKBnwKM/ytk7y9r6zg/Odt9fMqsU5PMwsh5IBj76/vM3Bz8wy85ZWZpY7bSs8+joHPzPLzAmMzCx3ImBrwcHPzHIm6fY6+JlZDvVg9UbNcPDrof7LNjJu2rM73q/cRMv7xtOwYRtDHl9NSLQObWTZ6ZNoHT6AoQ+vZMTdy7af3/Tiqyz6j4PZvPfgalQ/l/r3b+U7P/or/QcUaGgo8MBfx/Or6w7itNPncsJ7FrJ2bRMA1087mJkPj6OxscA5589i8gGrKYS46vJDmfP4mCr/iurxVJcSSDoR+BHQAEyLiEsreb9q2LrHQBZ9JZ1YXggmXTib9YeNoDCokZb3J2uth9+7jFG3vciK0yay7i2jWfeW0QAMePFV9rxyvgNfL9u6tR8XnX8MmzY10tBQ4HuX38fMh3cH4NbfTWb6TfvvdP6J710IwNlnvIvdhm/ikm//nc+ddRxRBwGge+qj21uxX5DusPpT4CTgIOCj6U6sdWvQ06+wdXQT20Y1URi4Y/mPthSgnV21h85oYd2RI3uzigaA2LQp+Xe/sbFAQ0NHuyIlJuzzCrNnJVvKrV3TzIb1/Zl8wOreqGjNKqR5PLo6alklw/cUYEFEPBcRW4Bfk+zEWreGzmxh3ZtHbX8/6tbFTLxoNsMeaaHlfa/dW3HozFU7nW+9p1+/4PKr/8wNt/yRxx4dyzPzkn+E3vehZ/nptLv53AUzGTJkCwDPPbsbR71tKf36Fdh9jw3st/8axozdWM3qV1Uy2ttQ0lHLKhn89gIWF71vd3dVSWdKmilpZuv6DRWsToVtKzDk8TWsO2JHS67lg3uz8FuH8cqUUQy/b/lOpzcvXE8M6MeWvQb1dk0NKBTEOZ86nk+ccjL7H7iaffZdy22/n8QZp53IZz91PKtamvm3s58A4K7b92XlywP50VX3cuZnH2fekyNpba3tVk0llXMb+2qqZPAraXfViJgaEUdGxJENQ/rus6/BT65l04RBtA7r/5rP1r15FEMe27mbNHRGi1t9NWDDhgHMmT2aI6YsZ83qZgoFESHu+ONE9j8w+TsrFPpx9RWHcs6njucbXzmawUO28uKSIVWueXW529u5jnZdrUu7dnn7L9+0/fWQJ1azZffmHScXgiGzVvl5X5UM220zgwcnXdoBA1o57IgVLFk0lBEjd3Rlj377Ul5YOAyApqZtNDVvA+DwI5ZTaO3H4heG9X7Fa0TbaG9fb/lVcrR3BjA53XH1RZKUcx+r4P2qRltaGTxvLStO23d72ehbFzNg+SYQbB3ZxIqP7fhs4Px1bBsxgK1jml97Mau4kaM28YULZ9CvX6B+8Lf7xvPIQ+P44kUzmLTfGiJg+bLBXP79wwHYbfhm/t93HqAQomVlM9/7VtZd2etPPYz2Ksn0VqGLSycDPySZ6nJtuhFhh5r2GR97/Od5FauPld/rf7Br1kGrZQ8+fx1rN77UoybZiAPHxnHXfqSkc6e/7cpHO8vhIek84FMkj8mujogfShoJ/AbYF3ge+D8RsTo9/yLgDKAVODci7uzu76ho+I6I2yNi/4h4XVeBz8z6jnJ0eyUdQhL4pgCHAu+VNJnuJS3PrO+3Xc2sV5Xxmd/rgYci4tWI2Ab8FfgQGZOWd/d3OPiZWWYZgt/otqls6XFm0WWeBI6RNErSIOBkkkHSnZKWA8VJy7ucPlcqr+01s0wybmbaYd7eiJgn6dvA3cB64HGSrGwd6VZy8o645WdmmZVrnl9EXBMRb4qIY4BVwHzSpOUAJSYt7xYHPzPLJAK2FfqVdHRF0tj0vxOADwM3kjFpeXd/h7u9ZpZZGScw3yxpFLCVJAn5akndSVqemYOfmWVSzgRGEfH2dspayJi0vDsc/Mwss3rYy9DBz8wyq/VNC0rh4GdmmUR4G3szyyXR6tSVZpZHfuZnZrnj7G1mlk+RPPfr6xz8zCwzj/aaWe6EBzzMLK/c7TWzXPJor5nlToSDn5nllKe6mFku+ZmfmeVOIAp1MNrb93+BmfW6KPHoiqTPS3pK0pOSbpTULGmkpLslzU//O6Lo/IskLZD0jKQTevIbHPzMLJt0wKOUozOS9gLOBY6MiEOABpK8vM7ba2Y1qlxNv+TR20BJjcAgkoREzttrZrUpQ8uvw7y9EfEi8D2SPB0vAWsj4i6qnbdX0uV0Ersj4tzu3tTM+q4ACoWe5+1Nn+V9AJgIrAF+K+njnVyrrHl7Oxvtndndi5pZHQugPPP8jgcWRsTLAJKmA0eT5u2NiJcqmbe3w+AXEdcXv5c0OCI2dPdGZlY/yjTPbxFwlKRBwEaSjG0zgQ0k+Xov5bV5e2+Q9H1gTyqdt1fSW4FrgCHABEmHAp+OiLO7e1Mz6+PKEPwi4mFJvwNmkeThfQyYShJraiJv7w+BE0iiLhHxuKRjuntDM+vrup7GUqqIuBi4eJfizdRK3t6IWCzt9GO7HW3NrA7kZHnbYklHAyFpAMmkxHmVrZaZ1ayAKH20t2aVMs/vLODfSebTvAgclr43s9xSiUft6rLlFxErgdN6oS5m1lfUQbe3y5afpEmS/iDpZUkrJP2vpEm9UTkzq1HlW95WNaV0e28AbgLGkcyt+S1wYyUrZWY1rG2ScylHDSsl+Cki/icitqXHL6n5mG5mlRRR2lHLOlvbOzJ9+RdJFwK/Jgl6/wLc1gt1M7NaVQejvZ0NeDxKEuzafuWniz4L4BuVqpSZ1TbVeKuuFJ2t7Z3YmxUxsz6iDwxmlKKkFR6SDgEOAprbyiLiF5WqlJnVstofzChFKRsbXAwcSxL8bgdOAh4AHPzM8qoOWn6ljPZ+hGSR8bKI+CRwKNBU0VqZWW0rlHjUsFK6vRsjoiBpm6RhJBsLepKzWV6VbzPTqiol+M2UNBy4mmQEeD092EDQzPq+ehjt7bLbGxFnR8SaiPgZ8C7g9LT7a2Z5VYblbZIOkDS76HhF0ud6K29vZ5Oc39TZZxExqyc3NrN8i4hnSHaJIs2/+yJwCzvy9l6aLrC4EPjyLnl79wT+LGn/7u7m3Fm397LO6g0c150bdqZp0avsf5Z71H3J7UtnV7sKlsGUE1aX5ToV6Pa+E3g2Il6Q9AGSGSaQ5O29D/gyRXl7gYWS2vL2PtidG3Y2yfkd3bmgmdW5IMvyttGSijNBTo2Iqe2cdyo7NkzZKW+vpOK8vQ8VfacyeXvNzDpUesuvw7y9bdId4t8PXNTFtcqat7eUeX5mZjtRlHaU6CRgVkQsT98vT/P1Usm8vQ5+ZpZdeTcz/Sg77xH6e5J8vfDavL2nSmqSNJEe5u0tZSdnSfq4pK+l7ydImtLdG5pZHShT8EsTlr8LmF5UfCnwLknz088uhSRvL8nGynOBO+iFvL1XkCxUOQ64BFgH3Ay8ubs3NbO+K2OXtlMR8SowapeyFmokb+9bIuJNkh5Lb746fUBpZnlV55uZttmaTkAMAEljqPkly2ZWSblY3gb8mGTW9VhJ3yTZzuq/K1orM6ttdZC9rZS8vb+S9ChJH1zAByNiXsVrZma1qYzP/KqplM1MJwCvAn8oLouIRZWsmJnVsDwEP5JMbW2JjJqBicAzJIuLzSyHVAdP/Uvp9r6h+H2628unOzjdzKxPyLy2NyJmSfIcP7M8y0O3V9L5RW/7AW8CXq5YjcystuVlwAMYWvR6G8kzwJsrUx0z6xPqPfilk5uHRMSXeqk+ZtYX1HPwk9QYEds6287ezPJH1P9o7yMkz/dmS/o98FtgQ9uHETG9oy+aWR3L0TO/kUALya4ubfP9gp23oDGzPKnz4Dc2Hel9kh1Br00d/HQz67Y6iACdbWzQAAxJj6FFr9sOM8upcm1jL2m4pN9JelrSPElvrXreXuCliLikJxc3szpVvpbfj4A7IuIj6T6hg4D/oBfy9nbW8uv7uxWaWflFMtpbytEZScOAY4BrACJiS0SsIcnPe3162vXAB9PX2/P2RsRCoC1vb7d0Fvza3UbazKxM+/lNIlkt9nNJj0maJmkwu+TtBYrz9i4u+n6P8vZ2GPwiYlV3L2pm9S3DM7/RkmYWHWcWXaaRZDrdlRFxOMlUugs7u207Zd3ugDtpuZllV56k5UuAJRHxcPr+dyTBb7mkcRHxkvP2mlntKLXL20WAjIhlwGJJB6RF7yRJS9kreXvd8jOzTERZV3icA/wqHel9DvgkSaPsJklnAIuAUyDJ2yupLW/vNnohb6+Z2U7KmLd3NtBet7gm8vaame2sDlZ4OPiZWXYOfmaWOzna1cXMbGcOfmaWR/W+mamZWbvc7TWz/Clt3W7Nc/Azs+wc/Mwsb8q8wqNqHPzMLDMV+n70c/Azs2z8zM/M8srdXjPLJwc/M8sjt/zMLJ8c/Mwsd6I+lrd5G3szy6Rtnl+ZkpY/L2mOpNmSZqZlvZK03MHPzLKLKO0ozTsi4rCiREcXkiQtnwzck75nl6TlJwJXSGro7k9w8DOzzMrV8utAryQt9zO/Hhqz5xa+9KNFjBi7jSjA7b8cxa3XjOHjX1jGSR9rYe2q5I/4598ax4x7hwEw8fUbOffbSxg8tJVCQZxz8mS2bva/Q5V02ef35uE/D2P46G1M/cszANz/h934n8v2YPH8Zn58+z/Y/9CNACxbPIBP/dOBjJ+0GYADj9jAed9estP1Lj59Ii8tGrD9WrmSbZLz6LbubGpqREzd5Wp3SQrgqvSznZKWSypOWv5Q0Xd7lLS8YsFP0rXAe4EVEXFIpe5Tba3bxNRL9mTBnEEMHNzKT+74B7PuHwrALVeP4Xc/G7vT+f0aggsuX8R3z53Ac3MHMnTENlq3tpeL2crp3f+yivd/ciXfPW/C9rJ9D9zE16Y9z4+/vPdrzh+3z2au/HP7ge2B23ejeXAdPPHvgQwDHp3l7QV4W0QsTQPc3ZKe7uy27ZR1u31ZyebGdST98rq2akV/FswZBMDGDQ0sXtDM6HFbOzz/iH9ax8J5zTw3dyAA61Y3Uig4+FXaG47awNARO2c5nDB5M3vvtznTdTZu6Mf0q8bwsc8tK2f1+hwVSju6EhFL0/+uAG4h6cYuT5OV0yeTlkfE/cCqSl2/Fu0+fguvO2QjT89KguH7PrmSK//8DOd/fxFDdtsGwPhJm4kQ37zhWX5y5z845ewVnV3SqmTZogGc/a79+eKH92POw4O3l1//nT3457NepmlgHUx0666gLAMekgZLGtr2Gng38CR5SVou6UzgTIBmBlW5Nt3XPKiVr057np99bU9eXd/AH68fxQ0/2J0IOP2CZZx58VK+f/4EGhqDQ6Zs4JyTJ7N5Yz8u/c2zzH9iILMfGFrtn2CpkWO38ssZcxk2spX5Twzkvz45kan3Pc2yF5pYurCJs76+lGWLB1S7mlVVphUeuwO3SIIkFt0QEXdImkEekpanDzinAgzTyD75z2lDY/DVac9z7/QR/P1PwwFYs7L/9s//9KtRXPKLhQC8/FJ/nnhwMK+kAyEz7h3Gfm/Y6OBXQwY0BQOakv+nJr9xI3vuu4UXn2vimdmDmD9nEJ+YchCtrbBmZSNf+uf9+O7NC6pc4yoow/+pEfEccGg75S30QtJyDzH2WHD+ZYtZPL+Z6VPHbC8dOXbHc7+jT1rL8880A/DofUOZeNAmmgYW6NcQvPGt61n0j+Zer7V1bE1LA61pe+KlFwbw4sIB7DFhC+87vYUbH3uKXzwyl8tuXcBekzbnMvCVc5JzNVW95dfXHTxlA8efsprn5jZzxd3J6ODPvzWOYz+4htcdvJEIWL5kAD++YDwA69c2Mv2qMVx++z+IEI/cO5RH7hlWzZ+QC9/6zD488eAQ1q5q5LQjDuJfv7CMoSNaueIre7G2pZGv/uskXnfwRv77xueY89AQfvHdPWhohIZ+wbmXLmHYiG73rupPRF1sZqoofRZ2tgtLNwLHAqOB5cDFEXFNZ98ZppHxFrXb2rUadefS2dWugmUw5YTFzHx8U4+mFwwdPj4OP+a8ks792x8ueLSLqS5VU7GWX0R8tFLXNrPqqvUubSnc7TWzbAKog26vg5+ZZdf3Y5+Dn5ll526vmeVSPYz2OviZWTZOXWlmeZRMcu770c/Bz8yyq4MdvRz8zCwzt/zMLH/8zM/M8qk+1vY6+JlZdnXQ7fWWVmaWTZRvG3sASQ2SHpP0x/S98/aaWY0qb97e84B5Re+dt9fMalSUeHRB0njgPcC0omLn7TWz2qRCyRP9usrb+0PgAqA4j0PfzttrZnUqyDLJucO8vZLa8no/KunYEq5V1ry9Dn5mlomIck1yfhvwfkknA83AMEm/JM3bm7b6+l7eXjOrY2UY8IiIiyJifETsSzKQcW9EfJy85O01sz6osvP8LiUPeXvNrI/J9syvtEtG3Afcl77ulby9Dn5mllmG0d6a5eBnZhllmsBcsxz8zCybwMHPzHKq7/d6HfzMLDtvZmpm+eTgZ2a5EwGtfb/f6+BnZtm55WdmueTgZ2a5E4BzeJhZ/gSEn/mZWd4EHvAws5zyMz8zyyUHPzPLn/rY2MA7OZtZNgEUCqUdnZDULOkRSY9LekrS19Ny5+01sxpVnry9m4HjIuJQ4DDgRElH4by9Zlab0uVtpRydXSWxPn3bPz2CXsrb6+BnZtkERBRKOkjz9hYdZxZfSlKDpNkkGdrujoiH2SVvL1Cct3dx0dedt9fMelnpKzw6zNsLkCYgOkzScOAWSYd0cq2y5u11y8/MsivPM7+iy8UakgRGJ5Lm7QVw3l4zqx0R5RrtHZO2+JA0EDgeeBrn7TWzmlWeeX7jgOvTEdt+wE0R8UdJD+K8vWZWe4Jo7XbM2XGViCeAw9spd95eM6tB3tLKzHLLW1qZWd4EEG75mVnuhDczNbOcKseAR7UpamhrGkkvAy9Uux4VMBpYWe1KWCb1+ne2T0SM6ckFJN1B8udTipURcWJP7lcpNRX86pWkmZ0t8bHa47+z+ucVHmaWSw5+ZpZLDn69Y2q1K2CZ+e+szvmZn5nlklt+ZpZLDn5mlksOfhUk6cQ0y9QCSRdWuz7WNUnXSloh6clq18Uqy8GvQtI9yn4KnAQcBHw0zT5lte06kt2Erc45+FXOFGBBRDwXEVuAX5Nkn7IaFhH3A6uqXQ+rPAe/yilrpikzKy8Hv8opa6YpMysvB7/KKWumKTMrLwe/ypkBTJY0UdIA4FSS7FNmVgMc/CokIrYBnwXuBOaRZKZ6qrq1sq5IuhF4EDhA0pI0g5jVIS9vM7NccsvPzHLJwc/McsnBz8xyycHPzHLJwc/McsnBrw+R1CpptqQnJf1W0qAeXOs6SR9JX0/rbNMFScdKOrob93he0muyfHVUvss56zPe678kfTFrHS2/HPz6lo0RcVhEHAJsAc4q/jDdSSaziPi3iJjbySnHApmDn1ktc/Dru/4G7Je2yv4i6QZgjqQGSd+VNEPSE5I+DaDETyTNlXQbMLbtQpLuk3Rk+vpESbMkPS7pHkn7kgTZz6etzrdLGiPp5vQeMyS9Lf3uKEl3SXpM0lW0v755J5JulfSopKcknbnLZ5eldblH0pi07HWS7ki/8zdJB5blT9Nyp7HaFbDsJDWS7BN4R1o0BTgkIhamAWRtRLxZUhPwd0l3AYcDBwBvAHYH5gLX7nLdMcDVwDHptUZGxCpJPwPWR8T30vNuAH4QEQ9ImkCyiuX1wMXAAxFxiaT3ADsFsw783/QeA4EZkm6OiBZgMDArIr4g6WvptT9LkljorIiYL+ktwBXAcd34Y7Scc/DrWwZKmp2+/htwDUl39JGIWJiWvxt4Y9vzPGA3YDJwDHBjRLQCSyXd2871jwLub7tWRHS0r93xwEHS9obdMElD03t8OP3ubZJWl/CbzpX0ofT13mldW4AC8Ju0/JfAdElD0t/726J7N5VwD7PXcPDrWzZGxGHFBWkQ2FBcBJwTEXfuct7JdL2llko4B5LHJW+NiI3t1KXk9ZKSjiUJpG+NiFcl3Qc0d3B6pPdds+ufgVl3+Jlf/bkT+Iyk/gCS9pc0GLgfODV9JjgOeEc7330Q+CdJE9PvjkzL1wFDi867i6QLSnreYenL+4HT0rKTgBFd1HU3YHUa+A4kaXm26Qe0tV4/RtKdfgVYKOmU9B6SdGgX9zBrl4Nf/ZlG8jxvVpqE5yqSFv4twHxgDnAl8NddvxgRL5M8p5su6XF2dDv/AHyobcADOBc4Mh1QmcuOUeevA8dImkXS/V7URV3vABolPQF8A3io6LMNwMGSHiV5pndJWn4acEZav6dwagDrJu/qYma55JafmeWSg5+Z5ZKDn5nlkoOfmeWSg5+Z5ZKDn5nlkoOfmeXS/wcVu2m4GfQb7wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_confusion_matrix(rf, X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "c24a49fc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x1a4d6dabfd0>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "plot_confusion_matrix(sv, X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "9e3a931d",
   "metadata": {},
   "outputs": [],
   "source": [
    "coupon_type = ['Bar', 'Carry out & Take away', 'Coffee House', \n",
    "               'Restaurant(<20)', 'Restaurant(20-50)']\n",
    "datasets = {}\n",
    "for type_name in coupon_type:\n",
    "    column_name = 'coupon_' + type_name\n",
    "    data = df[df[column_name] == 1].copy()\n",
    "    X, y = data.iloc[:, :-1], data.iloc[:, -1]\n",
    "    \n",
    "    X = X.drop(columns=['coupon_'+type_name for type_name in coupon_type])\n",
    "    \n",
    "    datasets[type_name] = (X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "17b4b6cb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--- DT ---\n",
      "Bar                      : 0.654(0.080)\n",
      "Carry out & Take away    : 0.562(0.059)\n",
      "Coffee House             : 0.605(0.098)\n",
      "Restaurant(<20)          : 0.283(0.227)\n",
      "Restaurant(20-50)        : 0.527(0.038)\n",
      "--- RF ---\n",
      "Bar                      : 0.698(0.056)\n",
      "Carry out & Take away    : 0.612(0.061)\n",
      "Coffee House             : 0.659(0.084)\n",
      "Restaurant(<20)          : 0.379(0.260)\n",
      "Restaurant(20-50)        : 0.541(0.070)\n",
      "--- SVM ---\n",
      "Bar                      : 0.568(0.145)\n",
      "Carry out & Take away    : 0.530(0.090)\n",
      "Coffee House             : 0.572(0.153)\n",
      "Restaurant(<20)          : 0.256(0.231)\n",
      "Restaurant(20-50)        : 0.504(0.137)\n"
     ]
    }
   ],
   "source": [
    "# Cross validation on different type of coupon\n",
    "# to compare with Bayesian rule sets\n",
    "# from paper: https://jmlr.org/papers/v18/16-003.html\n",
    "\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.impute import SimpleImputer\n",
    "\n",
    "estimators = [\n",
    "    DecisionTreeClassifier(criterion='entropy', max_depth=5, \n",
    "                           random_state=rng),\n",
    "    RandomForestClassifier(criterion='entropy', n_estimators=100,\n",
    "                           random_state=rng),\n",
    "    SVC(kernel='rbf', C=15.0)\n",
    "]\n",
    "\n",
    "for name, estimator in zip(['DT', 'RF', 'SVM'], estimators):\n",
    "    print('---', name, '---')\n",
    "    for coupon_type in datasets:\n",
    "        X, y = datasets[coupon_type]\n",
    "        scores = cross_val_score(\n",
    "            estimator, X, y, cv=5, n_jobs=-1, scoring='roc_auc'\n",
    "        )\n",
    "        mean = scores.mean()\n",
    "        std = scores.std()\n",
    "        print('%-25s: %.3f(%.3f)' % (coupon_type, mean, std))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0c24bf73",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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