07_Random_Under_Sampling.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "otherwise-uncertainty",
   "metadata": {},
   "source": [
    "# Flight Delay Prediction\n",
    "## Random Under Sampling to Handle Imbalance in ArrDel15\n",
    "- <strong>Environment Setup:</strong> import libraries and load the dataset\n",
    "- <strong>Imbalanced Data Analysis:</strong> understanding how the dataset balance is restored using random under sampling\n",
    "- <strong>Random Undersampling:</strong> or down-sampling the majority class is where the observations of the majority class are randomly removed to reduce its influence on the machine learning algorithm\n",
    "- <strong>Inference:</strong> understand how the model produces results and choosing the best classifier"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "laden-cookie",
   "metadata": {},
   "source": [
    "## Environment Setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "center-pressing",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/admin/.local/lib/python3.8/site-packages/numpy/lib/arraysetops.py:580: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
      "  mask |= (ar1 == a)\n"
     ]
    }
   ],
   "source": [
    "# Pre-requisites\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "# Store the classifier models to save time\n",
    "import joblib\n",
    "\n",
    "# Preprocessing\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# Classifiers from scikit-learn\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.ensemble import ExtraTreesClassifier\n",
    "\n",
    "# Performance metrics\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.metrics import f1_score\n",
    "from sklearn.metrics import precision_score\n",
    "from sklearn.metrics import recall_score\n",
    "from sklearn.metrics import classification_report\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import plot_confusion_matrix\n",
    "\n",
    "from sklearn.metrics import roc_curve\n",
    "from sklearn.metrics import roc_auc_score\n",
    "\n",
    "# Imbalanced dataset helpers\n",
    "from imblearn.under_sampling import RandomUnderSampler\n",
    "\n",
    "df = pd.read_csv(\"Data/flight_and_weather_encoded.csv\", index_col=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "technical-beaver",
   "metadata": {},
   "source": [
    "## Imbalanced Data Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "moderate-stuff",
   "metadata": {},
   "source": [
    "### Dataset Without Handling Imbalance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "spoken-madrid",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0    1463378\n",
      "1.0     388058\n",
      "Name: ArrDel15, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Plotting the percentage of observations that fall under each class\n",
    "ax = df[\"ArrDel15\"].value_counts().sort_values().plot(kind=\"barh\", color=[\"r\", \"g\"])\n",
    "ax.set_axisbelow(True)\n",
    "ax.grid()\n",
    "totals= []\n",
    "for i in ax.patches:\n",
    "    totals.append(i.get_width())\n",
    "total = sum(totals)\n",
    "for i in ax.patches:\n",
    "     ax.text(i.get_width()+.3, i.get_y()+.20, \n",
    "     str(round((i.get_width()/total)*100, 2))+'%', \n",
    "     fontsize=10, color='black')\n",
    "plt.title(\"ArrDel15\", fontsize=20)\n",
    "plt.xlabel(\"Count\", fontsize=14)\n",
    "plt.ylabel(\"Class\", fontsize=14)\n",
    "plt.show()\n",
    "print(df[\"ArrDel15\"].value_counts())\n",
    "fig = ax.get_figure()\n",
    "fig.savefig(\"Img/07_01_Dataset_ArrDel15_Class_Imbalance.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "hawaiian-warehouse",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Dataset shape: (1851436, 24)\n",
      "features_train shape: (1481148, 23) | features_test shape: (370288, 23)\n",
      "labels_train shape: (1481148,) | labels_test shape: (370288,)\n"
     ]
    }
   ],
   "source": [
    "features = df.loc[:, df.columns != \"ArrDel15\"]\n",
    "labels = np.asarray(df[\"ArrDel15\"])\n",
    "# Number of samples/observations/rows is greater than 100,000\n",
    "print(f\"\\nDataset shape: {df.shape}\")\n",
    "features_train, features_test, labels_train, labels_test = train_test_split(features, labels, test_size=0.20, random_state=42)\n",
    "print(f\"features_train shape: {features_train.shape} | features_test shape: {features_test.shape}\")\n",
    "print(f\"labels_train shape: {labels_train.shape} | labels_test shape: {labels_test.shape}\")\n",
    "# print(f\"{features_train.shape[1]} Features: {features_train.columns.to_list()}\")\n",
    "del features\n",
    "del labels"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "funded-gateway",
   "metadata": {},
   "source": [
    "### Training Data Without Handling Imbalance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "narrow-running",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0    1170484\n",
      "1.0     310664\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Plotting the percentage of observations that fall under each class\n",
    "ax = pd.DataFrame(labels_train).value_counts().sort_values().plot(kind=\"barh\", color=[\"r\", \"g\"])\n",
    "ax.set_axisbelow(True)\n",
    "ax.grid()\n",
    "totals= []\n",
    "for i in ax.patches:\n",
    "    totals.append(i.get_width())\n",
    "total = sum(totals)\n",
    "for i in ax.patches:\n",
    "     ax.text(i.get_width()+.3, i.get_y()+.20, \n",
    "     str(round((i.get_width()/total)*100, 2))+'%', \n",
    "     fontsize=10, color='black')\n",
    "plt.title(\"ArrDel15\", fontsize=20)\n",
    "plt.xlabel(\"Count\", fontsize=14)\n",
    "plt.ylabel(\"Class\", fontsize=14)\n",
    "plt.show()\n",
    "print(pd.DataFrame(labels_train).value_counts())\n",
    "fig = ax.get_figure()\n",
    "fig.savefig(\"Img/07_02_Training_ArrDel15_Class_Imbalance.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "verbal-imagination",
   "metadata": {},
   "source": [
    "## Random Undersampling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "stupid-reflection",
   "metadata": {},
   "outputs": [],
   "source": [
    "rus = RandomUnderSampler(random_state=42)\n",
    "features_train, labels_train = rus.fit_resample(features_train, labels_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "uniform-converter",
   "metadata": {},
   "source": [
    "### Balancing the Training Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "south-electron",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0    310664\n",
      "1.0    310664\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Plotting the percentage of observations that fall under each class\n",
    "ax = pd.DataFrame(labels_train).value_counts().sort_values().plot(kind=\"barh\", color=[\"r\", \"g\"])\n",
    "ax.set_axisbelow(True)\n",
    "ax.grid()\n",
    "totals= []\n",
    "for i in ax.patches:\n",
    "    totals.append(i.get_width())\n",
    "total = sum(totals)\n",
    "for i in ax.patches:\n",
    "     ax.text(i.get_width()+.3, i.get_y()+.20, \n",
    "     str(round((i.get_width()/total)*100, 2))+'%', \n",
    "     fontsize=10, color='black')\n",
    "plt.title(\"ArrDel15\", fontsize=20)\n",
    "plt.xlabel(\"Count\", fontsize=14)\n",
    "plt.ylabel(\"Class\", fontsize=14)\n",
    "plt.show()\n",
    "print(pd.DataFrame(labels_train).value_counts())\n",
    "fig = ax.get_figure()\n",
    "fig.savefig(\"Img/07_03_RandomUnderSampling_Training_ArrDel15_Class.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "greek-warrior",
   "metadata": {},
   "source": [
    "### Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "comparable-paragraph",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.94      0.93      0.93    292894\n",
      "         1.0       0.74      0.78      0.76     77394\n",
      "\n",
      "    accuracy                           0.90    370288\n",
      "   macro avg       0.84      0.85      0.85    370288\n",
      "weighted avg       0.90      0.90      0.90    370288\n",
      "\n",
      "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay object at 0x7ff617dc2af0>\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": [
    "# model = LogisticRegression(n_jobs=-1)\n",
    "# model.fit(features_train, labels_train)\n",
    "# joblib.dump(model, \"./RandomUnderSampling/LogisticRegression.joblib\")\n",
    "model = joblib.load(\"./RandomUnderSampling/LogisticRegression.joblib\")\n",
    "model_pred = model.predict(features_test)\n",
    "# print(confusion_matrix(labels_test, model_pred))\n",
    "print(classification_report(labels_test, model_pred))\n",
    "conf_mat_plot = plot_confusion_matrix(model, features_test, labels_test)\n",
    "plt.title(\"Logistic Regression\")\n",
    "print(conf_mat_plot)\n",
    "plt.savefig(\"Img/07_04_RandomUnderSampling_LR_Confusion_Matrix\")\n",
    "del model\n",
    "del model_pred"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "lesbian-watch",
   "metadata": {},
   "source": [
    "### Decision Tree Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "settled-georgia",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.94      0.79      0.86    292894\n",
      "         1.0       0.50      0.81      0.62     77394\n",
      "\n",
      "    accuracy                           0.79    370288\n",
      "   macro avg       0.72      0.80      0.74    370288\n",
      "weighted avg       0.85      0.79      0.81    370288\n",
      "\n",
      "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay object at 0x7ff66dfc0cd0>\n"
     ]
    },
    {
     "data": {
      "image/png": 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JC7niO+8xYuSZFXJslafmXv5n7agl5QAPE00X2x24KEwrGzcUWG9mRwEPAvdlqz3Z0rJtAV2P2w5Aw0OK6HTUDtbm1aFR46LdZT7fXovUdDrNWhVwdM/t1K6dWT0pf76zA0N/vgIV++P/8pjWnDpwI81aFSR/cDVAowY7Oa7bSiZO7QZAQWEOW7fX47O8Zixd2Wyf8l/psZxFy1qwcGlLADZtrV9m8jil12dM+vdRALw1owu9vrCC6FyueisK81SVtVQ32fxTcgKQa2aLzGwn8AzRdK9x8elfnwPOkoqnjapj5dK6LPyoAcf02gbAX+49lEuO787rLzTn0pvyyti65Hr+82oTWh26iyOP/Xyvcmvz6vCfV5py7uC16apxGTi01WY2bK7PLUPfZtSdL/LTIW9Tv+6uEst3bLsxukVw46v8+c6XuHDA7L3W3zL0bR6760W+/40PSCXOVs22sjr/EACKimqxZXtdmhyyI2vHdDCIev9zMlrKImmMpNWSPorFnpU0KyyLUzMCSDpc0vbYukdj2xwvaY6kXEkPpXKNpBaSJktaEH42D3GFcrmSZkvqlcmxZzOpljQdbNoyZlYAbARaFq9I0jBJMyTNWLOusPjqg8L2rbW4+4eHc+WI5bvPUocMX8lT78/lzG+tZ8KY1mXUkL6ez7eJZ/7YNm1SfvSODgz92Qpq1cyrrETk5BTR7bB1THjjGIbdeT6f76jNRV+fXUp544tdV/HLP/flul+dy6m9FoczT7jnz30Z+otvcd2vv84Xu63knJNzK+owDjqph/8zWTLwBNB/r/rNvmtmPc2sJ9GEgC/EVi9MrTOzK2PxkcDlQNewpOocDkwxs67AlPAdoqvsVNlhYfsyVYn/HM1slJn1NrPerVsefCPfFOyCu394OGd+az2nDty3o+jM89fzzsSmabYsu568JfVY+Vldrjr7GC49oTtr8upwTb+jyV9dm08+bMCvrzqcS0/oztv/bMofb+3If14pez9ujzX5jVizvhHzFrUB4K33utDtsJLP/NfkN2T2J4eyaUt9duyszbuzO9E1lF+7oREA2z+vy5RpR3LMEWt2x9u02AJE92QPabCTTVvqZfOwDgpJXf6b2VQgP926cLb5HeDp0uoIM6E2MbNpYbqVccB5YXX8inlssfg4i0wDmqVmVi1NNpNqSdPBpi0jqTbQlKjDqsowgwdu7Eynrjv4f1es2R1fvqju7s//ndSUTkeVfrlXUj1dvvA54+d8zLjpcxk3fS6t2+3i4UnzadGmgHHvztsdP+3cjfzo18s4eYD3/pfH+k0NWZ3fiE6HbgCgV/cVLF7RvMTy733UkS4d11OvbgG1ahXxpaNXsmRFM2rVKqLJIdHtmZycIk760lI+XRbV858POtPvlOis9au9P+WDee0pecql6iHV+5/hmWqr1JVoWIaVY1enAavMbEEs1kXSB5LeknRaiHUgulpOiV85tw0zqgKsBNrGtinransf2ez9fw/oKqkLUfK8ELi4WJnU9K//BS4AXg9/RaqMj6c3YspzLejyhe1cdfbRAAy5dQWvPt2SZQvrUasWtOmwk+vui/4981fX5kcDurFtcw6qBS893ppRb/6PT+c2SFvPCWd5r362PfTkSfxs2FvUrl1I3prG3Df6dE7ttZjrLvkvTRt/zq9//BoLl7bk5vv7s2VbPf4+qQeP3v4yZvDu7E5Mm92Z+nV38dsbXyUnp4icWsb7c9vzr7eif8d/Te3GbcPe4sl7x7Npaz3ufvSMSj7iilGO3v+1ZtZ7P3dzEXufpeYBnc1snaTjgZckHZtpZWZmkg4oBymbOUzSQOD3RI9UjTGzeySNAGaY2QRJ9YG/Al8mOr2/0MwWlVZn7y/Vt+mTOpVWxB1kzhjyw8pugiunqa8Of/8AEh3Nj2ljZ465IKOyL5wyssx9SToc+KeZ9YjFahOdsB1vZstK2O5N4Keh3BtmdkyIXwT0NbMrJM0Pn/PC5f2bZna0pD+Hz0+HbXaXK62tWX1O1cwmEs3BHY/dHvv8OfDtbLbBOVc5KuDh/7OB/8UTqqTWQL6ZFUo6gqiTaZGZ5UvaJKkP8C5wKfDHsFnqivne8PPlWPxaSc8AJwIby0qo4G9UOeeyIMk3qiQ9DfQluve6DLjDzEYT3VIs3kF1OjBC0i6gCLjSzFKdXFcTPUnQAHglLBAl0/GShgJLiDq+IDohHAjkAtuAIZm015Oqcy4rkkqqZnZRCfHL0sSeJ3rEKl35GUCPNPF1wFlp4gZcU87melJ1ziXPB6l2zrmEVcdXUDPhSdU5lzgzKPBBqp1zLjl++e+ccwnxe6rOOZcw86TqnHPJ8Y4q55xLiJnfU3XOuQSJQu/9d8655Pg9VeecS4jPpuqcc0my6L5qTeRJ1TmXFd7775xzCTHvqHLOuWT55b9zziWopvb+18zzc+dcVplFSTWTpSySxkhaLemjWOxOScslzQrLwNi6WyXlSpovqV8s3j/EciUNj8W7SHo3xJ+VVDfE64XvuWH94ZkcuydV51xWlGOK6rI8AfRPE3/QzHqGZSKApO5E06wcG7Z5RFKOpBzgYWAA0B24KJQFuC/UdRSwHhga4kOB9SH+YChXJk+qzrmsMMtsKbsem0o023ImBgHPmNkOM/uUaH6pE8KSa2aLzGwn8AwwSJKAM4HnwvZjgfNidY0Nn58DzgrlS+VJ1TmXOEMUFdXKaCGa0G9GbBmW4W6ulTQ73B5oHmIdgKWxMstCrKR4S2CDmRUUi+9VV1i/MZQvlSdV51xWWIYLsNbMeseWURlUPxI4EugJ5AH3J9z8/ea9/8655Fl2e//NbFXqs6THgH+Gr8uBTrGiHUOMEuLrgGaSaoez0Xj5VF3LJNUGmobypfIzVedcdpTjVLW8JLWLfT0fSD0ZMAG4MPTcdwG6AtOB94Cuoae/LlFn1oQwDfUbwAVh+8HAy7G6BofPFwCvh/Kl8jNV51xWJHWmKulpoC/RvddlwB1AX0k9idLyYuCKaJ/2saTxwFygALjGzApDPdcCk4AcYIyZfRx2cQvwjKRfAh8Ao0N8NPBXSblEHWUXZtLeEpOqpD9Syt8RM7sukx0452oeA4qKkkmqZnZRmvDoNLFU+XuAe9LEJwIT08QXET0dUDz+OfDtcjWW0s9UZ5S3MuecA8Klfc18o6rEpGpmY+PfJTU0s23Zb5Jzrjqoqe/+l9lRJekkSXOB/4XvX5L0SNZb5pyr2rLYUXUwy6T3//dAP8KjBGb2IXB6FtvknKvyMnvvvzoOupJR77+ZLS32dlZhdprjnKs2quFZaCYySapLJZ0MmKQ6wPXAvOw2yzlXpRlYQr3/VU0ml/9XAtcQvQe7gui1sGuy2CbnXLWgDJfqpcwzVTNbC1xSAW1xzlUnNfTyP5Pe/yMk/UPSmjBQ7MuSjqiIxjnnqjDv/S/R34DxQDugPfB34OlsNso5V8WlHv7PZKlmMkmqDc3sr2ZWEJYngfrZbphzrmpLapDqqqa0d/9bhI+vhPlcniH6+/Nd0rw/65xze6mhvf+ldVS9T5REU7+ZK2LrDLg1W41yzlV9qoZnoZko7d3/LhXZEOdcNVJNO6EykdEbVZJ6EM1AuPteqpmNy1ajnHNVXfXshMpEmUlV0h1EA8R2J7qXOgB4B/Ck6pwrWQ09U82k9/8C4CxgpZkNAb5ENFeLc86VrCjDpQxhttTVkj6KxX4r6X9hNtUXJTUL8cMlbZc0KyyPxrY5XtIcSbmSHkpNNy2phaTJkhaEn81DXKFcbthPr0wOO5Okut3MioACSU2A1ew9gZZzzu0t2edUnwD6F4tNBnqY2XHAJ+zdcb7QzHqG5cpYfCRwOdG8VV1jdQ4HpphZV2BK+A7RVXmq7LCwfZkySaozwl+Bx4ieCJgJ/DeTyp1zNZcss6UsZjaVaI6oeOy1MPspwDSiWVBLbks0UWATM5sWJu8bB5wXVg8CUoPyjy0WH2eRaUSzrsYnHEwrk3f/rw4fH5X0amjY7LK2c87VcJnfU20lKT590ygzG1WOPf0AeDb2vYukD4BNwM/N7G2iAaGWxcosCzGAtmaWFz6vBNqGzx2ApWm2yaMUpT38X+L9A0m9zGxmaRU751yG1ppZ7/3ZUNLPiGZNfSqE8oDOZrZO0vHAS5KOzbQ+MzPpwJ6wLe1M9f7S9g2ceSA73l+fzG5Iv/Y9K2PXbn/1r5mP1tR02X74X9JlwLnAWeGSHjPbAewIn9+XtBDoBixn71sEHUMMYJWkdmaWFy7vV4f4cvbuP4pvU6LSHv4/I4Pjcs65fRlZfU1VUn/gZuCr8QlJJbUG8s2sMIym1xVYZGb5kjZJ6gO8C1wK/DFsNgEYDNwbfr4ci18r6RngRGBj7DZBiTJ6+N8558otoTNVSU8TPSvfStIy4A6i3v56wOTwZNS00NN/OjBC0i6iB7auNLNUJ9fVRE8SNABeCQtEyXS8pKHAEuA7IT4RGAjkAtuAIZm015Oqcy4rkrr8N7OL0oRHl1D2eeD5EtbNAHqkia8jeha/eNzYj1lOPKk657LD36hKL7xV8D1Jt4fvnSWdkP2mOeeqNB/5v0SPACcBqVPwzcDDWWuRc67Ky/TB/+o4PGAml/8nmlmv8DAtZrZeUt0st8s5V9X5INUl2iUph3CiHh5ZyGAYBOdcTVYdz0Izkcnl/0PAi0AbSfcQDfv3q6y2yjlX9dXQe6qZvPv/lKT3iR45EHCemc3Lesucc1VXNb1fmolMBqnuTPTg6z/iMTP7LJsNc85VcZ5US/Qv9kwAWB/oAswHMh6kwDlX86iG9rxkcvn/xfj3MHrV1SUUd865Gq3cb1SZ2UxJJ2ajMc65asQv/9OTdEPsay2gF7Aiay1yzlV93lFVqsaxzwVE91jTDljgnHO7eVLdV3jov7GZ/bSC2uOcqy48qe5NUm0zK5B0SkU2yDlX9Qnv/U9nOtH901mSJgB/B7amVprZC1lum3OuqqrB91QzeU21PrCOaE6qc4FvhJ/OOVeyhF5TlTRG0mpJH8ViLSRNlrQg/Gwe4pL0kKRcSbPjE5hKGhzKL5A0OBY/XtKcsM1DClMJlLSPspSWVNuEnv+PgDnh58fh50elbOecc0m++/8E0L9YbDgwxcy6AlPCd4ABRPNSdQWGASMhSpBE07CcCJwA3BFLkiOBy2Pb9S9jH6UqLanmAIeEpXHsc2pxzrkSJTWeqplNBfKLhQcBY8PnscB5sfg4i0wDmoUZUvsBk80s38zWA5OB/mFdEzObFqZPGVesrnT7KFVp91TzzGxEJpU459w+Mr+n2krSjNj3UWY2qoxt2sZmNl0JtA2fOwBLY+WWhVhp8WVp4qXto1SlJdWaOcKsc+7AWbl6/9eaWe/93pWZSdntFivPPkq7/N9ndkHnnMtYdsdTXRUu3Qk/V4f4cqBTrFzHECst3jFNvLR9lKrEpBqbK9s558oty3NUTQBSPfiDgZdj8UvDUwB9gI3hEn4ScI6k5qGD6hxgUli3SVKf0Ot/abG60u2jVD5FtXMuOxK6IJf0NNCX6N7rMqJe/HuB8ZKGAkuA74TiE4GBQC7RONBDIDpJlHQ38F4oNyJ24ng10RMGDYBXwkIp+yiVJ1XnXPISnCrFzC4qYdU+tyhDD/41JdQzBhiTJj4D6JEmvi7dPsriSdU5lzhRc9+o8qTqnMsKT6rOOZckT6rOOZcgT6rOOZeQGjxKlSdV51x2eFJ1zrnk+CDVzjmXIL/8d865pCT48H9V40nVOZcdnlSdcy4Z/kaVc84lTEU1M6t6UnXOJc/vqTrnXLL88t8555LkSdU555JTU89US5ujyjnn9l9Cc1RJOlrSrNiySdKPJd0paXksPjC2za2SciXNl9QvFu8fYrmShsfiXSS9G+LPSqq7v4ftSdU5l7wwm2omS5lVmc03s55m1hM4nmialBfD6gdT68xsIoCk7sCFwLFAf+ARSTmScoCHgQFAd+CiUBbgvlDXUcB6YOj+HronVedc4lLPqWZh4r+zgIVmtqSUMoOAZ8xsh5l9SjRf1QlhyTWzRWa2E3gGGBQm/DsTeC5sPxY4r9wtCzypOueywyyzJZrQb0ZsGVZKrRcCT8e+XytptqQxYZZUgA7A0liZZSFWUrwlsMHMCorF94snVedcVpTjTHWtmfWOLaPS1hfd5/wm8PcQGgkcCfQE8oD7s35QGfDe/4Td8MBnnHj2Zjasrc0VZx4NwPduXMmAi9exMT/6df/l1+147/UmAHz32lX0vyifwiIx8uftef+tJrRuv5Ob/vAZzVoXgMHEJ1vy0ujWAFx6Ux4n9duEGWxYW5vf/bgz+avqVM7BVhONGuzgpiFv06XDeszgN385ndN6Lebknp+xq6AWK9Y04b7Rp7N1ez2O6bKaGwe/A4AET7zci3dmHk6nQzdw+5Wv766zXevN/OWl43l+cg++2nsRlw2aSed2G7jql4P4ZHHryjrUipOdh/8HADPNbBVA6ieApMeAf4avy4FOse06hhglxNcBzSTVDmer8fLllrWkKmkMcC6w2sz2mf413Mf4A9Ec3duAy8xsZrbaU1Fee7YFE/7Sipv+sHSv+IuPtea5R9vsFevc9XP6DtrAsDOOpkXbXdz77CKGntqYwgIxakR7cuc0pEGjQv706ifMnNqYzxbU57mRbRj323YADBq6hu/9ZBUPDe9YYcdXHf3o4mlMn9OROx85m9o5hdSrW0DD+h147PmvUFRUi2EXTOeSr3/IqOdO4NPlLbhixHkUFdWiRdNtPH7XC/xnVmeWrmzG5Xd+C4BaKuLvDzzNOzMPA+DT5c25/eGzueHSdyrzMCtcFsZTvYjYpb+kdmaWF76eD3wUPk8A/ibpAaA90BWYTnSrt6ukLkRJ80LgYjMzSW8AFxDdZx0MvLy/jczm5f8TRD1vJRlAdLBdgWFEp/JV3kfvHsLm9Zn9rTqp30befLkZu3bWYtXSeqxYXJejv7yN/NV1yJ3TEIDtW3NYmlufVu12AbBtS87u7es3KAq3pNz+atRgJ8d1y2Pi29FVRUFhDlu312PGxx0pKor+85i7qA2tm28FYMfO2rvjdesUpv399+q+ghWrG7NqXWMAPstrztKVzbJ/MAeZpHr/ASQ1Ar4GvBAL/0bSHEmzgTOAnwCY2cfAeGAu8CpwjZkVhrPQa4FJwDxgfCgLcAtwg6Rconuso/f3uLN2pmpmUyUdXkqRQcA4MzNgmqRmxf7yVCvfGLKWsy5Yz4LZDRh1V3u2bKxNq3a7mPd+o91l1ubVpeWhu/barm3HnRzZYzv/m9lwd+yyW/I4+9vr2boph5svOLLCjqE6OrTVZjZsbsAtP5jKkZ3y+WRJS/70t5P4fOeeWyoDTp3PG9OP2P39C0es5uYhU2nbcgu/erzv7iSbcuYJi5jybg3/dzFI8i++mW0lSnbx2PdLKX8PcE+a+ERgYpr4IqKnAw5YZXZUldQTtw9Jw1I9g7vYUSGNS9I/x7ZkyElf4OqvdSN/VR2G3bEio+3qNyzkF48v5tHb2+91hvrEfe34Xu/uvP5CM775g7XZanaNkJNTRLfD1jLhzS8w7K7z+XxHHS76+oe7119y7gcUFtXi/6YdtTs2b1EbhvziAq68exAXD/yQOrULdq+rnVPIyT2X8NaMLhV6HAejLD1SddCrEr3/ZjYq1TNYh3qV3Zxy27C2DkVFwky88lRLju65HYC1eXVo3X7n7nKt2u1k3croDCmntvGLxxfz+gvN+fcrzdLW+/qLzTl14Mast786W5PfiDXrGzFvUXS/+60ZXejWeR0A/U75hJOOW8o9o84guh23t8/ymrN9R226dFy/O3biF5fxyZJWrN/UcJ/yNU5Cb1RVNZWZVEvroatWWrTZc0l/8oCNLJ5fH4BprzWl76AN1KlbRNtOO+jQZSfzP2gIGDfcv5SlC+rzwqi9e4rbd9lzpn5Sv40sza16f2QOJus3NWR1fiM6HboBgF7dl7N4RTO+0mMpFw6Yzc/++DV27Nxzl+zQVpupVSu6Edi25WY6t9vIyrWNd68/88SFvD69hl/6k9WH/w96lflI1QSiB3efAU4ENlaH+6nDH1nCcSdtoWmLAp6cMZe/3t+W407aypHHbscMVi2ry0M3R731Sz6pz9R/NGPUm/MpLBR/uq0DRUXi2BO2cPa317Nobn0emTwf2PMY1tDb8uh45A6KimD18ro8dIv3/B+oh546mZ8Ne5PaOYXkrWnCfWNO59FfvEydOoX87sZXAJi7sA0P/vVUvth1JRcP/JCCwloUmfj9X09m05boj2T9urs4/tjlPDDu1L3qP7XXYq67+D80bfw5v75+EguXtuTmBwZU+HFWKLMaO0i1LEvdx5KeBvoCrYBVwB1AHQAzezQ8UvUnoicEtgFDzGxGWfU2UQs7UWdlpc0uO3b2/0plN8GV09RXbnnfzHrv7/aNm3W0L59+fUZl3/7HzQe0r4NNNnv/LypjvQHXZGv/zrnKVR0v7TPhb1Q555JnQA29/Pek6pzLjpqZUz2pOueywy//nXMuQTW199+TqnMuedX0wf5MeFJ1ziUuevi/ZmZVT6rOuexIfui/KsGTqnMuK/xM1TnnkuL3VJ1zLkk1993/KjH0n3OuCsp8NtUySVocRvmfJWlGiLWQNFnSgvCzeYhL0kOScsNMq71i9QwO5RdIGhyLHx/qzw3b7jvWY4Y8qTrnkmfJTqcSnGFmPWODrwwHpphZV2BK+A4lTNUkqQXRwE4nEo3yf0dsWuuRwOWx7UqbCqpUnlSdc9mR4JlqCQYBY8PnscB5sfg4i0wjmim1HdAPmGxm+Wa2HpgM9A/rmpjZtDDQ07hYXeXmSdU5lx3JjvxvwGuS3pc0LMTaxsZgXgm0DZ9LmqqptPiyNPH94h1VzrmsUFHG1/atUvdJg1FmNqpYmVPNbLmkNsBkSf+LrwzTTB8UPWOeVJ1zyTPK8/D/2rIGqTaz5eHnakkvEt0TXZWagTlcwq8OxUuaqmk50cD58fibId4xTfn94pf/zrnECUOW2VJmXVIjSY1Tn4FzgI+IpmRK9eAPBl4OnycAl4anAPqwZ6qmScA5kpqHDqpzgElh3SZJfUKv/6WxusrNz1Sdc9mR3BtVbYEXw1NOtYG/mdmrkt4DxksaCiwBvhPKTwQGArmEqZqi5li+pLuB90K5EWaWHz5fDTwBNABeCct+8aTqnMuOhJKqmS0CvpQmvg7YZ8K60qZqMrMxwJg08RlAjwNuLJ5UnXPZUL57qtWKJ1XnXFaUo/e/WvGk6pzLggN+sL/K8qTqnEue4UnVOecSVTOv/j2pOueywwepds65JHlSdc65hJhBYc28/vek6pzLDj9Tdc65BHlSdc65hBhQQ+eo8qTqnMsCA/N7qs45lwzDO6qccy5Rfk/VOecS5EnVOeeS4gOqOOdccgyooUP/+RxVzrnsMMtsKYOkTpLekDRX0seSrg/xOyUtlzQrLANj29wqKVfSfEn9YvH+IZYraXgs3kXSuyH+rKS6+3vYnlSdc1kQXlPNZClbAXCjmXUH+gDXSOoe1j1oZj3DMhEgrLsQOBboDzwiKUdSDvAwMADoDlwUq+e+UNdRwHpg6P4euSdV51zyDMyKMlrKrMosz8xmhs+bgXlAh1I2GQQ8Y2Y7zOxTogkATwhLrpktMrOdwDPAoDCD6pnAc2H7scB5+3fgnlSdc9lSZJkt0ErSjNgyrKQqJR0OfBl4N4SulTRb0pgw7TRECXdpbLNlIVZSvCWwwcwKisX3iydV51x2ZH5Pda2Z9Y4to9JVJ+kQ4Hngx2a2CRgJHAn0BPKA+yvmwErnvf/OueSZJdr7L6kOUUJ9ysxeiHZhq2LrHwP+Gb4uBzrFNu8YYpQQXwc0k1Q7nK3Gy5ebn6k657Ijud5/AaOBeWb2QCzeLlbsfOCj8HkCcKGkepK6AF2B6cB7QNfQ01+XqDNrgpkZ8AZwQdh+MPDy/h62n6k657LAsMLCpCo7Bfg+MEfSrBC7jaj3vifRU7GLgSsAzOxjSeOBuURPDlxjZoUAkq4FJgE5wBgz+zjUdwvwjKRfAh8QJfH94knVOZe8BIf+M7N3AKVZNbGUbe4B7kkTn5huOzNbRPR0wAHzpOqcyw4f+s8555JhgPkg1c45lxDzQaqdcy5RCXZUVSmyKjY8l6Q1wJLKbkcWtALWVnYjXLlU53+zw8ys9f5uLOlVot9PJtaaWf/93dfBpsol1epK0gwz613Z7XCZ838zl44//O+ccwnypOqccwnypHrwSDuIhDuo+b+Z24ffU3XOuQT5mapzziXIk6pzziXIk2oFK2nisdj6emHisdwwEdnhldBMF4QR5VdL+qiE9ZL0UPj3mi2pV0W30R1cPKlWoDImHksZCqwPE5A9SDQhmas8TxBNHleSAUTjdXYFhhGNRu9qME+qFSvtxGPFygwimngMoonIzgqD9LpKYGZTgfxSigwCxllkGtEI8u1KKe+qOU+qFaukicfSlglTO2wkmpjMHZwy+Td1NYgnVeecS5An1YpV2oRk+5SRVBtoSjQxmTs4ZfJv6moQT6oVK+3EY8XKTCCaeAyiicheN39D42A2Abg0PAXQB9hoZnmV3ShXeXw81QpkZgXpJh6TNAKYYWYTiCYc+6ukXKIOkgsrr8VO0tNAX6CVpGXAHUAdADN7lGi+o4FALrANGFI5LXUHC39N1TnnEuSX/845lyBPqs45lyBPqs45lyBPqs45lyBPqs45lyBPqtWQpEJJsyR9JOnvkhoeQF1PSLogfH48zQAw8bJ9JZ28H/tYLGmfmTdLihcrs6Wc+7pT0k/L20bnMuVJtXrabmY9zawHsBO4Mr4yvKlVbmb2QzObW0qRvkC5k6pz1Ykn1ervbeCocBb5tqQJwFxJOZJ+K+m9MA7oFbB7fNA/hTFf/w9ok6pI0puSeofP/SXNlPShpClh3NcrgZ+Es+TTJLWW9HzYx3uSTgnbtpT0mqSPJT0OlDkKl6SXJL0fthlWbN2DIT5FUusQO1LSq2GbtyUdk8hv07ky+BtV1Vg4Ix0AvBpCvYAeZvZpSEwbzewrkuoB/5b0GvBl4Gii8V7bAnOBMcXqbQ08Bpwe6mphZvmSHgW2mNnvQrm/AQ+a2TuSOhO9SfYForeS3jGzEZK+TjSGbFl+EPbRAHhP0vNmtg5oRPQ22k8k3R7qvpZoUr4rzWyBpBOBR4Az9+PX6Fy5eFKtnhpImhU+v0306uvJwHQz+zTEzwGOS90vJRq4pStwOvC0mRUCKyS9nqb+PsDUVF1mVtJ4o2cD3WPDwTaRdEjYx7fCtv+StD6DY7pO0vnhc6fQ1nVAEfBsiD8JvBD2cTLw99i+62WwD+cOmCfV6mm7mfWMB0Jy2RoPAT8ys0nFyg1MsB21gD5m9nmatmRMUl+iBH2SmW2T9CZQv4TiFva7ofjvwLmK4PdUa65JwFWS6gBI6iapETAV+G6459oOOCPNttOA0yV1Cdu2CPHNQONYudeAH6W+SOoZPk4FLg6xAUDzMtralGiKmW3h3mif2LpaRKN5Eep8x8w2AZ9K+nbYhyR9qYx9OJcIT6o11+NE90tnKprU7s9EVy4vAgvCunHAf4tvaGZriOZjekHSh+y5/P4HcH6qowq4DugdOsLmsucphLuIkvLHRLcBPiujra8CtSXNA+4lSuopW4ETwjGcCYwI8UuAoaF9H7PvtDXOZYWPUuWccwnyM1XnnEuQJ1XnnEuQJ1XnnEuQJ1XnnEuQJ1XnnEuQJ1XnnEuQJ1XnnEvQ/wcEE/R1AuWGIgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# model = DecisionTreeClassifier()\n",
    "# model.fit(features_train, labels_train)\n",
    "# joblib.dump(model, \"./RandomUnderSampling/DecisionTreeClassifier.joblib\")\n",
    "model = joblib.load(\"./RandomUnderSampling/DecisionTreeClassifier.joblib\")\n",
    "model_pred = model.predict(features_test)\n",
    "# print(confusion_matrix(labels_test, model_pred))\n",
    "print(classification_report(labels_test, model_pred))\n",
    "conf_mat_plot = plot_confusion_matrix(model, features_test, labels_test)\n",
    "plt.title(\"Decision Tree Classifier\")\n",
    "print(conf_mat_plot)\n",
    "plt.savefig(\"Img/07_05_RandomUnderSampling_DTC_Confusion_Matrix\")\n",
    "del model\n",
    "del model_pred"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cleared-looking",
   "metadata": {},
   "source": [
    "### Gradient Boosting Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "planned-produce",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.94      0.92      0.93    292894\n",
      "         1.0       0.73      0.79      0.76     77394\n",
      "\n",
      "    accuracy                           0.90    370288\n",
      "   macro avg       0.84      0.86      0.85    370288\n",
      "weighted avg       0.90      0.90      0.90    370288\n",
      "\n",
      "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay object at 0x7ff66d785940>\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": [
    "# model = GradientBoostingClassifier()\n",
    "# model.fit(features_train, labels_train)\n",
    "# joblib.dump(model, \"./RandomUnderSampling/GradientBoostingClassifier.joblib\")\n",
    "model = joblib.load(\"./RandomUnderSampling/GradientBoostingClassifier.joblib\")\n",
    "model_pred = model.predict(features_test)\n",
    "# print(confusion_matrix(labels_test, model_pred))\n",
    "print(classification_report(labels_test, model_pred))\n",
    "conf_mat_plot = plot_confusion_matrix(model, features_test, labels_test)\n",
    "plt.title(\"Gradient Boosting Classifier (XGBoost)\")\n",
    "print(conf_mat_plot)\n",
    "plt.savefig(\"Img/07_06_RandomUnderSampling_GBC_Confusion_Matrix\")\n",
    "del model\n",
    "del model_pred"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "intense-monthly",
   "metadata": {},
   "source": [
    "### Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "applied-forge",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.95      0.91      0.93    292894\n",
      "         1.0       0.70      0.81      0.75     77394\n",
      "\n",
      "    accuracy                           0.89    370288\n",
      "   macro avg       0.83      0.86      0.84    370288\n",
      "weighted avg       0.90      0.89      0.89    370288\n",
      "\n",
      "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay object at 0x7ff617dd97f0>\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": [
    "# model = RandomForestClassifier(n_jobs=-1)\n",
    "# model.fit(features_train, labels_train)\n",
    "# joblib.dump(model, \"./RandomUnderSampling/RandomForestClassifier.joblib\")\n",
    "model = joblib.load(\"./RandomUnderSampling/RandomForestClassifier.joblib\")\n",
    "model_pred = model.predict(features_test)\n",
    "# print(confusion_matrix(labels_test, model_pred))\n",
    "print(classification_report(labels_test, model_pred))\n",
    "conf_mat_plot = plot_confusion_matrix(model, features_test, labels_test)\n",
    "plt.title(\"Random Forest Classifier\")\n",
    "print(conf_mat_plot)\n",
    "plt.savefig(\"Img/07_07_RandomUnderSampling_RFC_Confusion_Matrix\")\n",
    "del model\n",
    "del model_pred"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "steady-conclusion",
   "metadata": {},
   "source": [
    "### Extra Trees Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "occupational-marketing",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.95      0.89      0.92    292894\n",
      "         1.0       0.67      0.82      0.74     77394\n",
      "\n",
      "    accuracy                           0.88    370288\n",
      "   macro avg       0.81      0.86      0.83    370288\n",
      "weighted avg       0.89      0.88      0.88    370288\n",
      "\n",
      "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay object at 0x7ff62e221880>\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": [
    "model = ExtraTreesClassifier(n_jobs=-1)\n",
    "model.fit(features_train, labels_train)\n",
    "# joblib.dump(model, \"./RandomUnderSampling/ExtraTreesClassifier.joblib\")\n",
    "# model = joblib.load(\"./RandomUnderSampling/ExtraTreesClassifier.joblib\")\n",
    "model_pred = model.predict(features_test)\n",
    "# print(confusion_matrix(labels_test, model_pred))\n",
    "print(classification_report(labels_test, model_pred))\n",
    "conf_mat_plot = plot_confusion_matrix(model, features_test, labels_test)\n",
    "plt.title(\"Extra Trees Classifier\")\n",
    "print(conf_mat_plot)\n",
    "plt.savefig(\"Img/07_08_RandomUnderSampling_ETC_Confusion_Matrix\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "superb-matrix",
   "metadata": {},
   "source": [
    "## Inference\n",
    "<table>\n",
    "    <thead>\n",
    "        <tr>\n",
    "            <th rowspan=\"3\">Classification Model</th>\n",
    "            <th colspan=\"8\">Performance Metric</th>\n",
    "        </tr>\n",
    "        <tr>\n",
    "            <th colspan=\"2\">Precision</th>\n",
    "            <th colspan=\"2\">Recall</th>\n",
    "            <th colspan=\"2\">F1 Score</th>\n",
    "            <th rowspan=\"2\" colspan=\"2\">Accuracy</th>\n",
    "        </tr>\n",
    "        <tr>\n",
    "            <th>0.0</th>\n",
    "            <th>1.0</th>\n",
    "            <th>0.0</th>\n",
    "            <th>1.0</th>\n",
    "            <th>0.0</th>\n",
    "            <th>1.0</th>\n",
    "        </tr>\n",
    "    </thead>\n",
    "    <tbody>\n",
    "        <tr>\n",
    "            <th>Logistic Regression</th>\n",
    "            <td>0.94</td>\n",
    "            <td>0.74</td>\n",
    "            <td>0.93</td>\n",
    "            <td>0.78</td>\n",
    "            <td>0.93</td>\n",
    "            <td>0.76</td>\n",
    "            <td>0.90</td>\n",
    "        </tr>\n",
    "        <tr>\n",
    "            <th>Decision Tree Classifier</th>\n",
    "            <td>0.94</td>\n",
    "            <td>0.50</td>\n",
    "            <td>0.79</td>\n",
    "            <td>0.81</td>\n",
    "            <td>0.86</td>\n",
    "            <td>0.62</td>\n",
    "            <td>0.79</td>\n",
    "        </tr>\n",
    "        <tr>\n",
    "            <th>Extra Trees Classifier</th>\n",
    "            <td>0.95</td>\n",
    "            <td>0.67</td>\n",
    "            <td>0.89</td>\n",
    "            <td>0.82</td>\n",
    "            <td>0.92</td>\n",
    "            <td>0.74</td>\n",
    "            <td>0.88</td>\n",
    "        </tr>\n",
    "        <tr>\n",
    "            <th>Gradient Boosting Classifier</th>\n",
    "            <td>0.94</td>\n",
    "            <td>0.73</td>\n",
    "            <td>0.92</td>\n",
    "            <td>0.79</td>\n",
    "            <td>0.93</td>\n",
    "            <td>0.76</td>\n",
    "            <td>0.90</td>\n",
    "        </tr>\n",
    "        <tr>\n",
    "            <th>Random Forest Classifier</th>\n",
    "            <td>0.95</td>\n",
    "            <td>0.70</td>\n",
    "            <td>0.91</td>\n",
    "            <td>0.81</td>\n",
    "            <td>0.93</td>\n",
    "            <td>0.75</td>\n",
    "            <td>0.89</td>\n",
    "        </tr>\n",
    "    </tbody>\n",
    "</table>"
   ]
  }
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