08_Random_Over_Sampling.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "experienced-mailing",
   "metadata": {},
   "source": [
    "# Flight Delay Prediction\n",
    "## Random Over 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 Oversampling:</strong> or up-sampling is where the minority class observations are randomly duplicated to increase the 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": "activated-audit",
   "metadata": {},
   "source": [
    "## Environment Setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "acting-transportation",
   "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.over_sampling import RandomOverSampler\n",
    "\n",
    "df = pd.read_csv(\"Data/flight_and_weather_encoded.csv\", index_col=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "satellite-burning",
   "metadata": {},
   "source": [
    "## Dataset Without Handling Imbalance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "interested-aquarium",
   "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/08_01_Dataset_ArrDel15_Class_Imbalance.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "backed-seeker",
   "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": "least-soundtrack",
   "metadata": {},
   "source": [
    "## Training Data Without Handling Imbalance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "innovative-power",
   "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/08_02_Training_ArrDel15_Class_Imbalance.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "laughing-container",
   "metadata": {},
   "source": [
    "## Random Oversampling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "weird-latest",
   "metadata": {},
   "outputs": [],
   "source": [
    "ros = RandomOverSampler(random_state=42)\n",
    "features_train, labels_train = ros.fit_resample(features_train, labels_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "controlled-florist",
   "metadata": {},
   "source": [
    "### Balancing the Training Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "conventional-diversity",
   "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    1170484\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/08_03_RandomOverSampling_Training_ArrDel15_Class.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "baking-minnesota",
   "metadata": {},
   "source": [
    "### Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "modified-window",
   "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 0x7f111dce40d0>\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, \"./RandomOverSampling/LogisticRegression.joblib\")\n",
    "model = joblib.load(\"./RandomOverSampling/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/08_04_RandomOverSampling_LR_Confusion_Matrix\")\n",
    "del model\n",
    "del model_pred"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "unsigned-unknown",
   "metadata": {},
   "source": [
    "### Decision Tree Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "exterior-uncertainty",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.92      0.92      0.92    292894\n",
      "         1.0       0.69      0.70      0.70     77394\n",
      "\n",
      "    accuracy                           0.87    370288\n",
      "   macro avg       0.81      0.81      0.81    370288\n",
      "weighted avg       0.87      0.87      0.87    370288\n",
      "\n",
      "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay object at 0x7f111dcc4580>\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 = DecisionTreeClassifier()\n",
    "# model.fit(features_train, labels_train)\n",
    "# joblib.dump(model, \"./RandomOverSampling/DecisionTreeClassifier.joblib\")\n",
    "model = joblib.load(\"./RandomOverSampling/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/08_05_RandomOverSampling_DTC_Confusion_Matrix\")\n",
    "del model\n",
    "del model_pred"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fancy-shelf",
   "metadata": {},
   "source": [
    "### Gradient Boosting Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "round-carry",
   "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 0x7f112092d850>\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, \"./RandomOverSampling/GradientBoostingClassifier.joblib\")\n",
    "model = joblib.load(\"./RandomOverSampling/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/08_06_RandomOverSampling_GBC_Confusion_Matrix\")\n",
    "del model\n",
    "del model_pred"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "prime-sphere",
   "metadata": {},
   "source": [
    "### Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "wrapped-uniform",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.93      0.96      0.94    292894\n",
      "         1.0       0.82      0.74      0.78     77394\n",
      "\n",
      "    accuracy                           0.91    370288\n",
      "   macro avg       0.88      0.85      0.86    370288\n",
      "weighted avg       0.91      0.91      0.91    370288\n",
      "\n",
      "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay object at 0x7f1120a8f070>\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, \"./RandomOverSampling/RandomForestClassifier.joblib\")\n",
    "model = joblib.load(\"./RandomOverSampling/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/08_07_RandomOverSampling_RFC_Confusion_Matrix\")\n",
    "del model\n",
    "del model_pred"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "perceived-macedonia",
   "metadata": {},
   "source": [
    "### Extra Trees Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "appropriate-junior",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.93      0.96      0.94    292894\n",
      "         1.0       0.82      0.73      0.77     77394\n",
      "\n",
      "    accuracy                           0.91    370288\n",
      "   macro avg       0.87      0.84      0.86    370288\n",
      "weighted avg       0.91      0.91      0.91    370288\n",
      "\n",
      "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay object at 0x7f1120980a90>\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, \"./RandomOverSampling/ExtraTreesClassifier.joblib\")\n",
    "# model = joblib.load(\"./RandomOverSampling/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/08_08_RandomOverSampling_ETC_Confusion_Matrix\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "narrative-combination",
   "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>0.92</td>\n",
    "            <td>0.69</td>\n",
    "            <td>0.92</td>\n",
    "            <td>0.70</td>\n",
    "            <td>0.92</td>\n",
    "            <td>0.70</td>\n",
    "            <td>0.87</td>\n",
    "        </tr>\n",
    "        <tr>\n",
    "            <th>Extra Trees Classifier</th>\n",
    "            <td>0.93</td>\n",
    "            <td>0.82</td>\n",
    "            <td>0.96</td>\n",
    "            <td>0.73</td>\n",
    "            <td>0.94</td>\n",
    "            <td>0.77</td>\n",
    "            <td>0.91</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>0.93</td>\n",
    "            <td>0.82</td>\n",
    "            <td>0.96</td>\n",
    "            <td>0.74</td>\n",
    "            <td>0.94</td>\n",
    "            <td>0.78</td>\n",
    "            <td>0.91</td>\n",
    "        </tr>\n",
    "    </tbody>\n",
    "</table>"
   ]
  }
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